From bd14fb7f8f95075c2fb21a0f1afd296a4648b7a3 Mon Sep 17 00:00:00 2001 From: bschilder Date: Thu, 25 Jun 2026 08:40:50 -0400 Subject: [PATCH 1/2] Workflow v2: parallel per-workspace matrix, failure/freshness alerts, keep-alive, badges MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - pipeline.yml restructured: discover → parallel build matrix (one isolated job per workspace, fail-fast:false) → deploy (collect artifacts, single atomic data commit + Vercel) → pages (main only) → alert (opens/append a GitHub issue on any failure). A workspace whose build fails no longer blocks the others, and the deploy rebuilds its dashboard from committed data so it never disappears from the site. - freshness.yml: independent Monday monitor that opens a "data stale" issue if data/ hasn't updated in >8 days (catches silent failures AND a schedule auto-disabled by inactivity). - keep-alive heartbeat: deploy always stamps .github/last_run.txt → at least one commit/week, so GitHub never auto-disables the cron after 60 idle days. - build_landing: stamps the last-updated date in the landing footer. - README: pipeline/freshness/CI status badges + auto-update note. Co-Authored-By: Claude Opus 4.8 (1M context) --- .github/last_run.txt | 1 + .github/workflows/freshness.yml | 54 ++++++++ .github/workflows/pipeline.yml | 215 +++++++++++++++++++------------- README.md | 6 + scripts/build_landing.py | 9 +- 5 files changed, 196 insertions(+), 89 deletions(-) create mode 100644 .github/last_run.txt create mode 100644 .github/workflows/freshness.yml diff --git a/.github/last_run.txt b/.github/last_run.txt new file mode 100644 index 0000000..1cf82ec --- /dev/null +++ b/.github/last_run.txt @@ -0,0 +1 @@ +2026-06-25T12:39:50Z diff --git a/.github/workflows/freshness.yml b/.github/workflows/freshness.yml new file mode 100644 index 0000000..9b10eb7 --- /dev/null +++ b/.github/workflows/freshness.yml @@ -0,0 +1,54 @@ +name: Data Freshness Monitor + +# Independent daily check: if the dashboards' data hasn't been updated in over +# a week, the weekly pipeline silently failed or its schedule was disabled. +# Opens (or appends to) a GitHub issue so it never goes unnoticed. +on: + schedule: + - cron: "0 12 * * 1" # Mondays noon UTC (the morning after the Sunday run) + workflow_dispatch: + +permissions: + contents: read + issues: write + +jobs: + check: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + with: + fetch-depth: 0 # need full history for the commit timestamp + - id: age + run: | + last=$(git log -1 --format=%ct -- data/ 2>/dev/null || echo 0) + now=$(date +%s) + days=$(( (now - last) / 86400 )) + echo "days=$days" >> "$GITHUB_OUTPUT" + echo "Data last updated $days days ago." + - name: Alert if stale + if: ${{ fromJSON(steps.age.outputs.days) > 8 }} + uses: actions/github-script@v7 + with: + script: | + const days = ${{ steps.age.outputs.days }}; + const title = "⚠️ PaperTrail data is stale"; + const body = `The dashboards' data has not been updated in **${days} days** ` + + `(expected weekly). The pipeline likely failed silently or its schedule was ` + + `auto-disabled. Check the [Pipeline workflow](${context.serverUrl}/${context.repo.owner}/${context.repo.repo}/actions/workflows/pipeline.yml).`; + const open = await github.rest.issues.listForRepo({ + owner: context.repo.owner, repo: context.repo.repo, + state: "open", labels: "data-stale", + }); + const existing = open.data.find(i => i.title === title); + if (existing) { + await github.rest.issues.createComment({ + owner: context.repo.owner, repo: context.repo.repo, + issue_number: existing.number, body, + }); + } else { + await github.rest.issues.create({ + owner: context.repo.owner, repo: context.repo.repo, + title, body, labels: ["data-stale"], + }); + } diff --git a/.github/workflows/pipeline.yml b/.github/workflows/pipeline.yml index eea6844..98e27f3 100644 --- a/.github/workflows/pipeline.yml +++ b/.github/workflows/pipeline.yml @@ -18,29 +18,52 @@ permissions: contents: write pages: write id-token: write + issues: write concurrency: group: "pipeline" cancel-in-progress: false jobs: - pipeline: + # ── Discover workspaces → matrix ────────────────────────────── + discover: runs-on: ubuntu-latest + outputs: + matrix: ${{ steps.gen.outputs.matrix }} steps: - uses: actions/checkout@v4 + - id: gen + env: + WS_INPUT: ${{ inputs.workspace }} + run: | + if [ -n "$WS_INPUT" ]; then + arr=$(WS="$WS_INPUT" python3 -c "import json,os; print(json.dumps([os.environ['WS']]))") + else + arr=$(ls config/*.yml 2>/dev/null | xargs -n1 basename | sed 's/\.yml$//' \ + | python3 -c "import sys,json; print(json.dumps([l.strip() for l in sys.stdin if l.strip()]))") + fi + echo "matrix=$arr" >> "$GITHUB_OUTPUT" + echo "Workspaces: $arr" - - name: Set up Python - uses: actions/setup-python@v5 + # ── Build each workspace in parallel (isolated) ─────────────── + build: + needs: discover + runs-on: ubuntu-latest + strategy: + fail-fast: false # one workspace failing must not block the others + matrix: + workspace: ${{ fromJSON(needs.discover.outputs.matrix) }} + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 with: python-version: "3.11" + - run: pip install -e ".[all,dev]" - - name: Install dependencies - run: pip install -e ".[all,dev]" - - - name: Run pipelines for all workspaces - timeout-minutes: 120 + - name: Build ${{ matrix.workspace }} + timeout-minutes: 90 env: - # Slack tokens — each config references its own via slack_token_secret + WS: ${{ matrix.workspace }} SLACK_BOT_TOKEN: ${{ secrets.SLACK_BOT_TOKEN }} SLACKBOT_STANDARDMODELBIO_TOKEN: ${{ secrets.SLACKBOT_STANDARDMODELBIO_TOKEN }} OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} @@ -49,63 +72,70 @@ jobs: OPENALEX_EMAIL: ${{ secrets.OPENALEX_EMAIL || format('{0}@users.noreply.github.com', github.actor) }} run: | set -e - - # Find config files — single workspace or all - if [ -n "${{ inputs.workspace }}" ]; then - CONFIGS="config/${{ inputs.workspace }}.yml" - elif [ -d config ]; then - CONFIGS=$(ls config/*.yml 2>/dev/null) + cfg="config/${WS}.yml" + SLUG=$(python -c "import yaml; c=yaml.safe_load(open('$cfg')); url=c.get('slack_workspace_url',''); print(url.split('//')[1].split('.')[0] if '//' in url else 'dashboard')") + TOKEN_SECRET=$(python -c "import yaml; c=yaml.safe_load(open('$cfg')); print(c.get('slack_token_secret', 'SLACK_BOT_TOKEN'))") + export SLACK_BOT_TOKEN="${!TOKEN_SECRET}" + mkdir -p "build/$SLUG" "data/$SLUG" + + if [ "${{ inputs.skip_scrape }}" = "true" ] && [ -f "data/$SLUG/papers_final.json" ]; then + papertrail -v run-pipeline -c "$cfg" -o "build/$SLUG" --skip-scrape --data-file "data/$SLUG/papers_final.json" else - CONFIGS="config.yml" + papertrail -v run-pipeline -c "$cfg" -o "build/$SLUG" fi - for cfg in $CONFIGS; do - echo "=========================================" - echo "Processing: $cfg" - echo "=========================================" - - # Extract workspace name from config - WORKSPACE=$(python -c "import yaml; c=yaml.safe_load(open('$cfg')); url=c.get('slack_workspace_url',''); name=url.split('//')[1].split('.')[0] if '//' in url else 'dashboard'; print(name)") - echo "Workspace: $WORKSPACE" - - # Set the correct Slack token for this workspace - TOKEN_SECRET=$(python -c "import yaml; c=yaml.safe_load(open('$cfg')); print(c.get('slack_token_secret', 'SLACK_BOT_TOKEN'))") - export SLACK_BOT_TOKEN="${!TOKEN_SECRET}" - - OUTDIR="build/$WORKSPACE" - DATADIR="data/$WORKSPACE" - mkdir -p "$OUTDIR" "$DATADIR" - - # Run pipeline - if [ "${{ inputs.skip_scrape }}" = "true" ] && [ -f "$DATADIR/papers_final.json" ]; then - echo "Skipping scrape, using existing data" - papertrail -v run-pipeline -c "$cfg" -o "$OUTDIR" --skip-scrape --data-file "$DATADIR/papers_final.json" - else - papertrail -v run-pipeline -c "$cfg" -o "$OUTDIR" || echo "WARNING: Pipeline failed for $WORKSPACE (may be missing token)" - fi + if [ ! -f "build/$SLUG/papers_final.json" ]; then + echo "::error::Pipeline produced no output for $SLUG"; exit 1 + fi + cp "build/$SLUG/papers_final.json" "data/$SLUG/papers_final.json" + echo "SLUG=$SLUG" >> "$GITHUB_ENV" - # Copy data for commit - if [ -f "$OUTDIR/papers_final.json" ]; then - cp "$OUTDIR/papers_final.json" "$DATADIR/papers_final.json" - fi - done + - name: Upload ${{ matrix.workspace }} artifact + uses: actions/upload-artifact@v4 + with: + name: ws-${{ matrix.workspace }} + path: | + build/${{ env.SLUG }}/dashboard.html + data/${{ env.SLUG }}/papers_final.json + if-no-files-found: error + retention-days: 7 + + # ── Collect built workspaces → commit + deploy (single atomic step) ── + deploy: + needs: build + if: ${{ always() && needs.build.result != 'cancelled' }} + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: "3.11" + - run: pip install -e ".[all,dev]" + + - name: Download built workspaces + uses: actions/download-artifact@v4 + with: + pattern: ws-* + merge-multiple: true # restores build// and data// in place - name: Build docs + dashboards run: | mkdocs build --strict - - # Deploy each workspace dashboard for cfg in config/*.yml; do - WORKSPACE=$(python -c "import yaml; c=yaml.safe_load(open('$cfg')); url=c.get('slack_workspace_url',''); name=url.split('//')[1].split('.')[0] if '//' in url else 'dashboard'; print(name)") - DASHBOARD="build/$WORKSPACE/dashboard.html" - if [ -f "$DASHBOARD" ]; then - mkdir -p "site/$WORKSPACE" - cp "$DASHBOARD" "site/$WORKSPACE/index.html" - echo "Deployed: site/$WORKSPACE/" + SLUG=$(python -c "import yaml; c=yaml.safe_load(open('$cfg')); url=c.get('slack_workspace_url',''); print(url.split('//')[1].split('.')[0] if '//' in url else 'dashboard')") + # If a workspace build failed (no fresh dashboard), rebuild from the + # committed data so it stays on the site rather than disappearing. + if [ ! -f "build/$SLUG/dashboard.html" ] && [ -f "data/$SLUG/papers_final.json" ]; then + echo "No fresh build for $SLUG — rebuilding dashboard from committed data" + TITLE=$(python -c "import yaml; print(yaml.safe_load(open('$cfg')).get('title','PaperTrail'))") + mkdir -p "build/$SLUG" + papertrail build "data/$SLUG/papers_final.json" -o "build/$SLUG/dashboard.html" --title "$TITLE" || true + fi + if [ -f "build/$SLUG/dashboard.html" ]; then + mkdir -p "site/$SLUG" + cp "build/$SLUG/dashboard.html" "site/$SLUG/index.html" fi done - - # Backward compat redirect mkdir -p site/dashboard echo "" > site/dashboard/index.html @@ -116,49 +146,35 @@ jobs: VERCEL_PROJECT_ID: ${{ secrets.VERCEL_PROJECT_ID }} run: | set -e - if [ -z "$VERCEL_TOKEN" ]; then - echo "VERCEL_TOKEN not set — skipping Vercel deploy (GitHub Pages deploy still runs)." - exit 0 + echo "VERCEL_TOKEN not set — skipping Vercel deploy (GitHub Pages still runs)."; exit 0 fi - npm install --global vercel@latest - - # Assemble a prebuilt static site via Vercel's Build Output API (v3). - # Using --prebuilt + VERCEL_PROJECT_ID pins the deploy to the - # "papertrail" project and skips any Vercel-side build step. OUT=.vercel/output/static rm -rf .vercel/output mkdir -p "$OUT" echo '{ "version": 3 }' > .vercel/output/config.json - - # Lab-picker landing page at the root (reads config/*.yml + data counts) python scripts/build_landing.py --out "$OUT/index.html" - - # One dashboard per workspace at // for cfg in config/*.yml; do - WORKSPACE=$(python -c "import yaml; c=yaml.safe_load(open('$cfg')); url=c.get('slack_workspace_url',''); name=url.split('//')[1].split('.')[0] if '//' in url else 'dashboard'; print(name)") - DASHBOARD="build/$WORKSPACE/dashboard.html" - if [ -f "$DASHBOARD" ]; then - mkdir -p "$OUT/$WORKSPACE" - cp "$DASHBOARD" "$OUT/$WORKSPACE/index.html" - echo "Staged for Vercel: /$WORKSPACE/" + SLUG=$(python -c "import yaml; c=yaml.safe_load(open('$cfg')); url=c.get('slack_workspace_url',''); print(url.split('//')[1].split('.')[0] if '//' in url else 'dashboard')") + if [ -f "build/$SLUG/dashboard.html" ]; then + mkdir -p "$OUT/$SLUG" + cp "build/$SLUG/dashboard.html" "$OUT/$SLUG/index.html" fi done - vercel deploy --prebuilt --prod --token="$VERCEL_TOKEN" --yes - - name: Commit updated data + - name: Commit updated data (+ keep-alive heartbeat) run: | git config user.name "github-actions[bot]" git config user.email "github-actions[bot]@users.noreply.github.com" - git add -f data/ + # Heartbeat: always stamp the run time so there is at least one commit + # per week. Keeps GitHub from auto-disabling the schedule after 60 days + # of inactivity, and doubles as the "data last updated" timestamp. + date -u +"%Y-%m-%dT%H:%M:%SZ" > .github/last_run.txt + git add -f data/ .github/last_run.txt if ! git diff --staged --quiet; then git commit -m "Update paper data [pipeline]" - # Reconcile against the branch we're running on (main for the cron, - # the PR branch for test runs). -X theirs keeps THIS run's freshly - # built data on the rare conflict where another run pushed data - # while we were building. Only data/ is regenerated, so this is safe. BRANCH="${GITHUB_REF_NAME}" git pull --rebase -X theirs origin "$BRANCH" || git rebase --abort git push origin "HEAD:${BRANCH}" @@ -169,17 +185,44 @@ jobs: with: path: site - deploy: - # GitHub Pages environment is restricted to the default branch, so this - # only runs on main. Gating it keeps PR-branch test runs from showing a - # false failure on the Pages step (Vercel deploy happens in the pipeline job). - if: github.ref == 'refs/heads/main' + # ── GitHub Pages (main only — the environment is default-branch-restricted) ── + pages: + needs: deploy + if: ${{ github.ref == 'refs/heads/main' }} environment: name: github-pages url: ${{ steps.deployment.outputs.page_url }} runs-on: ubuntu-latest - needs: pipeline steps: - name: Deploy to GitHub Pages id: deployment uses: actions/deploy-pages@v4 + + # ── Alert on any failure → open/append a GitHub issue ───────── + alert: + needs: [discover, build, deploy, pages] + if: ${{ failure() }} + runs-on: ubuntu-latest + steps: + - uses: actions/github-script@v7 + with: + script: | + const title = "⚠️ PaperTrail pipeline failed"; + const url = `${context.serverUrl}/${context.repo.owner}/${context.repo.repo}/actions/runs/${context.runId}`; + const body = `Scheduled/triggered pipeline run failed.\n\nRun: ${url}\nTriggered by: ${context.eventName}`; + const open = await github.rest.issues.listForRepo({ + owner: context.repo.owner, repo: context.repo.repo, + state: "open", labels: "pipeline-failure", + }); + const existing = open.data.find(i => i.title === title); + if (existing) { + await github.rest.issues.createComment({ + owner: context.repo.owner, repo: context.repo.repo, + issue_number: existing.number, body, + }); + } else { + await github.rest.issues.create({ + owner: context.repo.owner, repo: context.repo.repo, + title, body, labels: ["pipeline-failure"], + }); + } diff --git a/README.md b/README.md index a74e3c0..ab0f593 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,13 @@ # PaperTrail +[![Pipeline](https://github.com/bschilder/PaperTrail/actions/workflows/pipeline.yml/badge.svg)](https://github.com/bschilder/PaperTrail/actions/workflows/pipeline.yml) +[![Freshness](https://github.com/bschilder/PaperTrail/actions/workflows/freshness.yml/badge.svg)](https://github.com/bschilder/PaperTrail/actions/workflows/freshness.yml) +[![CI](https://github.com/bschilder/PaperTrail/actions/workflows/ci.yml/badge.svg)](https://github.com/bschilder/PaperTrail/actions/workflows/ci.yml) + **Every paper your team shares — found and mapped.** +> Dashboards rebuild & redeploy automatically every Sunday (GitHub Actions cron); the [landing page](https://papertrail-portal.vercel.app) footer shows the last data update. + PaperTrail automatically discovers papers shared across your Slack workspace, enriches them with metadata, computes LLM semantic embeddings, and builds an interactive visual dashboard with hierarchical topic clustering, AI-powered search, and full engagement metrics. ### Live Demos diff --git a/scripts/build_landing.py b/scripts/build_landing.py index 25430ee..164003c 100644 --- a/scripts/build_landing.py +++ b/scripts/build_landing.py @@ -63,7 +63,7 @@ def discover_workspaces(config_glob: str = "config/*.yml") -> list[dict]: return workspaces -def render(workspaces: list[dict]) -> str: +def render(workspaces: list[dict], updated: str) -> str: cards = [] for ws in workspaces: title = html.escape(ws["title"]) @@ -149,7 +149,7 @@ def render(workspaces: list[dict]) -> str: {cards_html} @@ -160,12 +160,15 @@ def main() -> None: parser = argparse.ArgumentParser(description="Build the PaperTrail landing page.") parser.add_argument("--out", default="vercel_site/index.html", help="Output HTML path.") parser.add_argument("--config-glob", default="config/*.yml", help="Glob for workspace configs.") + parser.add_argument("--updated", default=None, help="Last-updated date (default: today, UTC).") args = parser.parse_args() + from datetime import datetime, timezone + updated = args.updated or datetime.now(timezone.utc).strftime("%Y-%m-%d") workspaces = discover_workspaces(args.config_glob) out = Path(args.out) out.parent.mkdir(parents=True, exist_ok=True) - out.write_text(render(workspaces)) + out.write_text(render(workspaces, updated)) print(f"Wrote landing page with {len(workspaces)} workspace(s) -> {out}") for ws in workspaces: print(f" - {ws['slug']}: {ws['title']} ({ws['count']} papers)") From 3e89252af12e9b813b141ed3e799b40bc94ebdbf Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Thu, 25 Jun 2026 12:53:10 +0000 Subject: [PATCH 2/2] Update paper data [pipeline] --- .github/last_run.txt | 2 +- data/koolab/papers_final.json | 91650 +++++++++++----------- data/standardmodelbio/papers_final.json | 46404 +++++------ 3 files changed, 69220 insertions(+), 68836 deletions(-) diff --git a/.github/last_run.txt b/.github/last_run.txt index 1cf82ec..b0cae60 100644 --- a/.github/last_run.txt +++ b/.github/last_run.txt @@ -1 +1 @@ -2026-06-25T12:39:50Z +2026-06-25T12:53:09Z diff --git a/data/koolab/papers_final.json b/data/koolab/papers_final.json index 4970a5a..1eaa634 100644 --- a/data/koolab/papers_final.json +++ b/data/koolab/papers_final.json @@ -17,46 +17,46 @@ ], "projections": { "pca": [ - 0.036608628928661346, - 0.2878914177417755 + 0.03766646608710289, + 0.2867799401283264 ], "tsne": [ - -1.5122159719467163, - -6.449136734008789 + -1.485676884651184, + 5.015101909637451 ], "umap": [ - 4.902162551879883, - 7.4018874168396 + 5.154140949249268, + 6.631194114685059 ], "pca3d": [ - 0.03660792484879494, - 0.2879350185394287, - 0.019969569519162178 + 0.0376652367413044, + 0.2868226170539856, + 0.0194874070584774 ], "tsne3d": [ - -4.1990509033203125, - -0.7580049633979797, - -20.305208206176758 + -0.36294683814048767, + 21.158132553100586, + -4.346043109893799 ], "umap3d": [ - 4.423846244812012, - 3.5249953269958496, - 4.84058952331543 + 5.57200813293457, + 4.676988124847412, + 4.927947044372559 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, + "id": 13, "label": "Genomics Papers" } ], @@ -476,47 +476,47 @@ ], "projections": { "pca": [ - -0.12084031850099564, - 0.12388171255588531 + -0.12067153304815292, + 0.12434183806180954 ], "tsne": [ - -34.407432556152344, - -32.22675704956055 + -38.567867279052734, + 28.22692108154297 ], "umap": [ - 8.203802108764648, - 10.257899284362793 + 8.693854331970215, + 9.635007858276367 ], "pca3d": [ - -0.12084071338176727, - 0.1238919124007225, - -0.16687583923339844 + -0.1206718236207962, + 0.12435153126716614, + -0.16704949736595154 ], "tsne3d": [ - -13.668341636657715, - -2.5532467365264893, - -18.13385772705078 + -13.247257232666016, + -8.101638793945312, + -14.324665069580078 ], "umap3d": [ - 4.371124744415283, - 4.8976593017578125, - 1.3142033815383911 + 4.031065940856934, + 4.405668258666992, + 1.2997430562973022 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -955,46 +955,46 @@ "openalex_url": "https://openalex.org/W7165006462", "projections": { "pca": [ - -0.17788895964622498, - 0.14443054795265198 + -0.17736805975437164, + 0.14560410380363464 ], "tsne": [ - -1.971490740776062, - -31.756059646606445 + -9.08541488647461, + 28.466833114624023 ], "umap": [ - 4.6471405029296875, - 9.374425888061523 + 4.833735466003418, + 8.644646644592285 ], "pca3d": [ - -0.17788927257061005, - 0.14444851875305176, - 0.059018246829509735 + -0.1773684024810791, + 0.14562243223190308, + 0.05916467308998108 ], "tsne3d": [ - -4.800365924835205, - -17.484371185302734, - -2.4343667030334473 + -7.958785057067871, + 15.693798065185547, + -5.777600288391113 ], "umap3d": [ - 3.844165802001953, - 2.8292455673217773, - 3.028778076171875 + 4.523349285125732, + 3.229438304901123, + 3.644563913345337 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, + "id": 26, "label": "Medical AI" } ], @@ -1412,47 +1412,47 @@ "openalex_url": "https://openalex.org/W7163034720", "projections": { "pca": [ - -0.1745397001504898, - -0.11954740434885025 + -0.17486029863357544, + -0.11866398900747299 ], "tsne": [ - -39.75252914428711, - -15.31666088104248 + -35.93572235107422, + -0.4525897800922394 ], "umap": [ - 1.1960575580596924, - 7.322781085968018 + 1.5696178674697876, + 6.696624755859375 ], "pca3d": [ - -0.17453931272029877, - -0.11954697221517563, - -0.10183417052030563 + -0.17485997080802917, + -0.11866363883018494, + -0.10147874802350998 ], "tsne3d": [ - -24.507484436035156, - -16.747730255126953, - 7.712069034576416 + -21.18123435974121, + -0.6875502467155457, + -4.067680835723877 ], "umap3d": [ - 0.8288922905921936, - 4.7482171058654785, - 4.148055553436279 + 1.7017656564712524, + 5.008333206176758, + 4.843801021575928 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -1870,47 +1870,47 @@ "cited_by_count": 18, "projections": { "pca": [ - -0.2211088091135025, - -0.004878157749772072 + -0.2210707813501358, + -0.004145003389567137 ], "tsne": [ - -35.95864486694336, - -29.824556350708008 + -40.42808532714844, + 25.93913459777832 ], "umap": [ - 8.21578311920166, - 10.3543062210083 + 8.73612117767334, + 9.685729026794434 ], "pca3d": [ - -0.22110852599143982, - -0.004888350609689951, - -0.08208891749382019 + -0.22107064723968506, + -0.004155527334660292, + -0.08194752037525177 ], "tsne3d": [ - -16.47218132019043, - 1.0240150690078735, - -16.083139419555664 + -16.137020111083984, + -5.780574321746826, + -11.135612487792969 ], "umap3d": [ - 4.267348289489746, - 4.9769744873046875, - 1.2835592031478882 + 3.8979976177215576, + 4.450821399688721, + 1.2818589210510254 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -2385,47 +2385,47 @@ "openalex_url": "https://openalex.org/W7161731996", "projections": { "pca": [ - -0.0006609631818719208, - 0.08323509991168976 + -0.0005120601854287088, + 0.08429978787899017 ], "tsne": [ - -7.280508995056152, - -23.94295883178711 + -10.264994621276855, + 18.26449203491211 ], "umap": [ - 4.060107231140137, - 8.578813552856445 + 4.34894323348999, + 7.893043518066406 ], "pca3d": [ - -0.0006610904820263386, - 0.08324066549539566, - 0.14527486264705658 + -0.0005121899303048849, + 0.08430561423301697, + 0.1451493352651596 ], "tsne3d": [ - -5.122811317443848, - -8.819188117980957, - -2.1325125694274902 + -6.12649393081665, + 12.624225616455078, + -0.0018707162234932184 ], "umap3d": [ - 3.28143048286438, - 3.6617937088012695, - 3.2323966026306152 + 3.9193124771118164, + 4.046115398406982, + 3.6548259258270264 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -2859,47 +2859,47 @@ "openalex_url": "https://openalex.org/W7161734976", "projections": { "pca": [ - -0.1524869054555893, - 0.23441481590270996 + -0.1517292708158493, + 0.23536883294582367 ], "tsne": [ - -10.478422164916992, - -25.238231658935547 + -13.79234504699707, + 18.621097564697266 ], "umap": [ - 3.8596558570861816, - 8.662888526916504 + 4.071375846862793, + 7.992821216583252 ], "pca3d": [ - -0.1524871587753296, - 0.23441509902477264, - 0.17108938097953796 + -0.15172962844371796, + 0.23536920547485352, + 0.17087574303150177 ], "tsne3d": [ - -7.531057834625244, - -10.474862098693848, - -2.6959428787231445 + -9.151411056518555, + 13.947525024414062, + 0.7053180932998657 ], "umap3d": [ - 3.0266401767730713, - 3.5584933757781982, - 3.3389475345611572 + 3.72414231300354, + 3.968513011932373, + 3.746234893798828 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -3323,47 +3323,47 @@ "openalex_url": "https://openalex.org/W7161353890", "projections": { "pca": [ - -0.12780547142028809, - -0.0796385332942009 + -0.12787839770317078, + -0.07859834283590317 ], "tsne": [ - -36.571815490722656, - -17.857872009277344 + -39.34162139892578, + 14.046895980834961 ], "umap": [ - 1.5978002548217773, - 7.7918195724487305 + 1.9983996152877808, + 7.031013011932373 ], "pca3d": [ - -0.12780503928661346, - -0.07965796440839767, - 0.03925143927335739 + -0.12787801027297974, + -0.0786181092262268, + 0.03958294540643692 ], "tsne3d": [ - -21.826826095581055, - -13.064225196838379, - 3.917726755142212 + -21.927406311035156, + 5.2613372802734375, + -3.7107837200164795 ], "umap3d": [ - 1.3789249658584595, - 4.484113693237305, - 4.36135196685791 + 2.456368923187256, + 4.842362403869629, + 4.961950302124023 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { "id": 34, - "label": "Learning Methods" + "label": "Language Models" } ], "_embedding": [ @@ -3787,31 +3787,31 @@ "openalex_url": "https://openalex.org/W7160895307", "projections": { "pca": [ - -0.2843064069747925, - -0.10128802061080933 + -0.2842586934566498, + -0.10077424347400665 ], "tsne": [ - -36.74082946777344, - 10.089157104492188 + -33.19577407836914, + -15.343423843383789 ], "umap": [ - 2.4124155044555664, - 5.286053657531738 + 2.8022429943084717, + 4.729701995849609 ], "pca3d": [ - -0.2843058109283447, - -0.10130201280117035, - 0.012813522480428219 + -0.28425803780555725, + -0.10078884661197662, + 0.013275019824504852 ], "tsne3d": [ - -18.39615821838379, - 7.199637413024902, - 7.1951212882995605 + -17.607261657714844, + -17.43866539001465, + 9.122005462646484 ], "umap3d": [ - 2.1441328525543213, - 6.570691108703613, - 5.024363040924072 + 2.8206305503845215, + 7.089730262756348, + 4.86361837387085 ] }, "cluster_id": 2, @@ -3822,11 +3822,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -4246,31 +4246,31 @@ "openalex_url": "https://openalex.org/W7160864474", "projections": { "pca": [ - -0.22459106147289276, - -0.0905686616897583 + -0.22469814121723175, + -0.08962728083133698 ], "tsne": [ - -40.6803092956543, - 16.78523826599121 + -36.5538215637207, + -23.134828567504883 ], "umap": [ - 2.122340202331543, - 5.008158206939697 + 2.444370746612549, + 4.33075475692749 ], "pca3d": [ - -0.22459053993225098, - -0.09059581905603409, - -0.01566801592707634 + -0.22469770908355713, + -0.08965501189231873, + -0.015324647538363934 ], "tsne3d": [ - -20.46874237060547, - 13.011351585388184, - 0.5479435920715332 + -20.297677993774414, + -21.950021743774414, + 3.4335246086120605 ], "umap3d": [ - 1.8763031959533691, - 6.804922103881836, - 4.762242317199707 + 2.403860569000244, + 7.185818195343018, + 4.744833469390869 ] }, "cluster_id": 2, @@ -4281,11 +4281,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -4695,46 +4695,46 @@ "title": "Interpreting Language Model Parameters", "projections": { "pca": [ - -0.2080978900194168, - 0.0002159763389499858 + -0.20780566334724426, + 0.00048055005026981235 ], "tsne": [ - -29.25566291809082, - -17.62164306640625 + -33.54778289794922, + 15.152770042419434 ], "umap": [ - 1.9781699180603027, - 8.256367683410645 + 2.1641252040863037, + 7.478089809417725 ], "pca3d": [ - -0.20809796452522278, - 0.00022160258959047496, - 0.05997631698846817 + -0.2078056037425995, + 0.00048598687862977386, + 0.06023165583610535 ], "tsne3d": [ - -18.69321060180664, - -16.280345916748047, - 6.240851402282715 + -18.827871322631836, + 10.464539527893066, + -2.6711831092834473 ], "umap3d": [ - 1.7348785400390625, - 4.133039474487305, - 4.468310832977295 + 2.7298583984375, + 4.5762434005737305, + 5.166077136993408 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 1, + "label": "Attention Models" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -5159,31 +5159,31 @@ "openalex_url": "https://openalex.org/W4414806405", "projections": { "pca": [ - -0.19745321571826935, - -0.08123756945133209 + -0.19779786467552185, + -0.08029604703187943 ], "tsne": [ - -41.82378005981445, - 0.8838706612586975 + -39.5787239074707, + -6.9638447761535645 ], "umap": [ - 1.6362781524658203, - 6.420323371887207 + 1.9637500047683716, + 5.649574279785156 ], "pca3d": [ - -0.19745290279388428, - -0.08123835176229477, - -0.02091361954808235 + -0.19779765605926514, + -0.08029702305793762, + -0.020793406292796135 ], "tsne3d": [ - -19.366952896118164, - 2.1689202785491943, - -1.0285656452178955 + -18.519641876220703, + -9.869810104370117, + 3.319692373275757 ], "umap3d": [ - 1.6090517044067383, - 5.773589611053467, - 4.238720417022705 + 2.3675649166107178, + 6.054476261138916, + 4.609175205230713 ] }, "cluster_id": 2, @@ -5194,12 +5194,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -5608,31 +5608,31 @@ "title": "Discrete Flow Maps", "projections": { "pca": [ - -0.19712689518928528, - 0.008072269149124622 + -0.19691048562526703, + 0.007755146361887455 ], "tsne": [ - -40.70749282836914, - 19.98053550720215 + -36.030670166015625, + -26.124013900756836 ], "umap": [ - 2.263223171234131, - 4.848127365112305 + 2.7340164184570312, + 4.214965343475342 ], "pca3d": [ - -0.1971268206834793, - 0.008073895238339901, - -0.03595931455492973 + -0.19691039621829987, + 0.007757138926535845, + -0.03592746704816818 ], "tsne3d": [ - -19.23349380493164, - 14.505972862243652, - -3.85624098777771 + -18.85340690612793, + -26.026905059814453, + 3.771794557571411 ], "umap3d": [ - 2.126816511154175, - 7.04320764541626, - 4.729462146759033 + 2.6248178482055664, + 7.444838523864746, + 4.619888782501221 ] }, "cluster_id": 2, @@ -5643,11 +5643,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -6084,47 +6084,47 @@ "openalex_url": "https://openalex.org/W7154461809", "projections": { "pca": [ - -0.23417408764362335, - -0.013446231372654438 + -0.2342860996723175, + -0.01189212966710329 ], "tsne": [ - -32.43170928955078, - -20.477142333984375 + -36.02873992919922, + 15.511366844177246 ], "umap": [ - 1.995051622390747, - 7.592617988586426 + 2.1395792961120605, + 6.8876118659973145 ], "pca3d": [ - -0.2341739535331726, - -0.013452325016260147, - 0.006528998725116253 + -0.23428601026535034, + -0.011898779310286045, + 0.0067252423614263535 ], "tsne3d": [ - -18.82371711730957, - -12.883674621582031, - -0.625272810459137 + -22.041772842407227, + 9.400079727172852, + -4.879340171813965 ], "umap3d": [ - 1.5546534061431885, - 4.501062870025635, - 4.002015113830566 + 2.4236152172088623, + 4.781679153442383, + 4.656002998352051 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -6549,46 +6549,46 @@ "openalex_url": "https://openalex.org/W7131731790", "projections": { "pca": [ - -0.18295158445835114, - -0.0718168169260025 + -0.18300941586494446, + -0.07114183902740479 ], "tsne": [ - -36.48234939575195, - -11.3394193649292 + -41.45099639892578, + 11.554145812988281 ], "umap": [ - 0.95185387134552, - 8.047988891601562 + 1.2582523822784424, + 7.237607955932617 ], "pca3d": [ - -0.18295125663280487, - -0.07183074951171875, - -0.007623672019690275 + -0.18300911784172058, + -0.071155846118927, + -0.007288639899343252 ], "tsne3d": [ - -24.697097778320312, - -9.792269706726074, - 11.382225036621094 + -24.524883270263672, + 8.223570823669434, + 8.813079833984375 ], "umap3d": [ - 1.032884120941162, - 4.638324737548828, - 5.012637138366699 + 2.1268956661224365, + 4.916829586029053, + 5.707588195800781 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -7013,47 +7013,47 @@ "openalex_url": "https://openalex.org/W7140235390", "projections": { "pca": [ - -0.2037425935268402, - 0.004865437280386686 + -0.203688845038414, + 0.00585138751193881 ], "tsne": [ - -27.34996223449707, - -21.864347457885742 + -31.553266525268555, + 19.880380630493164 ], "umap": [ - 2.4243268966674805, - 8.480196952819824 + 2.689338445663452, + 7.685940742492676 ], "pca3d": [ - -0.20374225080013275, - 0.004831911064684391, - -0.048087384551763535 + -0.20368850231170654, + 0.005816829390823841, + -0.04785879701375961 ], "tsne3d": [ - -17.222692489624023, - -19.257230758666992, - 1.0915268659591675 + -17.810855865478516, + 10.496352195739746, + -8.7821683883667 ], "umap3d": [ - 1.8685534000396729, - 3.9318041801452637, - 4.031634330749512 + 2.872708797454834, + 4.2887091636657715, + 4.695191860198975 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 7, - "label": "Language Models" + "id": 25, + "label": "AI Agents" } ], "_embedding": [ @@ -7484,47 +7484,47 @@ "openalex_url": "https://openalex.org/W7140287209", "projections": { "pca": [ - -0.12968553602695465, - 0.12735338509082794 + -0.12927253544330597, + 0.12859557569026947 ], "tsne": [ - -8.828349113464355, - -21.9208927154541 + -11.001397132873535, + 16.48244285583496 ], "umap": [ - 3.979929208755493, - 8.448902130126953 + 4.138749599456787, + 7.792861461639404 ], "pca3d": [ - -0.12968572974205017, - 0.1273576319217682, - 0.148921400308609 + -0.1292727142572403, + 0.12859979271888733, + 0.14888125658035278 ], "tsne3d": [ - -6.607468605041504, - -8.32580852508545, - -0.0894787535071373 + -7.190848350524902, + 10.494319915771484, + 0.43047237396240234 ], "umap3d": [ - 3.168917417526245, - 3.6335175037384033, - 3.4369375705718994 + 3.8808982372283936, + 4.057066917419434, + 3.847742795944214 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -7945,47 +7945,47 @@ "openalex_url": "https://openalex.org/W7131288999", "projections": { "pca": [ - -0.11552193760871887, - 0.1177765280008316 + -0.11519402265548706, + 0.11915289610624313 ], "tsne": [ - -6.1497955322265625, - -26.23119354248047 + -9.919010162353516, + 21.143861770629883 ], "umap": [ - 4.076239109039307, - 8.715689659118652 + 4.345125198364258, + 8.052821159362793 ], "pca3d": [ - -0.11552224308252335, - 0.11778531223535538, - 0.17430274188518524 + -0.11519431322813034, + 0.11916184425354004, + 0.17426912486553192 ], "tsne3d": [ - -4.231645584106445, - -11.544706344604492, - -2.8832881450653076 + -6.628055572509766, + 15.262825965881348, + -0.8925212025642395 ], "umap3d": [ - 3.308563470840454, - 3.4626591205596924, - 3.156435012817383 + 4.001070499420166, + 3.835237503051758, + 3.632042407989502 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -8406,47 +8406,47 @@ "openalex_url": "https://openalex.org/W7140001187", "projections": { "pca": [ - -0.2215857058763504, - 0.16161903738975525 + -0.22096526622772217, + 0.1629444658756256 ], "tsne": [ - -21.686973571777344, - -16.464818954467773 + -26.54823875427246, + 13.985296249389648 ], "umap": [ - 2.6155812740325928, - 8.35013484954834 + 2.895573616027832, + 7.637655258178711 ], "pca3d": [ - -0.22158585488796234, - 0.16161365807056427, - 0.019837185740470886 + -0.2209654599428177, + 0.16293810307979584, + 0.01996583864092827 ], "tsne3d": [ - -10.468832969665527, - -15.49791431427002, - 8.058404922485352 + -12.963632583618164, + 6.102962493896484, + -5.190393924713135 ], "umap3d": [ - 2.3115084171295166, - 3.7165677547454834, - 4.3848748207092285 + 3.4361953735351562, + 4.273007392883301, + 4.957633018493652 ] }, - "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 1, - "label": "AI Scientific Research" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 0, - "label": "LLM Language Learning" + "id": 25, + "label": "AI Agents" } ], "_embedding": [ @@ -8859,31 +8859,31 @@ ], "projections": { "pca": [ - -0.1791706085205078, - 0.004475174471735954 + -0.1791231334209442, + 0.00522645516321063 ], "tsne": [ - -35.30265426635742, - 17.66460609436035 + -31.40957260131836, + -22.729576110839844 ], "umap": [ - 2.666757345199585, - 5.012789726257324 + 3.155649423599243, + 4.438242435455322 ], "pca3d": [ - -0.1791703701019287, - 0.0044601731933653355, - 0.06776995956897736 + -0.1791228950023651, + 0.005211133509874344, + 0.06778109818696976 ], "tsne3d": [ - -15.468660354614258, - 12.497183799743652, - 1.7618945837020874 + -14.843781471252441, + -20.844179153442383, + 3.2364938259124756 ], "umap3d": [ - 2.480041027069092, - 6.807621955871582, - 4.742099285125732 + 3.0264065265655518, + 7.156930446624756, + 4.472573280334473 ] }, "cluster_id": 2, @@ -8894,12 +8894,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, - "label": "Diffusion Models" + "id": 7, + "label": "Diffusion Gen" } ], "_embedding": [ @@ -9357,47 +9357,47 @@ "openalex_url": "https://openalex.org/W4416186944", "projections": { "pca": [ - -0.18939651548862457, - 0.04329228401184082 + -0.18929870426654816, + 0.044798921793699265 ], "tsne": [ - -20.246126174926758, - -24.9041805267334 + -20.780420303344727, + 14.746211051940918 ], "umap": [ - 2.9227185249328613, - 7.868234157562256 + 3.1321427822113037, + 7.119921684265137 ], "pca3d": [ - -0.18939614295959473, - 0.04326344281435013, - 0.14588528871536255 + -0.18929822742938995, + 0.04476974904537201, + 0.14592301845550537 ], "tsne3d": [ - -13.339590072631836, - -7.002171516418457, - -6.561496734619141 + -14.884934425354004, + 14.315389633178711, + 4.0949296951293945 ], "umap3d": [ - 2.312619209289551, - 4.310683250427246, - 3.572293758392334 + 3.1353931427001953, + 4.656641006469727, + 4.069344997406006 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { "id": 0, - "label": "LLM Language Learning" + "label": "Math AI" } ], "_embedding": [ @@ -9819,31 +9819,31 @@ "openalex_url": "https://openalex.org/W7134991936", "projections": { "pca": [ - -0.10214082151651382, - -0.02228524722158909 + -0.10213490575551987, + -0.021924015134572983 ], "tsne": [ - -32.80967712402344, - 24.817644119262695 + -22.706825256347656, + -14.868754386901855 ], "umap": [ - 3.3230392932891846, - 5.6329498291015625 + 3.5711100101470947, + 4.865169048309326 ], "pca3d": [ - -0.10214057564735413, - -0.02228924259543419, - 0.04302094504237175 + -0.10213463008403778, + -0.02192786894738674, + 0.043047621846199036 ], "tsne3d": [ - -12.925915718078613, - 19.981054306030273, - 2.998063802719116 + -13.925507545471191, + -9.613724708557129, + 10.723038673400879 ], "umap3d": [ - 3.0069432258605957, - 6.205009937286377, - 4.569491863250732 + 3.4598076343536377, + 6.76148796081543, + 4.226158142089844 ] }, "cluster_id": 2, @@ -9854,12 +9854,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 16, - "label": "Diffusion Models" + "id": 7, + "label": "Diffusion Gen" } ], "_embedding": [ @@ -10269,31 +10269,31 @@ "abstract": "A library for soft differentiable relaxations of common JAX functions. - a-paulus/softjax", "projections": { "pca": [ - -0.07066436856985092, - 0.10656339675188065 + -0.07013390958309174, + 0.10658149421215057 ], "tsne": [ - -32.790191650390625, - 24.858230590820312 + -22.65152359008789, + -14.852706909179688 ], "umap": [ - 3.4239890575408936, - 5.713698863983154 + 3.6579153537750244, + 4.936980724334717 ], "pca3d": [ - -0.07066435366868973, - 0.10655158758163452, - 0.07700719684362411 + -0.07013390213251114, + 0.10656949132680893, + 0.07688800245523453 ], "tsne3d": [ - -12.1156005859375, - 20.142704010009766, - 3.2062246799468994 + -13.24067211151123, + -9.522759437561035, + 11.129573822021484 ], "umap3d": [ - 3.0678534507751465, - 6.148877143859863, - 4.5908284187316895 + 3.5681190490722656, + 6.678349494934082, + 4.216238498687744 ] }, "cluster_id": 2, @@ -10304,12 +10304,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 16, - "label": "Diffusion Models" + "id": 7, + "label": "Diffusion Gen" } ], "_embedding": [ @@ -10719,31 +10719,31 @@ "abstract": "A library for soft differentiable relaxations of common PyTorch functions. - a-paulus/softtorch", "projections": { "pca": [ - -0.13450580835342407, - 0.04864295572042465 + -0.13417254388332367, + 0.04901072010397911 ], "tsne": [ - -32.777976989746094, - 24.884719848632812 + -22.64435577392578, + -14.820579528808594 ], "umap": [ - 3.4050838947296143, - 5.754432678222656 + 3.667565107345581, + 5.005276203155518 ], "pca3d": [ - -0.13450579345226288, - 0.04863310232758522, - 0.03273312747478485 + -0.13417242467403412, + 0.04900060594081879, + 0.032761938869953156 ], "tsne3d": [ - -12.299137115478516, - 20.10370445251465, - 3.8417181968688965 + -13.463515281677246, + -9.06224536895752, + 11.481719017028809 ], "umap3d": [ - 3.014827013015747, - 6.060295581817627, - 4.569143295288086 + 3.548928737640381, + 6.653581619262695, + 4.24084997177124 ] }, "cluster_id": 2, @@ -10754,12 +10754,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 16, - "label": "Diffusion Models" + "id": 7, + "label": "Diffusion Gen" } ], "_embedding": [ @@ -11199,46 +11199,46 @@ ], "projections": { "pca": [ - -0.2267550230026245, - -0.07282250374555588 + -0.22685298323631287, + -0.07168392837047577 ], "tsne": [ - -60.33283233642578, - 1.2511993646621704 + -52.9025764465332, + 3.238630771636963 ], "umap": [ - 0.3550262451171875, - 7.426161289215088 + 0.6454795598983765, + 6.584683418273926 ], "pca3d": [ - -0.22675465047359467, - -0.07283948361873627, - 0.005290704779326916 + -0.22685258090496063, + -0.07170115411281586, + 0.005625751800835133 ], "tsne3d": [ - -26.121421813964844, - 4.442331314086914, - 10.71288013458252 + -33.39686584472656, + 2.6464128494262695, + 2.3895349502563477 ], "umap3d": [ - 0.5718756914138794, - 5.564537048339844, - 4.960602283477783 + 1.5453698635101318, + 5.849573135375977, + 5.601089000701904 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -11646,31 +11646,31 @@ "title": "Distribution-Conditioned Transport", "projections": { "pca": [ - -0.2507312297821045, - -0.04586140811443329 + -0.25081223249435425, + -0.04555823281407356 ], "tsne": [ - -37.97426223754883, - 17.812185287475586 + -33.77690505981445, + -23.481727600097656 ], "umap": [ - 2.3331830501556396, - 4.98681116104126 + 2.725809097290039, + 4.355015754699707 ], "pca3d": [ - -0.2507312297821045, - -0.045838046818971634, - -0.031149422749876976 + -0.2508121132850647, + -0.04553428664803505, + -0.031059877946972847 ], "tsne3d": [ - -17.31668472290039, - 10.9982328414917, - -1.9071727991104126 + -18.115907669067383, + -21.892911911010742, + 1.806564211845398 ], "umap3d": [ - 2.262573719024658, - 6.852784633636475, - 4.650137901306152 + 2.6540462970733643, + 7.23753023147583, + 4.550224781036377 ] }, "cluster_id": 2, @@ -11681,11 +11681,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -12106,31 +12106,31 @@ "cited_by_count": 2, "projections": { "pca": [ - -0.16035795211791992, - 0.0020451077725738287 + -0.16037990152835846, + 0.002213898580521345 ], "tsne": [ - -39.261260986328125, - 1.041463017463684 + -38.1728401184082, + -3.9633772373199463 ], "umap": [ - 1.8406645059585571, - 6.617779731750488 + 2.105403184890747, + 5.888429641723633 ], "pca3d": [ - -0.16035781800746918, - 0.002038240898400545, - 0.09463565796613693 + -0.16037966310977936, + 0.0022071879357099533, + 0.09449514746665955 ], "tsne3d": [ - -17.62538719177246, - 0.17140716314315796, - 3.1204473972320557 + -18.986656188964844, + -6.076441764831543, + 8.016131401062012 ], "umap3d": [ - 1.6796817779541016, - 5.6165242195129395, - 4.486574172973633 + 2.5093932151794434, + 5.9572062492370605, + 4.952516078948975 ] }, "cluster_id": 2, @@ -12141,12 +12141,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 33, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -12534,7 +12534,11 @@ 0.0213, 0.021, 0.0643 - ] + ], + "affiliations": [ + "The University of Sydney" + ], + "openalex_url": "https://openalex.org/W7133557937" }, { "channel": "papers-dl", @@ -12564,47 +12568,47 @@ "journal": "ArXiv.org", "projections": { "pca": [ - -0.20240937173366547, - -0.05788176506757736 + -0.202408567070961, + -0.05699196830391884 ], "tsne": [ - -21.378095626831055, - 0.4879644215106964 + -26.731115341186523, + -0.8984631896018982 ], "umap": [ - 2.1330103874206543, - 7.5814595222473145 + 2.2950727939605713, + 6.811060428619385 ], "pca3d": [ - -0.2024087756872177, - -0.05791907384991646, - 0.020062124356627464 + -0.20240800082683563, + -0.05702917277812958, + 0.020326746627688408 ], "tsne3d": [ - -18.81511116027832, - 2.6378214359283447, - 10.983702659606934 + -20.30743408203125, + 1.1709589958190918, + 10.54814624786377 ], "umap3d": [ - 1.8295438289642334, - 4.783667087554932, - 5.054361820220947 + 2.950883626937866, + 5.376537322998047, + 5.505792140960693 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -12992,7 +12996,9 @@ 0.0362, 0.0344, -0.0045 - ] + ], + "cited_by_count": 0, + "openalex_url": "https://openalex.org/W7134093517" }, { "channel": "papers-dl", @@ -13024,31 +13030,31 @@ "journal": "ArXiv.org", "projections": { "pca": [ - -0.21653246879577637, - -0.10391619801521301 + -0.21681585907936096, + -0.10371693968772888 ], "tsne": [ - -50.41559600830078, - -20.98566436767578 + -53.66213607788086, + 14.428135871887207 ], "umap": [ - 0.3796769976615906, - 5.807396411895752 + 0.7680234909057617, + 4.85468864440918 ], "pca3d": [ - -0.21653218567371368, - -0.10389786213636398, - -0.07406314462423325 + -0.21681563556194305, + -0.10369853675365448, + -0.07390382885932922 ], "tsne3d": [ - -32.660072326660156, - -12.261262893676758, - -3.908484697341919 + -30.203800201416016, + -1.302807092666626, + -9.951828956604004 ], "umap3d": [ - 0.5016548037528992, - 5.54389762878418, - 3.4794745445251465 + 1.0818904638290405, + 5.446529865264893, + 4.1741132736206055 ] }, "cluster_id": 2, @@ -13059,12 +13065,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, - "label": "Training Noise" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -13452,7 +13458,9 @@ -0.0103, 0.0355, 0.0139 - ] + ], + "cited_by_count": 0, + "openalex_url": "https://openalex.org/W4417431300" }, { "channel": "papers-dl", @@ -13493,46 +13501,46 @@ "journal": "ArXiv.org", "projections": { "pca": [ - -0.09447356313467026, - -0.010843093506991863 + -0.09446791559457779, + -0.009408494457602501 ], "tsne": [ - -15.120619773864746, - -24.862394332885742 + -17.629594802856445, + 17.161148071289062 ], "umap": [ - 3.299617290496826, - 8.393035888671875 + 3.6138863563537598, + 7.616109371185303 ], "pca3d": [ - -0.09447313100099564, - -0.010881965048611164, - 0.16931858658790588 + -0.09446752071380615, + -0.009447748772799969, + 0.16945715248584747 ], "tsne3d": [ - -10.930246353149414, - -8.150800704956055, - -4.9947004318237305 + -11.240995407104492, + 14.688477516174316, + 5.808091640472412 ], "umap3d": [ - 2.649650812149048, - 4.0320281982421875, - 3.4366471767425537 + 3.345264196395874, + 4.38935661315918, + 3.91711163520813 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 32, + "id": 25, "label": "AI Agents" } ], @@ -13921,7 +13929,9 @@ 0.0586, 0.0342, 0.0013 - ] + ], + "cited_by_count": 0, + "openalex_url": "https://openalex.org/W7133267642" }, { "channel": "papers-dl", @@ -13952,31 +13962,31 @@ "doi": "10.48550/arxiv.2509.25170", "projections": { "pca": [ - -0.2004416137933731, - -0.10876146703958511 + -0.2007332742214203, + -0.10831762850284576 ], "tsne": [ - -39.408241271972656, - 17.882808685302734 + -35.12348937988281, + -23.865707397460938 ], "umap": [ - 2.261878728866577, - 4.872035026550293 + 2.7502126693725586, + 4.236865520477295 ], "pca3d": [ - -0.20044121146202087, - -0.108767569065094, - -0.03071187436580658 + -0.20073287189006805, + -0.10832365602254868, + -0.030500896275043488 ], "tsne3d": [ - -18.255104064941406, - 13.41294002532959, - -0.4016633629798889 + -17.621442794799805, + -22.645357131958008, + 4.576435089111328 ], "umap3d": [ - 2.1682376861572266, - 6.98159646987915, - 4.765028476715088 + 2.6081738471984863, + 7.349185466766357, + 4.640848636627197 ] }, "cluster_id": 2, @@ -13987,11 +13997,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -14380,7 +14390,9 @@ 0.0215, -0.015, -0.0473 - ] + ], + "cited_by_count": 0, + "openalex_url": "https://openalex.org/W4415337907" }, { "channel": "papers-dl", @@ -14401,31 +14413,31 @@ "abstract": "Lessons learned from working with discrete diffusion language models, and more or less speculative predictions about their future.", "projections": { "pca": [ - -0.29049479961395264, - 0.04910571873188019 + -0.2903158664703369, + 0.050722770392894745 ], "tsne": [ - -35.88576889038086, - 9.258419036865234 + -33.73093795776367, + -16.0968074798584 ], "umap": [ - 2.4224798679351807, - 5.374240398406982 + 2.876391887664795, + 4.844727516174316 ], "pca3d": [ - -0.2904951274394989, - 0.049124423414468765, - 0.09304431080818176 + -0.29031604528427124, + 0.05074112117290497, + 0.09321308881044388 ], "tsne3d": [ - -14.667147636413574, - 5.533018112182617, - 5.276998519897461 + -17.548973083496094, + -14.789464950561523, + 7.835221290588379 ], "umap3d": [ - 2.150662422180176, - 6.535004138946533, - 4.957784175872803 + 2.8346521854400635, + 6.972476482391357, + 4.84105110168457 ] }, "cluster_id": 2, @@ -14436,11 +14448,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -14869,46 +14881,46 @@ "journal": "ArXiv.org", "projections": { "pca": [ - -0.2915978729724884, - -0.09050553292036057 + -0.29173922538757324, + -0.08964444696903229 ], "tsne": [ - -57.47119140625, - -0.4729200303554535 + -47.7596549987793, + 6.793935775756836 ], "umap": [ - 0.3335927724838257, - 7.668074131011963 + 0.5363259315490723, + 6.801970481872559 ], "pca3d": [ - -0.2915972173213959, - -0.09053084999322891, - -0.07671533524990082 + -0.29173871874809265, + -0.08966966718435287, + -0.07636384665966034 ], "tsne3d": [ - -24.934585571289062, - 0.086138054728508, - 9.88199234008789 + -26.904407501220703, + 1.9112995862960815, + 8.218490600585938 ], "umap3d": [ - 0.5121113657951355, - 5.152071475982666, - 5.043582439422607 + 1.5479736328125, + 5.5193257331848145, + 5.8590240478515625 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -15297,7 +15309,9 @@ -0.0018, -0.002, -0.0138 - ] + ], + "cited_by_count": 0, + "openalex_url": "https://openalex.org/W4415067523" }, { "channel": "papers-dl", @@ -15317,31 +15331,31 @@ "doi": "10.1037/t02865-000", "projections": { "pca": [ - 0.03702429309487343, - 0.11060647666454315 + 0.037006478756666183, + 0.110664501786232 ], "tsne": [ - 43.88272476196289, - -19.155475616455078 + 40.68021011352539, + 23.896751403808594 ], "umap": [ - 8.513504981994629, - 8.067705154418945 + 8.855252265930176, + 7.607651233673096 ], "pca3d": [ - 0.03702394291758537, - 0.11060916632413864, - -0.16928690671920776 + 0.03700612857937813, + 0.11066699028015137, + -0.1695779263973236 ], "tsne3d": [ - 22.32450294494629, - 2.647078037261963, - -11.70090389251709 + 22.378345489501953, + -2.982959270477295, + 6.49917459487915 ], "umap3d": [ - 6.592933177947998, - 4.594797611236572, - 1.5141327381134033 + 6.257167339324951, + 4.504937171936035, + 0.7282886505126953 ] }, "cluster_id": 0, @@ -15352,12 +15366,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { "id": 31, - "label": "Genome Cancer" + "label": "Single Cell Seq" } ], "_embedding": [ @@ -15784,47 +15798,47 @@ "openalex_url": "https://openalex.org/W7130610581", "projections": { "pca": [ - -0.26008644700050354, - -0.00466705858707428 + -0.26005232334136963, + -0.0030563026666641235 ], "tsne": [ - -38.189476013183594, - -23.92499351501465 + -41.6650390625, + 18.4293270111084 ], "umap": [ - 1.84819495677948, - 7.434756278991699 + 2.2243051528930664, + 6.71611213684082 ], "pca3d": [ - -0.26008614897727966, - -0.004689509514719248, - 0.03250327333807945 + -0.260051965713501, + -0.003079300746321678, + 0.032803017646074295 ], "tsne3d": [ - -21.498146057128906, - -15.621575355529785, - -0.05284203961491585 + -22.34209442138672, + 6.469517230987549, + -8.810036659240723 ], "umap3d": [ - 1.006722331047058, - 4.5564961433410645, - 3.7119085788726807 + 1.8639750480651855, + 4.716251373291016, + 4.399662971496582 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -16243,31 +16257,31 @@ "journal": "ArXiv.org", "projections": { "pca": [ - -0.24482928216457367, - -0.121771901845932 + -0.24510428309440613, + -0.12067482620477676 ], "tsne": [ - -35.70648956298828, - 11.47386646270752 + -32.86909484863281, + -16.99042510986328 ], "umap": [ - 2.4505350589752197, - 5.363415718078613 + 2.886120319366455, + 4.809735298156738 ], "pca3d": [ - -0.24482901394367218, - -0.12176982313394547, - 0.02077604830265045 + -0.24510395526885986, + -0.12067277729511261, + 0.02114018052816391 ], "tsne3d": [ - -16.892929077148438, - 6.758357048034668, - 3.919785976409912 + -16.20664405822754, + -15.880958557128906, + 6.298907279968262 ], "umap3d": [ - 2.2400245666503906, - 6.497407913208008, - 4.871510028839111 + 2.8366706371307373, + 6.960339546203613, + 4.811172962188721 ] }, "cluster_id": 2, @@ -16278,11 +16292,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -16671,7 +16685,9 @@ -0.0185, -0.0015, -0.0614 - ] + ], + "cited_by_count": 0, + "openalex_url": "https://openalex.org/W7130237915" }, { "channel": "papers-dl", @@ -16700,31 +16716,31 @@ "journal": "ArXiv.org", "projections": { "pca": [ - -0.28521326184272766, - -0.13871122896671295 + -0.2855670154094696, + -0.13803261518478394 ], "tsne": [ - -40.578216552734375, - 13.492220878601074 + -36.849464416503906, + -20.311996459960938 ], "umap": [ - 1.9205580949783325, - 5.229482173919678 + 2.4167895317077637, + 4.635252952575684 ], "pca3d": [ - -0.285212904214859, - -0.13871367275714874, - -0.0019477956229820848 + -0.2855665683746338, + -0.13803502917289734, + -0.0016592085594311357 ], "tsne3d": [ - -21.300731658935547, - 10.951132774353027, - 2.354713201522827 + -20.641204833984375, + -19.512624740600586, + 4.741832733154297 ], "umap3d": [ - 1.7495529651641846, - 6.685699462890625, - 4.742166519165039 + 2.3637847900390625, + 7.044791221618652, + 4.826610088348389 ] }, "cluster_id": 2, @@ -16735,11 +16751,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -17128,7 +17144,9 @@ 0.0255, -0.059, -0.0572 - ] + ], + "cited_by_count": 0, + "openalex_url": "https://openalex.org/W7128096453" }, { "channel": "papers-dl", @@ -17183,46 +17201,46 @@ "journal": "Open MIND", "projections": { "pca": [ - -0.20022791624069214, - -0.10118170082569122 + -0.2004985213279724, + -0.10061932355165482 ], "tsne": [ - -28.216955184936523, - -7.9376091957092285 + -32.23080062866211, + 7.452698707580566 ], "umap": [ - 1.5702649354934692, - 7.961864948272705 + 1.8195844888687134, + 7.264180660247803 ], "pca3d": [ - -0.20022769272327423, - -0.10118072479963303, - 0.031036263331770897 + -0.20049813389778137, + -0.10061808675527573, + 0.031231436878442764 ], "tsne3d": [ - -15.383587837219238, - -7.348010540008545, - 6.1017961502075195 + -15.777575492858887, + 3.509002685546875, + 2.9291555881500244 ], "umap3d": [ - 1.6346689462661743, - 4.628329277038574, - 4.657581329345703 + 2.631260871887207, + 5.0972771644592285, + 5.253804683685303 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -17611,7 +17629,9 @@ 0.0056, -0.0053, -0.0024 - ] + ], + "cited_by_count": 0, + "openalex_url": "https://openalex.org/W7126071242" }, { "channel": "papers-dl", @@ -17642,46 +17662,46 @@ "journal": "Open MIND", "projections": { "pca": [ - -0.24302829802036285, - -0.12026771157979965 + -0.2432510256767273, + -0.11959801614284515 ], "tsne": [ - -26.55392074584961, - -8.768697738647461 + -33.30458450317383, + 6.350700378417969 ], "umap": [ - 1.6900432109832764, - 8.27979850769043 + 2.056551694869995, + 7.435840606689453 ], "pca3d": [ - -0.24302789568901062, - -0.12027022242546082, - 0.006744519341737032 + -0.24325060844421387, + -0.1196003407239914, + 0.0071256039664149284 ], "tsne3d": [ - -15.274324417114258, - -7.190013408660889, - 9.34625244140625 + -16.07798194885254, + 5.435523986816406, + 5.857166767120361 ], "umap3d": [ - 1.687570571899414, - 4.461169242858887, - 4.76215934753418 + 2.7515921592712402, + 4.994128704071045, + 5.352684497833252 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 1, + "label": "Attention Models" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -18070,7 +18090,9 @@ 0.0379, 0.0395, -0.0482 - ] + ], + "cited_by_count": 0, + "openalex_url": "https://openalex.org/W7128493546" }, { "channel": "papers-dl", @@ -18124,47 +18146,47 @@ "cited_by_count": 14, "projections": { "pca": [ - -0.09103265404701233, - 0.0681760385632515 + -0.0905710831284523, + 0.06842073798179626 ], "tsne": [ - -7.808195114135742, - -17.111196517944336 + -13.012611389160156, + 22.478302001953125 ], "umap": [ - 4.125734806060791, - 8.262922286987305 + 4.331121444702148, + 7.565573692321777 ], "pca3d": [ - -0.09103269129991531, - 0.06817683577537537, - 0.10836242139339447 + -0.09057115763425827, + 0.06842166185379028, + 0.10846664011478424 ], "tsne3d": [ - -8.064894676208496, - -12.252424240112305, - 4.262281894683838 + -4.514648914337158, + 8.271913528442383, + -3.352407455444336 ], "umap3d": [ - 3.418754816055298, - 3.8046345710754395, - 3.6808111667633057 + 4.259480953216553, + 4.200287818908691, + 4.031784534454346 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -18552,7 +18574,16 @@ 0.0261, 0.0756, 0.0141 - ] + ], + "affiliations": [ + "University of Washington", + "Allen Institute", + "University of Illinois Urbana-Champaign", + "University of North Carolina at Chapel Hill", + "Stanford University", + "Carnegie Mellon University" + ], + "openalex_url": "https://openalex.org/W7127589252" }, { "channel": "papers-dl", @@ -18583,46 +18614,46 @@ "journal": "Open MIND", "projections": { "pca": [ - -0.15812449157238007, - -0.16728003323078156 + -0.1584046632051468, + -0.16620059311389923 ], "tsne": [ - -31.485515594482422, - -9.527955055236816 + -36.939510345458984, + 8.520888328552246 ], "umap": [ - 1.4717894792556763, - 8.199319839477539 + 1.797910451889038, + 7.486255168914795 ], "pca3d": [ - -0.15812397003173828, - -0.16729816794395447, - 0.04381836578249931 + -0.1584041863679886, + -0.16621871292591095, + 0.044362347573041916 ], "tsne3d": [ - -18.243709564208984, - -7.933683395385742, - 10.982381820678711 + -18.941699981689453, + 3.7813994884490967, + 6.830366611480713 ], "umap3d": [ - 1.4011110067367554, - 4.5260210037231445, - 4.87835168838501 + 2.5402019023895264, + 5.012768745422363, + 5.563831329345703 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -19011,7 +19042,9 @@ 0.001, 0.0659, -0.032 - ] + ], + "cited_by_count": 0, + "openalex_url": "https://openalex.org/W7126178311" }, { "channel": "papers-dl", @@ -19046,46 +19079,46 @@ ], "projections": { "pca": [ - -0.23259779810905457, - -0.14261355996131897 + -0.2330135703086853, + -0.1416349560022354 ], "tsne": [ - -34.25142288208008, - -11.011924743652344 + -39.341697692871094, + 10.501874923706055 ], "umap": [ - 1.3083723783493042, - 8.067670822143555 + 1.4430516958236694, + 7.233522415161133 ], "pca3d": [ - -0.2325972020626068, - -0.14261353015899658, - -0.08304038643836975 + -0.23301322758197784, + -0.1416349560022354, + -0.0826333537697792 ], "tsne3d": [ - -22.38939094543457, - -9.737398147583008, - 8.849879264831543 + -21.801408767700195, + 6.05273962020874, + 8.814447402954102 ], "umap3d": [ - 1.071223258972168, - 4.529295921325684, - 4.824127674102783 + 2.249788284301758, + 4.90571928024292, + 5.545933246612549 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -19504,47 +19537,47 @@ "journal": "Open MIND", "projections": { "pca": [ - -0.20228125154972076, - -0.050856512039899826 + -0.20254912972450256, + -0.049873143434524536 ], "tsne": [ - -33.0677375793457, - -22.543588638305664 + -30.11263656616211, + 3.682586431503296 ], "umap": [ - 1.8322222232818604, - 7.2435808181762695 + 1.8690099716186523, + 6.2667365074157715 ], "pca3d": [ - -0.2022809535264969, - -0.050854649394750595, - -0.03707488253712654 + -0.20254890620708466, + -0.04987166449427605, + -0.03696407005190849 ], "tsne3d": [ - -17.93096923828125, - -12.855016708374023, - -3.983595848083496 + -22.49655532836914, + 12.575145721435547, + -4.399609088897705 ], "umap3d": [ - 1.439726710319519, - 4.950359344482422, - 4.009718894958496 + 2.264589786529541, + 5.1705522537231445, + 4.498452186584473 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -19932,7 +19965,9 @@ 0.0745, 0.0275, -0.0372 - ] + ], + "cited_by_count": 0, + "openalex_url": "https://openalex.org/W7125915072" }, { "channel": "papers-dl", @@ -19951,46 +19986,46 @@ "title": "Using Interpretability to Identify a Novel Class of Alzheimer's Biomarkers", "projections": { "pca": [ - -0.029890533536672592, - 0.14024458825588226 + -0.029417261481285095, + 0.1405046433210373 ], "tsne": [ - 3.3864030838012695, - -23.016572952270508 + -1.2277833223342896, + 22.484107971191406 ], "umap": [ - 6.143115997314453, - 8.267757415771484 + 6.243215084075928, + 7.818302631378174 ], "pca3d": [ - -0.02989097125828266, - 0.14026480913162231, - -0.08518011122941971 + -0.029417743906378746, + 0.14052419364452362, + -0.08526266366243362 ], "tsne3d": [ - 2.033238410949707, - -9.15843391418457, - 1.195339322090149 + 0.13738900423049927, + 15.229650497436523, + -2.989738941192627 ], "umap3d": [ - 5.421785831451416, - 3.48207426071167, - 2.9367551803588867 + 5.631283283233643, + 3.821974515914917, + 2.8351917266845703 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 26, + "id": 33, "label": "Genetic Disease" } ], @@ -20406,46 +20441,46 @@ "year": 2023, "projections": { "pca": [ - -0.26086878776550293, - -0.1623457670211792 + -0.2611451745033264, + -0.16171152889728546 ], "tsne": [ - -61.30802536010742, - -6.692930698394775 + -57.3079833984375, + 2.660893678665161 ], "umap": [ - 0.6174285411834717, - 7.23167610168457 + 0.8740858435630798, + 6.374914169311523 ], "pca3d": [ - -0.2608684301376343, - -0.1623571515083313, - -0.11582233011722565 + -0.26114463806152344, + -0.1617230772972107, + -0.11529093980789185 ], "tsne3d": [ - -27.35858154296875, - 4.053585052490234, - 3.4651572704315186 + -36.036781311035156, + -2.7207508087158203, + 2.915872812271118 ], "umap3d": [ - 0.7414547204971313, - 5.725358486175537, - 4.9238386154174805 + 1.6138451099395752, + 6.016660213470459, + 5.569130897521973 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -20861,47 +20896,47 @@ ], "projections": { "pca": [ - -0.12610921263694763, - -0.16528959572315216 + -0.12650269269943237, + -0.1646524965763092 ], "tsne": [ - -29.878633499145508, - 14.474849700927734 + -25.531085968017578, + -18.471847534179688 ], "umap": [ - 3.40348482131958, - 4.96644926071167 + 3.9191622734069824, + 4.286771297454834 ], "pca3d": [ - -0.1261088103055954, - -0.16529497504234314, - 0.07328542321920395 + -0.12650229036808014, + -0.16465790569782257, + 0.07360296696424484 ], "tsne3d": [ - -7.548672676086426, - 7.983310222625732, - 0.20635195076465607 + -11.147411346435547, + -21.13544273376465, + 7.186508655548096 ], "umap3d": [ - 3.2958226203918457, - 6.835926532745361, - 4.703097343444824 + 3.6099295616149902, + 7.222229957580566, + 4.10227632522583 ] }, - "cluster_id": 2, - "cluster_label": "Generative Models", + "cluster_id": 3, + "cluster_label": "Protein Design", "cluster_levels": [ { - "id": 2, - "label": "Generative Models" + "id": 3, + "label": "Protein Design" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 16, - "label": "Diffusion Models" + "id": 7, + "label": "Diffusion Gen" } ], "_embedding": [ @@ -21319,46 +21354,46 @@ "journal": "ArXiv.org", "projections": { "pca": [ - -0.19246649742126465, - -0.13874255120754242 + -0.19271089136600494, + -0.13777920603752136 ], "tsne": [ - -29.56075668334961, - -12.642523765563965 + -35.114253997802734, + 10.753305435180664 ], "umap": [ - 1.6849993467330933, - 8.220667839050293 + 1.8479838371276855, + 7.438892841339111 ], "pca3d": [ - -0.19246603548526764, - -0.1387527883052826, - -0.012522763572633266 + -0.19271045923233032, + -0.1377895325422287, + -0.012013277038931847 ], "tsne3d": [ - -17.75617027282715, - -11.424985885620117, - 7.287026882171631 + -19.14516258239746, + 5.715742588043213, + 1.0999281406402588 ], "umap3d": [ - 1.5060575008392334, - 4.326382637023926, - 4.729973793029785 + 2.568329095840454, + 4.8401875495910645, + 5.424071788787842 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 1, + "label": "Attention Models" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -21747,7 +21782,9 @@ 0.0241, -0.0006, -0.0145 - ] + ], + "cited_by_count": 0, + "openalex_url": "https://openalex.org/W4417448106" }, { "channel": "papers-dl", @@ -21781,47 +21818,47 @@ "cited_by_count": 25, "projections": { "pca": [ - -0.23608313500881195, - 0.02053717151284218 + -0.2362181544303894, + 0.021444275975227356 ], "tsne": [ - -31.3878173828125, - -22.93657875061035 + -36.968936920166016, + 18.37996482849121 ], "umap": [ - 1.8261369466781616, - 7.190950393676758 + 1.8973902463912964, + 6.2779035568237305 ], "pca3d": [ - -0.23608294129371643, - 0.020533345639705658, - -0.05560316890478134 + -0.23621802031993866, + 0.021440083160996437, + -0.05562679469585419 ], "tsne3d": [ - -17.20619010925293, - -15.905278205871582, - -4.548198699951172 + -24.47705078125, + 13.279852867126465, + -2.467252731323242 ], "umap3d": [ - 1.3588101863861084, - 4.992440700531006, - 4.033730983734131 + 2.274146556854248, + 5.1941447257995605, + 4.600846767425537 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -22209,7 +22246,8 @@ 0.0649, 0.0011, -0.0407 - ] + ], + "openalex_url": "https://openalex.org/W4226325574" }, { "channel": "papers-dl", @@ -22244,46 +22282,46 @@ "cited_by_count": 11, "projections": { "pca": [ - -0.2238207459449768, - 0.024604134261608124 + -0.22373409569263458, + 0.02604980766773224 ], "tsne": [ - -24.451244354248047, - -16.096120834350586 + -28.82479476928711, + 13.854703903198242 ], "umap": [ - 2.3703255653381348, - 8.37379264831543 + 2.5917534828186035, + 7.593898296356201 ], "pca3d": [ - -0.223820298910141, - 0.02458181232213974, - 0.04735242575407028 + -0.22373376786708832, + 0.026027053594589233, + 0.04756335914134979 ], "tsne3d": [ - -14.12921142578125, - -15.261886596679688, - 7.339230060577393 + -16.081466674804688, + 5.550442695617676, + -3.8792495727539062 ], "umap3d": [ - 2.084686279296875, - 3.868250608444214, - 4.35457706451416 + 3.11565899848938, + 4.393817901611328, + 5.004323482513428 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -22672,7 +22710,20 @@ 0.0084, -0.002, -0.0562 - ] + ], + "affiliations": [ + "Bexley Hall", + "Risk Management Solutions (United Kingdom)", + "Warsaw University of Technology", + "University College London", + "University of Toronto", + "Aisin (United States)", + "Stanford University", + "American Legacy Foundation", + "Berkeley College", + "University of California, Berkeley" + ], + "openalex_url": "https://openalex.org/W7124177090" }, { "channel": "papers-dl", @@ -22705,47 +22756,47 @@ "cited_by_count": 51, "projections": { "pca": [ - -0.0998208299279213, - 0.20266062021255493 + -0.09928738325834274, + 0.2040640264749527 ], "tsne": [ - -8.947175979614258, - -21.675962448120117 + -10.910887718200684, + 16.28146743774414 ], "umap": [ - 4.0110368728637695, - 8.474061012268066 + 4.189399719238281, + 7.821937561035156 ], "pca3d": [ - -0.09982110559940338, - 0.2026599496603012, - 0.09137926995754242 + -0.09928768873214722, + 0.2040632963180542, + 0.09122874587774277 ], "tsne3d": [ - -6.033580780029297, - -7.458610534667969, - 0.1288006752729416 + -7.314470291137695, + 9.707139015197754, + 0.9037549495697021 ], "umap3d": [ - 3.230970621109009, - 3.6630609035491943, - 3.3824729919433594 + 3.94846510887146, + 4.042037487030029, + 3.823516368865967 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -23133,7 +23184,8 @@ 0.0047, 0.0675, -0.0083 - ] + ], + "openalex_url": "https://openalex.org/W4405273923" }, { "channel": "papers-dl", @@ -23155,31 +23207,31 @@ "abstract": "We\u2019re on a journey to advance and democratize artificial intelligence through open source and open science.", "projections": { "pca": [ - -0.03617106005549431, - -0.04282637685537338 + -0.03614383190870285, + -0.04275500774383545 ], "tsne": [ - -21.57932472229004, - 36.01605224609375 + -19.885473251342773, + -17.98688316345215 ], "umap": [ - 3.819791793823242, - 4.982246398925781 + 4.2999796867370605, + 4.46130895614624 ], "pca3d": [ - -0.03617095947265625, - -0.04282064363360405, - 0.02311427891254425 + -0.036143720149993896, + -0.04274967312812805, + 0.02324981987476349 ], "tsne3d": [ - -8.631022453308105, - 9.3987455368042, - -5.105255603790283 + -7.732178211212158, + -21.70332908630371, + -3.2987992763519287 ], "umap3d": [ - 5.007543087005615, - 5.419999122619629, - 3.585721731185913 + 4.163777828216553, + 6.832446098327637, + 3.8447766304016113 ] }, "cluster_id": 3, @@ -23190,12 +23242,12 @@ "label": "Protein Design" }, { - "id": 7, - "label": "Diffusion Models" + "id": 9, + "label": "Protein Design" }, { - "id": 16, - "label": "Diffusion Models" + "id": 7, + "label": "Diffusion Gen" } ], "_embedding": [ @@ -23605,31 +23657,31 @@ "abstract": "We\u2019re on a journey to advance and democratize artificial intelligence through open source and open science.", "projections": { "pca": [ - 0.019996855407953262, - -0.0212690532207489 + 0.02009635977447033, + -0.021388044580817223 ], "tsne": [ - -21.574739456176758, - 36.02210235595703 + -19.870038986206055, + -17.984479904174805 ], "umap": [ - 3.968081474304199, - 4.997467041015625 + 4.478731632232666, + 4.4813408851623535 ], "pca3d": [ - 0.019996965304017067, - -0.021269338205456734, - 0.009185905568301678 + 0.020096447318792343, + -0.021388940513134003, + 0.009264351800084114 ], "tsne3d": [ - -8.611265182495117, - 9.419760704040527, - -5.114894866943359 + -7.714600086212158, + -21.710792541503906, + -3.321275472640991 ], "umap3d": [ - 5.16508150100708, - 5.368179798126221, - 3.4675981998443604 + 4.291775226593018, + 6.782628536224365, + 3.7403597831726074 ] }, "cluster_id": 3, @@ -23640,12 +23692,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 16, - "label": "Diffusion Models" + "id": 7, + "label": "Diffusion Gen" } ], "_embedding": [ @@ -24052,46 +24104,46 @@ "title": "Recursive Language Models", "projections": { "pca": [ - -0.2301565557718277, - 0.0003871515509672463 + -0.2297704964876175, + 0.0008686453220434487 ], "tsne": [ - -26.410751342773438, - -12.991127014160156 + -30.731849670410156, + 10.831988334655762 ], "umap": [ - 1.8172661066055298, - 8.413357734680176 + 2.24330472946167, + 7.6575493812561035 ], "pca3d": [ - -0.23015625774860382, - 0.0003789184847846627, - 0.058498870581388474 + -0.22977034747600555, + 0.0008606822229921818, + 0.05888565629720688 ], "tsne3d": [ - -14.988407135009766, - -11.690888404846191, - 6.018310546875 + -15.564718246459961, + 9.305153846740723, + 0.03198236599564552 ], "umap3d": [ - 2.0513222217559814, - 4.207271099090576, - 4.726293563842773 + 2.942641019821167, + 4.710052490234375, + 5.325252056121826 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 1, + "label": "Attention Models" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -24509,31 +24561,31 @@ "cited_by_count": 1, "projections": { "pca": [ - -0.21452942490577698, - -0.012327466160058975 + -0.21452465653419495, + -0.01138369832187891 ], "tsne": [ - -47.35257339477539, - -22.971466064453125 + -51.96299362182617, + 17.25033950805664 ], "umap": [ - 0.39970216155052185, - 5.873470306396484 + 0.7668228149414062, + 4.88894510269165 ], "pca3d": [ - -0.21452906727790833, - -0.012324538081884384, - -0.0671711415052414 + -0.21452447772026062, + -0.011380611918866634, + -0.06696435064077377 ], "tsne3d": [ - -29.30792808532715, - -13.029969215393066, - -5.79757022857666 + -27.271371841430664, + 0.2104298621416092, + -11.17495059967041 ], "umap3d": [ - 0.5201001763343811, - 5.455782890319824, - 3.4396681785583496 + 1.1781948804855347, + 5.338768005371094, + 4.184591293334961 ] }, "cluster_id": 2, @@ -24544,12 +24596,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, - "label": "Training Noise" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -24937,7 +24989,8 @@ 0.0297, 0.0208, -0.0491 - ] + ], + "openalex_url": "https://openalex.org/W4399794768" }, { "channel": "papers-dl", @@ -24966,46 +25019,46 @@ ], "projections": { "pca": [ - -0.14378710091114044, - -0.0991903692483902 + -0.14384357631206512, + -0.09856780618429184 ], "tsne": [ - -35.17279815673828, - -5.7208027839660645 + -42.95317840576172, + 7.562597274780273 ], "umap": [ - 0.7434744238853455, - 8.213656425476074 + 1.0967463254928589, + 7.378171920776367 ], "pca3d": [ - -0.1437867432832718, - -0.09921044111251831, - 0.027306174859404564 + -0.1438431739807129, + -0.09858809411525726, + 0.027687067165970802 ], "tsne3d": [ - -24.363677978515625, - -7.010679244995117, - 13.48827075958252 + -26.25010108947754, + 8.32260513305664, + 5.4456353187561035 ], "umap3d": [ - 0.8962944149971008, - 4.672860145568848, - 5.179159164428711 + 2.0525360107421875, + 5.062584400177002, + 5.899709224700928 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -25459,47 +25512,47 @@ "cited_by_count": 6, "projections": { "pca": [ - -0.23606041073799133, - 0.022162288427352905 + -0.23582114279270172, + 0.02296602725982666 ], "tsne": [ - -59.551612854003906, - -4.088625907897949 + -54.049217224121094, + 6.222288608551025 ], "umap": [ - 0.5013136267662048, - 7.7172393798828125 + 0.8461527824401855, + 6.752170562744141 ], "pca3d": [ - -0.23606005311012268, - 0.022138051688671112, - -0.025149473920464516 + -0.23582084476947784, + 0.022941596806049347, + -0.024918850511312485 ], "tsne3d": [ - -30.0975341796875, - 4.112667083740234, - 9.909846305847168 + -34.765724182128906, + -1.399288535118103, + 6.085842609405518 ], "umap3d": [ - 0.7289994955062866, - 5.422262191772461, - 5.109226226806641 + 1.7249679565429688, + 5.755741119384766, + 5.908992290496826 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, - "label": "Transformer Attention" + "id": 16, + "label": "Vision Transformers" } ], "_embedding": [ @@ -25887,7 +25940,8 @@ 0.0454, 0.0654, 0.0034 - ] + ], + "openalex_url": "https://openalex.org/W4417095222" }, { "channel": "papers-dl", @@ -25916,47 +25970,47 @@ "journal": "Repository for Publications and Research Data (ETH Zurich)", "projections": { "pca": [ - -0.18308284878730774, - -0.0010774931870400906 + -0.1830090582370758, + 0.00016348742065019906 ], "tsne": [ - -15.531593322753906, - -27.06401824951172 + -19.16008949279785, + 19.285715103149414 ], "umap": [ - 3.0566539764404297, - 8.308627128601074 + 3.2993662357330322, + 7.606450080871582 ], "pca3d": [ - -0.1830824911594391, - -0.0011014834744855762, - 0.07398796826601028 + -0.18300870060920715, + 0.00013861220213584602, + 0.07418566197156906 ], "tsne3d": [ - -11.013680458068848, - -11.17892837524414, - -7.6584553718566895 + -11.8858060836792, + 17.50798797607422, + 3.342468738555908 ], "umap3d": [ - 2.3953583240509033, - 3.99924373626709, - 3.610421657562256 + 3.229949474334717, + 4.302424907684326, + 4.139368057250977 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 0, - "label": "LLM Language Learning" + "id": 25, + "label": "AI Agents" } ], "_embedding": [ @@ -26344,7 +26398,9 @@ 0.0918, -0.0095, 0.0021 - ] + ], + "cited_by_count": 0, + "openalex_url": "https://openalex.org/W7125819367" }, { "channel": "papers-dl", @@ -26379,47 +26435,47 @@ "cited_by_count": 153, "projections": { "pca": [ - -0.21328333020210266, - -0.00739548122510314 + -0.21336282789707184, + -0.006094878073781729 ], "tsne": [ - -39.828407287597656, - -23.112375259399414 + -43.384735107421875, + 17.79554557800293 ], "umap": [ - 1.7568700313568115, - 7.335201263427734 + 2.227847099304199, + 6.493479251861572 ], "pca3d": [ - -0.21328319609165192, - -0.007407173048704863, - -0.009576682932674885 + -0.21336272358894348, + -0.006107080262154341, + -0.009394664317369461 ], "tsne3d": [ - -23.650829315185547, - -15.507253646850586, - -0.19274690747261047 + -23.599199295043945, + 4.920466423034668, + -9.38825798034668 ], "umap3d": [ - 0.8536162972450256, - 4.621796607971191, - 3.5898354053497314 + 1.661080002784729, + 4.771673202514648, + 4.3035783767700195 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -26807,7 +26863,13 @@ 0.0357, 0.0561, -0.0217 - ] + ], + "affiliations": [ + "Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute", + "Berlin Institute for the Foundations of Learning and Data", + "Technische Universit\u00e4t Berlin" + ], + "openalex_url": "https://openalex.org/W4386897466" }, { "channel": "papers-dl", @@ -26836,47 +26898,47 @@ "cited_by_count": 5, "projections": { "pca": [ - -0.22116857767105103, - -0.10836627334356308 + -0.22132620215415955, + -0.10790344327688217 ], "tsne": [ - -60.804691314697266, - -9.032038688659668 + -58.22749328613281, + 0.31547942757606506 ], "umap": [ - 0.267645001411438, - 6.711791038513184 + 0.49791622161865234, + 5.883087635040283 ], "pca3d": [ - -0.22116824984550476, - -0.1083778664469719, - -0.09346669912338257 + -0.22132590413093567, + -0.10791557282209396, + -0.09308673441410065 ], "tsne3d": [ - -29.188907623291016, - 6.347811698913574, - 2.2529876232147217 + -35.59105682373047, + -3.2974376678466797, + -0.38647523522377014 ], "umap3d": [ - 0.6153334975242615, - 6.221216678619385, - 4.701435565948486 + 1.3622088432312012, + 6.484292030334473, + 5.237283706665039 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 9, - "label": "Vision Transformers" + "id": 23, + "label": "Training Noise" } ], "_embedding": [ @@ -27264,7 +27326,8 @@ 0.0378, 0.0118, -0.037 - ] + ], + "openalex_url": "https://openalex.org/W3174039247" }, { "channel": "papers-dl", @@ -27291,47 +27354,47 @@ "year": 2021, "projections": { "pca": [ - -0.2315901666879654, - -0.15097171068191528 + -0.23193833231925964, + -0.15034422278404236 ], "tsne": [ - -61.199615478515625, - -10.613189697265625 + -59.46250915527344, + -0.41754981875419617 ], "umap": [ - 0.4823033809661865, - 6.708889484405518 + 0.6837716102600098, + 5.69878625869751 ], "pca3d": [ - -0.23158983886241913, - -0.15097448229789734, - -0.06369833648204803 + -0.23193784058094025, + -0.1503472626209259, + -0.06330884248018265 ], "tsne3d": [ - -30.0945987701416, - 7.09581995010376, - 0.7804838418960571 + -34.62454605102539, + -3.4200305938720703, + -2.0445261001586914 ], "umap3d": [ - 0.6664038300514221, - 6.1741437911987305, - 4.65141487121582 + 1.3189960718154907, + 6.428989410400391, + 5.1218366622924805 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 9, - "label": "Vision Transformers" + "id": 23, + "label": "Training Noise" } ], "_embedding": [ @@ -27750,46 +27813,46 @@ ], "projections": { "pca": [ - -0.17023207247257233, - -0.10465582460165024 + -0.17042946815490723, + -0.1039128452539444 ], "tsne": [ - -56.851585388183594, - 2.3123672008514404 + -52.24295425415039, + 7.6091790199279785 ], "umap": [ - 0.5892511606216431, - 7.1126275062561035 + 0.8592414259910583, + 6.2547430992126465 ], "pca3d": [ - -0.17023147642612457, - -0.10470231622457504, - -0.07804253697395325 + -0.17042884230613708, + -0.1039600819349289, + -0.07768699526786804 ], "tsne3d": [ - -27.325498580932617, - 7.967710971832275, - 7.132631301879883 + -30.547319412231445, + 6.409611225128174, + 2.1782584190368652 ], "umap3d": [ - 0.7538849711418152, - 5.800398349761963, - 4.9313859939575195 + 1.572373867034912, + 6.114504337310791, + 5.5335211753845215 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -28210,31 +28273,31 @@ ], "projections": { "pca": [ - 0.013211792334914207, - -0.030701786279678345 + 0.013138806447386742, + -0.03006211668252945 ], "tsne": [ - -31.12178611755371, - 16.455188751220703 + -27.495027542114258, + -20.97228240966797 ], "umap": [ - 3.2089953422546387, - 4.953040599822998 + 3.6065895557403564, + 4.398780822753906 ], "pca3d": [ - 0.013211913406848907, - -0.03070887178182602, - 0.09115973860025406 + 0.013138950802385807, + -0.03006920777261257, + 0.09120392054319382 ], "tsne3d": [ - -12.525157928466797, - 11.401354789733887, - 1.0965014696121216 + -11.954631805419922, + -18.96963882446289, + 1.775626301765442 ], "umap3d": [ - 3.067869186401367, - 6.8311872482299805, - 4.739817142486572 + 3.479689121246338, + 7.230893135070801, + 4.209066867828369 ] }, "cluster_id": 2, @@ -28245,12 +28308,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 16, - "label": "Diffusion Models" + "id": 7, + "label": "Diffusion Gen" } ], "_embedding": [ @@ -28675,31 +28738,31 @@ "openalex_url": "https://openalex.org/W4417268310", "projections": { "pca": [ - -0.14707721769809723, - -0.0011292711133137345 + -0.14711253345012665, + -5.058843089500442e-05 ], "tsne": [ - -33.52527618408203, - 2.9083943367004395 + -31.396888732910156, + -5.297332286834717 ], "umap": [ - 2.568208694458008, - 6.856886863708496 + 2.9875001907348633, + 6.16595458984375 ], "pca3d": [ - -0.14707717299461365, - -0.0011323338840156794, - 0.141776442527771 + -0.14711236953735352, + -5.338763730833307e-05, + 0.14180007576942444 ], "tsne3d": [ - -15.6869535446167, - 1.351630449295044, - -7.694514274597168 + -13.34361457824707, + -8.792086601257324, + -3.869157552719116 ], "umap3d": [ - 2.2549915313720703, - 5.110311031341553, - 3.766108512878418 + 2.8981103897094727, + 5.4984354972839355, + 4.146162033081055 ] }, "cluster_id": 2, @@ -28710,12 +28773,12 @@ "label": "Generative Models" }, { - "id": 4, - "label": "Language Models" + "id": 5, + "label": "Diffusion Models" }, { - "id": 33, - "label": "Neural Networks" + "id": 19, + "label": "Graph Neural Nets" } ], "_embedding": [ @@ -29137,46 +29200,46 @@ "openalex_url": "https://openalex.org/W4416530688", "projections": { "pca": [ - 0.0245977696031332, - -0.09093107283115387 + 0.024460313841700554, + -0.0909242033958435 ], "tsne": [ - 21.179460525512695, - -31.120481491088867 + 8.064794540405273, + 26.980213165283203 ], "umap": [ - 7.21744966506958, - 9.490330696105957 + 7.286957740783691, + 9.114137649536133 ], "pca3d": [ - 0.024597864598035812, - -0.09092864394187927, - -0.0885983407497406 + 0.024460406973958015, + -0.09092196077108383, + -0.08837084472179413 ], "tsne3d": [ - 9.008472442626953, - -1.236293077468872, - 10.098608016967773 + 1.209299087524414, + 2.7228586673736572, + -19.22433090209961 ], "umap3d": [ - 5.166037082672119, - 3.4388439655303955, - 1.5917125940322876 + 5.215033531188965, + 3.276007890701294, + 1.771246314048767 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -29584,46 +29647,46 @@ "abstract": "With SAM Audio, you can use simple text prompts to accurately separate any sound from any audio or audio-visual source.", "projections": { "pca": [ - -0.21143758296966553, - 0.1941162347793579 + -0.21073970198631287, + 0.1947442889213562 ], "tsne": [ - -27.07766342163086, - -3.81414532661438 + -33.11433792114258, + 2.587754011154175 ], "umap": [ - 1.006462812423706, - 7.903050899505615 + 1.4095579385757446, + 7.130996227264404 ], "pca3d": [ - -0.21143795549869537, - 0.19412964582443237, - -0.019691895693540573 + -0.21074000000953674, + 0.19475778937339783, + -0.019796475768089294 ], "tsne3d": [ - -20.64678192138672, - -11.447541236877441, - 14.628806114196777 + -19.251487731933594, + 9.83720588684082, + 11.189347267150879 ], "umap3d": [ - 1.33269202709198, - 4.846559047698975, - 4.999169826507568 + 2.3677823543548584, + 5.26400089263916, + 5.6981425285339355 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -30042,46 +30105,46 @@ "year": 2023, "projections": { "pca": [ - -0.2949041724205017, - -0.01436992734670639 + -0.2949034869670868, + -0.013178982771933079 ], "tsne": [ - -38.582244873046875, - -5.504546165466309 + -40.29052734375, + 1.0699148178100586 ], "umap": [ - 0.84596186876297, - 7.896207809448242 + 0.9302864670753479, + 6.968556880950928 ], "pca3d": [ - -0.29490387439727783, - -0.014387798495590687, - -0.07892750203609467 + -0.2949031591415405, + -0.013197421096265316, + -0.07864389568567276 ], "tsne3d": [ - -22.567426681518555, - -7.871973514556885, - 2.386598587036133 + -22.545589447021484, + 0.8095151782035828, + 1.102514624595642 ], "umap3d": [ - 0.7912858128547668, - 4.956358432769775, - 4.927681922912598 + 1.8902031183242798, + 5.271874904632568, + 5.714580059051514 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -30500,31 +30563,31 @@ "year": 2025, "projections": { "pca": [ - -0.10189913958311081, - -0.057658422738313675 + -0.10212122648954391, + -0.05759535729885101 ], "tsne": [ - -36.819908142089844, - 20.080020904541016 + -32.147274017333984, + -25.635427474975586 ], "umap": [ - 2.461862564086914, - 4.815046787261963 + 2.955673933029175, + 4.146705150604248 ], "pca3d": [ - -0.10189904272556305, - -0.05764339119195938, - 0.0928172618150711 + -0.10212109237909317, + -0.05757976323366165, + 0.09271740913391113 ], "tsne3d": [ - -14.633915901184082, - 13.801461219787598, - -2.2555158138275146 + -14.68968677520752, + -24.39039421081543, + 2.0390944480895996 ], "umap3d": [ - 2.471970558166504, - 7.069991111755371, - 4.77557373046875 + 2.871190309524536, + 7.4417500495910645, + 4.482454299926758 ] }, "cluster_id": 2, @@ -30535,11 +30598,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -30959,47 +31022,47 @@ "journal": "ArXiv.org", "projections": { "pca": [ - -0.19084714353084564, - 0.1915646642446518 + -0.1901887208223343, + 0.19172796607017517 ], "tsne": [ - -11.235568046569824, - -15.772765159606934 + -14.763665199279785, + 11.563730239868164 ], "umap": [ - 3.9257123470306396, - 7.909735202789307 + 4.1486663818359375, + 7.1652631759643555 ], "pca3d": [ - -0.1908475011587143, - 0.19158512353897095, - -0.03165271505713463 + -0.19018904864788055, + 0.1917482614517212, + -0.03179628774523735 ], "tsne3d": [ - -8.371011734008789, - -12.669217109680176, - 8.377096176147461 + -8.724287033081055, + 4.446927070617676, + -5.15700101852417 ], "umap3d": [ - 3.3728396892547607, - 3.497499942779541, - 4.1666975021362305 + 4.186229228973389, + 4.172264099121094, + 4.552319526672363 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -31387,7 +31450,9 @@ 0.0503, 0.0676, -0.0026 - ] + ], + "cited_by_count": 0, + "openalex_url": "https://openalex.org/W4417144655" }, { "channel": "papers-dl", @@ -31409,47 +31474,47 @@ "abstract": "We\u2019re on a journey to advance and democratize artificial intelligence through open source and open science.", "projections": { "pca": [ - -0.22728094458580017, - 0.12709365785121918 + -0.22685599327087402, + 0.12867918610572815 ], "tsne": [ - -15.574922561645508, - -20.921247482299805 + -16.1315860748291, + 13.46729564666748 ], "umap": [ - 3.465214967727661, - 8.117023468017578 + 3.729952096939087, + 7.441464900970459 ], "pca3d": [ - -0.22728082537651062, - 0.12708039581775665, - 0.15818478167057037 + -0.22685596346855164, + 0.12866567075252533, + 0.15818926692008972 ], "tsne3d": [ - -8.484503746032715, - -1.4107391834259033, - -3.7964513301849365 + -7.679715156555176, + 5.232187271118164, + 3.7328743934631348 ], "umap3d": [ - 2.96193528175354, - 4.073895454406738, - 3.8030824661254883 + 3.7853481769561768, + 4.622328281402588, + 4.186872482299805 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 0, + "label": "Math AI" } ], "_embedding": [ @@ -31878,47 +31943,47 @@ "cited_by_count": 21, "projections": { "pca": [ - -0.05567441135644913, - 0.11442955583333969 + -0.055121053010225296, + 0.11504921317100525 ], "tsne": [ - -2.376861333847046, - -27.177515029907227 + -5.366304874420166, + 23.186609268188477 ], "umap": [ - 4.508299827575684, - 8.695465087890625 + 4.903676986694336, + 8.059682846069336 ], "pca3d": [ - -0.05567461997270584, - 0.11442232131958008, - 0.1541275829076767 + -0.05512124300003052, + 0.11504193395376205, + 0.1541532576084137 ], "tsne3d": [ - 2.093367099761963, - -10.872522354125977, - -3.834064483642578 + -4.971640110015869, + 20.639272689819336, + -1.0126351118087769 ], "umap3d": [ - 3.7113840579986572, - 3.4008610248565674, - 3.059429883956909 + 4.2979326248168945, + 3.876228094100952, + 3.457502603530884 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, - "label": "Medical AI" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -32306,7 +32371,20 @@ -0.0214, 0.0651, -0.0933 - ] + ], + "affiliations": [ + "Princeton University", + "University of Central Florida", + "Defense Advanced Research Projects Agency", + "National Institute of Standards and Technology", + "Stanford University", + "University of Maryland, College Park", + "Palo Alto Institute", + "Carnegie Mellon University", + "Mohamed bin Zayed University of Artificial Intelligence", + "Harvard University" + ], + "openalex_url": "https://openalex.org/W4409868653" }, { "channel": "papers-dl", @@ -32337,31 +32415,31 @@ "journal": "ArXiv.org", "projections": { "pca": [ - 0.15131868422031403, - -0.020966365933418274 + 0.15151284635066986, + -0.02098235674202442 ], "tsne": [ - 9.2496337890625, - 13.624651908874512 + 10.976187705993652, + -10.993927001953125 ], "umap": [ - 6.765755653381348, - 5.71742057800293 + 7.016149520874023, + 5.505102157592773 ], "pca3d": [ - 0.15131875872612, - -0.0209831390529871, - 0.12224516272544861 + 0.15151290595531464, + -0.020999427884817123, + 0.12236103415489197 ], "tsne3d": [ - 2.3092243671417236, - 9.928597450256348, - -6.221209526062012 + 4.480638027191162, + -1.2095494270324707, + 4.861515998840332 ], "umap3d": [ - 5.752665996551514, - 5.144792556762695, - 3.680659294128418 + 6.105528831481934, + 5.5472092628479, + 2.6542983055114746 ] }, "cluster_id": 4, @@ -32372,11 +32450,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -32765,7 +32843,9 @@ -0.0459, 0.1265, -0.067 - ] + ], + "cited_by_count": 0, + "openalex_url": "https://openalex.org/W4415251282" }, { "channel": "papers-dl", @@ -32794,31 +32874,31 @@ "cited_by_count": 1, "projections": { "pca": [ - -0.2362392693758011, - -0.10961170494556427 + -0.2367933690547943, + -0.10851266980171204 ], "tsne": [ - -41.99153518676758, - -1.7749042510986328 + -40.0319938659668, + -5.578607559204102 ], "umap": [ - 1.533135175704956, - 6.543830871582031 + 1.8354434967041016, + 5.738260746002197 ], "pca3d": [ - -0.2362389862537384, - -0.1095890998840332, - -0.0073023284785449505 + -0.23679330945014954, + -0.10848935693502426, + -0.007205019239336252 ], "tsne3d": [ - -18.956317901611328, - -1.084134817123413, - -0.7808907628059387 + -19.05970001220703, + -7.141587734222412, + 4.229936122894287 ], "umap3d": [ - 1.5336861610412598, - 5.740152359008789, - 4.314561367034912 + 2.1990253925323486, + 5.9761199951171875, + 4.797982215881348 ] }, "cluster_id": 2, @@ -32829,12 +32909,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -33222,7 +33302,11 @@ 0.0343, 0.0122, -0.0205 - ] + ], + "affiliations": [ + "Queen's University" + ], + "openalex_url": "https://openalex.org/W4416978430" }, { "channel": "papers-dl", @@ -33256,46 +33340,46 @@ "cited_by_count": 625, "projections": { "pca": [ - -0.05678504332900047, - -0.0837472528219223 + -0.05714821070432663, + -0.08404964953660965 ], "tsne": [ - -30.541475296020508, - -31.484386444091797 + -34.61729049682617, + 28.217817306518555 ], "umap": [ - 7.688258647918701, - 9.730459213256836 + 7.779898166656494, + 9.266632080078125 ], "pca3d": [ - -0.05678500235080719, - -0.08371691405773163, - -0.09339538961648941 + -0.05714819207787514, + -0.08401883393526077, + -0.09338661283254623 ], "tsne3d": [ - -19.808748245239258, - -4.333373069763184, - -15.991986274719238 + -15.22280502319336, + -12.533724784851074, + -12.269505500793457 ], "umap3d": [ - 4.874860763549805, - 4.165476322174072, - 1.5744342803955078 + 4.683224678039551, + 3.987410306930542, + 1.566382646560669 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -33684,7 +33768,16 @@ 0.0009, 0.0218, -0.0225 - ] + ], + "affiliations": [ + "University of Freiburg", + "The Priory Hospital", + "Berlin Institute of Health at Charit\u00e9 - Universit\u00e4tsmedizin Berlin", + "University Medical Center Freiburg", + "University of Education Freiburg", + "University of T\u00fcbingen" + ], + "openalex_url": "https://openalex.org/W4406170795" }, { "channel": "papers-dl", @@ -33713,47 +33806,47 @@ "cited_by_count": 97, "projections": { "pca": [ - -0.15451684594154358, - -0.12993937730789185 + -0.1549527794122696, + -0.12993498146533966 ], "tsne": [ - -30.59032440185547, - -31.465471267700195 + -34.66954040527344, + 28.193069458007812 ], "umap": [ - 7.88955545425415, - 10.071229934692383 + 8.228251457214355, + 9.488154411315918 ], "pca3d": [ - -0.15451647341251373, - -0.1299346685409546, - -0.11821264028549194 + -0.15495231747627258, + -0.12993013858795166, + -0.11801329255104065 ], "tsne3d": [ - -19.751108169555664, - -4.271129131317139, - -15.956740379333496 + -15.25527572631836, + -12.438467025756836, + -12.26171588897705 ], "umap3d": [ - 4.347220420837402, - 4.799399375915527, - 1.4567897319793701 + 4.116601943969727, + 4.3846893310546875, + 1.4346145391464233 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -34141,7 +34234,8 @@ 0.0394, -0.0205, 0.0031 - ] + ], + "openalex_url": "https://openalex.org/W4302009014" }, { "channel": "papers-dl", @@ -34177,31 +34271,31 @@ "openalex_url": "https://openalex.org/W4399757981", "projections": { "pca": [ - -0.17187738418579102, - -0.034253060817718506 + -0.17194803059101105, + -0.03389928117394447 ], "tsne": [ - -48.59039306640625, - -14.434975624084473 + -50.00402069091797, + -10.363184928894043 ], "umap": [ - 0.8056047558784485, - 5.911718368530273 + 1.0922420024871826, + 5.153869152069092 ], "pca3d": [ - -0.17187723517417908, - -0.034237340092659, - -0.16351613402366638 + -0.1719479113817215, + -0.033883389085531235, + -0.1633419394493103 ], "tsne3d": [ - -27.749473571777344, - -6.696346282958984, - -7.383376598358154 + -24.755094528198242, + -4.776407718658447, + -6.661167621612549 ], "umap3d": [ - 0.645825982093811, - 5.9752702713012695, - 3.717118501663208 + 1.2310858964920044, + 5.9363484382629395, + 4.402299880981445 ] }, "cluster_id": 2, @@ -34212,12 +34306,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, - "label": "Training Noise" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -34633,31 +34727,31 @@ ], "projections": { "pca": [ - -0.2788660526275635, - -0.11377599090337753 + -0.2791304886341095, + -0.11238748580217361 ], "tsne": [ - -37.47054672241211, - 13.269168853759766 + -35.166988372802734, + -16.707006454467773 ], "umap": [ - 1.9479039907455444, - 5.878065586090088 + 2.286264657974243, + 5.268258571624756 ], "pca3d": [ - -0.27886590361595154, - -0.11376824975013733, - -0.03849847987294197 + -0.27913013100624084, + -0.1123800128698349, + -0.03809688240289688 ], "tsne3d": [ - -18.360300064086914, - 5.906105041503906, - 0.17015856504440308 + -17.625633239746094, + -13.830178260803223, + 3.0742335319519043 ], "umap3d": [ - 1.8203753232955933, - 6.135164260864258, - 4.345412254333496 + 2.4568076133728027, + 6.460753440856934, + 4.588751316070557 ] }, "cluster_id": 2, @@ -34668,12 +34762,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 33, - "label": "Neural Networks" + "id": 19, + "label": "Graph Neural Nets" } ], "_embedding": [ @@ -35097,47 +35191,47 @@ "cited_by_count": 11, "projections": { "pca": [ - -0.1551453024148941, - 0.0015336730284616351 + -0.15523675084114075, + 0.002469175262376666 ], "tsne": [ - -32.54922103881836, - -19.795005798339844 + -36.867828369140625, + 15.826990127563477 ], "umap": [ - 1.734528660774231, - 7.386753082275391 + 1.9807286262512207, + 6.643855094909668 ], "pca3d": [ - -0.15514527261257172, - 0.001535856630653143, - -0.04955286532640457 + -0.15523679554462433, + 0.002471114508807659, + -0.04948749765753746 ], "tsne3d": [ - -19.293338775634766, - -10.75283432006836, - -0.7209416031837463 + -23.86202049255371, + 9.483386039733887, + -3.5921647548675537 ], "umap3d": [ - 1.4689357280731201, - 4.668910026550293, - 4.027791500091553 + 2.29919171333313, + 4.894892692565918, + 4.701030254364014 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -35525,7 +35619,12 @@ 0.0498, 0.0139, -0.0198 - ] + ], + "affiliations": [ + "Princeton University", + "New York University" + ], + "openalex_url": "https://openalex.org/W4391448401" }, { "channel": "papers-dl", @@ -35567,47 +35666,47 @@ "cited_by_count": 2, "projections": { "pca": [ - -0.18691511452198029, - 0.11620917916297913 + -0.18661536276340485, + 0.11766326427459717 ], "tsne": [ - -19.623394012451172, - -22.84261703491211 + -19.483930587768555, + 13.770387649536133 ], "umap": [ - 3.237774133682251, - 7.972427845001221 + 3.4413774013519287, + 7.209061145782471 ], "pca3d": [ - -0.18691499531269073, - 0.11619056016206741, - 0.09797202795743942 + -0.1866152435541153, + 0.11764460802078247, + 0.09790901839733124 ], "tsne3d": [ - -12.548870086669922, - -4.889989852905273, - -3.7351620197296143 + -13.5606050491333, + 11.265203475952148, + 4.847803115844727 ], "umap3d": [ - 2.6782639026641846, - 4.14636754989624, - 3.546675205230713 + 3.4478893280029297, + 4.574129104614258, + 4.012377738952637 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { "id": 0, - "label": "LLM Language Learning" + "label": "Math AI" } ], "_embedding": [ @@ -35995,7 +36094,8 @@ 0.0592, 0.02, -0.0261 - ] + ], + "openalex_url": "https://openalex.org/W4416550248" }, { "channel": "papers-dl", @@ -36023,47 +36123,47 @@ ], "projections": { "pca": [ - -0.20239077508449554, - 0.05031602084636688 + -0.2021276354789734, + 0.05123572424054146 ], "tsne": [ - -24.685344696044922, - -16.739042282104492 + -29.574378967285156, + 14.235462188720703 ], "umap": [ - 2.4441514015197754, - 8.430618286132812 + 2.7109429836273193, + 7.648133754730225 ], "pca3d": [ - -0.20239077508449554, - 0.05031144618988037, - 0.0269240140914917 + -0.20212754607200623, + 0.05123080685734749, + 0.02706746757030487 ], "tsne3d": [ - -13.762691497802734, - -15.978401184082031, - 6.336623668670654 + -15.838665962219238, + 6.64044189453125, + -4.421416759490967 ], "umap3d": [ - 2.0966567993164062, - 3.8525304794311523, - 4.318124771118164 + 3.107713222503662, + 4.388288974761963, + 4.986077308654785 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 7, - "label": "Language Models" + "id": 25, + "label": "AI Agents" } ], "_embedding": [ @@ -36473,47 +36573,47 @@ "abstract": "OpenAI is exploring mechanistic interpretability to understand how neural networks reason. Our new sparse model approach could make AI systems more transparent and support safer, more reliable behavior.", "projections": { "pca": [ - -0.28077590465545654, - -0.0316033810377121 + -0.2809184193611145, + -0.030357714742422104 ], "tsne": [ - -37.49451446533203, - -18.397472381591797 + -39.7921257019043, + 14.884184837341309 ], "umap": [ - 1.4409617185592651, - 7.324908256530762 + 1.733163595199585, + 6.5815019607543945 ], "pca3d": [ - -0.2807758152484894, - -0.031592272222042084, - 0.007675435859709978 + -0.2809184193611145, + -0.03034639172255993, + 0.007859272882342339 ], "tsne3d": [ - -23.10239601135254, - -12.78416633605957, - 2.366926670074463 + -23.50803565979004, + 5.094846248626709, + -4.935754776000977 ], "umap3d": [ - 0.9701352715492249, - 4.734840393066406, - 4.012458801269531 + 1.874894380569458, + 4.9164605140686035, + 4.677639484405518 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -36935,31 +37035,31 @@ ], "projections": { "pca": [ - -0.21420252323150635, - -0.032019272446632385 + -0.21416570246219635, + -0.031240366399288177 ], "tsne": [ - -35.141998291015625, - 10.060506820678711 + -32.150367736816406, + -15.680559158325195 ], "umap": [ - 2.431694984436035, - 5.403632164001465 + 2.8639976978302, + 4.789707660675049 ], "pca3d": [ - -0.21420234441757202, - -0.03201981261372566, - 0.025186313316226006 + -0.21416543424129486, + -0.031240615993738174, + 0.025427894666790962 ], "tsne3d": [ - -16.859439849853516, - 5.179795265197754, - 6.0291547775268555 + -15.683502197265625, + -15.522485733032227, + 8.906110763549805 ], "umap3d": [ - 2.172307252883911, - 6.420518398284912, - 4.979460716247559 + 2.8465473651885986, + 6.973511219024658, + 4.9222493171691895 ] }, "cluster_id": 2, @@ -36970,11 +37070,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -37393,31 +37493,31 @@ "journal": "ArXiv.org", "projections": { "pca": [ - -0.21559301018714905, - -0.03980857878923416 + -0.2156260460615158, + -0.03942565247416496 ], "tsne": [ - -34.166534423828125, - 13.449872016906738 + -30.92954444885254, + -19.289470672607422 ], "umap": [ - 2.7681641578674316, - 5.130911827087402 + 3.0619351863861084, + 4.5836944580078125 ], "pca3d": [ - -0.21559281647205353, - -0.039799679070711136, - 0.004099186975508928 + -0.21562591195106506, + -0.03941672295331955, + 0.004235805012285709 ], "tsne3d": [ - -14.816984176635742, - 10.01931095123291, - 4.115991115570068 + -14.214414596557617, + -19.025165557861328, + 5.105369567871094 ], "umap3d": [ - 2.5971767902374268, - 6.697690010070801, - 4.761073112487793 + 3.0782783031463623, + 7.100461006164551, + 4.491468906402588 ] }, "cluster_id": 2, @@ -37428,11 +37528,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -37821,7 +37921,9 @@ -0.0098, -0.0091, -0.0409 - ] + ], + "cited_by_count": 0, + "openalex_url": "https://openalex.org/W4407386246" }, { "channel": "papers-dl", @@ -37848,31 +37950,31 @@ ], "projections": { "pca": [ - -0.1461794078350067, - -0.08245109021663666 + -0.14647455513477325, + -0.08194799721240997 ], "tsne": [ - -34.37017822265625, - 14.148377418518066 + -30.601829528808594, + -18.547405242919922 ], "umap": [ - 2.741825580596924, - 5.147113800048828 + 3.1195669174194336, + 4.651088237762451 ], "pca3d": [ - -0.14617928862571716, - -0.08242785930633545, - 0.04139983654022217 + -0.14647448062896729, + -0.0819244459271431, + 0.04148793965578079 ], "tsne3d": [ - -14.510357856750488, - 7.853030204772949, - 2.7349610328674316 + -13.946866989135742, + -16.35877227783203, + 4.86626672744751 ], "umap3d": [ - 2.5318596363067627, - 6.6144118309021, - 4.714451313018799 + 3.0730748176574707, + 7.036910057067871, + 4.465764045715332 ] }, "cluster_id": 2, @@ -37883,11 +37985,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -38304,47 +38406,47 @@ "journal": "ArXiv.org", "projections": { "pca": [ - -0.15061186254024506, - 0.001617670408450067 + -0.15048792958259583, + 0.0030863452702760696 ], "tsne": [ - -20.269868850708008, - -24.793991088867188 + -20.68915367126465, + 14.64574146270752 ], "umap": [ - 2.8969616889953613, - 7.79740571975708 + 3.087324619293213, + 7.041871070861816 ], "pca3d": [ - -0.15061143040657043, - 0.0015802369453012943, - 0.16888120770454407 + -0.15048754215240479, + 0.003048517508432269, + 0.16907048225402832 ], "tsne3d": [ - -13.106398582458496, - -6.76796817779541, - -5.817924976348877 + -14.429498672485352, + 13.742655754089355, + 4.4826178550720215 ], "umap3d": [ - 2.2988884449005127, - 4.364319801330566, - 3.583723306655884 + 3.132838487625122, + 4.713255882263184, + 4.065793991088867 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { "id": 0, - "label": "LLM Language Learning" + "label": "Math AI" } ], "_embedding": [ @@ -38732,7 +38834,9 @@ 0.0525, 0.014, -0.0178 - ] + ], + "cited_by_count": 0, + "openalex_url": "https://openalex.org/W4416017797" }, { "channel": "papers-dl", @@ -38753,47 +38857,47 @@ "title": "Introducing Nested Learning: A new ML paradigm for continual learning", "projections": { "pca": [ - -0.2600554823875427, - -0.0190420001745224 + -0.25994229316711426, + -0.018273383378982544 ], "tsne": [ - -31.250829696655273, - -23.601076126098633 + -36.28820037841797, + 18.680295944213867 ], "umap": [ - 1.8925834894180298, - 7.150341987609863 + 1.9642187356948853, + 6.240207672119141 ], "pca3d": [ - -0.26005539298057556, - -0.019029391929507256, - -0.024805961176753044 + -0.2599422335624695, + -0.018260840326547623, + -0.024512795731425285 ], "tsne3d": [ - -16.24491310119629, - -14.559701919555664, - -3.7866601943969727 + -22.678449630737305, + 13.371232986450195, + -2.151472806930542 ], "umap3d": [ - 1.4630006551742554, - 4.896774768829346, - 4.081395149230957 + 2.324042558670044, + 5.175950050354004, + 4.6032915115356445 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -39208,31 +39312,31 @@ "doi": "10.1016/j.cosrev.2026.100991", "projections": { "pca": [ - -0.19579647481441498, - -0.1056717112660408 + -0.19600175321102142, + -0.10506973415613174 ], "tsne": [ - -34.116477966308594, - 2.200227975845337 + -32.201820373535156, + -5.638910293579102 ], "umap": [ - 2.3301591873168945, - 6.657166481018066 + 2.7430527210235596, + 5.923684120178223 ], "pca3d": [ - -0.19579625129699707, - -0.10566849261522293, - 0.051779624074697495 + -0.19600139558315277, + -0.10506574809551239, + 0.052016861736774445 ], "tsne3d": [ - -16.785001754760742, - 3.0060629844665527, - -7.963843822479248 + -13.916888236999512, + -10.577424049377441, + -4.021945476531982 ], "umap3d": [ - 2.058892250061035, - 5.4272332191467285, - 3.912134885787964 + 2.697289228439331, + 5.70492696762085, + 4.198450088500977 ] }, "cluster_id": 2, @@ -39243,12 +39347,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 33, - "label": "Neural Networks" + "id": 19, + "label": "Graph Neural Nets" } ], "_embedding": [ @@ -39657,31 +39761,31 @@ "title": "The Principles of Diffusion Models", "projections": { "pca": [ - -0.26323196291923523, - 0.11402491480112076 + -0.262796014547348, + 0.11447589099407196 ], "tsne": [ - -36.76828384399414, - 14.86150074005127 + -33.20066833496094, + -20.166242599487305 ], "umap": [ - 2.3112714290618896, - 5.358932018280029 + 2.7766950130462646, + 4.718244552612305 ], "pca3d": [ - -0.2632322311401367, - 0.11405090987682343, - 0.04248051345348358 + -0.2627963423728943, + 0.11450177431106567, + 0.04237686097621918 ], "tsne3d": [ - -16.075817108154297, - 6.131143093109131, - 0.3641839027404785 + -15.471071243286133, + -14.152329444885254, + 2.6007299423217773 ], "umap3d": [ - 2.21764874458313, - 6.6099371910095215, - 4.687562942504883 + 2.787778854370117, + 6.984314918518066, + 4.618031024932861 ] }, "cluster_id": 2, @@ -39692,11 +39796,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -40114,46 +40218,46 @@ "cited_by_count": 3, "projections": { "pca": [ - -0.2180967777967453, - -0.11610626429319382 + -0.2184053361415863, + -0.11524295806884766 ], "tsne": [ - -30.958831787109375, - -14.20086669921875 + -33.323089599609375, + 11.06883716583252 ], "umap": [ - 1.7254717350006104, - 7.965635776519775 + 1.9618927240371704, + 7.173137664794922 ], "pca3d": [ - -0.21809646487236023, - -0.11610960215330124, - -0.05119369551539421 + -0.21840494871139526, + -0.11524640023708344, + -0.050850093364715576 ], "tsne3d": [ - -17.28308868408203, - -9.72854995727539, - 3.1044256687164307 + -16.759485244750977, + 0.9459341168403625, + 0.8579831123352051 ], "umap3d": [ - 1.63888418674469, - 4.470563888549805, - 4.570918083190918 + 2.693542718887329, + 4.89836311340332, + 5.154420852661133 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 1, + "label": "Attention Models" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -40542,7 +40646,8 @@ 0.017, -0.0002, -0.0329 - ] + ], + "openalex_url": "https://openalex.org/W4404318809" }, { "channel": "papers-dl", @@ -40564,31 +40669,31 @@ "abstract": "Introduction Deep neural networks (DNNs) are great at fitting data, but terrible at predicting when they don\u2019t know the right answer. Tackling this latter challenge (often called uncertainty quantification) is at the core of various problems in AI/ML. For example, reducing hallucinations in large language models, 1 or the classical balancing of exploration versus exploitation in reinforcement learning, neural bandits, and Bayesian optimization.\nA popular way to aim for both reliable predictions and calibrated uncertainty quantification is through Bayesian neural networks (BNNs). In theory they are elegant: instead of fixing the parameters of the DNN, you treat them as random variables. Then, conditioned on observed data, Bayes\u2019 rule gives you a posterior over the parameters. This, in principle, should allow for model averaging, uncertainty quantification, and sequential decision-making via algorithms like Thompson sampling.", "projections": { "pca": [ - -0.3188283145427704, - -0.09913661330938339 + -0.31968042254447937, + -0.09884025156497955 ], "tsne": [ - -50.338043212890625, - 7.134871959686279 + -50.69403839111328, + -16.95755958557129 ], "umap": [ - 0.9642793536186218, - 5.3931145668029785 + 1.3494964838027954, + 4.59820032119751 ], "pca3d": [ - -0.3188280463218689, - -0.09910562634468079, - -0.04698863625526428 + -0.31968021392822266, + -0.0988081619143486, + -0.04718005657196045 ], "tsne3d": [ - -27.670215606689453, - 0.16339485347270966, - -10.713839530944824 + -23.6461181640625, + -13.526040077209473, + -7.001660346984863 ], "umap3d": [ - 1.1132303476333618, - 6.578718185424805, - 3.565152883529663 + 1.487074613571167, + 6.506524562835693, + 3.987729072570801 ] }, "cluster_id": 2, @@ -40599,12 +40704,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -41023,31 +41128,31 @@ ], "projections": { "pca": [ - -0.2038123905658722, - -0.0791379064321518 + -0.20411469042301178, + -0.07794039696455002 ], "tsne": [ - -34.71792984008789, - 14.465603828430176 + -30.462360382080078, + -18.482234954833984 ], "umap": [ - 2.7096145153045654, - 5.224066257476807 + 3.058096170425415, + 4.634090423583984 ], "pca3d": [ - -0.2038121074438095, - -0.07913742959499359, - 0.08030425012111664 + -0.2041144222021103, + -0.07794000208377838, + 0.0804448202252388 ], "tsne3d": [ - -14.51434326171875, - 7.660154342651367, - 1.5656752586364746 + -14.00290584564209, + -15.619925498962402, + 3.988253593444824 ], "umap3d": [ - 2.5786848068237305, - 6.619579792022705, - 4.668756484985352 + 3.0752816200256348, + 6.99759578704834, + 4.42301082611084 ] }, "cluster_id": 2, @@ -41058,11 +41163,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -41483,46 +41588,46 @@ "journal": "ArXiv.org", "projections": { "pca": [ - -0.23917226493358612, - -0.10268967598676682 + -0.239231139421463, + -0.10167430341243744 ], "tsne": [ - -32.10080337524414, - -5.8732991218566895 + -40.991329193115234, + 6.225064754486084 ], "umap": [ - 1.0045162439346313, - 8.023528099060059 + 1.2712033987045288, + 7.240330696105957 ], "pca3d": [ - -0.23917162418365479, - -0.10272420197725296, - 0.02118331752717495 + -0.23923052847385406, + -0.10170989483594894, + 0.0216290894895792 ], "tsne3d": [ - -19.504737854003906, - -4.241552352905273, - 10.14155387878418 + -23.04094123840332, + 6.126776695251465, + 2.5004477500915527 ], "umap3d": [ - 0.9708875417709351, - 4.7116522789001465, - 4.942627906799316 + 2.097212076187134, + 5.14639139175415, + 5.668495178222656 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -41911,7 +42016,9 @@ 0.059, 0.0171, -0.0026 - ] + ], + "cited_by_count": 0, + "openalex_url": "https://openalex.org/W4415846678" }, { "channel": "papers-dl", @@ -41947,47 +42054,47 @@ "cited_by_count": 4, "projections": { "pca": [ - -0.23116272687911987, - 0.04185402765870094 + -0.23106302320957184, + 0.04295248165726662 ], "tsne": [ - -20.82602310180664, - -29.08344268798828 + -23.181882858276367, + 18.930030822753906 ], "umap": [ - 2.7787065505981445, - 7.974765777587891 + 3.0100765228271484, + 7.174866676330566 ], "pca3d": [ - -0.2311629205942154, - 0.041875001043081284, - 0.048283651471138 + -0.2310630977153778, + 0.042973995208740234, + 0.04830295592546463 ], "tsne3d": [ - -14.461896896362305, - -11.428497314453125, - -8.605069160461426 + -14.882744789123535, + 18.799468994140625, + 5.168132781982422 ], "umap3d": [ - 2.1305644512176514, - 4.3425140380859375, - 3.7298107147216797 + 2.925283908843994, + 4.608377456665039, + 4.236554145812988 ] }, - "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 1, - "label": "AI Scientific Research" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { "id": 0, - "label": "LLM Language Learning" + "label": "Math AI" } ], "_embedding": [ @@ -42375,7 +42482,12 @@ 0.1033, -0.0328, -0.0114 - ] + ], + "affiliations": [ + "Google (United Kingdom)", + "Google DeepMind (United Kingdom)" + ], + "openalex_url": "https://openalex.org/W4415437559" }, { "channel": "papers-dl", @@ -42410,31 +42522,31 @@ "journal": "ArXiv.org", "projections": { "pca": [ - -0.21363332867622375, - -0.1008666455745697 + -0.21384979784488678, + -0.10070115327835083 ], "tsne": [ - -40.47481918334961, - 19.200366973876953 + -35.88805389404297, + -25.350683212280273 ], "umap": [ - 2.241084098815918, - 4.866260528564453 + 2.6991381645202637, + 4.197324752807617 ], "pca3d": [ - -0.21363279223442078, - -0.10089271515607834, - -0.05814154073596001 + -0.21384930610656738, + -0.10072716325521469, + -0.05794041231274605 ], "tsne3d": [ - -18.672622680664062, - 14.172600746154785, - -2.217496871948242 + -18.131065368652344, + -24.496456146240234, + 4.125917911529541 ], "umap3d": [ - 2.1114091873168945, - 7.014358043670654, - 4.743924140930176 + 2.5780375003814697, + 7.413952350616455, + 4.624845027923584 ] }, "cluster_id": 2, @@ -42445,11 +42557,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -42838,7 +42950,9 @@ -0.0042, 0.0215, -0.0773 - ] + ], + "cited_by_count": 0, + "openalex_url": "https://openalex.org/W4414817007" }, { "channel": "papers-dl", @@ -42858,31 +42972,31 @@ "abstract": "Introducing RND1-30B, the largest open-source diffusion language model, trained via AR-to-Diffusion conversion with a simple, scalable recipe.", "projections": { "pca": [ - -0.22769814729690552, - -0.04714459553360939 + -0.2276359498500824, + -0.04647233337163925 ], "tsne": [ - -34.38394546508789, - 9.917338371276855 + -31.32731819152832, + -15.84210205078125 ], "umap": [ - 2.41585373878479, - 5.378232955932617 + 2.8605310916900635, + 4.8145318031311035 ], "pca3d": [ - -0.22769805788993835, - -0.04714573919773102, - 0.07301834225654602 + -0.22763566672801971, + -0.04647355526685715, + 0.07328490912914276 ], "tsne3d": [ - -18.502336502075195, - 5.659595489501953, - 5.105492115020752 + -14.206620216369629, + -15.59370231628418, + 7.8052659034729 ], "umap3d": [ - 2.1312172412872314, - 6.514781951904297, - 4.979205131530762 + 2.8661909103393555, + 7.006093502044678, + 4.833881378173828 ] }, "cluster_id": 2, @@ -42893,11 +43007,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -43313,31 +43427,31 @@ ], "projections": { "pca": [ - -0.08029358088970184, - -0.07507066428661346 + -0.08061232417821884, + -0.07483123242855072 ], "tsne": [ - -33.337249755859375, - 14.53110408782959 + -28.984052658081055, + -18.114179611206055 ], "umap": [ - 2.9079322814941406, - 5.092322826385498 + 3.3144872188568115, + 4.586954116821289 ], "pca3d": [ - -0.08029342442750931, - -0.07505853474140167, - 0.044139303267002106 + -0.08061221241950989, + -0.07481896132230759, + 0.04411606118083 ], "tsne3d": [ - -12.632246017456055, - 7.860895156860352, - 2.7369463443756104 + -12.189507484436035, + -16.28714942932129, + 4.732789516448975 ], "umap3d": [ - 2.7261598110198975, - 6.673247337341309, - 4.695507526397705 + 3.2109367847442627, + 7.086330890655518, + 4.364319801330566 ] }, "cluster_id": 2, @@ -43348,12 +43462,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 16, - "label": "Diffusion Models" + "id": 7, + "label": "Diffusion Gen" } ], "_embedding": [ @@ -43771,31 +43885,31 @@ "journal": "ArXiv.org", "projections": { "pca": [ - -0.24252170324325562, - -0.07200804352760315 + -0.24273169040679932, + -0.07085827738046646 ], "tsne": [ - -37.090084075927734, - 14.06751537322998 + -34.69710922241211, + -17.686975479125977 ], "umap": [ - 2.2086410522460938, - 5.4085540771484375 + 2.647940158843994, + 4.756381511688232 ], "pca3d": [ - -0.2425215095281601, - -0.07199636846780777, - -0.0378030389547348 + -0.242731511592865, + -0.07084692269563675, + -0.03754397854208946 ], "tsne3d": [ - -17.118528366088867, - 8.09727668762207, - 0.7718136310577393 + -16.472087860107422, + -16.046127319335938, + 2.8093230724334717 ], "umap3d": [ - 2.1047768592834473, - 6.575443744659424, - 4.644805431365967 + 2.6756300926208496, + 6.865849018096924, + 4.5318803787231445 ] }, "cluster_id": 2, @@ -43806,11 +43920,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -44199,7 +44313,9 @@ 0.0089, 0.018, -0.058 - ] + ], + "cited_by_count": 0, + "openalex_url": "https://openalex.org/W4414816462" }, { "channel": "papers-dl", @@ -44221,47 +44337,47 @@ "abstract": "How LoRA matches full training performance more broadly than expected.", "projections": { "pca": [ - -0.2282659113407135, - 0.20090271532535553 + -0.2277902215719223, + 0.20174860954284668 ], "tsne": [ - -21.249711990356445, - -9.609478950500488 + -21.701404571533203, + 7.765961647033691 ], "umap": [ - 3.522519111633301, - 7.321078777313232 + 3.7679293155670166, + 6.567900657653809 ], "pca3d": [ - -0.22826619446277618, - 0.20092207193374634, - -0.07956988364458084 + -0.2277907282114029, + 0.2017684280872345, + -0.07976469397544861 ], "tsne3d": [ - -15.95505142211914, - -9.452363014221191, - 1.6701308488845825 + -15.208609580993652, + -0.30175304412841797, + 0.5689042210578918 ], "umap3d": [ - 2.675278425216675, - 4.1534199714660645, - 4.592377185821533 + 3.6600096225738525, + 4.828134536743164, + 4.989205837249756 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -44649,7 +44765,8 @@ 0.0058, 0.0518, 0.0357 - ] + ], + "year": 2025 }, { "channel": "papers-dl", @@ -44668,31 +44785,31 @@ "title": "The Diffusion Duality", "projections": { "pca": [ - -0.2092573344707489, - 0.02834456041455269 + -0.2091759443283081, + 0.028928117826581 ], "tsne": [ - -31.750537872314453, - 12.791953086853027 + -30.7645206451416, + -21.582523345947266 ], "umap": [ - 2.666374921798706, - 5.188068866729736 + 3.1380155086517334, + 4.644951820373535 ], "pca3d": [ - -0.20925718545913696, - 0.028341494500637054, - -0.008821734227240086 + -0.2091757357120514, + 0.028924940153956413, + -0.008856878615915775 ], "tsne3d": [ - -16.605873107910156, - 12.34073543548584, - 4.099006175994873 + -15.412869453430176, + -19.305810928344727, + 1.3572043180465698 ], "umap3d": [ - 2.504826307296753, - 6.703448295593262, - 4.786928653717041 + 2.9663755893707275, + 7.028112888336182, + 4.588263988494873 ] }, "cluster_id": 2, @@ -44703,11 +44820,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -45134,31 +45251,31 @@ "cited_by_count": 7, "projections": { "pca": [ - -0.25662896037101746, - -0.05558455362915993 + -0.25684356689453125, + -0.05501456931233406 ], "tsne": [ - -39.762691497802734, - 2.2147984504699707 + -38.00031661987305, + -5.279372215270996 ], "umap": [ - 1.9944078922271729, - 6.298050403594971 + 2.2726402282714844, + 5.545421123504639 ], "pca3d": [ - -0.2566285729408264, - -0.055589281022548676, - 0.009597240015864372 + -0.256843239068985, + -0.055019229650497437, + 0.009619420394301414 ], "tsne3d": [ - -18.29265785217285, - 2.0769407749176025, - 1.9095395803451538 + -17.723583221435547, + -7.867259979248047, + 6.933140754699707 ], "umap3d": [ - 1.8112162351608276, - 5.8344340324401855, - 4.52836799621582 + 2.5887184143066406, + 6.246997356414795, + 4.891656398773193 ] }, "cluster_id": 2, @@ -45169,12 +45286,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 33, - "label": "Neural Networks" + "id": 19, + "label": "Graph Neural Nets" } ], "_embedding": [ @@ -45562,7 +45679,16 @@ -0.0219, 0.0008, -0.0299 - ] + ], + "affiliations": [ + "Northwestern University", + "Evanston Hospital", + "Virginia Tech", + "Brown University", + "The University of Texas at Dallas", + "The University of Texas at Austin" + ], + "openalex_url": "https://openalex.org/W4414700820" }, { "channel": "papers-dl", @@ -45596,31 +45722,31 @@ ], "projections": { "pca": [ - -0.22910188138484955, - -0.10330262780189514 + -0.22920459508895874, + -0.10250210016965866 ], "tsne": [ - -34.591548919677734, - 11.061534881591797 + -31.642946243286133, + -16.809953689575195 ], "umap": [ - 2.525177240371704, - 5.335440635681152 + 2.9484035968780518, + 4.7714762687683105 ], "pca3d": [ - -0.2291015386581421, - -0.10330589860677719, - 0.05638889595866203 + -0.2292042076587677, + -0.102505624294281, + 0.05676797777414322 ], "tsne3d": [ - -15.842203140258789, - 7.504209518432617, - 7.076838970184326 + -15.12037181854248, + -17.928632736206055, + 8.707204818725586 ], "umap3d": [ - 2.2458574771881104, - 6.554681301116943, - 4.986403465270996 + 2.9458792209625244, + 7.098327159881592, + 4.788780212402344 ] }, "cluster_id": 2, @@ -45631,11 +45757,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -46024,7 +46150,11 @@ -0.0135, -0.0002, -0.0505 - ] + ], + "year": 2025, + "journal": "ArXiv.org", + "cited_by_count": 0, + "openalex_url": "https://openalex.org/W4407632242" }, { "channel": "papers-dl", @@ -46061,46 +46191,46 @@ ], "projections": { "pca": [ - -0.16124840080738068, - -0.0866415798664093 + -0.1613467037677765, + -0.08601612597703934 ], "tsne": [ - -60.651954650878906, - -0.9910387992858887 + -54.37984085083008, + 4.213925361633301 ], "umap": [ - 0.4174668788909912, - 7.441263198852539 + 0.6117365956306458, + 6.6508941650390625 ], "pca3d": [ - -0.1612478643655777, - -0.08666913956403732, - -0.09179507195949554 + -0.16134630143642426, + -0.08604390174150467, + -0.09143250435590744 ], "tsne3d": [ - -25.130891799926758, - 2.6229724884033203, - 7.92095947265625 + -24.82600212097168, + -14.970355033874512, + 7.1927056312561035 ], "umap3d": [ - 0.7120473980903625, - 5.61545991897583, - 5.077195644378662 + 1.6558853387832642, + 6.007391929626465, + 5.686004161834717 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -46525,31 +46655,31 @@ "journal": "Journal of Machine Learning Research", "projections": { "pca": [ - -0.23910290002822876, - -0.026952411979436874 + -0.23904569447040558, + -0.026324285194277763 ], "tsne": [ - -36.73331069946289, - 15.773027420043945 + -32.684844970703125, + -21.29462432861328 ], "umap": [ - 2.60479474067688, - 5.158322811126709 + 2.978452205657959, + 4.573272228240967 ], "pca3d": [ - -0.23910295963287354, - -0.026921933516860008, - -0.027076655998826027 + -0.23904584348201752, + -0.02629336714744568, + -0.026849467307329178 ], "tsne3d": [ - -16.28310203552246, - 8.424912452697754, - -0.9153609275817871 + -15.773092269897461, + -16.019033432006836, + 0.9925056099891663 ], "umap3d": [ - 2.3661136627197266, - 6.619828224182129, - 4.649613857269287 + 2.8805909156799316, + 6.986520767211914, + 4.445655345916748 ] }, "cluster_id": 2, @@ -46560,11 +46690,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -46975,31 +47105,31 @@ "abstract": "A geometric framework for co-designing neural net optimizers with manifold constraints.", "projections": { "pca": [ - -0.24361376464366913, - -0.07166938483715057 + -0.24372036755084991, + -0.07124089449644089 ], "tsne": [ - -43.79825210571289, - 2.7550442218780518 + -38.45696258544922, + -8.136192321777344 ], "umap": [ - 1.5270389318466187, - 6.492862701416016 + 1.967299461364746, + 5.728943347930908 ], "pca3d": [ - -0.24361354112625122, - -0.07166995108127594, - 0.020572545006871223 + -0.2437201887369156, + -0.07124069333076477, + 0.020739886909723282 ], "tsne3d": [ - -19.97064208984375, - 1.0266085863113403, - -6.479277610778809 + -18.410764694213867, + -6.596670627593994, + -0.26526254415512085 ], "umap3d": [ - 1.5398831367492676, - 5.684576988220215, - 4.19935417175293 + 2.2099761962890625, + 6.0332536697387695, + 4.739725589752197 ] }, "cluster_id": 2, @@ -47010,12 +47140,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -47436,31 +47566,31 @@ ], "projections": { "pca": [ - -0.07297640293836594, - -0.12965935468673706 + -0.07327393442392349, + -0.12938658893108368 ], "tsne": [ - -33.05362319946289, - 15.458884239196777 + -28.52635383605957, + -19.455123901367188 ], "umap": [ - 3.035386085510254, - 5.0188117027282715 + 3.450045108795166, + 4.463041305541992 ], "pca3d": [ - -0.07297621667385101, - -0.12964658439159393, - 0.06427336484193802 + -0.07327371090650558, + -0.12937414646148682, + 0.06443476676940918 ], "tsne3d": [ - -12.578058242797852, - 9.793174743652344, - 2.453280210494995 + -12.100704193115234, + -18.00143814086914, + 3.7400550842285156 ], "umap3d": [ - 2.9054903984069824, - 6.80040979385376, - 4.717899322509766 + 3.2844650745391846, + 7.143746852874756, + 4.315309047698975 ] }, "cluster_id": 2, @@ -47471,12 +47601,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 16, - "label": "Diffusion Models" + "id": 7, + "label": "Diffusion Gen" } ], "_embedding": [ @@ -47892,46 +48022,46 @@ ], "projections": { "pca": [ - -0.25265732407569885, - -0.12417089194059372 + -0.2529142200946808, + -0.12305188924074173 ], "tsne": [ - -35.02751922607422, - -9.537566184997559 + -40.63301467895508, + 9.585530281066895 ], "umap": [ - 1.026131510734558, - 8.032772064208984 + 1.323262333869934, + 7.100805282592773 ], "pca3d": [ - -0.25265681743621826, - -0.12418442964553833, - -0.08053474128246307 + -0.2529137134552002, + -0.1230655238032341, + -0.08009511232376099 ], "tsne3d": [ - -23.456405639648438, - -8.125568389892578, - 7.506457328796387 + -22.454931259155273, + 3.8074567317962646, + 8.702263832092285 ], "umap3d": [ - 1.0572466850280762, - 4.6295647621154785, - 4.96450662612915 + 2.1388065814971924, + 5.021034240722656, + 5.635373115539551 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -48349,31 +48479,31 @@ ], "projections": { "pca": [ - 0.0583459846675396, - -0.08358312398195267 + 0.05822095274925232, + -0.08319389820098877 ], "tsne": [ - 7.1013898849487305, - 3.6782422065734863 + 9.262035369873047, + -2.044039487838745 ], "umap": [ - 6.810405731201172, - 5.915849208831787 + 7.109353065490723, + 5.692835330963135 ], "pca3d": [ - 0.05834609642624855, - -0.08357829600572586, - 0.06892888993024826 + 0.058221086859703064, + -0.08318869024515152, + 0.06910468637943268 ], "tsne3d": [ - 10.556373596191406, - 3.598968505859375, - -4.087026596069336 + 9.809310913085938, + -5.228736400604248, + -1.8861783742904663 ], "umap3d": [ - 5.988770008087158, - 5.054691791534424, - 3.5313165187835693 + 6.302999019622803, + 5.47014045715332, + 2.607269763946533 ] }, "cluster_id": 4, @@ -48384,11 +48514,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -48803,46 +48933,46 @@ "doi": "10.5281/zenodo.18578145", "projections": { "pca": [ - -0.28572189807891846, - 0.05264342948794365 + -0.2856174409389496, + 0.05410560593008995 ], "tsne": [ - -25.394832611083984, - -18.226016998291016 + -29.91827392578125, + 15.979310035705566 ], "umap": [ - 2.4070229530334473, - 8.37940502166748 + 2.5880868434906006, + 7.676243305206299 ], "pca3d": [ - -0.28572189807891846, - 0.05265141651034355, - 0.00635273614898324 + -0.2856174111366272, + 0.054113417863845825, + 0.00649129506200552 ], "tsne3d": [ - -13.678996086120605, - -17.12076187133789, - 4.500967979431152 + -16.021581649780273, + 9.035609245300293, + -4.913802146911621 ], "umap3d": [ - 1.9930329322814941, - 3.829545021057129, - 4.327640056610107 + 3.011481761932373, + 4.314338207244873, + 5.0088348388671875 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -49279,46 +49409,46 @@ ], "projections": { "pca": [ - -0.08898288756608963, - -0.1250222772359848 + -0.0891462117433548, + -0.12480884045362473 ], "tsne": [ - -58.80815505981445, - -6.319614410400391 + -54.755645751953125, + 1.9829962253570557 ], "umap": [ - 0.3884308636188507, - 7.0866827964782715 + 0.8056314587593079, + 6.294454097747803 ], "pca3d": [ - -0.08898235857486725, - -0.1250533014535904, - -0.08272767812013626 + -0.08914569765329361, + -0.12484009563922882, + -0.08234705030918121 ], "tsne3d": [ - -29.13071632385254, - 4.720844268798828, - 6.705610275268555 + -34.843971252441406, + 0.08915527909994125, + 3.03365421295166 ], "umap3d": [ - 0.6785739660263062, - 5.9049482345581055, - 4.973842620849609 + 1.527657151222229, + 6.136453151702881, + 5.530172348022461 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -49735,31 +49865,31 @@ ], "projections": { "pca": [ - -0.17947879433631897, - -0.044163577258586884 + -0.1794406920671463, + -0.04370605945587158 ], "tsne": [ - -34.71036148071289, - 12.057356834411621 + -31.893861770629883, + -17.837862014770508 ], "umap": [ - 2.4597432613372803, - 5.317390441894531 + 2.874584674835205, + 4.705052852630615 ], "pca3d": [ - -0.179478719830513, - -0.04414153844118118, - -0.04760798066854477 + -0.1794406622648239, + -0.043683972209692, + -0.047332391142845154 ], "tsne3d": [ - -15.75031566619873, - 9.65721607208252, - 6.035887718200684 + -15.402047157287598, + -19.48763084411621, + 6.797580242156982 ], "umap3d": [ - 2.3207409381866455, - 6.579999923706055, - 4.879112720489502 + 2.8242440223693848, + 7.065236568450928, + 4.723508834838867 ] }, "cluster_id": 2, @@ -49770,11 +49900,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -50197,31 +50327,31 @@ ], "projections": { "pca": [ - -0.0010296383406966925, - -0.07970796525478363 + -0.000910411705262959, + -0.0791170671582222 ], "tsne": [ - 6.775508880615234, - 22.84136390686035 + -21.357833862304688, + -22.57183837890625 ], "umap": [ - 4.751949310302734, - 4.479765892028809 + 5.21290922164917, + 4.140443325042725 ], "pca3d": [ - -0.0010294642997905612, - -0.07971392571926117, - 0.12258772552013397 + -0.0009102048352360725, + -0.07912316173315048, + 0.12296795099973679 ], "tsne3d": [ - -7.605554580688477, - 12.498144149780273, - -0.144384503364563 + -6.85334587097168, + -21.485416412353516, + 1.5716506242752075 ], "umap3d": [ - 4.698668956756592, - 6.2198944091796875, - 4.769529342651367 + 5.207979202270508, + 6.962699890136719, + 3.726236581802368 ] }, "cluster_id": 3, @@ -50232,12 +50362,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -50662,46 +50792,46 @@ "openalex_url": "https://openalex.org/W4409531882", "projections": { "pca": [ - -0.0842612162232399, - 0.047180190682411194 + -0.08393249660730362, + 0.047993265092372894 ], "tsne": [ - 0.36563730239868164, - -34.09829330444336 + -7.869776725769043, + 31.443349838256836 ], "umap": [ - 4.766139984130859, - 9.417191505432129 + 4.8897013664245605, + 8.669341087341309 ], "pca3d": [ - -0.08426119387149811, - 0.04717567563056946, - 0.04842343181371689 + -0.08393248915672302, + 0.047988131642341614, + 0.048702090978622437 ], "tsne3d": [ - -0.48005151748657227, - -19.323366165161133, - 0.5145058035850525 + -5.372620105743408, + 13.752917289733887, + -9.24992847442627 ], "umap3d": [ - 3.942570686340332, - 2.722533702850342, - 3.1428680419921875 + 4.664272785186768, + 3.1496524810791016, + 3.676417112350464 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, + "id": 26, "label": "Medical AI" } ], @@ -51125,46 +51255,46 @@ ], "projections": { "pca": [ - -0.14244095981121063, - -0.17820648849010468 + -0.14269550144672394, + -0.17772452533245087 ], "tsne": [ - -31.472946166992188, - -9.384355545043945 + -36.976016998291016, + 8.335494041442871 ], "umap": [ - 1.4439929723739624, - 8.215372085571289 + 1.7576099634170532, + 7.414900302886963 ], "pca3d": [ - -0.14244042336940765, - -0.1782231330871582, - 0.0165795236825943 + -0.14269496500492096, + -0.17774099111557007, + 0.01706862449645996 ], "tsne3d": [ - -18.1580867767334, - -7.328204154968262, - 10.088685035705566 + -19.71359634399414, + 3.9715099334716797, + 6.223021984100342 ], "umap3d": [ - 1.4286664724349976, - 4.522464275360107, - 4.890322685241699 + 2.5481114387512207, + 5.030786514282227, + 5.57811975479126 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -51581,31 +51711,31 @@ ], "projections": { "pca": [ - -0.2812806963920593, - -0.09798698127269745 + -0.2813962399959564, + -0.09708084166049957 ], "tsne": [ - -35.55867385864258, - 10.49828052520752 + -32.56428909301758, + -16.298816680908203 ], "umap": [ - 2.436702251434326, - 5.413052558898926 + 2.8312249183654785, + 4.892106533050537 ], "pca3d": [ - -0.2812803387641907, - -0.09798106551170349, - 0.02781928889453411 + -0.28139594197273254, + -0.09707500785589218, + 0.02823946438729763 ], "tsne3d": [ - -16.95766830444336, - 6.765340328216553, - 6.1664323806762695 + -16.311508178710938, + -16.77511215209961, + 8.229633331298828 ], "umap3d": [ - 2.184776782989502, - 6.457261085510254, - 4.995016574859619 + 2.8714308738708496, + 6.910141468048096, + 4.935468673706055 ] }, "cluster_id": 2, @@ -51616,11 +51746,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -52041,31 +52171,31 @@ "cited_by_count": 47, "projections": { "pca": [ - -0.04463068023324013, - -0.17797458171844482 + -0.04503462463617325, + -0.1778499186038971 ], "tsne": [ - -32.14947509765625, - 2.6634888648986816 + -30.01397132873535, + 24.669097900390625 ], "umap": [ - 2.492706775665283, - 6.547504425048828 + 2.909254550933838, + 5.895424842834473 ], "pca3d": [ - -0.04463046416640282, - -0.1779557764530182, - 0.14099687337875366 + -0.045034363865852356, + -0.17783044278621674, + 0.14119677245616913 ], "tsne3d": [ - -15.504474639892578, - 3.750337600708008, - -10.307292938232422 + -11.670202255249023, + -10.952329635620117, + -5.655294895172119 ], "umap3d": [ - 2.234804630279541, - 5.476902961730957, - 3.8896210193634033 + 2.847599744796753, + 5.816864490509033, + 4.086609363555908 ] }, "cluster_id": 2, @@ -52076,12 +52206,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 33, - "label": "Neural Networks" + "id": 19, + "label": "Graph Neural Nets" } ], "_embedding": [ @@ -52531,46 +52661,46 @@ "cited_by_count": 32, "projections": { "pca": [ - -0.14767302572727203, - 0.08761244267225266 + -0.14741051197052002, + 0.08835919201374054 ], "tsne": [ - -0.3161543905735016, - -39.016902923583984 + -10.482024192810059, + 35.56569290161133 ], "umap": [ - 4.727411270141602, - 9.620376586914062 + 4.94821310043335, + 8.889496803283691 ], "pca3d": [ - -0.14767304062843323, - 0.08760850131511688, - -0.03785425424575806 + -0.14741039276123047, + 0.08835499733686447, + -0.03775801137089729 ], "tsne3d": [ - -3.4739160537719727, - -23.90555763244629, - -1.2305278778076172 + -9.075372695922852, + 12.133267402648926, + -12.107865333557129 ], "umap3d": [ - 4.01630163192749, - 2.5105106830596924, - 3.167189598083496 + 4.86098051071167, + 2.9356112480163574, + 3.6633191108703613 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, + "id": 26, "label": "Medical AI" } ], @@ -52980,46 +53110,46 @@ "title": "How Attention Sinks Keep Language Models Stable", "projections": { "pca": [ - -0.1576516181230545, - 0.013290957547724247 + -0.15763147175312042, + 0.014467610977590084 ], "tsne": [ - -34.53739547729492, - -11.798206329345703 + -39.39702606201172, + 11.470831871032715 ], "umap": [ - 1.1333247423171997, - 8.083666801452637 + 1.383023977279663, + 7.236001491546631 ], "pca3d": [ - -0.15765146911144257, - 0.01328872237354517, - -0.01507328636944294 + -0.15763142704963684, + 0.014465131796896458, + -0.014923966489732265 ], "tsne3d": [ - -23.291284561157227, - -10.896617889404297, - 8.213766098022461 + -21.98622703552246, + 5.887032985687256, + 10.39564037322998 ], "umap3d": [ - 1.0271128416061401, - 4.497204780578613, - 4.839345455169678 + 2.2184934616088867, + 4.849906921386719, + 5.600592613220215 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -53429,46 +53559,46 @@ "title": "Achieving 10,000x training data reduction with high-fidelity labels", "projections": { "pca": [ - -0.24018071591854095, - -0.01309116743505001 + -0.2400767207145691, + -0.012336187064647675 ], "tsne": [ - -42.211727142333984, - -12.566210746765137 + -32.501041412353516, + -1.4430714845657349 ], "umap": [ - 0.2957732081413269, - 6.211977481842041 + 0.6412526369094849, + 5.283272743225098 ], "pca3d": [ - -0.24018079042434692, - -0.013063293881714344, - -0.08616137504577637 + -0.24007673561573029, + -0.01230824738740921, + -0.08588428050279617 ], "tsne3d": [ - -21.106157302856445, - -6.617641925811768, - -10.38901424407959 + -33.41958236694336, + -8.006525993347168, + -4.217377185821533 ], "umap3d": [ - 0.43511533737182617, - 6.192594051361084, - 3.9183461666107178 + 0.9456627368927002, + 6.203791618347168, + 4.623396396636963 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, + "id": 23, "label": "Training Noise" } ], @@ -53879,47 +54009,47 @@ "abstract": "A paper from Anthropic describing persona vectors and their applications to monitoring and controlling model behavior", "projections": { "pca": [ - -0.1267968863248825, - 0.18737024068832397 + -0.1262199878692627, + 0.18853358924388885 ], "tsne": [ - -36.44712829589844, - -25.322063446044922 + -40.0828857421875, + 19.968326568603516 ], "umap": [ - 2.0019352436065674, - 7.515207767486572 + 2.226280927658081, + 6.788239002227783 ], "pca3d": [ - -0.12679700553417206, - 0.18734420835971832, - -0.007011751644313335 + -0.12622006237506866, + 0.1885061413049698, + -0.007081550545990467 ], "tsne3d": [ - -19.107690811157227, - -15.121582984924316, - -0.37621209025382996 + -22.431791305541992, + 8.810375213623047, + -7.728797912597656 ], "umap3d": [ - 1.5058035850524902, - 4.518298625946045, - 3.761108160018921 + 2.2852842807769775, + 4.699310779571533, + 4.382477760314941 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -54334,46 +54464,46 @@ ], "projections": { "pca": [ - -0.271761417388916, - -0.13274458050727844 + -0.2719743549823761, + -0.13148777186870575 ], "tsne": [ - -29.981544494628906, - -14.468401908874512 + -35.1183967590332, + 12.214914321899414 ], "umap": [ - 1.5638333559036255, - 8.10988712310791 + 1.9243943691253662, + 7.343163013458252 ], "pca3d": [ - -0.2717609405517578, - -0.13275158405303955, - -0.04334834963083267 + -0.2719738781452179, + -0.13149523735046387, + -0.04283180832862854 ], "tsne3d": [ - -19.22930145263672, - -12.033440589904785, - 5.929532527923584 + -19.695390701293945, + 7.643486499786377, + 0.9207333326339722 ], "umap3d": [ - 1.4962154626846313, - 4.319267749786377, - 4.5853447914123535 + 2.5607755184173584, + 4.735077857971191, + 5.215428352355957 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -54791,31 +54921,31 @@ ], "projections": { "pca": [ - -0.15259020030498505, - -0.060813263058662415 + -0.15274988114833832, + -0.059661779552698135 ], "tsne": [ - -34.704708099365234, - 15.4815673828125 + -29.741647720336914, + -20.18385124206543 ], "umap": [ - 2.922212839126587, - 5.079473495483398 + 3.2466344833374023, + 4.512991428375244 ], "pca3d": [ - -0.15259003639221191, - -0.06081516668200493, - 0.09623925387859344 + -0.15274964272975922, + -0.05966376140713692, + 0.0964110717177391 ], "tsne3d": [ - -13.621057510375977, - 9.863871574401855, - 0.4642396569252014 + -13.05191707611084, + -17.272998809814453, + 1.886968731880188 ], "umap3d": [ - 2.762049913406372, - 6.734621524810791, - 4.707890033721924 + 3.149496555328369, + 7.153240203857422, + 4.329908847808838 ] }, "cluster_id": 2, @@ -54826,12 +54956,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 16, - "label": "Diffusion Models" + "id": 7, + "label": "Diffusion Gen" } ], "_embedding": [ @@ -55245,31 +55375,31 @@ ], "projections": { "pca": [ - -0.09702519327402115, - -0.026416251435875893 + -0.09718910604715347, + -0.02548474259674549 ], "tsne": [ - -36.155914306640625, - 4.318451881408691 + -36.041969299316406, + -8.27029037475586 ], "umap": [ - 2.5689408779144287, - 6.509305953979492 + 2.8037679195404053, + 5.637635707855225 ], "pca3d": [ - -0.09702485054731369, - -0.026430856436491013, - 0.06598898768424988 + -0.09718886017799377, + -0.025499377399683, + 0.06600389629602432 ], "tsne3d": [ - -13.716741561889648, - 1.7643989324569702, - -4.877692699432373 + -13.468591690063477, + -8.078521728515625, + -0.3637775480747223 ], "umap3d": [ - 2.4940593242645264, - 5.348320960998535, - 3.8026421070098877 + 3.0847067832946777, + 5.66060733795166, + 4.02890157699585 ] }, "cluster_id": 2, @@ -55280,12 +55410,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 33, - "label": "Neural Networks" + "id": 19, + "label": "Graph Neural Nets" } ], "_embedding": [ @@ -55702,47 +55832,47 @@ ], "projections": { "pca": [ - -0.2294866442680359, - -0.1656600534915924 + -0.22993676364421844, + -0.16473448276519775 ], "tsne": [ - -31.004587173461914, - -11.205269813537598 + -34.049190521240234, + 8.948308944702148 ], "umap": [ - 1.5069961547851562, - 7.709514617919922 + 1.761782169342041, + 6.913865566253662 ], "pca3d": [ - -0.22948621213436127, - -0.1656479686498642, - -0.02509523183107376 + -0.2299363762140274, + -0.16472184658050537, + -0.024686185643076897 ], "tsne3d": [ - -18.599027633666992, - -7.333314895629883, - 5.119719505310059 + -18.349857330322266, + 1.6441256999969482, + 3.61564302444458 ], "umap3d": [ - 1.4951415061950684, - 4.891517639160156, - 4.672577857971191 + 2.493180274963379, + 5.318051815032959, + 5.2499518394470215 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -56156,46 +56286,46 @@ ], "projections": { "pca": [ - -0.25508546829223633, - 0.0461236797273159 + -0.25491127371788025, + 0.047609757632017136 ], "tsne": [ - -25.636613845825195, - -17.78289222717285 + -30.284666061401367, + 15.383055686950684 ], "umap": [ - 2.3186817169189453, - 8.370182991027832 + 2.5373075008392334, + 7.698410511016846 ], "pca3d": [ - -0.2550852298736572, - 0.04611807316541672, - 0.0017468599835410714 + -0.2549111843109131, + 0.04760376363992691, + 0.0019768034107983112 ], "tsne3d": [ - -13.538344383239746, - -15.834614753723145, - 4.062773704528809 + -16.133398056030273, + 8.573589324951172, + -3.6481354236602783 ], "umap3d": [ - 1.948484182357788, - 3.8590943813323975, - 4.384050369262695 + 3.0243144035339355, + 4.356330394744873, + 5.0334248542785645 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -56613,47 +56743,47 @@ "cited_by_count": 33, "projections": { "pca": [ - -0.17056477069854736, - -0.015453411266207695 + -0.17079566419124603, + -0.01419829111546278 ], "tsne": [ - -32.61355209350586, - -20.320068359375 + -36.60847473144531, + 15.948493003845215 ], "umap": [ - 1.9433441162109375, - 7.41070556640625 + 2.0892393589019775, + 6.643600940704346 ], "pca3d": [ - -0.17056486010551453, - -0.015439167618751526, - -0.0183186624199152 + -0.17079570889472961, + -0.01418430544435978, + -0.01824454963207245 ], "tsne3d": [ - -19.759410858154297, - -12.117156028747559, - -0.4909423291683197 + -23.044469833374023, + 8.782962799072266, + -4.460156440734863 ], "umap3d": [ - 1.4904108047485352, - 4.646005630493164, - 3.9146504402160645 + 2.3583080768585205, + 4.875153064727783, + 4.517402648925781 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -57073,31 +57203,31 @@ ], "projections": { "pca": [ - -0.18807849287986755, - -0.13250930607318878 + -0.18837344646453857, + -0.1322927325963974 ], "tsne": [ - -45.811309814453125, - -16.282936096191406 + -40.313270568847656, + -14.396191596984863 ], "umap": [ - 1.4244410991668701, - 6.082204341888428 + 1.7959779500961304, + 5.484834671020508 ], "pca3d": [ - -0.18807806074619293, - -0.13250422477722168, - -0.0552808940410614 + -0.18837302923202515, + -0.1322871744632721, + -0.05501502752304077 ], "tsne3d": [ - -22.50448989868164, - -10.786069869995117, - -5.98241662979126 + -19.90022850036621, + -3.740326166152954, + -8.04308795928955 ], "umap3d": [ - 1.2022382020950317, - 5.5683135986328125, - 3.7118561267852783 + 1.8140647411346436, + 5.7017645835876465, + 4.199606418609619 ] }, "cluster_id": 2, @@ -57108,12 +57238,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -57527,47 +57657,47 @@ ], "projections": { "pca": [ - -0.23115858435630798, - -0.1689547300338745 + -0.23154276609420776, + -0.16795331239700317 ], "tsne": [ - -38.8017578125, - -14.809176445007324 + -35.29194259643555, + 0.27121517062187195 ], "umap": [ - 1.2628024816513062, - 7.395016670227051 + 1.6343488693237305, + 6.667853355407715 ], "pca3d": [ - -0.2311580628156662, - -0.1689600795507431, - -0.060926422476768494 + -0.23154228925704956, + -0.16795825958251953, + -0.06041956692934036 ], "tsne3d": [ - -24.19901466369629, - -14.309226036071777, - 6.772693157196045 + -20.80162811279297, + 1.5757801532745361, + -4.648832321166992 ], "umap3d": [ - 0.9004908800125122, - 4.666962623596191, - 4.158698081970215 + 1.781046748161316, + 4.922063827514648, + 4.918670177459717 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -57988,46 +58118,46 @@ ], "projections": { "pca": [ - -0.2940463423728943, - -0.12091613560914993 + -0.2941782772541046, + -0.12004885822534561 ], "tsne": [ - -58.31681823730469, - -1.6345785856246948 + -48.8428840637207, + 7.528402805328369 ], "umap": [ - 0.3750403821468353, - 7.660902976989746 + 0.5832802057266235, + 6.787841796875 ], "pca3d": [ - -0.29404568672180176, - -0.1209547147154808, - -0.11370010673999786 + -0.2941775918006897, + -0.12008850276470184, + -0.11318832635879517 ], "tsne3d": [ - -26.741104125976562, - -0.7416911721229553, - 12.56156063079834 + -29.342466354370117, + 2.884650945663452, + 9.190497398376465 ], "umap3d": [ - 0.5080953240394592, - 5.202526092529297, - 5.010625839233398 + 1.5814539194107056, + 5.527938365936279, + 5.82574462890625 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -58444,31 +58574,31 @@ ], "projections": { "pca": [ - -0.20318812131881714, - -0.10780366510152817 + -0.20356158912181854, + -0.10743855684995651 ], "tsne": [ - -43.86219024658203, - 1.3024100065231323 + -40.078800201416016, + -8.317252159118652 ], "umap": [ - 1.3993072509765625, - 6.46036958694458 + 1.8655054569244385, + 5.629181385040283 ], "pca3d": [ - -0.20318783819675446, - -0.10779863595962524, - -0.057550426572561264 + -0.20356136560440063, + -0.10743328928947449, + -0.057435542345047 ], "tsne3d": [ - -21.291872024536133, - 1.8871630430221558, - -4.455774784088135 + -20.130935668945312, + -9.046669960021973, + 0.09722477942705154 ], "umap3d": [ - 1.4813237190246582, - 5.740140914916992, - 4.11803674697876 + 2.200993776321411, + 6.006735324859619, + 4.627395153045654 ] }, "cluster_id": 2, @@ -58479,12 +58609,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -58901,31 +59031,31 @@ ], "projections": { "pca": [ - -0.2405567467212677, - -0.1552671194076538 + -0.24084128439426422, + -0.15462307631969452 ], "tsne": [ - -36.935428619384766, - 12.050126075744629 + -33.40101623535156, + -18.572429656982422 ], "umap": [ - 2.3427157402038574, - 5.248299598693848 + 2.6833724975585938, + 4.6929826736450195 ], "pca3d": [ - -0.2405562400817871, - -0.15527169406414032, - -0.07464192807674408 + -0.24084073305130005, + -0.15462788939476013, + -0.07418487966060638 ], "tsne3d": [ - -18.142263412475586, - 9.266993522644043, - 4.082049369812012 + -17.423093795776367, + -18.069385528564453, + 5.492913246154785 ], "umap3d": [ - 2.054394245147705, - 6.679718017578125, - 4.812017440795898 + 2.712627410888672, + 7.034132957458496, + 4.81149959564209 ] }, "cluster_id": 2, @@ -58936,11 +59066,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -59362,31 +59492,31 @@ ], "projections": { "pca": [ - -0.2022625058889389, - -0.05106176808476448 + -0.20250508189201355, + -0.05030477046966553 ], "tsne": [ - -33.41872024536133, - 13.287190437316895 + -29.74656867980957, + -17.118085861206055 ], "umap": [ - 2.7344601154327393, - 5.2223734855651855 + 3.091853380203247, + 4.643387794494629 ], "pca3d": [ - -0.2022622972726822, - -0.05104590207338333, - 0.03105132281780243 + -0.20250485837459564, + -0.05028883367776871, + 0.03109167516231537 ], "tsne3d": [ - -13.722670555114746, - 5.809411525726318, - 1.9967001676559448 + -13.44781494140625, + -14.064872741699219, + 5.001763343811035 ], "umap3d": [ - 2.528522491455078, - 6.65201473236084, - 4.70230770111084 + 3.031468391418457, + 6.92296838760376, + 4.49859619140625 ] }, "cluster_id": 2, @@ -59397,11 +59527,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -59815,31 +59945,31 @@ ], "projections": { "pca": [ - -0.18783019483089447, - -0.08228514343500137 + -0.1880178153514862, + -0.08153709024190903 ], "tsne": [ - -36.399993896484375, - 12.743462562561035 + -32.87272262573242, + -18.97535514831543 ], "umap": [ - 2.4389615058898926, - 5.189950466156006 + 2.8474953174591064, + 4.6632080078125 ], "pca3d": [ - -0.1878300905227661, - -0.08227294683456421, - -0.009623863734304905 + -0.18801765143871307, + -0.08152500540018082, + -0.009427501820027828 ], "tsne3d": [ - -17.296411514282227, - 9.175162315368652, - 2.9413580894470215 + -16.4854793548584, + -17.866252899169922, + 4.565987586975098 ], "umap3d": [ - 2.238485097885132, - 6.679803371429443, - 4.832326412200928 + 2.794193983078003, + 7.051998615264893, + 4.690718173980713 ] }, "cluster_id": 2, @@ -59850,11 +59980,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -60267,31 +60397,31 @@ ], "projections": { "pca": [ - -0.24175605177879333, - -0.11392559111118317 + -0.24190528690814972, + -0.11326448619365692 ], "tsne": [ - -40.77433395385742, - 12.602503776550293 + -37.28044128417969, + -19.578542709350586 ], "umap": [ - 1.857153058052063, - 5.307598114013672 + 2.3002066612243652, + 4.665226936340332 ], "pca3d": [ - -0.24175550043582916, - -0.11394698172807693, - -0.06585611402988434 + -0.2419048547744751, + -0.11328694969415665, + -0.06548431515693665 ], "tsne3d": [ - -21.946548461914062, - 9.658074378967285, - 3.600381851196289 + -21.27826690673828, + -18.042037963867188, + 5.307540416717529 ], "umap3d": [ - 1.5875828266143799, - 6.591913223266602, - 4.832258224487305 + 2.244997501373291, + 6.972527980804443, + 4.915841102600098 ] }, "cluster_id": 2, @@ -60302,11 +60432,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -60724,46 +60854,46 @@ ], "projections": { "pca": [ - -0.21822519600391388, - -0.08397024124860764 + -0.2182428538799286, + -0.08320610225200653 ], "tsne": [ - -28.455554962158203, - -13.752303123474121 + -32.345767974853516, + 10.896302223205566 ], "umap": [ - 1.721245527267456, - 8.27859878540039 + 1.960115671157837, + 7.369526386260986 ], "pca3d": [ - -0.21822479367256165, - -0.08399941027164459, - 0.025276223197579384 + -0.21824227273464203, + -0.08323553949594498, + 0.02571832574903965 ], "tsne3d": [ - -15.785996437072754, - -9.702803611755371, - 3.756481885910034 + -15.484477043151855, + 1.8747633695602417, + 0.38659968972206116 ], "umap3d": [ - 1.652277946472168, - 4.347341060638428, - 4.699143886566162 + 2.822580575942993, + 4.812194347381592, + 5.384110450744629 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 1, + "label": "Attention Models" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -61186,47 +61316,47 @@ ], "projections": { "pca": [ - -0.06660227477550507, - -0.04056652635335922 + -0.06673143059015274, + -0.04043470323085785 ], "tsne": [ - 14.39876651763916, - -35.889076232910156 + 3.4447391033172607, + 35.511436462402344 ], "umap": [ - 6.1534223556518555, - 9.678382873535156 + 6.3094916343688965, + 9.076224327087402 ], "pca3d": [ - -0.06660202145576477, - -0.0405675545334816, - -0.06841526925563812 + -0.06673116236925125, + -0.04043569415807724, + -0.06832617521286011 ], "tsne3d": [ - 6.7124714851379395, - -19.07929229736328, - 6.344141006469727 + -0.5244300961494446, + 14.918476104736328, + -19.10418701171875 ], "umap3d": [ - 4.829468250274658, - 2.864776372909546, - 2.1855502128601074 + 5.131994247436523, + 2.9246397018432617, + 2.605304479598999 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -61655,46 +61785,46 @@ "cited_by_count": 106, "projections": { "pca": [ - -0.038477201014757156, - 0.07601968944072723 + -0.03833741694688797, + 0.07662646472454071 ], "tsne": [ - -3.072469711303711, - -29.211870193481445 + -7.509072303771973, + 24.612199783325195 ], "umap": [ - 4.885037899017334, - 9.287141799926758 + 5.014187812805176, + 8.5556640625 ], "pca3d": [ - -0.03847682476043701, - 0.07598281651735306, - -0.01685788668692112 + -0.03833705931901932, + 0.07658888399600983, + -0.0168713741004467 ], "tsne3d": [ - -1.592602252960205, - -14.297818183898926, - -1.7985997200012207 + -5.059019565582275, + 16.287521362304688, + -4.1992363929748535 ], "umap3d": [ - 3.9026405811309814, - 2.889441967010498, - 2.966154098510742 + 4.638973712921143, + 3.1918461322784424, + 3.374556303024292 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, + "id": 26, "label": "Medical AI" } ], @@ -62112,47 +62242,47 @@ ], "projections": { "pca": [ - -0.19579274952411652, - -0.05121099576354027 + -0.19586916267871857, + -0.0503067746758461 ], "tsne": [ - -38.91779327392578, - -15.272249221801758 + -35.70427703857422, + 0.27017727494239807 ], "umap": [ - 1.211060643196106, - 7.433359146118164 + 1.6270840167999268, + 6.7563347816467285 ], "pca3d": [ - -0.19579240679740906, - -0.05121393874287605, - -0.1150461807847023 + -0.19586890935897827, + -0.05030976980924606, + -0.11472126841545105 ], "tsne3d": [ - -23.640111923217773, - -15.239872932434082, - 8.374542236328125 + -19.68659210205078, + 0.25848621129989624, + -3.65706205368042 ], "umap3d": [ - 0.8331961035728455, - 4.671746730804443, - 4.217837333679199 + 1.7714179754257202, + 4.886713027954102, + 4.9409966468811035 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -62570,31 +62700,31 @@ ], "projections": { "pca": [ - -0.05062314122915268, - -0.13937629759311676 + -0.050992295145988464, + -0.13944746553897858 ], "tsne": [ - -36.895511627197266, - 20.05626106262207 + -32.22137451171875, + -25.623205184936523 ], "umap": [ - 2.4907901287078857, - 4.7427897453308105 + 2.945006847381592, + 4.052280426025391 ], "pca3d": [ - -0.050622884184122086, - -0.13936224579811096, - 0.08767945319414139 + -0.05099203437566757, + -0.13943324983119965, + 0.08774203807115555 ], "tsne3d": [ - -15.313338279724121, - 13.899554252624512, - -1.6712654829025269 + -15.074830055236816, + -24.319101333618164, + 2.7913341522216797 ], "umap3d": [ - 2.4368953704833984, - 7.079775810241699, - 4.784401893615723 + 2.821240186691284, + 7.472101211547852, + 4.514330863952637 ] }, "cluster_id": 2, @@ -62605,11 +62735,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -63026,31 +63156,31 @@ ], "projections": { "pca": [ - -0.2406238168478012, - -0.13915127515792847 + -0.24103184044361115, + -0.1383627951145172 ], "tsne": [ - -42.223751068115234, - 2.925663709640503 + -38.501102447509766, + -9.780106544494629 ], "umap": [ - 1.7137004137039185, - 6.247241497039795 + 2.097015380859375, + 5.517598628997803 ], "pca3d": [ - -0.24062322080135345, - -0.13915903866291046, - -0.05120048671960831 + -0.24103128910064697, + -0.13837088644504547, + -0.0509193129837513 ], "tsne3d": [ - -19.15403175354004, - 3.464682102203369, - -3.912841558456421 + -17.951871871948242, + -9.799829483032227, + 0.02397553063929081 ], "umap3d": [ - 1.6692934036254883, - 5.8998613357543945, - 4.19115686416626 + 2.354393720626831, + 6.1984052658081055, + 4.559250354766846 ] }, "cluster_id": 2, @@ -63061,12 +63191,12 @@ "label": "Generative Models" }, { - "id": 1, - "label": "Network Learning" + "id": 5, + "label": "Diffusion Models" }, { - "id": 33, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -63482,31 +63612,31 @@ ], "projections": { "pca": [ - -0.22605961561203003, - -0.11370627582073212 + -0.22634008526802063, + -0.11311708390712738 ], "tsne": [ - -40.072959899902344, - 13.790627479553223 + -36.146934509277344, + -20.48769187927246 ], "umap": [ - 2.055708646774292, - 5.200684547424316 + 2.491297721862793, + 4.48504638671875 ], "pca3d": [ - -0.22605937719345093, - -0.11370179802179337, - -0.07327908277511597 + -0.22633974254131317, + -0.11311247944831848, + -0.07300005853176117 ], "tsne3d": [ - -19.744239807128906, - 10.867301940917969, - 3.45280385017395 + -19.204078674316406, + -19.108449935913086, + 5.317967414855957 ], "umap3d": [ - 1.9822824001312256, - 6.748263359069824, - 4.8426032066345215 + 2.525006055831909, + 7.0676493644714355, + 4.791114330291748 ] }, "cluster_id": 2, @@ -63517,11 +63647,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -63940,46 +64070,46 @@ ], "projections": { "pca": [ - -0.22357530891895294, - -0.10588272660970688 + -0.22367344796657562, + -0.1047225371003151 ], "tsne": [ - -35.1179313659668, - -5.698735237121582 + -43.00984191894531, + 7.493698596954346 ], "umap": [ - 0.6555039286613464, - 8.086124420166016 + 0.9759792685508728, + 7.267555236816406 ], "pca3d": [ - -0.2235746830701828, - -0.10591750591993332, - 0.06425084918737411 + -0.22367285192012787, + -0.10475780069828033, + 0.06468849629163742 ], "tsne3d": [ - -24.881542205810547, - -6.8199639320373535, - 13.026468276977539 + -26.48946762084961, + 7.748104095458984, + 5.576456069946289 ], "umap3d": [ - 0.8096475601196289, - 4.776274681091309, - 5.156652927398682 + 1.9968498945236206, + 5.103581428527832, + 5.896184921264648 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -64394,47 +64524,47 @@ ], "projections": { "pca": [ - -0.20431992411613464, - -0.11800567060709 + -0.2043268233537674, + -0.11728017777204514 ], "tsne": [ - -61.676273345947266, - -11.314395904541016 + -60.30777359008789, + -0.5072515606880188 ], "umap": [ - 0.7112075686454773, - 6.807053565979004 + 0.7711387872695923, + 5.847802639007568 ], "pca3d": [ - -0.2043195366859436, - -0.11802312731742859, - 0.00014633759565185755 + -0.20432637631893158, + -0.11729791015386581, + 0.0006863684975542128 ], "tsne3d": [ - -31.26799201965332, - 8.417801856994629, - 0.5451067090034485 + -34.96245193481445, + -3.49684739112854, + -3.766249418258667 ], "umap3d": [ - 0.7157928943634033, - 6.0746989250183105, - 4.7417683601379395 + 1.3624414205551147, + 6.325262069702148, + 5.14961576461792 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 9, - "label": "Vision Transformers" + "id": 23, + "label": "Training Noise" } ], "_embedding": [ @@ -64847,31 +64977,31 @@ ], "projections": { "pca": [ - -0.22109758853912354, - -0.07684405893087387 + -0.22134436666965485, + -0.07594980299472809 ], "tsne": [ - -41.19778823852539, - 3.9017438888549805 + -37.376312255859375, + -10.599271774291992 ], "umap": [ - 1.8962578773498535, - 6.078943252563477 + 2.2272722721099854, + 5.376680850982666 ], "pca3d": [ - -0.22109732031822205, - -0.07683044672012329, - 0.03213481605052948 + -0.22134414315223694, + -0.07593602687120438, + 0.03228086605668068 ], "tsne3d": [ - -17.95846176147461, - 4.252884864807129, - -2.612811803817749 + -17.366565704345703, + -11.375638008117676, + 1.2053148746490479 ], "umap3d": [ - 1.8308974504470825, - 6.020650386810303, - 4.211496353149414 + 2.444443941116333, + 6.306949138641357, + 4.536046504974365 ] }, "cluster_id": 2, @@ -64882,12 +65012,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 33, - "label": "Neural Networks" + "id": 19, + "label": "Graph Neural Nets" } ], "_embedding": [ @@ -65305,31 +65435,31 @@ ], "projections": { "pca": [ - -0.2191576510667801, - -0.08317666500806808 + -0.21929199993610382, + -0.08246833831071854 ], "tsne": [ - -35.47864532470703, - 11.044936180114746 + -32.48949432373047, + -16.830310821533203 ], "umap": [ - 2.409899950027466, - 5.338510513305664 + 2.825913190841675, + 4.768183708190918 ], "pca3d": [ - -0.2191573828458786, - -0.0831625759601593, - 0.009709839709103107 + -0.2192917764186859, + -0.0824541300535202, + 0.010007321834564209 ], "tsne3d": [ - -16.29994773864746, - 7.184911251068115, - 5.23420524597168 + -15.620575904846191, + -16.98255729675293, + 7.256982803344727 ], "umap3d": [ - 2.194775342941284, - 6.549182415008545, - 4.962674617767334 + 2.8281095027923584, + 7.030335903167725, + 4.864715099334717 ] }, "cluster_id": 2, @@ -65340,11 +65470,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -65778,31 +65908,31 @@ ], "projections": { "pca": [ - 0.027817483991384506, - 0.036747463047504425 + 0.028097867965698242, + 0.036410339176654816 ], "tsne": [ - -20.21462059020996, - 29.91002082824707 + -11.213330268859863, + -25.50330352783203 ], "umap": [ - 4.911566257476807, - 4.397191524505615 + 5.440776348114014, + 3.6793012619018555 ], "pca3d": [ - 0.027817463502287865, - 0.03675251826643944, - 0.11442708224058151 + 0.028097840026021004, + 0.03641536086797714, + 0.11429336667060852 ], "tsne3d": [ - 4.057318687438965, - 9.521353721618652, - 14.371053695678711 + 5.108220100402832, + -15.05305290222168, + 13.071917533874512 ], "umap3d": [ - 4.656740665435791, - 6.987726211547852, - 4.481952667236328 + 4.906152248382568, + 7.3420090675354, + 3.169450521469116 ] }, "cluster_id": 3, @@ -65813,12 +65943,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -66237,46 +66367,46 @@ ], "projections": { "pca": [ - -0.23937851190567017, - -0.11935845762491226 + -0.23962242901325226, + -0.11877016723155975 ], "tsne": [ - -46.171180725097656, - -2.2234790325164795 + -43.321781158447266, + -1.4874050617218018 ], "umap": [ - 0.8948785662651062, - 7.151383876800537 + 1.2786035537719727, + 6.391846656799316 ], "pca3d": [ - -0.2393779456615448, - -0.11937525868415833, - -0.03502390906214714 + -0.2396218627691269, + -0.11878698319196701, + -0.034722015261650085 ], "tsne3d": [ - -20.00010871887207, - -2.2703452110290527, - 3.7957966327667236 + -21.548770904541016, + -2.51686692237854, + 5.470696449279785 ], "umap3d": [ - 1.140053629875183, - 5.400557041168213, - 4.672063827514648 + 2.02759051322937, + 5.760648727416992, + 5.280134201049805 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -66697,31 +66827,31 @@ ], "projections": { "pca": [ - 0.022941824048757553, - -0.12081334739923477 + 0.02275296114385128, + -0.12049590051174164 ], "tsne": [ - 8.428558349609375, - 8.122276306152344 + 10.847749710083008, + -5.587683200836182 ], "umap": [ - 6.924075126647949, - 6.298527240753174 + 7.197268009185791, + 5.947539806365967 ], "pca3d": [ - 0.02294212020933628, - -0.12081858515739441, - -0.009804030880331993 + 0.022753262892365456, + -0.12050053477287292, + -0.009448107331991196 ], "tsne3d": [ - 7.235500335693359, - 3.3310415744781494, - -11.889389991760254 + 4.365799903869629, + 2.627329111099243, + 4.755157470703125 ], "umap3d": [ - 5.6719794273376465, - 4.930983066558838, - 3.378751516342163 + 5.9606852531433105, + 5.254177093505859, + 2.5278801918029785 ] }, "cluster_id": 4, @@ -66732,11 +66862,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -67172,46 +67302,46 @@ "cited_by_count": 27, "projections": { "pca": [ - -0.07010991126298904, - 0.2119605839252472 + -0.06950157135725021, + 0.2126944214105606 ], "tsne": [ - -4.495547771453857, - -24.744384765625 + -6.687503337860107, + 21.498781204223633 ], "umap": [ - 4.516493320465088, - 8.980484008789062 + 4.774911403656006, + 8.253466606140137 ], "pca3d": [ - -0.07011017203330994, - 0.21197408437728882, - 0.07935938984155655 + -0.06950204074382782, + 0.2127075493335724, + 0.07915165275335312 ], "tsne3d": [ - -1.3621821403503418, - -8.428030014038086, - 0.4354214370250702 + -3.641150951385498, + 12.684037208557129, + -3.8493802547454834 ], "umap3d": [ - 3.7218589782714844, - 3.2024645805358887, - 3.23559308052063 + 4.426581859588623, + 3.6136763095855713, + 3.7272064685821533 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, + "id": 26, "label": "Medical AI" } ], @@ -67632,47 +67762,47 @@ "cited_by_count": 44, "projections": { "pca": [ - -0.28568336367607117, - 0.22654183208942413 + -0.2850377559661865, + 0.22792048752307892 ], "tsne": [ - -12.836474418640137, - -30.358362197875977 + -18.306093215942383, + 23.417827606201172 ], "umap": [ - 1.9223872423171997, - 7.5270676612854 + 2.4110264778137207, + 6.828601360321045 ], "pca3d": [ - -0.28568392992019653, - 0.22655756771564484, - 0.0687473714351654 + -0.2850380837917328, + 0.22793619334697723, + 0.06858525425195694 ], "tsne3d": [ - -11.335368156433105, - -16.535850524902344, - -4.849550724029541 + -7.747413158416748, + 19.762454986572266, + 6.719547748565674 ], "umap3d": [ - 1.017116665840149, - 4.488286018371582, - 3.765406847000122 + 1.9941006898880005, + 4.698512077331543, + 4.459748268127441 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -68085,31 +68215,31 @@ ], "projections": { "pca": [ - -0.2569575607776642, - -0.07940158247947693 + -0.25719931721687317, + -0.07859978824853897 ], "tsne": [ - -37.546630859375, - 14.001011848449707 + -35.25900650024414, + -17.617849349975586 ], "umap": [ - 2.029853582382202, - 5.394397735595703 + 2.482722043991089, + 4.759474277496338 ], "pca3d": [ - -0.25695738196372986, - -0.07938213646411896, - -0.07636748254299164 + -0.2571991980075836, + -0.07858040183782578, + -0.07615424692630768 ], "tsne3d": [ - -18.26822853088379, - 7.708165168762207, - 0.5362231731414795 + -17.59420394897461, + -15.664287567138672, + 2.642655372619629 ], "umap3d": [ - 1.955062747001648, - 6.567286491394043, - 4.563279628753662 + 2.437448501586914, + 6.831666946411133, + 4.563101291656494 ] }, "cluster_id": 2, @@ -68120,11 +68250,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -68547,31 +68677,31 @@ "doi": "10.1101/2024.10.25.620272", "projections": { "pca": [ - -0.16839255392551422, - -0.05166313424706459 + -0.1685936450958252, + -0.050765201449394226 ], "tsne": [ - -41.91318893432617, - -0.7377369403839111 + -40.72645950317383, + -6.34905481338501 ], "umap": [ - 1.6039199829101562, - 6.383315086364746 + 1.8812994956970215, + 5.739876747131348 ], "pca3d": [ - -0.16839250922203064, - -0.05164124816656113, - -0.004788754042237997 + -0.168593630194664, + -0.050743404775857925, + -0.004675230011343956 ], "tsne3d": [ - -18.256519317626953, - 0.43178340792655945, - -1.9955801963806152 + -20.080596923828125, + -9.30738353729248, + 4.751964092254639 ], "umap3d": [ - 1.530687928199768, - 5.786700248718262, - 4.184521675109863 + 2.253108263015747, + 5.970921516418457, + 4.685275077819824 ] }, "cluster_id": 2, @@ -68582,12 +68712,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -69005,31 +69135,31 @@ ], "projections": { "pca": [ - -0.2971636652946472, - -0.05632064864039421 + -0.29727697372436523, + -0.05575300753116608 ], "tsne": [ - -39.31147003173828, - 14.616108894348145 + -35.33061981201172, + -20.789939880371094 ], "umap": [ - 2.0837669372558594, - 5.173348903656006 + 2.504333019256592, + 4.618435859680176 ], "pca3d": [ - -0.29716354608535767, - -0.056329887360334396, - -0.07785020023584366 + -0.29727649688720703, + -0.055762406438589096, + -0.07767491787672043 ], "tsne3d": [ - -20.90215492248535, - 9.10031795501709, - 0.37788301706314087 + -19.021657943725586, + -18.533313751220703, + 2.1546225547790527 ], "umap3d": [ - 1.9175220727920532, - 6.777956008911133, - 4.8138957023620605 + 2.497493267059326, + 7.110345840454102, + 4.7498955726623535 ] }, "cluster_id": 2, @@ -69040,11 +69170,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -69464,47 +69594,47 @@ ], "projections": { "pca": [ - -0.10094837844371796, - 0.034675586968660355 + -0.10075154900550842, + 0.034554436802864075 ], "tsne": [ - 9.429998397827148, - -35.624935150146484 + 0.38451656699180603, + 36.61491012573242 ], "umap": [ - 5.773396968841553, - 9.72923755645752 + 5.859618663787842, + 9.128697395324707 ], "pca3d": [ - -0.10094798356294632, - 0.034649066627025604, - -0.12901175022125244 + -0.10075119137763977, + 0.03452735021710396, + -0.12893913686275482 ], "tsne3d": [ - 3.417903423309326, - -16.9938907623291, - 4.357572555541992 + -3.0209829807281494, + 9.084556579589844, + -16.625795364379883 ], "umap3d": [ - 4.692075729370117, - 2.514390468597412, - 2.399031162261963 + 5.0736165046691895, + 2.590264081954956, + 2.8921031951904297 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -69914,47 +70044,47 @@ "abstract": "Anthropic's latest interpretability research: a new microscope to understand Claude's internal mechanisms", "projections": { "pca": [ - -0.196466863155365, - 0.1367574781179428 + -0.196133092045784, + 0.13829265534877777 ], "tsne": [ - -21.579727172851562, - -13.456707954406738 + -25.113849639892578, + 7.474405288696289 ], "umap": [ - 2.621526002883911, - 8.660723686218262 + 2.8239009380340576, + 7.9331817626953125 ], "pca3d": [ - -0.19646701216697693, - 0.13674499094486237, - 0.10965805500745773 + -0.19613315165042877, + 0.13827946782112122, + 0.10961441695690155 ], "tsne3d": [ - -12.762370109558105, - -15.331080436706543, - 1.638297200202942 + -9.440434455871582, + 6.4981231689453125, + 12.958233833312988 ], "umap3d": [ - 2.349616765975952, - 3.784848928451538, - 4.491443634033203 + 3.4335079193115234, + 4.484348297119141, + 5.000142574310303 ] }, - "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 1, - "label": "AI Scientific Research" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 7, - "label": "Language Models" + "id": 25, + "label": "AI Agents" } ], "_embedding": [ @@ -70398,46 +70528,46 @@ ], "projections": { "pca": [ - -0.23562468588352203, - -0.19599787890911102 + -0.23605841398239136, + -0.19536228477954865 ], "tsne": [ - -32.09044647216797, - -7.359869003295898 + -38.314048767089844, + 6.645442485809326 ], "umap": [ - 1.0274767875671387, - 7.995957851409912 + 1.2473483085632324, + 7.193781852722168 ], "pca3d": [ - -0.23562417924404144, - -0.1960037499666214, - 0.0012654942693188787 + -0.23605790734291077, + -0.19536782801151276, + 0.0017544727306813002 ], "tsne3d": [ - -20.43959617614746, - -3.607872724533081, - 7.660194396972656 + -21.899677276611328, + 2.891266107559204, + 5.101103782653809 ], "umap3d": [ - 1.140265941619873, - 4.852776527404785, - 4.991988182067871 + 2.224041700363159, + 5.2934041023254395, + 5.6648688316345215 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -70852,31 +70982,31 @@ ], "projections": { "pca": [ - 0.09059909731149673, - -0.10882794111967087 + 0.09044352173805237, + -0.109123595058918 ], "tsne": [ - -30.37822151184082, - 19.683767318725586 + -24.31734848022461, + -22.846576690673828 ], "umap": [ - 3.63136887550354, - 4.636274337768555 + 3.972651243209839, + 4.15433931350708 ], "pca3d": [ - 0.09059933573007584, - -0.10884067416191101, - 0.0884578749537468 + 0.09044384211301804, + -0.10913610458374023, + 0.08855200558900833 ], "tsne3d": [ - -10.724486351013184, - 14.673148155212402, - -0.568785548210144 + -9.975729942321777, + -23.394437789916992, + 2.4166762828826904 ], "umap3d": [ - 3.5015976428985596, - 6.9165568351745605, - 4.842178821563721 + 3.864393711090088, + 7.420263767242432, + 4.08344841003418 ] }, "cluster_id": 3, @@ -70887,12 +71017,12 @@ "label": "Protein Design" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 16, - "label": "Diffusion Models" + "id": 7, + "label": "Diffusion Gen" } ], "_embedding": [ @@ -71316,46 +71446,46 @@ "cited_by_count": 50, "projections": { "pca": [ - -0.2818405032157898, - -0.025467216968536377 + -0.2817668318748474, + -0.02416381426155567 ], "tsne": [ - -27.21326446533203, - -10.132099151611328 + -30.95949935913086, + 7.875594139099121 ], "umap": [ - 1.7724552154541016, - 8.128756523132324 + 2.0259416103363037, + 7.34818172454834 ], "pca3d": [ - -0.2818404734134674, - -0.02545807883143425, - 0.11174087971448898 + -0.2817665636539459, + -0.024154849350452423, + 0.1120215654373169 ], "tsne3d": [ - -16.674556732177734, - -6.4756293296813965, - 4.21287727355957 + -15.941326141357422, + 1.0259531736373901, + 4.011950969696045 ], "umap3d": [ - 1.66770601272583, - 4.49215030670166, - 4.713994026184082 + 2.7757480144500732, + 4.9010748863220215, + 5.263458251953125 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 1, + "label": "Attention Models" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -71771,46 +71901,46 @@ ], "projections": { "pca": [ - -0.2752894461154938, - -0.14702899754047394 + -0.2756887376308441, + -0.1464081108570099 ], "tsne": [ - -48.22809600830078, - -2.0782299041748047 + -45.624595642089844, + -1.6286523342132568 ], "umap": [ - 0.7586763501167297, - 7.21156120300293 + 0.9982341527938843, + 6.396349906921387 ], "pca3d": [ - -0.2752889096736908, - -0.1470523327589035, - -0.07956729829311371 + -0.2756880223751068, + -0.14643153548240662, + -0.07923280447721481 ], "tsne3d": [ - -22.72071647644043, - -3.8159284591674805, - 4.477898597717285 + -24.483640670776367, + -2.6187570095062256, + 7.568907737731934 ], "umap3d": [ - 0.874319851398468, - 5.358638763427734, - 4.735762119293213 + 1.8698326349258423, + 5.6909332275390625, + 5.418785095214844 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -72249,46 +72379,46 @@ ], "projections": { "pca": [ - -0.19285878539085388, - -0.0998021587729454 + -0.19305089116096497, + -0.09878692030906677 ], "tsne": [ - -25.67548179626465, - -13.785054206848145 + -29.76654624938965, + 11.47476863861084 ], "umap": [ - 2.1160941123962402, - 8.319082260131836 + 2.4039196968078613, + 7.588713645935059 ], "pca3d": [ - -0.19285859167575836, - -0.09980884194374084, - 0.04432494938373566 + -0.19305051863193512, + -0.09879341721534729, + 0.044748831540346146 ], "tsne3d": [ - -13.492389678955078, - -13.08023452758789, - 5.44595193862915 + -15.420042037963867, + 6.628049373626709, + -0.7470173239707947 ], "umap3d": [ - 1.9285072088241577, - 4.050921440124512, - 4.448101043701172 + 2.940279722213745, + 4.5194268226623535, + 5.079791069030762 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -72705,31 +72835,31 @@ ], "projections": { "pca": [ - -0.1928267627954483, - -0.09911271929740906 + -0.1930275559425354, + -0.09861105680465698 ], "tsne": [ - -35.747440338134766, - 12.756295204162598 + -32.28791427612305, + -18.783226013183594 ], "umap": [ - 2.507375717163086, - 5.201990127563477 + 2.904508113861084, + 4.669292449951172 ], "pca3d": [ - -0.19282647967338562, - -0.09909779578447342, - 0.0067636738531291485 + -0.19302737712860107, + -0.09859611839056015, + 0.006973607465624809 ], "tsne3d": [ - -16.263744354248047, - 8.315834999084473, - 3.3640944957733154 + -15.655713081359863, + -16.915138244628906, + 5.102730751037598 ], "umap3d": [ - 2.337938070297241, - 6.6256818771362305, - 4.827465534210205 + 2.850846529006958, + 7.046576976776123, + 4.679271697998047 ] }, "cluster_id": 2, @@ -72740,11 +72870,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -73160,47 +73290,47 @@ ], "projections": { "pca": [ - -0.24084042012691498, - -0.11318465322256088 + -0.24106040596961975, + -0.11198540776968002 ], "tsne": [ - -38.85857009887695, - -15.073957443237305 + -35.47321319580078, + 0.28839895129203796 ], "umap": [ - 1.2331939935684204, - 7.414278507232666 + 1.6268219947814941, + 6.796542644500732 ], "pca3d": [ - -0.24084001779556274, - -0.11318231374025345, - -0.07857708632946014 + -0.2410600781440735, + -0.11198316514492035, + -0.07807055860757828 ], "tsne3d": [ - -24.279909133911133, - -14.9759521484375, - 7.725432872772217 + -20.144229888916016, + 0.7848634719848633, + -4.227011203765869 ], "umap3d": [ - 0.8731452226638794, - 4.636823654174805, - 4.1993408203125 + 1.8231980800628662, + 4.869404315948486, + 4.951971054077148 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -73610,46 +73740,46 @@ "abstract": "DeepEP: an efficient expert-parallel communication library - deepseek-ai/DeepEP", "projections": { "pca": [ - -0.16356968879699707, - 0.03701469302177429 + -0.16327570378780365, + 0.037542056292295456 ], "tsne": [ - -25.81707000732422, - -7.808938503265381 + -32.43502426147461, + 5.406976222991943 ], "umap": [ - 1.730228304862976, - 8.309541702270508 + 2.0570547580718994, + 7.269787788391113 ], "pca3d": [ - -0.16356973350048065, - 0.03702089190483093, - 0.051844291388988495 + -0.16327570378780365, + 0.03754883632063866, + 0.051966242492198944 ], "tsne3d": [ - -14.574461936950684, - -6.002171039581299, - 10.566447257995605 + -15.410042762756348, + 5.806448936462402, + 7.473841667175293 ], "umap3d": [ - 1.7771728038787842, - 4.568042755126953, - 4.814748764038086 + 2.867466926574707, + 5.219738483428955, + 5.231087684631348 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 1, + "label": "Attention Models" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -74059,47 +74189,47 @@ ], "projections": { "pca": [ - -0.2120983600616455, - 0.2533642649650574 + -0.21141959726810455, + 0.2550555467605591 ], "tsne": [ - -10.324939727783203, - -24.84071159362793 + -13.621639251708984, + 18.178977966308594 ], "umap": [ - 3.8474793434143066, - 8.654130935668945 + 4.022399425506592, + 7.971742153167725 ], "pca3d": [ - -0.21209879219532013, - 0.25337323546409607, - 0.20202188193798065 + -0.21142002940177917, + 0.2550645172595978, + 0.20177794992923737 ], "tsne3d": [ - -6.250010013580322, - -11.227290153503418, - -2.9239413738250732 + -7.960665225982666, + 14.386807441711426, + 0.2202235609292984 ], "umap3d": [ - 3.076112985610962, - 3.5333282947540283, - 3.3655922412872314 + 3.708625078201294, + 3.987910270690918, + 3.76420521736145 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -74513,31 +74643,31 @@ ], "projections": { "pca": [ - -0.13660991191864014, - -0.11467157304286957 + -0.13686181604862213, + -0.1144799143075943 ], "tsne": [ - -39.08321762084961, - 17.258747100830078 + -35.09548568725586, + -23.153018951416016 ], "umap": [ - 2.221311569213867, - 4.921189308166504 + 2.6906485557556152, + 4.382441520690918 ], "pca3d": [ - -0.13660962879657745, - -0.1146654337644577, - -0.043312083929777145 + -0.1368616372346878, + -0.11447366327047348, + -0.043078456073999405 ], "tsne3d": [ - -18.243581771850586, - 11.88771915435791, - 0.2217073291540146 + -18.416337966918945, + -21.397531509399414, + 4.578898906707764 ], "umap3d": [ - 2.0789718627929688, - 6.972035884857178, - 4.768092155456543 + 2.564735174179077, + 7.301266670227051, + 4.6556715965271 ] }, "cluster_id": 2, @@ -74548,11 +74678,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -74979,47 +75109,47 @@ "cited_by_count": 5, "projections": { "pca": [ - -0.08160986751317978, - 0.011843673884868622 + -0.08153858035802841, + 0.012246877886354923 ], "tsne": [ - 13.1580171585083, - -33.36032485961914 + 5.218719959259033, + 34.16923904418945 ], "umap": [ - 6.150115489959717, - 9.759675025939941 + 6.209757328033447, + 9.167465209960938 ], "pca3d": [ - -0.08160964399576187, - 0.011824228800833225, - -0.14529834687709808 + -0.08153833448886871, + 0.012226855382323265, + -0.1451396942138672 ], "tsne3d": [ - 6.960757732391357, - -17.03398895263672, - 2.725926637649536 + 0.10900349169969559, + 11.8373441696167, + -17.64426612854004 ], "umap3d": [ - 4.86355447769165, - 2.674936294555664, - 2.0875234603881836 + 5.222717761993408, + 2.6682798862457275, + 2.5711867809295654 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -75436,47 +75566,47 @@ ], "projections": { "pca": [ - -0.11332669109106064, - 0.008398338221013546 + -0.11324281990528107, + 0.009389225393533707 ], "tsne": [ - -6.3697333335876465, - -22.225431442260742 + -11.710761070251465, + 20.761865615844727 ], "umap": [ - 4.006634712219238, - 8.65350341796875 + 4.292600631713867, + 7.9134297370910645 ], "pca3d": [ - -0.11332658678293228, - 0.00839674286544323, - 0.11410726606845856 + -0.1132427230477333, + 0.009387693367898464, + 0.11424310505390167 ], "tsne3d": [ - -6.78363561630249, - -8.312021255493164, - -6.272907257080078 + -5.912564277648926, + 14.309150695800781, + 3.549285650253296 ], "umap3d": [ - 3.2150824069976807, - 3.843519449234009, - 3.1918439865112305 + 3.8632700443267822, + 4.188089847564697, + 3.548844337463379 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -75887,31 +76017,31 @@ ], "projections": { "pca": [ - -0.24542079865932465, - 0.03855889290571213 + -0.2453736811876297, + 0.039734892547130585 ], "tsne": [ - -43.2475700378418, - 4.813855171203613 + -38.7386474609375, + -12.435214042663574 ], "umap": [ - 1.6867120265960693, - 5.9587836265563965 + 2.0503203868865967, + 5.451735973358154 ], "pca3d": [ - -0.24542099237442017, - 0.03858035057783127, - 0.06087517365813255 + -0.24537383019924164, + 0.03975643962621689, + 0.060823582112789154 ], "tsne3d": [ - -20.21294403076172, - 4.2719011306762695, - -0.32088205218315125 + -19.419200897216797, + -11.939062118530273, + 3.7482972145080566 ], "umap3d": [ - 1.695485234260559, - 5.99914026260376, - 4.240962982177734 + 2.280670404434204, + 6.289371967315674, + 4.647470951080322 ] }, "cluster_id": 2, @@ -75922,12 +76052,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -76343,46 +76473,46 @@ ], "projections": { "pca": [ - -0.25013434886932373, - -0.1379702240228653 + -0.2504659593105316, + -0.1369391530752182 ], "tsne": [ - -29.7330322265625, - -10.583741188049316 + -33.856258392333984, + 7.398453235626221 ], "umap": [ - 1.708602786064148, - 8.176162719726562 + 2.006165027618408, + 7.411456108093262 ], "pca3d": [ - -0.2501339018344879, - -0.13796281814575195, - -0.015996726229786873 + -0.2504655718803406, + -0.13693110644817352, + -0.015575986355543137 ], "tsne3d": [ - -16.459383010864258, - -7.87812614440918, - 8.698012351989746 + -17.023210525512695, + 4.997847080230713, + 4.842005729675293 ], "umap3d": [ - 1.6120461225509644, - 4.476486682891846, - 4.725602626800537 + 2.6644256114959717, + 4.95170783996582, + 5.425797462463379 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 1, + "label": "Attention Models" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -76805,31 +76935,31 @@ "openalex_url": "https://openalex.org/W4407570846", "projections": { "pca": [ - -0.2091594785451889, - -0.06434167176485062 + -0.20944225788116455, + -0.06351802498102188 ], "tsne": [ - -41.07324981689453, - 4.431934833526611 + -37.109519958496094, + -11.031251907348633 ], "umap": [ - 1.8865944147109985, - 6.020723819732666 + 2.2967913150787354, + 5.258761405944824 ], "pca3d": [ - -0.2091590166091919, - -0.06434652209281921, - 0.029040716588497162 + -0.2094418853521347, + -0.06352303922176361, + 0.02910035289824009 ], "tsne3d": [ - -17.424556732177734, - 5.455602169036865, - -2.991865873336792 + -17.025497436523438, + -12.407622337341309, + 0.42137768864631653 ], "umap3d": [ - 1.934041142463684, - 6.0898637771606445, - 4.250579833984375 + 2.4915966987609863, + 6.360687732696533, + 4.49881649017334 ] }, "cluster_id": 2, @@ -76840,12 +76970,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 33, - "label": "Neural Networks" + "id": 19, + "label": "Graph Neural Nets" } ], "_embedding": [ @@ -77263,47 +77393,47 @@ ], "projections": { "pca": [ - -0.15569019317626953, - -0.044710610061883926 + -0.15587866306304932, + -0.04391467198729515 ], "tsne": [ - -33.742801666259766, - -22.461868286132812 + -29.96274757385254, + 3.2829039096832275 ], "umap": [ - 1.6861933469772339, - 6.926619052886963 + 1.7608879804611206, + 6.097668647766113 ], "pca3d": [ - -0.15569020807743073, - -0.0446898527443409, - 0.03171748295426369 + -0.1558786928653717, + -0.04389345645904541, + 0.03178383409976959 ], "tsne3d": [ - -19.113964080810547, - -11.760210037231445, - -4.415456295013428 + -22.0435848236084, + 12.342286109924316, + -5.833942890167236 ], "umap3d": [ - 1.3227778673171997, - 5.126241683959961, - 3.892011880874634 + 2.1595942974090576, + 5.316575527191162, + 4.40062952041626 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -77715,47 +77845,47 @@ ], "projections": { "pca": [ - -0.22031617164611816, - -0.09401832520961761 + -0.22057561576366425, + -0.0927618071436882 ], "tsne": [ - -38.94462966918945, - -15.10197925567627 + -35.57133865356445, + 0.20482279360294342 ], "umap": [ - 1.2699862718582153, - 7.436275482177734 + 1.630956768989563, + 6.76661491394043 ], "pca3d": [ - -0.22031563520431519, - -0.09403252601623535, - -0.10200640559196472 + -0.2205752283334732, + -0.09277613461017609, + -0.1015876829624176 ], "tsne3d": [ - -23.92557716369629, - -15.55422592163086, - 7.138073444366455 + -20.850479125976562, + 0.7138587832450867, + -3.738429546356201 ], "umap3d": [ - 0.8872646689414978, - 4.626063823699951, - 4.190560340881348 + 1.813934564590454, + 4.88161563873291, + 4.956511974334717 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -78177,46 +78307,46 @@ ], "projections": { "pca": [ - -0.20588921010494232, - -0.0847809910774231 + -0.20597964525222778, + -0.08378523588180542 ], "tsne": [ - -27.33055877685547, - -11.777582168579102 + -31.50454330444336, + 9.317357063293457 ], "umap": [ - 1.9232115745544434, - 8.339176177978516 + 2.2662606239318848, + 7.561483860015869 ], "pca3d": [ - -0.20588886737823486, - -0.08479337394237518, - 0.06216602027416229 + -0.20597928762435913, + -0.08379751443862915, + 0.06255321949720383 ], "tsne3d": [ - -14.6819486618042, - -9.552128791809082, - 7.075977802276611 + -15.883504867553711, + 5.128264427185059, + 1.0807965993881226 ], "umap3d": [ - 1.8620283603668213, - 4.226254940032959, - 4.6325249671936035 + 2.9307212829589844, + 4.646060466766357, + 5.2154622077941895 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 1, + "label": "Attention Models" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -78631,47 +78761,47 @@ ], "projections": { "pca": [ - -0.32872357964515686, - -0.1198977530002594 + -0.3289984166622162, + -0.11859291791915894 ], "tsne": [ - -42.07474899291992, - -19.14754867553711 + -46.20771026611328, + 13.054971694946289 ], "umap": [ - 1.2107436656951904, - 7.221492290496826 + 1.528842806816101, + 6.436576843261719 ], "pca3d": [ - -0.3287232220172882, - -0.11990021169185638, - -0.009503954090178013 + -0.32899799942970276, + -0.11859562247991562, + -0.009080542251467705 ], "tsne3d": [ - -25.936859130859375, - -10.986188888549805, - 2.243567705154419 + -25.87070083618164, + 3.7319374084472656, + -5.179171562194824 ], "umap3d": [ - 0.8388875722885132, - 5.0596489906311035, - 4.02871036529541 + 1.5691251754760742, + 5.1898040771484375, + 4.750315189361572 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -79085,47 +79215,47 @@ ], "projections": { "pca": [ - -0.29319286346435547, - -0.11478585004806519 + -0.2933776080608368, + -0.11367423087358475 ], "tsne": [ - -39.297359466552734, - -14.065581321716309 + -34.55306625366211, + -0.12135092914104462 ], "umap": [ - 1.2411965131759644, - 7.274296283721924 + 1.6275893449783325, + 6.685214519500732 ], "pca3d": [ - -0.2931925356388092, - -0.11478796601295471, - -0.07901974022388458 + -0.2933772802352905, + -0.11367662250995636, + -0.07851435989141464 ], "tsne3d": [ - -25.799686431884766, - -14.105860710144043, - 7.1841230392456055 + -19.998233795166016, + 0.910587728023529, + -5.957576751708984 ], "umap3d": [ - 0.8870772123336792, - 4.735313892364502, - 4.156546115875244 + 1.7367674112319946, + 5.048183917999268, + 4.886256217956543 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -79540,47 +79670,47 @@ "journal": "Transactions on Machine Learning Research", "projections": { "pca": [ - -0.3006044328212738, - -0.12039153277873993 + -0.30092278122901917, + -0.11881984025239944 ], "tsne": [ - -42.81736755371094, - -21.022851943969727 + -44.43071365356445, + 12.237576484680176 ], "umap": [ - 1.2663506269454956, - 7.458676815032959 + 1.6166229248046875, + 6.603978633880615 ], "pca3d": [ - -0.3006039261817932, - -0.12041442841291428, - -0.028630735352635384 + -0.3009222149848938, + -0.11884324997663498, + -0.028167037293314934 ], "tsne3d": [ - -25.796302795410156, - -11.884393692016602, - -1.9697178602218628 + -24.387367248535156, + 0.6158802509307861, + -7.873211860656738 ], "umap3d": [ - 0.7288460731506348, - 4.895021915435791, - 3.8250892162323 + 1.542970895767212, + 5.041912078857422, + 4.6005964279174805 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -79998,31 +80128,31 @@ "doi": "10.1101/2023.12.06.570473", "projections": { "pca": [ - 0.1063789501786232, - -0.1353759616613388 + 0.10618146508932114, + -0.13532479107379913 ], "tsne": [ - -8.109367370605469, - 22.403417587280273 + 2.0825891494750977, + -29.07126235961914 ], "umap": [ - 5.292719841003418, - 4.415445804595947 + 5.786164283752441, + 4.006675720214844 ], "pca3d": [ - 0.10637909173965454, - -0.1353779137134552, - 0.23986831307411194 + 0.10618163645267487, + -0.13532687723636627, + 0.23997755348682404 ], "tsne3d": [ - -3.689516544342041, - 3.4824888706207275, - 5.935520172119141 + -6.555011749267578, + -12.630837440490723, + 3.4779012203216553 ], "umap3d": [ - 5.331233501434326, - 6.93595552444458, - 4.634974479675293 + 5.578688621520996, + 7.5578718185424805, + 2.9597628116607666 ] }, "cluster_id": 3, @@ -80033,11 +80163,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -80454,46 +80584,46 @@ ], "projections": { "pca": [ - -0.23972100019454956, - -0.11342895776033401 + -0.24003414809703827, + -0.1126302182674408 ], "tsne": [ - -37.254478454589844, - -9.760937690734863 + -42.67073440551758, + 10.14355754852295 ], "umap": [ - 0.6077771186828613, - 7.773335933685303 + 0.9023797512054443, + 6.855420112609863 ], "pca3d": [ - -0.23972073197364807, - -0.1134234070777893, - -0.10148107260465622 + -0.2400338500738144, + -0.11262431740760803, + -0.10114876180887222 ], "tsne3d": [ - -25.722450256347656, - -6.785003662109375, - 6.760941505432129 + -24.712574005126953, + 2.825791597366333, + 9.40381908416748 ], "umap3d": [ - 0.7051240801811218, - 4.9909443855285645, - 4.852050304412842 + 1.7095983028411865, + 5.3015971183776855, + 5.648709774017334 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -80909,46 +81039,46 @@ ], "projections": { "pca": [ - -0.20611461997032166, - 0.042298659682273865 + -0.2059193104505539, + 0.043079085648059845 ], "tsne": [ - -28.053447723388672, - -12.617579460144043 + -28.17775535583496, + 7.111351013183594 ], "umap": [ - 1.8041355609893799, - 8.179619789123535 + 2.0017406940460205, + 7.463308334350586 ], "pca3d": [ - -0.2061145305633545, - 0.04230067878961563, - 0.04832962155342102 + -0.20591925084590912, + 0.04308111220598221, + 0.04843893647193909 ], "tsne3d": [ - -15.424887657165527, - -11.460176467895508, - 11.859506607055664 + -13.356443405151367, + 3.722548007965088, + 4.013155460357666 ], "umap3d": [ - 1.6644948720932007, - 4.315536022186279, - 4.672636985778809 + 2.6983299255371094, + 4.8567986488342285, + 5.349437236785889 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 1, + "label": "Attention Models" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -81375,46 +81505,46 @@ ], "projections": { "pca": [ - -0.25914210081100464, - -0.06023965775966644 + -0.25917285680770874, + -0.0586104542016983 ], "tsne": [ - -28.926673889160156, - -15.81332015991211 + -33.59295654296875, + 13.466634750366211 ], "umap": [ - 2.0600361824035645, - 8.248734474182129 + 2.275315523147583, + 7.5297393798828125 ], "pca3d": [ - -0.2591418921947479, - -0.06024894863367081, - 0.10323753207921982 + -0.2591725289821625, + -0.05861956998705864, + 0.1036507710814476 ], "tsne3d": [ - -17.190805435180664, - -13.905213356018066, - 5.441009044647217 + -18.60157012939453, + 8.01839542388916, + -1.302719235420227 ], "umap3d": [ - 1.7462538480758667, - 4.128413677215576, - 4.346255302429199 + 2.8034160137176514, + 4.59503698348999, + 5.119524955749512 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 1, + "label": "Attention Models" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -81833,31 +81963,31 @@ "cited_by_count": 24, "projections": { "pca": [ - -0.10933665186166763, - 0.025821320712566376 + -0.10912476480007172, + 0.026714971289038658 ], "tsne": [ - -16.734525680541992, - 9.129981994628906 + -14.073423385620117, + -16.461172103881836 ], "umap": [ - 5.095066547393799, - 5.410209655761719 + 5.480906963348389, + 5.0769362449646 ], "pca3d": [ - -0.10933677107095718, - 0.02584017626941204, - 0.21162274479866028 + -0.10912486165761948, + 0.026733698323369026, + 0.21166405081748962 ], "tsne3d": [ - -2.827799081802368, - 11.623067855834961, - -2.4414045810699463 + 0.48179590702056885, + -8.26921558380127, + 10.021785736083984 ], "umap3d": [ - 4.606044292449951, - 5.756138324737549, - 4.278524398803711 + 5.051469802856445, + 6.236950874328613, + 3.404860496520996 ] }, "cluster_id": 3, @@ -81868,12 +81998,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, - "label": "Protein Design" + "id": 32, + "label": "Genome Protein" } ], "_embedding": [ @@ -82289,46 +82419,46 @@ ], "projections": { "pca": [ - -0.20181912183761597, - -0.12941443920135498 + -0.20207972824573517, + -0.12885694205760956 ], "tsne": [ - -34.59409713745117, - -14.452220916748047 + -34.59365463256836, + 5.61712646484375 ], "umap": [ - 0.7107826471328735, - 7.253998279571533 + 0.8978582620620728, + 6.560547351837158 ], "pca3d": [ - -0.20181871950626373, - -0.12942811846733093, - -0.08088408410549164 + -0.20207929611206055, + -0.12887060642242432, + -0.08047787100076675 ], "tsne3d": [ - -17.139883041381836, - -6.68159818649292, - 0.9934882521629333 + -25.164749145507812, + 3.668226957321167, + -0.5005343556404114 ], "umap3d": [ - 0.7514891028404236, - 5.587860107421875, - 4.805817604064941 + 1.7508569955825806, + 5.708015441894531, + 5.421875476837158 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -82752,47 +82882,47 @@ ], "projections": { "pca": [ - -0.10420194268226624, - 0.05930284783244133 + -0.10406740009784698, + 0.06070529296994209 ], "tsne": [ - -7.455548286437988, - -23.075225830078125 + -11.537997245788574, + 18.875600814819336 ], "umap": [ - 3.922135591506958, - 8.564019203186035 + 4.105331897735596, + 7.821161270141602 ], "pca3d": [ - -0.10420195013284683, - 0.05930542200803757, - 0.20841962099075317 + -0.10406745970249176, + 0.0607081763446331, + 0.20844389498233795 ], "tsne3d": [ - -6.846499919891357, - -8.013236045837402, - -4.23292875289917 + -6.751630783081055, + 12.779664039611816, + 2.8338067531585693 ], "umap3d": [ - 3.0443150997161865, - 3.7892396450042725, - 3.3750059604644775 + 3.80503511428833, + 4.165990829467773, + 3.7365431785583496 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -83236,47 +83366,47 @@ "openalex_url": "https://openalex.org/W4405669265", "projections": { "pca": [ - -0.20447851717472076, - 0.020101895555853844 + -0.20420333743095398, + 0.020642098039388657 ], "tsne": [ - 8.990216255187988, - -38.07072448730469 + -1.5004268884658813, + 38.654396057128906 ], "umap": [ - 5.76569128036499, - 9.662156105041504 + 5.906853199005127, + 9.052074432373047 ], "pca3d": [ - -0.20447854697704315, - 0.02009860798716545, - -0.06887359172105789 + -0.204203262925148, + 0.020638469606637955, + -0.06856051832437515 ], "tsne3d": [ - 3.4892690181732178, - -21.11016082763672, - 3.2062602043151855 + -5.240799903869629, + 12.531393051147461, + -17.69644546508789 ], "umap3d": [ - 4.580721855163574, - 2.767587184906006, - 2.4656500816345215 + 5.026252746582031, + 2.8437771797180176, + 2.9366931915283203 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -83695,31 +83825,31 @@ "cited_by_count": 3, "projections": { "pca": [ - -0.23806867003440857, - 0.06162199005484581 + -0.2377416044473648, + 0.06196938827633858 ], "tsne": [ - -39.41505813598633, - 8.678572654724121 + -38.058841705322266, + -15.534346580505371 ], "umap": [ - 2.273665428161621, - 5.362395286560059 + 2.601543664932251, + 4.784350872039795 ], "pca3d": [ - -0.23806875944137573, - 0.06163358315825462, - -0.17016899585723877 + -0.23774166405200958, + 0.06198129057884216, + -0.16995437443256378 ], "tsne3d": [ - 1.7789089679718018, - -25.723804473876953, - -5.475906848907471 + -19.16350746154785, + -17.728382110595703, + -8.12158203125 ], "umap3d": [ - 2.0041580200195312, - 6.599203109741211, - 4.182932376861572 + 2.4465980529785156, + 6.941989898681641, + 4.406280040740967 ] }, "cluster_id": 2, @@ -83730,11 +83860,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -84152,31 +84282,31 @@ ], "projections": { "pca": [ - -0.055160317569971085, - -0.06283020973205566 + -0.055246323347091675, + -0.06253348290920258 ], "tsne": [ - -33.16618347167969, - 16.381214141845703 + -28.786266326904297, + -20.58448600769043 ], "umap": [ - 2.9883041381835938, - 4.935956001281738 + 3.476907730102539, + 4.355355262756348 ], "pca3d": [ - -0.05516025051474571, - -0.06281773000955582, - 0.1144649013876915 + -0.05524623021483421, + -0.06252086162567139, + 0.11453353613615036 ], "tsne3d": [ - -13.622743606567383, - 11.163043975830078, - 3.3522276878356934 + -12.950648307800293, + -19.717226028442383, + 4.1146345138549805 ], "umap3d": [ - 2.9726548194885254, - 6.855533599853516, - 4.75712776184082 + 3.3018462657928467, + 7.228509902954102, + 4.348270416259766 ] }, "cluster_id": 2, @@ -84187,12 +84317,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 16, - "label": "Diffusion Models" + "id": 7, + "label": "Diffusion Gen" } ], "_embedding": [ @@ -84626,47 +84756,47 @@ ], "projections": { "pca": [ - -0.23050400614738464, - 0.07459355145692825 + -0.23021702468395233, + 0.07561667263507843 ], "tsne": [ - -20.718477249145508, - -23.634111404418945 + -20.3924617767334, + 13.54542350769043 ], "umap": [ - 2.8849270343780518, - 7.762033939361572 + 3.1055469512939453, + 7.046824932098389 ], "pca3d": [ - -0.23050367832183838, - 0.07456712424755096, - 0.05369150638580322 + -0.23021666705608368, + 0.07559029757976532, + 0.053749121725559235 ], "tsne3d": [ - -13.55664348602295, - -5.1018171310424805, - -4.811953544616699 + -14.867568969726562, + 11.763996124267578, + 4.26340389251709 ], "umap3d": [ - 2.326061248779297, - 4.3710479736328125, - 3.501572847366333 + 3.1151294708251953, + 4.732325077056885, + 4.077919006347656 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { "id": 0, - "label": "LLM Language Learning" + "label": "Math AI" } ], "_embedding": [ @@ -85086,47 +85216,47 @@ ], "projections": { "pca": [ - -0.16872191429138184, - -0.07690130174160004 + -0.16866272687911987, + -0.07615945488214493 ], "tsne": [ - -34.015953063964844, - -15.071857452392578 + -31.873912811279297, + 19.741872787475586 ], "umap": [ - 0.7801315188407898, - 7.561585903167725 + 0.921927273273468, + 6.624165058135986 ], "pca3d": [ - -0.16872158646583557, - -0.07693123072385788, - 0.012433175928890705 + -0.16866229474544525, + -0.07619025558233261, + 0.01283619087189436 ], "tsne3d": [ - -16.716920852661133, - -18.994428634643555, - 0.0941481813788414 + -18.40828514099121, + 11.430986404418945, + -8.568826675415039 ], "umap3d": [ - 0.7934324741363525, - 5.4916090965271, - 4.885381698608398 + 1.8639801740646362, + 5.72585916519165, + 5.463893413543701 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, - "label": "Transformer Attention" + "id": 16, + "label": "Vision Transformers" } ], "_embedding": [ @@ -85546,47 +85676,47 @@ ], "projections": { "pca": [ - -0.20642098784446716, - -0.011469773016870022 + -0.20643337070941925, + -0.010321075096726418 ], "tsne": [ - -25.01457977294922, - -14.185453414916992 + -29.656648635864258, + 11.861706733703613 ], "umap": [ - 2.4565348625183105, - 8.467755317687988 + 2.6658544540405273, + 7.717999458312988 ], "pca3d": [ - -0.20642060041427612, - -0.011474180035293102, - 0.09419213235378265 + -0.20643310248851776, + -0.0103255370631814, + 0.09438946098089218 ], "tsne3d": [ - -13.014357566833496, - -13.981054306030273, - 4.625060081481934 + -16.014408111572266, + 7.1120805740356445, + -1.6413486003875732 ], "umap3d": [ - 2.0916497707366943, - 3.935695171356201, - 4.340225696563721 + 3.094982862472534, + 4.478465557098389, + 4.935638904571533 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 7, - "label": "Language Models" + "id": 25, + "label": "AI Agents" } ], "_embedding": [ @@ -86008,31 +86138,31 @@ ], "projections": { "pca": [ - -0.10500501841306686, - -0.06440122425556183 + -0.10513857007026672, + -0.06408949941396713 ], "tsne": [ - -33.733848571777344, - 15.068534851074219 + -29.58538818359375, + -19.52597999572754 ], "umap": [ - 2.8450005054473877, - 5.1015238761901855 + 3.251131057739258, + 4.53729772567749 ], "pca3d": [ - -0.10500485450029373, - -0.06438703835010529, - -0.0019174428889527917 + -0.10513843595981598, + -0.06407531350851059, + -0.0018084667390212417 ], "tsne3d": [ - -14.039626121520996, - 9.908056259155273, - 2.7595276832580566 + -13.453344345092773, + -18.263643264770508, + 3.939769983291626 ], "umap3d": [ - 2.6627163887023926, - 6.715478897094727, - 4.725747108459473 + 3.157278537750244, + 7.099453449249268, + 4.413825511932373 ] }, "cluster_id": 2, @@ -86043,12 +86173,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 16, - "label": "Diffusion Models" + "id": 7, + "label": "Diffusion Gen" } ], "_embedding": [ @@ -86472,46 +86602,46 @@ ], "projections": { "pca": [ - -0.14717882871627808, - -0.13150142133235931 + -0.14742717146873474, + -0.13119067251682281 ], "tsne": [ - -30.317636489868164, - -8.331360816955566 + -35.97409439086914, + 7.1022467613220215 ], "umap": [ - 1.5737788677215576, - 8.265002250671387 + 1.7199954986572266, + 7.407704830169678 ], "pca3d": [ - -0.1471782624721527, - -0.13152198493480682, - 0.004071510396897793 + -0.14742659032344818, + -0.13121116161346436, + 0.0043924730271101 ], "tsne3d": [ - -17.27018165588379, - -4.947922706604004, - 7.849842548370361 + -20.11558723449707, + 5.812204837799072, + 4.580410003662109 ], "umap3d": [ - 1.5840201377868652, - 4.5222954750061035, - 4.8658061027526855 + 2.65110445022583, + 5.022655963897705, + 5.525924205780029 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -86932,31 +87062,31 @@ ], "projections": { "pca": [ - -0.1741810441017151, - 0.026565613225102425 + -0.17390084266662598, + 0.026881873607635498 ], "tsne": [ - -39.254207611083984, - 22.083908081054688 + -34.60545349121094, + -27.537147521972656 ], "umap": [ - 2.3838393688201904, - 4.808948516845703 + 2.7888214588165283, + 4.1981587409973145 ], "pca3d": [ - -0.17418068647384644, - 0.026533152908086777, - 0.02342926897108555 + -0.17390041053295135, + 0.02684888243675232, + 0.023532191291451454 ], "tsne3d": [ - -18.44875144958496, - 15.862946510314941, - -0.4023350775241852 + -16.79022216796875, + -24.572589874267578, + 6.114352703094482 ], "umap3d": [ - 2.2208077907562256, - 7.039253234863281, - 4.780815124511719 + 2.716402769088745, + 7.449280738830566, + 4.620295524597168 ] }, "cluster_id": 2, @@ -86967,11 +87097,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -87389,46 +87519,46 @@ ], "projections": { "pca": [ - -0.23317654430866241, - -0.037519969046115875 + -0.23321057856082916, + -0.03721405565738678 ], "tsne": [ - -62.10462951660156, - -6.981447219848633 + -58.16783142089844, + 2.797386646270752 ], "umap": [ - 0.651856541633606, - 7.185001850128174 + 0.7857577800750732, + 6.381411552429199 ], "pca3d": [ - -0.23317615687847137, - -0.03754083067178726, - -0.09055312722921371 + -0.23321016132831573, + -0.03723561391234398, + -0.09037306159734726 ], "tsne3d": [ - -26.1563777923584, - 4.547101974487305, - 2.224109172821045 + -36.76458740234375, + -4.161623477935791, + 2.4206125736236572 ], "umap3d": [ - 0.7558950781822205, - 5.816972255706787, - 4.926669120788574 + 1.6604076623916626, + 6.108142852783203, + 5.563015937805176 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -87855,31 +87985,31 @@ "cited_by_count": 305, "projections": { "pca": [ - -0.2555151879787445, - 0.022303011268377304 + -0.25554728507995605, + 0.022919293493032455 ], "tsne": [ - -50.92790603637695, - 14.25545883178711 + -47.10853958129883, + -23.86355972290039 ], "umap": [ - 1.3936076164245605, - 5.551261901855469 + 1.786752462387085, + 4.803981781005859 ], "pca3d": [ - -0.25551506876945496, - 0.022324981167912483, - 0.02278866432607174 + -0.25554734468460083, + 0.0229423139244318, + 0.022745436057448387 ], "tsne3d": [ - -23.998214721679688, - 9.884552001953125, - -8.513611793518066 + -27.96139907836914, + -17.45900535583496, + -1.7793787717819214 ], "umap3d": [ - 1.4975391626358032, - 6.360509872436523, - 4.053171157836914 + 2.023829698562622, + 6.595181465148926, + 4.39438533782959 ] }, "cluster_id": 2, @@ -87890,12 +88020,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -88306,47 +88436,47 @@ ], "projections": { "pca": [ - -0.26127946376800537, - 0.08544152975082397 + -0.26112356781959534, + 0.08629148453474045 ], "tsne": [ - -18.55470085144043, - -28.211305618286133 + -21.124624252319336, + 19.35321807861328 ], "umap": [ - 2.802724599838257, - 8.131987571716309 + 3.0043537616729736, + 7.355867862701416 ], "pca3d": [ - -0.2612793743610382, - 0.08543919771909714, - 0.023046229034662247 + -0.26112350821495056, + 0.08628888428211212, + 0.022972306236624718 ], "tsne3d": [ - -11.810541152954102, - -10.80460262298584, - -9.78326416015625 + -13.756341934204102, + 18.834192276000977, + 2.516223669052124 ], "umap3d": [ - 2.2279839515686035, - 4.16996431350708, - 3.7376627922058105 + 3.0047245025634766, + 4.503252029418945, + 4.363348960876465 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { "id": 0, - "label": "LLM Language Learning" + "label": "Math AI" } ], "_embedding": [ @@ -88755,47 +88885,47 @@ "title": "Genie 2: A large-scale foundation world model \u00e2\u0080\u0094 Google DeepMind", "projections": { "pca": [ - -0.21224211156368256, - -0.008266876451671124 + -0.21214058995246887, + -0.0074877627193927765 ], "tsne": [ - -25.341442108154297, - 2.5075457096099854 + -24.178627014160156, + 24.905017852783203 ], "umap": [ - 4.7119646072387695, - 6.579889297485352 + 3.4275245666503906, + 6.756002902984619 ], "pca3d": [ - -0.21224212646484375, - -0.008252676576375961, - 0.026340914890170097 + -0.212140753865242, + -0.007472646422684193, + 0.02658868581056595 ], "tsne3d": [ - -5.5125412940979, - -19.797636032104492, - 14.110023498535156 + -13.22251033782959, + 14.391382217407227, + 11.648784637451172 ], "umap3d": [ - 3.250584363937378, - 4.819788932800293, - 3.3924243450164795 + 3.5726194381713867, + 4.957326412200928, + 3.560664415359497 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 12, + "label": "AI Agents" }, { - "id": 10, - "label": "Code Development" + "id": 0, + "label": "Math AI" } ], "_embedding": [ @@ -89210,46 +89340,46 @@ ], "projections": { "pca": [ - -0.13825853168964386, - 0.040036074817180634 + -0.13812746107578278, + 0.04070870205760002 ], "tsne": [ - -2.9745090007781982, - -35.07707977294922 + -11.174178123474121, + 30.66685676574707 ], "umap": [ - 4.38613748550415, - 9.536516189575195 + 4.416934013366699, + 8.831402778625488 ], "pca3d": [ - -0.13825829327106476, - 0.04002522677183151, - 0.01851237565279007 + -0.13812728226184845, + 0.04069749265909195, + 0.01860499009490013 ], "tsne3d": [ - -5.27858829498291, - -20.705129623413086, - 1.3496371507644653 + -9.632564544677734, + 11.58864688873291, + -7.468413829803467 ], "umap3d": [ - 3.6465327739715576, - 2.659409284591675, - 3.3665759563446045 + 4.475039005279541, + 3.1613454818725586, + 3.9415934085845947 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, + "id": 26, "label": "Medical AI" } ], @@ -89664,46 +89794,46 @@ ], "projections": { "pca": [ - -0.26023146510124207, - -0.062095992267131805 + -0.26040270924568176, + -0.06144721806049347 ], "tsne": [ - -34.83759307861328, - -2.8306236267089844 + -38.36334991455078, + 2.3414831161499023 ], "umap": [ - 1.0489693880081177, - 7.700448989868164 + 1.3504718542099, + 7.089259624481201 ], "pca3d": [ - -0.2602308392524719, - -0.06210780143737793, - 0.07302932441234589 + -0.26040220260620117, + -0.06145831570029259, + 0.07312031835317612 ], "tsne3d": [ - -19.711402893066406, - -2.2649643421173096, - 7.677943229675293 + -21.764549255371094, + 1.5262491703033447, + 5.171069622039795 ], "umap3d": [ - 1.2759615182876587, - 5.174681663513184, - 4.881172180175781 + 2.278864860534668, + 5.624598503112793, + 5.355306148529053 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -90121,31 +90251,31 @@ ], "projections": { "pca": [ - -0.27008113265037537, - -0.12106325477361679 + -0.27033910155296326, + -0.12027724832296371 ], "tsne": [ - -49.71706771850586, - -20.373273849487305 + -53.27267837524414, + 14.993330001831055 ], "umap": [ - 0.33160391449928284, - 5.8795647621154785 + 0.6961716413497925, + 4.911990165710449 ], "pca3d": [ - -0.27008089423179626, - -0.1210387721657753, - -0.07184925675392151 + -0.2703389823436737, + -0.1202520951628685, + -0.07149367034435272 ], "tsne3d": [ - -31.439353942871094, - -10.887986183166504, - -4.0588274002075195 + -29.0014591217041, + -0.9215461015701294, + -8.581695556640625 ], "umap3d": [ - 0.42933928966522217, - 5.587261199951172, - 3.5068888664245605 + 1.094283103942871, + 5.506617546081543, + 4.217559337615967 ] }, "cluster_id": 2, @@ -90156,12 +90286,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, - "label": "Training Noise" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -90583,47 +90713,47 @@ ], "projections": { "pca": [ - -0.22129234671592712, - -0.012555921450257301 + -0.22113963961601257, + -0.011346978135406971 ], "tsne": [ - -24.54960823059082, - -13.258333206176758 + -28.849987030029297, + 10.988118171691895 ], "umap": [ - 2.4045839309692383, - 8.498403549194336 + 2.7321231365203857, + 7.786827087402344 ], "pca3d": [ - -0.22129188477993011, - -0.012582680210471153, - 0.06457721441984177 + -0.22113922238349915, + -0.011374169029295444, + 0.06493158638477325 ], "tsne3d": [ - -12.014744758605957, - -11.788373947143555, - 4.9378814697265625 + -13.36855697631836, + 8.17408275604248, + -0.5731557607650757 ], "umap3d": [ - 2.0963375568389893, - 3.9557712078094482, - 4.316794395446777 + 3.0681002140045166, + 4.402408599853516, + 4.91146183013916 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 7, - "label": "Language Models" + "id": 25, + "label": "AI Agents" } ], "_embedding": [ @@ -91050,46 +91180,46 @@ "cited_by_count": 4, "projections": { "pca": [ - -0.20028217136859894, - -0.05640174075961113 + -0.20019429922103882, + -0.05600280314683914 ], "tsne": [ - -62.017032623291016, - -2.634986639022827 + -56.43923568725586, + 5.96684455871582 ], "umap": [ - 0.4789874255657196, - 7.389531135559082 + 0.7295264005661011, + 6.578563213348389 ], "pca3d": [ - -0.20028169453144073, - -0.056440651416778564, - -0.03902238979935646 + -0.20019377768039703, + -0.05604210123419762, + -0.038705479353666306 ], "tsne3d": [ - -23.51422119140625, - 5.770862579345703, - 6.6852126121521 + -24.69476318359375, + -17.688695907592773, + 8.622933387756348 ], "umap3d": [ - 0.8524024486541748, - 5.80048942565918, - 5.163774013519287 + 1.7643089294433594, + 6.099940299987793, + 5.745194911956787 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -91506,46 +91636,46 @@ ], "projections": { "pca": [ - -0.15748381614685059, - 0.004279526881873608 + -0.15734440088272095, + 0.004997625946998596 ], "tsne": [ - -0.5388937592506409, - -34.50115966796875 + -8.867203712463379, + 31.4146671295166 ], "umap": [ - 4.603185176849365, - 9.582443237304688 + 4.798860549926758, + 8.896832466125488 ], "pca3d": [ - -0.15748348832130432, - 0.00426096748560667, - 0.0076806629076600075 + -0.15734410285949707, + 0.004978298209607601, + 0.007965173572301865 ], "tsne3d": [ - -1.922435998916626, - -19.953262329101562, - 0.6627883911132812 + -6.621650218963623, + 13.173280715942383, + -8.510856628417969 ], "umap3d": [ - 3.8808603286743164, - 2.5645864009857178, - 3.1993768215179443 + 4.639957427978516, + 3.013044834136963, + 3.8259923458099365 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, + "id": 26, "label": "Medical AI" } ], @@ -91969,47 +92099,47 @@ ], "projections": { "pca": [ - -0.17079958319664001, - -0.04450488090515137 + -0.17104682326316833, + -0.044079478830099106 ], "tsne": [ - 8.079452514648438, - -34.02918243408203 + -0.6706832051277161, + 34.62016296386719 ], "umap": [ - 5.721516132354736, - 9.630109786987305 + 5.825040817260742, + 9.007162094116211 ], "pca3d": [ - -0.17079925537109375, - -0.04451761394739151, - -0.17518721520900726 + -0.17104649543762207, + -0.04409243166446686, + -0.17503933608531952 ], "tsne3d": [ - 4.372357368469238, - -17.682510375976562, - 0.9324774146080017 + -1.9098471403121948, + 12.356881141662598, + -14.927083969116211 ], "umap3d": [ - 4.7345967292785645, - 2.5272321701049805, - 2.5578529834747314 + 5.215574741363525, + 2.682464122772217, + 2.972606658935547 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -92419,31 +92549,31 @@ "abstract": "A curated list for awesome discrete diffusion models resources. - kuleshov-group/awesome-discrete-diffusion-models", "projections": { "pca": [ - -0.218805193901062, - 0.19242683053016663 + -0.2182740420103073, + 0.19326409697532654 ], "tsne": [ - -28.867700576782227, - 16.046005249023438 + -28.12015724182129, + -23.691177368164062 ], "umap": [ - 2.876396417617798, - 5.085977554321289 + 3.330960512161255, + 4.530383586883545 ], "pca3d": [ - -0.2188054472208023, - 0.19243457913398743, - 0.03618422895669937 + -0.21827435493469238, + 0.1932716816663742, + 0.03596087172627449 ], "tsne3d": [ - -14.90347957611084, - 14.684114456176758, - 3.8869283199310303 + -13.317696571350098, + -19.700803756713867, + -0.8923673629760742 ], "umap3d": [ - 2.734968900680542, - 6.817328929901123, - 4.798360824584961 + 3.1863293647766113, + 7.164675712585449, + 4.462566375732422 ] }, "cluster_id": 2, @@ -92454,12 +92584,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 16, - "label": "Diffusion Models" + "id": 7, + "label": "Diffusion Gen" } ], "_embedding": [ @@ -92878,31 +93008,31 @@ ], "projections": { "pca": [ - -0.21198198199272156, - -0.10471636801958084 + -0.21223151683807373, + -0.10399508476257324 ], "tsne": [ - -35.44059753417969, - 14.688599586486816 + -29.643491744995117, + -18.561769485473633 ], "umap": [ - 2.5780718326568604, - 5.384428024291992 + 3.053058385848999, + 4.659777641296387 ], "pca3d": [ - -0.21198157966136932, - -0.10471111536026001, - -0.01973669044673443 + -0.21223114430904388, + -0.1039900854229927, + -0.019488021731376648 ], "tsne3d": [ - -15.500152587890625, - 9.490318298339844, - 1.2479281425476074 + -14.675875663757324, + -17.11749267578125, + 2.9361493587493896 ], "umap3d": [ - 2.3970487117767334, - 6.540616035461426, - 4.604160785675049 + 2.9756317138671875, + 6.878660202026367, + 4.424415111541748 ] }, "cluster_id": 2, @@ -92913,11 +93043,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -93333,31 +93463,31 @@ ], "projections": { "pca": [ - -0.1556997150182724, - -0.0609307698905468 + -0.15588794648647308, + -0.060511112213134766 ], "tsne": [ - -54.252193450927734, - 6.782125473022461 + -55.02652359008789, + -15.788122177124023 ], "umap": [ - 1.4135992527008057, - 5.578906059265137 + 2.286648988723755, + 4.976053237915039 ], "pca3d": [ - -0.15569943189620972, - -0.060918647795915604, - 0.028688885271549225 + -0.15588775277137756, + -0.06049871817231178, + 0.028719192370772362 ], "tsne3d": [ - -32.33675765991211, - 0.10116779804229736, - -9.09045124053955 + -29.456422805786133, + -13.542564392089844, + -6.589091777801514 ], "umap3d": [ - 1.5382673740386963, - 6.291956424713135, - 3.5798184871673584 + 1.8606153726577759, + 6.302591800689697, + 3.906743288040161 ] }, "cluster_id": 2, @@ -93368,12 +93498,12 @@ "label": "Generative Models" }, { - "id": 1, - "label": "Network Learning" + "id": 5, + "label": "Diffusion Models" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 22, + "label": "Diffusion Models" } ], "_embedding": [ @@ -93787,31 +93917,31 @@ ], "projections": { "pca": [ - -0.2152545303106308, - -0.07243240624666214 + -0.2156774252653122, + -0.07189866155385971 ], "tsne": [ - -47.805877685546875, - 8.18471622467041 + -48.00469970703125, + -16.124486923217773 ], "umap": [ - 1.2275381088256836, - 5.570901870727539 + 1.5554149150848389, + 4.850465774536133 ], "pca3d": [ - -0.21525417268276215, - -0.07241851091384888, - -0.02949446253478527 + -0.21567721664905548, + -0.07188418507575989, + -0.02946140244603157 ], "tsne3d": [ - -23.74108123779297, - -2.827265977859497, - -10.047423362731934 + -21.267366409301758, + -15.237090110778809, + -6.7927045822143555 ], "umap3d": [ - 1.311113715171814, - 6.379539966583252, - 3.9213814735412598 + 1.660141944885254, + 6.467751502990723, + 4.274825572967529 ] }, "cluster_id": 2, @@ -93822,12 +93952,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -94243,46 +94373,46 @@ ], "projections": { "pca": [ - -0.2590368688106537, - -0.014221210964024067 + -0.258989542722702, + -0.01290323305875063 ], "tsne": [ - -38.74073028564453, - -5.651373386383057 + -40.107215881347656, + 0.8780273795127869 ], "umap": [ - 1.196706771850586, - 7.783722877502441 + 1.290008306503296, + 6.937538146972656 ], "pca3d": [ - -0.25903651118278503, - -0.01423623412847519, - -0.05213373526930809 + -0.2589891254901886, + -0.012918459251523018, + -0.05177668109536171 ], "tsne3d": [ - -21.943038940429688, - -7.7680840492248535, - 1.6948909759521484 + -21.868087768554688, + 0.2211066633462906, + 0.930026113986969 ], "umap3d": [ - 1.1880526542663574, - 4.8568878173828125, - 4.70462703704834 + 2.1491377353668213, + 5.155735015869141, + 5.468303203582764 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -94689,31 +94819,31 @@ ], "projections": { "pca": [ - -0.1851557195186615, - 0.17724554240703583 + -0.18459978699684143, + 0.17781317234039307 ], "tsne": [ - -31.2789249420166, - 12.759151458740234 + -30.306055068969727, + -21.927141189575195 ], "umap": [ - 3.0199413299560547, - 5.192209243774414 + 3.351417064666748, + 4.661952018737793 ], "pca3d": [ - -0.18515582382678986, - 0.17725321650505066, - 0.048090286552906036 + -0.1846000701189041, + 0.17782063782215118, + 0.047843266278505325 ], "tsne3d": [ - -16.69800567626953, - 12.999093055725098, - 5.184656620025635 + -15.548787117004395, + -19.510000228881836, + 0.18471002578735352 ], "umap3d": [ - 2.761409282684326, - 6.6539626121521, - 4.746023654937744 + 3.272831678390503, + 7.0245771408081055, + 4.4695634841918945 ] }, "cluster_id": 2, @@ -94724,12 +94854,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 16, - "label": "Diffusion Models" + "id": 7, + "label": "Diffusion Gen" } ], "_embedding": [ @@ -95146,47 +95276,47 @@ "year": 2020, "projections": { "pca": [ - -0.27708688378334045, - 0.08698653429746628 + -0.2768310010433197, + 0.08802948892116547 ], "tsne": [ - -40.567047119140625, - -7.213351726531982 + -38.937740325927734, + -0.8400000929832458 ], "umap": [ - 1.532720685005188, - 7.404775142669678 + 1.6539053916931152, + 6.578540802001953 ], "pca3d": [ - -0.27708694338798523, - 0.08700161427259445, - -0.0394962839782238 + -0.27683112025260925, + 0.08804468810558319, + -0.039468199014663696 ], "tsne3d": [ - -20.415569305419922, - -7.709981441497803, - -1.2672338485717773 + -18.929067611694336, + -2.2542378902435303, + 0.24467137455940247 ], "umap3d": [ - 1.362958550453186, - 5.0017547607421875, - 4.38228178024292 + 2.1840381622314453, + 5.2171220779418945, + 5.092005729675293 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -95607,47 +95737,47 @@ "doi": "10.1101/2024.11.11.623004", "projections": { "pca": [ - -0.02745349146425724, - 0.141433984041214 + -0.02719506062567234, + 0.1420535296201706 ], "tsne": [ - -4.044961929321289, - -18.35553550720215 + -5.903059959411621, + 13.97299575805664 ], "umap": [ - 4.197158336639404, - 8.508797645568848 + 4.515878677368164, + 7.800994873046875 ], "pca3d": [ - -0.027453588321805, - 0.1414262354373932, - 0.18588794767856598 + -0.027195125818252563, + 0.1420455276966095, + 0.1855524778366089 ], "tsne3d": [ - -4.478308200836182, - -3.1502342224121094, - -1.915651798248291 + -4.41444206237793, + 9.238127708435059, + 7.098635673522949 ], "umap3d": [ - 3.458155632019043, - 3.803006172180176, - 3.191973924636841 + 4.059406757354736, + 4.319685459136963, + 3.4544289112091064 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 32, - "label": "AI Agents" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -96065,31 +96195,31 @@ "doi": "10.1145/3743128", "projections": { "pca": [ - -0.2120719850063324, - -0.11746557056903839 + -0.21242079138755798, + -0.11674443632364273 ], "tsne": [ - -33.516571044921875, - 2.1061363220214844 + -32.34299087524414, + -5.125296115875244 ], "umap": [ - 2.370328664779663, - 6.673225402832031 + 2.8141989707946777, + 5.9485650062561035 ], "pca3d": [ - -0.21207156777381897, - -0.11746302247047424, - 0.03977102413773537 + -0.21242040395736694, + -0.11674172431230545, + 0.03999100625514984 ], "tsne3d": [ - -16.43316650390625, - 2.637939929962158, - -9.31037425994873 + -13.301862716674805, + -10.388379096984863, + -5.28796911239624 ], "umap3d": [ - 2.094374179840088, - 5.423758029937744, - 3.87333083152771 + 2.742668390274048, + 5.728384494781494, + 4.16956090927124 ] }, "cluster_id": 2, @@ -96100,12 +96230,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 33, - "label": "Neural Networks" + "id": 19, + "label": "Graph Neural Nets" } ], "_embedding": [ @@ -96519,31 +96649,31 @@ ], "projections": { "pca": [ - -0.18545904755592346, - -0.14830268919467926 + -0.18602435290813446, + -0.1479528695344925 ], "tsne": [ - -53.371891021728516, - 6.641237735748291 + -54.131046295166016, + -15.650237083435059 ], "umap": [ - 1.0394818782806396, - 5.60463285446167 + 1.3939517736434937, + 4.696303367614746 ], "pca3d": [ - -0.18545874953269958, - -0.14828674495220184, - -0.04169318825006485 + -0.18602414429187775, + -0.14793628454208374, + -0.04156084731221199 ], "tsne3d": [ - -31.148643493652344, - -0.6057555675506592, - -8.821783065795898 + -28.28327751159668, + -12.844680786132812, + -6.602891445159912 ], "umap3d": [ - 1.1790482997894287, - 6.361893177032471, - 3.5634403228759766 + 1.5264829397201538, + 6.321457862854004, + 3.977762460708618 ] }, "cluster_id": 2, @@ -96554,12 +96684,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -96975,31 +97105,31 @@ ], "projections": { "pca": [ - -0.2472882866859436, - -0.128359854221344 + -0.2478000372648239, + -0.12723208963871002 ], "tsne": [ - -46.65628433227539, - 2.539085865020752 + -41.65581512451172, + -10.407693862915039 ], "umap": [ - 1.3065123558044434, - 6.451451301574707 + 1.7436774969100952, + 5.666621208190918 ], "pca3d": [ - -0.2472877949476242, - -0.1283746212720871, - -0.04879361018538475 + -0.24779950082302094, + -0.12724657356739044, + -0.04855272173881531 ], "tsne3d": [ - -23.229602813720703, - 3.4508697986602783, - -6.011168956756592 + -19.504304885864258, + -10.673951148986816, + -2.484217643737793 ], "umap3d": [ - 1.3545011281967163, - 5.663639068603516, - 3.9692933559417725 + 2.0097432136535645, + 5.924454689025879, + 4.51233434677124 ] }, "cluster_id": 2, @@ -97010,12 +97140,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -97425,47 +97555,47 @@ "title": "Easier, Better, Faster, Cuter", "projections": { "pca": [ - -0.1833094358444214, - 0.17169590294361115 + -0.18269328773021698, + 0.172652006149292 ], "tsne": [ - -20.785003662109375, - 0.7122970819473267 + -26.17857551574707, + -1.1450271606445312 ], "umap": [ - 2.433155059814453, - 7.572416305541992 + 2.6379971504211426, + 6.789520263671875 ], "pca3d": [ - -0.18330924212932587, - 0.17166250944137573, - 0.09964089840650558 + -0.18269319832324982, + 0.17261864244937897, + 0.09950776398181915 ], "tsne3d": [ - -17.702672958374023, - 3.1771605014801025, - 11.313223838806152 + -19.318944931030273, + 0.3630293011665344, + 11.108762741088867 ], "umap3d": [ - 2.083191394805908, - 4.721273422241211, - 5.1117143630981445 + 3.2234294414520264, + 5.386416435241699, + 5.527228355407715 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 0, - "label": "LLM Language Learning" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -97886,46 +98016,46 @@ ], "projections": { "pca": [ - -0.1831522136926651, - -0.08907749503850937 + -0.18333390355110168, + -0.08812598884105682 ], "tsne": [ - -25.82715606689453, - -11.925056457519531 + -29.96117401123047, + 9.80700397491455 ], "umap": [ - 1.9713459014892578, - 8.440096855163574 + 2.2866897583007812, + 7.654059410095215 ], "pca3d": [ - -0.18315181136131287, - -0.08908309042453766, - 0.09700225293636322 + -0.18333356082439423, + -0.0881311446428299, + 0.0973087027668953 ], "tsne3d": [ - -13.233455657958984, - -9.862927436828613, - 4.746186256408691 + -15.458038330078125, + 7.295241355895996, + 1.3996394872665405 ], "umap3d": [ - 1.9307410717010498, - 4.1838908195495605, - 4.584892749786377 + 2.938849687576294, + 4.663402080535889, + 5.229403495788574 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -98335,47 +98465,47 @@ "title": "Taking medical imaging embeddings 3D", "projections": { "pca": [ - -0.24916015565395355, - -0.024974169209599495 + -0.24905070662498474, + -0.024869350716471672 ], "tsne": [ - 8.51908016204834, - -37.67410659790039 + -1.4781166315078735, + 38.0806770324707 ], "umap": [ - 5.713850975036621, - 9.678175926208496 + 5.885885715484619, + 9.067938804626465 ], "pca3d": [ - -0.24915973842144012, - -0.02500351145863533, - -0.10753133893013 + -0.2490503340959549, + -0.024899130687117577, + -0.10726681351661682 ], "tsne3d": [ - 3.0295231342315674, - -19.76215362548828, - 3.536909580230713 + -4.87175989151001, + 11.4515380859375, + -16.805404663085938 ], "umap3d": [ - 4.653810977935791, - 2.613457202911377, - 2.4731502532958984 + 5.054662704467773, + 2.7455811500549316, + 2.969146966934204 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -98793,31 +98923,31 @@ "cited_by_count": 29, "projections": { "pca": [ - -0.08338754624128342, - -0.05867774039506912 + -0.08350852131843567, + -0.058443792164325714 ], "tsne": [ - -18.658010482788086, - 17.377206802368164 + -15.715624809265137, + -25.136756896972656 ], "umap": [ - 4.780043125152588, - 4.7426438331604 + 5.252373218536377, + 4.15927791595459 ], "pca3d": [ - -0.08338772505521774, - -0.05862840637564659, - 0.06493200361728668 + -0.08350877463817596, + -0.05839358642697334, + 0.06498252600431442 ], "tsne3d": [ - 3.1742682456970215, - 9.510231018066406, - 19.5528621673584 + 3.269732713699341, + -12.607901573181152, + 16.92125701904297 ], "umap3d": [ - 4.54918909072876, - 6.727571964263916, - 4.221748352050781 + 4.7681756019592285, + 7.116960048675537, + 3.098546266555786 ] }, "cluster_id": 3, @@ -98828,12 +98958,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { "id": 24, - "label": "Protein Design" + "label": "Protein Diffusion" } ], "_embedding": [ @@ -99271,46 +99401,46 @@ "cited_by_count": 92, "projections": { "pca": [ - -0.18609251081943512, - -0.011454826220870018 + -0.18590080738067627, + -0.01091639045625925 ], "tsne": [ - -25.565994262695312, - -15.068060874938965 + -27.72922706604004, + 11.759391784667969 ], "umap": [ - 2.1620450019836426, - 8.387727737426758 + 2.5244104862213135, + 7.528594970703125 ], "pca3d": [ - -0.18609221279621124, - -0.011462245136499405, - 0.024921484291553497 + -0.18590058386325836, + -0.0109239611774683, + 0.02519530989229679 ], "tsne3d": [ - -11.552985191345215, - -12.22899055480957, - 7.739816665649414 + -13.03602123260498, + 5.024143695831299, + -1.672271728515625 ], "umap3d": [ - 1.896068811416626, - 4.138015270233154, - 4.533527851104736 + 3.078526258468628, + 4.623355865478516, + 5.099616050720215 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -99726,31 +99856,31 @@ ], "projections": { "pca": [ - -0.21869538724422455, - -0.09030942618846893 + -0.21894216537475586, + -0.08954383432865143 ], "tsne": [ - -32.592567443847656, - 10.81264877319336 + -31.3538818359375, + -14.17679214477539 ], "umap": [ - 2.490241050720215, - 5.468007564544678 + 2.9622642993927, + 4.880557537078857 ], "pca3d": [ - -0.21869497001171112, - -0.09031559526920319, - -0.0065285139717161655 + -0.21894173324108124, + -0.08954963088035583, + -0.006337376777082682 ], "tsne3d": [ - -16.53462791442871, - 4.903489112854004, - 2.928778648376465 + -15.931764602661133, + -13.636589050292969, + 5.860283374786377 ], "umap3d": [ - 2.278421640396118, - 6.383119583129883, - 4.732189178466797 + 2.8580691814422607, + 6.804529190063477, + 4.697911739349365 ] }, "cluster_id": 2, @@ -99761,11 +99891,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -100182,31 +100312,31 @@ ], "projections": { "pca": [ - -0.2647354304790497, - -0.11718462407588959 + -0.26499027013778687, + -0.11651600152254105 ], "tsne": [ - -41.53480529785156, - 12.133448600769043 + -38.07281494140625, + -19.431297302246094 ], "umap": [ - 1.8296126127243042, - 5.326592922210693 + 2.232919692993164, + 4.689639091491699 ], "pca3d": [ - -0.26473483443260193, - -0.11719750612974167, - -0.09278853982686996 + -0.26498985290527344, + -0.11652977764606476, + -0.09247533977031708 ], "tsne3d": [ - -22.97398567199707, - 9.490029335021973, - 4.68084192276001 + -22.746986389160156, + -18.144752502441406, + 5.528282642364502 ], "umap3d": [ - 1.5418641567230225, - 6.577415466308594, - 4.872487545013428 + 2.2026381492614746, + 7.006636142730713, + 4.985428810119629 ] }, "cluster_id": 2, @@ -100217,11 +100347,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -100638,31 +100768,31 @@ ], "projections": { "pca": [ - 0.05835379287600517, - -0.04111131653189659 + 0.058371227234601974, + -0.04091682285070419 ], "tsne": [ - 12.385746002197266, - 4.179196834564209 + 19.05817413330078, + -9.356263160705566 ], "umap": [ - 6.562830448150635, - 7.14290189743042 + 6.635130405426025, + 6.873069763183594 ], "pca3d": [ - 0.05835389718413353, - -0.041122693568468094, - -0.03220003843307495 + 0.05837135761976242, + -0.040928710252046585, + -0.0319284126162529 ], "tsne3d": [ - 4.372004508972168, - -4.878048896789551, - -9.101024627685547 + 5.692359447479248, + 8.454583168029785, + -0.44931885600090027 ], "umap3d": [ - 5.695475101470947, - 3.968712568283081, - 3.3115530014038086 + 6.205225944519043, + 4.428047180175781, + 2.745675563812256 ] }, "cluster_id": 4, @@ -100673,12 +100803,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 11, - "label": "Genomic Language" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -101093,47 +101223,47 @@ ], "projections": { "pca": [ - -0.1843915432691574, - 0.030716845765709877 + -0.18444262444972992, + 0.03156038746237755 ], "tsne": [ - -34.697601318359375, - -31.954792022705078 + -38.72681427001953, + 27.89864730834961 ], "umap": [ - 8.217389106750488, - 10.292339324951172 + 8.726287841796875, + 9.653294563293457 ], "pca3d": [ - -0.1843913048505783, - 0.0307040773332119, - -0.15086661279201508 + -0.1844424307346344, + 0.03154675289988518, + -0.15080809593200684 ], "tsne3d": [ - -14.448064804077148, - -1.915869951248169, - -17.890235900878906 + -13.94088363647461, + -7.789700984954834, + -13.668633460998535 ], "umap3d": [ - 4.34567928314209, - 4.93509578704834, - 1.2653676271438599 + 4.01966667175293, + 4.413323879241943, + 1.231244683265686 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -101546,47 +101676,47 @@ ], "projections": { "pca": [ - -0.250485897064209, - -0.12592197954654694 + -0.2509653568267822, + -0.12478315085172653 ], "tsne": [ - -45.665103912353516, - -9.676790237426758 + -45.100337982177734, + -9.675837516784668 ], "umap": [ - 0.7955811619758606, - 6.462140083312988 + 1.2887884378433228, + 5.602307319641113 ], "pca3d": [ - -0.2504855692386627, - -0.12592442333698273, - -0.10312934964895248 + -0.25096502900123596, + -0.12478567659854889, + -0.10283578932285309 ], "tsne3d": [ - -25.495893478393555, - -6.09437894821167, - -0.3641868531703949 + -23.444950103759766, + -7.951732635498047, + -1.8228040933609009 ], "umap3d": [ - 0.9655440449714661, - 5.919144630432129, - 4.0296220779418945 + 1.70588219165802, + 5.879255771636963, + 4.774625778198242 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -102008,47 +102138,47 @@ ], "projections": { "pca": [ - -0.04474199190735817, - -0.1921537220478058 + -0.04522434622049332, + -0.19202081859111786 ], "tsne": [ - -32.02566909790039, - 15.767619132995605 + -27.412782669067383, + -19.700132369995117 ], "umap": [ - 3.4668948650360107, - 4.782095909118652 + 3.9315950870513916, + 4.240995407104492 ], "pca3d": [ - -0.04474171996116638, - -0.19214436411857605, - 0.12440191209316254 + -0.04522401839494705, + -0.1920115053653717, + 0.12462691962718964 ], "tsne3d": [ - -10.798429489135742, - 11.28880500793457, - 3.323730945587158 + -10.005556106567383, + -19.37854766845703, + 4.108395576477051 ], "umap3d": [ - 3.3460745811462402, - 6.88055944442749, - 4.778225421905518 + 3.6512954235076904, + 7.301466464996338, + 4.1345696449279785 ] }, - "cluster_id": 2, - "cluster_label": "Generative Models", + "cluster_id": 3, + "cluster_label": "Protein Design", "cluster_levels": [ { - "id": 2, - "label": "Generative Models" + "id": 3, + "label": "Protein Design" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 16, - "label": "Diffusion Models" + "id": 7, + "label": "Diffusion Gen" } ], "_embedding": [ @@ -102472,47 +102602,47 @@ "cited_by_count": 169, "projections": { "pca": [ - -0.06533878296613693, - 0.017033737152814865 + -0.06533072888851166, + 0.01746509037911892 ], "tsne": [ - 3.4238505363464355, - -27.476165771484375 + -2.1385765075683594, + 26.427518844604492 ], "umap": [ - 5.178608417510986, - 8.654597282409668 + 5.528561592102051, + 7.810482025146484 ], "pca3d": [ - -0.06533873826265335, - 0.017043691128492355, - 0.020644735544919968 + -0.06533068418502808, + 0.01747456006705761, + 0.02064554952085018 ], "tsne3d": [ - -0.6875101327896118, - -15.777677536010742, - 4.80132532119751 + 0.33227428793907166, + 11.307761192321777, + -5.221836090087891 ], "umap3d": [ - 4.334470748901367, - 3.79616117477417, - 2.9327070713043213 + 4.785621643066406, + 4.087428092956543, + 3.068554401397705 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, - "label": "Medical AI" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -102930,47 +103060,47 @@ "cited_by_count": 31, "projections": { "pca": [ - -0.17446643114089966, - 0.005298301577568054 + -0.17455025017261505, + 0.0066068521700799465 ], "tsne": [ - -39.48693084716797, - -23.30123519897461 + -43.060203552246094, + 17.85296630859375 ], "umap": [ - 1.8786805868148804, - 7.337716102600098 + 2.288675308227539, + 6.457397937774658 ], "pca3d": [ - -0.17446638643741608, - 0.005302614066749811, - 0.08946885168552399 + -0.17455022037029266, + 0.006610740441828966, + 0.08950862288475037 ], "tsne3d": [ - -23.507604598999023, - -17.05553436279297, - -0.09250389039516449 + -23.585880279541016, + 5.64578104019165, + -10.64447021484375 ], "umap3d": [ - 0.8943237066268921, - 4.648094654083252, - 3.57785701751709 + 1.6845359802246094, + 4.767604827880859, + 4.298816680908203 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -103397,47 +103527,47 @@ "openalex_url": "https://openalex.org/W4401944952", "projections": { "pca": [ - 0.04127587378025055, - 0.1545901894569397 + 0.0417027622461319, + 0.1548646092414856 ], "tsne": [ - 8.261195182800293, - -20.645301818847656 + 3.0574018955230713, + 17.222375869750977 ], "umap": [ - 6.461843013763428, - 8.70598030090332 + 6.7378411293029785, + 8.431591033935547 ], "pca3d": [ - 0.04127570614218712, - 0.15457570552825928, - 0.08739053457975388 + 0.0417025126516819, + 0.1548493355512619, + 0.08706098794937134 ], "tsne3d": [ - 2.568199634552002, - -2.9221701622009277, - 12.830867767333984 + 4.563709259033203, + 5.511270046234131, + -7.284923076629639 ], "umap3d": [ - 4.493668079376221, - 4.238494396209717, - 2.535928249359131 + 4.824392318725586, + 4.360661506652832, + 2.389059543609619 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 6, + "label": "Single Cell Data" }, { - "id": 26, - "label": "Genetic Disease" + "id": 28, + "label": "Single Cell Data" } ], "_embedding": [ @@ -103858,46 +103988,46 @@ ], "projections": { "pca": [ - -0.243719220161438, - -0.13413310050964355 + -0.24395520985126495, + -0.13319160044193268 ], "tsne": [ - -29.04896354675293, - -11.419447898864746 + -32.993202209472656, + 8.610946655273438 ], "umap": [ - 1.7572137117385864, - 8.244291305541992 + 1.9893145561218262, + 7.523037910461426 ], "pca3d": [ - -0.2437187284231186, - -0.1341402232646942, - 0.039774492383003235 + -0.24395470321178436, + -0.13319838047027588, + 0.040232591331005096 ], "tsne3d": [ - -16.178489685058594, - -8.809027671813965, - 6.628305435180664 + -16.785158157348633, + 4.412811279296875, + 2.4044225215911865 ], "umap3d": [ - 1.7114753723144531, - 4.340951442718506, - 4.7278876304626465 + 2.728353977203369, + 4.9097676277160645, + 5.406653881072998 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 1, + "label": "Attention Models" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -104319,31 +104449,31 @@ "cited_by_count": 30, "projections": { "pca": [ - -0.2337094396352768, - -0.14945487678050995 + -0.23429934680461884, + -0.14902961254119873 ], "tsne": [ - -50.508750915527344, - -11.171141624450684 + -54.12168884277344, + -10.381746292114258 ], "umap": [ - 0.7355632185935974, - 5.9054646492004395 + 1.0872077941894531, + 5.218347549438477 ], "pca3d": [ - -0.23370926082134247, - -0.1494326889514923, - -0.04224560037255287 + -0.23429910838603973, + -0.14900650084018707, + -0.04209703579545021 ], "tsne3d": [ - -32.798805236816406, - -4.1341447830200195, - -6.265348434448242 + -26.38046646118164, + -10.16871166229248, + -3.9570043087005615 ], "umap3d": [ - 0.643657386302948, - 6.1261725425720215, - 3.728098154067993 + 1.2206703424453735, + 6.106876850128174, + 4.270752429962158 ] }, "cluster_id": 2, @@ -104354,12 +104484,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, - "label": "Training Noise" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -104776,31 +104906,31 @@ ], "projections": { "pca": [ - -0.14548473060131073, - -0.041340723633766174 + -0.14558333158493042, + -0.04037797451019287 ], "tsne": [ - -33.4943733215332, - 2.7887980937957764 + -31.513324737548828, + -5.255234241485596 ], "umap": [ - 2.553311347961426, - 6.8043341636657715 + 2.9364287853240967, + 6.071174144744873 ], "pca3d": [ - -0.1454845815896988, - -0.041342414915561676, - 0.14654536545276642 + -0.1455831378698349, + -0.0403793528676033, + 0.14664575457572937 ], "tsne3d": [ - -15.486248016357422, - 1.7801662683486938, - -8.337852478027344 + -12.918554306030273, + -9.149864196777344, + -4.3808913230896 ], "umap3d": [ - 2.2451908588409424, - 5.239068508148193, - 3.7999401092529297 + 2.8467206954956055, + 5.525935173034668, + 4.170302867889404 ] }, "cluster_id": 2, @@ -104811,12 +104941,12 @@ "label": "Generative Models" }, { - "id": 4, - "label": "Language Models" + "id": 5, + "label": "Diffusion Models" }, { - "id": 33, - "label": "Neural Networks" + "id": 19, + "label": "Graph Neural Nets" } ], "_embedding": [ @@ -105224,46 +105354,46 @@ "abstract": "Sheng Lu, Irina Bigoulaeva, Rachneet Sachdeva, Harish Tayyar Madabushi, Iryna Gurevych. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.", "projections": { "pca": [ - -0.2604389786720276, - -0.008245103992521763 + -0.26039940118789673, + -0.00666276179254055 ], "tsne": [ - -28.950157165527344, - -15.818882942199707 + -33.64487838745117, + 13.485795974731445 ], "umap": [ - 2.122220277786255, - 8.197893142700195 + 2.26755952835083, + 7.520284652709961 ], "pca3d": [ - -0.2604387700557709, - -0.00825645960867405, - 0.06228925287723541 + -0.26039931178092957, + -0.006674443371593952, + 0.06260007619857788 ], "tsne3d": [ - -17.148948669433594, - -13.774975776672363, - 5.039599418640137 + -19.02348518371582, + 8.051445007324219, + -1.2645078897476196 ], "umap3d": [ - 1.717673659324646, - 4.111126899719238, - 4.36220645904541 + 2.7758901119232178, + 4.569278717041016, + 5.120975494384766 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 1, + "label": "Attention Models" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -105686,31 +105816,31 @@ "cited_by_count": 103, "projections": { "pca": [ - -0.05850420892238617, - 0.09423067420721054 + -0.058177605271339417, + 0.09419131278991699 ], "tsne": [ - 8.562545776367188, - -8.163330078125 + 9.866641998291016, + 10.375005722045898 ], "umap": [ - 7.495064735412598, - 6.4004950523376465 + 7.705191612243652, + 5.937534332275391 ], "pca3d": [ - -0.05850465968251228, - 0.09427443891763687, - -0.053142428398132324 + -0.05817808955907822, + 0.09423542767763138, + -0.05324992164969444 ], "tsne3d": [ - 7.54545259475708, - 4.063493251800537, - -20.47450828552246 + 0.9826703667640686, + -11.116002082824707, + -11.728081703186035 ], "umap3d": [ - 5.680934906005859, - 5.021559238433838, - 2.8246238231658936 + 5.754085063934326, + 5.102103233337402, + 1.9777674674987793 ] }, "cluster_id": 4, @@ -105721,12 +105851,12 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 1, - "label": "Regulatory Genomics" + "id": 17, + "label": "Genomic Models" } ], "_embedding": [ @@ -106147,47 +106277,47 @@ ], "projections": { "pca": [ - 0.09556037932634354, - 0.014839325100183487 + 0.09556210786104202, + 0.01544202957302332 ], "tsne": [ - -2.124377965927124, - -23.47157859802246 + -4.718058109283447, + 19.772539138793945 ], "umap": [ - 4.562243461608887, - 8.747530937194824 + 4.943215847015381, + 8.005160331726074 ], "pca3d": [ - 0.09556031227111816, - 0.01484636403620243, - 0.1382380574941635 + 0.09556194394826889, + 0.015448955819010735, + 0.1381906419992447 ], "tsne3d": [ - -2.2573564052581787, - -6.326612949371338, - -5.042550086975098 + -3.0428435802459717, + 12.148043632507324, + 2.4098293781280518 ], "umap3d": [ - 3.652388334274292, - 3.7079577445983887, - 2.9197144508361816 + 4.266235828399658, + 4.067138195037842, + 3.148508071899414 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, - "label": "Medical AI" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -106604,31 +106734,31 @@ ], "projections": { "pca": [ - -0.2283632755279541, - -0.11157441884279251 + -0.2286524921655655, + -0.11100282520055771 ], "tsne": [ - -41.74958038330078, - 14.283544540405273 + -38.366268157958984, + -20.8902587890625 ], "umap": [ - 1.9260890483856201, - 5.321977138519287 + 2.3891782760620117, + 4.6206231117248535 ], "pca3d": [ - -0.22836290299892426, - -0.11156980693340302, - -0.08933883160352707 + -0.22865214943885803, + -0.11099855601787567, + -0.08908651769161224 ], "tsne3d": [ - -21.692386627197266, - 12.481640815734863, - 4.067749500274658 + -21.896671295166016, + -18.972551345825195, + 2.7396392822265625 ], "umap3d": [ - 1.7190613746643066, - 6.649083137512207, - 4.6964006423950195 + 2.3185341358184814, + 6.982475280761719, + 4.796233177185059 ] }, "cluster_id": 2, @@ -106639,11 +106769,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -107065,46 +107195,46 @@ ], "projections": { "pca": [ - -0.2359364777803421, - 0.005292888265103102 + -0.23576447367668152, + 0.006672768387943506 ], "tsne": [ - -28.541059494018555, - -16.83184814453125 + -33.125762939453125, + 14.388508796691895 ], "umap": [ - 2.2010669708251953, - 8.314142227172852 + 2.3101725578308105, + 7.4213762283325195 ], "pca3d": [ - -0.2359362691640854, - 0.005283628590404987, - 0.05513564124703407 + -0.23576438426971436, + 0.006663118954747915, + 0.05545156076550484 ], "tsne3d": [ - -16.93744659423828, - -15.522143363952637, - 5.4958953857421875 + -18.320598602294922, + 8.444839477539062, + -2.820856809616089 ], "umap3d": [ - 1.766135334968567, - 4.033071041107178, - 4.309422492980957 + 2.775146245956421, + 4.521589279174805, + 5.043341159820557 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 1, + "label": "Attention Models" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -107541,47 +107671,47 @@ "openalex_url": "https://openalex.org/W3124938033", "projections": { "pca": [ - -0.06689301878213882, - 0.08889098465442657 + -0.06674790382385254, + 0.08896436542272568 ], "tsne": [ - 11.402003288269043, - -32.42559814453125 + 3.152616262435913, + 33.456661224365234 ], "umap": [ - 5.9945759773254395, - 9.54298210144043 + 6.191369533538818, + 8.89757251739502 ], "pca3d": [ - -0.0668928474187851, - 0.08887570351362228, - -0.1147596687078476 + -0.0667477399110794, + 0.08894877880811691, + -0.11488933116197586 ], "tsne3d": [ - 6.9580888748168945, - -14.4082670211792, - 0.5739254355430603 + 2.0951414108276367, + 14.907748222351074, + -17.204660415649414 ], "umap3d": [ - 4.99160623550415, - 2.7016334533691406, - 2.429936408996582 + 5.390639781951904, + 2.7675187587738037, + 2.789504289627075 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -108003,46 +108133,46 @@ "cited_by_count": 579, "projections": { "pca": [ - -0.29042932391166687, - -0.028826499357819557 + -0.29037782549858093, + -0.027296829968690872 ], "tsne": [ - -26.185670852661133, - -14.853477478027344 + -29.145933151245117, + 12.639444351196289 ], "umap": [ - 2.2324087619781494, - 8.363070487976074 + 2.4435410499572754, + 7.491523742675781 ], "pca3d": [ - -0.29042893648147583, - -0.02884024754166603, - 0.03716343268752098 + -0.2903776168823242, + -0.02731102891266346, + 0.03755282238125801 ], "tsne3d": [ - -13.190864562988281, - -13.426216125488281, - 7.663679122924805 + -14.801081657409668, + 5.056424140930176, + -2.200730800628662 ], "umap3d": [ - 1.9172909259796143, - 4.04758882522583, - 4.453525543212891 + 3.0670251846313477, + 4.533084869384766, + 5.041508674621582 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -108461,31 +108591,31 @@ ], "projections": { "pca": [ - -0.30900129675865173, - -0.10618356615304947 + -0.309089720249176, + -0.10515253990888596 ], "tsne": [ - -36.16545486450195, - 11.131016731262207 + -33.01411819458008, + -17.138916015625 ], "umap": [ - 2.3166661262512207, - 5.302370071411133 + 2.793654680252075, + 4.804750442504883 ], "pca3d": [ - -0.30900073051452637, - -0.10617923736572266, - -0.013764881528913975 + -0.30908942222595215, + -0.10514839738607407, + -0.013287005946040154 ], "tsne3d": [ - -17.74767303466797, - 7.449382305145264, - 4.9618377685546875 + -16.963207244873047, + -16.94491958618164, + 6.914281845092773 ], "umap3d": [ - 2.131866931915283, - 6.54768705368042, - 4.948418617248535 + 2.7785234451293945, + 7.0355224609375, + 4.894748687744141 ] }, "cluster_id": 2, @@ -108496,11 +108626,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -108923,47 +109053,47 @@ ], "projections": { "pca": [ - -0.12793496251106262, - -0.11849615722894669 + -0.12832748889923096, + -0.11747173964977264 ], "tsne": [ - -37.64131546020508, - -0.9216687679290771 + -41.3939323425293, + -2.2085156440734863 ], "umap": [ - 1.7007267475128174, - 6.824656963348389 + 1.9795546531677246, + 6.160895824432373 ], "pca3d": [ - -0.12793439626693726, - -0.11851996928453445, - 0.07604608684778214 + -0.12832696735858917, + -0.11749541014432907, + 0.0762576013803482 ], "tsne3d": [ - -20.30560302734375, - 0.5936885476112366, - 3.5948803424835205 + -20.421764373779297, + -3.9589710235595703, + 7.769114971160889 ], "umap3d": [ - 1.6121933460235596, - 5.3238525390625, - 4.562074661254883 + 2.5561089515686035, + 5.672367572784424, + 5.1244120597839355 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -109375,31 +109505,31 @@ ], "projections": { "pca": [ - -0.14745935797691345, - -0.06647759675979614 + -0.14747166633605957, + -0.06601211428642273 ], "tsne": [ - -46.902313232421875, - -12.349394798278809 + -44.9815559387207, + -13.404731750488281 ], "umap": [ - 1.008362889289856, - 6.051663875579834 + 1.3066418170928955, + 5.289543151855469 ], "pca3d": [ - -0.14745888113975525, - -0.06649985164403915, - -0.009007498621940613 + -0.14747126400470734, + -0.06603442877531052, + -0.008731257170438766 ], "tsne3d": [ - -23.515897750854492, - -7.357114315032959, - -5.853862285614014 + -19.87401580810547, + -6.089356899261475, + -6.2377471923828125 ], "umap3d": [ - 0.8604697585105896, - 6.065761089324951, - 4.06962251663208 + 1.5085641145706177, + 6.1144633293151855, + 4.488051414489746 ] }, "cluster_id": 2, @@ -109410,12 +109540,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -109830,31 +109960,31 @@ ], "projections": { "pca": [ - -0.08302651345729828, - -0.07465428858995438 + -0.08313039690256119, + -0.074308842420578 ], "tsne": [ - -33.65758514404297, - 15.925899505615234 + -28.901716232299805, + -19.45794105529785 ], "umap": [ - 3.0614519119262695, - 4.971052646636963 + 3.498509168624878, + 4.447773456573486 ], "pca3d": [ - -0.08302639424800873, - -0.07464650273323059, - 0.0875358060002327 + -0.08313025534152985, + -0.07430154830217361, + 0.08765079826116562 ], "tsne3d": [ - -12.503866195678711, - 9.67366886138916, - 4.239445686340332 + -11.911043167114258, + -18.3599910736084, + 5.399595260620117 ], "umap3d": [ - 2.924644708633423, - 6.768525123596191, - 4.72282600402832 + 3.3219001293182373, + 7.188393592834473, + 4.322082042694092 ] }, "cluster_id": 2, @@ -109865,12 +109995,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 16, - "label": "Diffusion Models" + "id": 7, + "label": "Diffusion Gen" } ], "_embedding": [ @@ -110285,31 +110415,31 @@ ], "projections": { "pca": [ - -0.24804355204105377, - -0.11044268310070038 + -0.24841858446598053, + -0.10982904583215714 ], "tsne": [ - -35.6875, - 13.407075881958008 + -31.969741821289062, + -20.219287872314453 ], "umap": [ - 2.4839165210723877, - 5.195461273193359 + 2.944582223892212, + 4.692622184753418 ], "pca3d": [ - -0.24804317951202393, - -0.11042564362287521, - 0.014660967513918877 + -0.24841822683811188, + -0.109811931848526, + 0.014788870699703693 ], "tsne3d": [ - -16.943906784057617, - 11.134181022644043, - 2.9869697093963623 + -16.148244857788086, + -18.735567092895508, + 2.7527451515197754 ], "umap3d": [ - 2.4257519245147705, - 6.708432674407959, - 4.763327121734619 + 2.8543145656585693, + 6.997100353240967, + 4.629859447479248 ] }, "cluster_id": 2, @@ -110320,11 +110450,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -110739,46 +110869,46 @@ ], "projections": { "pca": [ - -0.19654086232185364, - -0.1556827872991562 + -0.1967497020959854, + -0.1551506668329239 ], "tsne": [ - -32.461483001708984, - -2.51949405670166 + -40.56250762939453, + 4.063787460327148 ], "umap": [ - 0.8741556406021118, - 8.19372272491455 + 1.1193281412124634, + 7.440413475036621 ], "pca3d": [ - -0.19654040038585663, - -0.1556963175535202, - 0.024513673037290573 + -0.19674919545650482, + -0.15516416728496552, + 0.024936581030488014 ], "tsne3d": [ - -19.252338409423828, - -3.3285019397735596, - 13.39273738861084 + -24.297794342041016, + 9.84638786315918, + 3.4759974479675293 ], "umap3d": [ - 1.0708703994750977, - 4.766478061676025, - 5.241823673248291 + 2.2357981204986572, + 5.285126209259033, + 5.957705974578857 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -111201,46 +111331,46 @@ ], "projections": { "pca": [ - -0.24003653228282928, - -0.17202726006507874 + -0.2403995841741562, + -0.17113959789276123 ], "tsne": [ - -34.62884521484375, - -8.544841766357422 + -39.6301383972168, + 8.661863327026367 ], "umap": [ - 1.063150405883789, - 8.051803588867188 + 1.2880325317382812, + 7.223217010498047 ], "pca3d": [ - -0.24003615975379944, - -0.17202597856521606, - -0.013789748772978783 + -0.24039916694164276, + -0.17113816738128662, + -0.013336771167814732 ], "tsne3d": [ - -22.726076126098633, - -6.186978816986084, - 7.529947757720947 + -23.097585678100586, + 3.270374298095703, + 6.8674139976501465 ], "umap3d": [ - 1.1165106296539307, - 4.7192277908325195, - 5.0291643142700195 + 2.210710287094116, + 5.050119876861572, + 5.641129016876221 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -111649,47 +111779,47 @@ "abstract": "Schedule-Free Optimization in PyTorch. Contribute to facebookresearch/schedule_free development by creating an account on GitHub.", "projections": { "pca": [ - -0.10879619419574738, - 0.2335129976272583 + -0.10800333321094513, + 0.23415564000606537 ], "tsne": [ - -28.61020278930664, - 31.723957061767578 + -15.974590301513672, + -6.242514610290527 ], "umap": [ - 3.8945038318634033, - 6.507850170135498 + 4.246769905090332, + 5.989162921905518 ], "pca3d": [ - -0.10879649966955185, - 0.233534038066864, - 0.07043923437595367 + -0.10800386220216751, + 0.23417693376541138, + 0.07017365097999573 ], "tsne3d": [ - -12.228611946105957, - 16.58462905883789, - 12.872908592224121 + -12.016204833984375, + -4.2362847328186035, + 14.586902618408203 ], "umap3d": [ - 3.646338939666748, - 4.730212688446045, - 4.516457557678223 + 4.514444828033447, + 5.47503137588501, + 4.357738494873047 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 10, - "label": "Code Development" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -112107,31 +112237,31 @@ ], "projections": { "pca": [ - -0.2487557828426361, - -0.08202284574508667 + -0.24879665672779083, + -0.08120109885931015 ], "tsne": [ - -34.542808532714844, - 11.478943824768066 + -31.62431526184082, + -17.218658447265625 ], "umap": [ - 2.5178089141845703, - 5.311538219451904 + 2.9413840770721436, + 4.72791051864624 ], "pca3d": [ - -0.2487558275461197, - -0.0819890946149826, - 0.06450191140174866 + -0.24879662692546844, + -0.0811668336391449, + 0.06487217545509338 ], "tsne3d": [ - -15.210914611816406, - 8.258106231689453, - 6.257557392120361 + -14.742700576782227, + -18.50855255126953, + 7.684261798858643 ], "umap3d": [ - 2.25793194770813, - 6.54453182220459, - 4.9892706871032715 + 2.9005613327026367, + 7.048901081085205, + 4.813663959503174 ] }, "cluster_id": 2, @@ -112142,11 +112272,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -112568,31 +112698,31 @@ "cited_by_count": 6, "projections": { "pca": [ - 0.14144590497016907, - -0.12688235938549042 + 0.14123781025409698, + -0.1268644481897354 ], "tsne": [ - -5.566789627075195, - 27.645374298095703 + 2.142047643661499, + -27.631038665771484 ], "umap": [ - 5.574880123138428, - 4.194991111755371 + 6.287087440490723, + 3.7621283531188965 ], "pca3d": [ - 0.1414460390806198, - -0.12687887251377106, - 0.22259816527366638 + 0.1412379890680313, + -0.12686076760292053, + 0.22267380356788635 ], "tsne3d": [ - 1.1222858428955078, - 8.773635864257812, - 8.54029369354248 + -6.310014247894287, + -14.586212158203125, + 0.7446842789649963 ], "umap3d": [ - 5.37574577331543, - 6.660964488983154, - 4.9885101318359375 + 5.784046173095703, + 7.4891815185546875, + 3.2579264640808105 ] }, "cluster_id": 3, @@ -112603,12 +112733,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, - "label": "Protein Language" + "id": 3, + "label": "Protein Design" } ], "_embedding": [ @@ -113029,47 +113159,47 @@ "cited_by_count": 611, "projections": { "pca": [ - -0.23256933689117432, - 0.03601697087287903 + -0.232532799243927, + 0.03687744587659836 ], "tsne": [ - -25.3080997467041, - -18.7482852935791 + -29.83962059020996, + 16.55417251586914 ], "umap": [ - 2.536210298538208, - 8.41169548034668 + 2.835602283477783, + 7.7819108963012695 ], "pca3d": [ - -0.23256923258304596, - 0.036018624901771545, - -0.029365042224526405 + -0.23253273963928223, + 0.03687908872961998, + -0.029258254915475845 ], "tsne3d": [ - -13.29110336303711, - -17.823394775390625, - 3.9336814880371094 + -15.433845520019531, + 9.802416801452637, + -5.228967666625977 ], "umap3d": [ - 2.0490784645080566, - 3.7455875873565674, - 4.250263214111328 + 3.1116702556610107, + 4.148289680480957, + 4.861129283905029 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 7, - "label": "Language Models" + "id": 25, + "label": "AI Agents" } ], "_embedding": [ @@ -113483,31 +113613,31 @@ ], "projections": { "pca": [ - -0.14001646637916565, - -0.09337487071752548 + -0.14019422233104706, + -0.09267129749059677 ], "tsne": [ - -35.07060241699219, - 15.904153823852539 + -30.41179656982422, + -20.064104080200195 ], "umap": [ - 2.7874562740325928, - 5.095057487487793 + 3.160743474960327, + 4.513810634613037 ], "pca3d": [ - -0.14001639187335968, - -0.09335894882678986, - 0.06056458130478859 + -0.14019420742988586, + -0.09265521913766861, + 0.06076492369174957 ], "tsne3d": [ - -13.863533973693848, - 8.129167556762695, - -0.01682707853615284 + -13.172439575195312, + -15.495928764343262, + 2.4290881156921387 ], "umap3d": [ - 2.578758955001831, - 6.706641674041748, - 4.7020263671875 + 3.1016740798950195, + 7.083539962768555, + 4.390797138214111 ] }, "cluster_id": 2, @@ -113518,12 +113648,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, - "label": "Diffusion Models" + "id": 7, + "label": "Diffusion Gen" } ], "_embedding": [ @@ -113937,31 +114067,31 @@ ], "projections": { "pca": [ - -0.3072720468044281, - -0.16254130005836487 + -0.3076717257499695, + -0.16169963777065277 ], "tsne": [ - -33.85610580444336, - 1.5455771684646606 + -32.82483673095703, + -4.978377819061279 ], "umap": [ - 2.294182062149048, - 6.706220626831055 + 2.7067925930023193, + 5.953362941741943 ], "pca3d": [ - -0.3072715699672699, - -0.16254405677318573, - -0.04251260682940483 + -0.30767133831977844, + -0.1617019921541214, + -0.04208981990814209 ], "tsne3d": [ - -17.959985733032227, - 2.3962879180908203, - -9.271641731262207 + -14.795906066894531, + -10.469244956970215, + -5.605485916137695 ], "umap3d": [ - 2.0504376888275146, - 5.405703067779541, - 3.889660596847534 + 2.6599371433258057, + 5.667720317840576, + 4.263327121734619 ] }, "cluster_id": 2, @@ -113972,12 +114102,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 33, - "label": "Neural Networks" + "id": 19, + "label": "Graph Neural Nets" } ], "_embedding": [ @@ -114393,47 +114523,47 @@ ], "projections": { "pca": [ - -0.18238982558250427, - -0.11150823533535004 + -0.18271972239017487, + -0.11073940992355347 ], "tsne": [ - -45.6878776550293, - -10.066305160522461 + -45.18825912475586, + -10.026677131652832 ], "umap": [ - 0.932601273059845, - 6.40385103225708 + 1.29746675491333, + 5.5342936515808105 ], "pca3d": [ - -0.18238943815231323, - -0.11151853948831558, - -0.0861719399690628 + -0.18271932005882263, + -0.11074988543987274, + -0.08586011826992035 ], "tsne3d": [ - -25.90936279296875, - -7.128941059112549, - -0.08448272198438644 + -22.925718307495117, + -8.538814544677734, + -2.616309642791748 ], "umap3d": [ - 0.9640384912490845, - 5.943375587463379, - 4.032282829284668 + 1.7402528524398804, + 5.896727561950684, + 4.764927387237549 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -114871,47 +115001,47 @@ "cited_by_count": 46, "projections": { "pca": [ - -0.14197273552417755, - 0.11626414954662323 + -0.14154094457626343, + 0.11689057946205139 ], "tsne": [ - 12.661609649658203, - -36.64198684692383 + 3.966967821121216, + 37.209381103515625 ], "umap": [ - 5.729389190673828, - 9.719213485717773 + 5.775724411010742, + 9.128459930419922 ], "pca3d": [ - -0.14197281002998352, - 0.11625129729509354, - -0.021749284118413925 + -0.141540989279747, + 0.11687798798084259, + -0.02167145535349846 ], "tsne3d": [ - 7.033747673034668, - -21.490650177001953, - 4.2632551193237305 + -3.0913078784942627, + 14.163860321044922, + -19.91815948486328 ], "umap3d": [ - 4.440329551696777, - 2.782733917236328, - 2.3556103706359863 + 4.783432483673096, + 2.9508652687072754, + 2.998098611831665 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -115327,46 +115457,46 @@ ], "projections": { "pca": [ - -0.15333645045757294, - -0.060406167060136795 + -0.15334729850292206, + -0.05987443029880524 ], "tsne": [ - -62.09596252441406, - -3.828641176223755 + -56.78413772583008, + 4.9510602951049805 ], "umap": [ - 0.5291907787322998, - 7.413000106811523 + 0.8500538468360901, + 6.517271518707275 ], "pca3d": [ - -0.15333595871925354, - -0.06044461205601692, - -0.06457280367612839 + -0.1533466875553131, + -0.059913333505392075, + -0.0642809197306633 ], "tsne3d": [ - -24.98817253112793, - 5.757009983062744, - 5.571811199188232 + -25.73706817626953, + -16.736173629760742, + 7.983550548553467 ], "umap3d": [ - 0.8599829077720642, - 5.828655242919922, - 5.12499475479126 + 1.7726081609725952, + 6.179261207580566, + 5.730051040649414 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -115781,31 +115911,31 @@ ], "projections": { "pca": [ - -0.2435530573129654, - 0.009566346183419228 + -0.24358490109443665, + 0.010372551158070564 ], "tsne": [ - -37.99831771850586, - 12.612789154052734 + -34.78908157348633, + -18.407634735107422 ], "umap": [ - 2.2153146266937256, - 5.396308898925781 + 2.5847713947296143, + 4.8173723220825195 ], "pca3d": [ - -0.24355269968509674, - 0.00955850351601839, - -0.05662728101015091 + -0.24358467757701874, + 0.010363942012190819, + -0.0566006600856781 ], "tsne3d": [ - -19.308494567871094, - 6.603392601013184, - 2.1589059829711914 + -18.436859130859375, + -15.012253761291504, + 4.689211368560791 ], "umap3d": [ - 2.0735421180725098, - 6.54513692855835, - 4.639821529388428 + 2.6337051391601562, + 6.886777877807617, + 4.58765983581543 ] }, "cluster_id": 2, @@ -115816,11 +115946,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -116238,46 +116368,46 @@ "cited_by_count": 25, "projections": { "pca": [ - -0.18958835303783417, - -0.04164402559399605 + -0.18952208757400513, + -0.04053092375397682 ], "tsne": [ - -25.002079010009766, - -12.648031234741211 + -29.19331932067871, + 10.409290313720703 ], "umap": [ - 2.0594253540039062, - 8.39090633392334 + 2.3490889072418213, + 7.603216648101807 ], "pca3d": [ - -0.18958817422389984, - -0.04165443405508995, - 0.1167985275387764 + -0.18952181935310364, + -0.04054116830229759, + 0.1171482503414154 ], "tsne3d": [ - -13.260634422302246, - -11.66893196105957, - 3.6959238052368164 + -13.785343170166016, + 6.877339839935303, + 0.5128711462020874 ], "umap3d": [ - 1.9707751274108887, - 4.101390361785889, - 4.518335819244385 + 2.9793701171875, + 4.638031959533691, + 5.224883556365967 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -116695,31 +116825,31 @@ ], "projections": { "pca": [ - -0.2713283598423004, - -0.14828844368457794 + -0.27165380120277405, + -0.1475350260734558 ], "tsne": [ - -37.061038970947266, - 12.046453475952148 + -33.58015441894531, + -18.559206008911133 ], "umap": [ - 2.2395808696746826, - 5.252098560333252 + 2.5480051040649414, + 4.674693584442139 ], "pca3d": [ - -0.27132776379585266, - -0.14829568564891815, - -0.08185155689716339 + -0.27165329456329346, + -0.14754268527030945, + -0.08143551647663116 ], "tsne3d": [ - -18.403785705566406, - 8.330082893371582, - 3.762143135070801 + -17.666728973388672, + -17.12866973876953, + 5.518301486968994 ], "umap3d": [ - 2.0214297771453857, - 6.6495256423950195, - 4.901727676391602 + 2.6334924697875977, + 7.07407808303833, + 4.83378791809082 ] }, "cluster_id": 2, @@ -116730,11 +116860,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -117150,46 +117280,46 @@ ], "projections": { "pca": [ - -0.25193294882774353, - -0.16371360421180725 + -0.25233250856399536, + -0.16301649808883667 ], "tsne": [ - -50.71392822265625, - -2.554110288619995 + -45.80475997924805, + 0.3286886513233185 ], "umap": [ - 0.5927094221115112, - 7.17692232131958 + 0.9333252310752869, + 6.404551982879639 ], "pca3d": [ - -0.25193220376968384, - -0.16372644901275635, - -0.06091831997036934 + -0.25233194231987, + -0.16302898526191711, + -0.0605519637465477 ], "tsne3d": [ - -23.872314453125, - 0.32017984986305237, - 5.304622173309326 + -24.743284225463867, + -1.8615401983261108, + 3.818040132522583 ], "umap3d": [ - 0.7606696486473083, - 5.445621967315674, - 4.715337753295898 + 1.616217851638794, + 5.753851890563965, + 5.4215288162231445 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -117645,46 +117775,46 @@ "cited_by_count": 85, "projections": { "pca": [ - -0.20322321355342865, - -0.08387156575918198 + -0.20339113473892212, + -0.08279041200876236 ], "tsne": [ - -28.86860466003418, - -9.93478775024414 + -34.911865234375, + 8.464546203613281 ], "umap": [ - 1.8139762878417969, - 8.22092056274414 + 2.0089476108551025, + 7.502626895904541 ], "pca3d": [ - -0.20322297513484955, - -0.0838751271367073, - 0.04686346650123596 + -0.20339083671569824, + -0.08279367536306381, + 0.04723402112722397 ], "tsne3d": [ - -18.641054153442383, - -9.468032836914062, - 8.471933364868164 + -17.863134384155273, + 6.520977020263672, + 3.8279476165771484 ], "umap3d": [ - 1.65897798538208, - 4.458709239959717, - 4.761523246765137 + 2.6915481090545654, + 4.868764400482178, + 5.462613582611084 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 1, + "label": "Attention Models" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -118103,31 +118233,31 @@ ], "projections": { "pca": [ - -0.19487063586711884, - -0.08871287852525711 + -0.19508863985538483, + -0.08833658695220947 ], "tsne": [ - -40.34994125366211, - 18.69727325439453 + -35.914527893066406, + -24.85394287109375 ], "umap": [ - 2.2143163681030273, - 4.848310947418213 + 2.705294609069824, + 4.184656620025635 ], "pca3d": [ - -0.19487017393112183, - -0.08873030543327332, - -0.02182789333164692 + -0.19508813321590424, + -0.08835367113351822, + -0.021678218618035316 ], "tsne3d": [ - -20.087841033935547, - 13.79580307006836, - -1.553737998008728 + -19.552045822143555, + -23.97221565246582, + 4.0125579833984375 ], "umap3d": [ - 2.0836751461029053, - 6.983456611633301, - 4.6996026039123535 + 2.601919651031494, + 7.4206156730651855, + 4.618078231811523 ] }, "cluster_id": 2, @@ -118138,11 +118268,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -118557,46 +118687,46 @@ ], "projections": { "pca": [ - -0.20151840150356293, - -0.09908737987279892 + -0.20165356993675232, + -0.09793896228075027 ], "tsne": [ - -32.261077880859375, - -3.415093421936035 + -39.813697814941406, + 3.930462121963501 ], "umap": [ - 0.9469626545906067, - 8.139867782592773 + 1.170106291770935, + 7.403402805328369 ], "pca3d": [ - -0.20151783525943756, - -0.09911298751831055, - 0.04942965880036354 + -0.20165306329727173, + -0.09796494990587234, + 0.04982961714267731 ], "tsne3d": [ - -18.375587463378906, - -2.5707919597625732, - 11.824258804321289 + -23.661970138549805, + 8.976449012756348, + 1.8517965078353882 ], "umap3d": [ - 1.150551676750183, - 4.817182540893555, - 5.069238662719727 + 2.250884771347046, + 5.254676818847656, + 5.891964912414551 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -119011,31 +119141,31 @@ ], "projections": { "pca": [ - -0.19665434956550598, - -0.11530006676912308 + -0.19689838588237762, + -0.11490378528833389 ], "tsne": [ - -40.32316970825195, - 18.6641788482666 + -35.88724899291992, + -24.826086044311523 ], "umap": [ - 2.2138915061950684, - 4.87391996383667 + 2.6225059032440186, + 4.304284572601318 ], "pca3d": [ - -0.19665385782718658, - -0.11531534790992737, - -0.037432655692100525 + -0.19689790904521942, + -0.11491870135068893, + -0.03716818988323212 ], "tsne3d": [ - -19.742727279663086, - 13.261835098266602, - -1.9239884614944458 + -19.268739700317383, + -23.89409065246582, + 4.752476215362549 ], "umap3d": [ - 2.1212267875671387, - 7.039048194885254, - 4.725053310394287 + 2.607288122177124, + 7.424485206604004, + 4.589388847351074 ] }, "cluster_id": 2, @@ -119046,11 +119176,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -119463,31 +119593,31 @@ ], "projections": { "pca": [ - -0.19074058532714844, - -0.11627060920000076 + -0.1910609006881714, + -0.11569836735725403 ], "tsne": [ - -45.917659759521484, - 3.259418249130249 + -40.71757125854492, + -10.954221725463867 ], "umap": [ - 1.4687280654907227, - 6.524163722991943 + 1.9646912813186646, + 5.673193454742432 ], "pca3d": [ - -0.190740168094635, - -0.11627719551324844, - -0.024497687816619873 + -0.19106052815914154, + -0.11570494621992111, + -0.02431023307144642 ], "tsne3d": [ - -21.920413970947266, - 4.295294761657715, - -6.971794128417969 + -17.814722061157227, + -10.283905029296875, + -3.024939775466919 ], "umap3d": [ - 1.4858388900756836, - 5.658405303955078, - 3.9877543449401855 + 2.109666585922241, + 5.940262317657471, + 4.455450534820557 ] }, "cluster_id": 2, @@ -119498,12 +119628,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -119913,47 +120043,47 @@ "abstract": "We have identified how millions of concepts are represented inside Claude Sonnet, one of our deployed large language models. This is the first ever detailed look inside a modern, production-grade large language model.", "projections": { "pca": [ - -0.21910561621189117, - 0.1333613097667694 + -0.21866688132286072, + 0.13446977734565735 ], "tsne": [ - -21.509960174560547, - -13.332073211669922 + -25.094213485717773, + 7.388585567474365 ], "umap": [ - 2.5343525409698486, - 8.579142570495605 + 2.6686859130859375, + 7.794898509979248 ], "pca3d": [ - -0.21910572052001953, - 0.1333523988723755, - 0.0425858236849308 + -0.21866688132286072, + 0.1344601809978485, + 0.04260510951280594 ], "tsne3d": [ - -12.72868824005127, - -15.594887733459473, - 1.0767879486083984 + -9.78458023071289, + 5.95560359954834, + 13.160382270812988 ], "umap3d": [ - 2.2051022052764893, - 3.894331455230713, - 4.409350872039795 + 3.372363805770874, + 4.556239128112793, + 4.987770080566406 ] }, - "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 1, - "label": "AI Scientific Research" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 7, - "label": "Language Models" + "id": 25, + "label": "AI Agents" } ], "_embedding": [ @@ -120372,47 +120502,47 @@ ], "projections": { "pca": [ - -0.2770056128501892, - -0.145564466714859 + -0.277188777923584, + -0.1447100043296814 ], "tsne": [ - -32.529056549072266, - -10.47094440460205 + -37.4256591796875, + 9.841219902038574 ], "umap": [ - 1.396917462348938, - 8.174631118774414 + 1.635135531425476, + 7.38754940032959 ], "pca3d": [ - -0.27700528502464294, - -0.1455671638250351, - 0.015817837789654732 + -0.27718842029571533, + -0.1447124481201172, + 0.016311751678586006 ], "tsne3d": [ - -20.507640838623047, - -7.839242935180664, - 7.315480709075928 + -19.551097869873047, + 6.542475700378418, + 6.904921531677246 ], "umap3d": [ - 1.3106000423431396, - 4.437572956085205, - 4.825355052947998 + 2.425490140914917, + 4.898752689361572, + 5.550735950469971 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, - "label": "Transformer Attention" + "id": 34, + "label": "Language Models" } ], "_embedding": [ @@ -120825,46 +120955,46 @@ ], "projections": { "pca": [ - -0.20310711860656738, - -0.14281313121318817 + -0.20359274744987488, + -0.14212387800216675 ], "tsne": [ - -53.961727142333984, - -14.583918571472168 + -56.33317565917969, + -7.834875583648682 ], "umap": [ - -0.03796737641096115, - 6.331742763519287 + 0.22502924501895905, + 5.409324645996094 ], "pca3d": [ - -0.2031068354845047, - -0.14280113577842712, - -0.09701474756002426 + -0.2035924345254898, + -0.14211119711399078, + -0.09674230962991714 ], "tsne3d": [ - -31.136899948120117, - 0.19204479455947876, - -4.595323085784912 + -29.95646858215332, + -10.110747337341309, + -0.8329623341560364 ], "umap3d": [ - 0.08041311800479889, - 6.376222610473633, - 4.138444900512695 + 0.6912787556648254, + 6.429834365844727, + 4.83867883682251 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, + "id": 23, "label": "Training Noise" } ], @@ -121279,31 +121409,31 @@ ], "projections": { "pca": [ - 0.0567924864590168, - 0.041682641953229904 + 0.0568588525056839, + 0.04176726192235947 ], "tsne": [ - 4.635065078735352, - -24.35308837890625 + -12.790506362915039, + -22.409320831298828 ], "umap": [ - 4.583373069763184, - 4.351292133331299 + 5.193442344665527, + 3.7066454887390137 ], "pca3d": [ - 0.056792423129081726, - 0.04168655350804329, - 0.12303753942251205 + 0.05685882270336151, + 0.04177040606737137, + 0.1227988749742508 ], "tsne3d": [ - 1.2230126857757568, - -11.209145545959473, - 4.021046161651611 + -1.4350156784057617, + -13.594549179077148, + 17.09760284423828 ], "umap3d": [ - 4.596968650817871, - 7.019376277923584, - 4.447864532470703 + 4.753021717071533, + 7.448083877563477, + 3.1697566509246826 ] }, "cluster_id": 3, @@ -121314,12 +121444,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -121731,46 +121861,46 @@ ], "projections": { "pca": [ - -0.2039109766483307, - -0.21496400237083435 + -0.20433643460273743, + -0.21406687796115875 ], "tsne": [ - -32.66426086425781, - -1.4189441204071045 + -41.18361282348633, + 3.3491222858428955 ], "umap": [ - 0.8562119603157043, - 8.22939682006836 + 1.0383806228637695, + 7.483327388763428 ], "pca3d": [ - -0.2039104551076889, - -0.21497349441051483, - -0.02891373820602894 + -0.20433591306209564, + -0.21407607197761536, + -0.028300553560256958 ], "tsne3d": [ - -20.98151206970215, - -1.8887373208999634, - 12.093173027038574 + -26.154666900634766, + 9.823634147644043, + 1.909608006477356 ], "umap3d": [ - 1.090898036956787, - 4.818681716918945, - 5.184442520141602 + 2.3014397621154785, + 5.362828254699707, + 5.883302211761475 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -122189,31 +122319,31 @@ ], "projections": { "pca": [ - -0.2857230007648468, - -0.1439206749200821 + -0.2860817611217499, + -0.14313071966171265 ], "tsne": [ - -33.82769775390625, - 2.0307250022888184 + -32.42878341674805, + -5.178176403045654 ], "umap": [ - 2.3109164237976074, - 6.687319755554199 + 2.7270069122314453, + 5.9689483642578125 ], "pca3d": [ - -0.2857225239276886, - -0.14391843974590302, - 0.024245986714959145 + -0.28608134388923645, + -0.1431281417608261, + 0.024559350684285164 ], "tsne3d": [ - -17.13669776916504, - 1.9373700618743896, - -8.541715621948242 + -14.272564888000488, + -9.76321792602539, + -4.898036479949951 ], "umap3d": [ - 2.0680835247039795, - 5.409065246582031, - 3.8830883502960205 + 2.7145094871520996, + 5.697096824645996, + 4.208105564117432 ] }, "cluster_id": 2, @@ -122224,12 +122354,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 33, - "label": "Neural Networks" + "id": 19, + "label": "Graph Neural Nets" } ], "_embedding": [ @@ -122646,46 +122776,46 @@ ], "projections": { "pca": [ - -0.21316346526145935, - -0.08744586259126663 + -0.21339918673038483, + -0.08640140295028687 ], "tsne": [ - -29.593616485595703, - -14.70905876159668 + -34.33983612060547, + 12.292576789855957 ], "umap": [ - 1.8526408672332764, - 8.203365325927734 + 2.0500218868255615, + 7.4711833000183105 ], "pca3d": [ - -0.21316328644752502, - -0.08743671327829361, - -0.04269668459892273 + -0.21339891850948334, + -0.08639208227396011, + -0.04235294461250305 ], "tsne3d": [ - -17.917043685913086, - -12.328590393066406, - 5.697504043579102 + -18.516407012939453, + 7.443649768829346, + 0.35186249017715454 ], "umap3d": [ - 1.6618895530700684, - 4.2166314125061035, - 4.563970565795898 + 2.669603109359741, + 4.644706726074219, + 5.2091288566589355 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 1, + "label": "Attention Models" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -123101,31 +123231,31 @@ ], "projections": { "pca": [ - -0.23920024931430817, - -0.11623391509056091 + -0.2397950142621994, + -0.11634762585163116 ], "tsne": [ - -50.03077697753906, - 7.884776592254639 + -50.57096481323242, + -16.156469345092773 ], "umap": [ - 1.0461177825927734, - 5.404616832733154 + 1.433038592338562, + 4.618190765380859 ], "pca3d": [ - -0.2391999214887619, - -0.11621911078691483, - -0.07858896255493164 + -0.23979471623897552, + -0.11633224040269852, + -0.07861005514860153 ], "tsne3d": [ - -26.35694694519043, - -1.5139936208724976, - -10.948227882385254 + -24.307546615600586, + -15.322366714477539, + -6.238010883331299 ], "umap3d": [ - 1.1889206171035767, - 6.563320159912109, - 3.6275994777679443 + 1.5641480684280396, + 6.550134658813477, + 4.058833122253418 ] }, "cluster_id": 2, @@ -123136,12 +123266,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -123550,47 +123680,47 @@ "abstract": "While a growing number of algorithms for causal discovery of directed acyclic graphs from ob- servational and interventional data have been proposed, the robust identification of cyclic causal graphs in particular remains an open problem. Solutions to this challenge would have a consider- able impact in various application domains, including single-cell genomics, where gene regulatory networks are known to contain feedback loops. Recent work has shown promise in addressing this challenge by describing the expression states in a population of cells as the steady-state solution of a stochastic dynamical system. However, the current formulation cannot account for information on interventions in the population, and consequently, it ignores the associated causal inductive bi- ases, which are key assets to fully exploit the potential of available data for obtaining meaningful results and improving identifiability. In this work, we propose Bicycle, a method which (i) infers cyclic causal relationships from i.i.d. data, (ii) explicitly accounts for information on the perturba- tion state of cells by a realization of the independent causal mechanism principle and (iii) models causal effects in a latent space rather than on observed data. We benchmark Bicycle in the context of existing approaches, demonstrating improved recovery of simulated causal graphs and improved out-of-distribution prediction performance on unseen perturbations in real single-cell datasets.", "projections": { "pca": [ - 0.08687004446983337, - -0.03721403703093529 + 0.08660842478275299, + -0.03705616295337677 ], "tsne": [ - 33.207584381103516, - -19.747989654541016 + 25.424270629882812, + 25.84141731262207 ], "umap": [ - 8.419140815734863, - 9.99382495880127 + 8.75003433227539, + 9.338756561279297 ], "pca3d": [ - 0.08686990290880203, - -0.037193670868873596, - -0.06906315684318542 + 0.08660832792520523, + -0.0370357520878315, + -0.06913597881793976 ], "tsne3d": [ - 18.45149803161621, - 13.04403305053711, - 2.4703898429870605 + 12.956506729125977, + -6.994644641876221, + -10.902814865112305 ], "umap3d": [ - 4.755318641662598, - 4.843625545501709, - 1.1712844371795654 + 4.432097911834717, + 4.3664751052856445, + 1.0460264682769775 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -124016,47 +124146,47 @@ "cited_by_count": 107, "projections": { "pca": [ - -0.09479713439941406, - 0.09549639374017715 + -0.0944540947675705, + 0.09579971432685852 ], "tsne": [ - 13.18907642364502, - -36.6717643737793 + 4.418613433837891, + 37.29705810546875 ], "umap": [ - 5.947066783905029, - 9.742807388305664 + 6.043826580047607, + 9.1286039352417 ], "pca3d": [ - -0.09479733556509018, - 0.095501609146595, - -0.16927778720855713 + -0.09445428848266602, + 0.0958050936460495, + -0.16916120052337646 ], "tsne3d": [ - 8.180813789367676, - -20.91942596435547, - 5.261002540588379 + -2.1109702587127686, + 13.665339469909668, + -21.130735397338867 ], "umap3d": [ - 4.672718524932861, - 2.7578349113464355, - 2.2074551582336426 + 5.032404899597168, + 2.820294141769409, + 2.7019388675689697 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -124471,31 +124601,31 @@ ], "projections": { "pca": [ - -0.19279487431049347, - -0.09293349087238312 + -0.193001389503479, + -0.0921456441283226 ], "tsne": [ - -46.6312141418457, - -20.547237396240234 + -49.83907699584961, + 15.51965045928955 ], "umap": [ - 0.4508078396320343, - 5.84168004989624 + 0.7860287427902222, + 5.0184006690979 ], "pca3d": [ - -0.19279466569423676, - -0.09293495118618011, - -0.04415473714470863 + -0.1930011510848999, + -0.09214726090431213, + -0.04386572167277336 ], "tsne3d": [ - -27.286191940307617, - -10.789298057556152, - -3.3838775157928467 + -25.429866790771484, + -1.2977920770645142, + -7.848516941070557 ], "umap3d": [ - 0.47584667801856995, - 5.476297855377197, - 3.454529285430908 + 1.1201738119125366, + 5.371105194091797, + 4.1955766677856445 ] }, "cluster_id": 2, @@ -124506,12 +124636,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, - "label": "Training Noise" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -124928,47 +125058,47 @@ ], "projections": { "pca": [ - -0.19746823608875275, - -0.07209468632936478 + -0.19766275584697723, + -0.07225514203310013 ], "tsne": [ - 8.926995277404785, - -34.50540542602539 + 0.05542633309960365, + 35.39576721191406 ], "umap": [ - 5.711857795715332, - 9.827606201171875 + 5.791501522064209, + 9.127612113952637 ], "pca3d": [ - -0.19746775925159454, - -0.07211796194314957, - -0.1573113054037094 + -0.1976623237133026, + -0.07227838784456253, + -0.1571376472711563 ], "tsne3d": [ - 3.5610010623931885, - -16.652851104736328, - 2.4433627128601074 + -2.2399935722351074, + 10.359586715698242, + -15.382560729980469 ], "umap3d": [ - 4.764520645141602, - 2.440053701400757, - 2.5249695777893066 + 5.046710014343262, + 2.5603652000427246, + 2.9233734607696533 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -125381,46 +125511,46 @@ ], "projections": { "pca": [ - -0.29330211877822876, - -0.02646743133664131 + -0.29326653480529785, + -0.024888725951313972 ], "tsne": [ - -32.22732162475586, - -3.8252081871032715 + -39.35295867919922, + 3.884892225265503 ], "umap": [ - 1.0512049198150635, - 8.088753700256348 + 1.3985763788223267, + 7.380127906799316 ], "pca3d": [ - -0.2933015823364258, - -0.02648952230811119, - 0.07951713353395462 + -0.2932662069797516, + -0.02491130493581295, + 0.07982271164655685 ], "tsne3d": [ - -17.571697235107422, - -2.5382063388824463, - 11.161271095275879 + -23.135051727294922, + 8.460358619689941, + 1.1545395851135254 ], "umap3d": [ - 1.1967554092407227, - 4.719996452331543, - 4.915984153747559 + 2.2799971103668213, + 5.1902337074279785, + 5.752115726470947 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -125838,31 +125968,31 @@ "journal": "Journal of Machine Learning Research", "projections": { "pca": [ - -0.17686975002288818, - -0.08529412746429443 + -0.17712843418121338, + -0.08460380136966705 ], "tsne": [ - -47.597198486328125, - -5.438131809234619 + -43.34957504272461, + -5.683220386505127 ], "umap": [ - 0.8848851919174194, - 6.311804294586182 + 1.362288475036621, + 5.6973137855529785 ], "pca3d": [ - -0.17686954140663147, - -0.08529086410999298, - -0.047896627336740494 + -0.17712824046611786, + -0.08460037410259247, + -0.04769054427742958 ], "tsne3d": [ - -24.48188591003418, - 1.601183533668518, - -2.1838812828063965 + -23.726261138916016, + -10.406546592712402, + 2.238247871398926 ], "umap3d": [ - 0.9636904001235962, - 5.663309574127197, - 4.01821756362915 + 1.6717509031295776, + 5.81528377532959, + 4.779477119445801 ] }, "cluster_id": 2, @@ -125873,12 +126003,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -126299,46 +126429,46 @@ ], "projections": { "pca": [ - -0.1653898060321808, - -0.12634322047233582 + -0.16564640402793884, + -0.1254183053970337 ], "tsne": [ - -28.265453338623047, - -10.914487838745117 + -32.55655288696289, + 9.45129680633545 ], "umap": [ - 1.7763283252716064, - 8.216499328613281 + 1.95326828956604, + 7.412383079528809 ], "pca3d": [ - -0.16538940370082855, - -0.12634602189064026, - -0.012378590181469917 + -0.1656460016965866, + -0.1254211813211441, + -0.011928638443350792 ], "tsne3d": [ - -16.399045944213867, - -10.286712646484375, - 9.514694213867188 + -17.70747184753418, + 4.295224666595459, + -0.18181942403316498 ], "umap3d": [ - 1.668013095855713, - 4.458831787109375, - 4.739984512329102 + 2.784782648086548, + 4.928280830383301, + 5.321111679077148 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 1, + "label": "Attention Models" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -126765,47 +126895,47 @@ "cited_by_count": 117, "projections": { "pca": [ - -0.18380388617515564, - -0.008720185607671738 + -0.18380258977413177, + -0.00794768612831831 ], "tsne": [ - 2.9636406898498535, - -33.272891998291016 + -4.815382480621338, + 32.00503158569336 ], "umap": [ - 5.31084680557251, - 9.479938507080078 + 5.4829487800598145, + 8.868935585021973 ], "pca3d": [ - -0.18380331993103027, - -0.008741571567952633, - -0.11725519597530365 + -0.18380211293697357, + -0.007970103994011879, + -0.11701533198356628 ], "tsne3d": [ - 1.7741296291351318, - -17.13743019104004, - -1.356516718864441 + -5.25131368637085, + 16.42799949645996, + -11.593914985656738 ], "umap3d": [ - 4.354227542877197, - 2.608462333679199, - 2.8306612968444824 + 5.093234539031982, + 2.849367141723633, + 3.2698044776916504 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -127212,31 +127342,31 @@ "abstract": "Although popular, current diffusion models have several drawbacks which limit their usefulness for biological discovery. Class-labeled datasets, such as those common in scientific domains, are rife with internal structure. Current class-conditional diffusion models, however, implicitly model dif- fusion on all classes in a flat fashion, ignoring any known relationships between classes. To leverage this structure, we propose hierarchically branched diffusion models as a novel framework for class- conditional generation. We highlight several ad- vantages of branched diffusion models for scien- tific discovery: branched models are easily ex- tended to novel classes in a continual-learning setting, they enable more sophisticated forms of conditional generation, and they offer a novel in- terpretability into the conditional-generation pro- cess. We extensively evaluate branched diffusion models on several benchmark and large real-world biological datasets, including a real-world single- cell RNA-seq dataset, where our branched model leverages the intrinsic hierarchical structure be- tween human cell types.", "projections": { "pca": [ - -0.0711967870593071, - -0.11457884311676025 + -0.07154108583927155, + -0.11404915899038315 ], "tsne": [ - -32.247989654541016, - 14.990277290344238 + -28.008060455322266, + -18.925731658935547 ], "umap": [ - 3.1346607208251953, - 5.080283164978027 + 3.4385411739349365, + 4.607269287109375 ], "pca3d": [ - -0.07119661569595337, - -0.11456453055143356, - -0.042525023221969604 + -0.071540966629982, + -0.1140347272157669, + -0.04227049648761749 ], "tsne3d": [ - -11.56994915008545, - 9.265714645385742, - 0.27481281757354736 + -11.104179382324219, + -16.818344116210938, + 1.7620283365249634 ], "umap3d": [ - 2.982661724090576, - 6.7384419441223145, - 4.637500286102295 + 3.3397915363311768, + 7.104707717895508, + 4.199516773223877 ] }, "cluster_id": 2, @@ -127247,12 +127377,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 16, - "label": "Diffusion Models" + "id": 7, + "label": "Diffusion Gen" } ], "_embedding": [ @@ -127669,31 +127799,31 @@ ], "projections": { "pca": [ - -0.23898690938949585, - -0.0665164589881897 + -0.2391452193260193, + -0.0656159296631813 ], "tsne": [ - -37.308509826660156, - 13.863929748535156 + -35.03083038330078, + -17.41031837463379 ], "umap": [ - 2.205566644668579, - 5.4757771492004395 + 2.591440439224243, + 4.8409552574157715 ], "pca3d": [ - -0.2389868050813675, - -0.06650097668170929, - -0.02979065477848053 + -0.239144966006279, + -0.0656006932258606, + -0.029565313830971718 ], "tsne3d": [ - -17.68134117126465, - 6.8033881187438965, - 0.9520264863967896 + -17.019731521606445, + -14.93576431274414, + 3.3151848316192627 ], "umap3d": [ - 2.094529151916504, - 6.511261463165283, - 4.53420877456665 + 2.5980770587921143, + 6.818335056304932, + 4.517376899719238 ] }, "cluster_id": 2, @@ -127704,11 +127834,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -128129,47 +128259,47 @@ "cited_by_count": 32, "projections": { "pca": [ - -0.007914316840469837, - -0.032599709928035736 + -0.007793796714395285, + -0.03326474875211716 ], "tsne": [ - 16.99456214904785, - -32.95171356201172 + 9.062004089355469, + 33.86452102661133 ], "umap": [ - 6.6052069664001465, - 9.70876693725586 + 6.6152191162109375, + 9.287374496459961 ], "pca3d": [ - -0.007914362475275993, - -0.032586246728897095, - -0.11063113063573837 + -0.007793836761265993, + -0.033250655978918076, + -0.11046357452869415 ], "tsne3d": [ - 10.779547691345215, - -19.28531837463379, - 3.8776302337646484 + 0.1919834017753601, + 9.1398344039917, + -20.875768661499023 ], "umap3d": [ - 5.05550479888916, - 2.9623069763183594, - 1.7823988199234009 + 5.252893447875977, + 2.827639102935791, + 2.1729018688201904 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 6, + "label": "Single Cell Data" }, { "id": 5, - "label": "Single Cell Data" + "label": "Clinical Cancer" } ], "_embedding": [ @@ -128592,46 +128722,46 @@ "cited_by_count": 593, "projections": { "pca": [ - -0.16487015783786774, - 0.12991578876972198 + -0.16448554396629333, + 0.13086150586605072 ], "tsne": [ - -5.060575485229492, - -28.946979522705078 + -9.186391830444336, + 24.977230072021484 ], "umap": [ - 4.734626293182373, - 9.186142921447754 + 4.910821914672852, + 8.452338218688965 ], "pca3d": [ - -0.1648702174425125, - 0.12990963459014893, - -0.008448755368590355 + -0.16448555886745453, + 0.13085557520389557, + -0.008438081480562687 ], "tsne3d": [ - -4.21425724029541, - -14.871149063110352, - -2.8183236122131348 + -7.717288017272949, + 17.83382797241211, + -3.0181851387023926 ], "umap3d": [ - 3.746417284011841, - 2.995433807373047, - 2.986542224884033 + 4.354589462280273, + 3.420044422149658, + 3.482621431350708 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, + "id": 26, "label": "Medical AI" } ], @@ -129048,47 +129178,47 @@ ], "projections": { "pca": [ - -0.1956097036600113, - -0.10307330638170242 + -0.19585663080215454, + -0.10186542570590973 ], "tsne": [ - -37.33447265625, - -18.217504501342773 + -39.794761657714844, + 14.514400482177734 ], "umap": [ - 1.4281115531921387, - 7.554625511169434 + 1.773971676826477, + 6.7259745597839355 ], "pca3d": [ - -0.19560952484607697, - -0.10307496041059494, - -0.0284725409001112 + -0.19585637748241425, + -0.10186732560396194, + -0.028076432645320892 ], "tsne3d": [ - -22.865530014038086, - -13.108850479125977, - 3.2553930282592773 + -22.65904998779297, + 4.796825408935547, + -4.625820159912109 ], "umap3d": [ - 1.0124328136444092, - 4.601642608642578, - 4.032675743103027 + 1.9514349699020386, + 4.831657409667969, + 4.7912373542785645 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -129501,31 +129631,31 @@ ], "projections": { "pca": [ - -0.24580153822898865, - 0.05039294809103012 + -0.24557900428771973, + 0.0512237474322319 ], "tsne": [ - -39.27128601074219, - 12.204240798950195 + -34.72312927246094, + -19.72281265258789 ], "umap": [ - 2.0913479328155518, - 5.230074405670166 + 2.630557060241699, + 4.622705936431885 ], "pca3d": [ - -0.2458014190196991, - 0.050379782915115356, - 0.006054995581507683 + -0.24557901918888092, + 0.05120953544974327, + 0.006109151057898998 ], "tsne3d": [ - -20.972822189331055, - 7.210257530212402, - 3.8820390701293945 + -20.201379776000977, + -15.853205680847168, + 6.048390865325928 ], "umap3d": [ - 2.0736637115478516, - 6.678103923797607, - 4.862316131591797 + 2.669215679168701, + 7.05123233795166, + 4.706960678100586 ] }, "cluster_id": 2, @@ -129536,11 +129666,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -129955,31 +130085,31 @@ ], "projections": { "pca": [ - -0.07913406938314438, - -0.08493672311306 + -0.07955966889858246, + -0.08442187309265137 ], "tsne": [ - -33.57495880126953, - -17.301023483276367 + -34.939605712890625, + 22.45616340637207 ], "umap": [ - 1.6997908353805542, - 6.020615577697754 + 2.1512105464935303, + 5.259537220001221 ], "pca3d": [ - -0.07913408428430557, - -0.08490562438964844, - -0.08683032542467117 + -0.07955970615148544, + -0.08439063280820847, + -0.08677428960800171 ], "tsne3d": [ - -21.9663143157959, - -12.252228736877441, - -11.128637313842773 + -16.625579833984375, + -1.012763261795044, + -10.213397979736328 ], "umap3d": [ - 1.6375336647033691, - 5.759422302246094, - 3.655524492263794 + 2.0843491554260254, + 5.917794704437256, + 3.9529612064361572 ] }, "cluster_id": 2, @@ -129990,12 +130120,12 @@ "label": "Generative Models" }, { - "id": 1, - "label": "Network Learning" + "id": 5, + "label": "Diffusion Models" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 19, + "label": "Graph Neural Nets" } ], "_embedding": [ @@ -130448,46 +130578,46 @@ "cited_by_count": 142, "projections": { "pca": [ - -0.08392512053251266, - 0.008307726122438908 + -0.08369781821966171, + 0.00786409992724657 ], "tsne": [ - 26.430721282958984, - -26.386404037475586 + 16.22675323486328, + 30.061664581298828 ], "umap": [ - 7.433033466339111, - 9.392871856689453 + 7.5496625900268555, + 9.052492141723633 ], "pca3d": [ - -0.08392531424760818, - 0.008313866332173347, - -0.2390390932559967 + -0.08369801938533783, + 0.007870429195463657, + -0.23882722854614258 ], "tsne3d": [ - 16.96504783630371, - -9.613243103027344, - 11.739246368408203 + 12.301732063293457, + 4.3965325355529785, + -19.270774841308594 ], "umap3d": [ - 5.2481536865234375, - 3.4526824951171875, - 1.5742453336715698 + 5.347418785095215, + 3.3522253036499023, + 1.638035774230957 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -130901,47 +131031,47 @@ "doi": "10.1561/2400000049", "projections": { "pca": [ - -0.2958355247974396, - -0.08057045936584473 + -0.2960464060306549, + -0.07950934767723083 ], "tsne": [ - -18.62810707092285, - -28.088420867919922 + -21.228404998779297, + 19.339929580688477 ], "umap": [ - 2.5918405055999756, - 7.903451442718506 + 2.8030173778533936, + 7.181850433349609 ], "pca3d": [ - -0.29583513736724854, - -0.08057793229818344, - 0.005498350597918034 + -0.2960459887981415, + -0.07951689511537552, + 0.00577118992805481 ], "tsne3d": [ - -12.324616432189941, - -10.114270210266113, - -8.9588623046875 + -13.909664154052734, + 17.809537887573242, + 2.949542999267578 ], "umap3d": [ - 2.017545223236084, - 4.392265319824219, - 3.877268075942993 + 2.929560899734497, + 4.652839660644531, + 4.392912864685059 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { "id": 0, - "label": "LLM Language Learning" + "label": "Math AI" } ], "_embedding": [ @@ -131374,47 +131504,47 @@ "cited_by_count": 1121, "projections": { "pca": [ - -0.059947166591882706, - -0.008505010046064854 + -0.05985456332564354, + -0.008147948421537876 ], "tsne": [ - 13.11237621307373, - -36.38328552246094 + 4.241238594055176, + 36.92914581298828 ], "umap": [ - 6.007546424865723, - 9.769198417663574 + 6.078471660614014, + 9.175182342529297 ], "pca3d": [ - -0.05994705855846405, - -0.0085096200928092, - -0.058013543486595154 + -0.059854503720998764, + -0.008152440190315247, + -0.05779661610722542 ], "tsne3d": [ - 7.361038684844971, - -20.388599395751953, - 4.847260475158691 + -1.9860379695892334, + 13.713787078857422, + -20.098939895629883 ], "umap3d": [ - 4.68601131439209, - 2.74029541015625, - 2.2094550132751465 + 5.023163318634033, + 2.8223273754119873, + 2.702878475189209 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -131829,31 +131959,31 @@ ], "projections": { "pca": [ - -0.12846200168132782, - -0.1370534598827362 + -0.1288231909275055, + -0.1366744488477707 ], "tsne": [ - -45.15024948120117, - -17.268291473388672 + -41.62259292602539, + -14.496512413024902 ], "umap": [ - 1.6274909973144531, - 6.122472286224365 + 1.8960647583007812, + 5.282478332519531 ], "pca3d": [ - -0.12846167385578156, - -0.1370563805103302, - -0.09528017044067383 + -0.12882280349731445, + -0.13667762279510498, + -0.09495259076356888 ], "tsne3d": [ - -23.794509887695312, - -11.331814765930176, - -8.922406196594238 + -19.293241500854492, + -1.932094931602478, + -9.667685508728027 ], "umap3d": [ - 1.2124576568603516, - 5.715891361236572, - 3.740680456161499 + 1.8842984437942505, + 5.816259384155273, + 4.228559970855713 ] }, "cluster_id": 2, @@ -131864,12 +131994,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -132286,31 +132416,31 @@ ], "projections": { "pca": [ - 0.018774624913930893, - -0.053161248564720154 + 0.018914012238383293, + -0.0531935915350914 ], "tsne": [ - -30.485774993896484, - 19.301658630371094 + -24.68206214904785, + -22.643878936767578 ], "umap": [ - 3.3496499061584473, - 4.781247615814209 + 3.7662312984466553, + 4.224715709686279 ], "pca3d": [ - 0.018774859607219696, - -0.05317192152142525, - 0.0980854406952858 + 0.01891428232192993, + -0.053204383701086044, + 0.09826617687940598 ], "tsne3d": [ - -11.050431251525879, - 14.184597969055176, - 0.11029992997646332 + -10.322429656982422, + -22.600297927856445, + 2.244730234146118 ], "umap3d": [ - 3.208329200744629, - 6.917501449584961, - 4.76678991317749 + 3.6217615604400635, + 7.361581325531006, + 4.194444179534912 ] }, "cluster_id": 2, @@ -132321,12 +132451,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 16, - "label": "Diffusion Models" + "id": 7, + "label": "Diffusion Gen" } ], "_embedding": [ @@ -132742,47 +132872,47 @@ "doi": "10.1145/3589335.3651526", "projections": { "pca": [ - -0.2382735162973404, - -0.05116062983870506 + -0.23835687339305878, + -0.05012912303209305 ], "tsne": [ - -62.12802505493164, - -12.02623176574707 + -61.09868240356445, + -0.5781974196434021 ], "umap": [ - 0.7648407220840454, - 6.772805213928223 + 0.8305004835128784, + 5.744824409484863 ], "pca3d": [ - -0.23827312886714935, - -0.051189955323934555, - -0.06783188879489899 + -0.23835642635822296, + -0.05015933886170387, + -0.067539282143116 ], "tsne3d": [ - -30.961994171142578, - 8.920409202575684, - -1.3107037544250488 + -33.3141975402832, + -2.7674620151519775, + -4.683948993682861 ], "umap3d": [ - 0.7475263476371765, - 6.050872325897217, - 4.696721076965332 + 1.4324793815612793, + 6.29433536529541, + 5.123679161071777 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 23, + "label": "Training Noise" } ], "_embedding": [ @@ -133202,31 +133332,31 @@ "cited_by_count": 345, "projections": { "pca": [ - -0.13239258527755737, - -0.027245374396443367 + -0.1324751228094101, + -0.026890823617577553 ], "tsne": [ - -53.315303802490234, - 3.0859270095825195 + -30.401418685913086, + 25.532058715820312 ], "umap": [ - 2.3226871490478516, - 6.540281295776367 + 2.4809646606445312, + 5.928502082824707 ], "pca3d": [ - -0.1323927789926529, - -0.027210256084799767, - -0.009786654263734818 + -0.1324753761291504, + -0.026854852214455605, + -0.009693850763142109 ], "tsne3d": [ - -16.58650016784668, - 5.364608287811279, - -11.80373764038086 + -10.642257690429688, + -11.889771461486816, + -7.240732192993164 ], "umap3d": [ - 2.0840866565704346, - 5.5636067390441895, - 3.890953779220581 + 2.571272850036621, + 5.937336444854736, + 4.081791400909424 ] }, "cluster_id": 2, @@ -133237,12 +133367,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 33, - "label": "Neural Networks" + "id": 19, + "label": "Graph Neural Nets" } ], "_embedding": [ @@ -133661,46 +133791,46 @@ ], "projections": { "pca": [ - -0.20477041602134705, - -0.12530279159545898 + -0.20511601865291595, + -0.12426912039518356 ], "tsne": [ - -31.117870330810547, - -12.152839660644531 + -33.98685836791992, + 9.970890045166016 ], "umap": [ - 1.6050043106079102, - 7.9787702560424805 + 1.957640290260315, + 7.22573184967041 ], "pca3d": [ - -0.2047700583934784, - -0.12530502676963806, - -0.04841133952140808 + -0.20511558651924133, + -0.12427134811878204, + -0.04803848639130592 ], "tsne3d": [ - -18.608787536621094, - -9.233477592468262, - 4.8659586906433105 + -18.578588485717773, + 2.2089555263519287, + 1.977721095085144 ], "umap3d": [ - 1.5727423429489136, - 4.586419582366943, - 4.605453014373779 + 2.4741415977478027, + 5.029698848724365, + 5.162614345550537 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -134120,46 +134250,46 @@ ], "projections": { "pca": [ - -0.17980729043483734, - -0.11552519351243973 + -0.17995917797088623, + -0.11456195265054703 ], "tsne": [ - -33.593421936035156, - -10.171483039855957 + -38.54439926147461, + 9.93617057800293 ], "umap": [ - 1.3266115188598633, - 8.148036003112793 + 1.57828950881958, + 7.326805114746094 ], "pca3d": [ - -0.17980703711509705, - -0.11552540957927704, - 0.004875517915934324 + -0.17995896935462952, + -0.1145620197057724, + 0.005330211017280817 ], "tsne3d": [ - -22.229686737060547, - -8.905025482177734, - 7.303823471069336 + -21.132078170776367, + 4.282135963439941, + 8.13340950012207 ], "umap3d": [ - 1.2365983724594116, - 4.464770317077637, - 4.891263008117676 + 2.3191378116607666, + 4.880338191986084, + 5.610654830932617 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -134573,31 +134703,31 @@ ], "projections": { "pca": [ - -0.2506754994392395, - -0.039544813334941864 + -0.250858336687088, + -0.03880925476551056 ], "tsne": [ - -49.52671813964844, - -13.70975112915039 + -51.146575927734375, + -10.176814079284668 ], "umap": [ - 0.7058368921279907, - 5.9714555740356445 + 1.0059868097305298, + 5.180871963500977 ], "pca3d": [ - -0.25067511200904846, - -0.03953737020492554, - -0.15635979175567627 + -0.2508581876754761, + -0.03880179673433304, + -0.15617582201957703 ], "tsne3d": [ - -28.749608993530273, - -6.378017902374268, - -5.645053386688232 + -25.237422943115234, + -6.607615947723389, + -6.184996604919434 ], "umap3d": [ - 0.5885838866233826, - 5.952401638031006, - 3.6866369247436523 + 1.196379542350769, + 5.9352898597717285, + 4.396411418914795 ] }, "cluster_id": 2, @@ -134608,12 +134738,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, - "label": "Training Noise" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -135029,46 +135159,46 @@ ], "projections": { "pca": [ - -0.22496315836906433, - -0.14696244895458221 + -0.22525621950626373, + -0.14619673788547516 ], "tsne": [ - -29.19388198852539, - -11.658726692199707 + -33.06474304199219, + 8.439422607421875 ], "umap": [ - 1.731306791305542, - 8.216548919677734 + 2.025836944580078, + 7.468930244445801 ], "pca3d": [ - -0.22496280074119568, - -0.14695286750793457, - -0.0018762963591143489 + -0.2252558171749115, + -0.1461864709854126, + -0.0014216331765055656 ], "tsne3d": [ - -16.461688995361328, - -10.096819877624512, - 7.363554000854492 + -17.655397415161133, + 5.417564868927002, + 1.9614267349243164 ], "umap3d": [ - 1.6680010557174683, - 4.347753524780273, - 4.672556400299072 + 2.6815245151519775, + 4.90868616104126, + 5.409611701965332 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 1, + "label": "Attention Models" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -135476,47 +135606,47 @@ "title": "What to know about the Sora discontinuation | OpenAI Help Center", "projections": { "pca": [ - -0.19656513631343842, - 0.33506065607070923 + -0.19556333124637604, + 0.33522364497184753 ], "tsne": [ - -10.947188377380371, - -6.862163543701172 + -9.627973556518555, + 5.6684956550598145 ], "umap": [ - 4.136210918426514, - 7.699351787567139 + 4.314669609069824, + 6.986568927764893 ], "pca3d": [ - -0.196565642952919, - 0.3350619077682495, - -0.014972767792642117 + -0.19556385278701782, + 0.33522507548332214, + -0.015444264747202396 ], "tsne3d": [ - -6.4176459312438965, - -3.41196870803833, - 8.418717384338379 + -6.3194580078125, + 3.3394572734832764, + -1.560645580291748 ], "umap3d": [ - 3.5657503604888916, - 3.7563729286193848, - 4.234401226043701 + 4.48539400100708, + 4.510747909545898, + 4.448511123657227 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -135923,31 +136053,31 @@ "title": "Sample efficient reinforcement learning with active learning for molecular design - Chemical Science (RSC Publishing)", "projections": { "pca": [ - -0.1162298321723938, - -0.0017525922739878297 + -0.1160837858915329, + -0.0012179595651105046 ], "tsne": [ - -19.084989547729492, - 16.953365325927734 + -16.286630630493164, + -24.88601303100586 ], "umap": [ - 4.652913570404053, - 4.821694850921631 + 5.069951057434082, + 4.223867893218994 ], "pca3d": [ - -0.11623010039329529, - -0.0017172697698697448, - 0.18297962844371796 + -0.11608396470546722, + -0.001182415522634983, + 0.18296845257282257 ], "tsne3d": [ - 3.2518088817596436, - 9.208938598632812, - 20.738195419311523 + 4.055037498474121, + -12.258147239685059, + 17.7349853515625 ], "umap3d": [ - 4.3957839012146, - 6.714483261108398, - 4.215524673461914 + 4.668067455291748, + 7.081433296203613, + 3.1515374183654785 ] }, "cluster_id": 3, @@ -135958,12 +136088,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { "id": 24, - "label": "Protein Design" + "label": "Protein Diffusion" } ], "_embedding": [ @@ -136371,47 +136501,47 @@ ], "projections": { "pca": [ - -0.289704293012619, - 0.11600799113512039 + -0.2892889976501465, + 0.11670257896184921 ], "tsne": [ - -40.77577590942383, - -11.490113258361816 + -30.63642120361328, + -0.980101466178894 ], "umap": [ - 2.1290833950042725, - 6.867558479309082 + 1.8275316953659058, + 5.851141929626465 ], "pca3d": [ - -0.28970441222190857, - 0.11603430658578873, - 0.04077821224927902 + -0.2892892360687256, + 0.1167290210723877, + 0.040723394602537155 ], "tsne3d": [ - -18.999744415283203, - -6.8749237060546875, - -11.084197998046875 + -13.258742332458496, + -2.3472535610198975, + -4.273927688598633 ], "umap3d": [ - 1.533901572227478, - 5.25040340423584, - 3.985213279724121 + 2.1883440017700195, + 5.413366794586182, + 4.545285701751709 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { "id": 8, - "label": "Attention Vision" + "label": "Network Learning" }, { - "id": 33, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -136818,47 +136948,47 @@ "title": "Paper page - Grandmaster-Level Chess Without Search", "projections": { "pca": [ - -0.2251877635717392, - 0.08962159603834152 + -0.22489547729492188, + 0.09062780439853668 ], "tsne": [ - -22.790163040161133, - -22.304290771484375 + -36.45166015625, + 3.2833094596862793 ], "umap": [ - 2.5418882369995117, - 7.714266300201416 + 2.4637534618377686, + 6.923055648803711 ], "pca3d": [ - -0.22518767416477203, - 0.0896054059267044, - -0.012975494377315044 + -0.22489529848098755, + 0.09061121940612793, + -0.012959884479641914 ], "tsne3d": [ - -16.667415618896484, - -7.137113094329834, - -3.807729482650757 + -18.451444625854492, + 13.74178695678711, + 2.4911816120147705 ], "umap3d": [ - 1.9874604940414429, - 4.401012420654297, - 3.8083393573760986 + 2.8605823516845703, + 4.805704116821289, + 4.377833843231201 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 1, + "label": "Attention Models" }, { - "id": 0, - "label": "LLM Language Learning" + "id": 34, + "label": "Language Models" } ], "_embedding": [ @@ -137274,31 +137404,31 @@ ], "projections": { "pca": [ - -0.23523765802383423, - -0.03559034690260887 + -0.2351566106081009, + -0.035127442330121994 ], "tsne": [ - -49.681636810302734, - -15.798211097717285 + -50.084815979003906, + -11.720769882202148 ], "umap": [ - 0.7930861711502075, - 5.946314334869385 + 0.992610514163971, + 5.156674385070801 ], "pca3d": [ - -0.23523741960525513, - -0.03558727353811264, - -0.1507357358932495 + -0.2351563572883606, + -0.03512407839298248, + -0.15042747557163239 ], "tsne3d": [ - -29.36356544494629, - -8.729757308959961, - -8.006379127502441 + -25.596237182617188, + -5.35211706161499, + -9.133641242980957 ], "umap3d": [ - 0.6749564409255981, - 5.961442470550537, - 3.7267119884490967 + 1.1402391195297241, + 5.914587497711182, + 4.469385147094727 ] }, "cluster_id": 2, @@ -137309,12 +137439,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, - "label": "Training Noise" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -137722,47 +137852,47 @@ ], "projections": { "pca": [ - -0.033141251653432846, - 0.17630814015865326 + -0.03253600001335144, + 0.1761825531721115 ], "tsne": [ - -4.734064102172852, - -6.465198993682861 + -6.871952533721924, + 2.7176408767700195 ], "umap": [ - 4.812559127807617, - 7.265038013458252 + 4.972348213195801, + 6.548737525939941 ], "pca3d": [ - -0.033141735941171646, - 0.17633208632469177, - 0.010691864416003227 + -0.03253651782870293, + 0.17620718479156494, + 0.010497350245714188 ], "tsne3d": [ - -8.507735252380371, - -0.7368444800376892, - 13.276970863342285 + -3.7956771850585938, + 0.7948090434074402, + -5.657078742980957 ], "umap3d": [ - 4.346765041351318, - 3.707751750946045, - 4.594874382019043 + 5.212899208068848, + 4.738263130187988, + 4.595769882202148 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, - "label": "Genomics Papers" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -138194,46 +138324,46 @@ ], "projections": { "pca": [ - -0.18053261935710907, - -0.12849436700344086 + -0.18084155023097992, + -0.12750709056854248 ], "tsne": [ - -29.219308853149414, - -10.596169471740723 + -33.86320877075195, + 8.251932144165039 ], "umap": [ - 1.75818932056427, - 8.132040977478027 + 1.9677990674972534, + 7.363628387451172 ], "pca3d": [ - -0.18053211271762848, - -0.1284952014684677, - 0.01765947788953781 + -0.18084123730659485, + -0.1275072693824768, + 0.01807602494955063 ], "tsne3d": [ - -17.365388870239258, - -8.720385551452637, - 7.717751502990723 + -17.87697982788086, + 4.8637471199035645, + 3.419039011001587 ], "umap3d": [ - 1.7035808563232422, - 4.46432638168335, - 4.748277187347412 + 2.781597852706909, + 4.960070610046387, + 5.368732929229736 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 1, + "label": "Attention Models" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -138652,47 +138782,47 @@ ], "projections": { "pca": [ - -0.25414344668388367, - -0.07759218662977219 + -0.2542452812194824, + -0.07589518278837204 ], "tsne": [ - -30.753719329833984, - -5.296254634857178 + -37.16229248046875, + 4.19356107711792 ], "umap": [ - 1.3482682704925537, - 7.78007698059082 + 1.7757166624069214, + 6.944307327270508 ], "pca3d": [ - -0.2541428804397583, - -0.07762673497200012, - 0.03379887714982033 + -0.25424468517303467, + -0.07593033462762833, + 0.034217797219753265 ], "tsne3d": [ - -18.473203659057617, - -6.56577730178833, - -4.561679363250732 + -19.697532653808594, + 12.475955963134766, + 3.236879348754883 ], "umap3d": [ - 1.2519512176513672, - 4.718916416168213, - 4.389969825744629 + 2.3441436290740967, + 5.007284164428711, + 5.040481090545654 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, - "label": "Transformer Attention" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -139102,31 +139232,31 @@ "abstract": "Various attribution methods have been developed to explain deep neural networks (DNNs) by inferring the attribution/importance/contribution score of each input variable to the final output. However, existing attribution methods are often built upon different heuristics. There remains a lack of a unified theoretical understanding of why these methods are effective and how they are related. Furthermore, there is still no universally accepted criterion to compare whether one attribution method is preferable over another. In this paper, we resort to Taylor interactions and for the first time, we discover that fourteen existing attribution methods, which define attributions based on fully different heuristics, actually share the same core mechanism. Specifically, we prove that attribution scores of input variables estimated by the fourteen attribution methods can all be mathematically reformulated as a weighted allocation of two typical types of effects, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i>, independent effects of each input variable and interaction effects between input variables. The essential difference among these attribution methods lies in the weights of allocating different effects. Inspired by these insights, we propose three principles for fairly allocating the effects, which serve as new criteria to evaluate the faithfulness of attribution methods. In summary, this study can be considered as a new unified perspective to revisit fourteen attribution methods, which theoretically clarifies essential similarities and differences among these methods. Besides, the proposed new principles enable people to make a direct and fair comparison among different methods under the unified perspective.", "projections": { "pca": [ - -0.25474318861961365, - -0.05239499732851982 + -0.25505921244621277, + -0.051647085696458817 ], "tsne": [ - -48.68027114868164, - -21.957683563232422 + -51.883026123046875, + 15.252503395080566 ], "umap": [ - 0.3388751447200775, - 5.762426853179932 + 0.7345033288002014, + 4.8311767578125 ], "pca3d": [ - -0.2547430992126465, - -0.052368469536304474, - -0.1387965828180313 + -0.2550591230392456, + -0.051620274782180786, + -0.1386798471212387 ], "tsne3d": [ - -30.61589241027832, - -13.5130033493042, - -2.8198955059051514 + -27.935375213623047, + -2.368391513824463, + -10.99426555633545 ], "umap3d": [ - 0.4028128981590271, - 5.418643951416016, - 3.4313931465148926 + 1.0410385131835938, + 5.362401962280273, + 4.136903285980225 ] }, "cluster_id": 2, @@ -139137,12 +139267,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, - "label": "Training Noise" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -139563,31 +139693,31 @@ "journal": "Transactions on Machine Learning Research", "projections": { "pca": [ - -0.2256108969449997, - 0.03267031908035278 + -0.2258484661579132, + 0.03342696651816368 ], "tsne": [ - -51.14266586303711, - 8.749468803405762 + -52.18413162231445, + -17.606460571289062 ], "umap": [ - 0.8928209543228149, - 5.433038234710693 + 1.3070123195648193, + 4.610260963439941 ], "pca3d": [ - -0.22561101615428925, - 0.03269147500395775, - -0.03872263804078102 + -0.22584858536720276, + 0.03344835713505745, + -0.038903459906578064 ], "tsne3d": [ - -28.563861846923828, - -2.4588940143585205, - -11.70839786529541 + -24.638513565063477, + -12.848403930664062, + -9.074847221374512 ], "umap3d": [ - 1.0509861707687378, - 6.528015613555908, - 3.5285069942474365 + 1.4403785467147827, + 6.456300735473633, + 4.039260387420654 ] }, "cluster_id": 2, @@ -139598,12 +139728,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -140018,47 +140148,47 @@ ], "projections": { "pca": [ - -0.22396747767925262, - -0.12225509434938431 + -0.22433774173259735, + -0.12158448249101639 ], "tsne": [ - -35.41790008544922, - -14.005345344543457 + -34.796207427978516, + 5.044834613800049 ], "umap": [ - 1.1881388425827026, - 6.645261764526367 + 1.394300103187561, + 5.507033824920654 ], "pca3d": [ - -0.22396712005138397, - -0.12224870920181274, - -0.03371262550354004 + -0.22433729469776154, + -0.1215776801109314, + -0.03341777250170708 ], "tsne3d": [ - -16.29930305480957, - -5.511363983154297, - 0.09028159826993942 + -24.41502571105957, + 2.321138620376587, + -1.3179185390472412 ], "umap3d": [ - 1.0826060771942139, - 5.899815082550049, - 4.255516529083252 + 1.8335421085357666, + 5.7969489097595215, + 4.965306282043457 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -140471,31 +140601,31 @@ ], "projections": { "pca": [ - -0.16107743978500366, - -0.08645953983068466 + -0.1613818258047104, + -0.08598256856203079 ], "tsne": [ - -44.97100830078125, - -13.030160903930664 + -47.95440673828125, + -11.876445770263672 ], "umap": [ - 1.215636968612671, - 6.3555192947387695 + 1.424517035484314, + 5.340066432952881 ], "pca3d": [ - -0.16107742488384247, - -0.08644550293684006, - -0.09679878503084183 + -0.16138173639774323, + -0.08596808463335037, + -0.09661640971899033 ], "tsne3d": [ - -26.258214950561523, - -8.975234031677246, - -11.32822036743164 + -22.757368087768555, + -5.116661071777344, + -13.056424140930176 ], "umap3d": [ - 0.9833325147628784, - 5.775582313537598, - 3.681450605392456 + 1.6213890314102173, + 5.708219051361084, + 4.231489658355713 ] }, "cluster_id": 2, @@ -140506,12 +140636,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -140930,47 +141060,47 @@ "cited_by_count": 252, "projections": { "pca": [ - -0.2068597674369812, - 0.07758131623268127 + -0.2066323459148407, + 0.07900030165910721 ], "tsne": [ - -20.83884620666504, - -24.402542114257812 + -20.89141845703125, + 13.961752891540527 ], "umap": [ - 2.883484125137329, - 7.751873970031738 + 3.0985870361328125, + 7.082074165344238 ], "pca3d": [ - -0.2068594992160797, - 0.07754373550415039, - 0.051587335765361786 + -0.20663194358348846, + 0.07896260172128677, + 0.051630306988954544 ], "tsne3d": [ - -13.878761291503906, - -5.700856685638428, - -6.4034295082092285 + -15.79762077331543, + 13.29279899597168, + 4.100066661834717 ], "umap3d": [ - 2.3087551593780518, - 4.3486127853393555, - 3.551621437072754 + 3.068636417388916, + 4.739649295806885, + 4.10078239440918 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { "id": 0, - "label": "LLM Language Learning" + "label": "Math AI" } ], "_embedding": [ @@ -141388,47 +141518,47 @@ ], "projections": { "pca": [ - -0.25731566548347473, - 0.059601400047540665 + -0.257271945476532, + 0.060901619493961334 ], "tsne": [ - -20.003034591674805, - -21.392961502075195 + -22.918842315673828, + 16.41553497314453 ], "umap": [ - 2.7520694732666016, - 8.039628982543945 + 3.175956964492798, + 7.358712673187256 ], "pca3d": [ - -0.2573154866695404, - 0.059599619358778, - 0.0062192887999117374 + -0.2572718560695648, + 0.060900140553712845, + 0.006242448464035988 ], "tsne3d": [ - -12.854354858398438, - -5.501039505004883, - -2.033590793609619 + -12.859376907348633, + 11.297398567199707, + 6.677369117736816 ], "umap3d": [ - 2.4139227867126465, - 4.267749786376953, - 3.726872682571411 + 3.1712801456451416, + 4.661081790924072, + 4.333429336547852 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { "id": 0, - "label": "LLM Language Learning" + "label": "Math AI" } ], "_embedding": [ @@ -141854,31 +141984,31 @@ "openalex_url": "https://openalex.org/W4390604658", "projections": { "pca": [ - -0.2580884099006653, - -0.012327468022704124 + -0.2581895887851715, + -0.011244053021073341 ], "tsne": [ - -47.16073989868164, - -21.771366119384766 + -50.94072341918945, + 16.413557052612305 ], "umap": [ - 0.3805096745491028, - 5.846388816833496 + 0.679007351398468, + 4.92243766784668 ], "pca3d": [ - -0.25808829069137573, - -0.012323930859565735, - -0.08789719641208649 + -0.25818943977355957, + -0.011240553110837936, + -0.08774936199188232 ], "tsne3d": [ - -27.992019653320312, - -13.005823135375977, - -3.4739162921905518 + -25.66139793395996, + -0.9036760926246643, + -9.985023498535156 ], "umap3d": [ - 0.48961302638053894, - 5.369842052459717, - 3.4512529373168945 + 1.1091725826263428, + 5.327671051025391, + 4.167946815490723 ] }, "cluster_id": 2, @@ -141889,12 +142019,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, - "label": "Training Noise" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -142319,47 +142449,47 @@ "doi": "10.1101/2024.01.06.24300926", "projections": { "pca": [ - 0.06378880143165588, - -0.0615653470158577 + 0.06360090523958206, + -0.061581555753946304 ], "tsne": [ - 14.086438179016113, - -32.0234375 + 6.102867603302002, + 33.01093673706055 ], "umap": [ - 6.260293483734131, - 9.71710205078125 + 6.343411445617676, + 9.11989688873291 ], "pca3d": [ - 0.0637889876961708, - -0.061564769595861435, - -0.1522207111120224 + 0.0636010617017746, + -0.06158101186156273, + -0.15207868814468384 ], "tsne3d": [ - 9.57868480682373, - -16.13007354736328, - 2.904515027999878 + 2.080299139022827, + 10.985613822937012, + -18.24408531188965 ], "umap3d": [ - 4.961546897888184, - 2.726724863052368, - 2.053004741668701 + 5.251511096954346, + 2.691995859146118, + 2.4876065254211426 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -142793,31 +142923,31 @@ "cited_by_count": 416, "projections": { "pca": [ - -0.0797247663140297, - -0.08274693787097931 + -0.07974836230278015, + -0.0828615352511406 ], "tsne": [ - -16.707569122314453, - 14.207182884216309 + -10.083683013916016, + -14.87221622467041 ], "umap": [ - 5.791758060455322, - 4.917854309082031 + 5.763936519622803, + 4.473907947540283 ], "pca3d": [ - -0.07972490787506104, - -0.08271130174398422, - 0.1500076800584793 + -0.07974845170974731, + -0.08282540738582611, + 0.15012896060943604 ], "tsne3d": [ - -0.2066792994737625, - -12.370878219604492, - -23.308889389038086 + 0.19824574887752533, + 21.225629806518555, + 11.529342651367188 ], "umap3d": [ - 5.027242660522461, - 6.493457317352295, - 4.162721633911133 + 5.350385665893555, + 6.911197662353516, + 2.851886510848999 ] }, "cluster_id": 3, @@ -142828,11 +142958,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -143251,47 +143381,47 @@ "cited_by_count": 1, "projections": { "pca": [ - -0.2792205214500427, - 0.09458400309085846 + -0.2787603735923767, + 0.095531165599823 ], "tsne": [ - -43.93903732299805, - -24.285844802856445 + -47.434410095214844, + 19.761005401611328 ], "umap": [ - 1.7957969903945923, - 7.067250728607178 + 2.223224639892578, + 6.302338600158691 ], "pca3d": [ - -0.2792202830314636, - 0.09455959498882294, - -0.051444586366415024 + -0.27876025438308716, + 0.09550608694553375, + -0.05130118131637573 ], "tsne3d": [ - -25.614900588989258, - -17.40030288696289, - -5.032129287719727 + -19.848474502563477, + 1.495896339416504, + -11.015789031982422 ], "umap3d": [ - 1.0168288946151733, - 4.808380126953125, - 3.4653725624084473 + 1.6366182565689087, + 4.897149085998535, + 4.238362789154053 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -143711,47 +143841,47 @@ ], "projections": { "pca": [ - -0.24837851524353027, - 0.1592831015586853 + -0.24796153604984283, + 0.16014057397842407 ], "tsne": [ - -14.664688110351562, - -16.470531463623047 + -9.29093074798584, + 7.807655334472656 ], "umap": [ - 4.104250907897949, - 7.781286239624023 + 4.640042781829834, + 6.9703521728515625 ], "pca3d": [ - -0.24837864935398102, - 0.15929384529590607, - 0.06366366147994995 + -0.2479618489742279, + 0.16015145182609558, + 0.06349804997444153 ], "tsne3d": [ - -8.24979019165039, - -5.811587810516357, - 8.204102516174316 + -9.679869651794434, + -0.15293382108211517, + -2.3842647075653076 ], "umap3d": [ - 3.67633318901062, - 3.8480472564697266, - 4.056962013244629 + 4.6236162185668945, + 4.506962299346924, + 4.272043704986572 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -144170,47 +144300,47 @@ "doi": "10.1101/2023.12.12.571389", "projections": { "pca": [ - 0.1476374864578247, - -0.07501600682735443 + 0.14731159806251526, + -0.07517478615045547 ], "tsne": [ - 29.63235855102539, - -17.06141471862793 + 22.855947494506836, + 21.869020462036133 ], "umap": [ - 8.51849365234375, - 9.383410453796387 + 8.631272315979004, + 8.887797355651855 ], "pca3d": [ - 0.14763732254505157, - -0.07498612999916077, - -0.07569799572229385 + 0.14731141924858093, + -0.07514443248510361, + -0.07570498436689377 ], "tsne3d": [ - 16.512855529785156, - 11.833202362060547, - 7.283972263336182 + 11.510313034057617, + -4.6674675941467285, + -15.715462684631348 ], "umap3d": [ - 5.493457794189453, - 4.564890384674072, - 1.098605751991272 + 5.090109348297119, + 4.202688217163086, + 0.8968537449836731 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -144627,31 +144757,31 @@ ], "projections": { "pca": [ - -0.22539456188678741, - -0.08506152033805847 + -0.22569504380226135, + -0.08406463265419006 ], "tsne": [ - -44.008907318115234, - 8.356194496154785 + -43.47249984741211, + -15.86658763885498 ], "umap": [ - 1.4952754974365234, - 5.972498893737793 + 1.6418582201004028, + 5.114388942718506 ], "pca3d": [ - -0.2253940999507904, - -0.08506517112255096, - -0.09487572312355042 + -0.22569474577903748, + -0.0840686783194542, + -0.09463226050138474 ], "tsne3d": [ - -26.01972198486328, - 7.19175910949707, - -2.1707897186279297 + -19.812725067138672, + -8.835063934326172, + -8.966724395751953 ], "umap3d": [ - 1.0560792684555054, - 6.2925920486450195, - 4.368862628936768 + 1.6912163496017456, + 6.450779438018799, + 4.658730506896973 ] }, "cluster_id": 2, @@ -144662,12 +144792,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -145082,31 +145212,31 @@ ], "projections": { "pca": [ - -0.25019174814224243, - -0.0977993905544281 + -0.25051170587539673, + -0.0969620943069458 ], "tsne": [ - -47.73593521118164, - -18.22084617614746 + -52.470821380615234, + -3.5174875259399414 ], "umap": [ - 0.7427978515625, - 6.226616859436035 + 0.9233383536338806, + 5.380801200866699 ], "pca3d": [ - -0.2501913011074066, - -0.09782209992408752, - -0.10063058882951736 + -0.2505110800266266, + -0.09698465466499329, + -0.10037777572870255 ], "tsne3d": [ - -30.770776748657227, - -7.853326320648193, - -0.4251386523246765 + -29.012462615966797, + -3.2003588676452637, + -5.092700481414795 ], "umap3d": [ - 0.567389965057373, - 5.751189708709717, - 3.866755485534668 + 1.1860235929489136, + 5.718156814575195, + 4.565667629241943 ] }, "cluster_id": 2, @@ -145117,11 +145247,11 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, + "id": 23, "label": "Training Noise" } ], @@ -145538,31 +145668,31 @@ ], "projections": { "pca": [ - -0.21648572385311127, - -0.029378585517406464 + -0.21682670712471008, + -0.02846582792699337 ], "tsne": [ - -50.719635009765625, - -18.399242401123047 + -53.37305450439453, + -13.793658256530762 ], "umap": [ - 0.6354864835739136, - 5.600642681121826 + 0.9447643160820007, + 4.814857482910156 ], "pca3d": [ - -0.21648548543453217, - -0.029355820268392563, - -0.1346643716096878 + -0.21682661771774292, + -0.028442583978176117, + -0.1345938742160797 ], "tsne3d": [ - -30.374853134155273, - -4.298337936401367, - -8.706649780273438 + -27.194698333740234, + -9.818331718444824, + -8.440939903259277 ], "umap3d": [ - 0.6812789440155029, - 6.056777000427246, - 3.4764645099639893 + 1.1411468982696533, + 5.795156955718994, + 4.101436138153076 ] }, "cluster_id": 2, @@ -145573,12 +145703,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, - "label": "Training Noise" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -145993,46 +146123,46 @@ ], "projections": { "pca": [ - -0.30165335536003113, - -0.11652469635009766 + -0.3019794821739197, + -0.11568880826234818 ], "tsne": [ - -54.006893157958984, - -12.938319206237793 + -55.593135833740234, + -6.1481428146362305 ], "umap": [ - 0.018144847825169563, - 6.36896276473999 + 0.2874011695384979, + 5.428597450256348 ], "pca3d": [ - -0.30165308713912964, - -0.11650402098894119, - -0.0956539660692215 + -0.3019791543483734, + -0.11566749215126038, + -0.09532222896814346 ], "tsne3d": [ - -28.934925079345703, - 2.2792859077453613, - -1.6930168867111206 + -29.119117736816406, + -7.011099815368652, + 1.305816411972046 ], "umap3d": [ - 0.23268963396549225, - 6.278799057006836, - 4.120236396789551 + 0.8450407385826111, + 6.357490539550781, + 4.839011192321777 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, + "id": 23, "label": "Training Noise" } ], @@ -146455,46 +146585,46 @@ ], "projections": { "pca": [ - -0.22156324982643127, - -0.12856081128120422 + -0.22173786163330078, + -0.12794129550457 ], "tsne": [ - -59.404541015625, - -3.9716079235076904 + -53.97124481201172, + 6.075773239135742 ], "umap": [ - 0.3345934748649597, - 7.647317886352539 + 0.6660417318344116, + 6.706798553466797 ], "pca3d": [ - -0.22156251966953278, - -0.12859858572483063, - -0.114774689078331 + -0.22173714637756348, + -0.12797923386096954, + -0.11431410163640976 ], "tsne3d": [ - -29.568660736083984, - 3.3919506072998047, - 9.43543529510498 + -33.81072998046875, + -1.3791618347167969, + 5.785053253173828 ], "umap3d": [ - 0.5353158116340637, - 5.519674301147461, - 5.1220173835754395 + 1.5982757806777954, + 5.774213790893555, + 5.957488536834717 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -146907,47 +147037,47 @@ ], "projections": { "pca": [ - -0.25076279044151306, - -0.12276960164308548 + -0.2511833608150482, + -0.12181222438812256 ], "tsne": [ - -41.186767578125, - -20.267805099487305 + -45.80076599121094, + 14.879364013671875 ], "umap": [ - 1.3313392400741577, - 6.9455060958862305 + 1.6743888854980469, + 6.153398513793945 ], "pca3d": [ - -0.25076231360435486, - -0.12277620285749435, - -0.060843612998723984 + -0.25118288397789, + -0.12181895971298218, + -0.06056121364235878 ], "tsne3d": [ - -26.327150344848633, - -12.949345588684082, - 0.9312084913253784 + -25.73190689086914, + 3.40360426902771, + -7.22272253036499 ], "umap3d": [ - 0.813034176826477, - 5.079051494598389, - 3.8264806270599365 + 1.4916315078735352, + 5.222659587860107, + 4.489272594451904 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -147365,47 +147495,47 @@ ], "projections": { "pca": [ - -0.26929205656051636, - -0.13022908568382263 + -0.26977837085723877, + -0.12941880524158478 ], "tsne": [ - -41.247440338134766, - -2.714146137237549 + -40.322208404541016, + -5.096723556518555 ], "umap": [ - 1.356837272644043, - 6.596485137939453 + 1.5732191801071167, + 5.828491687774658 ], "pca3d": [ - -0.26929184794425964, - -0.13021191954612732, - -0.03323886916041374 + -0.2697781026363373, + -0.1294010877609253, + -0.033014193177223206 ], "tsne3d": [ - -17.915369033813477, - -2.818770408630371, - -1.4308384656906128 + -17.438644409179688, + -6.537482261657715, + 3.3781912326812744 ], "umap3d": [ - 1.3913072347640991, - 5.773705959320068, - 4.251059055328369 + 2.007633924484253, + 5.881118297576904, + 4.873691082000732 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -147822,46 +147952,46 @@ ], "projections": { "pca": [ - -0.26883527636528015, - -0.19511055946350098 + -0.2694959342479706, + -0.19481095671653748 ], "tsne": [ - -47.39548873901367, - -2.9411137104034424 + -45.17303466796875, + -2.6692206859588623 ], "umap": [ - 0.7909707427024841, - 6.919397354125977 + 1.1261578798294067, + 6.172786712646484 ], "pca3d": [ - -0.2688349187374115, - -0.1950974017381668, - -0.13555769622325897 + -0.26949542760849, + -0.1947970986366272, + -0.1352814882993698 ], "tsne3d": [ - -22.931642532348633, - -2.218750238418579, - 2.6604959964752197 + -24.138980865478516, + -4.775622367858887, + 6.415414810180664 ], "umap3d": [ - 0.9914544820785522, - 5.519846439361572, - 4.475607395172119 + 1.7891912460327148, + 5.8422417640686035, + 5.198078155517578 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { "id": 8, - "label": "Attention Vision" + "label": "Network Learning" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -148275,46 +148405,46 @@ ], "projections": { "pca": [ - -0.25986242294311523, - -0.1692284643650055 + -0.260324627161026, + -0.1689072549343109 ], "tsne": [ - -47.480506896972656, - -2.953958511352539 + -45.21962356567383, + -2.631178617477417 ], "umap": [ - 0.7403415441513062, - 7.028708457946777 + 1.0111209154129028, + 6.264856338500977 ], "pca3d": [ - -0.2598619759082794, - -0.16923363506793976, - -0.10440009832382202 + -0.26032397150993347, + -0.1689121127128601, + -0.10408591479063034 ], "tsne3d": [ - -22.96700096130371, - -1.548033595085144, - 2.891430139541626 + -23.874011993408203, + -4.486055850982666, + 5.943603992462158 ], "umap3d": [ - 0.8816285729408264, - 5.4951677322387695, - 4.60206937789917 + 1.776267170906067, + 5.831925392150879, + 5.307934284210205 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -148731,31 +148861,31 @@ "cited_by_count": 59, "projections": { "pca": [ - -0.05245596170425415, - -0.08503900468349457 + -0.052845314145088196, + -0.08539092540740967 ], "tsne": [ - -17.281883239746094, - 20.006322860717773 + -13.297122955322266, + -26.373706817626953 ], "umap": [ - 4.89246129989624, - 4.515097141265869 + 5.3975090980529785, + 3.94101619720459 ], "pca3d": [ - -0.05245604366064072, - -0.08499626070261002, - -0.0020863707177340984 + -0.05284542962908745, + -0.08534716814756393, + -0.0021909417118877172 ], "tsne3d": [ - 0.15881678462028503, - 10.114328384399414, - 18.061695098876953 + 1.1576509475708008, + -16.196706771850586, + 17.189678192138672 ], "umap3d": [ - 4.71569299697876, - 6.924959659576416, - 4.366942405700684 + 4.972982883453369, + 7.323665618896484, + 3.029304265975952 ] }, "cluster_id": 3, @@ -148766,12 +148896,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -149185,47 +149315,47 @@ ], "projections": { "pca": [ - -0.13485975563526154, - 0.03620128706097603 + -0.1348477005958557, + 0.03696012496948242 ], "tsne": [ - -35.84867858886719, - -31.498435974121094 + -40.03866958618164, + 27.454490661621094 ], "umap": [ - 8.241751670837402, - 10.3395357131958 + 8.762236595153809, + 9.635272979736328 ], "pca3d": [ - -0.13485966622829437, - 0.036205872893333435, - -0.04322073981165886 + -0.13484764099121094, + 0.03696473687887192, + -0.04325297474861145 ], "tsne3d": [ - -15.761910438537598, - 0.2689005434513092, - -18.172439575195312 + -15.651152610778809, + -6.07130765914917, + -13.353019714355469 ], "umap3d": [ - 4.373099327087402, - 4.942255020141602, - 1.2343113422393799 + 3.9999947547912598, + 4.4597883224487305, + 1.2006858587265015 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -149638,31 +149768,31 @@ ], "projections": { "pca": [ - -0.3210911154747009, - -0.07960096001625061 + -0.3213995695114136, + -0.07896657288074493 ], "tsne": [ - -40.57218551635742, - -0.30816036462783813 + -37.10434341430664, + -6.620567798614502 ], "umap": [ - 1.851251482963562, - 6.433657646179199 + 1.9950426816940308, + 5.612015724182129 ], "pca3d": [ - -0.32109084725379944, - -0.07958270609378815, - -0.0006856596446596086 + -0.3213992416858673, + -0.07894766330718994, + -0.0005714474827982485 ], "tsne3d": [ - -16.67600440979004, - -2.5112597942352295, - -5.989325523376465 + -15.596129417419434, + -4.618561267852783, + -3.770655632019043 ], "umap3d": [ - 1.6970380544662476, - 5.6743693351745605, - 4.161386966705322 + 2.3307509422302246, + 5.943799018859863, + 4.556699752807617 ] }, "cluster_id": 2, @@ -149673,12 +149803,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 33, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -150093,46 +150223,46 @@ ], "projections": { "pca": [ - -0.22605031728744507, - 0.0066853733733296394 + -0.22600264847278595, + 0.0077117132022976875 ], "tsne": [ - -26.769426345825195, - -16.638248443603516 + -30.990110397338867, + 14.115763664245605 ], "umap": [ - 2.1990339756011963, - 8.400898933410645 + 2.3895998001098633, + 7.698591232299805 ], "pca3d": [ - -0.22605018317699432, - 0.006694921758025885, - -0.011520147323608398 + -0.2260025143623352, + 0.007721229922026396, + -0.011280816979706287 ], "tsne3d": [ - -15.0165376663208, - -14.320075988769531, - 3.525449752807617 + -14.40068531036377, + 8.986041069030762, + -3.0169806480407715 ], "umap3d": [ - 1.8816007375717163, - 3.87872576713562, - 4.450943470001221 + 2.8456647396087646, + 4.41483211517334, + 5.154261589050293 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -150559,31 +150689,31 @@ "openalex_url": "https://openalex.org/W4388520288", "projections": { "pca": [ - -0.0599583275616169, - 0.013692224398255348 + -0.060046058148145676, + 0.014181246981024742 ], "tsne": [ - -9.762425422668457, - 18.595664978027344 + 3.7177541255950928, + -7.763075351715088 ], "umap": [ - 5.169826984405518, - 4.951006889343262 + 5.5926971435546875, + 4.350062370300293 ], "pca3d": [ - -0.05995851382613182, - 0.013724792748689651, - 0.02127169631421566 + -0.060046251863241196, + 0.014214606955647469, + 0.021187543869018555 ], "tsne3d": [ - 4.8673014640808105, - 7.772869110107422, - -19.401643753051758 + 2.107943534851074, + 12.310680389404297, + 17.47096824645996 ], "umap3d": [ - 5.250558853149414, - 6.720410346984863, - 4.113498210906982 + 5.363396167755127, + 7.158028602600098, + 2.4582679271698 ] }, "cluster_id": 3, @@ -150594,11 +150724,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 9, + "label": "Protein Design" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -151029,31 +151159,31 @@ "cited_by_count": 4, "projections": { "pca": [ - -0.08651261776685715, - -0.055554501712322235 + -0.08674319833517075, + -0.05509562790393829 ], "tsne": [ - -18.564512252807617, - 17.975614547729492 + -15.364510536193848, + -25.59622573852539 ], "umap": [ - 4.727631092071533, - 4.688239574432373 + 5.181139945983887, + 4.007908821105957 ], "pca3d": [ - -0.08651271462440491, - -0.055507879704236984, - 0.06229095906019211 + -0.08674333244562149, + -0.05504857748746872, + 0.062274184077978134 ], "tsne3d": [ - 3.309689998626709, - 10.374143600463867, - 18.744089126586914 + 2.459311008453369, + -13.348455429077148, + 16.610279083251953 ], "umap3d": [ - 4.4978203773498535, - 6.827921390533447, - 4.2419915199279785 + 4.6909894943237305, + 7.223646640777588, + 3.109395742416382 ] }, "cluster_id": 3, @@ -151064,12 +151194,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -151483,46 +151613,46 @@ ], "projections": { "pca": [ - -0.2874418795108795, - -0.15876558423042297 + -0.2878267765045166, + -0.15812529623508453 ], "tsne": [ - -53.48405456542969, - -3.3678133487701416 + -49.62162780761719, + 2.408360719680786 ], "umap": [ - 0.2592240571975708, - 7.248568058013916 + 0.5052746534347534, + 6.522479057312012 ], "pca3d": [ - -0.28744128346443176, - -0.15879887342453003, - -0.13046707212924957 + -0.28782615065574646, + -0.1581585705280304, + -0.13002701103687286 ], "tsne3d": [ - -27.065723419189453, - -0.10878664255142212, - 6.360219955444336 + -29.04665184020996, + -1.0264041423797607, + 4.064397811889648 ], "umap3d": [ - 0.35430556535720825, - 5.453981399536133, - 4.852658748626709 + 1.3390889167785645, + 5.755832672119141, + 5.685217380523682 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -151935,46 +152065,46 @@ ], "projections": { "pca": [ - -0.22654861211776733, - -0.030622728168964386 + -0.22636722028255463, + -0.029916169121861458 ], "tsne": [ - -26.76410484313965, - -14.58922290802002 + -30.31453514099121, + 12.585107803344727 ], "umap": [ - 2.090721368789673, - 8.411767959594727 + 2.260423421859741, + 7.587441444396973 ], "pca3d": [ - -0.2265484482049942, - -0.03062879480421543, - 0.027105461806058884 + -0.22636686265468597, + -0.029922401532530785, + 0.027479493990540504 ], "tsne3d": [ - -13.782709121704102, - -12.407133102416992, - 8.648299217224121 + -15.128730773925781, + 4.074646949768066, + -1.1583133935928345 ], "umap3d": [ - 1.8521369695663452, - 4.099541664123535, - 4.570003986358643 + 2.9235875606536865, + 4.67156457901001, + 5.263769149780273 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 1, + "label": "Attention Models" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -152396,31 +152526,31 @@ "cited_by_count": 108, "projections": { "pca": [ - -0.1743375062942505, - -0.1241350769996643 + -0.17477358877658844, + -0.12312889844179153 ], "tsne": [ - -29.57000160217285, - 2.677198886871338 + -34.306251525878906, + -7.225833892822266 ], "umap": [ - 2.2455122470855713, - 6.205491065979004 + 2.4357450008392334, + 5.742491245269775 ], "pca3d": [ - -0.17433740198612213, - -0.12410503625869751, - 0.07233138382434845 + -0.1747734546661377, + -0.12309829890727997, + 0.07252465933561325 ], "tsne3d": [ - -13.695066452026367, - -0.10666296631097794, - -1.1519173383712769 + -14.094942092895508, + -5.166486740112305, + 1.9200470447540283 ], "umap3d": [ - 2.1209237575531006, - 5.558847427368164, - 4.311991214752197 + 2.817781925201416, + 5.928921699523926, + 4.592723369598389 ] }, "cluster_id": 2, @@ -152431,12 +152561,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 33, - "label": "Neural Networks" + "id": 19, + "label": "Graph Neural Nets" } ], "_embedding": [ @@ -152850,46 +152980,46 @@ ], "projections": { "pca": [ - -0.27396270632743835, - -0.13047899305820465 + -0.27407488226890564, + -0.12970493733882904 ], "tsne": [ - -56.19585037231445, - 0.47442546486854553 + -46.09463882446289, + 6.529025554656982 ], "umap": [ - 0.4390331506729126, - 7.9409942626953125 + 0.7085518836975098, + 7.089365005493164 ], "pca3d": [ - -0.2739621102809906, - -0.13053174316883087, - -0.05234137922525406 + -0.2740741968154907, + -0.1297583431005478, + -0.05184030532836914 ], "tsne3d": [ - -26.420686721801758, - -4.607098579406738, - 11.6751127243042 + -27.860990524291992, + 5.350265026092529, + 5.53401517868042 ], "umap3d": [ - 0.5835514664649963, - 4.959512233734131, - 5.147696495056152 + 1.7073962688446045, + 5.315236568450928, + 5.913719654083252 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -153311,31 +153441,31 @@ "cited_by_count": 701, "projections": { "pca": [ - -0.24683257937431335, - -0.06685642898082733 + -0.247202068567276, + -0.06632006168365479 ], "tsne": [ - -39.268489837646484, - 1.2171074151992798 + -38.108272552490234, + -4.231174468994141 ], "umap": [ - 1.848114252090454, - 6.537557125091553 + 2.069230079650879, + 5.7992472648620605 ], "pca3d": [ - -0.24683240056037903, - -0.06683933734893799, - -0.020469652488827705 + -0.24720187485218048, + -0.0663023442029953, + -0.020486092194914818 ], "tsne3d": [ - -17.81252098083496, - 0.22837470471858978, - 1.9388442039489746 + -19.468379974365234, + -6.937760829925537, + 7.450013637542725 ], "umap3d": [ - 1.6604955196380615, - 5.678471565246582, - 4.393226623535156 + 2.4944052696228027, + 5.992836952209473, + 4.853625297546387 ] }, "cluster_id": 2, @@ -153346,12 +153476,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 33, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -153767,31 +153897,31 @@ ], "projections": { "pca": [ - -0.1824621558189392, - -0.1459173709154129 + -0.183025524020195, + -0.14572952687740326 ], "tsne": [ - -53.283390045166016, - 7.252408504486084 + -54.046607971191406, + -16.418771743774414 ], "umap": [ - 1.0213507413864136, - 5.514747142791748 + 1.397995114326477, + 4.704135417938232 ], "pca3d": [ - -0.18246176838874817, - -0.14589951932430267, - -0.06398153305053711 + -0.18302516639232635, + -0.1457108110189438, + -0.0638616532087326 ], "tsne3d": [ - -30.06580352783203, - 0.7979512214660645, - -9.183720588684082 + -27.399072647094727, + -14.051193237304688, + -5.685685634613037 ], "umap3d": [ - 1.1663134098052979, - 6.426692008972168, - 3.551042079925537 + 1.560551643371582, + 6.387035846710205, + 3.9620087146759033 ] }, "cluster_id": 2, @@ -153802,12 +153932,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -154227,31 +154357,31 @@ ], "projections": { "pca": [ - -0.24713116884231567, - -0.07742998003959656 + -0.24736423790454865, + -0.07715298235416412 ], "tsne": [ - -49.80696105957031, - -15.209863662719727 + -50.669822692871094, + -11.453439712524414 ], "umap": [ - 0.7403367161750793, - 5.8957648277282715 + 1.048399567604065, + 5.086249351501465 ], "pca3d": [ - -0.24713104963302612, - -0.07740237563848495, - -0.1110079362988472 + -0.24736417829990387, + -0.0771244466304779, + -0.11085245013237 ], "tsne3d": [ - -29.209461212158203, - -8.414928436279297, - -6.666833877563477 + -24.82463836669922, + -6.043307781219482, + -8.317525863647461 ], "umap3d": [ - 0.7152189612388611, - 6.020299434661865, - 3.6734097003936768 + 1.1950135231018066, + 5.920469760894775, + 4.34499979019165 ] }, "cluster_id": 2, @@ -154262,12 +154392,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, - "label": "Training Noise" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -154685,47 +154815,47 @@ ], "projections": { "pca": [ - -0.1679985523223877, - -0.09791464358568192 + -0.16828499734401703, + -0.09749642759561539 ], "tsne": [ - -42.65635681152344, - -2.7739789485931396 + -40.21258544921875, + -3.5789597034454346 ], "umap": [ - 1.4427680969238281, - 6.726024627685547 + 1.764166235923767, + 6.0179924964904785 ], "pca3d": [ - -0.16799825429916382, - -0.09791234135627747, - 0.013285073451697826 + -0.16828468441963196, + -0.09749343246221542, + 0.013412274420261383 ], "tsne3d": [ - -19.637483596801758, - -2.302696466445923, - 0.5397772192955017 + -19.654481887817383, + -5.227834701538086, + 4.636973857879639 ], "umap3d": [ - 1.455245852470398, - 5.567030906677246, - 4.430411338806152 + 2.1960034370422363, + 5.87336540222168, + 4.955287456512451 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -155138,31 +155268,31 @@ ], "projections": { "pca": [ - -0.25553205609321594, - -0.07696868479251862 + -0.2557910680770874, + -0.07661498337984085 ], "tsne": [ - -44.129486083984375, - -15.622844696044922 + -54.50064468383789, + -12.088207244873047 ], "umap": [ - 0.9949578046798706, - 5.848433017730713 + 1.4279788732528687, + 5.188124179840088 ], "pca3d": [ - -0.25553205609321594, - -0.07694423943758011, - -0.03191845118999481 + -0.2557908594608307, + -0.07659012079238892, + -0.03181631490588188 ], "tsne3d": [ - -34.34886932373047, - -4.129733085632324, - 2.394576072692871 + -30.476655960083008, + 3.463552474975586, + -3.6647117137908936 ], "umap3d": [ - 0.9796110391616821, - 5.826793670654297, - 3.6507773399353027 + 1.4980541467666626, + 5.918199062347412, + 4.194475173950195 ] }, "cluster_id": 2, @@ -155173,12 +155303,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -155608,47 +155738,47 @@ ], "projections": { "pca": [ - -0.06613630801439285, - -0.009550686925649643 + -0.06604832410812378, + -0.009510461241006851 ], "tsne": [ - 13.329767227172852, - -33.751861572265625 + 5.337296009063721, + 34.59970474243164 ], "umap": [ - 6.166279315948486, - 9.782563209533691 + 6.257389545440674, + 9.15192985534668 ], "pca3d": [ - -0.06613600999116898, - -0.009577411226928234, - -0.20583541691303253 + -0.0660480186343193, + -0.009537755511701107, + -0.20560979843139648 ], "tsne3d": [ - 8.230788230895996, - -17.432937622070312, - 3.36786150932312 + 0.4003903865814209, + 11.67332649230957, + -18.992328643798828 ], "umap3d": [ - 4.866197109222412, - 2.6874139308929443, - 2.0549957752227783 + 5.174459457397461, + 2.6707773208618164, + 2.5987768173217773 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -156067,31 +156197,31 @@ "journal": "Journal of Machine Learning Research", "projections": { "pca": [ - -0.22155967354774475, - -0.0090738944709301 + -0.2214525192975998, + -0.008941588923335075 ], "tsne": [ - -43.31816101074219, - 20.01457405090332 + -38.5733757019043, + -26.820613861083984 ], "umap": [ - 1.3325761556625366, - 6.325786590576172 + 1.9804654121398926, + 5.621512413024902 ], "pca3d": [ - -0.22155974805355072, - -0.009056167677044868, - -0.0025110957212746143 + -0.22145256400108337, + -0.008922694250941277, + -0.00239941687323153 ], "tsne3d": [ - -17.166669845581055, - 11.976329803466797, - -5.5921549797058105 + -18.491714477539062, + -24.922548294067383, + -0.0779610350728035 ], "umap3d": [ - 1.5504891872406006, - 6.031502723693848, - 4.3466691970825195 + 2.224597930908203, + 6.209010601043701, + 4.739410400390625 ] }, "cluster_id": 2, @@ -156102,12 +156232,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -156525,31 +156655,31 @@ ], "projections": { "pca": [ - -0.1903243511915207, - -0.1273261308670044 + -0.19061340391635895, + -0.12693409621715546 ], "tsne": [ - -39.056095123291016, - 17.727636337280273 + -34.917999267578125, + -23.629884719848633 ], "umap": [ - 2.286916971206665, - 4.829421043395996 + 2.75447940826416, + 4.220057487487793 ], "pca3d": [ - -0.19032403826713562, - -0.12732461094856262, - -0.015377771109342575 + -0.19061298668384552, + -0.12693241238594055, + -0.015128285624086857 ], "tsne3d": [ - -18.01759910583496, - 12.224864959716797, - -0.959433376789093 + -18.43294334411621, + -22.214216232299805, + 3.4664175510406494 ], "umap3d": [ - 2.124778985977173, - 6.975032329559326, - 4.745516300201416 + 2.6242408752441406, + 7.408794403076172, + 4.528471946716309 ] }, "cluster_id": 2, @@ -156560,11 +156690,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -156977,47 +157107,47 @@ ], "projections": { "pca": [ - -0.26714780926704407, - -0.1316768079996109 + -0.267545610666275, + -0.1306871771812439 ], "tsne": [ - -41.121315002441406, - -10.026825904846191 + -53.22285079956055, + -0.8536768555641174 ], "umap": [ - 1.1212939023971558, - 6.84507942199707 + 1.3630162477493286, + 5.988392353057861 ], "pca3d": [ - -0.267147421836853, - -0.131653293967247, - -0.08300638198852539 + -0.26754531264305115, + -0.130663201212883, + -0.08267561346292496 ], "tsne3d": [ - -32.77926254272461, - -0.2869213819503784, - 4.1638264656066895 + -31.888288497924805, + 1.747802972793579, + -0.7717939019203186 ], "umap3d": [ - 1.1307024955749512, - 5.544754981994629, - 4.239716053009033 + 1.9151848554611206, + 5.618716716766357, + 4.802845001220703 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -157431,31 +157561,31 @@ ], "projections": { "pca": [ - 0.012027141638100147, - -0.055226583033800125 + 0.011955857276916504, + -0.05461851507425308 ], "tsne": [ - -17.047588348388672, - 17.577802658081055 + -14.32201099395752, + -24.256139755249023 ], "umap": [ - 4.857801914215088, - 4.860966682434082 + 5.32656192779541, + 4.316817283630371 ], "pca3d": [ - 0.012026901356875896, - -0.05518478527665138, - 0.13901284337043762 + 0.011955617927014828, + -0.05457647889852524, + 0.1391417384147644 ], "tsne3d": [ - 2.382660150527954, - 8.243165016174316, - 17.863561630249023 + 3.3716957569122314, + -11.935729026794434, + 14.608240127563477 ], "umap3d": [ - 4.559924125671387, - 6.516441822052002, - 4.2128095626831055 + 4.8026442527771, + 6.939402103424072, + 3.1667838096618652 ] }, "cluster_id": 3, @@ -157466,11 +157596,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -157889,47 +158019,47 @@ "cited_by_count": 588, "projections": { "pca": [ - -0.183549702167511, - 0.04488655924797058 + -0.1831742525100708, + 0.0454871729016304 ], "tsne": [ - 12.47497844696045, - -35.507816314697266 + 4.7047014236450195, + 36.2597770690918 ], "umap": [ - 5.95566463470459, - 9.766891479492188 + 6.009941577911377, + 9.154254913330078 ], "pca3d": [ - -0.1835499107837677, - 0.04488561302423477, - -0.09891724586486816 + -0.183174267411232, + 0.045486003160476685, + -0.09858033061027527 ], "tsne3d": [ - 6.641449928283691, - -19.753082275390625, - 3.276181936264038 + -2.3070502281188965, + 12.187198638916016, + -19.19872283935547 ], "umap3d": [ - 4.639922142028809, - 2.7167904376983643, - 2.2668416500091553 + 5.031183242797852, + 2.7250523567199707, + 2.8010213375091553 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -158356,31 +158486,31 @@ ], "projections": { "pca": [ - -0.12737664580345154, - -0.1311115026473999 + -0.127732515335083, + -0.13042275607585907 ], "tsne": [ - -33.560302734375, - -17.230619430541992 + -34.92723083496094, + 22.397811889648438 ], "umap": [ - 1.7389734983444214, - 6.158493518829346 + 1.9863038063049316, + 5.504334449768066 ], "pca3d": [ - -0.12737642228603363, - -0.13109871745109558, - -0.01626991108059883 + -0.1277322769165039, + -0.13040991127490997, + -0.015950655564665794 ], "tsne3d": [ - -22.17865753173828, - -12.109460830688477, - -10.847450256347656 + -16.89179229736328, + -1.094149112701416, + -10.0692720413208 ], "umap3d": [ - 1.5894701480865479, - 5.5887532234191895, - 3.8307809829711914 + 2.073930263519287, + 5.786525726318359, + 4.177409648895264 ] }, "cluster_id": 2, @@ -158391,12 +158521,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 33, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -158812,47 +158942,47 @@ ], "projections": { "pca": [ - -0.26196709275245667, - -0.09016827493906021 + -0.262055903673172, + -0.0889277383685112 ], "tsne": [ - -35.20694351196289, - -1.099952220916748 + -32.26215744018555, + 1.1470261812210083 ], "umap": [ - 1.3053123950958252, - 7.648857593536377 + 1.6640281677246094, + 6.771072864532471 ], "pca3d": [ - -0.26196685433387756, - -0.09016688168048859, - 0.02407594956457615 + -0.2620556056499481, + -0.08892654627561569, + 0.024503696709871292 ], "tsne3d": [ - -19.890987396240234, - -6.154631614685059, - 4.522627830505371 + -18.501314163208008, + 0.42608967423439026, + 4.897372722625732 ], "umap3d": [ - 1.422326683998108, - 5.1371235847473145, - 4.7858734130859375 + 2.4606430530548096, + 5.558536529541016, + 5.306821823120117 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -159268,46 +159398,46 @@ ], "projections": { "pca": [ - -0.2074221968650818, - -0.14528077840805054 + -0.20794416964054108, + -0.14508680999279022 ], "tsne": [ - -43.1862907409668, - -11.747754096984863 + -48.22114181518555, + -14.297237396240234 ], "umap": [ - 0.2130155712366104, - 6.0860161781311035 + 0.6221112012863159, + 5.12623405456543 ], "pca3d": [ - -0.20742200314998627, - -0.1452469527721405, - -0.0837077647447586 + -0.20794397592544556, + -0.14505217969417572, + -0.08352691680192947 ], "tsne3d": [ - -21.97115135192871, - -5.222269535064697, - -11.017133712768555 + -32.680973052978516, + -9.375391960144043, + -4.182313919067383 ], "umap3d": [ - 0.3981591761112213, - 6.332705497741699, - 3.989145278930664 + 1.030037760734558, + 6.343478679656982, + 4.584754943847656 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, + "id": 23, "label": "Training Noise" } ], @@ -159716,47 +159846,47 @@ "journal": "Nature", "projections": { "pca": [ - 0.1209971159696579, - 0.0896923840045929 + 0.121566541492939, + 0.08868777751922607 ], "tsne": [ - 29.235187530517578, - -12.890941619873047 + 23.871477127075195, + 18.190303802490234 ], "umap": [ - 7.855169296264648, - 8.730941772460938 + 8.108423233032227, + 8.39711856842041 ], "pca3d": [ - 0.12099678069353104, - 0.0896962434053421, - 0.005121357273310423 + 0.12156624346971512, + 0.08869167417287827, + 0.005030520725995302 ], "tsne3d": [ - 17.338653564453125, - -10.390020370483398, - 6.789222717285156 + 14.035192489624023, + 1.0555944442749023, + -16.280109405517578 ], "umap3d": [ - 5.937310218811035, - 3.6992831230163574, - 1.5781868696212769 + 5.839943885803223, + 3.623032569885254, + 1.2465533018112183 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 12, - "label": "Genetic Variants" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -160170,47 +160300,47 @@ ], "projections": { "pca": [ - -0.21293345093727112, - -0.04368089139461517 + -0.21328161656856537, + -0.042708203196525574 ], "tsne": [ - -34.852413177490234, - -30.6330509185791 + -39.166927337646484, + 26.523941040039062 ], "umap": [ - 8.28774642944336, - 10.283917427062988 + 8.74880599975586, + 9.623823165893555 ], "pca3d": [ - -0.21293345093727112, - -0.043665993958711624, - -0.08366655558347702 + -0.2132815718650818, + -0.04269329085946083, + -0.08359725773334503 ], "tsne3d": [ - -14.572651863098145, - -0.8489053249359131, - -16.16054916381836 + -13.80660343170166, + -6.4695143699646, + -11.788890838623047 ], "umap3d": [ - 4.3462347984313965, - 4.954672336578369, - 1.269788384437561 + 4.011995792388916, + 4.424199104309082, + 1.2468900680541992 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -160624,31 +160754,31 @@ ], "projections": { "pca": [ - -0.2520633935928345, - -0.10022924840450287 + -0.25244632363319397, + -0.09950442612171173 ], "tsne": [ - -39.61223602294922, - -1.014689326286316 + -35.629390716552734, + -5.997673988342285 ], "umap": [ - 1.4058854579925537, - 6.387144565582275 + 1.7413455247879028, + 5.621863842010498 ], "pca3d": [ - -0.2520630359649658, - -0.10021473467350006, - 0.023573296144604683 + -0.2524460256099701, + -0.09948956966400146, + 0.023686189204454422 ], "tsne3d": [ - -16.034160614013672, - -1.0948362350463867, - -3.680002450942993 + -15.79233169555664, + -5.274247169494629, + -1.0185133218765259 ], "umap3d": [ - 1.36545991897583, - 5.677484035491943, - 4.190675735473633 + 1.9367480278015137, + 6.0158305168151855, + 4.844848155975342 ] }, "cluster_id": 2, @@ -160659,12 +160789,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -161080,31 +161210,31 @@ ], "projections": { "pca": [ - 0.11566872894763947, - -0.1727834939956665 + 0.11523427069187164, + -0.17301006615161896 ], "tsne": [ - 60.21295166015625, - -1.077677845954895 + 45.34941482543945, + 24.30320930480957 ], "umap": [ - 9.12640380859375, - 6.411837100982666 + 9.333023071289062, + 5.979698181152344 ], "pca3d": [ - 0.11566879600286484, - -0.17276574671268463, - 0.038290224969387054 + 0.115234375, + -0.1729918271303177, + 0.038462620228528976 ], "tsne3d": [ - 22.97191047668457, - 7.464797496795654, - -10.80193042755127 + 20.78136444091797, + -4.727170944213867, + 2.3607468605041504 ], "umap3d": [ - 7.146078109741211, - 5.586307525634766, - 1.9595280885696411 + 6.626972198486328, + 5.490036487579346, + 0.6693466305732727 ] }, "cluster_id": 0, @@ -161115,12 +161245,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 2, + "label": "DNA Regulation" }, { - "id": 29, - "label": "Transcription Binding" + "id": 21, + "label": "RNA Splicing" } ], "_embedding": [ @@ -161538,46 +161668,46 @@ ], "projections": { "pca": [ - -0.1992053985595703, - -0.1727304607629776 + -0.19948086142539978, + -0.17197343707084656 ], "tsne": [ - -32.54096603393555, - -8.138875961303711 + -38.908077239990234, + 7.4722394943237305 ], "umap": [ - 1.059448003768921, - 8.207135200500488 + 1.226291298866272, + 7.4006571769714355 ], "pca3d": [ - -0.19920486211776733, - -0.17274747788906097, - 0.014413263648748398 + -0.1994803100824356, + -0.17199020087718964, + 0.014971056021749973 ], "tsne3d": [ - -20.821533203125, - -6.96552038192749, - 10.451708793640137 + -21.92755699157715, + 5.966207027435303, + 5.522714138031006 ], "umap3d": [ - 1.1415221691131592, - 4.594198226928711, - 5.1549787521362305 + 2.361548662185669, + 5.087979793548584, + 5.7939019203186035 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -161986,47 +162116,47 @@ "abstract": "We use GPT-4 to automatically write explanations for the behavior of neurons in large language models and to score those explanations. We release a dataset of these (imperfect) explanations and scores for every neuron in GPT-2.", "projections": { "pca": [ - -0.22732877731323242, - 0.004415777511894703 + -0.22730538249015808, + 0.005708449985831976 ], "tsne": [ - -36.62070083618164, - -19.79581642150879 + -40.95012664794922, + 15.393573760986328 ], "umap": [ - 1.8675228357315063, - 7.709993839263916 + 2.2225725650787354, + 7.160531997680664 ], "pca3d": [ - -0.22732870280742645, - 0.004422943107783794, - 0.059300798922777176 + -0.2273053228855133, + 0.005714972037822008, + 0.0594959631562233 ], "tsne3d": [ - -22.597925186157227, - -15.611300468444824, - 3.2629287242889404 + -21.03217124938965, + 5.840071678161621, + -5.973879337310791 ], "umap3d": [ - 1.3389534950256348, - 4.414357662200928, - 4.048401832580566 + 2.2272255420684814, + 4.661323070526123, + 4.715205192565918 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 1, + "label": "Attention Models" }, { "id": 34, - "label": "Learning Methods" + "label": "Language Models" } ], "_embedding": [ @@ -162438,31 +162568,31 @@ ], "projections": { "pca": [ - -0.27133262157440186, - -0.11875218152999878 + -0.2720123827457428, + -0.11902930587530136 ], "tsne": [ - -50.83287811279297, - 7.357469081878662 + -51.301185607910156, + -16.21661376953125 ], "umap": [ - 0.9221311807632446, - 5.428826808929443 + 1.279916763305664, + 4.614090442657471 ], "pca3d": [ - -0.27133259177207947, - -0.11872909963130951, - -0.10408823937177658 + -0.27201205492019653, + -0.11900526285171509, + -0.10419312864542007 ], "tsne3d": [ - -27.49108123779297, - -1.4758687019348145, - -10.144502639770508 + -25.227216720581055, + -14.09079647064209, + -6.293930530548096 ], "umap3d": [ - 1.04414701461792, - 6.528566360473633, - 3.549072027206421 + 1.4607502222061157, + 6.438871383666992, + 4.013849258422852 ] }, "cluster_id": 2, @@ -162473,12 +162603,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -162892,31 +163022,31 @@ ], "projections": { "pca": [ - -0.2244006097316742, - -0.14489521086215973 + -0.22497716546058655, + -0.1440829187631607 ], "tsne": [ - -42.96565246582031, - -0.013992181047797203 + -41.52930450439453, + -7.360391616821289 ], "umap": [ - 1.5709768533706665, - 6.252254962921143 + 1.8901466131210327, + 5.504088401794434 ], "pca3d": [ - -0.22440028190612793, - -0.14488321542739868, - -0.006639265920966864 + -0.22497689723968506, + -0.14407016336917877, + -0.006495920475572348 ], "tsne3d": [ - -20.328611373901367, - 0.31621354818344116, - -2.7919881343841553 + -20.18320655822754, + -8.254794120788574, + 2.3449132442474365 ], "umap3d": [ - 1.606616497039795, - 5.9468674659729, - 4.201282501220703 + 2.213076591491699, + 6.147243976593018, + 4.690228462219238 ] }, "cluster_id": 2, @@ -162927,12 +163057,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -163345,31 +163475,31 @@ ], "projections": { "pca": [ - -0.28464528918266296, - -0.07133253663778305 + -0.28489527106285095, + -0.0704958364367485 ], "tsne": [ - -47.71708297729492, - -20.84343147277832 + -50.91914749145508, + 15.702177047729492 ], "umap": [ - 0.3980071544647217, - 5.770166873931885 + 0.7266682982444763, + 4.862306118011475 ], "pca3d": [ - -0.284645140171051, - -0.07131743431091309, - -0.15688824653625488 + -0.2848951518535614, + -0.07048030942678452, + -0.15663300454616547 ], "tsne3d": [ - -28.91745376586914, - -11.346268653869629, - -3.6947243213653564 + -26.346345901489258, + -2.2314932346343994, + -8.942569732666016 ], "umap3d": [ - 0.4325828552246094, - 5.53661584854126, - 3.404153347015381 + 1.0576324462890625, + 5.456704139709473, + 4.1582722663879395 ] }, "cluster_id": 2, @@ -163380,12 +163510,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, - "label": "Training Noise" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -163799,31 +163929,31 @@ ], "projections": { "pca": [ - -0.2441544383764267, - -0.09585712105035782 + -0.2442924529314041, + -0.09522794187068939 ], "tsne": [ - -39.115638732910156, - 6.219642162322998 + -34.490291595458984, + -12.072549819946289 ], "umap": [ - 1.981614351272583, - 5.984703540802002 + 2.361844301223755, + 5.395990371704102 ], "pca3d": [ - -0.24415412545204163, - -0.09585247188806534, - -0.0718003660440445 + -0.24429215490818024, + -0.09522292762994766, + -0.07145757228136063 ], "tsne3d": [ - -20.88365364074707, - 7.091742992401123, - -4.642146110534668 + -15.836325645446777, + -15.755924224853516, + -4.485848426818848 ], "umap3d": [ - 1.7807873487472534, - 6.084690093994141, - 4.208967685699463 + 2.4001407623291016, + 6.2344536781311035, + 4.447418689727783 ] }, "cluster_id": 2, @@ -163834,12 +163964,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 33, - "label": "Neural Networks" + "id": 19, + "label": "Graph Neural Nets" } ], "_embedding": [ @@ -164257,31 +164387,31 @@ ], "projections": { "pca": [ - -0.22314926981925964, - -0.0885949432849884 + -0.22361819446086884, + -0.08838066458702087 ], "tsne": [ - -44.219139099121094, - 7.451490879058838 + -44.852325439453125, + -16.78329849243164 ], "umap": [ - 1.4952481985092163, - 5.868620872497559 + 1.5307691097259521, + 4.929706573486328 ], "pca3d": [ - -0.2231488823890686, - -0.08858754485845566, - -0.03846973553299904 + -0.22361774742603302, + -0.08837303519248962, + -0.03851078078150749 ], "tsne3d": [ - -25.15181541442871, - 6.596267223358154, - -3.3782236576080322 + -20.648303985595703, + -11.32798957824707, + -7.380475044250488 ], "umap3d": [ - 1.3081086874008179, - 6.305467128753662, - 4.296530723571777 + 1.7239031791687012, + 6.499054431915283, + 4.376704216003418 ] }, "cluster_id": 2, @@ -164292,12 +164422,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -164713,31 +164843,31 @@ "year": 2022, "projections": { "pca": [ - -0.11055035889148712, - -0.003756946651265025 + -0.11043371260166168, + -0.004111521877348423 ], "tsne": [ - -32.59672164916992, - 18.17410659790039 + -28.40382957458496, + -21.799972534179688 ], "umap": [ - 3.0742347240448, - 4.9032464027404785 + 3.542851448059082, + 4.355987071990967 ], "pca3d": [ - -0.11055047810077667, - -0.0037337264511734247, - 0.015231708064675331 + -0.11043379455804825, + -0.004088331013917923, + 0.015185803174972534 ], "tsne3d": [ - -13.380131721496582, - 12.1505126953125, - 4.932092666625977 + -12.456491470336914, + -21.32745933532715, + 4.448824882507324 ], "umap3d": [ - 3.08607816696167, - 6.920389175415039, - 4.814099311828613 + 3.433655261993408, + 7.268787860870361, + 4.285031318664551 ] }, "cluster_id": 2, @@ -164748,12 +164878,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 16, - "label": "Diffusion Models" + "id": 7, + "label": "Diffusion Gen" } ], "_embedding": [ @@ -165167,47 +165297,47 @@ ], "projections": { "pca": [ - -0.19835494458675385, - -0.03945315256714821 + -0.19851206243038177, + -0.038199543952941895 ], "tsne": [ - -33.64369583129883, - -22.355113983154297 + -30.064197540283203, + 3.3733623027801514 ], "umap": [ - 1.6199116706848145, - 7.007284641265869 + 1.7084680795669556, + 6.210798740386963 ], "pca3d": [ - -0.19835485517978668, - -0.03944801539182663, - 0.013771726749837399 + -0.19851195812225342, + -0.038194440305233, + 0.013928570784628391 ], "tsne3d": [ - -18.843965530395508, - -12.024224281311035, - -3.8544836044311523 + -22.179399490356445, + 11.91168212890625, + -5.4039225578308105 ], "umap3d": [ - 1.3216805458068848, - 5.107843399047852, - 3.9297070503234863 + 2.1561636924743652, + 5.240115165710449, + 4.467333793640137 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -165624,46 +165754,46 @@ ], "projections": { "pca": [ - -0.18154971301555634, - -0.16067290306091309 + -0.18187065422534943, + -0.16005201637744904 ], "tsne": [ - -31.404882431030273, - -11.940540313720703 + -33.72286605834961, + 10.055224418640137 ], "umap": [ - 1.5815424919128418, - 7.856092929840088 + 1.870328426361084, + 7.181864261627197 ], "pca3d": [ - -0.18154923617839813, - -0.16066862642765045, - -0.009595540352165699 + -0.1818702518939972, + -0.1600472778081894, + -0.009173568338155746 ], "tsne3d": [ - -17.954631805419922, - -8.477087020874023, - 4.120384693145752 + -17.608917236328125, + 1.4241318702697754, + 2.193948268890381 ], "umap3d": [ - 1.5786843299865723, - 4.637580871582031, - 4.68440055847168 + 2.549560785293579, + 5.071560859680176, + 5.261228561401367 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -166079,46 +166209,46 @@ ], "projections": { "pca": [ - -0.2229674607515335, - -0.005288350395858288 + -0.2230544090270996, + -0.004463090095669031 ], "tsne": [ - -41.48404312133789, - -17.441560745239258 + -50.62554168701172, + 9.735228538513184 ], "umap": [ - 0.7305993437767029, - 7.030683517456055 + 1.071918249130249, + 6.3502278327941895 ], "pca3d": [ - -0.22296734154224396, - -0.005297521594911814, - -0.06877612322568893 + -0.22305437922477722, + -0.004472531843930483, + -0.06872303038835526 ], "tsne3d": [ - -32.12403106689453, - -3.487760305404663, - 8.154814720153809 + -32.01715087890625, + 4.880990505218506, + 4.569849491119385 ], "umap3d": [ - 0.5204054117202759, - 5.199059963226318, - 4.27374267578125 + 1.3507745265960693, + 5.46786642074585, + 5.129975318908691 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -166536,31 +166666,31 @@ ], "projections": { "pca": [ - -0.26023393869400024, - -0.17028915882110596 + -0.2606470584869385, + -0.1695130467414856 ], "tsne": [ - -43.0650634765625, - 2.254483938217163 + -39.13833999633789, + -8.954450607299805 ], "umap": [ - 1.4626682996749878, - 6.381465911865234 + 1.959963083267212, + 5.5273847579956055 ], "pca3d": [ - -0.2602333724498749, - -0.17031347751617432, - 0.00045898009557276964 + -0.26064643263816833, + -0.1695375144481659, + 0.0008511191117577255 ], "tsne3d": [ - -19.49158477783203, - 2.490370273590088, - -5.295746326446533 + -19.128950119018555, + -8.029427528381348, + -1.345506191253662 ], "umap3d": [ - 1.4976266622543335, - 5.810464859008789, - 4.145734786987305 + 2.1552035808563232, + 6.06815242767334, + 4.664409637451172 ] }, "cluster_id": 2, @@ -166571,12 +166701,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -166991,31 +167121,31 @@ "doi": "10.1101/cshperspect.a041469", "projections": { "pca": [ - -0.074496328830719, - -0.0037759444676339626 + -0.0744018629193306, + -0.0025524371303617954 ], "tsne": [ - -10.347579002380371, - 21.208818435668945 + -0.35025152564048767, + -27.678762435913086 ], "umap": [ - 5.281094074249268, - 4.742496967315674 + 5.69268798828125, + 4.205048084259033 ], "pca3d": [ - -0.0744963213801384, - -0.003781245555728674, - 0.22683627903461456 + -0.07440178841352463, + -0.002558441599830985, + 0.22689467668533325 ], "tsne3d": [ - -7.2050089836120605, - 5.0716142654418945, - 5.271451950073242 + -6.278902053833008, + -10.896245002746582, + 7.906216144561768 ], "umap3d": [ - 5.0899338722229, - 6.615054607391357, - 4.5402679443359375 + 5.358123302459717, + 7.164337635040283, + 3.274587869644165 ] }, "cluster_id": 3, @@ -167026,11 +167156,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 9, + "label": "Protein Design" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -167447,47 +167577,47 @@ "journal": "Transactions on Machine Learning Research", "projections": { "pca": [ - -0.230250284075737, - -0.08773331344127655 + -0.23046861588954926, + -0.08706476539373398 ], "tsne": [ - -44.53697967529297, - -5.373120307922363 + -45.60626983642578, + -6.519748210906982 ], "umap": [ - 0.9509004950523376, - 6.404530048370361 + 1.4829401969909668, + 5.785611629486084 ], "pca3d": [ - -0.2302497923374176, - -0.08775023370981216, - -0.08329936861991882 + -0.23046813905239105, + -0.08708187937736511, + -0.08303740620613098 ], "tsne3d": [ - -23.00118637084961, - -2.2003488540649414, - -3.54953932762146 + -22.83896255493164, + -4.981077194213867, + 2.45904278755188 ], "umap3d": [ - 1.1049237251281738, - 5.8032097816467285, - 4.322685718536377 + 1.9050509929656982, + 5.955225944519043, + 4.882778644561768 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -167898,47 +168028,47 @@ ], "projections": { "pca": [ - -0.16926993429660797, - 0.15764544904232025 + -0.16899935901165009, + 0.15839481353759766 ], "tsne": [ - -34.15422058105469, - -28.784997940063477 + -38.899227142333984, + 24.512754440307617 ], "umap": [ - 8.16003704071045, - 10.327300071716309 + 8.65892219543457, + 9.690430641174316 ], "pca3d": [ - -0.1692703515291214, - 0.15768374502658844, - -0.006374675780534744 + -0.1689998209476471, + 0.15843325853347778, + -0.006660023704171181 ], "tsne3d": [ - -15.335247993469238, - -1.4601880311965942, - -13.880395889282227 + -13.614479064941406, + -3.587080240249634, + -7.908369064331055 ], "umap3d": [ - 4.113631725311279, - 4.978835105895996, - 1.4310413599014282 + 3.8475029468536377, + 4.472782611846924, + 1.4800519943237305 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -168355,31 +168485,31 @@ "doi": "10.1038/s42256-023-00657-x", "projections": { "pca": [ - -0.25085529685020447, - -0.06188215687870979 + -0.2510392665863037, + -0.06111359968781471 ], "tsne": [ - -47.42036437988281, - -22.297332763671875 + -51.56258773803711, + 16.7044677734375 ], "umap": [ - 0.34255969524383545, - 5.819419860839844 + 0.6792517900466919, + 4.863880634307861 ], "pca3d": [ - -0.2508549690246582, - -0.06187397986650467, - -0.10460201650857925 + -0.2510389983654022, + -0.06110480800271034, + -0.10436288267374039 ], "tsne3d": [ - -29.052202224731445, - -12.663531303405762, - -4.691165447235107 + -26.829004287719727, + -0.5817156434059143, + -10.452657699584961 ], "umap3d": [ - 0.42280638217926025, - 5.475573539733887, - 3.3781888484954834 + 1.0838125944137573, + 5.3709893226623535, + 4.1493024826049805 ] }, "cluster_id": 2, @@ -168390,12 +168520,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, - "label": "Training Noise" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -168809,31 +168939,31 @@ "cited_by_count": 81, "projections": { "pca": [ - -0.21575911343097687, - -0.0170659888535738 + -0.21579314768314362, + -0.01661008782684803 ], "tsne": [ - -36.008724212646484, - 1.3233399391174316 + -32.04464340209961, + -7.355297088623047 ], "umap": [ - 1.9202462434768677, - 6.457722187042236 + 2.219808578491211, + 5.723703384399414 ], "pca3d": [ - -0.21575915813446045, - -0.01704636588692665, - 0.005931132473051548 + -0.21579328179359436, + -0.016589274629950523, + 0.00597945973277092 ], "tsne3d": [ - -17.139135360717773, - 5.215992450714111, - -7.3757805824279785 + -14.132174491882324, + -12.394173622131348, + -2.8092405796051025 ], "umap3d": [ - 1.72593092918396, - 5.596137523651123, - 4.204981803894043 + 2.4079034328460693, + 5.979319095611572, + 4.560702800750732 ] }, "cluster_id": 2, @@ -168844,12 +168974,12 @@ "label": "Generative Models" }, { - "id": 1, - "label": "Network Learning" + "id": 5, + "label": "Diffusion Models" }, { - "id": 33, - "label": "Neural Networks" + "id": 19, + "label": "Graph Neural Nets" } ], "_embedding": [ @@ -169288,47 +169418,47 @@ "cited_by_count": 318, "projections": { "pca": [ - -0.07982071489095688, - 0.025442808866500854 + -0.07989519089460373, + 0.02573273330926895 ], "tsne": [ - 8.652316093444824, - -31.191965103149414 + 0.7411943674087524, + 32.10310745239258 ], "umap": [ - 5.934212684631348, - 9.464826583862305 + 6.067960262298584, + 8.87883186340332 ], "pca3d": [ - -0.07982051372528076, - 0.025436291471123695, - -0.17273515462875366 + -0.0798950046300888, + 0.02572610042989254, + -0.17271921038627625 ], "tsne3d": [ - 6.505558490753174, - -15.517848014831543, - -2.2227303981781006 + 1.254041075706482, + 15.486591339111328, + -14.539318084716797 ], "umap3d": [ - 4.896190166473389, - 2.6227457523345947, - 2.5232934951782227 + 5.4011383056640625, + 2.7777132987976074, + 2.9238572120666504 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -169744,46 +169874,46 @@ ], "projections": { "pca": [ - -0.08289484679698944, - 0.06235208362340927 + -0.08254238963127136, + 0.06286628544330597 ], "tsne": [ - -1.419966697692871, - -35.04332733154297 + -9.835311889648438, + 31.437232971191406 ], "umap": [ - 4.543630599975586, - 9.597513198852539 + 4.715668201446533, + 8.873083114624023 ], "pca3d": [ - -0.08289478719234467, - 0.06234264746308327, - 0.08431221544742584 + -0.082542285323143, + 0.06285656988620758, + 0.0844758078455925 ], "tsne3d": [ - -3.0069544315338135, - -20.433366775512695, - 1.4186433553695679 + -7.256777763366699, + 12.215385437011719, + -7.5108723640441895 ], "umap3d": [ - 3.806251049041748, - 2.5907952785491943, - 3.2581021785736084 + 4.592949390411377, + 3.072451114654541, + 3.8567700386047363 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, + "id": 26, "label": "Medical AI" } ], @@ -170199,31 +170329,31 @@ "journal": "OpenReview", "projections": { "pca": [ - -0.22660423815250397, - -0.14390471577644348 + -0.22699669003486633, + -0.14374282956123352 ], "tsne": [ - -51.728389739990234, - -10.404972076416016 + -53.01963424682617, + -8.88725757598877 ], "umap": [ - 0.49083927273750305, - 6.094816207885742 + 0.7668121457099915, + 5.3356475830078125 ], "pca3d": [ - -0.22660373151302338, - -0.14390158653259277, - -0.08386233448982239 + -0.22699613869190216, + -0.14373929798603058, + -0.08363629132509232 ], "tsne3d": [ - -30.64362907409668, - -4.381920337677002, - -4.4269514083862305 + -27.72314453125, + -8.135965347290039, + -5.187929153442383 ], "umap3d": [ - 0.3608108162879944, - 6.020020484924316, - 3.893646717071533 + 1.0626732110977173, + 5.975673675537109, + 4.591122627258301 ] }, "cluster_id": 2, @@ -170234,11 +170364,11 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, + "id": 23, "label": "Training Noise" } ], @@ -170653,46 +170783,46 @@ ], "projections": { "pca": [ - -0.2030937373638153, - -0.11876636743545532 + -0.20316095650196075, + -0.1183028444647789 ], "tsne": [ - -33.00538635253906, - -6.728821754455566 + -40.47319793701172, + 7.060183525085449 ], "umap": [ - 0.7639205455780029, - 8.146036148071289 + 1.022937297821045, + 7.276280403137207 ], "pca3d": [ - -0.20309311151504517, - -0.11879739165306091, - -0.0362263023853302 + -0.20316028594970703, + -0.11833398044109344, + -0.03578556329011917 ], "tsne3d": [ - -22.008846282958984, - -4.620464324951172, - 10.077102661132812 + -23.730792999267578, + 4.81285285949707, + 4.268306255340576 ], "umap3d": [ - 0.9036681652069092, - 4.72116756439209, - 5.197985649108887 + 2.1496472358703613, + 5.154819488525391, + 5.871527194976807 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -171107,47 +171237,47 @@ ], "projections": { "pca": [ - -0.2639589309692383, - -0.19214940071105957 + -0.26456141471862793, + -0.19152362644672394 ], "tsne": [ - -44.008270263671875, - -1.992530345916748 + -41.745140075683594, + -4.222049713134766 ], "umap": [ - 1.1904999017715454, - 6.638273239135742 + 1.5832114219665527, + 5.972535610198975 ], "pca3d": [ - -0.2639583945274353, - -0.1921539455652237, - -0.08036155998706818 + -0.26456090807914734, + -0.19152775406837463, + -0.0800456777215004 ], "tsne3d": [ - -21.344104766845703, - -0.9156343936920166, - 0.21691788733005524 + -21.59650421142578, + -6.521385192871094, + 4.835037708282471 ], "umap3d": [ - 1.311032772064209, - 5.639315128326416, - 4.412801265716553 + 2.1501851081848145, + 5.908140182495117, + 4.971480369567871 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -171554,47 +171684,47 @@ "title": "HuggingChat - Chat with AI models", "projections": { "pca": [ - -0.21223944425582886, - 0.12616708874702454 + -0.21175667643547058, + 0.12721218168735504 ], "tsne": [ - -21.126779556274414, - -5.241167068481445 + -21.02356719970703, + -3.913644552230835 ], "umap": [ - 3.1907341480255127, - 7.792391777038574 + 3.495368003845215, + 7.271487712860107 ], "pca3d": [ - -0.21223948895931244, - 0.12615396082401276, - 0.06508703529834747 + -0.2117566168308258, + 0.12719860672950745, + 0.06513076275587082 ], "tsne3d": [ - -8.695942878723145, - 3.4822025299072266, - -11.835820198059082 + -9.777507781982422, + 1.6742616891860962, + 7.057783603668213 ], "umap3d": [ - 3.055868625640869, - 4.478501319885254, - 3.9617345333099365 + 3.861745834350586, + 5.051742076873779, + 4.16146183013916 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { "id": 0, - "label": "LLM Language Learning" + "label": "Math AI" } ], "_embedding": [ @@ -172023,47 +172153,47 @@ ], "projections": { "pca": [ - -0.19727760553359985, - 0.09594370424747467 + -0.19687002897262573, + 0.09636809676885605 ], "tsne": [ - -7.181387901306152, - -37.594764709472656 + -15.093413352966309, + 29.81283950805664 ], "umap": [ - 4.75623893737793, - 8.390223503112793 + 3.7096245288848877, + 7.235691547393799 ], "pca3d": [ - -0.19727778434753418, - 0.09594547748565674, - -0.022638564929366112 + -0.19687001407146454, + 0.09636987000703812, + -0.02260708063840866 ], "tsne3d": [ - -5.458274841308594, - -19.406599044799805, - -8.312740325927734 + -14.40024185180664, + 12.845115661621094, + -13.285696029663086 ], "umap3d": [ - 3.728808879852295, - 4.084310054779053, - 3.46648907661438 + 4.497348785400391, + 4.356260776519775, + 3.5401713848114014 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 17, - "label": "Medical AI" + "id": 0, + "label": "Math AI" } ], "_embedding": [ @@ -172480,47 +172610,47 @@ "doi": "10.1038/s41467-024-44824-z", "projections": { "pca": [ - -0.1061304435133934, - 0.05551386997103691 + -0.10587960481643677, + 0.05548698082566261 ], "tsne": [ - 9.466580390930176, - -35.6545295715332 + 0.4196139872074127, + 36.65464782714844 ], "umap": [ - 5.77108097076416, - 9.747888565063477 + 5.823493480682373, + 9.114205360412598 ], "pca3d": [ - -0.10613007843494415, - 0.055484838783741, - -0.15253278613090515 + -0.1058792918920517, + 0.05545724555850029, + -0.1524529606103897 ], "tsne3d": [ - 3.432450771331787, - -16.918474197387695, - 4.6680402755737305 + -3.053415536880493, + 8.866328239440918, + -16.7849063873291 ], "umap3d": [ - 4.679878234863281, - 2.532395839691162, - 2.417393922805786 + 5.09899377822876, + 2.6087117195129395, + 2.8850295543670654 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -172939,47 +173069,47 @@ "cited_by_count": 2, "projections": { "pca": [ - -0.2376529723405838, - -0.16967463493347168 + -0.23820596933364868, + -0.16901233792304993 ], "tsne": [ - -44.05868148803711, - 0.5628179311752319 + -40.71580505371094, + -7.984432220458984 ], "umap": [ - 1.2946866750717163, - 6.521055698394775 + 1.6657943725585938, + 5.6879754066467285 ], "pca3d": [ - -0.23765243589878082, - -0.16969004273414612, - -0.04967980831861496 + -0.23820537328720093, + -0.1690274327993393, + -0.04942743852734566 ], "tsne3d": [ - -21.65795135498047, - 1.8470739126205444, - -3.0988364219665527 + -20.758878707885742, + -9.59812068939209, + 1.1080876588821411 ], "umap3d": [ - 1.3469648361206055, - 5.738852024078369, - 4.173524379730225 + 2.0600478649139404, + 5.9724225997924805, + 4.704496383666992 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -173387,46 +173517,46 @@ "abstract": "StableLM: Stability AI Language Models. Contribute to Stability-AI/StableLM development by creating an account on GitHub.", "projections": { "pca": [ - -0.20451919734477997, - 0.07999864220619202 + -0.20425833761692047, + 0.0806349515914917 ], "tsne": [ - -23.608234405517578, - -10.965843200683594 + -27.05933380126953, + 9.68648624420166 ], "umap": [ - 2.1382129192352295, - 8.44145393371582 + 2.5528995990753174, + 7.625083923339844 ], "pca3d": [ - -0.20451904833316803, - 0.07998892664909363, - 0.12002819031476974 + -0.20425818860530853, + 0.08062535524368286, + 0.11996931582689285 ], "tsne3d": [ - -12.443695068359375, - -9.754448890686035, - 10.0128812789917 + -12.148580551147461, + 3.6772818565368652, + 0.8303552269935608 ], "umap3d": [ - 2.0267040729522705, - 4.030998706817627, - 4.462477207183838 + 3.0866518020629883, + 4.68014669418335, + 5.153104782104492 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -173840,31 +173970,31 @@ ], "projections": { "pca": [ - 0.07164306938648224, - -0.17030774056911469 + 0.07135407626628876, + -0.16996996104717255 ], "tsne": [ - 3.7466471195220947, - 35.891204833984375 + 0.9471288323402405, + -11.666939735412598 ], "umap": [ - 6.792267799377441, - 5.056554794311523 + 7.091625690460205, + 4.826420783996582 ], "pca3d": [ - 0.07164312899112701, - -0.17029237747192383, - 0.1009945496916771 + 0.07135424762964249, + -0.16995388269424438, + 0.10134530812501907 ], "tsne3d": [ - 7.449510097503662, - 14.927501678466797, - 5.269095420837402 + 8.183126449584961, + -10.175930976867676, + -0.012933586724102497 ], "umap3d": [ - 5.573031425476074, - 5.449297904968262, - 4.264005184173584 + 6.2597246170043945, + 6.231452941894531, + 3.2359468936920166 ] }, "cluster_id": 4, @@ -173875,12 +174005,12 @@ "label": "RNA Sequence Models" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 14, - "label": "Genomic Models" + "id": 20, + "label": "Protein Language" } ], "_embedding": [ @@ -174316,46 +174446,46 @@ ], "projections": { "pca": [ - -0.25725144147872925, - -0.14853285253047943 + -0.2575704753398895, + -0.1478072702884674 ], "tsne": [ - -58.76861572265625, - -6.307894706726074 + -54.70192337036133, + 1.967740535736084 ], "umap": [ - 0.33541139960289, - 7.140877723693848 + 0.6815505027770996, + 6.291954040527344 ], "pca3d": [ - -0.25725099444389343, - -0.14854976534843445, - -0.11222156882286072 + -0.2575698792934418, + -0.14782433211803436, + -0.11174914985895157 ], "tsne3d": [ - -29.028507232666016, - 4.3283233642578125, - 6.57911491394043 + -34.52056884765625, + -0.11192771047353745, + 2.9794344902038574 ], "umap3d": [ - 0.5307685732841492, - 5.82346773147583, - 4.843759536743164 + 1.465144157409668, + 6.0799641609191895, + 5.531698703765869 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -174780,47 +174910,47 @@ "openalex_url": "https://openalex.org/W4323865263", "projections": { "pca": [ - -0.10226186364889145, - -0.03601968660950661 + -0.10229888558387756, + -0.03569034859538078 ], "tsne": [ - 10.08985424041748, - -31.280071258544922 + 2.3246631622314453, + 31.950031280517578 ], "umap": [ - 5.608240604400635, - 9.440924644470215 + 5.842112064361572, + 8.84924602508545 ], "pca3d": [ - -0.10226185619831085, - -0.03599489480257034, - -0.14454509317874908 + -0.10229890793561935, + -0.035665176808834076, + -0.14427615702152252 ], "tsne3d": [ - 8.407265663146973, - -17.237632751464844, - -1.359118938446045 + 1.1537699699401855, + 12.893659591674805, + -13.637146949768066 ], "umap3d": [ - 4.794689178466797, - 2.5910987854003906, - 2.6273560523986816 + 5.2974324226379395, + 2.7791051864624023, + 3.069857358932495 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -175232,47 +175362,47 @@ ], "projections": { "pca": [ - -0.2072543054819107, - 0.2773534953594208 + -0.2066836804151535, + 0.27902859449386597 ], "tsne": [ - -13.497655868530273, - -14.454565048217773 + -12.233670234680176, + 7.823511123657227 ], "umap": [ - 3.9428958892822266, - 7.830999851226807 + 4.219954013824463, + 7.089231967926025 ], "pca3d": [ - -0.2072545886039734, - 0.27735719084739685, - 0.0994528979063034 + -0.20668408274650574, + 0.2790320813655853, + 0.09906190633773804 ], "tsne3d": [ - -4.528615474700928, - -6.9694085121154785, - 8.873344421386719 + -10.792926788330078, + -0.023117488250136375, + -7.586177825927734 ], "umap3d": [ - 3.428368091583252, - 3.6303532123565674, - 4.1010003089904785 + 4.338366985321045, + 4.343658447265625, + 4.416638374328613 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -175687,31 +175817,31 @@ ], "projections": { "pca": [ - -0.22917145490646362, - -0.06582105904817581 + -0.2294146865606308, + -0.06522329896688461 ], "tsne": [ - -42.97412109375, - 19.90121078491211 + -38.27046585083008, + -26.637535095214844 ], "umap": [ - 2.2565865516662598, - 4.930564880371094 + 2.6701431274414062, + 4.342078685760498 ], "pca3d": [ - -0.2291712462902069, - -0.06581894308328629, - 0.015755711123347282 + -0.22941440343856812, + -0.06522088497877121, + 0.015831027179956436 ], "tsne3d": [ - -17.203596115112305, - 12.107958793640137, - -4.51582145690918 + -18.18149757385254, + -24.44192123413086, + 0.7943791151046753 ], "umap3d": [ - 2.0873429775238037, - 6.885978698730469, - 4.629858016967773 + 2.516632556915283, + 7.282367706298828, + 4.476261615753174 ] }, "cluster_id": 2, @@ -175722,11 +175852,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -176142,47 +176272,47 @@ ], "projections": { "pca": [ - -0.22507160902023315, - -0.07686429470777512 + -0.22519245743751526, + -0.07612641155719757 ], "tsne": [ - -31.446062088012695, - -23.035991668701172 + -36.881954193115234, + 18.38680076599121 ], "umap": [ - 1.7943600416183472, - 7.284693717956543 + 1.8945859670639038, + 6.309566020965576 ], "pca3d": [ - -0.2250714749097824, - -0.07685776054859161, - 0.008089682087302208 + -0.2251923382282257, + -0.07612016797065735, + 0.008335116319358349 ], "tsne3d": [ - -17.281124114990234, - -15.125628471374512, - -4.123970985412598 + -23.742053985595703, + 12.87476634979248, + -2.4691522121429443 ], "umap3d": [ - 1.4062873125076294, - 4.89445686340332, - 4.084469318389893 + 2.263432741165161, + 5.207100868225098, + 4.609766483306885 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -176623,47 +176753,47 @@ "cited_by_count": 283, "projections": { "pca": [ - -0.16421820223331451, - 0.11189670115709305 + -0.16395442187786102, + 0.11340553313493729 ], "tsne": [ - -14.245488166809082, - -23.19994354248047 + -15.657718658447266, + 16.544334411621094 ], "umap": [ - 3.4640605449676514, - 8.278761863708496 + 3.758279800415039, + 7.595914840698242 ], "pca3d": [ - -0.16421838104724884, - 0.11188995093107224, - 0.10019094496965408 + -0.16395458579063416, + 0.11339827626943588, + 0.10017158836126328 ], "tsne3d": [ - -10.939805030822754, - -10.951108932495117, - -2.850806951522827 + -9.768264770507812, + 12.478694915771484, + 3.4684715270996094 ], "umap3d": [ - 2.71504545211792, - 3.8510076999664307, - 3.572761058807373 + 3.533625841140747, + 4.292232036590576, + 4.039597034454346 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 0, + "label": "Math AI" } ], "_embedding": [ @@ -177077,31 +177207,31 @@ ], "projections": { "pca": [ - -0.2665588855743408, - -0.040587302297353745 + -0.266813188791275, + -0.039828963577747345 ], "tsne": [ - -43.22489547729492, - -11.407586097717285 + -48.1497802734375, + -14.5631742477417 ], "umap": [ - 0.6360646486282349, - 5.834717273712158 + 1.1682264804840088, + 5.013574123382568 ], "pca3d": [ - -0.2665589153766632, - -0.040555503219366074, - -0.03038773126900196 + -0.26681315898895264, + -0.0397966206073761, + -0.030330032110214233 ], "tsne3d": [ - -22.55784797668457, - -4.760499000549316, - -12.1006441116333 + -32.629695892333984, + -10.450765609741211, + -5.057613372802734 ], "umap3d": [ - 0.8297057747840881, - 6.347919464111328, - 3.8893914222717285 + 1.4520221948623657, + 6.263678073883057, + 4.3273797035217285 ] }, "cluster_id": 2, @@ -177112,12 +177242,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, - "label": "Training Noise" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -177537,47 +177667,47 @@ ], "projections": { "pca": [ - -0.14176520705223083, - 0.005820567719638348 + -0.1419629454612732, + 0.006707113701850176 ], "tsne": [ - -35.42316436767578, - -31.335416793823242 + -39.4882698059082, + 27.294918060302734 ], "umap": [ - 8.262746810913086, - 10.296228408813477 + 8.753239631652832, + 9.634529113769531 ], "pca3d": [ - -0.14176517724990845, - 0.005835963878780603, - -0.07272622734308243 + -0.14196288585662842, + 0.006722342222929001, + -0.07275599986314774 ], "tsne3d": [ - -14.952296257019043, - -0.5042411684989929, - -17.6651611328125 + -14.611565589904785, + -6.623206615447998, + -13.075329780578613 ], "umap3d": [ - 4.364754676818848, - 4.921642303466797, - 1.2280851602554321 + 3.989773750305176, + 4.43648624420166, + 1.2135469913482666 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -177993,47 +178123,47 @@ "journal": "Advances in Neural Information Processing Systems", "projections": { "pca": [ - -0.1481780707836151, - 0.0971684530377388 + -0.14793221652507782, + 0.09765568375587463 ], "tsne": [ - -35.95303726196289, - -31.700328826904297 + -40.14202117919922, + 27.64366912841797 ], "umap": [ - 8.221707344055176, - 10.321622848510742 + 8.780036926269531, + 9.604522705078125 ], "pca3d": [ - -0.14817839860916138, - 0.09719114750623703, - -0.11352117359638214 + -0.1479324996471405, + 0.09767840057611465, + -0.11357150971889496 ], "tsne3d": [ - -14.841848373413086, - 0.5223682522773743, - -18.7454776763916 + -14.932089805603027, + -5.483879566192627, + -13.909841537475586 ], "umap3d": [ - 4.398596286773682, - 4.907872676849365, - 1.217605471611023 + 3.994687080383301, + 4.430752754211426, + 1.2281156778335571 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -178448,46 +178578,46 @@ ], "projections": { "pca": [ - -0.27515527606010437, - -0.08845189213752747 + -0.27555355429649353, + -0.08741214871406555 ], "tsne": [ - -48.173980712890625, - -8.120220184326172 + -48.641780853271484, + -6.7887139320373535 ], "umap": [ - 0.6615310311317444, - 6.357849597930908 + 0.9672111868858337, + 5.4510931968688965 ], "pca3d": [ - -0.27515503764152527, - -0.08844085037708282, - -0.18150383234024048 + -0.2755534052848816, + -0.08740086853504181, + -0.1812584102153778 ], "tsne3d": [ - -28.18532943725586, - 0.4093123972415924, - -3.674015760421753 + -28.01111602783203, + -9.259020805358887, + 1.4783233404159546 ], "umap3d": [ - 0.5007072687149048, - 6.03069543838501, - 4.043452739715576 + 1.296763300895691, + 6.1059393882751465, + 4.736276149749756 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, + "id": 23, "label": "Training Noise" } ], @@ -178901,31 +179031,31 @@ ], "projections": { "pca": [ - -0.14633262157440186, - -0.025900552049279213 + -0.1463334858417511, + -0.025595160201191902 ], "tsne": [ - -47.276031494140625, - -21.768356323242188 + -51.036521911621094, + 16.542579650878906 ], "umap": [ - 0.3408764600753784, - 5.764149188995361 + 0.6802247762680054, + 4.909523963928223 ], "pca3d": [ - -0.14633244276046753, - -0.02588932402431965, - -0.12757758796215057 + -0.1463334858417511, + -0.025584176182746887, + -0.12738673388957977 ], "tsne3d": [ - -28.719852447509766, - -13.7930269241333, - -3.6632580757141113 + -25.527854919433594, + -1.4733176231384277, + -10.942699432373047 ], "umap3d": [ - 0.4452485144138336, - 5.5089569091796875, - 3.3751840591430664 + 1.0462993383407593, + 5.435556888580322, + 4.138685703277588 ] }, "cluster_id": 2, @@ -178936,12 +179066,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, - "label": "Training Noise" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -179359,31 +179489,31 @@ "doi": "10.1038/s42256-024-00886-8", "projections": { "pca": [ - -0.2134697139263153, - -0.060534197837114334 + -0.21358153223991394, + -0.05951704457402229 ], "tsne": [ - -41.06603240966797, - 3.7250640392303467 + -37.25935363769531, + -10.298240661621094 ], "umap": [ - 1.8999704122543335, - 6.151182174682617 + 2.2650012969970703, + 5.40807580947876 ], "pca3d": [ - -0.21346935629844666, - -0.060538940131664276, - 0.0433146208524704 + -0.21358127892017365, + -0.059522271156311035, + 0.04349159076809883 ], "tsne3d": [ - -17.157665252685547, - 3.9092609882354736, - -3.7455034255981445 + -16.3604793548584, + -10.799535751342773, + 0.44039610028266907 ], "umap3d": [ - 1.8576668500900269, - 5.913057804107666, - 4.187507152557373 + 2.477640151977539, + 6.274990081787109, + 4.515883445739746 ] }, "cluster_id": 2, @@ -179394,12 +179524,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 33, - "label": "Neural Networks" + "id": 19, + "label": "Graph Neural Nets" } ], "_embedding": [ @@ -179816,46 +179946,46 @@ ], "projections": { "pca": [ - -0.3081820607185364, - -0.17249763011932373 + -0.30864179134368896, + -0.17153804004192352 ], "tsne": [ - -47.662994384765625, - -2.3797621726989746 + -45.09834289550781, + -1.9117684364318848 ], "umap": [ - 0.7018886208534241, - 7.3367180824279785 + 0.9983159303665161, + 6.486954689025879 ], "pca3d": [ - -0.308181494474411, - -0.1725151240825653, - -0.08285161852836609 + -0.30864110589027405, + -0.17155574262142181, + -0.08242501318454742 ], "tsne3d": [ - -22.751583099365234, - -2.589293956756592, - 4.001847743988037 + -23.88685417175293, + -3.6817679405212402, + 7.17922830581665 ], "umap3d": [ - 0.7809976935386658, - 5.272924423217773, - 4.759584426879883 + 1.7972755432128906, + 5.6323137283325195, + 5.492614269256592 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -180269,31 +180399,31 @@ ], "projections": { "pca": [ - -0.2764398157596588, - -0.06784803420305252 + -0.2767527401447296, + -0.06745143234729767 ], "tsne": [ - -50.0357780456543, - 9.281023025512695 + -52.040931701660156, + -18.23653221130371 ], "umap": [ - 1.033294916152954, - 5.5432634353637695 + 1.5321365594863892, + 4.792316436767578 ], "pca3d": [ - -0.27643951773643494, - -0.06785088032484055, - -0.10570216178894043 + -0.27675241231918335, + -0.0674542486667633, + -0.10558464378118515 ], "tsne3d": [ - -27.651655197143555, - -3.8614940643310547, - -11.427337646484375 + -23.5253849029541, + -11.717687606811523, + -9.50235366821289 ], "umap3d": [ - 1.091059684753418, - 6.44859504699707, - 3.8222155570983887 + 1.4869935512542725, + 6.557084083557129, + 4.228586196899414 ] }, "cluster_id": 2, @@ -180304,12 +180434,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -180723,31 +180853,31 @@ ], "projections": { "pca": [ - -0.2868686616420746, - -0.06701505184173584 + -0.28731465339660645, + -0.06681685894727707 ], "tsne": [ - -51.68870544433594, - 7.610944747924805 + -52.43278884887695, + -16.48590660095215 ], "umap": [ - 0.8826351165771484, - 5.4546942710876465 + 1.2864469289779663, + 4.638581275939941 ], "pca3d": [ - -0.2868686318397522, - -0.06698807328939438, - -0.11645953357219696 + -0.2873145341873169, + -0.06678907573223114, + -0.11649759858846664 ], "tsne3d": [ - -29.4539794921875, - -1.5807665586471558, - -10.22953987121582 + -26.122835159301758, + -12.76404857635498, + -7.968494415283203 ], "umap3d": [ - 1.0467798709869385, - 6.487273216247559, - 3.561772108078003 + 1.4273205995559692, + 6.404160976409912, + 4.029029846191406 ] }, "cluster_id": 2, @@ -180758,12 +180888,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -181176,47 +181306,47 @@ ], "projections": { "pca": [ - -0.07038920372724533, - -0.08552124351263046 + -0.0705074816942215, + -0.08543339371681213 ], "tsne": [ - -59.176048278808594, - -8.528759002685547 + -56.60737228393555, + -0.028871677815914154 ], "umap": [ - 0.4862375259399414, - 6.6801371574401855 + 0.6437035799026489, + 6.0425238609313965 ], "pca3d": [ - -0.07038884609937668, - -0.08554593473672867, - -0.109505794942379 + -0.07050709426403046, + -0.08545870333909988, + -0.10924909263849258 ], "tsne3d": [ - -30.935056686401367, - 6.613426685333252, - 4.926968574523926 + -37.02330780029297, + -0.18843460083007812, + -0.5616415143013 ], "umap3d": [ - 0.8068222999572754, - 6.145538330078125, - 4.841696262359619 + 1.521690845489502, + 6.458193778991699, + 5.309023857116699 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, - "label": "Training Noise" + "id": 16, + "label": "Vision Transformers" } ], "_embedding": [ @@ -181624,47 +181754,47 @@ "abstract": "A playbook for systematically maximizing the performance of deep learning models. - google-research/tuning_playbook", "projections": { "pca": [ - -0.2100306898355484, - 0.0581333227455616 + -0.2098962664604187, + 0.05879846587777138 ], "tsne": [ - -39.101890563964844, - -3.230471611022949 + -28.473445892333984, + -9.019692420959473 ], "umap": [ - 1.462849497795105, - 7.0635199546813965 + 1.9972684383392334, + 6.094742298126221 ], "pca3d": [ - -0.21003051102161407, - 0.05814630165696144, - -0.01148379035294056 + -0.2098962813615799, + 0.05881163477897644, + -0.01152553129941225 ], "tsne3d": [ - -16.682994842529297, - -3.4045541286468506, - 2.9906110763549805 + -17.59235191345215, + -2.612400531768799, + 4.883368492126465 ], "umap3d": [ - 1.6757891178131104, - 5.456074237823486, - 4.618949890136719 + 2.4926679134368896, + 5.834183692932129, + 4.989799499511719 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -182081,46 +182211,46 @@ ], "projections": { "pca": [ - -0.27308574318885803, - -0.17158545553684235 + -0.2734105587005615, + -0.17105956375598907 ], "tsne": [ - -54.32689666748047, - -4.231163501739502 + -50.75078201293945, + 2.7013485431671143 ], "umap": [ - 0.17358578741550446, - 7.314579486846924 + 0.48107343912124634, + 6.561490535736084 ], "pca3d": [ - -0.27308520674705505, - -0.17160923779010773, - -0.15567706525325775 + -0.2734099328517914, + -0.17108367383480072, + -0.15516169369220734 ], "tsne3d": [ - -28.00163459777832, - 1.569178581237793, - 7.0122833251953125 + -30.895648956298828, + -1.5133845806121826, + 4.6249494552612305 ], "umap3d": [ - 0.3771231770515442, - 5.566654682159424, - 4.955618858337402 + 1.3508470058441162, + 5.7870612144470215, + 5.757809638977051 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -182561,46 +182691,46 @@ ], "projections": { "pca": [ - -0.11334345489740372, - 0.0305348988622427 + -0.11315301060676575, + 0.030856864526867867 ], "tsne": [ - -0.8896169066429138, - -35.082183837890625 + -9.391655921936035, + 31.778234481811523 ], "umap": [ - 4.613653659820557, - 9.603520393371582 + 4.786778926849365, + 8.905786514282227 ], "pca3d": [ - -0.11334332078695297, - 0.030532652512192726, - -0.025584740564227104 + -0.11315290629863739, + 0.03085421770811081, + -0.025386646389961243 ], "tsne3d": [ - -2.566162109375, - -21.06099510192871, - 0.31693345308303833 + -7.203526020050049, + 11.99453067779541, + -8.70547103881836 ], "umap3d": [ - 3.8945300579071045, - 2.4885334968566895, - 3.173293352127075 + 4.655593395233154, + 2.9927051067352295, + 3.8018810749053955 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, + "id": 26, "label": "Medical AI" } ], @@ -183019,46 +183149,46 @@ ], "projections": { "pca": [ - -0.2894721031188965, - -0.08767292648553848 + -0.28961700201034546, + -0.08641904592514038 ], "tsne": [ - -56.00950622558594, - -3.841310501098633 + -50.44105529785156, + 4.961117744445801 ], "umap": [ - 0.19682978093624115, - 7.438888072967529 + 0.5004196763038635, + 6.645148277282715 ], "pca3d": [ - -0.2894715368747711, - -0.08771388977766037, - -0.07767745107412338 + -0.2896164059638977, + -0.08646095544099808, + -0.07727442681789398 ], "tsne3d": [ - -30.63433265686035, - -2.218963384628296, - 8.41933822631836 + -30.837051391601562, + 3.505291223526001, + 5.019171237945557 ], "umap3d": [ - 0.28295576572418213, - 5.262615203857422, - 4.900080680847168 + 1.3155754804611206, + 5.623721122741699, + 5.791711330413818 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -183471,46 +183601,46 @@ ], "projections": { "pca": [ - -0.24179448187351227, - -0.10417597740888596 + -0.24192599952220917, + -0.10300237685441971 ], "tsne": [ - -30.043834686279297, - -10.274728775024414 + -34.16398620605469, + 7.861515045166016 ], "umap": [ - 1.8283675909042358, - 7.980639934539795 + 1.924651026725769, + 7.326233386993408 ], "pca3d": [ - -0.24179403483867645, - -0.10418938100337982, - -0.003065236145630479 + -0.24192553758621216, + -0.10301575064659119, + -0.0026027720887213945 ], "tsne3d": [ - -17.776527404785156, - -7.547757148742676, - 7.450415134429932 + -18.16870880126953, + 3.8775811195373535, + 4.374973297119141 ], "umap3d": [ - 1.6640188694000244, - 4.608195781707764, - 4.961300373077393 + 2.7551703453063965, + 5.140312671661377, + 5.529374599456787 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 1, + "label": "Attention Models" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -183927,31 +184057,31 @@ "year": 2022, "projections": { "pca": [ - -0.2659046947956085, - -0.039659179747104645 + -0.26605406403541565, + -0.03925253078341484 ], "tsne": [ - -50.38507843017578, - -6.500892639160156 + -49.64883041381836, + -8.343579292297363 ], "umap": [ - 0.8469674587249756, - 5.867458343505859 + 1.122846007347107, + 5.1463623046875 ], "pca3d": [ - -0.2659045159816742, - -0.03966236487030983, - -0.13592393696308136 + -0.2660537660121918, + -0.039255596697330475, + -0.13582392036914825 ], "tsne3d": [ - -27.377548217773438, - -3.0156757831573486, - -5.627492904663086 + -26.151308059692383, + -4.833988189697266, + -3.589977741241455 ], "umap3d": [ - 0.7993603348731995, - 6.125726222991943, - 3.9562864303588867 + 1.3002431392669678, + 6.113104820251465, + 4.570997714996338 ] }, "cluster_id": 2, @@ -183962,12 +184092,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -184382,47 +184512,47 @@ ], "projections": { "pca": [ - -0.29801249504089355, - -0.12071164697408676 + -0.2983042001724243, + -0.12015382945537567 ], "tsne": [ - -53.99250030517578, - -10.416888236999512 + -53.71059036254883, + -5.2034993171691895 ], "umap": [ - 0.20011574029922485, - 6.7844743728637695 + 0.5980291366577148, + 5.9504475593566895 ], "pca3d": [ - -0.29801225662231445, - -0.12071423977613449, - -0.11438043415546417 + -0.29830387234687805, + -0.12015590071678162, + -0.11403757333755493 ], "tsne3d": [ - -28.937402725219727, - -3.537637948989868, - 1.934622883796692 + -28.870328903198242, + -3.732177972793579, + 0.43240514397621155 ], "umap3d": [ - 0.29420986771583557, - 5.754673480987549, - 4.375138282775879 + 1.174088954925537, + 5.883780002593994, + 5.13941764831543 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 9, - "label": "Vision Transformers" + "id": 23, + "label": "Training Noise" } ], "_embedding": [ @@ -184840,46 +184970,46 @@ ], "projections": { "pca": [ - -0.23880814015865326, - -0.10425017774105072 + -0.23922099173069, + -0.10326184332370758 ], "tsne": [ - -31.235219955444336, - -13.297609329223633 + -32.362850189208984, + 12.240347862243652 ], "umap": [ - 1.8617385625839233, - 8.214554786682129 + 2.152930498123169, + 7.577237129211426 ], "pca3d": [ - -0.2388080209493637, - -0.10423069447278976, - -0.009861016646027565 + -0.23922084271907806, + -0.10324215143918991, + -0.009598402306437492 ], "tsne3d": [ - -15.985755920410156, - -12.73745059967041, - 2.5291833877563477 + -19.37462615966797, + 2.2834360599517822, + 0.006254630163311958 ], "umap3d": [ - 1.725053310394287, - 4.271692752838135, - 4.6084303855896 + 2.6544723510742188, + 4.692777156829834, + 5.243473052978516 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 1, + "label": "Attention Models" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -185292,47 +185422,47 @@ ], "projections": { "pca": [ - -0.30814850330352783, - -0.12803736329078674 + -0.30862492322921753, + -0.12733127176761627 ], "tsne": [ - -44.76783752441406, - -3.489393711090088 + -44.25944519042969, + -3.8062446117401123 ], "umap": [ - 1.2838244438171387, - 6.770345687866211 + 1.5942474603652954, + 5.960208892822266 ], "pca3d": [ - -0.30814799666404724, - -0.12802846729755402, - -0.0229847002774477 + -0.3086244463920593, + -0.12732182443141937, + -0.022798588499426842 ], "tsne3d": [ - -22.05978775024414, - -3.950164318084717, - 1.2846366167068481 + -24.119441986083984, + -7.265092849731445, + 7.4332275390625 ], "umap3d": [ - 1.1515142917633057, - 5.461313724517822, - 4.176643371582031 + 1.8545684814453125, + 5.747501373291016, + 4.817785739898682 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -185747,31 +185877,31 @@ ], "projections": { "pca": [ - -0.2348678857088089, - -0.07493606209754944 + -0.2350103259086609, + -0.07442109286785126 ], "tsne": [ - -50.229522705078125, - -13.945304870605469 + -51.50643539428711, + -11.165635108947754 ], "umap": [ - 0.6317076683044434, - 5.931857585906982 + 0.935886025428772, + 5.164805889129639 ], "pca3d": [ - -0.23486775159835815, - -0.07491841167211533, - -0.113764189183712 + -0.23501001298427582, + -0.07440301030874252, + -0.11348426342010498 ], "tsne3d": [ - -27.81473159790039, - -7.9057817459106445, - -5.11860466003418 + -23.49618911743164, + -6.584874629974365, + -6.773464679718018 ], "umap3d": [ - 0.5490269660949707, - 5.962215423583984, - 3.686199426651001 + 1.154300570487976, + 5.897036552429199, + 4.366993427276611 ] }, "cluster_id": 2, @@ -185782,12 +185912,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, - "label": "Training Noise" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -186204,46 +186334,46 @@ ], "projections": { "pca": [ - -0.21748311817646027, - -0.17055349051952362 + -0.2178294062614441, + -0.16977527737617493 ], "tsne": [ - -31.822139739990234, - -1.8013360500335693 + -41.51457977294922, + 4.0045366287231445 ], "umap": [ - 0.8362400531768799, - 8.197504997253418 + 0.9794387221336365, + 7.383851528167725 ], "pca3d": [ - -0.21748247742652893, - -0.17056898772716522, - -0.047056764364242554 + -0.21782885491847992, + -0.16979047656059265, + -0.046568043529987335 ], "tsne3d": [ - -20.27787971496582, - -1.513827919960022, - 13.70329761505127 + -25.08330726623535, + 11.28264331817627, + 2.2050764560699463 ], "umap3d": [ - 1.0509684085845947, - 4.775097370147705, - 5.186192035675049 + 2.191100597381592, + 5.332243919372559, + 5.951000690460205 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -186656,31 +186786,31 @@ ], "projections": { "pca": [ - -0.21393071115016937, - -0.1291714459657669 + -0.21424250304698944, + -0.12843921780586243 ], "tsne": [ - -47.663307189941406, - -11.531956672668457 + -45.80779266357422, + -12.345096588134766 ], "umap": [ - 0.8899390697479248, - 6.154634952545166 + 1.1568593978881836, + 5.403914451599121 ], "pca3d": [ - -0.21393026411533356, - -0.12917527556419373, - -0.015169509686529636 + -0.214242085814476, + -0.12844318151474, + -0.014833019115030766 ], "tsne3d": [ - -24.702749252319336, - -6.507363796234131, - -4.817492961883545 + -21.063291549682617, + -6.064114093780518, + -4.9275898933410645 ], "umap3d": [ - 0.798182487487793, - 6.092946529388428, - 4.065652847290039 + 1.4289441108703613, + 6.0278520584106445, + 4.534276962280273 ] }, "cluster_id": 2, @@ -186691,12 +186821,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -187119,31 +187249,31 @@ "openalex_url": "https://openalex.org/W4306735081", "projections": { "pca": [ - 0.04395827278494835, - -0.08193326741456985 + 0.043705929070711136, + -0.08191320300102234 ], "tsne": [ - -9.137682914733887, - 19.615169525146484 + 3.871328830718994, + -30.593549728393555 ], "umap": [ - 5.24006986618042, - 4.695345878601074 + 5.6685967445373535, + 4.221433162689209 ], "pca3d": [ - 0.04395824670791626, - -0.08191145211458206, - 0.1452454775571823 + 0.04370591416954994, + -0.08189129084348679, + 0.14515894651412964 ], "tsne3d": [ - -6.026914596557617, - 2.6188979148864746, - 4.004693984985352 + -9.313946723937988, + -11.806551933288574, + 2.8716611862182617 ], "umap3d": [ - 5.327452182769775, - 6.902249813079834, - 4.393978595733643 + 5.470740795135498, + 7.425889492034912, + 2.7479286193847656 ] }, "cluster_id": 3, @@ -187154,11 +187284,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 9, + "label": "Protein Design" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -187575,46 +187705,46 @@ ], "projections": { "pca": [ - -0.2873583734035492, - -0.1342194676399231 + -0.2876701056957245, + -0.13356952369213104 ], "tsne": [ - -53.86271286010742, - -10.579825401306152 + -53.679054260253906, + -5.414595603942871 ], "umap": [ - 0.3952726423740387, - 6.585632801055908 + 0.7574617862701416, + 5.705677032470703 ], "pca3d": [ - -0.287357896566391, - -0.134216770529747, - -0.12868747115135193 + -0.28766965866088867, + -0.13356654345989227, + -0.12831027805805206 ], "tsne3d": [ - -29.299055099487305, - -3.8829851150512695, - 1.2333875894546509 + -28.77519989013672, + -3.9566407203674316, + -0.3626507818698883 ], "umap3d": [ - 0.3598831295967102, - 5.802816390991211, - 4.112007141113281 + 1.1333935260772705, + 5.911225318908691, + 4.800139904022217 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, + "id": 23, "label": "Training Noise" } ], @@ -188023,47 +188153,47 @@ ], "projections": { "pca": [ - -0.1334531456232071, - 0.13613493740558624 + -0.13305439054965973, + 0.1374337375164032 ], "tsne": [ - -3.084874391555786, - 0.950692355632782 + -12.995630264282227, + 3.8206911087036133 ], "umap": [ - 4.733567714691162, - 7.009625434875488 + 4.8540802001953125, + 6.699403285980225 ], "pca3d": [ - -0.13345327973365784, - 0.13612517714500427, - 0.05168040841817856 + -0.13305450975894928, + 0.13742388784885406, + 0.05169150233268738 ], "tsne3d": [ - -7.537342071533203, - 0.5140444040298462, - -4.8400959968566895 + -2.230581760406494, + 3.226532459259033, + 1.3505127429962158 ], "umap3d": [ - 4.259705543518066, - 4.230719566345215, - 3.9726924896240234 + 4.915736198425293, + 4.777430534362793, + 3.9053397178649902 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, - "label": "Genomics Papers" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -188480,31 +188610,31 @@ "journal": "Transactions on Machine Learning Research", "projections": { "pca": [ - -0.2196958214044571, - -0.08993737399578094 + -0.21999791264533997, + -0.08913785964250565 ], "tsne": [ - -49.31350326538086, - -14.283726692199707 + -50.93023681640625, + -10.236366271972656 ], "umap": [ - 0.748982310295105, - 5.953454494476318 + 0.9972814917564392, + 5.202422142028809 ], "pca3d": [ - -0.21969547867774963, - -0.08992856740951538, - -0.10288099944591522 + -0.21999762952327728, + -0.08912859857082367, + -0.10264202207326889 ], "tsne3d": [ - -29.46075439453125, - -6.584426403045654, - -6.439675807952881 + -25.96871566772461, + -6.0297956466674805, + -6.846024036407471 ], "umap3d": [ - 0.6514995098114014, - 5.965596675872803, - 3.6584835052490234 + 1.1820069551467896, + 5.881265163421631, + 4.381193161010742 ] }, "cluster_id": 2, @@ -188515,12 +188645,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, - "label": "Training Noise" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -188935,31 +189065,31 @@ ], "projections": { "pca": [ - -0.2865388095378876, - -0.1466256082057953 + -0.28711700439453125, + -0.14610569179058075 ], "tsne": [ - -48.35990524291992, - 5.323596477508545 + -48.849220275878906, + -17.9864444732666 ], "umap": [ - 1.0988097190856934, - 5.735331058502197 + 1.4595087766647339, + 4.783317565917969 ], "pca3d": [ - -0.2865384519100189, - -0.1466153860092163, - -0.1414596438407898 + -0.2871165871620178, + -0.1460949033498764, + -0.14123943448066711 ], "tsne3d": [ - -24.884681701660156, - 1.1221007108688354, - -7.910052299499512 + -21.80870246887207, + -12.99964427947998, + -3.3714358806610107 ], "umap3d": [ - 1.1537370681762695, - 6.340989112854004, - 3.7315237522125244 + 1.6257933378219604, + 6.3582868576049805, + 4.1865410804748535 ] }, "cluster_id": 2, @@ -188970,12 +189100,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -189391,31 +189521,31 @@ ], "projections": { "pca": [ - -0.23890215158462524, - -0.0632527619600296 + -0.23908676207065582, + -0.06204891577363014 ], "tsne": [ - -44.19474411010742, - -7.47008752822876 + -46.288570404052734, + -7.933732032775879 ], "umap": [ - 1.1628291606903076, - 6.2769856452941895 + 1.474359154701233, + 5.53513765335083 ], "pca3d": [ - -0.23890186846256256, - -0.06325285136699677, - -0.11574309319257736 + -0.23908649384975433, + -0.062048763036727905, + -0.11542505025863647 ], "tsne3d": [ - -23.521806716918945, - -3.5048038959503174, - -5.547973155975342 + -23.08098793029785, + -4.335947513580322, + -2.0067250728607178 ], "umap3d": [ - 1.0567831993103027, - 5.778714656829834, - 3.850193500518799 + 1.6926820278167725, + 5.767551422119141, + 4.376246929168701 ] }, "cluster_id": 2, @@ -189426,12 +189556,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -189850,46 +189980,46 @@ ], "projections": { "pca": [ - -0.26605212688446045, - -0.09900692850351334 + -0.2661886215209961, + -0.09843683987855911 ], "tsne": [ - -56.08709716796875, - -1.7568957805633545 + -48.78276443481445, + 5.215548992156982 ], "umap": [ - 0.10717370361089706, - 7.618475437164307 + 0.3848177492618561, + 6.758191108703613 ], "pca3d": [ - -0.26605138182640076, - -0.09906690567731857, - -0.1468024104833603 + -0.26618799567222595, + -0.09849773347377777, + -0.14641697704792023 ], "tsne3d": [ - -27.311546325683594, - -1.7087541818618774, - 9.850494384765625 + -28.318174362182617, + 2.043773651123047, + 6.136103630065918 ], "umap3d": [ - 0.2888227701187134, - 5.2915472984313965, - 5.058749675750732 + 1.3679906129837036, + 5.591828346252441, + 5.9056220054626465 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -190304,47 +190434,47 @@ ], "projections": { "pca": [ - -0.27307575941085815, - -0.15332640707492828 + -0.2734680473804474, + -0.15235793590545654 ], "tsne": [ - -47.000160217285156, - 2.501004457473755 + -41.90687942504883, + -10.446837425231934 ], "umap": [ - 1.1039079427719116, - 6.495275974273682 + 1.5241163969039917, + 5.706380844116211 ], "pca3d": [ - -0.27307504415512085, - -0.15335769951343536, - -0.08876851946115494 + -0.27346745133399963, + -0.15238985419273376, + -0.08836062252521515 ], "tsne3d": [ - -23.992198944091797, - 3.325840711593628, - -5.892407417297363 + -20.14201545715332, + -10.77138614654541, + -2.7352824211120605 ], "umap3d": [ - 1.0247044563293457, - 5.640583038330078, - 3.997913360595703 + 1.581742286682129, + 5.744235515594482, + 4.544497013092041 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -190771,47 +190901,47 @@ "cited_by_count": 449, "projections": { "pca": [ - -0.17953699827194214, - -0.03187589719891548 + -0.17973671853542328, + -0.030375415459275246 ], "tsne": [ - -20.209869384765625, - -25.589031219482422 + -21.247480392456055, + 15.232173919677734 ], "umap": [ - 2.7271921634674072, - 7.754893779754639 + 2.9055659770965576, + 6.975734710693359 ], "pca3d": [ - -0.17953667044639587, - -0.0318877249956131, - 0.05916869640350342 + -0.17973637580871582, + -0.03038702718913555, + 0.059264495968818665 ], "tsne3d": [ - -13.99250602722168, - -8.199053764343262, - -5.808011531829834 + -14.511597633361816, + 15.062202453613281, + 5.384464263916016 ], "umap3d": [ - 2.16041898727417, - 4.444887161254883, - 3.7334582805633545 + 2.989619255065918, + 4.772997856140137, + 4.2261505126953125 ] }, - "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 1, - "label": "AI Scientific Research" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { "id": 0, - "label": "LLM Language Learning" + "label": "Math AI" } ], "_embedding": [ @@ -191230,47 +191360,47 @@ ], "projections": { "pca": [ - -0.22925709187984467, - -0.00621920358389616 + -0.2293972223997116, + -0.005007138010114431 ], "tsne": [ - -42.01763916015625, - -23.536216735839844 + -45.45338439941406, + 18.68998908996582 ], "umap": [ - 1.8068915605545044, - 7.018496513366699 + 2.2626399993896484, + 6.3175554275512695 ], "pca3d": [ - -0.2292570322751999, - -0.006200379692018032, - -0.02928079292178154 + -0.2293972671031952, + -0.004988015163689852, + -0.02916024811565876 ], "tsne3d": [ - -24.476455688476562, - -14.456110954284668, - -3.7266600131988525 + -23.107141494750977, + 1.4804928302764893, + -10.666299819946289 ], "umap3d": [ - 0.8808808326721191, - 4.795986652374268, - 3.495635747909546 + 1.6301906108856201, + 4.920473575592041, + 4.221292495727539 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -191686,47 +191816,47 @@ ], "projections": { "pca": [ - -0.27077534794807434, - -0.16394755244255066 + -0.2712498903274536, + -0.16335947811603546 ], "tsne": [ - -48.41232681274414, - -6.819640159606934 + -49.27680206298828, + -5.802755832672119 ], "umap": [ - 0.9188438653945923, - 6.4980034828186035 + 1.3313549757003784, + 5.793463230133057 ], "pca3d": [ - -0.2707749009132385, - -0.16394931077957153, - -0.06311675161123276 + -0.27124935388565063, + -0.16336072981357574, + -0.0628058910369873 ], "tsne3d": [ - -25.730304718017578, - -0.23847393691539764, - -2.693824052810669 + -25.56135368347168, + -9.796187400817871, + 0.7003831267356873 ], "umap3d": [ - 0.9462012648582458, - 5.760759353637695, - 4.223311424255371 + 1.6617859601974487, + 5.965989589691162, + 4.889213562011719 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -192155,47 +192285,47 @@ "cited_by_count": 27, "projections": { "pca": [ - -0.16525141894817352, - -0.022906264290213585 + -0.16542603075504303, + -0.022864222526550293 ], "tsne": [ - 8.923975944519043, - -34.30548095703125 + 0.09676489233970642, + 35.19914627075195 ], "umap": [ - 5.822699069976807, - 9.781096458435059 + 5.85698938369751, + 9.148433685302734 ], "pca3d": [ - -0.165251225233078, - -0.02290886454284191, - -0.17122358083724976 + -0.16542579233646393, + -0.02286658063530922, + -0.17115958034992218 ], "tsne3d": [ - 4.070657730102539, - -16.07986831665039, - 1.8667224645614624 + -1.3766483068466187, + 10.591772079467773, + -15.140948295593262 ], "umap3d": [ - 4.73720121383667, - 2.511194944381714, - 2.4466915130615234 + 5.103564262390137, + 2.5852580070495605, + 2.875601053237915 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -192615,31 +192745,31 @@ "cited_by_count": 36, "projections": { "pca": [ - -0.09793999791145325, - 0.13370652496814728 + -0.0979110598564148, + 0.13419756293296814 ], "tsne": [ - -51.67832565307617, - 9.851444244384766 + -51.28950500488281, + -19.515796661376953 ], "umap": [ - 0.9322613477706909, - 5.370201110839844 + 1.4084463119506836, + 4.53150749206543 ], "pca3d": [ - -0.09794037789106369, - 0.13374267518520355, - -0.00656242948025465 + -0.09791156649589539, + 0.13423475623130798, + -0.006943719461560249 ], "tsne3d": [ - -27.92251205444336, - 0.00505277281627059, - -13.05331039428711 + -23.674095153808594, + -14.698068618774414, + -9.545576095581055 ], "umap3d": [ - 1.1439439058303833, - 6.615082740783691, - 3.5597636699676514 + 1.5058858394622803, + 6.514124870300293, + 3.9925758838653564 ] }, "cluster_id": 2, @@ -192650,12 +192780,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -193073,46 +193203,46 @@ "cited_by_count": 582, "projections": { "pca": [ - -0.17949165403842926, - 0.10689353942871094 + -0.17887768149375916, + 0.10711508244276047 ], "tsne": [ - -6.950828552246094, - -37.651397705078125 + -14.956808090209961, + 30.008926391601562 ], "umap": [ - 4.916159152984619, - 9.43069839477539 + 4.9118475914001465, + 8.768688201904297 ], "pca3d": [ - -0.17949178814888, - 0.10689020156860352, - -0.06718923896551132 + -0.1788778007030487, + 0.10711153596639633, + -0.06699203699827194 ], "tsne3d": [ - -5.262712478637695, - -19.83435821533203, - -7.882397651672363 + -13.906134605407715, + 13.052518844604492, + -13.020576477050781 ], "umap3d": [ - 4.196747303009033, - 2.692498207092285, - 3.050640821456909 + 4.855043888092041, + 3.0482945442199707, + 3.63729190826416 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, + "id": 26, "label": "Medical AI" } ], @@ -193552,47 +193682,47 @@ "openalex_url": "https://openalex.org/W4289523162", "projections": { "pca": [ - -0.2676418423652649, - 0.006732470355927944 + -0.2675778865814209, + 0.00796756986528635 ], "tsne": [ - -21.852209091186523, - -23.781635284423828 + -21.570571899414062, + 13.15904712677002 ], "umap": [ - 2.7176342010498047, - 7.738785266876221 + 2.898956537246704, + 6.958785533905029 ], "pca3d": [ - -0.26764142513275146, - 0.00673058582469821, - 0.039631783962249756 + -0.2675777077674866, + 0.007965728640556335, + 0.03982597216963768 ], "tsne3d": [ - -15.314428329467773, - -5.347137451171875, - -5.152871608734131 + -16.507246017456055, + 11.953489303588867, + 3.1683707237243652 ], "umap3d": [ - 2.1932194232940674, - 4.4142255783081055, - 3.633631706237793 + 2.9533793926239014, + 4.823366165161133, + 4.2429962158203125 ] }, - "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 1, - "label": "AI Scientific Research" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { "id": 0, - "label": "LLM Language Learning" + "label": "Math AI" } ], "_embedding": [ @@ -194007,47 +194137,47 @@ ], "projections": { "pca": [ - -0.26797571778297424, - -0.1885014772415161 + -0.2685909569263458, + -0.18764108419418335 ], "tsne": [ - -45.30551528930664, - -6.694960594177246 + -45.84687805175781, + -5.270787715911865 ], "umap": [ - 1.0160906314849854, - 6.6467061042785645 + 1.415266990661621, + 5.834798812866211 ], "pca3d": [ - -0.26797521114349365, - -0.18849359452724457, - -0.10440544784069061 + -0.26859045028686523, + -0.18763253092765808, + -0.10402752459049225 ], "tsne3d": [ - -23.686813354492188, - -3.784536123275757, - -0.8903932571411133 + -24.049022674560547, + -6.563695907592773, + 0.6452473998069763 ], "umap3d": [ - 1.1025584936141968, - 5.602299690246582, - 4.32554817199707 + 1.9554402828216553, + 5.848752498626709, + 4.907932758331299 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -194460,47 +194590,47 @@ ], "projections": { "pca": [ - -0.17514881491661072, - 0.016986992210149765 + -0.17515482008457184, + 0.018170759081840515 ], "tsne": [ - -41.0365104675293, - -23.859664916992188 + -44.42424774169922, + 18.79656982421875 ], "umap": [ - 1.820281744003296, - 7.160440921783447 + 2.301971673965454, + 6.4021124839782715 ], "pca3d": [ - -0.1751486212015152, - 0.01698438823223114, - 0.03839537128806114 + -0.17515473067760468, + 0.01816784217953682, + 0.03846048191189766 ], "tsne3d": [ - -23.479928970336914, - -14.779887199401855, - -2.4372434616088867 + -23.513214111328125, + 3.110722780227661, + -11.035951614379883 ], "umap3d": [ - 0.8441051840782166, - 4.716579914093018, - 3.4817869663238525 + 1.5798298120498657, + 4.808096885681152, + 4.213798522949219 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -194916,31 +195046,31 @@ "year": 2022, "projections": { "pca": [ - -0.21258768439292908, - 0.11633910238742828 + -0.21242555975914001, + 0.1164669468998909 ], "tsne": [ - -47.42068862915039, - 6.465863227844238 + -48.474761962890625, + -19.178653717041016 ], "umap": [ - 1.2560594081878662, - 5.512923240661621 + 1.6538256406784058, + 4.720150947570801 ], "pca3d": [ - -0.21258795261383057, - 0.11636444926261902, - 0.00240808236412704 + -0.2124258279800415, + 0.1164928749203682, + 0.0021068574860692024 ], "tsne3d": [ - -23.136476516723633, - 0.05565771088004112, - -9.393675804138184 + -20.834457397460938, + -15.468039512634277, + -3.9776978492736816 ], "umap3d": [ - 1.3716111183166504, - 6.536435604095459, - 3.8227617740631104 + 1.8017364740371704, + 6.607416152954102, + 4.176298141479492 ] }, "cluster_id": 2, @@ -194951,12 +195081,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -195369,31 +195499,31 @@ ], "projections": { "pca": [ - -0.23677925765514374, - -0.10967817902565002 + -0.23712840676307678, + -0.10855873674154282 ], "tsne": [ - -46.9697380065918, - -14.162491798400879 + -48.29609298706055, + -10.395392417907715 ], "umap": [ - 0.9717079997062683, - 6.107205867767334 + 1.2164556980133057, + 5.315241813659668 ], "pca3d": [ - -0.23677872121334076, - -0.10968432575464249, - -0.08854268491268158 + -0.2371281385421753, + -0.10856492072343826, + -0.08819267898797989 ], "tsne3d": [ - -26.225677490234375, - -8.223857879638672, - -9.28840446472168 + -24.118743896484375, + -3.1555447578430176, + -4.646689414978027 ], "umap3d": [ - 0.8521791696548462, - 5.919658660888672, - 3.781108856201172 + 1.4081758260726929, + 5.982186794281006, + 4.395377159118652 ] }, "cluster_id": 2, @@ -195404,12 +195534,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -195826,47 +195956,47 @@ ], "projections": { "pca": [ - -0.22861814498901367, - 0.07214374095201492 + -0.22825887799263, + 0.07303585857152939 ], "tsne": [ - -31.141626358032227, - -23.85712242126465 + -36.04087829589844, + 18.813432693481445 ], "umap": [ - 1.9618582725524902, - 7.108130931854248 + 1.9913657903671265, + 6.128170490264893 ], "pca3d": [ - -0.2286183387041092, - 0.07214716821908951, - -0.05888086557388306 + -0.22825895249843597, + 0.07303889095783234, + -0.05869609862565994 ], "tsne3d": [ - -15.479894638061523, - -14.225190162658691, - -3.601820468902588 + -21.986080169677734, + 13.761144638061523, + -1.965206265449524 ], "umap3d": [ - 1.4678527116775513, - 4.940138339996338, - 4.0169477462768555 + 2.3041114807128906, + 5.1851301193237305, + 4.552737236022949 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -196286,46 +196416,46 @@ ], "projections": { "pca": [ - -0.32511064410209656, - -0.16456031799316406 + -0.32552477717399597, + -0.16388602554798126 ], "tsne": [ - -54.17327880859375, - -3.3622002601623535 + -49.96476745605469, + 2.9591801166534424 ], "umap": [ - 0.24605342745780945, - 7.360299587249756 + 0.5188419222831726, + 6.543501377105713 ], "pca3d": [ - -0.3251100182533264, - -0.16458286345005035, - -0.14993824064731598 + -0.3255241811275482, + -0.16390834748744965, + -0.1494787484407425 ], "tsne3d": [ - -26.55167007446289, - 0.6163125038146973, - 7.760213851928711 + -29.589815139770508, + -0.9363673329353333, + 5.639250755310059 ], "umap3d": [ - 0.4148452579975128, - 5.475314617156982, - 4.980867385864258 + 1.3630973100662231, + 5.79183292388916, + 5.735551834106445 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -196742,47 +196872,47 @@ ], "projections": { "pca": [ - -0.23641276359558105, - -0.08193299174308777 + -0.2367440015077591, + -0.08097705990076065 ], "tsne": [ - -46.26505661010742, - -10.07811450958252 + -45.618812561035156, + -10.132057189941406 ], "umap": [ - 0.7845038175582886, - 6.345795631408691 + 1.2681347131729126, + 5.562735557556152 ], "pca3d": [ - -0.23641248047351837, - -0.08193594217300415, - -0.09663151204586029 + -0.23674376308918, + -0.0809800997376442, + -0.09642422199249268 ], "tsne3d": [ - -26.854259490966797, - -6.359410285949707, - -1.0069873332977295 + -24.177785873413086, + -8.177251815795898, + -3.0558242797851562 ], "umap3d": [ - 0.8644545078277588, - 5.956863880157471, - 4.003628253936768 + 1.4834777116775513, + 5.884298324584961, + 4.726876735687256 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -197200,46 +197330,46 @@ "cited_by_count": 281, "projections": { "pca": [ - -0.2543504238128662, - 0.04607566073536873 + -0.2541148066520691, + 0.04742554947733879 ], "tsne": [ - -25.834257125854492, - -16.88524055480957 + -29.095340728759766, + 15.389116287231445 ], "umap": [ - 2.2158684730529785, - 8.445680618286133 + 2.4587559700012207, + 7.659077167510986 ], "pca3d": [ - -0.2543504536151886, - 0.046081412583589554, - -0.03898066654801369 + -0.25411486625671387, + 0.047431185841560364, + -0.038659121841192245 ], "tsne3d": [ - -15.574237823486328, - -17.514392852783203, - 5.7280378341674805 + -17.96906852722168, + 7.926575660705566, + -4.957452774047852 ], "umap3d": [ - 1.9536479711532593, - 3.8238043785095215, - 4.379004955291748 + 2.994713068008423, + 4.338514804840088, + 5.058223247528076 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -197653,31 +197783,31 @@ ], "projections": { "pca": [ - -0.21745386719703674, - -0.14957959949970245 + -0.2179555743932724, + -0.14908531308174133 ], "tsne": [ - -43.23326110839844, - -0.2098529189825058 + -42.14115524291992, + -7.452613353729248 ], "umap": [ - 1.1799042224884033, - 6.346199035644531 + 1.6408932209014893, + 5.564862251281738 ], "pca3d": [ - -0.2174535095691681, - -0.14957398176193237, - -0.07467974722385406 + -0.21795518696308136, + -0.14907868206501007, + -0.07446051388978958 ], "tsne3d": [ - -20.755586624145508, - -0.3776925802230835, - -3.391130208969116 + -20.743324279785156, + -7.530470371246338, + 1.950066089630127 ], "umap3d": [ - 1.3498756885528564, - 5.8771233558654785, - 4.1461076736450195 + 2.104142904281616, + 6.0737738609313965, + 4.723155498504639 ] }, "cluster_id": 2, @@ -197688,12 +197818,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -198114,47 +198244,47 @@ ], "projections": { "pca": [ - -0.3203767240047455, - -0.09308770298957825 + -0.32068386673927307, + -0.09163454920053482 ], "tsne": [ - -43.24867248535156, - -5.815124988555908 + -44.664947509765625, + -6.2888360023498535 ], "umap": [ - 1.1922101974487305, - 6.785039901733398 + 1.48722505569458, + 5.9127678871154785 ], "pca3d": [ - -0.3203764855861664, - -0.0930839255452156, - 0.011718637309968472 + -0.32068362832069397, + -0.0916302353143692, + 0.012040423229336739 ], "tsne3d": [ - -21.106840133666992, - -4.455539703369141, - -3.473031520843506 + -22.60723304748535, + -3.679565668106079, + 0.6445629000663757 ], "umap3d": [ - 1.1655174493789673, - 5.486522197723389, - 4.306795597076416 + 2.0360567569732666, + 5.647813320159912, + 4.91834831237793 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -198571,46 +198701,46 @@ "doi": "10.1145/3505244", "projections": { "pca": [ - -0.33077818155288696, - -0.1197514608502388 + -0.3309125006198883, + -0.11864063143730164 ], "tsne": [ - -56.03023147583008, - -2.5436458587646484 + -48.041812896728516, + 5.126202583312988 ], "umap": [ - 0.24052612483501434, - 7.722692012786865 + 0.4988856017589569, + 6.831507682800293 ], "pca3d": [ - -0.3307773768901825, - -0.11980022490024567, - -0.09876637160778046 + -0.3309117555618286, + -0.11869033426046371, + -0.09821204841136932 ], "tsne3d": [ - -28.703737258911133, - -3.1228747367858887, - 9.70566177368164 + -28.601591110229492, + 3.2621073722839355, + 4.643341541290283 ], "umap3d": [ - 0.3828947842121124, - 5.175995349884033, - 5.0023651123046875 + 1.4826961755752563, + 5.520657539367676, + 5.890216827392578 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -199025,31 +199155,31 @@ ], "projections": { "pca": [ - -0.2245693951845169, - -0.06686893850564957 + -0.2247612029314041, + -0.06598703563213348 ], "tsne": [ - -46.791412353515625, - -16.31607437133789 + -39.324493408203125, + -14.91565227508545 ], "umap": [ - 1.5708361864089966, - 5.787779808044434 + 2.156273603439331, + 5.014644145965576 ], "pca3d": [ - -0.22456902265548706, - -0.06687655299901962, - -0.07856978476047516 + -0.22476078569889069, + -0.06599468737840652, + -0.07829691469669342 ], "tsne3d": [ - -24.532119750976562, - -10.56326675415039, - -5.4268012046813965 + -21.851051330566406, + -3.843578577041626, + -8.542848587036133 ], "umap3d": [ - 1.3917936086654663, - 6.039485454559326, - 3.9381930828094482 + 1.7634950876235962, + 6.026266098022461, + 4.218189716339111 ] }, "cluster_id": 2, @@ -199060,12 +199190,12 @@ "label": "Generative Models" }, { - "id": 1, - "label": "Network Learning" + "id": 5, + "label": "Diffusion Models" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 22, + "label": "Diffusion Models" } ], "_embedding": [ @@ -199481,47 +199611,47 @@ "journal": "Advances in Neural Information Processing Systems", "projections": { "pca": [ - -0.24897676706314087, - 0.005036513786762953 + -0.24871279299259186, + 0.005719628185033798 ], "tsne": [ - -42.9852294921875, - -6.438685417175293 + -44.79568099975586, + -7.319526672363281 ], "umap": [ - 1.1011091470718384, - 6.4659528732299805 + 1.5469205379486084, + 5.712345600128174 ], "pca3d": [ - -0.2489766776561737, - 0.005022676195949316, - -0.026170892640948296 + -0.2487126588821411, + 0.005706198513507843, + -0.025914981961250305 ], "tsne3d": [ - -20.844558715820312, - -3.5518298149108887, - -5.155071258544922 + -21.16706085205078, + -4.656073570251465, + -0.3832501471042633 ], "umap3d": [ - 1.1901530027389526, - 5.642319679260254, - 4.167950630187988 + 2.0718092918395996, + 5.770036697387695, + 4.7732415199279785 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -199935,47 +200065,47 @@ ], "projections": { "pca": [ - -0.26318293809890747, - -0.1456419974565506 + -0.26360708475112915, + -0.1447555273771286 ], "tsne": [ - -43.64565658569336, - -5.817711353302002 + -44.92156219482422, + -6.729618072509766 ], "umap": [ - 1.0407829284667969, - 6.433968544006348 + 1.5084314346313477, + 5.674145698547363 ], "pca3d": [ - -0.26318255066871643, - -0.1456291824579239, - -0.08761201798915863 + -0.26360663771629333, + -0.14474210143089294, + -0.08727900683879852 ], "tsne3d": [ - -21.83238410949707, - -3.4147560596466064, - -3.750974655151367 + -22.119211196899414, + -4.726986885070801, + 0.7810445427894592 ], "umap3d": [ - 1.2147544622421265, - 5.763366222381592, - 4.1766510009765625 + 1.9069348573684692, + 5.889565944671631, + 4.766729831695557 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -200392,46 +200522,46 @@ ], "projections": { "pca": [ - -0.335358202457428, - -0.13103237748146057 + -0.3357263505458832, + -0.13023220002651215 ], "tsne": [ - -50.16617965698242, - -8.74089241027832 + -51.43980026245117, + -6.373312950134277 ], "umap": [ - 0.478279173374176, - 6.293147087097168 + 0.772733747959137, + 5.515623092651367 ], "pca3d": [ - -0.33535778522491455, - -0.13101816177368164, - -0.13760139048099518 + -0.3357261121273041, + -0.13021765649318695, + -0.13723479211330414 ], "tsne3d": [ - -28.76679039001465, - -2.115877628326416, - -2.493880033493042 + -28.006755828857422, + -6.548581600189209, + -1.9273943901062012 ], "umap3d": [ - 0.42490556836128235, - 5.955733299255371, - 3.998708724975586 + 1.1295913457870483, + 5.9716973304748535, + 4.769698619842529 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, + "id": 23, "label": "Training Noise" } ], @@ -200853,47 +200983,47 @@ ], "projections": { "pca": [ - -0.25206059217453003, - -0.08982507139444351 + -0.2523319125175476, + -0.08921333402395248 ], "tsne": [ - -43.47526931762695, - -8.973489761352539 + -47.542972564697266, + -8.811514854431152 ], "umap": [ - 0.857184886932373, - 6.396243095397949 + 1.2016512155532837, + 5.371504306793213 ], "pca3d": [ - -0.2520602345466614, - -0.08982690423727036, - -0.13390477001667023 + -0.25233152508735657, + -0.08921469748020172, + -0.1336432695388794 ], "tsne3d": [ - -23.775630950927734, - -4.266701698303223, - -8.440366744995117 + -24.473787307739258, + -1.5654383897781372, + -2.042173147201538 ], "umap3d": [ - 0.9181914925575256, - 5.967963695526123, - 4.076736927032471 + 1.5919815301895142, + 6.124748229980469, + 4.650857448577881 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -201305,47 +201435,47 @@ ], "projections": { "pca": [ - -0.25443124771118164, - -0.08995012193918228 + -0.25468742847442627, + -0.08927779644727707 ], "tsne": [ - -44.82231140136719, - -19.29404640197754 + -48.85765075683594, + 12.363504409790039 ], "umap": [ - 0.841307520866394, - 6.84007453918457 + 1.0276741981506348, + 5.84402322769165 ], "pca3d": [ - -0.25443121790885925, - -0.08993840217590332, - -0.06676454842090607 + -0.2546873092651367, + -0.08926577121019363, + -0.06651627272367477 ], "tsne3d": [ - -29.696422576904297, - -10.018171310424805, - 1.9754503965377808 + -27.374120712280273, + -0.053662680089473724, + -4.502650737762451 ], "umap3d": [ - 0.5918535590171814, - 5.294289588928223, - 3.89774751663208 + 1.3417119979858398, + 5.395467758178711, + 4.609764575958252 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 23, + "label": "Training Noise" } ], "_embedding": [ @@ -201753,46 +201883,46 @@ "abstract": "Beyond the Imitation Game collaborative benchmark for measuring and extrapolating the capabilities of language models - google/BIG-bench", "projections": { "pca": [ - -0.1906042993068695, - 0.05889463797211647 + -0.1903226226568222, + 0.06002894416451454 ], "tsne": [ - -24.081806182861328, - -11.117829322814941 + -27.5274715423584, + 9.478120803833008 ], "umap": [ - 2.0873055458068848, - 8.35466480255127 + 2.4686851501464844, + 7.582515716552734 ], "pca3d": [ - -0.19060419499874115, - 0.05889159440994263, - 0.11759775131940842 + -0.19032251834869385, + 0.06002619117498398, + 0.11773503571748734 ], "tsne3d": [ - -12.556317329406738, - -9.802457809448242, - 8.534801483154297 + -13.110467910766602, + 4.652220249176025, + 1.282443881034851 ], "umap3d": [ - 2.13127064704895, - 4.13216495513916, - 4.52484130859375 + 3.175123691558838, + 4.730321884155273, + 5.166843414306641 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -202204,47 +202334,47 @@ "abstract": "Promoting openness in scientific communication and the peer-review process", "projections": { "pca": [ - -0.1375383585691452, - 0.247914656996727 + -0.13640914857387543, + 0.24760955572128296 ], "tsne": [ - -8.618048667907715, - -15.501946449279785 + -14.789389610290527, + 9.762397766113281 ], "umap": [ - 4.102340221405029, - 7.936986923217773 + 4.333364009857178, + 7.3875861167907715 ], "pca3d": [ - -0.1375390887260437, - 0.24795040488243103, - 0.04647848382592201 + -0.13641007244586945, + 0.24764542281627655, + 0.04633956775069237 ], "tsne3d": [ - -6.391075134277344, - -12.202719688415527, - 5.2879486083984375 + -5.545235633850098, + 7.498076915740967, + -4.9515557289123535 ], "umap3d": [ - 3.474062204360962, - 3.564929723739624, - 4.044809818267822 + 4.3609232902526855, + 4.121660232543945, + 4.345194339752197 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -202657,47 +202787,47 @@ ], "projections": { "pca": [ - -0.17998576164245605, - 0.015197779051959515 + -0.1799623966217041, + 0.016454681754112244 ], "tsne": [ - -40.2802619934082, - -23.41461753845215 + -43.75754165649414, + 18.22652244567871 ], "umap": [ - 1.8138548135757446, - 7.200472354888916 + 2.249950885772705, + 6.451668739318848 ], "pca3d": [ - -0.1799856424331665, - 0.015181375667452812, - 0.00038530866731889546 + -0.17996223270893097, + 0.016437644138932228, + 0.0005417768843472004 ], "tsne3d": [ - -23.701122283935547, - -16.223316192626953, - -1.3481615781784058 + -22.531835556030273, + 4.542507171630859, + -10.101923942565918 ], "umap3d": [ - 0.8158380389213562, - 4.657498359680176, - 3.52836275100708 + 1.5924423933029175, + 4.78472900390625, + 4.229766368865967 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -203117,31 +203247,31 @@ ], "projections": { "pca": [ - -0.2765404284000397, - -0.15656043589115143 + -0.2772156596183777, + -0.15629909932613373 ], "tsne": [ - -51.1329460144043, - 6.773179531097412 + -51.71467208862305, + -15.80856704711914 ], "umap": [ - 0.8965753316879272, - 5.480415344238281 + 1.3092416524887085, + 4.648004531860352 ], "pca3d": [ - -0.27654018998146057, - -0.1565348505973816, - -0.11061476916074753 + -0.2772153317928314, + -0.15627235174179077, + -0.11049138009548187 ], "tsne3d": [ - -28.1015682220459, - -0.965962290763855, - -8.859417915344238 + -25.328983306884766, + -12.570515632629395, + -6.232379913330078 ], "umap3d": [ - 1.0677025318145752, - 6.464439392089844, - 3.612700939178467 + 1.4247621297836304, + 6.425466537475586, + 4.049600124359131 ] }, "cluster_id": 2, @@ -203152,12 +203282,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -203565,46 +203695,46 @@ "abstract": "Probing the representations of Vision Transformers. - sayakpaul/probing-vits", "projections": { "pca": [ - -0.1979226917028427, - -0.0005904281279072165 + -0.19779536128044128, + 3.564800135791302e-05 ], "tsne": [ - -57.01138687133789, - -4.301496505737305 + -51.33882522583008, + 5.452273368835449 ], "umap": [ - 0.13779088854789734, - 7.46290922164917 + 0.4397803843021393, + 6.5658159255981445 ], "pca3d": [ - -0.1979224532842636, - -0.000606003450229764, - -0.05923699215054512 + -0.19779518246650696, + 1.9883114873664454e-05, + -0.059051137417554855 ], "tsne3d": [ - -31.97551155090332, - -1.184159517288208, - 9.445059776306152 + -32.00979232788086, + 3.3708975315093994, + 6.583040237426758 ], "umap3d": [ - 0.2983296513557434, - 5.332935333251953, - 4.939933776855469 + 1.3011071681976318, + 5.6836113929748535, + 5.80232572555542 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -204025,47 +204155,47 @@ ], "projections": { "pca": [ - -0.25136929750442505, - -0.15909609198570251 + -0.25179725885391235, + -0.15851062536239624 ], "tsne": [ - -45.59922409057617, - -2.4934279918670654 + -42.865882873535156, + -1.3996186256408691 ], "umap": [ - 1.1845132112503052, - 7.252175807952881 + 1.5878320932388306, + 6.348476409912109 ], "pca3d": [ - -0.25136885046958923, - -0.15909600257873535, - -0.031275440007448196 + -0.25179678201675415, + -0.1585100293159485, + -0.030961811542510986 ], "tsne3d": [ - -18.926773071289062, - -3.1285223960876465, - 3.529620409011841 + -20.2803955078125, + -2.143552303314209, + 5.038881778717041 ], "umap3d": [ - 1.3862272500991821, - 5.332355499267578, - 4.656932353973389 + 2.2305407524108887, + 5.702796936035156, + 5.15018367767334 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -204480,46 +204610,46 @@ ], "projections": { "pca": [ - -0.3007754981517792, - -0.17204025387763977 + -0.30125513672828674, + -0.17154185473918915 ], "tsne": [ - -53.327571868896484, - -4.016579627990723 + -49.55562973022461, + 1.9338414669036865 ], "umap": [ - 0.23503315448760986, - 7.2134904861450195 + 0.5073839426040649, + 6.515585422515869 ], "pca3d": [ - -0.3007749319076538, - -0.17205709218978882, - -0.11886373162269592 + -0.3012545704841614, + -0.1715587079524994, + -0.11850657314062119 ], "tsne3d": [ - -28.440452575683594, - -0.7437688112258911, - 6.172722339630127 + -28.10253143310547, + -0.5807563066482544, + 5.118544578552246 ], "umap3d": [ - 0.3476884067058563, - 5.399049758911133, - 4.743884563446045 + 1.3175164461135864, + 5.750303268432617, + 5.641213893890381 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -204967,31 +205097,31 @@ "cited_by_count": 700, "projections": { "pca": [ - -0.16269715130329132, - 0.04011199623346329 + -0.16272495687007904, + 0.04063748940825462 ], "tsne": [ - -38.37576675415039, - 1.3097957372665405 + -37.131046295166016, + -3.932904005050659 ], "umap": [ - 2.000504970550537, - 6.661845684051514 + 2.195840835571289, + 5.887197017669678 ], "pca3d": [ - -0.16269689798355103, - 0.040101733058691025, - 0.016599835827946663 + -0.16272476315498352, + 0.04062682390213013, + 0.016403520479798317 ], "tsne3d": [ - -16.508039474487305, - 0.2170805186033249, - 1.0681618452072144 + -20.13442039489746, + -8.07644271850586, + 8.280083656311035 ], "umap3d": [ - 1.8120239973068237, - 5.358278274536133, - 4.2839555740356445 + 2.6329808235168457, + 5.805552005767822, + 4.71493673324585 ] }, "cluster_id": 2, @@ -205002,12 +205132,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 33, - "label": "Neural Networks" + "id": 19, + "label": "Graph Neural Nets" } ], "_embedding": [ @@ -205421,46 +205551,46 @@ ], "projections": { "pca": [ - -0.3062218427658081, - -0.07741695642471313 + -0.30637869238853455, + -0.07644083350896835 ], "tsne": [ - -55.573612213134766, - -3.746678113937378 + -50.58592987060547, + 4.220663547515869 ], "umap": [ - 0.10930832475423813, - 7.5163421630859375 + 0.35558873414993286, + 6.668737411499023 ], "pca3d": [ - -0.3062211573123932, - -0.07745744287967682, - -0.14533363282680511 + -0.3063781261444092, + -0.07648216933012009, + -0.14497582614421844 ], "tsne3d": [ - -30.10404396057129, - -0.035885654389858246, - 8.302159309387207 + -31.397146224975586, + 1.6659778356552124, + 4.313314914703369 ], "umap3d": [ - 0.23397019505500793, - 5.358860015869141, - 4.937601089477539 + 1.296722173690796, + 5.673605442047119, + 5.840038776397705 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -205876,31 +206006,31 @@ ], "projections": { "pca": [ - -0.0874437540769577, - -0.11588719487190247 + -0.08809514343738556, + -0.11596452444791794 ], "tsne": [ - -12.265616416931152, - 18.90155601501465 + -15.325825691223145, + -20.776567459106445 ], "umap": [ - 1.1260489225387573, - 5.319305896759033 + 1.4505563974380493, + 4.575748443603516 ], "pca3d": [ - -0.0874437615275383, - -0.11584401875734329, - 0.029078729450702667 + -0.08809521049261093, + -0.11592061817646027, + 0.028930557891726494 ], "tsne3d": [ - -8.732588768005371, - 2.693129062652588, - 1.2641130685806274 + -10.180283546447754, + -13.869199752807617, + -1.7150990962982178 ], "umap3d": [ - 1.3609716892242432, - 6.561063289642334, - 3.670698642730713 + 1.6679096221923828, + 6.554637908935547, + 3.8989579677581787 ] }, "cluster_id": 2, @@ -205911,12 +206041,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -206332,31 +206462,31 @@ ], "projections": { "pca": [ - -0.2257661521434784, - -0.08793681859970093 + -0.22596995532512665, + -0.08731353282928467 ], "tsne": [ - -47.41815948486328, - -14.34947395324707 + -48.752986907958984, + -10.265671730041504 ], "umap": [ - 1.0050910711288452, - 6.049840927124023 + 1.235543966293335, + 5.191885948181152 ], "pca3d": [ - -0.2257658690214157, - -0.08792871236801147, - -0.11299504339694977 + -0.22596964240074158, + -0.08730512857437134, + -0.11271094530820847 ], "tsne3d": [ - -26.48906707763672, - -7.618412494659424, - -8.105212211608887 + -24.332473754882812, + -3.784259557723999, + -5.653748989105225 ], "umap3d": [ - 0.8052042126655579, - 5.893972396850586, - 3.7841875553131104 + 1.3956499099731445, + 5.948420524597168, + 4.420314788818359 ] }, "cluster_id": 2, @@ -206367,12 +206497,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -206785,31 +206915,31 @@ ], "projections": { "pca": [ - -0.1550147831439972, - -0.07949421554803848 + -0.15518155694007874, + -0.07874234020709991 ], "tsne": [ - -45.68878173828125, - -14.277596473693848 + -43.935977935791016, + -14.651468276977539 ], "umap": [ - 1.2712876796722412, - 6.1150031089782715 + 1.4820196628570557, + 5.342694282531738 ], "pca3d": [ - -0.15501447021961212, - -0.07949770241975784, - -0.09495926648378372 + -0.15518131852149963, + -0.07874594628810883, + -0.09463833272457123 ], "tsne3d": [ - -23.8177490234375, - -8.072504043579102, - -8.246764183044434 + -20.067230224609375, + -7.135573387145996, + -8.655324935913086 ], "umap3d": [ - 0.9100348353385925, - 6.0713582038879395, - 4.095737457275391 + 1.5506309270858765, + 6.173523902893066, + 4.584538459777832 ] }, "cluster_id": 2, @@ -206820,12 +206950,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -207241,46 +207371,46 @@ ], "projections": { "pca": [ - -0.3289937376976013, - -0.0893624871969223 + -0.3291924297809601, + -0.08871664851903915 ], "tsne": [ - -54.4884147644043, - -3.683542013168335 + -50.187374114990234, + 3.1224474906921387 ], "umap": [ - 0.09083452820777893, - 7.51180362701416 + 0.3685343265533447, + 6.648831367492676 ], "pca3d": [ - -0.3289931118488312, - -0.08939921855926514, - -0.13732244074344635 + -0.3291919231414795, + -0.08875381201505661, + -0.13697290420532227 ], "tsne3d": [ - -28.014305114746094, - 0.5083411335945129, - 7.699072360992432 + -30.47428321838379, + -0.3710004687309265, + 4.960345268249512 ], "umap3d": [ - 0.24943006038665771, - 5.365407943725586, - 5.002748489379883 + 1.3198598623275757, + 5.7265944480896, + 5.842494964599609 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -207696,46 +207826,46 @@ ], "projections": { "pca": [ - -0.2609598636627197, - -0.18023933470249176 + -0.2612961530685425, + -0.17929039895534515 ], "tsne": [ - -53.11005783081055, - 0.06221278756856918 + -45.533241271972656, + 4.269709587097168 ], "umap": [ - 0.6039057970046997, - 7.730314254760742 + 0.9129630923271179, + 6.86165714263916 ], "pca3d": [ - -0.2609592080116272, - -0.1802549511194229, - -0.04698389023542404 + -0.2612955570220947, + -0.17930635809898376, + -0.04643111303448677 ], "tsne3d": [ - -23.054149627685547, - -2.8812224864959717, - 8.713639259338379 + -24.486831665039062, + 2.788902759552002, + 3.560380697250366 ], "umap3d": [ - 0.8515816330909729, - 5.08718204498291, - 4.987125873565674 + 1.8540672063827515, + 5.506739139556885, + 5.7251715660095215 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -208151,47 +208281,47 @@ ], "projections": { "pca": [ - -0.2934751510620117, - -0.10390105843544006 + -0.29377779364585876, + -0.10263188928365707 ], "tsne": [ - -40.69195556640625, - -6.5098795890808105 + -39.54298400878906, + -1.0264912843704224 ], "umap": [ - 1.4785634279251099, - 7.261727809906006 + 1.6454195976257324, + 6.4521098136901855 ], "pca3d": [ - -0.2934747040271759, - -0.10390354692935944, - -0.040552832186222076 + -0.2937772572040558, + -0.10263396799564362, + -0.04021679610013962 ], "tsne3d": [ - -20.654552459716797, - -5.947535037994385, - -0.7232897281646729 + -20.019025802612305, + -2.6564431190490723, + 1.5890852212905884 ], "umap3d": [ - 1.3475929498672485, - 5.227311134338379, - 4.3754119873046875 + 2.1830241680145264, + 5.314150810241699, + 5.0810227394104 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -208605,31 +208735,31 @@ ], "projections": { "pca": [ - -0.30694594979286194, - 0.0016593647887930274 + -0.30682939291000366, + 0.002942456165328622 ], "tsne": [ - -41.67885208129883, - -13.546710014343262 + -33.2762336730957, + -1.1849594116210938 ], "umap": [ - 0.7151749730110168, - 6.104689598083496 + 0.8457523584365845, + 5.345114231109619 ], "pca3d": [ - -0.3069459795951843, - 0.0016656245570629835, - -0.06430767476558685 + -0.3068292438983917, + 0.002948824781924486, + -0.06397644430398941 ], "tsne3d": [ - -21.58605194091797, - -7.785693168640137, - -9.291343688964844 + -21.72530746459961, + -1.365679383277893, + -6.230408668518066 ], "umap3d": [ - 0.638145387172699, - 5.906659126281738, - 3.7105348110198975 + 1.1505510807037354, + 5.840129852294922, + 4.487111568450928 ] }, "cluster_id": 2, @@ -208640,11 +208770,11 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, + "id": 23, "label": "Training Noise" } ], @@ -209070,47 +209200,47 @@ "openalex_url": "https://openalex.org/W2786715987", "projections": { "pca": [ - -0.2517969012260437, - 0.09692972153425217 + -0.2515116035938263, + 0.09777823835611343 ], "tsne": [ - -40.80524444580078, - -24.417037963867188 + -44.1191520690918, + 19.29978370666504 ], "umap": [ - 1.8392529487609863, - 7.179874897003174 + 2.2769880294799805, + 6.424610614776611 ], "pca3d": [ - -0.25179675221443176, - 0.09692179411649704, - -0.024425480514764786 + -0.25151145458221436, + 0.09776975214481354, + -0.02442566305398941 ], "tsne3d": [ - -22.8397274017334, - -16.26264762878418, - -2.8672993183135986 + -22.3587646484375, + 3.8657608032226562, + -11.844077110290527 ], "umap3d": [ - 0.8264738321304321, - 4.722106456756592, - 3.4686262607574463 + 1.5720555782318115, + 4.806262969970703, + 4.195621013641357 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -209525,47 +209655,47 @@ "doi": "10.1109/TETCI.2021.3100641", "projections": { "pca": [ - -0.21282820403575897, - -0.04387295991182327 + -0.2130180150270462, + -0.04287067428231239 ], "tsne": [ - -39.93370819091797, - -20.264244079589844 + -44.78239059448242, + 14.96022891998291 ], "umap": [ - 1.252760648727417, - 7.110823154449463 + 1.57973051071167, + 6.3871684074401855 ], "pca3d": [ - -0.21282809972763062, - -0.0438588447868824, - -0.05901306867599487 + -0.21301792562007904, + -0.042856816202402115, + -0.05881807580590248 ], "tsne3d": [ - -26.786155700683594, - -14.476133346557617, - 1.7573930025100708 + -26.588520050048828, + 4.724603176116943, + -7.961893081665039 ], "umap3d": [ - 0.6966113448143005, - 4.929991722106934, - 3.8196401596069336 + 1.4223634004592896, + 5.047720432281494, + 4.527215003967285 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -209984,31 +210114,31 @@ "cited_by_count": 1, "projections": { "pca": [ - -0.2886541187763214, - -0.15162000060081482 + -0.2891657054424286, + -0.15097959339618683 ], "tsne": [ - -49.33357620239258, - -21.373247146606445 + -52.296024322509766, + 14.479340553283691 ], "umap": [ - 0.40309613943099976, - 5.8400750160217285 + 0.7109373211860657, + 4.888678550720215 ], "pca3d": [ - -0.2886536717414856, - -0.15160365402698517, - -0.08245405554771423 + -0.2891653776168823, + -0.15096263587474823, + -0.08217451721429825 ], "tsne3d": [ - -31.498762130737305, - -11.521926879882812, - -1.9085707664489746 + -28.918603897094727, + -3.01572322845459, + -9.048803329467773 ], "umap3d": [ - 0.40700674057006836, - 5.464240074157715, - 3.423351287841797 + 1.0822510719299316, + 5.420490264892578, + 4.197645664215088 ] }, "cluster_id": 2, @@ -210019,12 +210149,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, - "label": "Training Noise" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -210433,31 +210563,31 @@ "abstract": "This blog post focuses on a promising new direction for generative modeling. We can learn score functions (gradients of log probability density functions) on a large number of noise-perturbed data distributions, then generate samples with Langevin-type sampling. The resulting generative models, often called score-based generative models, has several important advantages over existing model families: GAN-level sample quality without adversarial training, flexible model architectures, exact log-likelihood computation, and inverse problem solving without re-training models. In this blog post, we will show you in more detail the intuition, basic concepts, and potential applications of score-based generative models.", "projections": { "pca": [ - -0.24941067397594452, - -0.09577788412570953 + -0.24956287443637848, + -0.09491673111915588 ], "tsne": [ - -42.70906066894531, - 14.832304000854492 + -39.39353942871094, + -21.357051849365234 ], "umap": [ - 1.8538529872894287, - 5.361373424530029 + 2.2201507091522217, + 4.649659156799316 ], "pca3d": [ - -0.24941033124923706, - -0.09577035158872604, - -0.012874292209744453 + -0.24956250190734863, + -0.09490933269262314, + -0.012537693604826927 ], "tsne3d": [ - -22.766223907470703, - 13.521565437316895, - 3.552410125732422 + -22.7010555267334, + -19.52397918701172, + 1.6271134614944458 ], "umap3d": [ - 1.6581064462661743, - 6.617528915405273, - 4.607321739196777 + 2.2224836349487305, + 6.929529190063477, + 4.741842269897461 ] }, "cluster_id": 2, @@ -210468,11 +210598,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -210888,31 +211018,31 @@ ], "projections": { "pca": [ - -0.24432896077632904, - -0.11813999712467194 + -0.2448100745677948, + -0.1172804981470108 ], "tsne": [ - -43.311336517333984, - 0.9084306359291077 + -40.27573776245117, + -7.947534084320068 ], "umap": [ - 1.5689326524734497, - 6.422842502593994 + 1.9906915426254272, + 5.635135650634766 ], "pca3d": [ - -0.2443285882472992, - -0.11814237385988235, - 0.0018978598527610302 + -0.24480968713760376, + -0.11728280782699585, + 0.0020009104628115892 ], "tsne3d": [ - -20.531522750854492, - 2.104238510131836, - -3.050218105316162 + -19.628400802612305, + -9.556175231933594, + 1.303395390510559 ], "umap3d": [ - 1.6294621229171753, - 5.797510623931885, - 4.161412239074707 + 2.3542604446411133, + 6.026385307312012, + 4.614035129547119 ] }, "cluster_id": 2, @@ -210923,12 +211053,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -211348,31 +211478,31 @@ "cited_by_count": 6, "projections": { "pca": [ - -0.19346176087856293, - -0.1781630665063858 + -0.19394312798976898, + -0.17736724019050598 ], "tsne": [ - -45.114253997802734, - 1.1234499216079712 + -41.067569732666016, + -8.888100624084473 ], "umap": [ - 1.4520999193191528, - 6.489940166473389 + 1.7078193426132202, + 5.666991710662842 ], "pca3d": [ - -0.19346129894256592, - -0.1781679093837738, - -0.011191263794898987 + -0.19394268095493317, + -0.17737196385860443, + -0.010811952874064445 ], "tsne3d": [ - -22.23067855834961, - 3.7164878845214844, - -3.2272279262542725 + -20.88388442993164, + -11.279458999633789, + 0.17648246884346008 ], "umap3d": [ - 1.4339836835861206, - 5.6638503074646, - 4.044082164764404 + 2.0878982543945312, + 5.93716287612915, + 4.583479404449463 ] }, "cluster_id": 2, @@ -211383,12 +211513,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -211800,46 +211930,46 @@ ], "projections": { "pca": [ - -0.1432655155658722, - 0.001786382170394063 + -0.14314004778862, + 0.0025890618562698364 ], "tsne": [ - -35.27830123901367, - -9.319547653198242 + -41.02341079711914, + 9.475293159484863 ], "umap": [ - 0.9772593379020691, - 8.146403312683105 + 1.2403663396835327, + 7.180572032928467 ], "pca3d": [ - -0.14326529204845428, - 0.0017741642659530044, - -0.017252802848815918 + -0.14313985407352448, + 0.002577079925686121, + -0.017101304605603218 ], "tsne3d": [ - -24.102699279785156, - -8.565420150756836, - 6.446130275726318 + -22.120361328125, + 2.9186031818389893, + 9.642973899841309 ], "umap3d": [ - 1.0390726327896118, - 4.636929035186768, - 5.085615634918213 + 2.242759943008423, + 5.009281158447266, + 5.717313289642334 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -212249,47 +212379,47 @@ "title": "Simulating matter on the quantum scale with AI \u00e2\u0080\u0094 Google DeepMind", "projections": { "pca": [ - -0.20711863040924072, - 0.05601993203163147 + -0.20704349875450134, + 0.05670856311917305 ], "tsne": [ - -26.787059783935547, - 3.0992839336395264 + -33.421295166015625, + -8.784632682800293 ], "umap": [ - 4.440064907073975, - 6.664986610412598 + 3.3520030975341797, + 6.697824001312256 ], "pca3d": [ - -0.2071186900138855, - 0.05604447424411774, - 0.13168533146381378 + -0.20704366266727448, + 0.05673409625887871, + 0.13153550028800964 ], "tsne3d": [ - -4.077444076538086, - -19.444080352783203, - 12.395533561706543 + -13.07741928100586, + 12.26296329498291, + 12.117855072021484 ], "umap3d": [ - 3.108165740966797, - 4.764901161193848, - 3.430088520050049 + 3.521421194076538, + 4.902324676513672, + 3.6576061248779297 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 12, + "label": "AI Agents" }, { - "id": 10, - "label": "Code Development" + "id": 0, + "label": "Math AI" } ], "_embedding": [ @@ -212702,47 +212832,47 @@ ], "projections": { "pca": [ - -0.310101181268692, - -0.15829508006572723 + -0.31052085757255554, + -0.15746335685253143 ], "tsne": [ - -43.34734344482422, - -18.883718490600586 + -47.379798889160156, + 12.61970329284668 ], "umap": [ - 0.8483178019523621, - 6.814993381500244 + 1.2998746633529663, + 6.13865852355957 ], "pca3d": [ - -0.31010085344314575, - -0.15827521681785583, - -0.15663763880729675 + -0.3105206787586212, + -0.1574433445930481, + -0.15618915855884552 ], "tsne3d": [ - -27.320920944213867, - -9.865535736083984, - 2.5913124084472656 + -30.83516502380371, + -3.522495985031128, + 9.150856018066406 ], "umap3d": [ - 0.6261532306671143, - 5.230710506439209, - 3.912919521331787 + 1.3267806768417358, + 5.365479946136475, + 4.676647663116455 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -213158,31 +213288,31 @@ ], "projections": { "pca": [ - -0.22237445414066315, - -0.09220527857542038 + -0.22262433171272278, + -0.0915633961558342 ], "tsne": [ - -48.31674575805664, - -11.817300796508789 + -46.37057876586914, + -12.21639633178711 ], "umap": [ - 0.6657789945602417, - 6.033979415893555 + 1.0443460941314697, + 5.339459419250488 ], "pca3d": [ - -0.22237391769886017, - -0.09220752120018005, - -0.09011905640363693 + -0.22262397408485413, + -0.09156567603349686, + -0.08987340331077576 ], "tsne3d": [ - -26.220989227294922, - -6.9684271812438965, - -4.810206890106201 + -22.28835678100586, + -6.547771453857422, + -5.409552097320557 ], "umap3d": [ - 0.6817250847816467, - 6.093188762664795, - 4.01161003112793 + 1.3083161115646362, + 6.032688617706299, + 4.502845764160156 ] }, "cluster_id": 2, @@ -213193,12 +213323,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, - "label": "Training Noise" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -213626,47 +213756,47 @@ "cited_by_count": 430, "projections": { "pca": [ - -0.22888214886188507, - 0.08157666772603989 + -0.22855451703071594, + 0.08293528854846954 ], "tsne": [ - -21.5943603515625, - -24.542095184326172 + -21.043684005737305, + 13.081832885742188 ], "umap": [ - 2.8718321323394775, - 7.676585674285889 + 3.0703012943267822, + 6.965235710144043 ], "pca3d": [ - -0.22888198494911194, - 0.08156931400299072, - 0.04608985409140587 + -0.22855442762374878, + 0.08292773365974426, + 0.04616847261786461 ], "tsne3d": [ - -14.465927124023438, - -4.437677383422852, - -6.550275802612305 + -15.751554489135742, + 12.721898078918457, + 5.359327793121338 ], "umap3d": [ - 2.2632944583892822, - 4.405063629150391, - 3.5486252307891846 + 3.0369949340820312, + 4.807258605957031, + 4.077891826629639 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { "id": 0, - "label": "LLM Language Learning" + "label": "Math AI" } ], "_embedding": [ @@ -214079,31 +214209,31 @@ ], "projections": { "pca": [ - -0.2220451682806015, - -0.1585828959941864 + -0.2224566787481308, + -0.15812543034553528 ], "tsne": [ - -41.27307891845703, - 6.7526326179504395 + -36.3670654296875, + -13.189173698425293 ], "umap": [ - 1.8181005716323853, - 5.698862075805664 + 2.275430202484131, + 4.901251792907715 ], "pca3d": [ - -0.22204476594924927, - -0.15857741236686707, - -0.03916361182928085 + -0.2224562019109726, + -0.15811942517757416, + -0.03885805606842041 ], "tsne3d": [ - -18.166580200195312, - 7.85328483581543, - -4.646612167358398 + -17.983509063720703, + -15.204001426696777, + -2.0146827697753906 ], "umap3d": [ - 1.8612622022628784, - 6.362434387207031, - 4.263427734375 + 2.356916904449463, + 6.650096893310547, + 4.484728813171387 ] }, "cluster_id": 2, @@ -214114,12 +214244,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 22, + "label": "Diffusion Models" } ], "_embedding": [ @@ -214536,46 +214666,46 @@ ], "projections": { "pca": [ - -0.2232348769903183, - -0.13805584609508514 + -0.22339744865894318, + -0.13731108605861664 ], "tsne": [ - -61.92897033691406, - -4.413508415222168 + -56.897708892822266, + 4.511056423187256 ], "umap": [ - 0.5418700575828552, - 7.362931251525879 + 0.8402684330940247, + 6.447326183319092 ], "pca3d": [ - -0.22323434054851532, - -0.13808247447013855, - -0.06024938076734543 + -0.22339682281017303, + -0.13733826577663422, + -0.0597432516515255 ], "tsne3d": [ - -25.894367218017578, - 5.578445911407471, - 4.932821273803711 + -33.99833679199219, + -3.318807363510132, + 2.954770088195801 ], "umap3d": [ - 0.7460667490959167, - 5.792524337768555, - 5.023228168487549 + 1.6677613258361816, + 6.088910102844238, + 5.697833061218262 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -214992,47 +215122,47 @@ ], "projections": { "pca": [ - -0.2275913655757904, - -0.15414820611476898 + -0.22805938124656677, + -0.15342183411121368 ], "tsne": [ - -46.38240051269531, - -7.508382797241211 + -47.0334358215332, + -6.972519874572754 ], "umap": [ - 0.8314090967178345, - 6.3018903732299805 + 1.007283329963684, + 5.412154197692871 ], "pca3d": [ - -0.2275909185409546, - -0.1541382223367691, - -0.11686942726373672 + -0.2280590534210205, + -0.1534116268157959, + -0.11650720983743668 ], "tsne3d": [ - -24.788257598876953, - -3.580810070037842, - -3.803460121154785 + -24.85793685913086, + -4.728448390960693, + -0.5212158560752869 ], "umap3d": [ - 0.7632664442062378, - 6.089906215667725, - 4.079616069793701 + 1.4236993789672852, + 6.108092308044434, + 4.568196773529053 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 23, + "label": "Training Noise" } ], "_embedding": [ @@ -215448,31 +215578,31 @@ ], "projections": { "pca": [ - -0.28987690806388855, - -0.09722919017076492 + -0.29039016366004944, + -0.09690789133310318 ], "tsne": [ - -49.25013732910156, - 5.942865371704102 + -49.62042236328125, + -17.793460845947266 ], "umap": [ - 1.086563229560852, - 5.54041051864624 + 1.4352608919143677, + 4.711284637451172 ], "pca3d": [ - -0.28987643122673035, - -0.09722869098186493, - -0.028343066573143005 + -0.2903895676136017, + -0.0969068631529808, + -0.02832482010126114 ], "tsne3d": [ - -25.975465774536133, - 1.3251110315322876, - -9.224982261657715 + -23.308250427246094, + -13.445052146911621, + -3.868361473083496 ], "umap3d": [ - 1.1985301971435547, - 6.481688022613525, - 3.721623182296753 + 1.616477608680725, + 6.481656551361084, + 4.135647773742676 ] }, "cluster_id": 2, @@ -215483,12 +215613,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -215904,47 +216034,47 @@ ], "projections": { "pca": [ - -0.213669091463089, - -0.10908550024032593 + -0.21382072567939758, + -0.10857986658811569 ], "tsne": [ - -58.2583122253418, - -9.50777530670166 + -55.869171142578125, + -1.0980130434036255 ], "umap": [ - 0.5256110429763794, - 6.703575611114502 + 0.6949962973594666, + 5.951025009155273 ], "pca3d": [ - -0.21366864442825317, - -0.10910143703222275, - -0.1448187530040741 + -0.21382024884223938, + -0.10859594494104385, + -0.1444242000579834 ], "tsne3d": [ - -32.1247673034668, - 4.931791305541992, - 4.280452728271484 + -35.161319732666016, + 0.3735617697238922, + -1.4524410963058472 ], "umap3d": [ - 0.7064361572265625, - 6.119148254394531, - 4.697106838226318 + 1.492286205291748, + 6.419475078582764, + 5.214439868927002 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 9, - "label": "Vision Transformers" + "id": 23, + "label": "Training Noise" } ], "_embedding": [ @@ -216359,47 +216489,47 @@ "journal": "Journal of Machine Learning Research", "projections": { "pca": [ - -0.20623958110809326, - 0.07354763895273209 + -0.20608240365982056, + 0.07512716203927994 ], "tsne": [ - -41.1246223449707, - -23.38488006591797 + -44.596282958984375, + 18.363065719604492 ], "umap": [ - 1.8112949132919312, - 7.192124843597412 + 2.2400729656219482, + 6.44232702255249 ], "pca3d": [ - -0.20623958110809326, - 0.07353973388671875, - -0.0061863684095442295 + -0.2060823291540146, + 0.07511866837739944, + -0.006092119961977005 ], "tsne3d": [ - -24.71659278869629, - -16.009727478027344, - -2.552421808242798 + -22.058259963989258, + 3.0419023036956787, + -10.229736328125 ], "umap3d": [ - 0.8458778262138367, - 4.686758041381836, - 3.4755804538726807 + 1.5595178604125977, + 4.783113956451416, + 4.226876735687256 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -216818,46 +216948,46 @@ ], "projections": { "pca": [ - -0.05822422727942467, - -0.03769097849726677 + -0.05811891704797745, + -0.03715706244111061 ], "tsne": [ - 0.3632594645023346, - -34.13344955444336 + -7.924307823181152, + 31.465377807617188 ], "umap": [ - 4.699097633361816, - 9.442695617675781 + 4.9125847816467285, + 8.694446563720703 ], "pca3d": [ - -0.05822421982884407, - -0.037677861750125885, - 0.0483720488846302 + -0.05811891332268715, + -0.03714420646429062, + 0.04872091859579086 ], "tsne3d": [ - -0.5558627843856812, - -19.461130142211914, - 1.3285911083221436 + -4.999959468841553, + 13.521947860717773, + -8.616741180419922 ], "umap3d": [ - 4.021429538726807, - 2.720269203186035, - 3.1792542934417725 + 4.702639579772949, + 3.1512844562530518, + 3.706037759780884 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, + "id": 26, "label": "Medical AI" } ], @@ -217272,31 +217402,31 @@ ], "projections": { "pca": [ - 0.049596190452575684, - -0.08512027561664581 + 0.049432914704084396, + -0.08494655787944794 ], "tsne": [ - 2.461177110671997, - 20.111894607543945 + 8.241297721862793, + -31.535860061645508 ], "umap": [ - 5.524601936340332, - 4.790771484375 + 6.092097759246826, + 4.338695049285889 ], "pca3d": [ - 0.049596235156059265, - -0.08510864526033401, - 0.18505875766277313 + 0.04943300038576126, + -0.08493462204933167, + 0.18510785698890686 ], "tsne3d": [ - -3.243525981903076, - 6.613950252532959, - -0.9360291361808777 + 1.764381766319275, + -18.102807998657227, + -3.729807138442993 ], "umap3d": [ - 5.396828651428223, - 6.569071292877197, - 4.412702560424805 + 5.6181559562683105, + 7.147252082824707, + 2.784724712371826 ] }, "cluster_id": 3, @@ -217307,11 +217437,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -217719,47 +217849,47 @@ "abstract": "Certi\ufb01ed patch defenses can guarantee robustness of an image classi\ufb01er to arbitrary changes within a bounded contiguous region. But, currently, this robustness comes at a cost of degraded standard accuracies and slower inference times. We demonstrate how using vision transformers enables signi\ufb01cantly better certi\ufb01ed patch robustness that is also more computationally ef\ufb01cient and does not incur a substantial drop in standard accuracy. These improvements stem from the inherent ability of the vision transformer to gracefully handle largely masked images.1", "projections": { "pca": [ - -0.251986563205719, - -0.012840267270803452 + -0.2519479990005493, + -0.012153581716120243 ], "tsne": [ - -53.271034240722656, - -7.578396797180176 + -51.26874542236328, + 0.23639240860939026 ], "umap": [ - 0.2518867254257202, - 6.7126078605651855 + 0.537535548210144, + 5.799785614013672 ], "pca3d": [ - -0.25198590755462646, - -0.012894069775938988, - -0.0989278256893158 + -0.2519473433494568, + -0.012208196334540844, + -0.09876079112291336 ], "tsne3d": [ - -32.53418731689453, - -4.478343486785889, - -1.9506547451019287 + -29.933618545532227, + -0.9949291348457336, + 0.41926705837249756 ], "umap3d": [ - 0.24936069548130035, - 5.674661636352539, - 4.305962085723877 + 1.0630488395690918, + 5.804584980010986, + 5.047120571136475 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 9, - "label": "Vision Transformers" + "id": 23, + "label": "Training Noise" } ], "_embedding": [ @@ -218170,47 +218300,47 @@ "cited_by_count": 6, "projections": { "pca": [ - -0.2590996026992798, - 0.06455596536397934 + -0.25884249806404114, + 0.06511864811182022 ], "tsne": [ - -43.55332565307617, - -26.12702751159668 + -46.28764343261719, + 21.34158706665039 ], "umap": [ - 1.7254143953323364, - 6.934967994689941 + 2.2529499530792236, + 6.192224025726318 ], "pca3d": [ - -0.2590997815132141, - 0.06459356099367142, - -0.06328751891851425 + -0.25884273648262024, + 0.06515714526176453, + -0.06320744752883911 ], "tsne3d": [ - -22.16524887084961, - -15.947991371154785, - -6.191120147705078 + -22.551790237426758, + 0.5596244931221008, + -13.614989280700684 ], "umap3d": [ - 1.032867193222046, - 4.960698127746582, - 3.4442875385284424 + 1.6926915645599365, + 5.059840202331543, + 4.091263294219971 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -218637,31 +218767,31 @@ "cited_by_count": 880, "projections": { "pca": [ - -0.2550751268863678, - -0.00022975404863245785 + -0.25511419773101807, + 0.0008624332840554416 ], "tsne": [ - -37.889373779296875, - 3.2784836292266846 + -34.40913772583008, + -8.957742691040039 ], "umap": [ - 2.135225296020508, - 6.30858039855957 + 2.469377279281616, + 5.598011493682861 ], "pca3d": [ - -0.2550753355026245, - -0.000210423837415874, - 0.09563828259706497 + -0.25511428713798523, + 0.0008822527015581727, + 0.09564638882875443 ], "tsne3d": [ - -15.193502426147461, - 2.5066866874694824, - -1.9947783946990967 + -15.241903305053711, + -9.173622131347656, + 2.387878656387329 ], "umap3d": [ - 2.057009696960449, - 5.727517604827881, - 4.197736740112305 + 2.637925148010254, + 6.046769618988037, + 4.494348526000977 ] }, "cluster_id": 2, @@ -218672,12 +218802,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 33, - "label": "Neural Networks" + "id": 19, + "label": "Graph Neural Nets" } ], "_embedding": [ @@ -219092,31 +219222,31 @@ ], "projections": { "pca": [ - -0.2795308232307434, - -0.14416275918483734 + -0.2799736261367798, + -0.14323079586029053 ], "tsne": [ - -44.08778381347656, - -4.860045433044434 + -43.679439544677734, + -7.1309356689453125 ], "umap": [ - 1.1845864057540894, - 6.4007887840271 + 1.567487359046936, + 5.559587001800537 ], "pca3d": [ - -0.2795305550098419, - -0.14415012300014496, - -0.07881560176610947 + -0.27997323870658875, + -0.143217533826828, + -0.07849109917879105 ], "tsne3d": [ - -21.491025924682617, - -1.7603611946105957, - -3.2467904090881348 + -21.681930541992188, + -6.190592288970947, + 1.7975794076919556 ], "umap3d": [ - 1.2686885595321655, - 5.930106163024902, - 4.163870811462402 + 2.0538909435272217, + 6.096514701843262, + 4.767863750457764 ] }, "cluster_id": 2, @@ -219127,12 +219257,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -219566,31 +219696,31 @@ "cited_by_count": 916, "projections": { "pca": [ - -0.27014538645744324, - 0.0029487491119652987 + -0.2702374756336212, + 0.0036363310646265745 ], "tsne": [ - -50.91273498535156, - 14.276527404785156 + -47.084476470947266, + -23.875492095947266 ], "umap": [ - 1.572214961051941, - 5.54352331161499 + 2.1754815578460693, + 4.810879707336426 ], "pca3d": [ - -0.27014514803886414, - 0.002957479562610388, - -0.06449978053569794 + -0.2702372670173645, + 0.003645648481324315, + -0.06445308774709702 ], "tsne3d": [ - -23.980310440063477, - 9.91267204284668, - -8.478732109069824 + -27.961034774780273, + -17.48673439025879, + -1.7339396476745605 ], "umap3d": [ - 1.7475091218948364, - 6.411365985870361, - 4.355825424194336 + 2.2680811882019043, + 6.779712677001953, + 4.5803704261779785 ] }, "cluster_id": 2, @@ -219601,12 +219731,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 22, + "label": "Diffusion Models" } ], "_embedding": [ @@ -220022,46 +220152,46 @@ ], "projections": { "pca": [ - -0.3225502371788025, - -0.15558621287345886 + -0.3229236602783203, + -0.1548210233449936 ], "tsne": [ - -54.37999725341797, - -1.3786512613296509 + -47.83677673339844, + 3.991197347640991 ], "umap": [ - 0.16172759234905243, - 7.504758358001709 + 0.40736567974090576, + 6.651749134063721 ], "pca3d": [ - -0.32254964113235474, - -0.15562184154987335, - -0.14838449656963348 + -0.32292306423187256, + -0.15485689043998718, + -0.14790743589401245 ], "tsne3d": [ - -26.765213012695312, - -3.052229642868042, - 8.022339820861816 + -27.256439208984375, + 1.3558112382888794, + 4.4130167961120605 ], "umap3d": [ - 0.2517980933189392, - 5.347876071929932, - 4.959835052490234 + 1.3303197622299194, + 5.634302139282227, + 5.807063102722168 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -220478,46 +220608,46 @@ "doi": "10.23915/distill.00032", "projections": { "pca": [ - -0.3273239731788635, - -0.132969930768013 + -0.32764607667922974, + -0.13201846182346344 ], "tsne": [ - -53.30501174926758, - 2.4218900203704834 + -46.99946594238281, + 1.97687566280365 ], "umap": [ - 0.35450220108032227, - 7.221662521362305 + 0.5728643536567688, + 6.421841144561768 ], "pca3d": [ - -0.327323853969574, - -0.13295486569404602, - -0.0910157710313797 + -0.3276458978652954, + -0.1320028305053711, + -0.09060720354318619 ], "tsne3d": [ - -17.935089111328125, - 5.703029155731201, - -11.964296340942383 + -27.057403564453125, + 0.36878690123558044, + 0.7215073108673096 ], "umap3d": [ - 0.38116464018821716, - 5.349699974060059, - 4.703177452087402 + 1.3507640361785889, + 5.661282062530518, + 5.552609920501709 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -220933,46 +221063,46 @@ ], "projections": { "pca": [ - -0.3295440971851349, - -0.13452863693237305 + -0.329876184463501, + -0.13339616358280182 ], "tsne": [ - -55.43374252319336, - -3.378091812133789 + -50.03300476074219, + 4.214648723602295 ], "umap": [ - 0.12217910587787628, - 7.462168216705322 + 0.3863222002983093, + 6.622732162475586 ], "pca3d": [ - -0.32954344153404236, - -0.13456778228282928, - -0.13059990108013153 + -0.329875648021698, + -0.13343626260757446, + -0.13015370070934296 ], "tsne3d": [ - -29.60283851623535, - -1.3293389081954956, - 7.827885150909424 + -30.27374839782715, + 2.308009386062622, + 4.795115947723389 ], "umap3d": [ - 0.22861239314079285, - 5.358621597290039, - 4.934353828430176 + 1.2743638753890991, + 5.686441898345947, + 5.806267261505127 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -221387,47 +221517,47 @@ "doi": "10.1145/3546577", "projections": { "pca": [ - -0.23338603973388672, - 0.00034426976344548166 + -0.2333209365606308, + 0.001719359657727182 ], "tsne": [ - -39.66319274902344, - -20.670394897460938 + -44.19635009765625, + 15.330887794494629 ], "umap": [ - 1.5766222476959229, - 7.319671630859375 + 1.8660165071487427, + 6.528104305267334 ], "pca3d": [ - -0.2333860695362091, - 0.0003546658845152706, - 0.007729690056294203 + -0.2333209365606308, + 0.0017293341225013137, + 0.008024883456528187 ], "tsne3d": [ - -25.861377716064453, - -15.224466323852539, - 1.5630779266357422 + -25.738534927368164, + 5.328929424285889, + -8.330909729003906 ], "umap3d": [ - 0.8117672801017761, - 4.699431419372559, - 3.77207612991333 + 1.6223245859146118, + 4.830937385559082, + 4.45889949798584 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -221843,47 +221973,47 @@ ], "projections": { "pca": [ - -0.3026478886604309, - -0.12787765264511108 + -0.30301347374916077, + -0.12681426107883453 ], "tsne": [ - -45.79549789428711, - -6.937129497528076 + -46.67698287963867, + -5.509641170501709 ], "umap": [ - 0.7489051818847656, - 6.375851154327393 + 1.0384889841079712, + 5.688039302825928 ], "pca3d": [ - -0.3026474416255951, - -0.12786759436130524, - -0.13114850223064423 + -0.3030130863189697, + -0.12680388987064362, + -0.1307513415813446 ], "tsne3d": [ - -24.553857803344727, - -3.1660990715026855, - -1.6835449934005737 + -25.242599487304688, + -5.946645259857178, + 0.6302326917648315 ], "umap3d": [ - 0.6872984766960144, - 5.847469329833984, - 4.173985958099365 + 1.5198765993118286, + 5.999200344085693, + 4.72065544128418 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 23, + "label": "Training Noise" } ], "_embedding": [ @@ -222295,31 +222425,31 @@ ], "projections": { "pca": [ - -0.2871265709400177, - -0.06891144812107086 + -0.2874701917171478, + -0.06834697723388672 ], "tsne": [ - -51.98637390136719, - 6.2385334968566895 + -52.71597671508789, + -15.016847610473633 ], "umap": [ - 0.7844660878181458, - 5.530912399291992 + 1.2119972705841064, + 4.723924160003662 ], "pca3d": [ - -0.28712618350982666, - -0.06891002506017685, - -0.11585766077041626 + -0.28746989369392395, + -0.06834525614976883, + -0.11574117839336395 ], "tsne3d": [ - -29.285741806030273, - -2.4026646614074707, - -8.33244514465332 + -26.368059158325195, + -11.169918060302734, + -6.801621913909912 ], "umap3d": [ - 0.9057325124740601, - 6.365522861480713, - 3.569650888442993 + 1.3321115970611572, + 6.312008380889893, + 4.096977233886719 ] }, "cluster_id": 2, @@ -222330,12 +222460,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -222746,47 +222876,47 @@ ], "projections": { "pca": [ - -0.20981095731258392, - -0.1210460215806961 + -0.21014192700386047, + -0.12020981311798096 ], "tsne": [ - -40.544334411621094, - -6.7021965980529785 + -39.37881088256836, + -0.8672604560852051 ], "umap": [ - 1.4253140687942505, - 7.427549362182617 + 1.5566222667694092, + 6.642985820770264 ], "pca3d": [ - -0.20981062948703766, - -0.12104012817144394, - -0.06840455532073975 + -0.2101415991783142, + -0.12020353227853775, + -0.06803057342767715 ], "tsne3d": [ - -20.622039794921875, - -6.649290084838867, - -0.6015072464942932 + -19.745159149169922, + -2.175915479660034, + 1.201694130897522 ], "umap3d": [ - 1.2841784954071045, - 4.992886066436768, - 4.481532096862793 + 2.1673078536987305, + 5.223482608795166, + 5.161922454833984 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -223201,31 +223331,31 @@ ], "projections": { "pca": [ - -0.281751811504364, - -0.12173572927713394 + -0.28206485509872437, + -0.12116183340549469 ], "tsne": [ - -39.45476531982422, - 13.425621032714844 + -36.14942932128906, + -19.391281127929688 ], "umap": [ - 1.9871057271957397, - 5.258330345153809 + 2.380221128463745, + 4.6480512619018555 ], "pca3d": [ - -0.2817513942718506, - -0.12174282222986221, - -0.06160769611597061 + -0.28206437826156616, + -0.12116928398609161, + -0.06132934242486954 ], "tsne3d": [ - -20.67806625366211, - 9.283434867858887, - 2.107273817062378 + -19.771652221679688, + -17.571409225463867, + 3.8473737239837646 ], "umap3d": [ - 1.800828456878662, - 6.70295524597168, - 4.775337219238281 + 2.3765134811401367, + 7.032397747039795, + 4.780510902404785 ] }, "cluster_id": 2, @@ -223236,11 +223366,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -223658,46 +223788,46 @@ ], "projections": { "pca": [ - -0.30303066968917847, - -0.1805296540260315 + -0.3032492697238922, + -0.17976324260234833 ], "tsne": [ - -55.64582443237305, - -0.8738594055175781 + -46.59540939331055, + 5.338756084442139 ], "umap": [ - 0.48960405588150024, - 7.901180267333984 + 0.7675930857658386, + 7.133797645568848 ], "pca3d": [ - -0.303029865026474, - -0.18057170510292053, - -0.10131610184907913 + -0.30324843525886536, + -0.1798059344291687, + -0.10064379870891571 ], "tsne3d": [ - -24.55343246459961, - -3.8092188835144043, - 10.049078941345215 + -26.012348175048828, + 3.9639384746551514, + 4.989060401916504 ], "umap3d": [ - 0.6810265183448792, - 4.925017356872559, - 5.158750534057617 + 1.8181363344192505, + 5.337100028991699, + 5.90883207321167 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -224111,46 +224241,46 @@ ], "projections": { "pca": [ - -0.17509004473686218, - -0.04352511838078499 + -0.1749812513589859, + -0.04308401048183441 ], "tsne": [ - -35.62786102294922, - -4.906557083129883 + -43.947509765625, + 6.942982196807861 ], "umap": [ - 0.5983797907829285, - 8.150991439819336 + 0.9543509483337402, + 7.276493549346924 ], "pca3d": [ - -0.17508956789970398, - -0.043555472046136856, - -0.01029909960925579 + -0.17498084902763367, + -0.04311482235789299, + -0.010074320249259472 ], "tsne3d": [ - -25.484678268432617, - -6.013426303863525, - 14.704191207885742 + -27.98919677734375, + 8.452798843383789, + 4.751111030578613 ], "umap3d": [ - 0.8130167126655579, - 4.762515544891357, - 5.194741725921631 + 1.9221402406692505, + 5.116580009460449, + 5.975011348724365 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -224568,46 +224698,46 @@ ], "projections": { "pca": [ - -0.24826475977897644, - -0.11492161452770233 + -0.24845245480537415, + -0.11424008011817932 ], "tsne": [ - -56.463409423828125, - -1.3752715587615967 + -48.24140167236328, + 5.7266106605529785 ], "umap": [ - 0.16431675851345062, - 7.66855001449585 + 0.44160887598991394, + 6.76486349105835 ], "pca3d": [ - -0.24826423823833466, - -0.11496579647064209, - -0.14708977937698364 + -0.2484518438577652, + -0.11428482830524445, + -0.14667095243930817 ], "tsne3d": [ - -26.447311401367188, - -2.6130921840667725, - 10.230318069458008 + -27.22054100036621, + 2.7224910259246826, + 6.233410358428955 ], "umap3d": [ - 0.35354194045066833, - 5.218776702880859, - 5.066668510437012 + 1.444908857345581, + 5.54237174987793, + 5.9383416175842285 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -225041,47 +225171,47 @@ "doi": "10.1101/2021.06.27.449937", "projections": { "pca": [ - -0.07934683561325073, - 0.02964130975306034 + -0.07941871136426926, + 0.030177270993590355 ], "tsne": [ - 8.664986610412598, - -31.21426010131836 + 0.7425755262374878, + 32.1122932434082 ], "umap": [ - 5.805585861206055, - 9.501063346862793 + 6.078418731689453, + 8.895601272583008 ], "pca3d": [ - -0.07934661954641342, - 0.029634809121489525, - -0.1670890748500824 + -0.07941852509975433, + 0.030170662328600883, + -0.1670478731393814 ], "tsne3d": [ - 6.4812774658203125, - -15.79371452331543, - -2.015763282775879 + 1.2436772584915161, + 15.249103546142578, + -14.46016788482666 ], "umap3d": [ - 4.854921340942383, - 2.5961248874664307, - 2.593087673187256 + 5.418489456176758, + 2.7774946689605713, + 2.9682836532592773 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -225494,31 +225624,31 @@ ], "projections": { "pca": [ - -0.27353981137275696, - -0.10600078105926514 + -0.274250864982605, + -0.10588991641998291 ], "tsne": [ - -49.71198272705078, - 6.234919548034668 + -50.1140022277832, + -17.547176361083984 ], "umap": [ - 0.9488162398338318, - 5.424458980560303 + 1.3499503135681152, + 4.624344825744629 ], "pca3d": [ - -0.27353933453559875, - -0.10598281770944595, - -0.041297201067209244 + -0.27425041794776917, + -0.10587102174758911, + -0.041437067091464996 ], "tsne3d": [ - -26.693370819091797, - 0.9981202483177185, - -9.646717071533203 + -23.8853759765625, + -13.456604957580566, + -4.55035400390625 ], "umap3d": [ - 1.1211282014846802, - 6.517005443572998, - 3.592996597290039 + 1.5095899105072021, + 6.484500885009766, + 4.025179386138916 ] }, "cluster_id": 2, @@ -225529,12 +225659,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -225949,31 +226079,31 @@ ], "projections": { "pca": [ - -0.2765198349952698, - -0.12365930527448654 + -0.27709150314331055, + -0.12343151122331619 ], "tsne": [ - -47.93675994873047, - 6.1153883934021 + -48.623878479003906, + -18.50508689880371 ], "umap": [ - 1.1692051887512207, - 5.64504337310791 + 1.4817593097686768, + 4.75695276260376 ], "pca3d": [ - -0.27651944756507874, - -0.12364297360181808, - -0.12222562730312347 + -0.2770911455154419, + -0.1234143003821373, + -0.12212169170379639 ], "tsne3d": [ - -24.164642333984375, - -0.018860969692468643, - -8.552512168884277 + -21.2933349609375, + -14.302240371704102, + -3.982058048248291 ], "umap3d": [ - 1.2277888059616089, - 6.436478614807129, - 3.766961097717285 + 1.732043981552124, + 6.512582302093506, + 4.196402072906494 ] }, "cluster_id": 2, @@ -225984,12 +226114,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -226407,31 +226537,31 @@ ], "projections": { "pca": [ - -0.32238826155662537, - -0.11564329266548157 + -0.3229014277458191, + -0.1148565262556076 ], "tsne": [ - -52.624755859375, - 6.320395469665527 + -53.35478591918945, + -15.207757949829102 ], "umap": [ - 0.8407988548278809, - 5.588406085968018 + 1.2162214517593384, + 4.72742223739624 ], "pca3d": [ - -0.32238784432411194, - -0.11563761532306671, - -0.10933947563171387 + -0.32290107011795044, + -0.1148502305150032, + -0.10915382951498032 ], "tsne3d": [ - -30.443864822387695, - -1.58390212059021, - -8.532092094421387 + -27.52971649169922, + -11.933121681213379, + -6.868933200836182 ], "umap3d": [ - 0.8956089019775391, - 6.304943084716797, - 3.566880226135254 + 1.331446647644043, + 6.219113349914551, + 4.116130352020264 ] }, "cluster_id": 2, @@ -226442,12 +226572,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -226861,46 +226991,46 @@ ], "projections": { "pca": [ - -0.3048895001411438, - -0.14932510256767273 + -0.3052225112915039, + -0.14841777086257935 ], "tsne": [ - -54.881202697753906, - -2.885974407196045 + -49.409271240234375, + 3.636446952819824 ], "umap": [ - 0.07715639472007751, - 7.520574569702148 + 0.33245620131492615, + 6.637526035308838 ], "pca3d": [ - -0.3048887252807617, - -0.14937151968479156, - -0.10747223347425461 + -0.3052217364311218, + -0.14846491813659668, + -0.10700245201587677 ], "tsne3d": [ - -28.036767959594727, - -1.502925992012024, - 7.68159818649292 + -29.566741943359375, + 0.9316225647926331, + 4.064789295196533 ], "umap3d": [ - 0.2263643443584442, - 5.328197956085205, - 4.9507317543029785 + 1.2684217691421509, + 5.703908443450928, + 5.844442844390869 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -227316,31 +227446,31 @@ ], "projections": { "pca": [ - -0.2297791689634323, - -0.07969291508197784 + -0.22994789481163025, + -0.07970263063907623 ], "tsne": [ - -41.01801681518555, - 18.428321838378906 + -36.649784088134766, + -24.786930084228516 ], "umap": [ - 2.13173508644104, - 4.923069477081299 + 2.6120898723602295, + 4.223513603210449 ], "pca3d": [ - -0.2297787368297577, - -0.07969161868095398, - -0.08311096578836441 + -0.2299475520849228, + -0.07970084995031357, + -0.08298463374376297 ], "tsne3d": [ - -20.606996536254883, - 12.499991416931152, - -2.672560453414917 + -20.56633186340332, + -24.495080947875977, + 5.063906669616699 ], "umap3d": [ - 2.0715126991271973, - 7.005272388458252, - 4.718763828277588 + 2.549581289291382, + 7.392667293548584, + 4.615713596343994 ] }, "cluster_id": 2, @@ -227351,11 +227481,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -227772,47 +227902,47 @@ ], "projections": { "pca": [ - -0.18727277219295502, - -0.08564101159572601 + -0.1875353902578354, + -0.08502902835607529 ], "tsne": [ - -37.26803207397461, - -30.778778076171875 + -37.718902587890625, + 27.289337158203125 ], "umap": [ - 8.14401912689209, - 10.390681266784668 + 8.705758094787598, + 9.695533752441406 ], "pca3d": [ - -0.18727248907089233, - -0.08563597500324249, - -0.1554456353187561 + -0.1875350922346115, + -0.08502410352230072, + -0.1551954597234726 ], "tsne3d": [ - -16.664424896240234, - -2.4488885402679443, - -17.557022094726562 + -15.852084159851074, + -8.779563903808594, + -12.980886459350586 ], "umap3d": [ - 4.192144393920898, - 4.9757585525512695, - 1.3089241981506348 + 3.9047398567199707, + 4.451372146606445, + 1.3049958944320679 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -228227,31 +228357,31 @@ ], "projections": { "pca": [ - -0.2657898962497711, - -0.0697879046201706 + -0.26584771275520325, + -0.06926633417606354 ], "tsne": [ - -39.11107635498047, - 15.640364646911621 + -35.04402542114258, + -21.6831111907959 ], "umap": [ - 2.1856496334075928, - 5.0912604331970215 + 2.5884132385253906, + 4.600630283355713 ], "pca3d": [ - -0.265789657831192, - -0.06979057192802429, - -0.10082310438156128 + -0.2658472955226898, + -0.0692690908908844, + -0.10049106180667877 ], "tsne3d": [ - -19.680416107177734, - 10.277445793151855, - -0.46281516551971436 + -18.22344398498535, + -19.71673583984375, + 3.421607494354248 ], "umap3d": [ - 1.9639474153518677, - 6.810580730438232, - 4.810623645782471 + 2.570952892303467, + 7.196951866149902, + 4.693680286407471 ] }, "cluster_id": 2, @@ -228262,11 +228392,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -228675,46 +228805,46 @@ "abstract": "In this post, I explain two techniques for visualising attention that address the problem of lack of token identifiability in higher layers of Transformers w...", "projections": { "pca": [ - -0.24508611857891083, - 0.08721019327640533 + -0.2448912113904953, + 0.08820749074220657 ], "tsne": [ - -36.822208404541016, - -7.476779937744141 + -44.568565368652344, + 10.510978698730469 ], "umap": [ - 0.6222784519195557, - 7.895343780517578 + 0.9454512000083923, + 6.952199935913086 ], "pca3d": [ - -0.24508608877658844, - 0.08719239383935928, - -0.08702974021434784 + -0.24489113688468933, + 0.08818864077329636, + -0.0870378315448761 ], "tsne3d": [ - -27.555973052978516, - -7.00460147857666, - 10.038765907287598 + -27.40621566772461, + 5.899282455444336, + 8.185402870178223 ], "umap3d": [ - 0.5533044934272766, - 4.790675640106201, - 4.750916481018066 + 1.674018144607544, + 5.121404647827148, + 5.6447038650512695 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -229127,47 +229257,47 @@ ], "projections": { "pca": [ - -0.29653313755989075, - -0.15905433893203735 + -0.29697489738464355, + -0.15827937424182892 ], "tsne": [ - -46.17153549194336, - -4.548593997955322 + -43.11045837402344, + -4.39809513092041 ], "umap": [ - 0.7823981642723083, - 6.70653772354126 + 1.159509539604187, + 5.838023662567139 ], "pca3d": [ - -0.2965327203273773, - -0.15903714299201965, - -0.09878383576869965 + -0.29697462916374207, + -0.15826167166233063, + -0.09840980172157288 ], "tsne3d": [ - -23.250547409057617, - 1.0523890256881714, - 0.3944932818412781 + -23.94904136657715, + -8.241196632385254, + 4.305613040924072 ], "umap3d": [ - 0.8761675953865051, - 5.685380935668945, - 4.451415538787842 + 1.8199055194854736, + 5.948073387145996, + 4.941226482391357 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -229586,47 +229716,47 @@ ], "projections": { "pca": [ - -0.26998114585876465, - -0.1766456514596939 + -0.27043527364730835, + -0.1761222779750824 ], "tsne": [ - -60.782318115234375, - -9.011174201965332 + -58.19086456298828, + 0.31958070397377014 ], "umap": [ - 0.47000598907470703, - 6.8103837966918945 + 0.6460516452789307, + 5.866142272949219 ], "pca3d": [ - -0.2699808180332184, - -0.17664678394794464, - -0.1097954660654068 + -0.27043473720550537, + -0.17612341046333313, + -0.10939031094312668 ], "tsne3d": [ - -28.53838348388672, - 6.488583087921143, - 1.8709666728973389 + -34.93534851074219, + -3.016850709915161, + -0.23526592552661896 ], "umap3d": [ - 0.7209434509277344, - 6.186774253845215, - 4.73481559753418 + 1.4475892782211304, + 6.468321800231934, + 5.2592997550964355 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 9, - "label": "Vision Transformers" + "id": 23, + "label": "Training Noise" } ], "_embedding": [ @@ -230045,46 +230175,46 @@ "cited_by_count": 474, "projections": { "pca": [ - -0.20560933649539948, - 0.12558893859386444 + -0.2055007368326187, + 0.1264072060585022 ], "tsne": [ - 2.298830270767212, - -31.715312957763672 + -4.965428352355957, + 30.623271942138672 ], "umap": [ - 4.917914390563965, - 9.477398872375488 + 5.0799736976623535, + 8.855673789978027 ], "pca3d": [ - -0.20560914278030396, - 0.12558302283287048, - -0.10252238810062408 + -0.20550064742565155, + 0.12640103697776794, + -0.1026940643787384 ], "tsne3d": [ - 0.8843552470207214, - -15.528307914733887, - -0.23521125316619873 + -6.5709357261657715, + 18.130373001098633, + -10.51346492767334 ], "umap3d": [ - 4.002593517303467, - 2.64060378074646, - 2.9428937435150146 + 4.719839572906494, + 2.988524913787842, + 3.4863052368164062 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, + "id": 26, "label": "Medical AI" } ], @@ -230501,31 +230631,31 @@ "doi": "10.1101/2021.05.30.446360", "projections": { "pca": [ - 0.06575187295675278, - -0.11939089745283127 + 0.06546806544065475, + -0.11956653743982315 ], "tsne": [ - 8.532095909118652, - 18.10430145263672 + 11.303013801574707, + -3.0021655559539795 ], "umap": [ - 6.866927146911621, - 5.92685604095459 + 7.259054660797119, + 5.34036111831665 ], "pca3d": [ - 0.06575208157300949, - -0.119389109313488, - 0.04129090905189514 + 0.06546828895807266, + -0.11956412345170975, + 0.041457291692495346 ], "tsne3d": [ - 8.290043830871582, - 5.2535080909729, - -3.2000248432159424 + 9.728485107421875, + -8.083511352539062, + -2.008467674255371 ], "umap3d": [ - 6.057523250579834, - 5.345964431762695, - 3.6946635246276855 + 6.360128402709961, + 5.77622652053833, + 2.586376905441284 ] }, "cluster_id": 4, @@ -230536,11 +230666,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -230954,47 +231084,47 @@ ], "projections": { "pca": [ - -0.280852347612381, - -0.1530429720878601 + -0.281396746635437, + -0.1519480049610138 ], "tsne": [ - -43.03303909301758, - -3.6812424659729004 + -41.158180236816406, + -3.5821616649627686 ], "umap": [ - 1.3381537199020386, - 6.735381603240967 + 1.6937634944915771, + 5.966607570648193 ], "pca3d": [ - -0.28085193037986755, - -0.15303482115268707, - -0.04307646304368973 + -0.2813962996006012, + -0.15193898975849152, + -0.04277766868472099 ], "tsne3d": [ - -20.99431610107422, - -3.4396297931671143, - -0.6694441437721252 + -20.236703872680664, + -4.356436729431152, + 2.7791748046875 ], "umap3d": [ - 1.4102474451065063, - 5.553761959075928, - 4.358684062957764 + 2.2013535499572754, + 5.806641578674316, + 4.896588325500488 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -231406,31 +231536,31 @@ ], "projections": { "pca": [ - -0.2955474555492401, - -0.09592726081609726 + -0.29579076170921326, + -0.09523449093103409 ], "tsne": [ - -41.25120544433594, - 13.581868171691895 + -37.66056823730469, + -20.3980770111084 ], "umap": [ - 1.8917298316955566, - 5.289219379425049 + 2.2870988845825195, + 4.637282848358154 ], "pca3d": [ - -0.2955473065376282, - -0.09592712670564651, - -0.09199817478656769 + -0.2957903742790222, + -0.09523455798625946, + -0.09172555059194565 ], "tsne3d": [ - -21.4890079498291, - 11.233389854431152, - 4.068842887878418 + -21.66468620300293, + -18.50055694580078, + 3.8009324073791504 ], "umap3d": [ - 1.7033904790878296, - 6.638570785522461, - 4.816735744476318 + 2.2853264808654785, + 6.952740669250488, + 4.898995876312256 ] }, "cluster_id": 2, @@ -231441,11 +231571,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -231854,46 +231984,46 @@ "abstract": "Trains Transformer model variants. Data isn't shuffled between batches. - facebookresearch/transformer-sequential", "projections": { "pca": [ - -0.21854421496391296, - 0.05068186670541763 + -0.21826286613941193, + 0.05157243832945824 ], "tsne": [ - -33.63853454589844, - -5.394827842712402 + -42.36623764038086, + 6.183789253234863 ], "umap": [ - 0.6861402988433838, - 8.046146392822266 + 1.0087859630584717, + 7.285355567932129 ], "pca3d": [ - -0.21854375302791595, - 0.0506407804787159, - -0.06606743484735489 + -0.2182624191045761, + 0.05153016373515129, + -0.06590918451547623 ], "tsne3d": [ - -23.331497192382812, - -3.909209728240967, - 12.432921409606934 + -25.730205535888672, + 5.799675464630127, + 2.9090828895568848 ], "umap3d": [ - 0.8288117051124573, - 4.686838626861572, - 5.107648849487305 + 2.0365495681762695, + 5.142309188842773, + 5.926362991333008 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -232309,47 +232439,47 @@ ], "projections": { "pca": [ - -0.2899639904499054, - -0.15653273463249207 + -0.29051855206489563, + -0.15565957129001617 ], "tsne": [ - -38.31760787963867, - -17.732826232910156 + -43.60487365722656, + 4.42909049987793 ], "umap": [ - 1.1897045373916626, - 7.426782131195068 + 1.4935802221298218, + 6.611021518707275 ], "pca3d": [ - -0.2899637818336487, - -0.15651743113994598, - -0.0985143706202507 + -0.2905181646347046, + -0.15564371645450592, + -0.09818634390830994 ], "tsne3d": [ - -24.248960494995117, - -12.643204689025879, - 3.913299083709717 + -23.116931915283203, + 3.304054021835327, + -4.7483086585998535 ], "umap3d": [ - 0.8053834438323975, - 4.798974514007568, - 4.123330116271973 + 1.6863664388656616, + 5.025895118713379, + 4.8861870765686035 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -232767,47 +232897,47 @@ ], "projections": { "pca": [ - -0.288461834192276, - -0.11097543686628342 + -0.2887064814567566, + -0.11007659882307053 ], "tsne": [ - -55.983642578125, - -8.909554481506348 + -53.59128952026367, + -0.614811360836029 ], "umap": [ - 0.3443780839443207, - 6.921554088592529 + 0.6823619604110718, + 5.925478935241699 ], "pca3d": [ - -0.28846144676208496, - -0.11098252236843109, - -0.11269646883010864 + -0.28870612382888794, + -0.11008362472057343, + -0.1123170480132103 ], "tsne3d": [ - -31.736278533935547, - 0.45932435989379883, - 4.809291839599609 + -32.275733947753906, + 1.2056246995925903, + 0.461311936378479 ], "umap3d": [ - 0.4680558741092682, - 5.9296875, - 4.533247470855713 + 1.2804844379425049, + 6.114688396453857, + 5.1703948974609375 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 9, - "label": "Vision Transformers" + "id": 23, + "label": "Training Noise" } ], "_embedding": [ @@ -233229,46 +233359,46 @@ ], "projections": { "pca": [ - -0.2983839511871338, - -0.10750295966863632 + -0.2985469698905945, + -0.10684949904680252 ], "tsne": [ - -52.76552200317383, - -4.103268146514893 + -49.01877212524414, + 1.618082046508789 ], "umap": [ - 0.16476449370384216, - 7.381416320800781 + 0.47154539823532104, + 6.5623459815979 ], "pca3d": [ - -0.2983836233615875, - -0.10751467198133469, - -0.08992179483175278 + -0.29854661226272583, + -0.10686128586530685, + -0.08954979479312897 ], "tsne3d": [ - -25.398229598999023, - -1.6257110834121704, - 7.097290515899658 + -27.319664001464844, + -0.529087483882904, + 6.460083961486816 ], "umap3d": [ - 0.3273438811302185, - 5.318593978881836, - 4.8838958740234375 + 1.295036792755127, + 5.656473159790039, + 5.745821952819824 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -233686,31 +233816,31 @@ ], "projections": { "pca": [ - -0.2594144940376282, - -0.1089915931224823 + -0.25964224338531494, + -0.10803065448999405 ], "tsne": [ - -43.9030647277832, - 8.479471206665039 + -43.402923583984375, + -15.910717964172363 ], "umap": [ - 1.5467791557312012, - 5.867794990539551 + 1.6798816919326782, + 5.114063739776611 ], "pca3d": [ - -0.25941407680511475, - -0.10901202261447906, - -0.1062796339392662 + -0.2596418559551239, + -0.10805141925811768, + -0.10589773207902908 ], "tsne3d": [ - -26.65662384033203, - 7.840306758880615, - -2.3035736083984375 + -19.038331985473633, + -9.149847984313965, + -8.751180648803711 ], "umap3d": [ - 1.1256698369979858, - 6.332723617553711, - 4.4833083152771 + 1.7659019231796265, + 6.523805141448975, + 4.793334007263184 ] }, "cluster_id": 2, @@ -233721,12 +233851,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -234140,31 +234270,31 @@ ], "projections": { "pca": [ - -0.30038371682167053, - 0.000985487480647862 + -0.300434947013855, + 0.0019043631618842483 ], "tsne": [ - -47.77604293823242, - -21.42424201965332 + -51.236968994140625, + 15.902888298034668 ], "umap": [ - 0.3284039795398712, - 5.765267848968506 + 0.6791043281555176, + 4.8293657302856445 ], "pca3d": [ - -0.30038362741470337, - 0.0010041798232123256, - -0.17491008341312408 + -0.3004348576068878, + 0.0019232453778386116, + -0.17472705245018005 ], "tsne3d": [ - -29.059364318847656, - -12.464258193969727, - -2.730555772781372 + -26.301273345947266, + -2.4095282554626465, + -10.226542472839355 ], "umap3d": [ - 0.4181908071041107, - 5.467229843139648, - 3.383774518966675 + 1.0473088026046753, + 5.380465507507324, + 4.1364359855651855 ] }, "cluster_id": 2, @@ -234175,12 +234305,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, - "label": "Training Noise" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -234593,46 +234723,46 @@ ], "projections": { "pca": [ - -0.22932426631450653, - -0.08627339452505112 + -0.2295168787240982, + -0.08553005754947662 ], "tsne": [ - -36.26142501831055, - -15.651856422424316 + -30.320383071899414, + 6.032829284667969 ], "umap": [ - 1.8550218343734741, - 7.942600250244141 + 2.184971809387207, + 7.094759941101074 ], "pca3d": [ - -0.2293243259191513, - -0.08623988926410675, - -0.08208564668893814 + -0.22951672971248627, + -0.08549592643976212, + -0.08173349499702454 ], "tsne3d": [ - -19.348268508911133, - -16.207386016845703, - 10.019835472106934 + -19.40878677368164, + 4.010843753814697, + -2.4846994876861572 ], "umap3d": [ - 1.6793748140335083, - 4.303680896759033, - 4.578178405761719 + 2.4890668392181396, + 4.911101818084717, + 5.0985331535339355 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 1, + "label": "Attention Models" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -235051,46 +235181,46 @@ ], "projections": { "pca": [ - -0.2677691578865051, - -0.11987493932247162 + -0.2679271101951599, + -0.11937946826219559 ], "tsne": [ - -60.22247314453125, - -8.231436729431152 + -57.40886306762695, + 0.7063706517219543 ], "umap": [ - 0.37586942315101624, - 6.828475475311279 + 0.5928399562835693, + 6.1220879554748535 ], "pca3d": [ - -0.26776841282844543, - -0.11990629881620407, - -0.12662182748317719 + -0.2679266035556793, + -0.11941211670637131, + -0.12619730830192566 ], "tsne3d": [ - -29.26931381225586, - 6.903491020202637, - 3.865412473678589 + -36.84016799926758, + -2.10628342628479, + -0.06799811124801636 ], "umap3d": [ - 0.7383420467376709, - 6.173187732696533, - 4.815132141113281 + 1.5127878189086914, + 6.43477725982666, + 5.382682800292969 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -235508,47 +235638,47 @@ ], "projections": { "pca": [ - -0.1718796044588089, - -0.03546403720974922 + -0.1721210479736328, + -0.03489801660180092 ], "tsne": [ - -38.83331298828125, - -3.0036277770996094 + -28.367881774902344, + -9.426836967468262 ], "umap": [ - 1.554830551147461, - 6.876551628112793 + 2.179628372192383, + 5.620716094970703 ], "pca3d": [ - -0.17187942564487457, - -0.03545286878943443, - 0.012054129503667355 + -0.1721208691596985, + -0.03488622233271599, + 0.012033365666866302 ], "tsne3d": [ - -15.477152824401855, - -3.149355411529541, - 2.2514050006866455 + -16.377670288085938, + -3.302849292755127, + 4.944468975067139 ], "umap3d": [ - 2.1129536628723145, - 5.906871795654297, - 4.604655742645264 + 2.7337210178375244, + 6.113222122192383, + 4.7989959716796875 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 5, + "label": "Diffusion Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 19, + "label": "Graph Neural Nets" } ], "_embedding": [ @@ -235964,46 +236094,46 @@ ], "projections": { "pca": [ - -0.23247820138931274, - -0.10587356984615326 + -0.23260748386383057, + -0.10473528504371643 ], "tsne": [ - -29.639984130859375, - -12.916359901428223 + -35.498008728027344, + 10.993842124938965 ], "umap": [ - 1.6434401273727417, - 8.192107200622559 + 1.8941876888275146, + 7.412178039550781 ], "pca3d": [ - -0.23247796297073364, - -0.1058630421757698, - 0.012710453942418098 + -0.23260727524757385, + -0.10472478717565536, + 0.013169942423701286 ], "tsne3d": [ - -17.671310424804688, - -12.371736526489258, - 8.285994529724121 + -19.854877471923828, + 5.3380913734436035, + 0.11944156140089035 ], "umap3d": [ - 1.5981125831604004, - 4.333252906799316, - 4.712917327880859 + 2.6303305625915527, + 4.838009834289551, + 5.356372833251953 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 1, + "label": "Attention Models" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -236417,46 +236547,46 @@ ], "projections": { "pca": [ - -0.26251429319381714, - -0.12685659527778625 + -0.26268264651298523, + -0.12608546018600464 ], "tsne": [ - -34.53477096557617, - -10.926513671875 + -39.54410934448242, + 10.42341423034668 ], "umap": [ - 1.0231953859329224, - 8.01962661743164 + 1.3301467895507812, + 7.155913829803467 ], "pca3d": [ - -0.2625138759613037, - -0.12685643136501312, - -0.08197096735239029 + -0.26268237829208374, + -0.1260855495929718, + -0.08147253096103668 ], "tsne3d": [ - -22.361780166625977, - -10.049970626831055, - 9.950665473937988 + -21.886720657348633, + 7.119339942932129, + 8.63421630859375 ], "umap3d": [ - 0.9577622413635254, - 4.575898170471191, - 4.873520374298096 + 2.1050102710723877, + 4.891658306121826, + 5.606316089630127 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -236871,46 +237001,46 @@ ], "projections": { "pca": [ - -0.3111324906349182, - -0.11010780185461044 + -0.3112814724445343, + -0.10929302126169205 ], "tsne": [ - -54.64603042602539, - -1.069322109222412 + -47.77731704711914, + 4.304115295410156 ], "umap": [ - 0.138447105884552, - 7.556012153625488 + 0.3128124475479126, + 6.717801094055176 ], "pca3d": [ - -0.3111318051815033, - -0.11015521734952927, - -0.1430208832025528 + -0.31128060817718506, + -0.10934128612279892, + -0.14256441593170166 ], "tsne3d": [ - -27.155792236328125, - -3.8933074474334717, - 8.757076263427734 + -27.162460327148438, + 2.34541392326355, + 3.8621416091918945 ], "umap3d": [ - 0.21347366273403168, - 5.283325672149658, - 5.038492679595947 + 1.3408275842666626, + 5.564169406890869, + 5.929051876068115 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -237325,31 +237455,31 @@ "doi": "10.1088/0954-3899/38/12/124178", "projections": { "pca": [ - -0.08177106827497482, - 0.10814088582992554 + -0.08152173459529877, + 0.10832888633012772 ], "tsne": [ - -30.615703582763672, - 9.967966079711914 + -29.957136154174805, + -12.788592338562012 ], "umap": [ - 2.4955761432647705, - 6.105376720428467 + 2.890904426574707, + 5.4461164474487305 ], "pca3d": [ - -0.08177124708890915, - 0.10815751552581787, - 0.01773053966462612 + -0.08152198791503906, + 0.10834577679634094, + 0.01743651181459427 ], "tsne3d": [ - -13.732034683227539, - 3.5712201595306396, - -1.0807017087936401 + -13.911396026611328, + -10.28189754486084, + 3.303513765335083 ], "umap3d": [ - 2.3418145179748535, - 5.819065570831299, - 4.328710556030273 + 2.9923768043518066, + 6.243496417999268, + 4.478848934173584 ] }, "cluster_id": 2, @@ -237360,12 +237490,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 33, - "label": "Neural Networks" + "id": 19, + "label": "Graph Neural Nets" } ], "_embedding": [ @@ -237779,46 +237909,46 @@ ], "projections": { "pca": [ - -0.2866760194301605, - -0.10276791453361511 + -0.28700292110443115, + -0.10211023688316345 ], "tsne": [ - -50.143558502197266, - -8.692740440368652 + -51.44447708129883, + -6.284685134887695 ], "umap": [ - 0.5111357569694519, - 6.306784629821777 + 0.8139104843139648, + 5.505614757537842 ], "pca3d": [ - -0.28667572140693665, - -0.1027582585811615, - -0.09367357939481735 + -0.2870027422904968, + -0.10210023075342178, + -0.09343034029006958 ], "tsne3d": [ - -28.537883758544922, - -2.042165756225586, - -1.7012784481048584 + -27.624759674072266, + -6.661840438842773, + -1.2661586999893188 ], "umap3d": [ - 0.40014612674713135, - 5.900920867919922, - 3.943964719772339 + 1.170480728149414, + 5.946988105773926, + 4.691608428955078 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, + "id": 23, "label": "Training Noise" } ], @@ -238232,46 +238362,46 @@ ], "projections": { "pca": [ - -0.3153262436389923, - -0.12765690684318542 + -0.31556060910224915, + -0.1267077624797821 ], "tsne": [ - -55.570579528808594, - -4.464670181274414 + -51.412986755371094, + 3.4118077754974365 ], "umap": [ - 0.09527118504047394, - 7.447292804718018 + 0.39430147409439087, + 6.4845147132873535 ], "pca3d": [ - -0.31532543897628784, - -0.12768417596817017, - -0.14593194425106049 + -0.31556010246276855, + -0.12673552334308624, + -0.14545048773288727 ], "tsne3d": [ - -30.535200119018555, - -0.0767640769481659, - 6.437884330749512 + -31.753873825073242, + 1.6595722436904907, + 2.4651131629943848 ], "umap3d": [ - 0.24921317398548126, - 5.507443904876709, - 4.912144184112549 + 1.2506417036056519, + 5.77229642868042, + 5.714828014373779 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -238689,46 +238819,46 @@ "doi": "10.1088/1742-5468/ac9830", "projections": { "pca": [ - -0.27369147539138794, - -0.11838389188051224 + -0.27396219968795776, + -0.1174798235297203 ], "tsne": [ - -55.862266540527344, - -1.786047101020813 + -49.04542922973633, + 5.091360092163086 ], "umap": [ - 0.13458316028118134, - 7.613651752471924 + 0.3271011412143707, + 6.745737552642822 ], "pca3d": [ - -0.27369099855422974, - -0.11841901391744614, - -0.15256281197071075 + -0.2739616930484772, + -0.11751536279916763, + -0.15214578807353973 ], "tsne3d": [ - -26.747652053833008, - -0.6359025239944458, - 9.543869972229004 + -29.354263305664062, + 2.3973333835601807, + 6.663577556610107 ], "umap3d": [ - 0.29572466015815735, - 5.282783031463623, - 5.0727033615112305 + 1.4198498725891113, + 5.575014591217041, + 5.974697589874268 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -238740,7 +238870,7 @@ 0.0405, 0.0506, -0.0079, - 0.0227, + 0.0226, 0.0341, -0.0202, 0.0185, @@ -239146,46 +239276,46 @@ ], "projections": { "pca": [ - -0.27107954025268555, - -0.1285761296749115 + -0.2714037001132965, + -0.12785597145557404 ], "tsne": [ - -54.950096130371094, - -2.029667615890503 + -49.10799026489258, + 4.227481842041016 ], "umap": [ - 0.025613835081458092, - 7.499945640563965 + 0.3656036853790283, + 6.622995853424072 ], "pca3d": [ - -0.27107885479927063, - -0.12860693037509918, - -0.07464686036109924 + -0.2714032530784607, + -0.1278870850801468, + -0.07431376725435257 ], "tsne3d": [ - -27.921497344970703, - 0.4463747441768646, - 9.5501070022583 + -29.39794921875, + 0.5120468735694885, + 6.87300968170166 ], "umap3d": [ - 0.26503801345825195, - 5.342324256896973, - 5.0483784675598145 + 1.2984862327575684, + 5.6988844871521, + 5.914605140686035 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -239598,46 +239728,46 @@ ], "projections": { "pca": [ - -0.30083000659942627, - -0.1749328225851059 + -0.301308274269104, + -0.17435306310653687 ], "tsne": [ - -51.49479675292969, - -3.508800745010376 + -48.443477630615234, + 0.02309633418917656 ], "umap": [ - 0.2600041627883911, - 7.152284622192383 + 0.5681971907615662, + 6.304873943328857 ], "pca3d": [ - -0.30082935094833374, - -0.17494070529937744, - -0.10175325721502304 + -0.30130764842033386, + -0.17436061799526215, + -0.10134457796812057 ], "tsne3d": [ - -26.65941047668457, - -0.21096506714820862, - 3.9743213653564453 + -28.005104064941406, + -3.0299839973449707, + 3.799645185470581 ], "umap3d": [ - 0.43239015340805054, - 5.55746603012085, - 4.677978992462158 + 1.387331247329712, + 5.8109331130981445, + 5.522480487823486 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -240053,46 +240183,46 @@ ], "projections": { "pca": [ - -0.34982606768608093, - -0.18614573776721954 + -0.35024818778038025, + -0.18512362241744995 ], "tsne": [ - -53.666221618652344, - -2.3948001861572266 + -48.435211181640625, + 2.7715370655059814 ], "umap": [ - 0.20301109552383423, - 7.328308582305908 + 0.44745534658432007, + 6.543669700622559 ], "pca3d": [ - -0.34982550144195557, - -0.1861775666475296, - -0.09576944261789322 + -0.35024747252464294, + -0.18515586853027344, + -0.09522368758916855 ], "tsne3d": [ - -28.046859741210938, - -2.7647690773010254, - 6.267460823059082 + -28.308135986328125, + 0.7478263974189758, + 2.78788685798645 ], "umap3d": [ - 0.24756330251693726, - 5.324833393096924, - 4.859155178070068 + 1.2930978536605835, + 5.687531471252441, + 5.716212272644043 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -240508,31 +240638,31 @@ ], "projections": { "pca": [ - -0.26517587900161743, - -0.1584675908088684 + -0.2657470703125, + -0.1578969657421112 ], "tsne": [ - -47.24562072753906, - -10.319808006286621 + -45.071258544921875, + -11.407122611999512 ], "umap": [ - 1.0094209909439087, - 6.1464715003967285 + 1.4913829565048218, + 5.274399757385254 ], "pca3d": [ - -0.26517555117607117, - -0.1584673821926117, - -0.07944668084383011 + -0.26574671268463135, + -0.15789660811424255, + -0.07921333611011505 ], "tsne3d": [ - -25.34918785095215, - -7.036177158355713, - -2.6062541007995605 + -20.604368209838867, + -7.463116645812988, + -2.9306106567382812 ], "umap3d": [ - 0.9866014122962952, - 6.077259540557861, - 3.9832546710968018 + 1.6487581729888916, + 6.0977091789245605, + 4.57705020904541 ] }, "cluster_id": 2, @@ -240543,12 +240673,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -240956,47 +241086,47 @@ "abstract": "From BiT (928 million parameters (opens in new tab)) to GPT-3 (175 billion parameters (opens in new tab)), state-of-the-art machine learning models are rapidly growing in size. With the greater expressivity and easier trainability of these models come skyrocketing training costs, deployment difficulties, and even climate impact. As a result, we\u2019re witnessing exciting and emerging […]", "projections": { "pca": [ - -0.2580825686454773, - -0.06174152344465256 + -0.2583724856376648, + -0.06093759462237358 ], "tsne": [ - -41.629051208496094, - -4.755339622497559 + -45.03337860107422, + 0.4010191559791565 ], "umap": [ - 1.1214100122451782, - 7.34600830078125 + 1.4238107204437256, + 6.469850540161133 ], "pca3d": [ - -0.2580822706222534, - -0.06172621622681618, - -0.034411702305078506 + -0.2583722770214081, + -0.06092183291912079, + -0.03428558260202408 ], "tsne3d": [ - -22.509340286254883, - 0.7364101409912109, - 5.933125019073486 + -23.34312629699707, + -1.6373738050460815, + 3.3530054092407227 ], "umap3d": [ - 1.2516069412231445, - 5.149773597717285, - 4.647012710571289 + 2.2372641563415527, + 5.541739463806152, + 5.20521354675293 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -241412,46 +241542,46 @@ ], "projections": { "pca": [ - -0.3103488087654114, - -0.1682715117931366 + -0.31085440516471863, + -0.16760966181755066 ], "tsne": [ - -53.71900177001953, - -5.4143147468566895 + -50.4923210144043, + 1.3396313190460205 ], "umap": [ - 0.2256300300359726, - 7.11134147644043 + 0.5068300366401672, + 6.226022243499756 ], "pca3d": [ - -0.31034842133522034, - -0.16830086708068848, - -0.13692200183868408 + -0.31085386872291565, + -0.16763943433761597, + -0.13654005527496338 ], "tsne3d": [ - -29.576213836669922, - -2.2198116779327393, - 5.720174789428711 + -29.731576919555664, + 0.0836448073387146, + 2.0670948028564453 ], "umap3d": [ - 0.21112148463726044, - 5.4633893966674805, - 4.562719821929932 + 1.1635887622833252, + 5.696500778198242, + 5.50271463394165 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { "id": 8, - "label": "Attention Vision" + "label": "Network Learning" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -241867,47 +241997,47 @@ ], "projections": { "pca": [ - -0.21533319354057312, - -0.08466365188360214 + -0.21539181470870972, + -0.08419153839349747 ], "tsne": [ - -27.100122451782227, - -4.974461555480957 + -33.51286315917969, + 3.4661295413970947 ], "umap": [ - 1.4638371467590332, - 7.917197227478027 + 1.6778777837753296, + 7.222863674163818 ], "pca3d": [ - -0.2153330296278, - -0.08464539796113968, - -0.029990699142217636 + -0.21539150178432465, + -0.08417291939258575, + -0.029641879722476006 ], "tsne3d": [ - -19.69235610961914, - -12.679011344909668, - 12.881919860839844 + -17.644466400146484, + 9.143264770507812, + 9.721407890319824 ], "umap3d": [ - 1.5508081912994385, - 4.749373435974121, - 4.844339370727539 + 2.4437386989593506, + 5.325059413909912, + 5.493859767913818 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, - "label": "Transformer Attention" + "id": 34, + "label": "Language Models" } ], "_embedding": [ @@ -242320,47 +242450,47 @@ ], "projections": { "pca": [ - -0.16419488191604614, - -0.1407504379749298 + -0.16443566977977753, + -0.14071786403656006 ], "tsne": [ - -61.571983337402344, - -11.25227165222168 + -60.20035171508789, + -0.5678091049194336 ], "umap": [ - 0.618194580078125, - 6.798679351806641 + 0.7639074921607971, + 5.805445194244385 ], "pca3d": [ - -0.16419441998004913, - -0.1407555639743805, - -0.09780000150203705 + -0.16443535685539246, + -0.14072291553020477, + -0.09744298458099365 ], "tsne3d": [ - -30.800033569335938, - 7.626443862915039, - -0.21086083352565765 + -34.07118606567383, + -3.079775810241699, + -3.193045139312744 ], "umap3d": [ - 0.7068158388137817, - 6.097835540771484, - 4.700020790100098 + 1.3684800863265991, + 6.365087032318115, + 5.171389579772949 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 9, - "label": "Vision Transformers" + "id": 23, + "label": "Training Noise" } ], "_embedding": [ @@ -242778,46 +242908,46 @@ ], "projections": { "pca": [ - -0.3323613405227661, - -0.1414477676153183 + -0.33264467120170593, + -0.14054125547409058 ], "tsne": [ - -55.36552429199219, - -2.6668524742126465 + -49.45306396484375, + 4.28945255279541 ], "umap": [ - 0.0014126566238701344, - 7.541004180908203 + 0.3592495620250702, + 6.6612162590026855 ], "pca3d": [ - -0.3323605954647064, - -0.14148245751857758, - -0.14037375152111053 + -0.33264410495758057, + -0.14057661592960358, + -0.13988818228244781 ], "tsne3d": [ - -28.451601028442383, - -1.1763277053833008, - 8.8579683303833 + -29.423704147338867, + 1.3730103969573975, + 5.320671558380127 ], "umap3d": [ - 0.2244425117969513, - 5.334338188171387, - 5.0107102394104 + 1.2796467542648315, + 5.6484880447387695, + 5.879484176635742 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -243226,46 +243356,46 @@ "abstract": "Can we swap softmax attention for energy-based attention?", "projections": { "pca": [ - -0.21459545195102692, - -0.023857127875089645 + -0.21459436416625977, + -0.023247910663485527 ], "tsne": [ - -38.008750915527344, - -8.403315544128418 + -44.88352584838867, + 8.989921569824219 ], "umap": [ - 0.6548821330070496, - 7.887463569641113 + 0.9843905568122864, + 6.97176456451416 ], "pca3d": [ - -0.21459567546844482, - -0.023846490308642387, - -0.03695137798786163 + -0.21459443867206573, + -0.02323693409562111, + -0.03680681437253952 ], "tsne3d": [ - -27.058151245117188, - -9.157880783081055, - 8.001700401306152 + -25.72747802734375, + 4.943172931671143, + 10.804017066955566 ], "umap3d": [ - 0.6983197331428528, - 4.7754716873168945, - 4.883520126342773 + 1.9228813648223877, + 5.087475776672363, + 5.667271614074707 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -243680,46 +243810,46 @@ "doi": "10.3389/fcomp.2022.1041703", "projections": { "pca": [ - -0.3034939467906952, - -0.18175168335437775 + -0.3040388822555542, + -0.1808941662311554 ], "tsne": [ - -52.18933868408203, - -2.6813671588897705 + -47.9663200378418, + 1.1945923566818237 ], "umap": [ - 0.2792469561100006, - 7.094755172729492 + 0.6096903681755066, + 6.252935409545898 ], "pca3d": [ - -0.30349355936050415, - -0.18175646662712097, - -0.09660933166742325 + -0.3040383458137512, + -0.18089869618415833, + -0.0961892306804657 ], "tsne3d": [ - -26.82915496826172, - -1.7869210243225098, - 5.0140252113342285 + -27.338062286376953, + -1.0827407836914062, + 2.6108131408691406 ], "umap3d": [ - 0.4102182686328888, - 5.503242492675781, - 4.605556011199951 + 1.3403643369674683, + 5.730637550354004, + 5.470611572265625 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { "id": 8, - "label": "Attention Vision" + "label": "Network Learning" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -244138,46 +244268,46 @@ ], "projections": { "pca": [ - -0.25354495644569397, - -0.15535154938697815 + -0.25405657291412354, + -0.1548651158809662 ], "tsne": [ - -51.78617477416992, - -3.020820140838623 + -48.21944046020508, + 0.5808513164520264 ], "umap": [ - 0.3205585777759552, - 7.14894962310791 + 0.5853040218353271, + 6.304148197174072 ], "pca3d": [ - -0.25354433059692383, - -0.15536527335643768, - -0.13219347596168518 + -0.2540561556816101, + -0.1548784226179123, + -0.13188353180885315 ], "tsne3d": [ - -25.925453186035156, - -0.6558567881584167, - 5.106436252593994 + -27.17148208618164, + -1.8306258916854858, + 3.81068754196167 ], "umap3d": [ - 0.4503861665725708, - 5.573909759521484, - 4.6834845542907715 + 1.385002613067627, + 5.822196960449219, + 5.53269100189209 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -244593,31 +244723,31 @@ ], "projections": { "pca": [ - -0.26774007081985474, - -0.14087674021720886 + -0.2682872414588928, + -0.13993051648139954 ], "tsne": [ - -49.135555267333984, - -21.18581771850586 + -52.24638748168945, + 14.653514862060547 ], "umap": [ - 0.39271143078804016, - 5.8004841804504395 + 0.7479623556137085, + 4.927884578704834 ], "pca3d": [ - -0.26773983240127563, - -0.14085756242275238, - -0.1170566976070404 + -0.26828694343566895, + -0.13991089165210724, + -0.11675543338060379 ], "tsne3d": [ - -30.982133865356445, - -11.823832511901855, - -2.7924551963806152 + -28.45440673828125, + -2.076179265975952, + -9.285590171813965 ], "umap3d": [ - 0.4206446707248688, - 5.49678897857666, - 3.4556474685668945 + 1.0872273445129395, + 5.401113510131836, + 4.198848724365234 ] }, "cluster_id": 2, @@ -244628,12 +244758,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, - "label": "Training Noise" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -245048,31 +245178,31 @@ "doi": "10.1109/TPAMI.2021.3116668", "projections": { "pca": [ - -0.36133044958114624, - -0.15948322415351868 + -0.36169278621673584, + -0.15849904716014862 ], "tsne": [ - -43.803409576416016, - 14.423294067382812 + -40.36865997314453, + -21.23137664794922 ], "umap": [ - 1.7104476690292358, - 5.401491165161133 + 2.1399173736572266, + 4.82649040222168 ], "pca3d": [ - -0.3613298535346985, - -0.1594904512166977, - -0.041914161294698715 + -0.36169227957725525, + -0.15850649774074554, + -0.041437748819589615 ], "tsne3d": [ - -24.733762741088867, - 12.326464653015137, - 4.040256977081299 + -24.5949649810791, + -18.679378509521484, + 2.7094979286193848 ], "umap3d": [ - 1.5791548490524292, - 6.503417491912842, - 4.722685813903809 + 2.1378347873687744, + 6.838537693023682, + 4.900025367736816 ] }, "cluster_id": 2, @@ -245083,12 +245213,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 22, + "label": "Diffusion Models" } ], "_embedding": [ @@ -245504,46 +245634,46 @@ ], "projections": { "pca": [ - -0.2032909095287323, - -0.17150023579597473 + -0.2035382241010666, + -0.17044426500797272 ], "tsne": [ - -31.29833221435547, - -7.154733180999756 + -37.45248794555664, + 6.745946884155273 ], "umap": [ - 1.0921317338943481, - 8.120624542236328 + 1.3042725324630737, + 7.222024917602539 ], "pca3d": [ - -0.20329026877880096, - -0.1715231090784073, - 0.03331379219889641 + -0.20353764295578003, + -0.17046760022640228, + 0.033907700330019 ], "tsne3d": [ - -19.85696029663086, - -5.158125877380371, - 8.840003967285156 + -22.02001190185547, + 4.663859844207764, + 3.157932996749878 ], "umap3d": [ - 1.1469001770019531, - 4.739130020141602, - 4.96872615814209 + 2.3029205799102783, + 5.216104507446289, + 5.795011520385742 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -245957,46 +246087,46 @@ ], "projections": { "pca": [ - -0.30798131227493286, - -0.11500631272792816 + -0.30824488401412964, + -0.11366104334592819 ], "tsne": [ - -36.51318359375, - -9.202791213989258 + -42.71961975097656, + 9.842477798461914 ], "umap": [ - 0.6844267845153809, - 7.89621114730835 + 0.9585292935371399, + 6.950920581817627 ], "pca3d": [ - -0.3079809844493866, - -0.11501892656087875, - -0.09752839058637619 + -0.3082444369792938, + -0.1136738657951355, + -0.09709557145833969 ], "tsne3d": [ - -25.06610870361328, - -7.11316442489624, - 8.030244827270508 + -24.59295082092285, + 3.8928205966949463, + 8.587984085083008 ], "umap3d": [ - 0.69545578956604, - 4.794373035430908, - 4.925411224365234 + 1.8790931701660156, + 5.1158366203308105, + 5.699113368988037 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -246412,46 +246542,46 @@ ], "projections": { "pca": [ - -0.20996980369091034, - -0.10189773142337799 + -0.21023587882518768, + -0.1014462262392044 ], "tsne": [ - -58.11414337158203, - -9.54409122467041 + -55.735595703125, + -1.1491546630859375 ], "umap": [ - 0.3547901511192322, - 6.869338512420654 + 0.5937067270278931, + 6.109740257263184 ], "pca3d": [ - -0.20996925234794617, - -0.10191798210144043, - -0.16767805814743042 + -0.21023547649383545, + -0.10146630555391312, + -0.16737791895866394 ], "tsne3d": [ - -32.29323196411133, - 4.448428630828857, - 4.203619003295898 + -34.72270584106445, + 0.49579253792762756, + -1.438286542892456 ], "umap3d": [ - 0.5210870504379272, - 5.845903396606445, - 4.714498043060303 + 1.3705105781555176, + 6.140387058258057, + 5.369800090789795 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -246868,46 +246998,46 @@ ], "projections": { "pca": [ - -0.24227072298526764, - -0.08004167675971985 + -0.24236348271369934, + -0.07902392745018005 ], "tsne": [ - -58.467342376708984, - -0.8828647136688232 + -48.43159103393555, + 7.943923473358154 ], "umap": [ - 0.3935050368309021, - 7.749992847442627 + 0.6106750965118408, + 6.806938648223877 ], "pca3d": [ - -0.2422703355550766, - -0.08005814254283905, - -0.05331224948167801 + -0.2423631101846695, + -0.07904083281755447, + -0.052943430840969086 ], "tsne3d": [ - -25.367631912231445, - -0.06172811612486839, - 12.075297355651855 + -28.01495933532715, + 2.1240077018737793, + 9.739611625671387 ], "umap3d": [ - 0.47216564416885376, - 5.140343189239502, - 5.040297985076904 + 1.514893889427185, + 5.476288318634033, + 5.8607025146484375 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -247321,46 +247451,46 @@ ], "projections": { "pca": [ - -0.24366159737110138, - -0.02907731756567955 + -0.24349838495254517, + -0.027947353199124336 ], "tsne": [ - -34.724308013916016, - -5.663055419921875 + -43.168373107910156, + 7.195951461791992 ], "umap": [ - 0.7024815678596497, - 8.072176933288574 + 0.9915264248847961, + 7.259709358215332 ], "pca3d": [ - -0.2436610460281372, - -0.029112864285707474, - 0.015851333737373352 + -0.24349789321422577, + -0.02798386849462986, + 0.016251491382718086 ], "tsne3d": [ - -24.79197120666504, - -5.44117546081543, - 13.03543472290039 + -26.6767578125, + 7.0403642654418945, + 4.408268451690674 ], "umap3d": [ - 0.7938510775566101, - 4.7154974937438965, - 5.050878047943115 + 1.9850914478302002, + 5.07009220123291, + 5.880333423614502 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -247775,47 +247905,47 @@ ], "projections": { "pca": [ - -0.264021098613739, - -0.028387121856212616 + -0.26403674483299255, + -0.027233928442001343 ], "tsne": [ - -39.26736831665039, - -20.225841522216797 + -43.84708786010742, + 14.627938270568848 ], "umap": [ - 1.1547143459320068, - 7.517109394073486 + 1.4680509567260742, + 6.580094814300537 ], "pca3d": [ - -0.26402080059051514, - -0.028382251039147377, - -0.05874745920300484 + -0.26403677463531494, + -0.027229586616158485, + -0.05845094099640846 ], "tsne3d": [ - -26.26747703552246, - -15.685858726501465, - 2.7774598598480225 + -26.574352264404297, + 6.330629348754883, + -8.031414985656738 ], "umap3d": [ - 0.7120040059089661, - 4.7055439949035645, - 4.138869762420654 + 1.569871425628662, + 4.909554958343506, + 4.815621852874756 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -248231,46 +248361,46 @@ ], "projections": { "pca": [ - -0.2674669325351715, - -0.10411281138658524 + -0.2675279974937439, + -0.10288330912590027 ], "tsne": [ - -36.332679748535156, - -7.546838760375977 + -44.003631591796875, + 9.926722526550293 ], "umap": [ - 0.6852691769599915, - 7.973289966583252 + 0.9638847708702087, + 7.137832164764404 ], "pca3d": [ - -0.2674662470817566, - -0.10415659844875336, - -0.04457967355847359 + -0.26752740144729614, + -0.10292837768793106, + -0.0440651997923851 ], "tsne3d": [ - -26.168987274169922, - -6.610902309417725, - 9.799206733703613 + -26.309951782226562, + 5.304352283477783, + 7.58922815322876 ], "umap3d": [ - 0.6803902387619019, - 4.75739860534668, - 4.933864116668701 + 1.8380717039108276, + 5.075148105621338, + 5.687106609344482 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -248685,46 +248815,46 @@ ], "projections": { "pca": [ - -0.25964584946632385, - -0.06821352243423462 + -0.2598693072795868, + -0.06755025684833527 ], "tsne": [ - -48.55415344238281, - -7.138309478759766 + -49.43094253540039, + -6.046154975891113 ], "umap": [ - 0.6515278816223145, - 6.369485378265381 + 1.0170338153839111, + 5.559871673583984 ], "pca3d": [ - -0.2596455514431, - -0.0682143047451973, - -0.10179092735052109 + -0.2598689794540405, + -0.06755124777555466, + -0.10163825005292892 ], "tsne3d": [ - -26.427244186401367, - -0.4606160521507263, - -2.9550728797912598 + -26.176578521728516, + -9.588030815124512, + 0.33024823665618896 ], "umap3d": [ - 0.5732918977737427, - 5.912227630615234, - 3.994849443435669 + 1.3607184886932373, + 5.9329729080200195, + 4.786868572235107 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, + "id": 23, "label": "Training Noise" } ], @@ -249140,46 +249270,46 @@ ], "projections": { "pca": [ - -0.2928433418273926, - -0.08375469595193863 + -0.2932720482349396, + -0.08297542482614517 ], "tsne": [ - -50.1926383972168, - -9.842472076416016 + -51.71809387207031, + -7.526637554168701 ], "umap": [ - 0.5310563445091248, - 6.34535026550293 + 0.7370638251304626, + 5.4909820556640625 ], "pca3d": [ - -0.29284319281578064, - -0.08374672383069992, - -0.10293513536453247 + -0.29327186942100525, + -0.08296715468168259, + -0.10281835496425629 ], "tsne3d": [ - -28.77410316467285, - -4.353142738342285, - -2.2842516899108887 + -26.771793365478516, + -8.946361541748047, + -1.861327052116394 ], "umap3d": [ - 0.41456934809684753, - 5.853644371032715, - 3.984031915664673 + 1.133895754814148, + 5.867003917694092, + 4.809845447540283 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, + "id": 23, "label": "Training Noise" } ], @@ -249603,46 +249733,46 @@ "cited_by_count": 8, "projections": { "pca": [ - 0.047682955861091614, - -0.09173192083835602 + 0.04775014519691467, + -0.09125275164842606 ], "tsne": [ - 5.023122787475586, - 34.21896743774414 + 2.9145667552948, + -11.93656063079834 ], "umap": [ - 6.665369033813477, - 4.901843547821045 + 7.058655738830566, + 4.670721054077148 ], "pca3d": [ - 0.04768288508057594, - -0.0917128175497055, - 0.12793338298797607 + 0.04775005578994751, + -0.09123383462429047, + 0.12830038368701935 ], "tsne3d": [ - 6.798181533813477, - 12.106669425964355, - -2.5237181186676025 + 7.20464563369751, + -7.49540376663208, + 1.4727805852890015 ], "umap3d": [ - 5.507628917694092, - 5.486203193664551, - 4.469964027404785 + 6.173741340637207, + 6.444943428039551, + 3.3595404624938965 ] }, - "cluster_id": 3, - "cluster_label": "Protein Design", + "cluster_id": 4, + "cluster_label": "RNA Sequence Models", "cluster_levels": [ { - "id": 3, - "label": "Protein Design" + "id": 4, + "label": "RNA Sequence Models" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -250056,47 +250186,47 @@ ], "projections": { "pca": [ - -0.23794880509376526, - -0.12436725944280624 + -0.23832491040229797, + -0.12364746630191803 ], "tsne": [ - -44.96012496948242, - -18.99846649169922 + -48.573123931884766, + 12.606620788574219 ], "umap": [ - 0.8873447179794312, - 6.607909202575684 + 1.2130677700042725, + 5.670998573303223 ], "pca3d": [ - -0.2379484474658966, - -0.12437053769826889, - -0.16156814992427826 + -0.23832456767559052, + -0.12365113943815231, + -0.16123874485492706 ], "tsne3d": [ - -28.96677017211914, - -9.780659675598145, - 0.8607437610626221 + -27.603534698486328, + -0.924981415271759, + -5.454342365264893 ], "umap3d": [ - 0.65720134973526, - 5.461491584777832, - 3.8591785430908203 + 1.361584186553955, + 5.533345699310303, + 4.510967254638672 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -250509,47 +250639,47 @@ ], "projections": { "pca": [ - -0.217794269323349, - -0.0287308506667614 + -0.2178434431552887, + -0.027738075703382492 ], "tsne": [ - -44.02605438232422, - -23.1734561920166 + -47.503395080566406, + 18.902334213256836 ], "umap": [ - 1.7215747833251953, - 6.8813958168029785 + 2.0956034660339355, + 6.2327470779418945 ], "pca3d": [ - -0.21779398620128632, - -0.02873840741813183, - -0.08546584844589233 + -0.2178431749343872, + -0.027746213600039482, + -0.0852275937795639 ], "tsne3d": [ - -26.27473258972168, - -15.889886856079102, - -6.190049171447754 + -20.275115966796875, + -0.4213600754737854, + -10.111332893371582 ], "umap3d": [ - 1.0493665933609009, - 4.991208553314209, - 3.520392417907715 + 1.6465953588485718, + 5.040992736816406, + 4.289740085601807 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -250961,46 +251091,46 @@ ], "projections": { "pca": [ - -0.2541896402835846, - -0.12040317058563232 + -0.25434547662734985, + -0.11929363757371902 ], "tsne": [ - -61.49748992919922, - -7.525653839111328 + -57.893394470214844, + 2.1293816566467285 ], "umap": [ - 0.6024513840675354, - 7.092650413513184 + 0.8429766297340393, + 6.1497931480407715 ], "pca3d": [ - -0.25418922305107117, - -0.12040520459413528, - -0.060182683169841766 + -0.2543451189994812, + -0.1192963644862175, + -0.05963588133454323 ], "tsne3d": [ - -27.287246704101562, - 5.692864894866943, - 2.7282538414001465 + -35.30229187011719, + -3.388312816619873, + 1.5555503368377686 ], "umap3d": [ - 0.7725980877876282, - 5.994086265563965, - 4.909284591674805 + 1.6169853210449219, + 6.271223068237305, + 5.520790100097656 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { "id": 8, - "label": "Attention Vision" + "label": "Network Learning" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -251137,7 +251267,7 @@ -0.0069, 0.0422, 0.0282, - -0.0463, + -0.0464, 0.059, 0.0244, 0.0059, @@ -251414,31 +251544,31 @@ ], "projections": { "pca": [ - -0.19166435301303864, - -0.12870578467845917 + -0.19195188581943512, + -0.128042072057724 ], "tsne": [ - -45.94612121582031, - 3.3028512001037598 + -40.71955490112305, + -10.999054908752441 ], "umap": [ - 1.6386280059814453, - 6.419186592102051 + 2.1817142963409424, + 5.700078010559082 ], "pca3d": [ - -0.19166408479213715, - -0.1287076473236084, - 0.05125182121992111 + -0.19195149838924408, + -0.12804386019706726, + 0.0515102818608284 ], "tsne3d": [ - -22.23698616027832, - 4.456833362579346, - -7.470979690551758 + -17.7950439453125, + -10.6592435836792, + -3.4293227195739746 ], "umap3d": [ - 1.6353694200515747, - 5.6394124031066895, - 3.961313009262085 + 2.2926406860351562, + 5.911831378936768, + 4.3404541015625 ] }, "cluster_id": 2, @@ -251449,12 +251579,12 @@ "label": "Generative Models" }, { - "id": 1, - "label": "Network Learning" + "id": 5, + "label": "Diffusion Models" }, { "id": 19, - "label": "Neural Networks" + "label": "Graph Neural Nets" } ], "_embedding": [ @@ -251865,46 +251995,46 @@ ], "projections": { "pca": [ - -0.18235181272029877, - -0.022112557664513588 + -0.18237462639808655, + -0.021296696737408638 ], "tsne": [ - -57.02412033081055, - 2.8438777923583984 + -52.35301971435547, + 8.013290405273438 ], "umap": [ - 0.5482131838798523, - 7.1620354652404785 + 1.0060791969299316, + 6.253547668457031 ], "pca3d": [ - -0.18235141038894653, - -0.02215452492237091, - -0.0009778749663382769 + -0.18237416446208954, + -0.02133948542177677, + -0.0008464587153866887 ], "tsne3d": [ - -27.940597534179688, - 8.637934684753418, - 8.206743240356445 + -31.551084518432617, + 7.16567850112915, + 1.767320990562439 ], "umap3d": [ - 0.7378339767456055, - 5.596266269683838, - 4.763114929199219 + 1.5309195518493652, + 5.864861488342285, + 5.504038333892822 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -252318,46 +252448,46 @@ ], "projections": { "pca": [ - -0.22641316056251526, - -0.11479230970144272 + -0.22669371962547302, + -0.11385579407215118 ], "tsne": [ - -60.27780532836914, - 1.423865795135498 + -53.21116256713867, + 3.387657403945923 ], "umap": [ - 0.376954048871994, - 7.401634216308594 + 0.626788318157196, + 6.6033830642700195 ], "pca3d": [ - -0.22641287744045258, - -0.11480890214443207, - -0.05997519940137863 + -0.22669318318367004, + -0.11387231945991516, + -0.0596335344016552 ], "tsne3d": [ - -25.000324249267578, - 4.023566722869873, - 9.978434562683105 + -24.282838821411133, + -13.856939315795898, + 5.070910453796387 ], "umap3d": [ - 0.7122964859008789, - 5.722108840942383, - 5.054152011871338 + 1.6542619466781616, + 6.0630693435668945, + 5.576839923858643 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -252777,46 +252907,46 @@ ], "projections": { "pca": [ - -0.26710066199302673, - -0.1492869257926941 + -0.2673730254173279, + -0.14890971779823303 ], "tsne": [ - -56.40534210205078, - -2.8724138736724854 + -49.748130798339844, + 5.026462078094482 ], "umap": [ - -0.01395786739885807, - 7.580099582672119 + 0.3187430500984192, + 6.700425624847412 ], "pca3d": [ - -0.2671000063419342, - -0.14932319521903992, - -0.10622528940439224 + -0.2673724889755249, + -0.14894621074199677, + -0.10582083463668823 ], "tsne3d": [ - -29.376497268676758, - -0.7683200836181641, - 10.357410430908203 + -31.017234802246094, + 1.1533890962600708, + 6.4746623039245605 ], "umap3d": [ - 0.1553962528705597, - 5.318517684936523, - 5.0944390296936035 + 1.266772985458374, + 5.643956184387207, + 5.909544467926025 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -253232,47 +253362,47 @@ ], "projections": { "pca": [ - -0.13533997535705566, - -0.13546022772789001 + -0.1354895979166031, + -0.13515418767929077 ], "tsne": [ - -55.64021682739258, - 0.49374738335609436 + -45.48835754394531, + 6.329793930053711 ], "umap": [ - 0.3637278974056244, - 7.914424896240234 + 0.560531735420227, + 7.057276248931885 ], "pca3d": [ - -0.13533928990364075, - -0.13550634682178497, - -0.06813664734363556 + -0.13548901677131653, + -0.13520130515098572, + -0.06770781427621841 ], "tsne3d": [ - -26.460800170898438, - -4.236968994140625, - 13.348952293395996 + -28.59600257873535, + 6.152072429656982, + 4.349057674407959 ], "umap3d": [ - 0.5379544496536255, - 4.9727630615234375, - 5.175172328948975 + 1.6717631816864014, + 5.330545902252197, + 5.966634750366211 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, - "label": "Transformer Attention" + "id": 16, + "label": "Vision Transformers" } ], "_embedding": [ @@ -253686,31 +253816,31 @@ ], "projections": { "pca": [ - -0.24138614535331726, - -0.11596763134002686 + -0.24159084260463715, + -0.1151413694024086 ], "tsne": [ - -43.95326614379883, - 8.870251655578613 + -43.05439758300781, + -16.19668197631836 ], "umap": [ - 1.5932586193084717, - 5.8446831703186035 + 1.7692114114761353, + 5.010942459106445 ], "pca3d": [ - -0.2413858026266098, - -0.1159936860203743, - -0.09245403856039047 + -0.24159042537212372, + -0.11516823619604111, + -0.09205269813537598 ], "tsne3d": [ - -26.11152458190918, - 7.403242588043213, - -1.1103099584579468 + -19.92571449279785, + -9.469196319580078, + -9.717601776123047 ], "umap3d": [ - 1.0805072784423828, - 6.3773417472839355, - 4.569229602813721 + 1.740046501159668, + 6.618954658508301, + 4.8732829093933105 ] }, "cluster_id": 2, @@ -253721,12 +253851,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -254143,47 +254273,47 @@ ], "projections": { "pca": [ - -0.25168779492378235, - -0.00453382171690464 + -0.2518778443336487, + -0.0031136551406234503 ], "tsne": [ - -35.42496871948242, - -30.002025604248047 + -39.77968978881836, + 25.973949432373047 ], "umap": [ - 8.215546607971191, - 10.349446296691895 + 8.72840690612793, + 9.691239356994629 ], "pca3d": [ - -0.2516876459121704, - -0.004524920601397753, - -0.08183322846889496 + -0.2518777847290039, + -0.003105133306235075, + -0.08172126114368439 ], "tsne3d": [ - -15.823182106018066, - -0.22052167356014252, - -15.83491039276123 + -15.13012409210205, + -6.603621959686279, + -11.176324844360352 ], "umap3d": [ - 4.27764368057251, - 4.973486423492432, - 1.2826007604599 + 3.938115358352661, + 4.427572250366211, + 1.2608894109725952 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -254603,47 +254733,47 @@ ], "projections": { "pca": [ - 0.02617032453417778, - 0.0535731241106987 + 0.02631010115146637, + 0.053505461663007736 ], "tsne": [ - 3.4990315437316895, - -27.316057205200195 + -2.0273778438568115, + 26.32608413696289 ], "umap": [ - 5.2726731300354, - 8.537121772766113 + 5.589488983154297, + 7.704498291015625 ], "pca3d": [ - 0.02617020159959793, - 0.0535941943526268, - 0.006475162226706743 + 0.026309918612241745, + 0.053526345640420914, + 0.006336971186101437 ], "tsne3d": [ - -0.780436098575592, - -15.654764175415039, - 5.680850028991699 + 0.9635540246963501, + 10.850870132446289, + -4.887022495269775 ], "umap3d": [ - 4.3886027336120605, - 3.957534074783325, - 2.8682422637939453 + 4.809695243835449, + 4.213132858276367, + 3.0066640377044678 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, - "label": "Medical AI" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -255054,46 +255184,46 @@ ], "projections": { "pca": [ - -0.2382112592458725, - -0.14452281594276428 + -0.23853060603141785, + -0.14406324923038483 ], "tsne": [ - -52.51389694213867, - -1.4732537269592285 + -46.764862060546875, + 3.412498712539673 ], "umap": [ - 0.4229945242404938, - 7.466498374938965 + 0.6327839493751526, + 6.515235424041748 ], "pca3d": [ - -0.23821084201335907, - -0.1445377916097641, - -0.09641853719949722 + -0.23853006958961487, + -0.14407816529273987, + -0.0960359051823616 ], "tsne3d": [ - -25.48259925842285, - -3.839913845062256, - 6.933730125427246 + -25.88826560974121, + 0.9204378724098206, + 5.772613525390625 ], "umap3d": [ - 0.49254536628723145, - 5.226501941680908, - 4.776514053344727 + 1.4517234563827515, + 5.52846622467041, + 5.686911106109619 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -255509,31 +255639,31 @@ ], "projections": { "pca": [ - -0.2513626515865326, - -0.08027805387973785 + -0.2514917552471161, + -0.07882783561944962 ], "tsne": [ - -44.717811584472656, - 10.048413276672363 + -41.73826599121094, + -16.68186378479004 ], "umap": [ - 1.544753074645996, - 5.857165336608887 + 1.7298883199691772, + 5.124894618988037 ], "pca3d": [ - -0.2513623833656311, - -0.08029259741306305, - -0.04602906480431557 + -0.2514914870262146, + -0.07884342223405838, + -0.045617979019880295 ], "tsne3d": [ - -26.62247657775879, - 7.394539833068848, - 1.0136445760726929 + -21.333463668823242, + -9.906671524047852, + -11.035446166992188 ], "umap3d": [ - 1.0416244268417358, - 6.346462249755859, - 4.592884540557861 + 1.7396873235702515, + 6.619372844696045, + 4.923741817474365 ] }, "cluster_id": 2, @@ -255544,12 +255674,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -255963,31 +256093,31 @@ ], "projections": { "pca": [ - -0.29157522320747375, - -0.10052742063999176 + -0.2918154001235962, + -0.09982060641050339 ], "tsne": [ - -42.36128234863281, - 12.743407249450684 + -37.577030181884766, + -21.685272216796875 ], "umap": [ - 1.903831124305725, - 5.140236854553223 + 2.361318588256836, + 4.613896369934082 ], "pca3d": [ - -0.29157495498657227, - -0.10053133964538574, - -0.020722107961773872 + -0.29181501269340515, + -0.09982415288686752, + -0.020475095137953758 ], "tsne3d": [ - -22.712669372558594, - 10.676408767700195, - 6.632245063781738 + -22.782978057861328, + -20.401885986328125, + 6.029300689697266 ], "umap3d": [ - 1.737160325050354, - 6.689631938934326, - 4.84980583190918 + 2.39567494392395, + 7.145013332366943, + 4.874297618865967 ] }, "cluster_id": 2, @@ -255998,11 +256128,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -256419,47 +256549,47 @@ ], "projections": { "pca": [ - -0.29904529452323914, - -0.1467522233724594 + -0.2995269298553467, + -0.14618149399757385 ], "tsne": [ - -46.63871383666992, - -6.207981586456299 + -46.909549713134766, + -4.570921897888184 ], "umap": [ - 0.7140361070632935, - 6.6278839111328125 + 1.0233596563339233, + 5.879919528961182 ], "pca3d": [ - -0.29904457926750183, - -0.1467580646276474, - -0.12434840202331543 + -0.2995264232158661, + -0.14618699252605438, + -0.12403922528028488 ], "tsne3d": [ - -25.050264358520508, - -2.150794744491577, - -0.07652828842401505 + -25.37895393371582, + -4.555672645568848, + 1.7877007722854614 ], "umap3d": [ - 0.7713375687599182, - 5.771085262298584, - 4.286980628967285 + 1.5748077630996704, + 5.891672611236572, + 5.013746738433838 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 23, + "label": "Training Noise" } ], "_embedding": [ @@ -256873,46 +257003,46 @@ ], "projections": { "pca": [ - -0.2926010191440582, - -0.21046970784664154 + -0.2931129038333893, + -0.21016687154769897 ], "tsne": [ - -49.652259826660156, - -3.355246067047119 + -47.404541015625, + -1.770688533782959 ], "umap": [ - 0.5512317419052124, - 6.867575168609619 + 0.8173612952232361, + 6.09871244430542 ], "pca3d": [ - -0.29260027408599854, - -0.21047282218933105, - -0.13785718381404877 + -0.2931123971939087, + -0.2101694941520691, + -0.13739177584648132 ], "tsne3d": [ - -25.75801658630371, - -2.0259318351745605, - 2.984719753265381 + -26.581567764282227, + -3.4859097003936768, + 5.1267547607421875 ], "umap3d": [ - 0.6299495100975037, - 5.620761394500732, - 4.528059482574463 + 1.4801838397979736, + 5.829911231994629, + 5.314366817474365 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -257327,47 +257457,47 @@ ], "projections": { "pca": [ - -0.2569418251514435, - -0.11413788795471191 + -0.2573445439338684, + -0.11324284970760345 ], "tsne": [ - -45.398902893066406, - -9.044100761413574 + -44.83167266845703, + -9.213569641113281 ], "umap": [ - 0.975705623626709, - 6.508406639099121 + 1.409690260887146, + 5.691399097442627 ], "pca3d": [ - -0.2569415867328644, - -0.11413098126649857, - -0.08562284708023071 + -0.25734421610832214, + -0.11323584616184235, + -0.08538105338811874 ], "tsne3d": [ - -24.586021423339844, - -5.406219005584717, - -0.3812377452850342 + -23.431346893310547, + -7.484023094177246, + -0.8462231755256653 ], "umap3d": [ - 1.092991590499878, - 5.776943683624268, - 4.184418201446533 + 1.9237293004989624, + 5.878554344177246, + 4.83389139175415 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -257773,435 +257903,455 @@ ], "projections": { "pca": [ - -0.1299125701189041, - 0.28189295530319214 + -0.26080283522605896, + -0.19773036241531372 ], "tsne": [ - -9.67375659942627, - 2.737661838531494 + -49.87290954589844, + -16.561904907226562 ], "umap": [ - 4.730323314666748, - 7.40037727355957 + 1.4281245470046997, + 4.732872009277344 ], "pca3d": [ - -0.12991315126419067, - 0.2819162607192993, - 0.002792235231027007 + -0.26080241799354553, + -0.1977051943540573, + -0.08296659588813782 ], "tsne3d": [ - -3.2576067447662354, - 21.287872314453125, - -11.535642623901367 + -22.931621551513672, + -13.66627025604248, + -6.138346195220947 ], "umap3d": [ - 4.357021331787109, - 3.540057420730591, - 4.831231117248535 + 1.5340797901153564, + 6.504029273986816, + 4.089582443237305 ] }, - "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 1, - "label": "AI Scientific Research" + "id": 2, + "label": "Generative Models" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 8, + "label": "Network Learning" }, { - "id": 18, - "label": "Genomics Papers" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ - -0.0793, - -0.0028, - -0.0321, - -0.0011, - 0.0287, - 0.0595, - -0.0615, - 0.0541, - -0.0211, + -0.0707, + -0.02, + -0.0438, + 0.0202, + 0.056, + 0.0806, + 0.0081, + -0.0254, + 0.0737, + -0.0173, + -0.0062, + -0.0868, + 0.0166, + 0.0405, + 0.0469, + 0.0272, + 0.0056, + 0.0034, + 0.0398, + -0.0383, + 0.04, + -0.0375, + 0.0131, + -0.002, + 0.0615, + -0.0095, + 0.0321, + -0.0066, + -0.0393, + -0.2422, + 0.0275, + -0.013, + 0.0073, + 0.0096, + -0.0248, + 0.0119, + -0.0068, + -0.0074, + -0.0161, + 0.0116, + 0.03, + 0.028, + -0.019, + 0.0071, + 0.0312, + -0.0132, + 0.0025, + -0.0251, + -0.0544, 0.007, - -0.0294, - 0.0353, - 0.0139, + 0.0285, + -0.0291, + 0.0403, + 0.0614, + 0.0022, + 0.0221, + 0.0305, + 0.0516, + 0.0238, + 0.0478, + -0.0115, + 0.0372, + -0.1145, + -0.0002, + 0.0047, 0.0002, - -0.062, - 0.0017, - 0.0281, + -0.0349, + -0.0412, + -0.001, + 0.028, + 0.0435, + 0.0123, + -0.0078, + 0.0655, + 0.0308, + 0.0156, + 0.0517, + 0.0159, + 0.0252, + -0.0127, + 0.0516, + 0.0461, + 0.0356, + -0.0246, + -0.0154, + -0.0044, + 0.0102, + -0.045, + -0.0627, + -0.051, + -0.0589, -0.0223, - 0.0117, - 0.0487, - 0.0499, - -0.0574, - -0.0106, + -0.0248, + -0.0375, + -0.0527, + 0.0232, + 0.0183, + -0.0147, + 0.0142, + 0.3474, + 0.0233, + -0.0, + -0.0222, + 0.0613, + -0.0162, + -0.0288, + -0.0276, + -0.0219, + -0.0159, + 0.0385, + -0.0352, + -0.0182, + -0.0221, + -0.0453, + -0.0148, + 0.0049, + 0.0827, + 0.0172, + -0.0084, + -0.0381, + -0.0248, + -0.0088, + 0.056, + -0.0046, + -0.009, + -0.0242, + -0.0471, + 0.0983, + 0.029, + 0.0209, + 0.0263, + -0.0184, + -0.0834, + -0.0066, + 0.0845, + -0.0295, + 0.0345, + -0.0285, + -0.0394, + 0.028, + -0.0494, + 0.014, + 0.0697, + 0.008, + -0.1207, + 0.0814, + -0.055, + 0.0012, + -0.0271, + -0.0764, + 0.0076, + 0.0326, + -0.0216, + -0.0213, + 0.0047, + 0.0417, + -0.0267, + -0.0134, + -0.1078, + -0.0176, + -0.0323, + -0.0506, + -0.0182, + 0.1249, + -0.003, + -0.0092, + -0.0452, + -0.0199, + 0.0071, + -0.0104, + 0.0359, + 0.0074, + 0.022, + 0.0265, -0.0086, - -0.0606, - 0.0648, - -0.0463, - -0.0554, - -0.0898, - -0.1565, - -0.04, - 0.033, - -0.0026, + 0.0393, + -0.0883, + 0.0008, + -0.024, + 0.0462, + -0.0228, + -0.0573, + 0.0118, 0.0125, - 0.0599, - -0.0387, - 0.0182, - 0.0327, - 0.0524, - -0.0315, - -0.0251, - -0.0167, - 0.0083, - 0.0062, - 0.0686, - -0.0866, - 0.0114, - -0.0004, - 0.0587, - -0.0324, - -0.0881, - -0.0052, - 0.0343, - 0.0091, - -0.0595, - 0.0236, - 0.0215, - 0.007, - 0.0043, - 0.046, - 0.0647, - 0.0281, - -0.1737, - 0.1049, - 0.0303, - 0.0292, - -0.0675, - -0.0278, - 0.0229, - -0.0606, - -0.0507, - 0.0095, - 0.0305, - 0.0052, - 0.0261, - 0.0034, - -0.0181, - -0.0041, - -0.0106, - 0.0081, - -0.0203, - 0.0629, - 0.0336, - -0.0382, - -0.0325, - 0.0033, - 0.0464, - -0.0052, - 0.0607, - -0.0369, - -0.0025, + 0.0197, + -0.0189, + -0.0003, + -0.0591, + -0.0208, + 0.0167, + -0.0117, + 0.0038, + -0.0476, + 0.0076, + 0.001, + -0.0243, + 0.0121, + 0.0386, + -0.0383, + 0.0044, + -0.0166, + 0.0205, -0.0312, - -0.0106, - 0.0233, - -0.0734, - -0.0193, - 0.0177, - 0.0049, - 0.0379, - 0.3473, - -0.0413, - -0.0245, - 0.0713, - -0.0233, - -0.0352, - 0.0035, - -0.0017, - -0.0416, - -0.0013, - 0.0557, - -0.039, - -0.0177, - 0.0298, - -0.0311, - 0.0173, - 0.0329, - 0.0136, - 0.0045, - -0.0336, - -0.0353, - -0.0461, - 0.0058, - -0.0128, - -0.0106, - 0.0233, - -0.0406, - 0.0181, - 0.0972, - 0.0302, - 0.0261, - -0.0179, - 0.0343, - -0.0544, - -0.035, - 0.0496, + -0.0321, + -0.0322, + 0.0084, + -0.0001, + 0.0105, + 0.0073, + 0.0231, + -0.041, + -0.0032, + 0.0091, + -0.0349, 0.0094, - 0.0095, + 0.0342, + 0.0346, + 0.076, + -0.0598, + -0.0143, + 0.0535, + -0.0222, + 0.0083, + -0.2652, + -0.0144, + 0.0031, + -0.0351, + 0.0823, + -0.0726, + 0.0185, + 0.0102, + 0.0211, + 0.0396, + 0.0189, + 0.0636, + -0.0525, + -0.0448, + 0.0063, + 0.0166, + 0.0199, + -0.0093, + -0.0811, + 0.0096, + 0.0029, + 0.0177, + -0.0237, + -0.0868, + 0.0365, + 0.0612, + 0.1721, + -0.0585, + 0.0869, + -0.0012, -0.0078, - 0.0173, - 0.0281, - 0.0073, - 0.0064, - -0.0425, + 0.0548, + -0.0193, + -0.013, + 0.0418, + 0.0285, + 0.0773, + 0.0347, + -0.0401, + -0.0094, + -0.0363, + 0.017, + -0.0131, + -0.1176, + -0.0765, + -0.0138, + -0.0527, + 0.0077, + -0.062, + -0.0122, + 0.0552, + 0.0384, + 0.0659, + -0.0236, + -0.0575, + 0.0086, + -0.0859, + 0.0125, + -0.0359, + -0.0271, + 0.0362, + -0.0581, + -0.0349, -0.0112, - -0.085, - 0.1258, - -0.0132, - 0.0294, - 0.0054, - -0.0233, - 0.0427, - 0.0625, - -0.0198, - -0.0043, - 0.0247, - -0.0075, - -0.0036, - 0.0261, - 0.0532, - 0.0692, - -0.0207, - -0.0454, + 0.0316, + -0.0471, + 0.0014, + -0.018, + 0.0215, + 0.001, + -0.0291, + 0.0696, + 0.0129, + 0.1056, + 0.102, 0.0008, - 0.1459, - -0.0267, - -0.0715, - -0.0065, - -0.0384, - -0.0522, - 0.0175, - 0.0123, - 0.0495, - 0.0126, + -0.0149, + 0.0006, + -0.1038, + -0.007, + 0.0961, + 0.0074, 0.0346, - 0.0438, - 0.0174, - 0.0311, - -0.0095, - 0.0222, - -0.0288, - -0.0353, - -0.0213, - -0.0553, - 0.0182, - -0.0056, - -0.0229, + 0.0487, + 0.0009, + 0.0213, + 0.0477, + -0.0047, + 0.0091, + 0.053, + -0.0219, + -0.0248, + -0.0442, + -0.0228, + 0.011, + 0.0436, + -0.2474, + 0.0164, + 0.019, + 0.0456, + -0.03, -0.0067, - -0.0156, - -0.0055, - 0.0151, - -0.0476, - -0.0302, - -0.0241, - 0.0156, - 0.006, - -0.0186, - -0.0924, - 0.0019, - 0.0554, - -0.1252, - 0.1043, - 0.0125, - -0.0022, - 0.0349, - 0.0105, - -0.0362, - -0.0026, - -0.0503, - 0.0267, - -0.0097, - -0.0212, - 0.0103, - 0.0204, - -0.0102, - -0.079, - 0.0023, + 0.0759, + -0.0335, + 0.011, + -0.0563, + -0.0066, + 0.0839, + 0.0916, + 0.0403, + -0.0802, + -0.021, + 0.0473, + -0.0395, + 0.0494, -0.0133, - -0.0002, - 0.0096, - 0.0014, - -0.0036, - -0.0267, - -0.0393, - -0.2999, - -0.0019, - -0.0266, - -0.0049, - -0.0357, - -0.0364, - 0.0591, - 0.0419, - 0.0217, - 0.068, - 0.0259, - 0.0635, - -0.0414, - -0.0247, - -0.0028, - 0.0246, - -0.006, - -0.0352, - -0.0246, - 0.0233, - 0.0226, - 0.004, - -0.0042, - 0.0207, - 0.0442, - -0.0287, - 0.1412, - -0.0792, - 0.079, - 0.0335, - 0.0253, - 0.1314, - -0.0161, - -0.1513, - -0.008, - 0.0376, - -0.0039, - 0.0726, - 0.0019, - -0.0438, - 0.0146, - 0.0374, - 0.0404, - -0.0776, - 0.0011, - -0.0571, - 0.0019, - -0.0132, - -0.0211, - 0.055, - -0.0539, - -0.0208, - 0.0561, - 0.083, - 0.0002, - -0.0163, - -0.0116, - -0.0093, - -0.0498, - -0.0443, - 0.008, - -0.0266, - 0.0271, - -0.1393, - -0.0099, - -0.0237, - -0.0367, - -0.0217, - 0.0344, - -0.0512, - -0.029, - -0.0316, - -0.0069, - 0.0445, - -0.0103, - 0.0275, - 0.0164, + -0.0216, + 0.0079, + 0.1688, -0.0481, - -0.0114, - -0.0367, - -0.0095, - 0.0673, - 0.0796, - 0.0316, - 0.0354, - 0.0066, - -0.015, - -0.0065, - -0.0209, - -0.0116, - -0.0148, - -0.008, - -0.0282, - 0.0296, - 0.066, - 0.0235, - -0.2484, - 0.0346, - -0.0296, - 0.0681, - 0.0192, - 0.0508, - 0.0307, - -0.0101, - -0.026, - -0.0603, - -0.0201, - 0.0426, - -0.0366, - -0.0003, - 0.0286, - -0.036, - 0.0403, - 0.0424, - 0.0455, - 0.0381, - -0.0521, - 0.0393, - 0.1294, - 0.0022, - 0.002, - 0.0104, - 0.0174, - 0.033, + 0.0575, 0.0064, - -0.0466, - 0.0139, - -0.0243, - 0.0812, - 0.0145, - -0.0179, - 0.0523, - -0.0251, - 0.0446, - 0.0616, - -0.0261, - 0.0095, - -0.0134, - -0.0675, - 0.0196, - 0.0451, - 0.0688, - -0.0003, - 0.0111, - -0.0315, - -0.005, - -0.0038, - -0.0358, - -0.043, - 0.0426, - -0.0005, - 0.0484, - 0.0139, - -0.0177, - -0.0706, - -0.0508, - 0.0006, - -0.0414, - 0.1083, - 0.0574, - -0.004 - ] + 0.0041, + -0.0163, + -0.0017, + -0.0196, + -0.0088, + 0.006, + 0.0631, + -0.0299, + 0.0481, + 0.1218, + -0.0138, + 0.005, + 0.0398, + -0.0388, + 0.0373, + -0.0147, + 0.0186, + -0.0363, + 0.0737, + 0.0099, + 0.0475, + -0.0091, + -0.0364, + -0.0249, + -0.0001, + 0.0142, + 0.0312, + -0.0481, + 0.0362, + 0.0024, + 0.0188, + -0.0439, + -0.0504, + -0.0551, + 0.0374, + -0.071, + 0.063, + -0.0295, + -0.0673 + ], + "abstract": "Scaling Bayesian inference to the large parameter spaces of deep neural networks requires restrictive approximations. We propose performing inference over only a small subset of the model parameters while keeping all others as point estimates. This enables us to use expressive posterior approximations that are intractable in the full model. In particular, we develop a practical and scalable Bayesian deep learning method that \ufb01rst trains a point estimate, and then infers a full covariance Gaussian posterior approximation over a subnetwork. We propose a subnetwork selection procedure which aims to optimally preserve posterior uncertainty. Empirical studies demonstrate the e\ufb00ectiveness of our approach. 1. Subnetwork Posterior Approximation Deep neural networks (DNNs) still su\ufb00er from critical shortcomings that make them un\ufb01t for important applications. For instance, DNNs tend to be poorly calibrated and overcon\ufb01dent in their predictions, especially when there is a shift in the train and test distributions (Nguyen et al., 2015; Guo et al., 2017). To reliably inform decision making, DNNs must be able to robustly quantify the uncertainty in their predictions, which is particularly important in safety-critical areas such as healthcare or autonomous driving (Amodei et al., 2016). Bayesian modeling (Ghahramani, 2015) presents a principled way to capture model uncertainty, i.e., uncertainty about the choice of weights W which arises due to multiple plausible explanations of the training data {y, X}. Here, y is the target (e.g. classi\ufb01cation label) and X are the features. A prior distribution p(W) is speci\ufb01ed over the Bayesian neural network (BNN) weights. We wish to infer their full posterior distribution p(W|y, X) \u221d p(y|X, W) p(W). To make predictions, we then estimate the posterior predictive distribution that averages the network\u2019s predictions across all possible settings of the weights, weighted by their posterior probability, i.e. p(y\u2217|X\u2217, y, X) = R W p(y\u2217|X\u2217, W)p(W|y, X)dW. \u2217equal contribution \u00a9 E. Daxberger, E. Nalisnick, J.U. Allingham, J. Antor\u00b4an & J.M. Hern\u00b4andez-Lobato. Subnetwork Inference for Bayesian Deep Learning x1 x2 h2 h1 h3 y 0.5 0.5 0.3 0.3 0.9 0.9 0.1 0.1 0.3 0.3 0.5 0.5 0.7 0.7 0.5 0.5 0.1 0.1 (a) Point Estimation x1 x2 h2 h1 h3 y 0.5 0.5 0.3 0.3 0.9 0.9 0.1 0.1 0.3 0.3 0.5 0.5 0.7 0.7 0.5 0.5 0.1 0.1 (b) Subnet Selection x1 x2 h2 h1 h3 y 0.5 0.5 0.1 0.1 0.3 0.3 0.7 0.7 (c) Bayes. Inference x1 x2 h2 h1 h3 y 0.5 0.5 0.1 0.1 0.3 0.3 0.7 0.7 (d) Prediction Figure 1: Schematic illustration of our proposed approach. (a) We train a neural network using standard techniques to obtain a point estimate of the weights. (b) We identify a small subnetwork within the NN. (c) We estimate a posterior distribution over the selected subnetwork using Bayesian inference techniques. (d) We make predictions using the full network comprising of both Bayesian and deterministic weights. Unfortunately, due to the size of modern deep neural networks, it is not", + "title": "Expressive yet Tractable Bayesian Deep Learning via Subnetwork Inference", + "authors": [ + "Erik Daxberger", + "Eric Nalisnick", + "James Urquhart Allingham", + "Javier Antor\u00e1n", + "Jos\u00e9 Miguel Hern\u00e1ndez-Lobato" + ], + "affiliations": [ + "University of Cambridge", + "Max Planck Institute for Intelligent Systems, T\u00fcbingen", + "University of Amsterdam", + "Microsoft Research", + "The Alan Turing Institute" + ], + "year": 2021, + "cited_by_count": 12, + "openalex_url": "https://openalex.org/W3096396514", + "journal": "OpenReview" }, { "channel": "papers-dl", @@ -258225,47 +258375,47 @@ ], "projections": { "pca": [ - -0.270328164100647, - -0.12030589580535889 + -0.2706845700740814, + -0.11931678652763367 ], "tsne": [ - -43.39167404174805, - -15.561619758605957 + -43.42744445800781, + 1.6533961296081543 ], "umap": [ - 1.3433152437210083, - 6.976190567016602 + 1.4636772871017456, + 5.97463846206665 ], "pca3d": [ - -0.2703278660774231, - -0.12028690427541733, - -0.03776206076145172 + -0.2706843912601471, + -0.11929742246866226, + -0.037469636648893356 ], "tsne3d": [ - -34.00079345703125, - -4.045444011688232, - 3.6436760425567627 + -30.072431564331055, + 4.047691822052002, + -2.622314691543579 ], "umap3d": [ - 1.0034832954406738, - 5.312897682189941, - 3.852895498275757 + 1.8632230758666992, + 5.426034450531006, + 4.451554775238037 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { "id": 8, - "label": "Attention Vision" + "label": "Network Learning" }, { - "id": 34, - "label": "Learning Methods" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -258678,46 +258828,46 @@ ], "projections": { "pca": [ - -0.28488993644714355, - -0.17626570165157318 + -0.28541216254234314, + -0.17592079937458038 ], "tsne": [ - -49.403621673583984, - -3.227734088897705 + -47.290016174316406, + -1.7961071729660034 ], "umap": [ - 0.5790488123893738, - 6.984694957733154 + 0.7427179217338562, + 6.194347858428955 ], "pca3d": [ - -0.2848893702030182, - -0.17626632750034332, - -0.14116251468658447 + -0.285411536693573, + -0.17592084407806396, + -0.1408485472202301 ], "tsne3d": [ - -25.033443450927734, - -2.357212781906128, - 3.392387628555298 + -26.060672760009766, + -3.5856759548187256, + 5.805963516235352 ], "umap3d": [ - 0.6538779735565186, - 5.583420753479004, - 4.515141010284424 + 1.4790507555007935, + 5.825865268707275, + 5.300049781799316 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { "id": 8, - "label": "Attention Vision" + "label": "Network Learning" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -259124,46 +259274,46 @@ ], "projections": { "pca": [ - -0.10108143091201782, - 0.2820306122303009 + -0.09938640892505646, + 0.2786502540111542 ], "tsne": [ - -9.676074981689453, - 2.7377679347991943 + -2.5585551261901855, + 6.401304244995117 ], "umap": [ - 4.810601234436035, - 7.365847110748291 + 5.162701606750488, + 6.57328462600708 ], "pca3d": [ - -0.10108198970556259, - 0.2820495367050171, - -0.0011362106306478381 + -0.09938700497150421, + 0.2786686420440674, + -0.0015238283667713404 ], "tsne3d": [ - -3.2717628479003906, - 21.292448043823242, - -11.511906623840332 + -1.0971770286560059, + 22.358741760253906, + -7.159818172454834 ], "umap3d": [ - 4.4675679206848145, - 3.5193912982940674, - 4.868099689483643 + 5.552043914794922, + 4.669249534606934, + 4.888633728027344 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, + "id": 13, "label": "Genomics Papers" } ], @@ -259576,31 +259726,31 @@ ], "projections": { "pca": [ - -0.20667052268981934, - -0.030727475881576538 + -0.20679382979869843, + -0.030115466564893723 ], "tsne": [ - -45.51728439331055, - 13.037759780883789 + -41.57979965209961, + -19.531803131103516 ], "umap": [ - 1.569274663925171, - 5.428901672363281 + 1.973356008529663, + 4.727530479431152 ], "pca3d": [ - -0.2066703736782074, - -0.03071197308599949, - 0.020129045471549034 + -0.20679360628128052, + -0.03009965643286705, + 0.020183082669973373 ], "tsne3d": [ - -24.451663970947266, - 11.873133659362793, - 0.30766406655311584 + -22.550914764404297, + -16.43946075439453, + 0.47033262252807617 ], "umap3d": [ - 1.5538538694381714, - 6.564728736877441, - 4.506860256195068 + 2.0296754837036133, + 6.738273620605469, + 4.651428699493408 ] }, "cluster_id": 2, @@ -259611,12 +259761,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 22, + "label": "Diffusion Models" } ], "_embedding": [ @@ -260035,31 +260185,31 @@ "cited_by_count": 21, "projections": { "pca": [ - -0.28843072056770325, - -0.09225726127624512 + -0.28871485590934753, + -0.09112436324357986 ], "tsne": [ - -50.29035568237305, - -6.528578281402588 + -49.614959716796875, + -8.115177154541016 ], "umap": [ - 0.6777141094207764, - 6.194448471069336 + 1.026343822479248, + 5.382659435272217 ], "pca3d": [ - -0.2884300947189331, - -0.09225665777921677, - -0.10331186652183533 + -0.28871458768844604, + -0.09112387150526047, + -0.10301297158002853 ], "tsne3d": [ - -27.150039672851562, - -2.889673948287964, - -4.925999641418457 + -25.933391571044922, + -4.9019904136657715, + -2.977123737335205 ], "umap3d": [ - 0.6702901721000671, - 5.954975605010986, - 3.920903444290161 + 1.253175139427185, + 5.984920978546143, + 4.740438461303711 ] }, "cluster_id": 2, @@ -260070,11 +260220,11 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, + "id": 23, "label": "Training Noise" } ], @@ -260498,47 +260648,47 @@ "openalex_url": "https://openalex.org/W3119904598", "projections": { "pca": [ - -0.31963565945625305, - -0.10254304111003876 + -0.3200005888938904, + -0.10162780433893204 ], "tsne": [ - -40.509552001953125, - -19.351516723632812 + -45.420936584472656, + 14.278932571411133 ], "umap": [ - 1.07500422000885, - 6.921459674835205 + 1.1103706359863281, + 6.066458702087402 ], "pca3d": [ - -0.3196355104446411, - -0.10253196209669113, - -0.12237904220819473 + -0.32000041007995605, + -0.10161661356687546, + -0.1220829039812088 ], "tsne3d": [ - -27.61321258544922, - -13.157440185546875, - 2.2012476921081543 + -27.541412353515625, + 3.459557056427002, + -7.280076503753662 ], "umap3d": [ - 0.5238171815872192, - 5.274684906005859, - 3.9966957569122314 + 1.2793346643447876, + 5.415619373321533, + 4.7688798904418945 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { "id": 8, - "label": "Attention Vision" + "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -260953,46 +261103,46 @@ ], "projections": { "pca": [ - -0.23959511518478394, - -0.02593093179166317 + -0.23958580195903778, + -0.02535100467503071 ], "tsne": [ - -56.56450271606445, - -12.647323608398438 + -57.377010345458984, + -4.647983551025391 ], "umap": [ - -0.13773781061172485, - 6.382498264312744 + 0.1968146413564682, + 5.456758975982666 ], "pca3d": [ - -0.23959486186504364, - -0.025935493409633636, - -0.07955970615148544 + -0.23958559334278107, + -0.0253556240350008, + -0.07934596389532089 ], "tsne3d": [ - -32.40947341918945, - 1.9182292222976685, - 0.7116026282310486 + -32.698150634765625, + -5.750222682952881, + 1.2798190116882324 ], "umap3d": [ - 0.11819466203451157, - 6.287474632263184, - 4.329418182373047 + 0.7508339285850525, + 6.404062271118164, + 4.923910140991211 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, + "id": 23, "label": "Training Noise" } ], @@ -261409,46 +261559,46 @@ ], "projections": { "pca": [ - -0.2783110737800598, - -0.08695518970489502 + -0.27844271063804626, + -0.08615287393331528 ], "tsne": [ - -57.48851776123047, - -2.730123281478882 + -49.58434295654297, + 6.2542877197265625 ], "umap": [ - 0.19692069292068481, - 7.670942783355713 + 0.4003223478794098, + 6.740128040313721 ], "pca3d": [ - -0.2783104479312897, - -0.0869959369301796, - -0.12788939476013184 + -0.2784421145915985, + -0.08619414269924164, + -0.12751664221286774 ], "tsne3d": [ - -28.850387573242188, - -2.6596691608428955, - 11.340193748474121 + -32.39130783081055, + -0.39214658737182617, + 6.8462066650390625 ], "umap3d": [ - 0.32151657342910767, - 5.353442192077637, - 5.090626239776611 + 1.4846562147140503, + 5.6553955078125, + 6.054895877838135 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -261866,46 +262016,46 @@ ], "projections": { "pca": [ - -0.2965845763683319, - -0.11399894207715988 + -0.2968294620513916, + -0.11305546015501022 ], "tsne": [ - -54.015480041503906, - -2.2365036010742188 + -48.35234832763672, + 3.213691234588623 ], "umap": [ - 0.16324393451213837, - 7.486413478851318 + 0.5091837048530579, + 6.6379499435424805 ], "pca3d": [ - -0.29658421874046326, - -0.11402265727519989, - -0.1012016013264656 + -0.296828955411911, + -0.11307978630065918, + -0.10081622004508972 ], "tsne3d": [ - -28.98149871826172, - -4.0269036293029785, - 6.981369495391846 + -28.915332794189453, + 2.507399559020996, + 2.1169321537017822 ], "umap3d": [ - 0.25794312357902527, - 5.221928119659424, - 4.90685510635376 + 1.3491333723068237, + 5.554948329925537, + 5.826159477233887 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -262320,47 +262470,47 @@ ], "projections": { "pca": [ - -0.29564356803894043, - -0.15492257475852966 + -0.29620596766471863, + -0.15374988317489624 ], "tsne": [ - -41.44728088378906, - -20.318927764892578 + -46.043128967285156, + 14.76872730255127 ], "umap": [ - 1.2784579992294312, - 7.016615867614746 + 1.569987416267395, + 6.110251426696777 ], "pca3d": [ - -0.2956431806087494, - -0.15490959584712982, - 0.0055529214441776276 + -0.29620546102523804, + -0.1537366360425949, + 0.005844921804964542 ], "tsne3d": [ - -25.657480239868164, - -12.371051788330078, - 0.25232985615730286 + -25.45746421813965, + 2.3791112899780273, + -7.183789253234863 ], "umap3d": [ - 0.7906806468963623, - 5.086617469787598, - 3.850984811782837 + 1.514685869216919, + 5.244699954986572, + 4.570606708526611 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -262785,47 +262935,47 @@ "cited_by_count": 1098, "projections": { "pca": [ - -0.2861713171005249, - 0.04325219243764877 + -0.2858945429325104, + 0.04445663094520569 ], "tsne": [ - -20.915950775146484, - -29.115734100341797 + -23.27723503112793, + 18.91446304321289 ], "umap": [ - 2.8151004314422607, - 8.014822006225586 + 2.9873270988464355, + 7.246679782867432 ], "pca3d": [ - -0.28617116808891296, - 0.043246377259492874, - 0.086419977247715 + -0.2858944535255432, + 0.04445118457078934, + 0.08659326285123825 ], "tsne3d": [ - -14.831074714660645, - -11.88491439819336, - -7.781562805175781 + -15.453317642211914, + 18.54847526550293, + 5.9080810546875 ], "umap3d": [ - 2.1623668670654297, - 4.282114028930664, - 3.7237284183502197 + 2.9774200916290283, + 4.600285530090332, + 4.2198486328125 ] }, - "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 1, - "label": "AI Scientific Research" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { "id": 0, - "label": "LLM Language Learning" + "label": "Math AI" } ], "_embedding": [ @@ -263245,46 +263395,46 @@ "cited_by_count": 25, "projections": { "pca": [ - -0.2789059579372406, - -0.07527703046798706 + -0.2790307402610779, + -0.07436276227235794 ], "tsne": [ - -52.250892639160156, - -8.149609565734863 + -52.04185104370117, + -4.508559226989746 ], "umap": [ - 0.49620556831359863, - 6.3307271003723145 + 0.7355366349220276, + 5.559455394744873 ], "pca3d": [ - -0.2789055407047272, - -0.07530546933412552, - 0.0038461985532194376 + -0.27903032302856445, + -0.07439150661230087, + 0.0041140359826385975 ], "tsne3d": [ - -30.962121963500977, - -5.785874843597412, - -0.4453069567680359 + -29.443378448486328, + -4.51170015335083, + -3.513012170791626 ], "umap3d": [ - 0.3571822941303253, - 5.796485424041748, - 4.085020542144775 + 1.0732430219650269, + 5.854593276977539, + 4.81537389755249 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, + "id": 23, "label": "Training Noise" } ], @@ -263699,31 +263849,31 @@ ], "projections": { "pca": [ - -0.25068584084510803, - -0.09629932045936584 + -0.2509244680404663, + -0.09539008140563965 ], "tsne": [ - -44.55354690551758, - 11.782068252563477 + -40.678592681884766, + -18.3433837890625 ], "umap": [ - 1.6737066507339478, - 5.423511981964111 + 1.9499543905258179, + 4.701099395751953 ], "pca3d": [ - -0.2506854832172394, - -0.09631004929542542, - -0.04197967052459717 + -0.2509239912033081, + -0.09540082514286041, + -0.041686732321977615 ], "tsne3d": [ - -24.97688865661621, - 9.242691993713379, - 1.3211820125579834 + -23.023433685302734, + -15.185667991638184, + 2.5975773334503174 ], "umap3d": [ - 1.3844887018203735, - 6.561119079589844, - 4.675528526306152 + 1.9195321798324585, + 6.812161922454834, + 4.896635055541992 ] }, "cluster_id": 2, @@ -263734,12 +263884,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 22, + "label": "Diffusion Models" } ], "_embedding": [ @@ -264154,47 +264304,47 @@ ], "projections": { "pca": [ - -0.23926055431365967, - -0.0555121973156929 + -0.23950748145580292, + -0.054167166352272034 ], "tsne": [ - -41.876502990722656, - -22.06298065185547 + -45.152320861816406, + 17.24628257751465 ], "umap": [ - 1.5746911764144897, - 7.141297817230225 + 1.9497134685516357, + 6.390637397766113 ], "pca3d": [ - -0.23926031589508057, - -0.055508069694042206, - -0.030200131237506866 + -0.23950739204883575, + -0.05416301265358925, + -0.029972415417432785 ], "tsne3d": [ - -25.35354232788086, - -13.480793952941895, - -1.8605306148529053 + -24.13836669921875, + 1.8260618448257446, + -8.997726440429688 ], "umap3d": [ - 0.7574136853218079, - 4.861099720001221, - 3.5418872833251953 + 1.4985222816467285, + 4.9840264320373535, + 4.326881408691406 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -264609,31 +264759,31 @@ ], "projections": { "pca": [ - -0.22323893010616302, - -0.09202997386455536 + -0.2235526740550995, + -0.09164445847272873 ], "tsne": [ - -44.97650909423828, - 13.056286811828613 + -41.112281799316406, + -19.60080909729004 ], "umap": [ - 1.6890074014663696, - 5.345324993133545 + 2.1114397048950195, + 4.669785976409912 ], "pca3d": [ - -0.2232387661933899, - -0.09201483428478241, - -0.05632441118359566 + -0.22355249524116516, + -0.09162893146276474, + -0.05618791654706001 ], "tsne3d": [ - -24.322181701660156, - 11.368560791015625, - 1.1576144695281982 + -22.73703384399414, + -16.60073471069336, + 1.4178460836410522 ], "umap3d": [ - 1.5791276693344116, - 6.62538480758667, - 4.653669834136963 + 2.096467971801758, + 6.873878479003906, + 4.809451580047607 ] }, "cluster_id": 2, @@ -264644,12 +264794,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 22, + "label": "Diffusion Models" } ], "_embedding": [ @@ -265065,31 +265215,31 @@ ], "projections": { "pca": [ - -0.2945001721382141, - -0.13773709535598755 + -0.2951348125934601, + -0.13751885294914246 ], "tsne": [ - -51.060585021972656, - 6.740767478942871 + -51.62040710449219, + -15.783181190490723 ], "umap": [ - 0.9194840788841248, - 5.451043605804443 + 1.2858480215072632, + 4.611835479736328 ], "pca3d": [ - -0.2944997549057007, - -0.1377089023590088, - -0.08423636108636856 + -0.2951345443725586, + -0.1374894231557846, + -0.08415631949901581 ], "tsne3d": [ - -27.722509384155273, - -1.11607825756073, - -8.750432968139648 + -25.001420974731445, + -12.444602012634277, + -6.162940502166748 ], "umap3d": [ - 1.0569469928741455, - 6.50583553314209, - 3.577951192855835 + 1.4535971879959106, + 6.459408283233643, + 4.033957004547119 ] }, "cluster_id": 2, @@ -265100,12 +265250,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -265520,47 +265670,47 @@ ], "projections": { "pca": [ - -0.2225593477487564, - -0.17065973579883575 + -0.22306115925312042, + -0.17006230354309082 ], "tsne": [ - -45.298431396484375, - -1.226867914199829 + -42.4419059753418, + -2.785952568054199 ], "umap": [ - 1.2360862493515015, - 6.744409561157227 + 1.673282265663147, + 5.993147373199463 ], "pca3d": [ - -0.22255881130695343, - -0.170669823884964, - -0.01926865614950657 + -0.22306057810783386, + -0.17007224261760712, + -0.01899702288210392 ], "tsne3d": [ - -20.917987823486328, - -0.6801446676254272, - 2.7166998386383057 + -21.58411979675293, + -4.425618648529053, + 6.440443992614746 ], "umap3d": [ - 1.3213750123977661, - 5.552727222442627, - 4.495668411254883 + 2.2292680740356445, + 5.861571788787842, + 5.135408878326416 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -265972,31 +266122,31 @@ ], "projections": { "pca": [ - -0.31554993987083435, - -0.1501469910144806 + -0.31629839539527893, + -0.14973041415214539 ], "tsne": [ - -48.54444122314453, - 6.853824138641357 + -49.31671142578125, + -16.492361068725586 ], "umap": [ - 1.0938715934753418, - 5.636195659637451 + 1.4077696800231934, + 4.775446891784668 ], "pca3d": [ - -0.3155495822429657, - -0.1501207798719406, - -0.09871828556060791 + -0.3162981867790222, + -0.149703249335289, + -0.09862389415502548 ], "tsne3d": [ - -24.960289001464844, - -1.3418374061584473, - -8.728327751159668 + -21.929533004760742, + -13.931756019592285, + -5.622748851776123 ], "umap3d": [ - 1.1894001960754395, - 6.417990684509277, - 3.795337438583374 + 1.560515284538269, + 6.470978736877441, + 4.130359172821045 ] }, "cluster_id": 2, @@ -266007,12 +266157,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -266428,31 +266578,31 @@ ], "projections": { "pca": [ - -0.31413713097572327, - -0.07294678688049316 + -0.314375102519989, + -0.07233614474534988 ], "tsne": [ - -49.99237060546875, - -12.979947090148926 + -51.68566131591797, + -9.711777687072754 ], "umap": [ - 0.5659559369087219, - 6.143674373626709 + 0.8656678199768066, + 5.319850921630859 ], "pca3d": [ - -0.3141367435455322, - -0.07295234501361847, - -0.14016292989253998 + -0.3143746852874756, + -0.07234148681163788, + -0.13994166254997253 ], "tsne3d": [ - -29.313983917236328, - -5.814683437347412, - -4.696242809295654 + -26.083877563476562, + -7.28488826751709, + -5.658199787139893 ], "umap3d": [ - 0.4904855787754059, - 5.966590881347656, - 3.811009407043457 + 1.0713776350021362, + 5.9379096031188965, + 4.489345550537109 ] }, "cluster_id": 2, @@ -266463,11 +266613,11 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, + "id": 23, "label": "Training Noise" } ], @@ -266883,46 +267033,46 @@ ], "projections": { "pca": [ - -0.30662089586257935, - -0.1513109654188156 + -0.3071316182613373, + -0.1507025510072708 ], "tsne": [ - -50.08950424194336, - -10.316353797912598 + -51.66958236694336, + -7.747007846832275 ], "umap": [ - 0.5316244959831238, - 6.3255720138549805 + 0.8303313255310059, + 5.543487548828125 ], "pca3d": [ - -0.3066205382347107, - -0.1513124406337738, - -0.11451634764671326 + -0.30713117122650146, + -0.1507040113210678, + -0.11423537880182266 ], "tsne3d": [ - -28.27937126159668, - -5.297244548797607, - -1.481697916984558 + -25.910842895507812, + -8.436226844787598, + -3.031129837036133 ], "umap3d": [ - 0.44993048906326294, - 5.869348526000977, - 3.9870948791503906 + 1.1562151908874512, + 5.891907691955566, + 4.781398773193359 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, + "id": 23, "label": "Training Noise" } ], @@ -267336,46 +267486,46 @@ ], "projections": { "pca": [ - -0.295024573802948, - -0.07957996428012848 + -0.29516369104385376, + -0.07900954782962799 ], "tsne": [ - -49.346744537353516, - -4.060570240020752 + -47.645320892333984, + -2.6115925312042236 ], "umap": [ - 0.5876925587654114, - 6.564725399017334 + 0.8044362664222717, + 5.935368537902832 ], "pca3d": [ - -0.29502418637275696, - -0.07959172874689102, - -0.15640459954738617 + -0.29516327381134033, + -0.07902127504348755, + -0.1561090350151062 ], "tsne3d": [ - -25.8947811126709, - -3.8279786109924316, - 2.7108161449432373 + -27.17621612548828, + -4.120355606079102, + 7.020838260650635 ], "umap3d": [ - 0.6670179963111877, - 5.750937461853027, - 4.363656044006348 + 1.3587241172790527, + 5.962371349334717, + 5.056788444519043 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, + "id": 23, "label": "Training Noise" } ], @@ -267782,47 +267932,47 @@ ], "projections": { "pca": [ - -0.15038539469242096, - 0.3535410761833191 + -0.14912085235118866, + 0.353301465511322 ], "tsne": [ - -8.409290313720703, - -14.623220443725586 + -14.376959800720215, + 9.14607048034668 ], "umap": [ - 4.213634014129639, - 7.535478591918945 + 4.49833869934082, + 6.917013645172119 ], "pca3d": [ - -0.15038622915744781, - 0.3535624146461487, - -0.04137532785534859 + -0.14912158250808716, + 0.3533225655555725, + -0.04174623638391495 ], "tsne3d": [ - -4.477102756500244, - -12.801466941833496, - 6.326851844787598 + -6.5176873207092285, + 7.444709300994873, + -6.874080657958984 ], "umap3d": [ - 3.681635618209839, - 3.5577847957611084, - 4.2654709815979 + 4.658729553222656, + 4.311278343200684, + 4.613377094268799 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -268237,47 +268387,47 @@ ], "projections": { "pca": [ - -0.19041313230991364, - -0.012224466539919376 + -0.19042058289051056, + -0.010838784277439117 ], "tsne": [ - -41.22907257080078, - -23.1411075592041 + -44.72709274291992, + 18.150976181030273 ], "umap": [ - 1.7959232330322266, - 7.159188270568848 + 2.198446750640869, + 6.365920066833496 ], "pca3d": [ - -0.1904129981994629, - -0.012231221422553062, - 2.0129647964495234e-05 + -0.19042043387889862, + -0.010845751501619816, + 0.00026487160357646644 ], "tsne3d": [ - -25.737632751464844, - -15.666056632995605, - -2.0621018409729004 + -22.187986373901367, + 2.9158849716186523, + -9.105100631713867 ], "umap3d": [ - 0.7686596512794495, - 4.766772270202637, - 3.454594850540161 + 1.5259002447128296, + 4.851222991943359, + 4.2155351638793945 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -268695,31 +268845,31 @@ ], "projections": { "pca": [ - -0.25606662034988403, - -0.14028802514076233 + -0.2564948797225952, + -0.13904936611652374 ], "tsne": [ - -42.445526123046875, - -0.761913537979126 + -40.882171630859375, + -6.367064952850342 ], "umap": [ - 1.4537259340286255, - 6.445343017578125 + 1.7578927278518677, + 5.6190924644470215 ], "pca3d": [ - -0.256066232919693, - -0.1402777135372162, - -0.02737274579703808 + -0.2564944922924042, + -0.13903862237930298, + -0.027044452726840973 ], "tsne3d": [ - -19.664552688598633, - 0.49333029985427856, - -1.5825145244598389 + -19.927696228027344, + -8.734926223754883, + 3.597593307495117 ], "umap3d": [ - 1.4330440759658813, - 5.8170318603515625, - 4.228208541870117 + 2.13027286529541, + 5.991698741912842, + 4.721920013427734 ] }, "cluster_id": 2, @@ -268730,12 +268880,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -269150,31 +269300,31 @@ ], "projections": { "pca": [ - -0.2528383135795593, - -0.0879933312535286 + -0.2532021999359131, + -0.08719611912965775 ], "tsne": [ - -49.223915100097656, - -21.506793975830078 + -52.16405487060547, + 14.52296257019043 ], "umap": [ - 0.3582506775856018, - 5.780496597290039 + 0.7249029278755188, + 4.880514621734619 ], "pca3d": [ - -0.2528381049633026, - -0.08797059953212738, - -0.03260605409741402 + -0.25320208072662354, + -0.08717284351587296, + -0.03247364982962608 ], "tsne3d": [ - -31.23851203918457, - -12.403218269348145, - -1.9639779329299927 + -28.689258575439453, + -2.8603243827819824, + -9.855427742004395 ], "umap3d": [ - 0.4382339417934418, - 5.432961463928223, - 3.471040725708008 + 1.1151145696640015, + 5.438048839569092, + 4.238046169281006 ] }, "cluster_id": 2, @@ -269185,12 +269335,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, - "label": "Training Noise" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -269602,46 +269752,46 @@ ], "projections": { "pca": [ - -0.22307346761226654, - -0.1676839292049408 + -0.22342906892299652, + -0.16691195964813232 ], "tsne": [ - -28.840219497680664, - -9.130457878112793 + -35.64223098754883, + 9.482620239257812 ], "umap": [ - 1.758504867553711, - 8.172255516052246 + 1.969052791595459, + 7.4829816818237305 ], "pca3d": [ - -0.2230731099843979, - -0.16767297685146332, - 0.044767554849386215 + -0.22342868149280548, + -0.16690047085285187, + 0.0452403798699379 ], "tsne3d": [ - -19.166339874267578, - -11.027542114257812, - 9.803234100341797 + -18.831113815307617, + 8.049223899841309, + 3.364584445953369 ], "umap3d": [ - 1.6279621124267578, - 4.541722297668457, - 4.839987754821777 + 2.674302101135254, + 4.963013172149658, + 5.492512226104736 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 1, + "label": "Attention Models" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -270058,47 +270208,47 @@ ], "projections": { "pca": [ - -0.27127793431282043, - 0.021791547536849976 + -0.27126389741897583, + 0.023280713707208633 ], "tsne": [ - -42.59841537475586, - -23.770910263061523 + -46.005035400390625, + 19.04807472229004 ], "umap": [ - 1.7495172023773193, - 7.046277046203613 + 2.199925184249878, + 6.261885643005371 ], "pca3d": [ - -0.27127784490585327, - 0.021801726892590523, - -0.006182842422276735 + -0.2712637186050415, + 0.023290924727916718, + -0.006017752457410097 ], "tsne3d": [ - -24.615291595458984, - -15.270384788513184, - -4.604750633239746 + -22.10961151123047, + 1.0435374975204468, + -11.033063888549805 ], "umap3d": [ - 0.921448290348053, - 4.830825328826904, - 3.4748358726501465 + 1.5862373113632202, + 4.901852607727051, + 4.199668884277344 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -270513,31 +270663,31 @@ ], "projections": { "pca": [ - 0.095149464905262, - -0.15068131685256958 + 0.09485325217247009, + -0.15086844563484192 ], "tsne": [ - -15.616312026977539, - 19.146127700805664 + -12.319490432739258, + -24.750221252441406 ], "umap": [ - 4.912049293518066, - 4.530369758605957 + 5.513842582702637, + 3.7855334281921387 ], "pca3d": [ - 0.09514962881803513, - -0.15066713094711304, - 0.06592363864183426 + 0.09485337883234024, + -0.15085424482822418, + 0.06608240306377411 ], "tsne3d": [ - 2.1519670486450195, - 7.4459547996521, - 15.389073371887207 + 2.7408201694488525, + -13.505897521972656, + 12.346756935119629 ], "umap3d": [ - 4.6970601081848145, - 6.922149181365967, - 4.399304389953613 + 4.953799247741699, + 7.339445114135742, + 3.108328104019165 ] }, "cluster_id": 3, @@ -270548,12 +270698,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -270967,31 +271117,31 @@ ], "projections": { "pca": [ - -0.23464123904705048, - -0.20753423869609833 + -0.23521395027637482, + -0.20687581598758698 ], "tsne": [ - -47.31684875488281, - 2.800565481185913 + -42.186824798583984, + -10.439864158630371 ], "umap": [ - 1.249466061592102, - 6.3796186447143555 + 1.596727728843689, + 5.612635612487793 ], "pca3d": [ - -0.23464077711105347, - -0.207529217004776, - -0.08961863815784454 + -0.235213503241539, + -0.20687048137187958, + -0.0892089232802391 ], "tsne3d": [ - -24.262948989868164, - 2.257382869720459, - -6.661544322967529 + -20.895498275756836, + -11.745992660522461, + -2.6036789417266846 ], "umap3d": [ - 1.1885157823562622, - 5.758861541748047, - 3.9241347312927246 + 1.7929346561431885, + 5.919544219970703, + 4.459835529327393 ] }, "cluster_id": 2, @@ -271002,12 +271152,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -271422,47 +271572,47 @@ ], "projections": { "pca": [ - -0.2105458825826645, - -0.07912464439868927 + -0.21084323525428772, + -0.07802640646696091 ], "tsne": [ - -35.77827835083008, - -29.6573486328125 + -40.058223724365234, + 25.678205490112305 ], "umap": [ - 8.17400074005127, - 10.432490348815918 + 8.72048568725586, + 9.734911918640137 ], "pca3d": [ - -0.2105453610420227, - -0.07914448529481888, - -0.08317146450281143 + -0.2108427733182907, + -0.07804706692695618, + -0.08295704424381256 ], "tsne3d": [ - -16.88519859313965, - -0.38142624497413635, - -15.751060485839844 + -15.886075019836426, + -7.1878275871276855, + -10.758066177368164 ], "umap3d": [ - 4.204217910766602, - 5.004783630371094, - 1.3196022510528564 + 3.869091033935547, + 4.458616256713867, + 1.301274061203003 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -271876,46 +272026,46 @@ ], "projections": { "pca": [ - -0.25945183634757996, - -0.060641031712293625 + -0.25958284735679626, + -0.060563959181308746 ], "tsne": [ - -51.95891189575195, - -9.822501182556152 + -53.163211822509766, + -8.2947998046875 ], "umap": [ - 0.40116122364997864, - 6.22902774810791 + 0.70781010389328, + 5.396763324737549 ], "pca3d": [ - -0.25945132970809937, - -0.06064358353614807, - -0.07756884396076202 + -0.2595824897289276, + -0.060565948486328125, + -0.0774742141366005 ], "tsne3d": [ - -31.187700271606445, - -3.9091339111328125, - -3.4854259490966797 + -28.62139129638672, + -7.871213912963867, + -4.530023574829102 ], "umap3d": [ - 0.38049352169036865, - 5.963773727416992, - 3.958970308303833 + 1.050862193107605, + 5.946689128875732, + 4.699840545654297 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, + "id": 23, "label": "Training Noise" } ], @@ -272334,47 +272484,47 @@ ], "projections": { "pca": [ - -0.2372114360332489, - -0.05863790959119797 + -0.23708322644233704, + -0.05807223170995712 ], "tsne": [ - -27.10832405090332, - -4.874702453613281 + -33.466556549072266, + 3.3928864002227783 ], "umap": [ - 1.439318299293518, - 8.052796363830566 + 1.6850271224975586, + 7.302994251251221 ], "pca3d": [ - -0.23721134662628174, - -0.05862298235297203, - 0.017956499010324478 + -0.2370830923318863, + -0.05805695801973343, + 0.01834983378648758 ], "tsne3d": [ - -19.908809661865234, - -12.282549858093262, - 13.246747970581055 + -18.05575180053711, + 9.304997444152832, + 10.041977882385254 ], "umap3d": [ - 1.5293021202087402, - 4.709383964538574, - 4.896854400634766 + 2.5590455532073975, + 5.152123928070068, + 5.613208293914795 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, - "label": "Transformer Attention" + "id": 34, + "label": "Language Models" } ], "_embedding": [ @@ -272789,47 +272939,47 @@ ], "projections": { "pca": [ - -0.32320109009742737, - -0.18607868254184723 + -0.32362690567970276, + -0.1852773278951645 ], "tsne": [ - -48.62677764892578, - -2.021481990814209 + -46.092445373535156, + -1.4756968021392822 ], "umap": [ - 0.8746489882469177, - 7.473851203918457 + 0.9854467511177063, + 6.469576835632324 ], "pca3d": [ - -0.3232007920742035, - -0.18607373535633087, - -0.08467425405979156 + -0.32362642884254456, + -0.1852722316980362, + -0.08415287733078003 ], "tsne3d": [ - -21.73375129699707, - -4.507328033447266, - 5.137908935546875 + -24.337060928344727, + -1.5330219268798828, + 6.8485798835754395 ], "umap3d": [ - 0.927497386932373, - 5.289426326751709, - 4.78818416595459 + 1.9484692811965942, + 5.633538246154785, + 5.489459991455078 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, - "label": "Transformer Attention" + "id": 16, + "label": "Vision Transformers" } ], "_embedding": [ @@ -273241,31 +273391,31 @@ ], "projections": { "pca": [ - -0.2799258828163147, - -0.10997732728719711 + -0.2803337275981903, + -0.10972993075847626 ], "tsne": [ - -51.71422576904297, - -10.62045669555664 + -53.45246124267578, + -9.25893497467041 ], "umap": [ - 0.7348435521125793, - 5.707292556762695 + 1.0189841985702515, + 4.956390380859375 ], "pca3d": [ - -0.27992549538612366, - -0.10995059460401535, - -0.08028535544872284 + -0.28033360838890076, + -0.10970229655504227, + -0.08016680926084518 ], "tsne3d": [ - -31.095714569091797, - -3.6151275634765625, - -5.245504379272461 + -27.88979721069336, + -9.279614448547363, + -5.205201148986816 ], "umap3d": [ - 0.6942320466041565, - 6.284803867340088, - 3.6373250484466553 + 1.1566526889801025, + 6.191988945007324, + 4.241080284118652 ] }, "cluster_id": 2, @@ -273276,12 +273426,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -273697,31 +273847,31 @@ ], "projections": { "pca": [ - -0.2215033322572708, - -0.11384366452693939 + -0.22166043519973755, + -0.11316531151533127 ], "tsne": [ - -43.80317306518555, - 11.856646537780762 + -39.748470306396484, + -18.327722549438477 ], "umap": [ - 1.796633243560791, - 5.329982280731201 + 2.1883866786956787, + 4.642332553863525 ], "pca3d": [ - -0.2215026617050171, - -0.11386896669864655, - -0.05213581398129463 + -0.22165989875793457, + -0.11319071054458618, + -0.051747653633356094 ], "tsne3d": [ - -23.343467712402344, - 8.792616844177246, - 1.5936802625656128 + -23.1516056060791, + -15.043869972229004, + 4.166475772857666 ], "umap3d": [ - 1.598199486732483, - 6.62186336517334, - 4.856019973754883 + 2.0963587760925293, + 6.9436187744140625, + 4.967789173126221 ] }, "cluster_id": 2, @@ -273732,11 +273882,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -274151,31 +274301,31 @@ ], "projections": { "pca": [ - -0.2671889662742615, - -0.04019458219408989 + -0.26727530360221863, + -0.039854515343904495 ], "tsne": [ - -49.83345031738281, - -15.960399627685547 + -50.02693176269531, + -12.031868934631348 ], "umap": [ - 0.9264752864837646, - 5.905561923980713 + 1.2268859148025513, + 5.111494064331055 ], "pca3d": [ - -0.2671889066696167, - -0.040167901664972305, - -0.1108446791768074 + -0.2672751843929291, + -0.039826877415180206, + -0.11070454120635986 ], "tsne3d": [ - -30.397689819335938, - -8.771659851074219, - -8.112165451049805 + -25.84977149963379, + -6.161642551422119, + -9.730562210083008 ], "umap3d": [ - 0.8218784332275391, - 6.032939434051514, - 3.804323434829712 + 1.3396443128585815, + 6.166627883911133, + 4.575928211212158 ] }, "cluster_id": 2, @@ -274186,12 +274336,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -274605,31 +274755,31 @@ "doi": "10.3390/e23121608", "projections": { "pca": [ - -0.2768933176994324, - -0.03613276779651642 + -0.2772284150123596, + -0.03555293008685112 ], "tsne": [ - -51.73160934448242, - 7.9968767166137695 + -52.40179443359375, + -16.735553741455078 ], "umap": [ - 0.8413086533546448, - 5.450699329376221 + 1.2211962938308716, + 4.5625386238098145 ], "pca3d": [ - -0.2768931984901428, - -0.03610944375395775, - -0.12487666308879852 + -0.27722838521003723, + -0.03552877530455589, + -0.12484908849000931 ], "tsne3d": [ - -29.428802490234375, - -1.0602505207061768, - -11.278849601745605 + -25.8236083984375, + -13.808839797973633, + -8.350506782531738 ], "umap3d": [ - 0.9914258122444153, - 6.488658428192139, - 3.5163841247558594 + 1.4005329608917236, + 6.429303169250488, + 4.025824546813965 ] }, "cluster_id": 2, @@ -274640,12 +274790,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -275058,46 +275208,46 @@ "doi": "10.4208/csiam-am.2020", "projections": { "pca": [ - -0.29530060291290283, - -0.114176906645298 + -0.2954292595386505, + -0.11369473487138748 ], "tsne": [ - -51.05998992919922, - 0.23172099888324738 + -49.326133728027344, + -2.867647171020508 ], "umap": [ - 0.4813123047351837, - 6.8754401206970215 + 0.7326442003250122, + 6.1702680587768555 ], "pca3d": [ - -0.29530012607574463, - -0.1141742691397667, - -0.11714643239974976 + -0.2954288423061371, + -0.1136915311217308, + -0.11675897240638733 ], "tsne3d": [ - -27.131254196166992, - 0.9191017150878906, - 1.4434514045715332 + -27.799901962280273, + -6.526312351226807, + 4.956278324127197 ], "umap3d": [ - 0.5479573607444763, - 5.613953590393066, - 4.473470687866211 + 1.4487855434417725, + 5.82774019241333, + 5.333733558654785 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -275510,46 +275660,46 @@ ], "projections": { "pca": [ - -0.2589481770992279, - -0.0833040103316307 + -0.25907811522483826, + -0.08185335248708725 ], "tsne": [ - -30.380069732666016, - -11.992652893066406 + -34.0624885559082, + 9.626477241516113 ], "umap": [ - 1.771706223487854, - 8.118256568908691 + 2.002772569656372, + 7.416661262512207 ], "pca3d": [ - -0.25894787907600403, - -0.0832974910736084, - 0.03891707584261894 + -0.2590779662132263, + -0.08184646815061569, + 0.039359141141176224 ], "tsne3d": [ - -18.29092025756836, - -9.55996322631836, - 5.945158004760742 + -18.74703598022461, + 3.351284980773926, + 2.1032516956329346 ], "umap3d": [ - 1.6935440301895142, - 4.370821475982666, - 4.634747505187988 + 2.7143397331237793, + 4.8892130851745605, + 5.33333683013916 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 1, + "label": "Attention Models" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -275974,46 +276124,46 @@ ], "projections": { "pca": [ - -0.32123002409935, - -0.11554061621427536 + -0.32136479020118713, + -0.1144033670425415 ], "tsne": [ - -54.95333480834961, - -2.6308836936950684 + -48.89116287231445, + 3.7673838138580322 ], "umap": [ - 0.10245124250650406, - 7.5445942878723145 + 0.33508819341659546, + 6.663351058959961 ], "pca3d": [ - -0.3212292790412903, - -0.1155836433172226, - -0.11582434177398682 + -0.321364164352417, + -0.114447221159935, + -0.1152728721499443 ], "tsne3d": [ - -28.812397003173828, - -2.7923874855041504, - 8.01858139038086 + -29.154090881347656, + 2.1159555912017822, + 3.5608227252960205 ], "umap3d": [ - 0.19463084638118744, - 5.282375335693359, - 4.978679180145264 + 1.2559155225753784, + 5.6187591552734375, + 5.883939266204834 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -276429,31 +276579,31 @@ ], "projections": { "pca": [ - -0.1288813352584839, - -0.0595993846654892 + -0.1292518675327301, + -0.0595034621655941 ], "tsne": [ - -48.361122131347656, - 9.155648231506348 + -47.153533935546875, + -17.19594383239746 ], "umap": [ - 1.3271045684814453, - 5.467875957489014 + 1.830910325050354, + 4.666985988616943 ], "pca3d": [ - -0.12888111174106598, - -0.05959256365895271, - -0.08745134621858597 + -0.1292516589164734, + -0.05949657782912254, + -0.08746553212404251 ], "tsne3d": [ - -24.864694595336914, - -2.0750732421875, - -12.0617094039917 + -22.866559982299805, + -16.790834426879883, + -6.420390605926514 ], "umap3d": [ - 1.495192050933838, - 6.522556304931641, - 3.9065890312194824 + 1.8238403797149658, + 6.599921226501465, + 4.1497039794921875 ] }, "cluster_id": 2, @@ -276464,12 +276614,12 @@ "label": "Generative Models" }, { - "id": 1, - "label": "Network Learning" + "id": 5, + "label": "Diffusion Models" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -276884,31 +277034,31 @@ ], "projections": { "pca": [ - -0.15744997560977936, - -0.11881706863641739 + -0.1578110307455063, + -0.11880972236394882 ], "tsne": [ - -37.10237121582031, - 19.80732536315918 + -32.459228515625, + -25.413711547851562 ], "umap": [ - 2.4064207077026367, - 4.815025329589844 + 2.873424530029297, + 4.156579971313477 ], "pca3d": [ - -0.1574498414993286, - -0.11878890544176102, - 0.012125130742788315 + -0.15781085193157196, + -0.11878100037574768, + 0.012151150032877922 ], "tsne3d": [ - -15.693808555603027, - 12.976553916931152, - -2.381654977798462 + -15.863851547241211, + -23.761138916015625, + 2.0777344703674316 ], "umap3d": [ - 2.3020753860473633, - 7.031113624572754, - 4.74044942855835 + 2.7077395915985107, + 7.369905948638916, + 4.554566860198975 ] }, "cluster_id": 2, @@ -276919,11 +277069,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -277341,31 +277491,31 @@ ], "projections": { "pca": [ - -0.21804623305797577, - -0.08903022855520248 + -0.21845294535160065, + -0.08824136853218079 ], "tsne": [ - -36.502899169921875, - 3.9000022411346436 + -36.16184616088867, + -8.215597152709961 ], "umap": [ - 2.3036630153656006, - 6.443669319152832 + 2.5680084228515625, + 5.68003511428833 ], "pca3d": [ - -0.21804600954055786, - -0.08901706337928772, - 0.0005590985529124737 + -0.21845264732837677, + -0.08822766691446304, + 0.0006509372615255415 ], "tsne3d": [ - -14.943711280822754, - 1.673895001411438, - -4.785767555236816 + -14.572994232177734, + -8.45263385772705, + -0.5399411916732788 ], "umap3d": [ - 2.209839105606079, - 5.5594096183776855, - 4.011334419250488 + 2.770906686782837, + 5.827876091003418, + 4.337814807891846 ] }, "cluster_id": 2, @@ -277376,12 +277526,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 33, - "label": "Neural Networks" + "id": 19, + "label": "Graph Neural Nets" } ], "_embedding": [ @@ -277794,47 +277944,47 @@ ], "projections": { "pca": [ - -0.21851199865341187, - 0.05728890374302864 + -0.2182413786649704, + 0.058396026492118835 ], "tsne": [ - -43.31304168701172, - -23.703350067138672 + -46.70338439941406, + 19.19850730895996 ], "umap": [ - 1.7509198188781738, - 6.985292911529541 + 2.2231340408325195, + 6.288895130157471 ], "pca3d": [ - -0.2185121476650238, - 0.05729889124631882, - -0.09148776531219482 + -0.21824152767658234, + 0.05840573087334633, + -0.09125714749097824 ], "tsne3d": [ - -24.862842559814453, - -15.37860107421875, - -5.901068687438965 + -21.37059783935547, + -0.02186749130487442, + -11.174362182617188 ], "umap3d": [ - 1.0566238164901733, - 4.856357097625732, - 3.5218780040740967 + 1.7244834899902344, + 4.968053340911865, + 4.267719745635986 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -278249,46 +278399,46 @@ ], "projections": { "pca": [ - -0.2047465443611145, - -0.14689524471759796 + -0.20487108826637268, + -0.14584027230739594 ], "tsne": [ - -34.378692626953125, - -6.735993385314941 + -41.82618713378906, + 7.497733116149902 ], "umap": [ - 0.7195411920547485, - 8.075271606445312 + 1.0036524534225464, + 7.2655463218688965 ], "pca3d": [ - -0.20474591851234436, - -0.14692792296409607, - -0.018526677042245865 + -0.20487046241760254, + -0.14587347209453583, + -0.017930416390299797 ], "tsne3d": [ - -24.042465209960938, - -5.650259971618652, - 11.210216522216797 + -25.345703125, + 6.006109237670898, + 5.301624298095703 ], "umap3d": [ - 0.7961367964744568, - 4.719287395477295, - 5.128579616546631 + 1.9855114221572876, + 5.102404594421387, + 5.908921718597412 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -278704,47 +278854,47 @@ ], "projections": { "pca": [ - -0.30413132905960083, - -0.1695016473531723 + -0.3046494722366333, + -0.16862383484840393 ], "tsne": [ - -43.86471176147461, - -1.7351932525634766 + -42.31127166748047, + -4.829582214355469 ], "umap": [ - 1.0919945240020752, - 6.597590923309326 + 1.5357881784439087, + 5.813974380493164 ], "pca3d": [ - -0.30413103103637695, - -0.16948585212230682, - -0.11136467754840851 + -0.3046491742134094, + -0.16860780119895935, + -0.1110076755285263 ], "tsne3d": [ - -21.96861457824707, - 0.39289674162864685, - -0.36710435152053833 + -22.396883010864258, + -7.9362945556640625, + 4.120093822479248 ], "umap3d": [ - 1.187927484512329, - 5.658162593841553, - 4.392392635345459 + 1.9529225826263428, + 5.94780969619751, + 4.9072675704956055 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -279158,31 +279308,31 @@ ], "projections": { "pca": [ - -0.27122214436531067, - -0.10287436097860336 + -0.2713673412799835, + -0.10211759060621262 ], "tsne": [ - -42.077308654785156, - 12.253955841064453 + -38.60914611816406, + -19.764484405517578 ], "umap": [ - 1.810328483581543, - 5.313238143920898 + 2.2393815517425537, + 4.659943580627441 ], "pca3d": [ - -0.2712218463420868, - -0.10289264470338821, - -0.07195889949798584 + -0.27136704325675964, + -0.10213649272918701, + -0.07161372900009155 ], "tsne3d": [ - -23.672792434692383, - 10.229633331298828, - 5.348394393920898 + -23.62147331237793, + -18.958698272705078, + 5.46254825592041 ], "umap3d": [ - 1.594443678855896, - 6.614399433135986, - 4.869705677032471 + 2.2338969707489014, + 7.03151273727417, + 4.977534294128418 ] }, "cluster_id": 2, @@ -279193,11 +279343,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -279615,31 +279765,31 @@ "cited_by_count": 5640, "projections": { "pca": [ - -0.2654131054878235, - -0.08040321618318558 + -0.26568397879600525, + -0.08004040271043777 ], "tsne": [ - -38.947105407714844, - 13.555437088012695 + -35.71354675292969, + -19.32538414001465 ], "umap": [ - 2.132472276687622, - 5.2259931564331055 + 2.5179567337036133, + 4.613612651824951 ], "pca3d": [ - -0.26541295647621155, - -0.08038751035928726, - -0.03372720628976822 + -0.26568377017974854, + -0.08002462238073349, + -0.03362329304218292 ], "tsne3d": [ - -19.48479461669922, - 9.34922981262207, - 1.8341816663742065 + -18.826927185058594, + -17.339984893798828, + 3.411816358566284 ], "umap3d": [ - 1.943970799446106, - 6.667929172515869, - 4.787703037261963 + 2.556914806365967, + 7.018752098083496, + 4.731364727020264 ] }, "cluster_id": 2, @@ -279650,11 +279800,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -280068,31 +280218,31 @@ ], "projections": { "pca": [ - -0.0006429022760130465, - -0.17955414950847626 + -0.0010537499329075217, + -0.17944900691509247 ], "tsne": [ - -9.130575180053711, - 22.473636627197266 + 1.6635452508926392, + -28.656957626342773 ], "umap": [ - 5.24672269821167, - 4.359134674072266 + 5.709521293640137, + 3.9538512229919434 ], "pca3d": [ - -0.0006426807376556098, - -0.17953984439373016, - 0.12483768165111542 + -0.00105352234095335, + -0.17943431437015533, + 0.12503643333911896 ], "tsne3d": [ - -3.4804883003234863, - 2.159546136856079, - 7.244696617126465 + -7.493894100189209, + -14.262569427490234, + 2.9221317768096924 ], "umap3d": [ - 5.28507661819458, - 6.907515048980713, - 4.599717140197754 + 5.519266128540039, + 7.597193717956543, + 2.9692091941833496 ] }, "cluster_id": 3, @@ -280103,7 +280253,7 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { @@ -280520,47 +280670,47 @@ ], "projections": { "pca": [ - -0.2984888255596161, - -0.18589580059051514 + -0.2990506887435913, + -0.18522317707538605 ], "tsne": [ - -44.530460357666016, - -0.5911015272140503 + -42.46316146850586, + -5.877871513366699 ], "umap": [ - 1.1877137422561646, - 6.507482528686523 + 1.4616483449935913, + 5.749395370483398 ], "pca3d": [ - -0.2984881103038788, - -0.1858995258808136, - -0.09486868232488632 + -0.29905012249946594, + -0.18522650003433228, + -0.09452714771032333 ], "tsne3d": [ - -22.70585823059082, - 1.1453027725219727, - -2.223445177078247 + -22.189788818359375, + -9.45367431640625, + 1.9551622867584229 ], "umap3d": [ - 1.2753016948699951, - 5.691872596740723, - 4.248087406158447 + 2.0039005279541016, + 5.965224266052246, + 4.814085960388184 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -280975,31 +281125,31 @@ ], "projections": { "pca": [ - -0.16625963151454926, - -0.13166409730911255 + -0.16669267416000366, + -0.13126230239868164 ], "tsne": [ - -41.32258605957031, - 6.788172721862793 + -36.369468688964844, + -13.229342460632324 ], "umap": [ - 1.7796292304992676, - 5.691622734069824 + 2.236875534057617, + 4.983739852905273 ], "pca3d": [ - -0.16625939309597015, - -0.13164933025836945, - -0.062141239643096924 + -0.16669246554374695, + -0.13124674558639526, + -0.06194262579083443 ], "tsne3d": [ - -18.025821685791016, - 8.069143295288086, - -5.123769283294678 + -18.287782669067383, + -15.323880195617676, + -2.3402297496795654 ], "umap3d": [ - 1.8449883460998535, - 6.3022589683532715, - 4.163452625274658 + 2.3742523193359375, + 6.6070170402526855, + 4.375803470611572 ] }, "cluster_id": 2, @@ -281010,12 +281160,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 22, + "label": "Diffusion Models" } ], "_embedding": [ @@ -281431,46 +281581,46 @@ "doi": "10.1609/aaai.v36i1.19938", "projections": { "pca": [ - -0.2960854172706604, - -0.17582526803016663 + -0.29652440547943115, + -0.1751607060432434 ], "tsne": [ - -50.31016159057617, - -5.442205429077148 + -48.71444320678711, + -3.7693822383880615 ], "umap": [ - 0.573988676071167, - 6.667488098144531 + 0.847303032875061, + 6.009934902191162 ], "pca3d": [ - -0.29608485102653503, - -0.17582620680332184, - -0.12979333102703094 + -0.2965238690376282, + -0.17516128718852997, + -0.12936177849769592 ], "tsne3d": [ - -26.036706924438477, - -0.3666917681694031, - 1.1658567190170288 + -26.721370697021484, + -5.577826976776123, + 3.8698995113372803 ], "umap3d": [ - 0.5429860949516296, - 5.7270917892456055, - 4.40949821472168 + 1.4180424213409424, + 5.860941410064697, + 5.229937553405762 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, + "id": 23, "label": "Training Noise" } ], @@ -281886,46 +282036,46 @@ ], "projections": { "pca": [ - -0.24097609519958496, - -0.09672809392213821 + -0.24121412634849548, + -0.09620805829763412 ], "tsne": [ - -51.06486129760742, - -1.294941782951355 + -46.93505096435547, + 0.729302704334259 ], "umap": [ - 0.349852979183197, - 7.117131233215332 + 0.6838061809539795, + 6.204893589019775 ], "pca3d": [ - -0.24097560346126556, - -0.09674686938524246, - -0.06674912571907043 + -0.2412136048078537, + -0.09622711688280106, + -0.06651207059621811 ], "tsne3d": [ - -24.298757553100586, - 2.071429491043091, - 3.6123151779174805 + -25.882366180419922, + -1.468045949935913, + 1.5309427976608276 ], "umap3d": [ - 0.6158169507980347, - 5.499415397644043, - 4.622112274169922 + 1.4458853006362915, + 5.734192848205566, + 5.394307613372803 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { "id": 8, - "label": "Attention Vision" + "label": "Network Learning" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -282350,47 +282500,47 @@ ], "projections": { "pca": [ - -0.29418623447418213, - -0.08335305750370026 + -0.29434603452682495, + -0.08239050954580307 ], "tsne": [ - -56.92723846435547, - -9.127321243286133 + -54.65547561645508, + -0.3707149922847748 ], "umap": [ - 0.2930978536605835, - 6.877742767333984 + 0.5795986652374268, + 5.954446315765381 ], "pca3d": [ - -0.29418590664863586, - -0.0833682119846344, - -0.11581466346979141 + -0.294345498085022, + -0.08240603655576706, + -0.11543124914169312 ], "tsne3d": [ - -31.541297912597656, - 2.3114051818847656, - 4.422386169433594 + -33.23347854614258, + -0.4016573131084442, + 0.21002726256847382 ], "umap3d": [ - 0.4083288311958313, - 5.980074405670166, - 4.644619941711426 + 1.2273391485214233, + 6.2626118659973145, + 5.295062065124512 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 9, - "label": "Vision Transformers" + "id": 23, + "label": "Training Noise" } ], "_embedding": [ @@ -282806,31 +282956,31 @@ ], "projections": { "pca": [ - -0.269446462392807, - -0.11422307044267654 + -0.269703209400177, + -0.11370684951543808 ], "tsne": [ - -44.89384841918945, - 13.652565956115723 + -41.34975051879883, + -20.29418182373047 ], "umap": [ - 1.442366600036621, - 5.426974296569824 + 1.8630239963531494, + 4.765078544616699 ], "pca3d": [ - -0.2694459855556488, - -0.11422053724527359, - -0.08559887856245041 + -0.2697027325630188, + -0.11370405554771423, + -0.0853077694773674 ], "tsne3d": [ - -25.22771644592285, - 11.964995384216309, - 2.286724328994751 + -24.223098754882812, + -17.183719635009766, + 1.5454002618789673 ], "umap3d": [ - 1.4719111919403076, - 6.553404808044434, - 4.568790912628174 + 1.9661697149276733, + 6.802820205688477, + 4.786273956298828 ] }, "cluster_id": 2, @@ -282841,12 +282991,12 @@ "label": "Generative Models" }, { - "id": 1, - "label": "Network Learning" + "id": 5, + "label": "Diffusion Models" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -283261,46 +283411,46 @@ ], "projections": { "pca": [ - -0.27448901534080505, - -0.10422329604625702 + -0.27469420433044434, + -0.10389602929353714 ], "tsne": [ - -52.104618072509766, - -9.397503852844238 + -53.101295471191406, + -7.602989196777344 ], "umap": [ - 0.40233489871025085, - 6.346746444702148 + 0.6333244442939758, + 5.521058082580566 ], "pca3d": [ - -0.2744885981082916, - -0.10422731190919876, - -0.09926716983318329 + -0.2746937870979309, + -0.10389992594718933, + -0.09899106621742249 ], "tsne3d": [ - -30.939809799194336, - -3.7193169593811035, - -2.373746156692505 + -28.893627166748047, + -7.058511257171631, + -3.7603042125701904 ], "umap3d": [ - 0.2914578914642334, - 5.895461559295654, - 4.019373893737793 + 1.031341791152954, + 5.910003662109375, + 4.798804759979248 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, + "id": 23, "label": "Training Noise" } ], @@ -283725,46 +283875,46 @@ "doi": "10.64898/2026.06.15.731445", "projections": { "pca": [ - 0.07231441885232925, - -0.15076114237308502 + 0.07190844416618347, + -0.1508922278881073 ], "tsne": [ - 22.653547286987305, - -25.701805114746094 + 13.620671272277832, + 26.21711540222168 ], "umap": [ - 7.57656717300415, - 9.453703880310059 + 7.667505264282227, + 9.134378433227539 ], "pca3d": [ - 0.07231451570987701, - -0.15074951946735382, - -0.11096526682376862 + 0.07190859317779541, + -0.15088045597076416, + -0.11071678251028061 ], "tsne3d": [ - 11.625386238098145, - -12.341483116149902, - 8.616552352905273 + 6.283516883850098, + 2.6043360233306885, + -17.00408172607422 ], "umap3d": [ - 5.304687023162842, - 3.7584335803985596, - 1.5314626693725586 + 5.229741096496582, + 3.471033811569214, + 1.5047045946121216 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -284182,47 +284332,47 @@ "doi": "10.64898/2026.06.12.731871", "projections": { "pca": [ - 0.10570797324180603, - -0.022825175896286964 + 0.1058027371764183, + -0.022267945110797882 ], "tsne": [ - 1.6248329877853394, - 13.47951602935791 + 11.840574264526367, + -15.909503936767578 ], "umap": [ - 6.129487991333008, - 5.551938533782959 + 6.471033573150635, + 5.276096343994141 ], "pca3d": [ - 0.10570815950632095, - -0.022857418283820152, - 0.21721325814723969 + 0.10580296814441681, + -0.02230042777955532, + 0.2172836810350418 ], "tsne3d": [ - -0.49884122610092163, - 1.8232252597808838, - -3.9316015243530273 + 2.2464189529418945, + -2.6826047897338867, + 10.170624732971191 ], "umap3d": [ - 5.704143524169922, - 5.854134559631348, - 3.871659517288208 + 5.725229263305664, + 6.227933406829834, + 2.5812957286834717 ] }, - "cluster_id": 3, - "cluster_label": "Protein Design", + "cluster_id": 4, + "cluster_label": "RNA Sequence Models", "cluster_levels": [ { - "id": 3, - "label": "Protein Design" + "id": 4, + "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, - "label": "Genomic Models" + "id": 32, + "label": "Genome Protein" } ], "_embedding": [ @@ -284644,31 +284794,31 @@ "doi": "10.1038/s41592-026-03124-8", "projections": { "pca": [ - 0.16465961933135986, - -0.08074236661195755 + 0.1645241677761078, + -0.08081836253404617 ], "tsne": [ - 21.430511474609375, - 1.5581600666046143 + 23.371341705322266, + 0.8708102703094482 ], "umap": [ - 7.766451835632324, - 6.920167446136475 + 7.909669399261475, + 6.515968322753906 ], "pca3d": [ - 0.16465982794761658, - -0.08074165880680084, - -0.025929570198059082 + 0.16452419757843018, + -0.08081740140914917, + -0.025757096707820892 ], "tsne3d": [ - 12.481348037719727, - 1.1195026636123657, - -8.744770050048828 + 13.4800443649292, + 4.861355304718018, + 5.690295219421387 ], "umap3d": [ - 6.40044641494751, - 4.616293430328369, - 2.691816568374634 + 6.4642744064331055, + 4.781008243560791, + 1.7622077465057373 ] }, "cluster_id": 4, @@ -284679,12 +284829,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -285110,46 +285260,46 @@ "cited_by_count": 6, "projections": { "pca": [ - -0.0070540108717978, - -0.11045993864536285 + -0.007283175829797983, + -0.1101389229297638 ], "tsne": [ - 21.966320037841797, - -21.46860694885254 + 14.432238578796387, + 22.267202377319336 ], "umap": [ - 7.656125545501709, - 9.46959114074707 + 7.655666351318359, + 9.158586502075195 ], "pca3d": [ - -0.00705388467758894, - -0.11044283956289291, - -0.13015078008174896 + -0.007283109240233898, + -0.11012163013219833, + -0.12980705499649048 ], "tsne3d": [ - 10.941754341125488, - -7.181753635406494, - 10.519079208374023 + 7.775939464569092, + 2.07309889793396, + -13.799117088317871 ], "umap3d": [ - 5.186841011047363, - 3.754409074783325, - 1.4798107147216797 + 5.154796600341797, + 3.5399649143218994, + 1.4365708827972412 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -285558,47 +285708,47 @@ ], "projections": { "pca": [ - -0.13694718480110168, - 0.2808941602706909 + -0.13608448207378387, + 0.28095099329948425 ], "tsne": [ - -8.797212600708008, - -6.817934036254883 + -8.359302520751953, + 4.290228843688965 ], "umap": [ - 4.273925304412842, - 7.497913360595703 + 4.499200820922852, + 6.715699195861816 ], "pca3d": [ - -0.13694772124290466, - 0.2809240221977234, - -0.014269035309553146 + -0.13608503341674805, + 0.2809811234474182, + -0.014664297923445702 ], "tsne3d": [ - -8.146750450134277, - -2.3806040287017822, - 11.632630348205566 + -5.389428615570068, + 1.8896116018295288, + -4.50663948059082 ], "umap3d": [ - 3.782113790512085, - 3.6627016067504883, - 4.387102127075195 + 4.7973761558532715, + 4.592695236206055, + 4.5396270751953125 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -286004,47 +286154,47 @@ ], "projections": { "pca": [ - 0.02718973346054554, - -0.04812254756689072 + 0.027179846540093422, + -0.04822727292776108 ], "tsne": [ - 33.33375930786133, - -25.29996681213379 + 19.302974700927734, + 16.250219345092773 ], "umap": [ - 8.210726737976074, - 8.784907341003418 + 8.301770210266113, + 8.471135139465332 ], "pca3d": [ - 0.027189569547772408, - -0.048108577728271484, - -0.029959527775645256 + 0.027179725468158722, + -0.04821321740746498, + -0.029843229800462723 ], "tsne3d": [ - 7.620737552642822, - -2.0701539516448975, - 14.989906311035156 + 4.665526866912842, + -3.516658306121826, + -20.668537139892578 ], "umap3d": [ - 5.849269390106201, - 4.439769744873047, - 1.504220724105835 + 5.680749893188477, + 4.196455001831055, + 0.9848616719245911 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 12, - "label": "Genetic Variants" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -286469,31 +286619,31 @@ "doi": "10.64898/2026.05.29.728882", "projections": { "pca": [ - 0.2612745463848114, - 0.04482930898666382 + 0.26134416460990906, + 0.04423552379012108 ], "tsne": [ - 38.256107330322266, - 10.597938537597656 + 44.59513854980469, + -2.8987972736358643 ], "umap": [ - 8.634193420410156, - 7.744090557098389 + 8.895624160766602, + 7.342528343200684 ], "pca3d": [ - 0.2612743377685547, - 0.04481407254934311, - -0.09926265478134155 + 0.2613440454006195, + 0.04422075301408768, + -0.09941525757312775 ], "tsne3d": [ - 19.67912483215332, - 3.0086028575897217, - 9.943285942077637 + 21.190847396850586, + 8.198945999145508, + 10.858839988708496 ], "umap3d": [ - 7.485588550567627, - 4.140385150909424, - 1.9845155477523804 + 7.232107639312744, + 4.19382905960083, + 0.8598793148994446 ] }, "cluster_id": 0, @@ -286504,12 +286654,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -286931,31 +287081,31 @@ "doi": "10.64898/2026.05.22.727141", "projections": { "pca": [ - 0.2023496776819229, - 0.0103954141959548 + 0.2024243175983429, + 0.010276536457240582 ], "tsne": [ - 27.067184448242188, - 22.27285385131836 + 32.384639739990234, + -25.045862197875977 ], "umap": [ - 8.542049407958984, - 5.664761543273926 + 8.822089195251465, + 5.36431884765625 ], "pca3d": [ - 0.20234975218772888, - 0.010379007086157799, - -0.06012214347720146 + 0.20242437720298767, + 0.01026027463376522, + -0.06011345982551575 ], "tsne3d": [ - 14.170305252075195, - 24.059511184692383, - 4.4776105880737305 + 19.80967903137207, + -10.15352725982666, + 10.014055252075195 ], "umap3d": [ - 7.592532157897949, - 4.972161293029785, - 3.1754086017608643 + 7.678280353546143, + 5.383675575256348, + 1.5661470890045166 ] }, "cluster_id": 4, @@ -286966,11 +287116,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -287404,31 +287554,31 @@ "cited_by_count": 21, "projections": { "pca": [ - 0.21284663677215576, - -0.08487360179424286 + 0.21266663074493408, + -0.08523382991552353 ], "tsne": [ - 27.89115333557129, - -8.668747901916504 + 24.737689971923828, + 10.696982383728027 ], "umap": [ - 8.425102233886719, - 7.724608421325684 + 8.637103080749512, + 7.6922383308410645 ], "pca3d": [ - 0.2128465324640274, - -0.0848664790391922, - -0.10653343796730042 + 0.21266651153564453, + -0.08522678911685944, + -0.10638868063688278 ], "tsne3d": [ - 14.128236770629883, - -1.2615312337875366, - -5.4486565589904785 + 15.293575286865234, + 4.019155025482178, + -2.9458494186401367 ], "umap3d": [ - 6.315091609954834, - 4.503988265991211, - 1.7406115531921387 + 6.130276679992676, + 4.333485126495361, + 1.0547730922698975 ] }, "cluster_id": 0, @@ -287439,12 +287589,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { "id": 31, - "label": "Genome Cancer" + "label": "Single Cell Seq" } ], "_embedding": [ @@ -287866,31 +288016,31 @@ "doi": "10.1101/2024.11.22.624813", "projections": { "pca": [ - 0.23677094280719757, - -0.03180331364274025 + 0.23669064044952393, + -0.032521605491638184 ], "tsne": [ - 44.88554000854492, - -3.3642361164093018 + 31.798053741455078, + 15.70242977142334 ], "umap": [ - 9.158676147460938, - 7.767807483673096 + 9.443239212036133, + 7.464128494262695 ], "pca3d": [ - 0.23677092790603638, - -0.031805235892534256, - -0.15543490648269653 + 0.2366907000541687, + -0.03252352401614189, + -0.15547975897789001 ], "tsne3d": [ - 15.499360084533691, - -1.0285283327102661, - 1.2801063060760498 + 17.301443099975586, + -0.1463482677936554, + -7.411263465881348 ], "umap3d": [ - 7.000453948974609, - 4.407888412475586, - 1.2645745277404785 + 6.833850383758545, + 4.174682140350342, + 0.26463618874549866 ] }, "cluster_id": 0, @@ -287901,12 +288051,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 20, - "label": "Chromatin Regulation" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -288332,47 +288482,47 @@ "doi": "10.64898/2026.03.06.710116", "projections": { "pca": [ - 0.2352461963891983, - 0.08137694001197815 + 0.2353675663471222, + 0.08068396151065826 ], "tsne": [ - 30.95576286315918, - 11.235677719116211 + 33.5606803894043, + -3.1526103019714355 ], "umap": [ - 7.862010955810547, - 7.855103015899658 + 7.993357181549072, + 7.3955397605896 ], "pca3d": [ - 0.23524592816829681, - 0.08136416226625443, - -0.14523713290691376 + 0.23536726832389832, + 0.08067045360803604, + -0.1455373913049698 ], "tsne3d": [ - 21.012500762939453, - -9.898395538330078, - -7.7670817375183105 + 22.38737678527832, + 15.049870491027832, + -0.1473674327135086 ], "umap3d": [ - 7.0946946144104, - 3.7511589527130127, - 2.458228826522827 + 7.26012659072876, + 3.8990120887756348, + 1.6034995317459106 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -288816,31 +288966,31 @@ "openalex_url": "https://openalex.org/W7161624394", "projections": { "pca": [ - 0.2970915734767914, - 0.1779889017343521 + 0.2974892854690552, + 0.1773446500301361 ], "tsne": [ - 44.00020217895508, - 6.327101707458496 + 44.49648666381836, + 1.7853803634643555 ], "umap": [ - 9.28928279876709, - 7.614241600036621 + 9.475364685058594, + 6.997581958770752 ], "pca3d": [ - 0.2970912456512451, - 0.17797765135765076, - -0.04628729820251465 + 0.2974889278411865, + 0.17733325064182281, + -0.046796876937150955 ], "tsne3d": [ - 23.680866241455078, - 1.2871439456939697, - 7.746634006500244 + 26.117595672607422, + 1.7868019342422485, + -12.410396575927734 ], "umap3d": [ - 7.8155317306518555, - 4.334314823150635, - 1.4654786586761475 + 7.4347147941589355, + 4.2398457527160645, + 0.2480403482913971 ] }, "cluster_id": 0, @@ -288851,11 +289001,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -289264,47 +289414,47 @@ ], "projections": { "pca": [ - -0.1791328340768814, - 0.2786407470703125 + -0.17861223220825195, + 0.27967923879623413 ], "tsne": [ - -38.40143585205078, - -21.349018096923828 + -46.37459182739258, + 16.014366149902344 ], "umap": [ - 1.3444790840148926, - 7.0897440910339355 + 1.8112465143203735, + 6.420128345489502 ], "pca3d": [ - -0.17913325130939484, - 0.2786446809768677, - -0.0748521015048027 + -0.17861269414424896, + 0.27968284487724304, + -0.07528950273990631 ], "tsne3d": [ - -28.6743221282959, - -15.558652877807617, - 1.5321402549743652 + -27.95589828491211, + 4.453136920928955, + -9.646203994750977 ], "umap3d": [ - 0.7108615636825562, - 4.872377395629883, - 3.8275234699249268 + 1.5001025199890137, + 4.972856044769287, + 4.495716094970703 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -289727,31 +289877,31 @@ "doi": "10.1038/s41587-026-03130-3", "projections": { "pca": [ - 0.16952988505363464, - 0.17474891245365143 + 0.16995105147361755, + 0.17414303123950958 ], "tsne": [ - 34.37820816040039, - 23.324710845947266 + 51.667633056640625, + -4.254312515258789 ], "umap": [ - 9.074901580810547, - 6.81727933883667 + 9.18571662902832, + 5.967801094055176 ], "pca3d": [ - 0.16952934861183167, - 0.17476946115493774, - -0.10765296220779419 + 0.1699504852294922, + 0.17416435480117798, + -0.10807961970567703 ], "tsne3d": [ - 19.85985565185547, - 20.05219841003418, - 2.6175715923309326 + 25.440799713134766, + -8.28933334350586, + 9.075759887695312 ], "umap3d": [ - 7.609715461730957, - 4.767449855804443, - 2.2051925659179688 + 7.381406784057617, + 4.742671966552734, + 0.779188334941864 ] }, "cluster_id": 0, @@ -289762,12 +289912,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 20, - "label": "Chromatin Regulation" + "id": 21, + "label": "RNA Splicing" } ], "_embedding": [ @@ -290185,46 +290335,46 @@ "doi": "10.64898/2026.05.11.724149", "projections": { "pca": [ - 0.12721842527389526, - -0.05183706060051918 + 0.12697972357273102, + -0.05237630009651184 ], "tsne": [ - 11.99487590789795, - -21.113075256347656 + 5.310775279998779, + 21.0607967376709 ], "umap": [ - 6.946095943450928, - 9.202125549316406 + 7.072414398193359, + 8.708643913269043 ], "pca3d": [ - 0.12721818685531616, - -0.05181014537811279, - -0.0732862800359726 + 0.12697957456111908, + -0.05234939232468605, + -0.07338553667068481 ], "tsne3d": [ - 2.417250156402588, - -9.50997257232666, - 9.853705406188965 + 5.180535316467285, + 8.793743133544922, + -8.734328269958496 ], "umap3d": [ - 4.855854034423828, - 4.016556262969971, - 1.903408169746399 + 4.8954877853393555, + 3.918299436569214, + 1.8259741067886353 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -290646,31 +290796,31 @@ "doi": "10.64898/2026.05.11.724344", "projections": { "pca": [ - 0.08429622650146484, - -0.1637829691171646 + 0.08393540233373642, + -0.1639796942472458 ], "tsne": [ - 17.56658363342285, - 31.754722595214844 + 21.03909683227539, + -30.118114471435547 ], "umap": [ - 7.8528523445129395, - 4.767025947570801 + 8.241117477416992, + 4.274612903594971 ], "pca3d": [ - 0.0842963233590126, - -0.16376952826976776, - 0.09216413646936417 + 0.08393561840057373, + -0.16396564245224, + 0.09235896915197372 ], "tsne3d": [ - 3.772412061691284, - 22.18828582763672, - 4.173221111297607 + 11.281530380249023, + -15.663113594055176, + 6.80331563949585 ], "umap3d": [ - 7.1575026512146, - 6.163745403289795, - 3.9556758403778076 + 7.257969856262207, + 6.643991947174072, + 2.2018682956695557 ] }, "cluster_id": 4, @@ -290681,11 +290831,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -291122,46 +291272,46 @@ "cited_by_count": 1, "projections": { "pca": [ - 0.1614939570426941, - -0.030426444485783577 + 0.16143760085105896, + -0.03100416623055935 ], "tsne": [ - 20.12897300720215, - -19.36228370666504 + 13.916391372680664, + 19.60848617553711 ], "umap": [ - 7.709779262542725, - 9.189957618713379 + 7.601555347442627, + 8.939411163330078 ], "pca3d": [ - 0.16149389743804932, - -0.030418116599321365, - -0.11499446630477905 + 0.16143758594989777, + -0.03099535033106804, + -0.11497370898723602 ], "tsne3d": [ - 9.700848579406738, - -5.603293418884277, - 6.702571392059326 + 8.422993659973145, + 3.187121629714966, + -9.344710350036621 ], "umap3d": [ - 5.505485534667969, - 3.720268726348877, - 1.619045376777649 + 5.411633491516113, + 3.5406076908111572, + 1.5295056104660034 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -291600,31 +291750,31 @@ "cited_by_count": 2, "projections": { "pca": [ - 0.31500670313835144, - 0.05417461693286896 + 0.31517329812049866, + 0.05312119051814079 ], "tsne": [ - 48.85219955444336, - 5.2892165184021 + 46.060550689697266, + 3.5254316329956055 ], "umap": [ - 9.791132926940918, - 7.346993446350098 + 10.026803970336914, + 6.777677536010742 ], "pca3d": [ - 0.3150064945220947, - 0.054169584065675735, - -0.09307845681905746 + 0.31517311930656433, + 0.053116198629140854, + -0.09337469190359116 ], "tsne3d": [ - 25.306116104125977, - 2.5037004947662354, - 3.815659761428833 + 27.35478973388672, + 1.5114198923110962, + -9.24445629119873 ], "umap3d": [ - 8.076258659362793, - 4.661867141723633, - 1.2689268589019775 + 7.512055397033691, + 4.505331516265869, + -0.1360519826412201 ] }, "cluster_id": 0, @@ -291635,12 +291785,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 20, - "label": "Chromatin Regulation" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -292055,31 +292205,31 @@ "doi": "10.1038/s43588-026-00981-3", "projections": { "pca": [ - 0.1321895718574524, - -0.013041011057794094 + 0.1320783942937851, + -0.01286555640399456 ], "tsne": [ - 31.932334899902344, - 0.8912445902824402 + 29.151119232177734, + 3.7399699687957764 ], "umap": [ - 8.67588996887207, - 7.002270221710205 + 8.85692310333252, + 6.64238166809082 ], "pca3d": [ - 0.13218924403190613, - -0.013036365620791912, - 0.002868250710889697 + 0.13207818567752838, + -0.012860652059316635, + 0.0028096043970435858 ], "tsne3d": [ - 15.594353675842285, - 5.339238166809082, - -3.372797966003418 + 18.775964736938477, + 4.824422836303711, + 5.894711494445801 ], "umap3d": [ - 6.833218097686768, - 5.012751579284668, - 2.092158794403076 + 6.5087785720825195, + 4.905213356018066, + 0.8911863565444946 ] }, "cluster_id": 0, @@ -292090,12 +292240,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -292571,31 +292721,31 @@ "cited_by_count": 6, "projections": { "pca": [ - 0.13022786378860474, - -0.027253516018390656 + 0.13022872805595398, + -0.027081483975052834 ], "tsne": [ - 4.8144755363464355, - 10.623286247253418 + 14.43281078338623, + -13.015336036682129 ], "umap": [ - 6.700251579284668, - 6.182093143463135 + 6.9354448318481445, + 6.015082359313965 ], "pca3d": [ - 0.13022813200950623, - -0.027276482433080673, - -0.022732267156243324 + 0.1302289515733719, + -0.027104103937745094, + -0.022559529170393944 ], "tsne3d": [ - 1.7454850673675537, - 0.1380731165409088, - -7.38381814956665 + 4.835671424865723, + 1.5263937711715698, + 9.407347679138184 ], "umap3d": [ - 5.878885269165039, - 4.923678874969482, - 3.531350612640381 + 6.142897605895996, + 5.316119194030762, + 2.6752195358276367 ] }, "cluster_id": 4, @@ -292606,11 +292756,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -293019,47 +293169,47 @@ "abstract": "Official release of Genos models. Contribute to zhejianglab/Genos development by creating an account on GitHub.", "projections": { "pca": [ - -0.100452721118927, - 0.2527570426464081 + -0.099617138504982, + 0.25306957960128784 ], "tsne": [ - -29.435264587402344, - 29.96630096435547 + -15.29367446899414, + -4.189826488494873 ], "umap": [ - 4.026117324829102, - 6.600294589996338 + 4.387485980987549, + 6.066659450531006 ], "pca3d": [ - -0.10045307874679565, - 0.252755343914032, - 0.062284793704748154 + -0.099617600440979, + 0.25306808948516846, + 0.06196638569235802 ], "tsne3d": [ - -13.379368782043457, - 14.343565940856934, - 11.006953239440918 + -13.741401672363281, + -2.0177900791168213, + 13.951455116271973 ], "umap3d": [ - 3.7138679027557373, - 4.575560569763184, - 4.447233200073242 + 4.585323333740234, + 5.344594955444336, + 4.278982639312744 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 10, - "label": "Code Development" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -293492,31 +293642,31 @@ "doi": "10.64898/2026.04.07.717039", "projections": { "pca": [ - 0.22764603793621063, - -0.04698728397488594 + 0.2274288684129715, + -0.047226682305336 ], "tsne": [ - 46.096500396728516, - -7.637857437133789 + 44.078514099121094, + 9.699975967407227 ], "umap": [ - 9.667498588562012, - 8.039657592773438 + 9.929999351501465, + 7.572325706481934 ], "pca3d": [ - 0.22764594852924347, - -0.04699994996190071, - -0.10767140984535217 + 0.2274288684129715, + -0.047239746898412704, + -0.10768485069274902 ], "tsne3d": [ - 24.638532638549805, - -4.409409999847412, - -7.773480415344238 + 25.205087661743164, + 8.844427108764648, + 0.5173357725143433 ], "umap3d": [ - 6.995007514953613, - 4.564855575561523, - 1.0356559753417969 + 6.511996269226074, + 4.285103797912598, + -0.017451951280236244 ] }, "cluster_id": 0, @@ -293527,12 +293677,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -293948,31 +294098,31 @@ ], "projections": { "pca": [ - 0.2001103013753891, - -0.06954333931207657 + 0.19993856549263, + -0.06978947669267654 ], "tsne": [ - 40.64802932739258, - 4.343404769897461 + 35.9860725402832, + 4.030509948730469 ], "umap": [ - 9.28499698638916, - 7.210055351257324 + 9.344852447509766, + 7.000189304351807 ], "pca3d": [ - 0.20011040568351746, - -0.06954675167798996, - 0.05405266955494881 + 0.19993863999843597, + -0.06979333609342575, + 0.05402733385562897 ], "tsne3d": [ - 24.02608871459961, - 0.00556097412481904, - -4.877005577087402 + 17.849349975585938, + -3.111292600631714, + -2.669811248779297 ], "umap3d": [ - 6.7118048667907715, - 5.309549808502197, - 1.3922849893569946 + 6.329493045806885, + 4.906066417694092, + 0.3222973942756653 ] }, "cluster_id": 0, @@ -293983,11 +294133,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -294414,31 +294564,31 @@ "openalex_url": "https://openalex.org/W4390904763", "projections": { "pca": [ - 0.22220127284526825, - 0.0173258725553751 + 0.22212857007980347, + 0.01681489497423172 ], "tsne": [ - 56.55299377441406, - -4.696493148803711 + 50.373809814453125, + 12.580095291137695 ], "umap": [ - 10.154837608337402, - 7.314446449279785 + 10.364258766174316, + 6.9118523597717285 ], "pca3d": [ - 0.22220107913017273, - 0.017321016639471054, - -0.08633063733577728 + 0.22212843596935272, + 0.01680939272046089, + -0.08660288155078888 ], "tsne3d": [ - 31.599342346191406, - 1.4062917232513428, - -7.168457984924316 + 30.357019424438477, + 3.624253988265991, + 1.015069603919983 ], "umap3d": [ - 7.681239128112793, - 5.141064167022705, - 0.9584299921989441 + 7.011122703552246, + 4.912055492401123, + -0.3442184329032898 ] }, "cluster_id": 0, @@ -294449,12 +294599,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -294874,31 +295024,31 @@ "doi": "10.64898/2025.12.10.693551", "projections": { "pca": [ - 0.2558929920196533, - 0.0006974871503189206 + 0.25593915581703186, + 0.0003299751551821828 ], "tsne": [ - 50.705421447753906, - 1.5028527975082397 + 56.01413345336914, + 13.562520980834961 ], "umap": [ - 10.372776985168457, - 7.230074882507324 + 10.48995304107666, + 6.80785608291626 ], "pca3d": [ - 0.25589293241500854, - 0.0006890446529723704, - 0.010050015524029732 + 0.25593915581703186, + 0.0003209470887668431, + 0.00987319927662611 ], "tsne3d": [ - 29.584047317504883, - 5.2623701095581055, - 3.740203380584717 + 32.928985595703125, + 1.60060715675354, + 3.5427188873291016 ], "umap3d": [ - 7.834150791168213, - 5.144073009490967, - 0.8787313103675842 + 7.238798141479492, + 4.8261590003967285, + -0.5549936294555664 ] }, "cluster_id": 0, @@ -294909,12 +295059,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -295338,46 +295488,46 @@ "cited_by_count": 2, "projections": { "pca": [ - 0.03140667453408241, - -0.18889270722866058 + 0.030801651999354362, + -0.18861053884029388 ], "tsne": [ - 21.089876174926758, - -22.468841552734375 + 13.328221321105957, + 22.777803421020508 ], "umap": [ - 7.5543437004089355, - 9.321516036987305 + 7.552493572235107, + 9.107878684997559 ], "pca3d": [ - 0.03140689805150032, - -0.18887613713741302, - -0.08020097017288208 + 0.030801841989159584, + -0.18859325349330902, + -0.07988464832305908 ], "tsne3d": [ - 9.776097297668457, - -9.367944717407227, - 8.007987976074219 + 6.300777435302734, + 2.790666103363037, + -14.308412551879883 ], "umap3d": [ - 5.206448078155518, - 3.8458759784698486, - 1.4873794317245483 + 5.113426208496094, + 3.640306234359741, + 1.5618691444396973 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -295817,31 +295967,31 @@ "openalex_url": "https://openalex.org/W7140849072", "projections": { "pca": [ - 0.20719967782497406, - 0.10216600447893143 + 0.20749010145664215, + 0.10194573551416397 ], "tsne": [ - 34.56841278076172, - -5.530520915985107 + 35.25758361816406, + 7.4822845458984375 ], "umap": [ - 8.955464363098145, - 7.664368152618408 + 9.241503715515137, + 7.258958339691162 ], "pca3d": [ - 0.20719937980175018, - 0.10215999186038971, - -0.12443973124027252 + 0.20748981833457947, + 0.10193906724452972, + -0.12461874634027481 ], "tsne3d": [ - 15.772444725036621, - -4.479907512664795, - -2.057835817337036 + 17.532711029052734, + 6.151686668395996, + -5.593089580535889 ], "umap3d": [ - 7.297026634216309, - 4.462277889251709, - 1.5590407848358154 + 6.925436973571777, + 4.233392715454102, + 0.48718783259391785 ] }, "cluster_id": 0, @@ -295852,11 +296002,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -296278,31 +296428,31 @@ "cited_by_count": 1, "projections": { "pca": [ - -0.017000805586576462, - -0.007180624175816774 + -0.01698174700140953, + -0.0066096908412873745 ], "tsne": [ - 18.915584564208984, - 26.827369689941406 + 23.010160446166992, + -26.653182983398438 ], "umap": [ - 7.82073450088501, - 4.8999128341674805 + 8.106525421142578, + 4.459195613861084 ], "pca3d": [ - -0.017000870779156685, - -0.007164026144891977, - 0.10339349508285522 + -0.016981812193989754, + -0.0065923877991735935, + 0.1034480631351471 ], "tsne3d": [ - 5.824450969696045, - 20.010940551757812, - 4.9579758644104 + 12.377744674682617, + -13.291608810424805, + 8.642077445983887 ], "umap3d": [ - 7.086688041687012, - 6.009397506713867, - 3.834352493286133 + 7.181003570556641, + 6.5445027351379395, + 2.1824114322662354 ] }, "cluster_id": 4, @@ -296313,11 +296463,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -296760,31 +296910,31 @@ "openalex_url": "https://openalex.org/W4409502882", "projections": { "pca": [ - 0.2131856232881546, - 0.10995426028966904 + 0.21355405449867249, + 0.10957513004541397 ], "tsne": [ - 48.953006744384766, - -3.954785108566284 + 50.17804718017578, + 5.999899864196777 ], "umap": [ - 10.245121002197266, - 7.756921291351318 + 10.383082389831543, + 7.4209208488464355 ], "pca3d": [ - 0.21318547427654266, - 0.10994244366884232, - -0.06145738437771797 + 0.21355384588241577, + 0.10956220328807831, + -0.061688411980867386 ], "tsne3d": [ - 28.514427185058594, - -6.365813732147217, - -3.8371567726135254 + 30.55243492126465, + 10.427242279052734, + -1.7590365409851074 ], "umap3d": [ - 7.308788776397705, - 4.924610137939453, - 0.7447846531867981 + 6.599306583404541, + 4.539653301239014, + -0.38932591676712036 ] }, "cluster_id": 0, @@ -296795,12 +296945,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -297223,46 +297373,46 @@ "doi": "10.64898/2026.04.11.717183", "projections": { "pca": [ - 0.07623536884784698, - -0.045303825289011 + 0.07614007592201233, + -0.044970132410526276 ], "tsne": [ - 12.059418678283691, - -22.327838897705078 + 6.108193397521973, + 20.263389587402344 ], "umap": [ - 6.7338151931762695, - 9.142217636108398 + 6.869082927703857, + 8.72024154663086 ], "pca3d": [ - 0.07623539119958878, - -0.04530131071805954, - 0.08081211894750595 + 0.07614011317491531, + -0.04496782273054123, + 0.08087649941444397 ], "tsne3d": [ - 3.714675188064575, - -7.758721351623535, - 10.783742904663086 + 3.8943774700164795, + 7.280356407165527, + -9.709859848022461 ], "umap3d": [ - 4.559876918792725, - 3.9126832485198975, - 2.1170690059661865 + 4.758840084075928, + 3.931942939758301, + 2.1764721870422363 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -297698,31 +297848,31 @@ "doi": "10.64898/2026.04.10.717844", "projections": { "pca": [ - 0.1474878042936325, - -0.08150321990251541 + 0.14735804498195648, + -0.08152605593204498 ], "tsne": [ - 22.22591781616211, - 12.654387474060059 + 24.83646583557129, + -6.396315097808838 ], "umap": [ - 7.16176700592041, - 7.252498149871826 + 7.260660648345947, + 6.826401710510254 ], "pca3d": [ - 0.14748799800872803, - -0.0815264955163002, - -0.0636020079255104 + 0.14735828340053558, + -0.08154995739459991, + -0.06336679309606552 ], "tsne3d": [ - 9.856391906738281, - -5.470199108123779, - -12.837960243225098 + 8.984701156616211, + 11.178138732910156, + 5.133561611175537 ], "umap3d": [ - 6.773909091949463, - 3.9128847122192383, - 3.3309123516082764 + 7.1700334548950195, + 4.401318073272705, + 2.4033920764923096 ] }, "cluster_id": 4, @@ -297733,12 +297883,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -298157,31 +298307,31 @@ ], "projections": { "pca": [ - 0.18808986246585846, - -0.08122675120830536 + 0.18811942636966705, + -0.0807897076010704 ], "tsne": [ - 8.609451293945312, - 12.177667617797852 + 10.950624465942383, + -9.662867546081543 ], "umap": [ - 6.756988048553467, - 5.657282829284668 + 7.024388313293457, + 5.366606712341309 ], "pca3d": [ - 0.1880899965763092, - -0.08124848455190659, - 0.19929824769496918 + 0.1881195604801178, + -0.0808115303516388, + 0.19953730702400208 ], "tsne3d": [ - 3.787642002105713, - 8.093055725097656, - -6.317526340484619 + 5.479151725769043, + -2.2237799167633057, + 3.2517707347869873 ], "umap3d": [ - 5.823903560638428, - 5.346997261047363, - 3.809708595275879 + 6.09244441986084, + 5.816827297210693, + 2.595994710922241 ] }, "cluster_id": 4, @@ -298192,11 +298342,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -298617,31 +298767,31 @@ "doi": "10.1101/2025.11.10.687627", "projections": { "pca": [ - 0.22838418185710907, - -0.11442030221223831 + 0.228061243891716, + -0.11456276476383209 ], "tsne": [ - 34.17396545410156, - -1.4681047201156616 + 29.01535987854004, + 7.133513450622559 ], "umap": [ - 8.529926300048828, - 6.9031219482421875 + 8.724909782409668, + 6.510986328125 ], "pca3d": [ - 0.22838422656059265, - -0.11441617459058762, - -0.04305747523903847 + 0.22806130349636078, + -0.11455841362476349, + -0.0429120697081089 ], "tsne3d": [ - 16.60979652404785, - 1.4011605978012085, - -5.062839984893799 + 18.333099365234375, + 2.9736032485961914, + -0.47634491324424744 ], "umap3d": [ - 6.7437310218811035, - 5.014781951904297, - 2.071537733078003 + 6.490578651428223, + 4.924617290496826, + 1.042217493057251 ] }, "cluster_id": 0, @@ -298652,12 +298802,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -299094,31 +299244,31 @@ "cited_by_count": 3, "projections": { "pca": [ - 0.26166242361068726, - -0.0029737409204244614 + 0.2615748643875122, + -0.0035801015328615904 ], "tsne": [ - 38.5147819519043, - -4.711392879486084 + 39.28913116455078, + 11.297415733337402 ], "umap": [ - 9.289045333862305, - 7.949258804321289 + 9.531792640686035, + 7.5005669593811035 ], "pca3d": [ - 0.26166224479675293, - -0.0029789365362375975, - -0.16922953724861145 + 0.26157477498054504, + -0.0035855891183018684, + -0.16931571066379547 ], "tsne3d": [ - 20.10502052307129, - -4.430871486663818, - -0.12515351176261902 + 22.707977294921875, + 5.722508430480957, + -5.478640079498291 ], "umap3d": [ - 7.265218257904053, - 4.3547139167785645, - 1.3195079565048218 + 6.7964653968811035, + 4.095676898956299, + 0.2745974361896515 ] }, "cluster_id": 0, @@ -299129,12 +299279,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -299558,31 +299708,31 @@ "doi": "10.1126/sciadv.aec9801", "projections": { "pca": [ - 0.23451054096221924, - 0.20912566781044006 + 0.2351805865764618, + 0.2085629105567932 ], "tsne": [ - 45.805931091308594, - 6.405383110046387 + 47.28601837158203, + 1.0051177740097046 ], "umap": [ - 9.488845825195312, - 7.201083660125732 + 9.631999015808105, + 6.802253723144531 ], "pca3d": [ - 0.23451003432273865, - 0.20910699665546417, - 0.005221621133387089 + 0.23518021404743195, + 0.20854362845420837, + 0.00474512530490756 ], "tsne3d": [ - 21.318157196044922, - 4.923644542694092, - 3.8372206687927246 + 26.176380157470703, + -2.8691465854644775, + -10.504900932312012 ], "umap3d": [ - 7.93345308303833, - 4.420112609863281, - 1.4842209815979004 + 7.571906089782715, + 4.3149094581604, + 0.13588528335094452 ] }, "cluster_id": 0, @@ -299593,11 +299743,11 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -300024,47 +300174,47 @@ "doi": "10.64898/2026.04.06.716850", "projections": { "pca": [ - 0.040279146283864975, - 0.06900408118963242 + 0.04055572673678398, + 0.06963502615690231 ], "tsne": [ - -3.703425645828247, - -21.794315338134766 + -6.352636337280273, + 18.045780181884766 ], "umap": [ - 4.5383524894714355, - 8.7426118850708 + 4.766124725341797, + 7.980229377746582 ], "pca3d": [ - 0.04027916118502617, - 0.06899413466453552, - 0.14929834008216858 + 0.04055572673678398, + 0.06962510943412781, + 0.1492476612329483 ], "tsne3d": [ - -4.511249542236328, - -8.594273567199707, - -6.888320446014404 + -3.9209892749786377, + 15.265826225280762, + 2.891395092010498 ], "umap3d": [ - 3.604440927505493, - 3.7545275688171387, - 2.89888334274292 + 4.202102184295654, + 4.043128490447998, + 3.2574524879455566 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, - "label": "Medical AI" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -300479,31 +300629,31 @@ ], "projections": { "pca": [ - 0.03763652965426445, - -0.12398672103881836 + 0.0373949259519577, + -0.12349635362625122 ], "tsne": [ - 8.71756362915039, - 8.459576606750488 + 11.002731323242188, + -5.373110771179199 ], "umap": [ - 6.81263542175293, - 6.132598876953125 + 7.082634449005127, + 5.747125148773193 ], "pca3d": [ - 0.03763662651181221, - -0.123971126973629, - 0.02084261365234852 + 0.03739501163363457, + -0.12348032742738724, + 0.021170135587453842 ], "tsne3d": [ - 5.8425211906433105, - 3.973702907562256, - -11.097905158996582 + 4.462032794952393, + 1.5258259773254395, + 3.587171792984009 ], "umap3d": [ - 5.630474090576172, - 5.035076141357422, - 3.398906707763672 + 5.874539852142334, + 5.260557651519775, + 2.512237787246704 ] }, "cluster_id": 4, @@ -300514,11 +300664,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -300935,31 +301085,31 @@ "doi": "10.64898/2026.03.17.712488", "projections": { "pca": [ - 0.23435546457767487, - 0.008750971406698227 + 0.2343447506427765, + 0.008340942673385143 ], "tsne": [ - 49.704036712646484, - 8.917383193969727 + 52.22958755493164, + 4.417752742767334 ], "umap": [ - 9.899874687194824, - 7.130588531494141 + 10.134069442749023, + 6.811580181121826 ], "pca3d": [ - 0.23435521125793457, - 0.008736887946724892, - -0.09794145077466965 + 0.23434461653232574, + 0.008326444774866104, + -0.09808691591024399 ], "tsne3d": [ - 29.283906936645508, - 3.1389236450195312, - -6.219947338104248 + 28.824851989746094, + 0.2320556789636612, + 2.3429465293884277 ], "umap3d": [ - 7.710036277770996, - 4.9795613288879395, - 1.405785083770752 + 7.250020503997803, + 4.791421890258789, + 0.2396499663591385 ] }, "cluster_id": 0, @@ -300970,12 +301120,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -301383,47 +301533,47 @@ ], "projections": { "pca": [ - -0.1432625949382782, - 0.2950085997581482 + -0.1422465741634369, + 0.2950855791568756 ], "tsne": [ - -7.615551948547363, - -6.404182434082031 + -7.594738960266113, + 3.52093505859375 ], "umap": [ - 4.383120536804199, - 7.340210914611816 + 4.582851409912109, + 6.691171646118164 ], "pca3d": [ - -0.14326322078704834, - 0.2950402796268463, - 0.013255644589662552 + -0.14224731922149658, + 0.29511746764183044, + 0.0129046980291605 ], "tsne3d": [ - -8.793343544006348, - -3.0772383213043213, - 13.720014572143555 + -5.800680637359619, + 0.7387464046478271, + -6.4409284591674805 ], "umap3d": [ - 3.8784291744232178, - 3.664766550064087, - 4.533161163330078 + 4.873574733734131, + 4.608376502990723, + 4.678149223327637 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -301853,31 +302003,31 @@ "doi": "10.1038/s41592-026-03057-2", "projections": { "pca": [ - 0.20259663462638855, - -0.05025150254368782 + 0.20248888432979584, + -0.05076555162668228 ], "tsne": [ - 45.932254791259766, - -9.660371780395508 + 43.615631103515625, + 11.019415855407715 ], "umap": [ - 9.838900566101074, - 8.042140007019043 + 10.133238792419434, + 7.6340718269348145 ], "pca3d": [ - 0.2025967240333557, - -0.050265710800886154, - -0.10191196948289871 + 0.20248892903327942, + -0.050780218094587326, + -0.10188606381416321 ], "tsne3d": [ - 22.648237228393555, - -2.8116230964660645, - -8.577010154724121 + 23.4300594329834, + 7.038395881652832, + 1.4219890832901 ], "umap3d": [ - 6.892409324645996, - 4.869688510894775, - 0.9399096369743347 + 6.35099458694458, + 4.494924068450928, + -0.09283889085054398 ] }, "cluster_id": 0, @@ -301888,12 +302038,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -302312,46 +302462,46 @@ "cited_by_count": 5, "projections": { "pca": [ - 0.10747066140174866, - 0.03775752708315849 + 0.10747723281383514, + 0.03772592172026634 ], "tsne": [ - 24.534589767456055, - 4.880525588989258 + 30.71593475341797, + -13.355576515197754 ], "umap": [ - 7.002591133117676, - 7.861440658569336 + 7.193748950958252, + 7.455043315887451 ], "pca3d": [ - 0.10747060924768448, - 0.03774746134877205, - -0.14674076437950134 + 0.10747718065977097, + 0.037715863436460495, + -0.14677934348583221 ], "tsne3d": [ - 9.650755882263184, - -14.485930442810059, - -10.47539234161377 + 10.155131340026855, + 17.518861770629883, + -0.12090729922056198 ], "umap3d": [ - 6.534228801727295, - 3.4026525020599365, - 2.929086208343506 + 7.002135276794434, + 3.71588397026062, + 2.2916908264160156 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 26, + "id": 33, "label": "Genetic Disease" } ], @@ -302760,46 +302910,46 @@ ], "projections": { "pca": [ - 0.12989462912082672, - 0.16112424433231354 + 0.13060398399829865, + 0.1604388952255249 ], "tsne": [ - -0.14333225786685944, - -7.141947269439697 + -0.12248419970273972, + 5.880643844604492 ], "umap": [ - 5.117903709411621, - 7.344869613647461 + 5.314013957977295, + 6.596837997436523 ], "pca3d": [ - 0.12989403307437897, - 0.16114172339439392, - -0.009532625786960125 + 0.1306033432483673, + 0.16045670211315155, + -0.009704284369945526 ], "tsne3d": [ - -1.1701050996780396, - -1.0857328176498413, - -20.376739501953125 + 1.0937938690185547, + 23.741350173950195, + -4.908752918243408 ], "umap3d": [ - 4.666433811187744, - 3.4564449787139893, - 4.948927402496338 + 5.728589057922363, + 4.679935455322266, + 4.934437274932861 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, + "id": 13, "label": "Genomics Papers" } ], @@ -303215,46 +303365,46 @@ "doi": "10.1038/s41592-026-03055-4", "projections": { "pca": [ - -0.02867717854678631, - 0.16069217026233673 + -0.028168706223368645, + 0.16106651723384857 ], "tsne": [ - 10.487018585205078, - -22.3344783782959 + 5.1186747550964355, + 19.07996940612793 ], "umap": [ - 6.675253868103027, - 9.15097713470459 + 6.8190155029296875, + 8.696837425231934 ], "pca3d": [ - -0.028677407652139664, - 0.16068711876869202, - 0.016631530597805977 + -0.028168991208076477, + 0.16106130182743073, + 0.016446124762296677 ], "tsne3d": [ - 1.1599860191345215, - -7.089670181274414, - 11.017966270446777 + 2.1356701850891113, + 8.545743942260742, + -8.212949752807617 ], "umap3d": [ - 4.531630992889404, - 3.9156339168548584, - 2.1872644424438477 + 4.746130466461182, + 3.9748332500457764, + 2.1970021724700928 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -303679,47 +303829,47 @@ "cited_by_count": 4, "projections": { "pca": [ - -0.07782396674156189, - 0.019820233806967735 + -0.07758457213640213, + 0.020930130034685135 ], "tsne": [ - -0.371390700340271, - -26.163280487060547 + -10.065781593322754, + 22.959230422973633 ], "umap": [ - 4.857785224914551, - 8.831833839416504 + 4.973635673522949, + 8.077201843261719 ], "pca3d": [ - -0.07782415300607681, - 0.019830677658319473, - 0.15476453304290771 + -0.07758473604917526, + 0.020940929651260376, + 0.15502546727657318 ], "tsne3d": [ - -2.0680623054504395, - -13.031428337097168, - -5.163361072540283 + -5.5320048332214355, + 17.56624984741211, + -0.37679895758628845 ], "umap3d": [ - 3.7575221061706543, - 3.5032899379730225, - 3.0261402130126953 + 4.498342514038086, + 3.7159812450408936, + 3.3925271034240723 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, - "label": "Medical AI" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -304129,31 +304279,31 @@ "doi": "10.1002/gepi.70023", "projections": { "pca": [ - 0.041221827268600464, - 0.09444703906774521 + 0.04155021160840988, + 0.09471466392278671 ], "tsne": [ - 23.931568145751953, - 13.818285942077637 + 23.390483856201172, + -9.082623481750488 ], "umap": [ - 7.228302001953125, - 7.343079090118408 + 7.4313554763793945, + 7.032818794250488 ], "pca3d": [ - 0.041221823543310165, - 0.09442226588726044, - -0.10387527197599411 + 0.041550152003765106, + 0.09468936175107956, + -0.10387866944074631 ], "tsne3d": [ - 8.604546546936035, - -7.015251159667969, - -11.029406547546387 + 8.041651725769043, + 12.713062286376953, + 3.1925032138824463 ], "umap3d": [ - 6.794989109039307, - 3.8543083667755127, - 3.212341785430908 + 7.177392482757568, + 4.339815139770508, + 2.330173969268799 ] }, "cluster_id": 4, @@ -304164,12 +304314,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -304592,46 +304742,46 @@ "cited_by_count": 17148, "projections": { "pca": [ - -0.027201363816857338, - 0.3150945007801056 + -0.026071902364492416, + 0.31393107771873474 ], "tsne": [ - -12.543323516845703, - 3.661883592605591 + -3.0758211612701416, + 8.065932273864746 ], "umap": [ - 4.6514739990234375, - 7.271902084350586 + 5.0179524421691895, + 6.516083240509033 ], "pca3d": [ - -0.027201807126402855, - 0.3150998651981354, - -0.05913318693637848 + -0.026072440668940544, + 0.3139367997646332, + -0.05963815376162529 ], "tsne3d": [ - -5.538225173950195, - 21.905014038085938, - -8.216705322265625 + -4.2817182540893555, + -7.411709308624268, + -17.833898544311523 ], "umap3d": [ - 4.367964744567871, - 3.6573097705841064, - 4.8369951248168945 + 5.357503890991211, + 4.736993312835693, + 4.7425737380981445 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, + "id": 13, "label": "Genomics Papers" } ], @@ -305048,31 +305198,31 @@ "doi": "10.1038/s41467-025-67750-0", "projections": { "pca": [ - 0.2001509964466095, - 0.03698664903640747 + 0.2002086192369461, + 0.03682221472263336 ], "tsne": [ - 43.04883575439453, - 11.16288948059082 + 30.62645721435547, + 2.8783082962036133 ], "umap": [ - 8.579562187194824, - 7.4097137451171875 + 8.909937858581543, + 7.034328937530518 ], "pca3d": [ - 0.20015113055706024, - 0.036963917315006256, - 0.005300619173794985 + 0.2002086639404297, + 0.036799728870391846, + 0.005123801063746214 ], "tsne3d": [ - 13.916930198669434, - 3.816455364227295, - 5.6213812828063965 + 19.94682502746582, + 5.205942630767822, + 8.419509887695312 ], "umap3d": [ - 7.460843086242676, - 4.28110408782959, - 2.114133834838867 + 7.290410995483398, + 4.373425006866455, + 0.8877366185188293 ] }, "cluster_id": 0, @@ -305083,12 +305233,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -305505,31 +305655,31 @@ "doi": "10.64898/2026.03.10.710786", "projections": { "pca": [ - 0.14647473394870758, - 0.009384599514305592 + 0.146588996052742, + 0.009234423749148846 ], "tsne": [ - 13.647977828979492, - 13.209845542907715 + 16.565082550048828, + -7.098757266998291 ], "umap": [ - 6.745181560516357, - 6.62601375579834 + 7.1286444664001465, + 6.242244720458984 ], "pca3d": [ - 0.14647483825683594, - 0.009369568899273872, - 0.015032033436000347 + 0.14658907055854797, + 0.009219150990247726, + 0.015008287504315376 ], "tsne3d": [ - 7.7366862297058105, - 2.6372478008270264, - -5.862135887145996 + 4.559961795806885, + 0.6857055425643921, + 6.8556365966796875 ], "umap3d": [ - 6.143308639526367, - 4.568840503692627, - 3.4598498344421387 + 6.375760078430176, + 5.119090557098389, + 2.5831761360168457 ] }, "cluster_id": 4, @@ -305540,12 +305690,12 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 11, - "label": "Genomic Language" + "id": 17, + "label": "Genomic Models" } ], "_embedding": [ @@ -305989,31 +306139,31 @@ "openalex_url": "https://openalex.org/W7134244049", "projections": { "pca": [ - 0.1754532754421234, - -0.012424650602042675 + 0.17545834183692932, + -0.012832875363528728 ], "tsne": [ - -1.7728383541107178, - 9.82927417755127 + 9.378344535827637, + -33.56023025512695 ], "umap": [ - 5.480922222137451, - 4.860879898071289 + 6.18770170211792, + 4.5491743087768555 ], "pca3d": [ - 0.17545314133167267, - -0.01241963729262352, - 0.11766430735588074 + 0.17545829713344574, + -0.01282725390046835, + 0.11748962104320526 ], "tsne3d": [ - 6.848743438720703, - 2.62699031829834, - 11.804274559020996 + 1.0266956090927124, + -20.994062423706055, + -2.6454474925994873 ], "umap3d": [ - 5.203999042510986, - 6.617558002471924, - 4.329270362854004 + 5.40847635269165, + 7.185427188873291, + 2.864041566848755 ] }, "cluster_id": 3, @@ -306024,11 +306174,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -306449,31 +306599,31 @@ ], "projections": { "pca": [ - 0.12146484851837158, - -0.16060201823711395 + 0.12116194516420364, + -0.16055209934711456 ], "tsne": [ - 8.362678527832031, - 11.088762283325195 + 10.495442390441895, + -8.632959365844727 ], "umap": [ - 6.9155778884887695, - 5.756497383117676 + 7.0714640617370605, + 5.470731735229492 ], "pca3d": [ - 0.12146510183811188, - -0.16061028838157654, - 0.012994321063160896 + 0.12116231769323349, + -0.1605599969625473, + 0.013352573849260807 ], "tsne3d": [ - 5.565885066986084, - 9.110703468322754, - -5.9459943771362305 + 6.330883026123047, + -2.409306287765503, + 1.3055516481399536 ], "umap3d": [ - 5.728695392608643, - 5.136842727661133, - 3.7276511192321777 + 6.092432022094727, + 5.618435382843018, + 2.6797401905059814 ] }, "cluster_id": 4, @@ -306484,11 +306634,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -306914,46 +307064,46 @@ "doi": "10.64898/2026.03.05.709973", "projections": { "pca": [ - 0.06253248453140259, - -0.07220868766307831 + 0.06248967722058296, + -0.0724424496293068 ], "tsne": [ - 22.484580993652344, - -23.37640953063965 + 14.396360397338867, + 24.26423454284668 ], "umap": [ - 7.544404029846191, - 9.41222095489502 + 7.603236198425293, + 9.020373344421387 ], "pca3d": [ - 0.06253251433372498, - -0.07219700515270233, - -0.09865374118089676 + 0.06248965114355087, + -0.07243067771196365, + -0.09841714054346085 ], "tsne3d": [ - 11.512353897094727, - -9.847355842590332, - 11.24217700958252 + 9.086137771606445, + 5.106334686279297, + -15.489924430847168 ], "umap3d": [ - 5.246359825134277, - 3.6855998039245605, - 1.482147216796875 + 5.1448869705200195, + 3.489675283432007, + 1.4452075958251953 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -307376,47 +307526,47 @@ "openalex_url": "https://openalex.org/W7133544650", "projections": { "pca": [ - 0.04018877446651459, - 0.24270451068878174 + 0.04090212658047676, + 0.24217620491981506 ], "tsne": [ - 29.453264236450195, - 2.576300621032715 + 35.08547592163086, + -4.503905773162842 ], "umap": [ - 7.308850288391113, - 8.00071907043457 + 7.645615100860596, + 7.6401166915893555 ], "pca3d": [ - 0.040188394486904144, - 0.24269568920135498, - -0.08129751682281494 + 0.04090166836977005, + 0.24216635525226593, + -0.0816735103726387 ], "tsne3d": [ - 12.539896011352539, - -9.348617553710938, - -5.5713114738464355 + 13.281747817993164, + 15.944061279296875, + -4.685126781463623 ], "umap3d": [ - 6.545645236968994, - 3.4888432025909424, - 2.62331485748291 + 7.070625305175781, + 3.725309133529663, + 1.981534481048584 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -307838,31 +307988,31 @@ "doi": "10.1038/s41467-026-69966-0", "projections": { "pca": [ - 0.11768385022878647, - -0.11379791796207428 + 0.11747707426548004, + -0.113246850669384 ], "tsne": [ - 0.3883728086948395, - 18.014514923095703 + 9.802591323852539, + -25.35982894897461 ], "umap": [ - 6.489030838012695, - 5.207404136657715 + 6.669013500213623, + 4.777648448944092 ], "pca3d": [ - 0.11768415570259094, - -0.1138196513056755, - 0.20262254774570465 + 0.11747734993696213, + -0.11326878517866135, + 0.2028030902147293 ], "tsne3d": [ - 4.058938980102539, - 2.3453524112701416, - 2.242222309112549 + 2.7879247665405273, + -6.39907169342041, + -6.894693851470947 ], "umap3d": [ - 6.067119598388672, - 6.110983848571777, - 4.183724880218506 + 6.350474834442139, + 6.6820454597473145, + 2.526094913482666 ] }, "cluster_id": 3, @@ -307873,12 +308023,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 14, - "label": "Genomic Models" + "id": 32, + "label": "Genome Protein" } ], "_embedding": [ @@ -308313,31 +308463,31 @@ "doi": "10.1038/s41467-026-69918-8", "projections": { "pca": [ - 0.3221624493598938, - 0.07981482148170471 + 0.32237306237220764, + 0.07889453321695328 ], "tsne": [ - 37.960689544677734, - 4.622840404510498 + 42.5977783203125, + 4.915585517883301 ], "umap": [ - 9.081822395324707, - 8.378682136535645 + 9.287501335144043, + 7.863600730895996 ], "pca3d": [ - 0.322162389755249, - 0.079791359603405, - -0.09844832122325897 + 0.3223729431629181, + 0.07887058705091476, + -0.09874577075242996 ], "tsne3d": [ - 23.871728897094727, - 6.536224842071533, - 10.120758056640625 + 18.360698699951172, + 11.958576202392578, + -4.694244384765625 ], "umap3d": [ - 7.108365058898926, - 4.12647008895874, - 1.2582542896270752 + 6.844709873199463, + 3.8507707118988037, + 0.38743674755096436 ] }, "cluster_id": 0, @@ -308348,12 +308498,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -308761,47 +308911,47 @@ "abstract": "Cerebras is the go-to platform for fast and effortless AI training. Learn more at cerebras.ai.", "projections": { "pca": [ - -0.18474093079566956, - 0.21963092684745789 + -0.1840338557958603, + 0.22048525512218475 ], "tsne": [ - -9.203927040100098, - -27.773405075073242 + -13.419950485229492, + 20.177221298217773 ], "umap": [ - 3.889483690261841, - 8.664807319641113 + 4.069247722625732, + 8.113214492797852 ], "pca3d": [ - -0.1847410798072815, - 0.21962237358093262, - 0.044805269688367844 + -0.18403390049934387, + 0.2204764485359192, + 0.044643089175224304 ], "tsne3d": [ - -7.711259841918945, - -13.248034477233887, - -0.7924884557723999 + -10.526293754577637, + 15.471631050109863, + -2.0722789764404297 ], "umap3d": [ - 3.0840959548950195, - 3.5692074298858643, - 3.3645427227020264 + 3.8852293491363525, + 4.045003414154053, + 3.913675308227539 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -309209,31 +309359,31 @@ ], "projections": { "pca": [ - 0.14395825564861298, - 0.11208368837833405 + 0.14447548985481262, + 0.11176955699920654 ], "tsne": [ - 39.072837829589844, - 2.3361403942108154 + 37.06187057495117, + 5.454887866973877 ], "umap": [ - 9.111552238464355, - 7.537555694580078 + 9.46588134765625, + 7.139909744262695 ], "pca3d": [ - 0.14395807683467865, - 0.112065389752388, - 0.07590890675783157 + 0.14447535574436188, + 0.11175131052732468, + 0.07576332986354828 ], "tsne3d": [ - 21.53236198425293, - -1.7699602842330933, - -4.529237747192383 + 20.30747413635254, + 8.277687072753906, + 0.2378920167684555 ], "umap3d": [ - 6.8850908279418945, - 4.985684394836426, - 1.5949345827102661 + 6.421611785888672, + 4.646955490112305, + 0.5487545728683472 ] }, "cluster_id": 0, @@ -309244,11 +309394,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -309697,47 +309847,47 @@ "cited_by_count": 5, "projections": { "pca": [ - 0.2830391228199005, - 0.026916423812508583 + 0.28300538659095764, + 0.026531878858804703 ], "tsne": [ - 29.10579490661621, - 9.967240333557129 + 32.74209976196289, + -5.7129435539245605 ], "umap": [ - 7.609347820281982, - 7.831903457641602 + 7.829357624053955, + 7.475765705108643 ], "pca3d": [ - 0.28303906321525574, - 0.026902474462985992, - -0.16514097154140472 + 0.2830052673816681, + 0.026517672464251518, + -0.16528424620628357 ], "tsne3d": [ - 15.133909225463867, - -8.54039478302002, - -10.672024726867676 + 15.257768630981445, + 14.129809379577637, + 2.9514923095703125 ], "umap3d": [ - 7.048855781555176, - 3.6282942295074463, - 2.7053699493408203 + 7.331202507019043, + 3.8472506999969482, + 1.8479492664337158 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -310186,46 +310336,46 @@ "openalex_url": "https://openalex.org/W4410386467", "projections": { "pca": [ - 0.019362173974514008, - 0.1984189748764038 + 0.019897600635886192, + 0.19910262525081635 ], "tsne": [ - -4.054551601409912, - -30.24098014831543 + -6.3987579345703125, + 25.285768508911133 ], "umap": [ - 4.772522449493408, - 8.971534729003906 + 5.060234546661377, + 8.376442909240723 ], "pca3d": [ - 0.019361943006515503, - 0.19839824736118317, - 0.022738797590136528 + 0.0198973398655653, + 0.1990811973810196, + 0.02255469560623169 ], "tsne3d": [ - -1.4781125783920288, - -13.099754333496094, - 0.5358486175537109 + -5.35534143447876, + 18.76185417175293, + -4.6867146492004395 ], "umap3d": [ - 3.8369805812835693, - 3.1909570693969727, - 3.0947327613830566 + 4.545997142791748, + 3.6602561473846436, + 3.5048320293426514 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, + "id": 26, "label": "Medical AI" } ], @@ -310644,47 +310794,47 @@ ], "projections": { "pca": [ - 0.21183963119983673, - -0.10874620825052261 + 0.21152760088443756, + -0.10916949808597565 ], "tsne": [ - 28.42287826538086, - -16.630346298217773 + 21.946062088012695, + 21.000761032104492 ], "umap": [ - 8.501717567443848, - 9.205816268920898 + 8.708749771118164, + 8.810589790344238 ], "pca3d": [ - 0.21183951199054718, - -0.10873134434223175, - -0.05446162819862366 + 0.2115275114774704, + -0.10915427654981613, + -0.054422326385974884 ], "tsne3d": [ - 15.06775951385498, - 10.672208786010742, - 8.571503639221191 + 9.797128677368164, + -4.33947229385376, + -16.729768753051758 ], "umap3d": [ - 5.580930709838867, - 4.66979455947876, - 1.1558482646942139 + 5.260488986968994, + 4.317845821380615, + 0.8399599194526672 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -311112,31 +311262,31 @@ "openalex_url": "https://openalex.org/W7125408118", "projections": { "pca": [ - 0.04946016147732735, - 0.22978489100933075 + 0.05002719163894653, + 0.23031941056251526 ], "tsne": [ - -3.648616313934326, - 6.251267910003662 + -0.07096046209335327, + -3.864349842071533 ], "umap": [ - 5.662647724151611, - 6.490065097808838 + 5.823686599731445, + 5.969077110290527 ], "pca3d": [ - 0.04945956915616989, - 0.22979281842708588, - 0.13944405317306519 + 0.05002668499946594, + 0.23032760620117188, + 0.1390267163515091 ], "tsne3d": [ - -2.7876226902008057, - 11.873905181884766, - -8.294461250305176 + -3.946821451187134, + 3.674609899520874, + 7.41124153137207 ], "umap3d": [ - 4.7511138916015625, - 4.552130222320557, - 3.8453404903411865 + 5.257163047790527, + 5.086953639984131, + 3.4628050327301025 ] }, "cluster_id": 4, @@ -311147,12 +311297,12 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 11, - "label": "Genomic Language" + "id": 13, + "label": "Genomics Papers" } ], "_embedding": [ @@ -311569,47 +311719,47 @@ "doi": "10.1038/s44320-025-00131-3", "projections": { "pca": [ - 0.13551990687847137, - 0.09776095300912857 + 0.13564319908618927, + 0.09803762286901474 ], "tsne": [ - 30.959781646728516, - -18.07564353942871 + 23.639190673828125, + 23.394498825073242 ], "umap": [ - 8.523140907287598, - 9.440692901611328 + 8.657339096069336, + 9.012104034423828 ], "pca3d": [ - 0.13551945984363556, - 0.09778023511171341, - -0.038120970129966736 + 0.13564269244670868, + 0.09805664420127869, + -0.03839327394962311 ], "tsne3d": [ - 16.791494369506836, - 8.548463821411133, - 5.404977798461914 + 11.123024940490723, + -7.653012275695801, + -15.826859474182129 ], "umap3d": [ - 5.454634189605713, - 4.511181354522705, - 1.0549746751785278 + 5.0057501792907715, + 4.185776710510254, + 0.8653867840766907 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -312021,46 +312171,46 @@ ], "projections": { "pca": [ - 0.07537530362606049, - -0.0005883316043764353 + 0.07537069171667099, + -0.00033972118399105966 ], "tsne": [ - 26.527198791503906, - 3.114351749420166 + 29.62627410888672, + -11.185578346252441 ], "umap": [ - 6.965216636657715, - 7.772650718688965 + 7.130141258239746, + 7.334402561187744 ], "pca3d": [ - 0.07537539303302765, - -0.0006064481567591429, - -0.12302570790052414 + 0.07537074387073517, + -0.00035838212352246046, + -0.12289879471063614 ], "tsne3d": [ - 6.919970512390137, - -10.919676780700684, - -7.331898212432861 + 7.206939697265625, + 14.51204776763916, + -2.959573745727539 ], "umap3d": [ - 6.316141128540039, - 3.550990104675293, - 2.923577308654785 + 6.735584259033203, + 4.009047985076904, + 2.4573867321014404 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 26, + "id": 33, "label": "Genetic Disease" } ], @@ -312497,47 +312647,47 @@ "doi": "10.64898/2026.02.18.706454", "projections": { "pca": [ - 0.09983742237091064, - -0.06822536140680313 + 0.09964202344417572, + -0.0677885040640831 ], "tsne": [ - 28.162569046020508, - -18.534095764160156 + 20.797494888305664, + 22.71392822265625 ], "umap": [ - 8.426218032836914, - 9.438732147216797 + 8.552632331848145, + 9.020458221435547 ], "pca3d": [ - 0.09983733296394348, - -0.0682014524936676, - 0.037201616913080215 + 0.09964190423488617, + -0.06776431202888489, + 0.03730370104312897 ], "tsne3d": [ - 13.513216018676758, - 9.633116722106934, - 5.892769813537598 + 7.787546634674072, + -5.458385467529297, + -15.47079086303711 ], "umap3d": [ - 5.384564399719238, - 4.555629730224609, - 1.1818865537643433 + 5.053213596343994, + 4.221104621887207, + 0.9475369453430176 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -312957,31 +313107,31 @@ "doi": "10.1038/s41467-026-68422-3", "projections": { "pca": [ - 0.28693729639053345, - 0.08445572853088379 + 0.287159264087677, + 0.08359067142009735 ], "tsne": [ - 47.56349182128906, - -2.8731448650360107 + 50.3994255065918, + 4.606602191925049 ], "umap": [ - 9.591400146484375, - 7.976729869842529 + 9.759140014648438, + 7.524692058563232 ], "pca3d": [ - 0.2869369685649872, - 0.08445096760988235, - -0.11852862685918808 + 0.2871590554714203, + 0.08358555287122726, + -0.11883369833230972 ], "tsne3d": [ - 26.890317916870117, - -6.484793663024902, - -1.79142427444458 + 30.612974166870117, + 11.5393705368042, + -4.069674491882324 ], "umap3d": [ - 7.383154392242432, - 4.538916110992432, - 1.09512197971344 + 6.89677095413208, + 4.28515625, + -0.01746601238846779 ] }, "cluster_id": 0, @@ -312992,12 +313142,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -313430,47 +313580,47 @@ "cited_by_count": 3, "projections": { "pca": [ - 0.22459536790847778, - 0.09447106719017029 + 0.22475717961788177, + 0.09432555735111237 ], "tsne": [ - 29.397335052490234, - -0.8480230569839478 + 31.242752075195312, + 0.3951212763786316 ], "umap": [ - 8.235986709594727, - 7.807592868804932 + 8.388774871826172, + 7.404171943664551 ], "pca3d": [ - 0.2245953232049942, - 0.09445001929998398, - -0.11707407236099243 + 0.22475706040859222, + 0.0943044051527977, + -0.11729831248521805 ], "tsne3d": [ - 12.238615036010742, - -9.407011985778809, - -1.4953579902648926 + 10.833221435546875, + 13.021353721618652, + -8.896998405456543 ], "umap3d": [ - 7.119980335235596, - 3.957035779953003, - 2.283945083618164 + 7.260948657989502, + 4.018054485321045, + 1.2514997720718384 ] }, - "cluster_id": 0, - "cluster_label": "Chromatin Regulation", + "cluster_id": 6, + "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 0, - "label": "Chromatin Regulation" + "id": 6, + "label": "Single-Cell Data" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -313887,31 +314037,31 @@ "doi": "10.64898/2026.02.05.703637", "projections": { "pca": [ - 0.18334142863750458, - -0.125722736120224 + 0.18309682607650757, + -0.12571927905082703 ], "tsne": [ - 10.52834415435791, - 11.479455947875977 + 15.300492286682129, + -5.257920265197754 ], "umap": [ - 7.432250499725342, - 6.105457782745361 + 7.688575267791748, + 5.857283592224121 ], "pca3d": [ - 0.18334171175956726, - -0.12572993338108063, - 0.006410528905689716 + 0.18309713900089264, + -0.12572653591632843, + 0.006646620109677315 ], "tsne3d": [ - 4.982895851135254, - 11.124101638793945, - -8.338725090026855 + 9.551279067993164, + -1.104362964630127, + 0.8715285062789917 ], "umap3d": [ - 6.205638408660889, - 5.180635452270508, - 3.0251047611236572 + 6.257994651794434, + 5.429571628570557, + 2.1442530155181885 ] }, "cluster_id": 4, @@ -313922,11 +314072,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -314348,31 +314498,31 @@ "doi": "10.1101/2025.07.16.665183", "projections": { "pca": [ - 0.21619316935539246, - 0.011001763865351677 + 0.2161608189344406, + 0.010761911980807781 ], "tsne": [ - 27.030378341674805, - 21.873926162719727 + 32.79079818725586, + -25.12187385559082 ], "umap": [ - 8.5886869430542, - 5.686407566070557 + 8.740954399108887, + 5.317342758178711 ], "pca3d": [ - 0.2161930799484253, - 0.010987174697220325, - -0.10735681653022766 + 0.21616080403327942, + 0.01074749045073986, + -0.10739891231060028 ], "tsne3d": [ - 14.047487258911133, - 24.922609329223633, - 5.270720958709717 + 19.456998825073242, + -10.181333541870117, + 11.10813045501709 ], "umap3d": [ - 7.578013896942139, - 4.874830722808838, - 3.1738688945770264 + 7.69150972366333, + 5.333521366119385, + 1.5937254428863525 ] }, "cluster_id": 4, @@ -314383,11 +314533,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -314808,31 +314958,31 @@ ], "projections": { "pca": [ - 0.13974860310554504, - -0.0934135839343071 + 0.1397453099489212, + -0.0933268591761589 ], "tsne": [ - 9.09636116027832, - 12.236128807067871 + 11.437005043029785, + -9.567821502685547 ], "umap": [ - 6.889942169189453, - 5.78389835357666 + 7.110243320465088, + 5.513424873352051 ], "pca3d": [ - 0.1397487372159958, - -0.09342721849679947, - 0.08282604813575745 + 0.1397455781698227, + -0.09334034472703934, + 0.08311042934656143 ], "tsne3d": [ - 4.369800090789795, - 8.605515480041504, - -7.41386604309082 + 6.530961513519287, + -3.0930919647216797, + 3.1875901222229004 ], "umap3d": [ - 5.862350940704346, - 5.227016925811768, - 3.6522622108459473 + 6.16131591796875, + 5.672749996185303, + 2.5855116844177246 ] }, "cluster_id": 4, @@ -314843,11 +314993,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -315274,31 +315424,31 @@ "cited_by_count": 2, "projections": { "pca": [ - 0.025296125560998917, - -0.12964457273483276 + 0.02525295317173004, + -0.12929169833660126 ], "tsne": [ - 12.912521362304688, - 31.381458282470703 + 16.54168128967285, + -28.500228881835938 ], "umap": [ - 7.64718770980835, - 4.765232086181641 + 7.991801738739014, + 4.310925483703613 ], "pca3d": [ - 0.02529618889093399, - -0.12963062524795532, - 0.18036314845085144 + 0.02525310032069683, + -0.12927721440792084, + 0.18071098625659943 ], "tsne3d": [ - -2.0010902881622314, - 23.69180679321289, - 1.255156397819519 + 10.888433456420898, + -18.125699996948242, + 1.0403376817703247 ], "umap3d": [ - 6.772005558013916, - 6.167983055114746, - 3.892704725265503 + 6.9879021644592285, + 6.663426399230957, + 2.2504947185516357 ] }, "cluster_id": 4, @@ -315309,11 +315459,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -315738,31 +315888,31 @@ "openalex_url": "https://openalex.org/W7126157416", "projections": { "pca": [ - 0.11467042565345764, - -0.14533062279224396 + 0.11431077867746353, + -0.14548245072364807 ], "tsne": [ - 21.182506561279297, - -2.3029494285583496 + 23.57884407043457, + 5.674686431884766 ], "umap": [ - 8.258744239807129, - 6.660059928894043 + 8.446950912475586, + 6.4221882820129395 ], "pca3d": [ - 0.114670529961586, - -0.14532208442687988, - -0.05985012277960777 + 0.11431088298559189, + -0.14547371864318848, + -0.059598810970783234 ], "tsne3d": [ - 17.3314151763916, - 4.9313578605651855, - -10.340826034545898 + 13.250232696533203, + 2.943138599395752, + 1.2122516632080078 ], "umap3d": [ - 6.600939750671387, - 4.985971927642822, - 2.3259410858154297 + 6.4228973388671875, + 5.0685811042785645, + 1.326257586479187 ] }, "cluster_id": 4, @@ -315773,12 +315923,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -316201,31 +316351,31 @@ "doi": "10.64898/2026.02.02.703275", "projections": { "pca": [ - 0.03434990718960762, - -0.08157926797866821 + 0.034186847507953644, + -0.08088669180870056 ], "tsne": [ - 6.117010593414307, - 7.515689373016357 + 8.931784629821777, + -5.120713710784912 ], "umap": [ - 6.41911506652832, - 6.4687724113464355 + 6.715640544891357, + 6.071986675262451 ], "pca3d": [ - 0.03435000777244568, - -0.08158235996961594, - 0.09511636942625046 + 0.034186966717243195, + -0.08088960498571396, + 0.09534463286399841 ], "tsne3d": [ - 3.8845317363739014, - 1.7150013446807861, - -12.264913558959961 + 2.365053176879883, + 3.765253782272339, + 2.5581865310668945 ], "umap3d": [ - 5.505140781402588, - 4.593658447265625, - 3.6506595611572266 + 6.01537561416626, + 5.084468841552734, + 2.9449422359466553 ] }, "cluster_id": 4, @@ -316236,12 +316386,12 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 11, - "label": "Genomic Language" + "id": 17, + "label": "Genomic Models" } ], "_embedding": [ @@ -316679,47 +316829,47 @@ "openalex_url": "https://openalex.org/W7128052975", "projections": { "pca": [ - 0.011015312746167183, - 0.2248552441596985 + 0.011691181920468807, + 0.2253708690404892 ], "tsne": [ - -2.4201366901397705, - -27.26960563659668 + -5.347165584564209, + 23.285619735717773 ], "umap": [ - 4.703117370605469, - 8.841212272644043 + 4.9863362312316895, + 8.129600524902344 ], "pca3d": [ - 0.011014917865395546, - 0.22485923767089844, - 0.03881184011697769 + 0.011690766550600529, + 0.22537484765052795, + 0.03857384994626045 ], "tsne3d": [ - 1.473071813583374, - -11.694963455200195, - -3.145742893218994 + -4.161280155181885, + 20.758981704711914, + -1.9468169212341309 ], "umap3d": [ - 3.7738754749298096, - 3.3665802478790283, - 3.0392415523529053 + 4.355285167694092, + 3.7323496341705322, + 3.4679553508758545 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, - "label": "Medical AI" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -317139,31 +317289,31 @@ "doi": "10.64898/2026.02.01.702969", "projections": { "pca": [ - 0.24166202545166016, - -0.04279747232794762 + 0.24156509339809418, + -0.04287903755903244 ], "tsne": [ - 21.954557418823242, - 1.895038366317749 + 24.0180606842041, + 0.8555445075035095 ], "umap": [ - 7.9440999031066895, - 7.159923076629639 + 8.025543212890625, + 6.735601425170898 ], "pca3d": [ - 0.2416621893644333, - -0.04281774163246155, - -0.0925917997956276 + 0.24156522750854492, + -0.042899299412965775, + -0.09248848259449005 ], "tsne3d": [ - 13.418150901794434, - -0.34974342584609985, - -8.655290603637695 + 14.65882396697998, + 5.536538124084473, + 4.924314975738525 ], "umap3d": [ - 6.604295253753662, - 4.479767799377441, - 2.569382429122925 + 6.640749454498291, + 4.595889568328857, + 1.6096159219741821 ] }, "cluster_id": 4, @@ -317174,12 +317324,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -317588,47 +317738,47 @@ "title": "MPRA training data processing \u00b7 Issue #7 \u00b7 vansteensellab/PARM \u00b7 GitHub", "projections": { "pca": [ - -0.1772986650466919, - 0.1975477933883667 + -0.1765962541103363, + 0.19793276488780975 ], "tsne": [ - -30.467866897583008, - 30.580820083618164 + -14.274555206298828, + -4.599419593811035 ], "umap": [ - 4.104886054992676, - 6.8399739265441895 + 4.426929950714111, + 6.265647888183594 ], "pca3d": [ - -0.1772991120815277, - 0.19755762815475464, - 0.004084293264895678 + -0.1765964776277542, + 0.19794262945652008, + 0.003912239335477352 ], "tsne3d": [ - -13.321249008178711, - 13.218889236450195, - 13.223357200622559 + -12.82430362701416, + -2.7401204109191895, + 11.675636291503906 ], "umap3d": [ - 3.7406208515167236, - 4.314660549163818, - 4.448657035827637 + 4.646622180938721, + 5.0430989265441895, + 4.367534160614014 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 10, - "label": "Code Development" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -318038,47 +318188,47 @@ "abstract": "Contribute to vansteensellab/PARM_preprocessing_pipeline development by creating an account on GitHub.", "projections": { "pca": [ - -0.10378658026456833, - 0.1864265501499176 + -0.1031346470117569, + 0.18666143715381622 ], "tsne": [ - -30.185544967651367, - 30.57533836364746 + -14.554593086242676, + -4.725220680236816 ], "umap": [ - 3.9743056297302246, - 6.57814359664917 + 4.342248439788818, + 6.107634544372559 ], "pca3d": [ - -0.10378672182559967, - 0.1864227056503296, - 0.054569315165281296 + -0.10313490033149719, + 0.18665766716003418, + 0.054335761815309525 ], "tsne3d": [ - -13.36088752746582, - 13.754347801208496, - 13.044008255004883 + -12.719734191894531, + -2.7933642864227295, + 12.199945449829102 ], "umap3d": [ - 3.665971517562866, - 4.647896766662598, - 4.502801895141602 + 4.554877758026123, + 5.3503875732421875, + 4.307287693023682 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 10, - "label": "Code Development" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -318515,31 +318665,31 @@ "cited_by_count": 3, "projections": { "pca": [ - 0.19541768729686737, - -0.06740391254425049 + 0.1952170878648758, + -0.06768301874399185 ], "tsne": [ - 34.08542251586914, - -1.8906978368759155 + 29.456287384033203, + 7.247354030609131 ], "umap": [ - 9.135469436645508, - 7.003794193267822 + 9.340225219726562, + 6.723812580108643 ], "pca3d": [ - 0.19541767239570618, - -0.06739768385887146, - -0.053039006888866425 + 0.19521713256835938, + -0.06767715513706207, + -0.05302337184548378 ], "tsne3d": [ - 17.213794708251953, - 2.6014819145202637, - -4.7629714012146 + 19.58741569519043, + 2.474900484085083, + -0.23545923829078674 ], "umap3d": [ - 6.911879062652588, - 5.015239238739014, - 1.5834252834320068 + 6.4731316566467285, + 4.758326053619385, + 0.5228866934776306 ] }, "cluster_id": 0, @@ -318550,11 +318700,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -318972,31 +319122,31 @@ ], "projections": { "pca": [ - 0.14892740547657013, - -0.1254546344280243 + 0.1486193984746933, + -0.12514515221118927 ], "tsne": [ - 17.794004440307617, - -0.7732205986976624 + 22.32623863220215, + 3.862809419631958 ], "umap": [ - 8.233447074890137, - 6.619404315948486 + 8.360920906066895, + 6.178223133087158 ], "pca3d": [ - 0.14892740547657013, - -0.12544956803321838, - 0.022517705336213112 + 0.14861953258514404, + -0.12514005601406097, + 0.02274446003139019 ], "tsne3d": [ - 14.637335777282715, - 3.576601505279541, - -7.072425842285156 + 16.336566925048828, + 2.4400620460510254, + 3.8113622665405273 ], "umap3d": [ - 6.47847843170166, - 5.168543338775635, - 2.4107558727264404 + 6.369525909423828, + 5.1227617263793945, + 1.3478355407714844 ] }, "cluster_id": 4, @@ -319007,12 +319157,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -319435,31 +319585,31 @@ "openalex_url": "https://openalex.org/W7126195413", "projections": { "pca": [ - 0.09115448594093323, - -0.15406586229801178 + 0.09072631597518921, + -0.15387345850467682 ], "tsne": [ - 20.53470802307129, - 0.726273238658905 + 22.0434627532959, + 0.7187289595603943 ], "umap": [ - 7.648203372955322, - 6.871965408325195 + 7.882687091827393, + 6.51401424407959 ], "pca3d": [ - 0.0911545529961586, - -0.1540469080209732, - -0.09342370182275772 + 0.0907263308763504, + -0.15385399758815765, + -0.09308498352766037 ], "tsne3d": [ - 12.76453685760498, - 1.3940069675445557, - -11.285733222961426 + 11.874984741210938, + 4.178536415100098, + 4.539615631103516 ], "umap3d": [ - 6.283708095550537, - 4.661948204040527, - 2.6503708362579346 + 6.387185573577881, + 4.767930507659912, + 1.8352396488189697 ] }, "cluster_id": 4, @@ -319470,12 +319620,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -319894,31 +320044,31 @@ "doi": "10.1093/bioinformatics/btag048", "projections": { "pca": [ - 0.1410558819770813, - -0.052529916167259216 + 0.14082849025726318, + -0.052863672375679016 ], "tsne": [ - 62.53173065185547, - 1.8670481443405151 + 49.439964294433594, + 24.84412384033203 ], "umap": [ - 9.883228302001953, - 6.4616217613220215 + 10.003396034240723, + 5.9577741622924805 ], "pca3d": [ - 0.14105568826198578, - -0.052504319697618484, - -0.09837058931589127 + 0.14082841575145721, + -0.052837714552879333, + -0.09843181818723679 ], "tsne3d": [ - 27.643802642822266, - 8.878824234008789, - -7.7237677574157715 + 25.04730796813965, + -6.0615034103393555, + -1.065531611442566 ], "umap3d": [ - 7.730295658111572, - 5.653890132904053, - 1.5648669004440308 + 7.150964736938477, + 5.482115745544434, + 0.015296204015612602 ] }, "cluster_id": 0, @@ -319929,12 +320079,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -320362,31 +320512,31 @@ "doi": "10.64898/2026.01.29.702547", "projections": { "pca": [ - 0.30120256543159485, - 0.015516324900090694 + 0.30125364661216736, + 0.015001569874584675 ], "tsne": [ - 37.29658126831055, - 3.5090346336364746 + 41.492496490478516, + 4.130058765411377 ], "umap": [ - 8.806890487670898, - 7.663858890533447 + 8.906787872314453, + 7.322179317474365 ], "pca3d": [ - 0.30120253562927246, - 0.015502577647566795, - -0.011818921193480492 + 0.30125367641448975, + 0.014987997710704803, + -0.011984997428953648 ], "tsne3d": [ - 22.341211318969727, - 4.109944820404053, - 9.735591888427734 + 17.488447189331055, + 9.242057800292969, + -2.5053598880767822 ], "umap3d": [ - 7.3294854164123535, - 4.413780689239502, - 1.9736193418502808 + 7.088068008422852, + 4.409931182861328, + 0.7731532454490662 ] }, "cluster_id": 0, @@ -320397,12 +320547,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -320823,46 +320973,46 @@ "doi": "10.1038/s43588-025-00945-z", "projections": { "pca": [ - 0.06735167652368546, - -0.05156134068965912 + 0.06733943521976471, + -0.05183778330683708 ], "tsne": [ - 19.47911834716797, - -25.000946044921875 + 11.983659744262695, + 23.247556686401367 ], "umap": [ - 7.445018291473389, - 9.279156684875488 + 7.469854831695557, + 8.95615005493164 ], "pca3d": [ - 0.06735165417194366, - -0.051549311727285385, - -0.07988334447145462 + 0.06733935326337814, + -0.05182540416717529, + -0.07972364872694016 ], "tsne3d": [ - 9.861739158630371, - -9.493218421936035, - 12.97006607055664 + 10.083617210388184, + 7.131789207458496, + -14.158610343933105 ], "umap3d": [ - 5.08466100692749, - 3.7043638229370117, - 1.670038104057312 + 5.117283344268799, + 3.55433988571167, + 1.6410853862762451 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -321289,31 +321439,31 @@ "openalex_url": "https://openalex.org/W4385568450", "projections": { "pca": [ - 0.27922844886779785, - 0.05324646085500717 + 0.27922359108924866, + 0.05284696817398071 ], "tsne": [ - 55.17498016357422, - 6.9521355628967285 + 54.00982666015625, + 17.861698150634766 ], "umap": [ - 10.449427604675293, - 6.925262451171875 + 10.601953506469727, + 6.4454450607299805 ], "pca3d": [ - 0.27922824025154114, - 0.05325392261147499, - -0.0950189158320427 + 0.2792232036590576, + 0.05285394564270973, + -0.09535988420248032 ], "tsne3d": [ - 29.399690628051758, - 5.582669734954834, - 0.6284548044204712 + 30.745737075805664, + -0.9509967565536499, + -3.4987540245056152 ], "umap3d": [ - 8.210407257080078, - 5.305001258850098, - 1.0273176431655884 + 7.471911907196045, + 5.0536956787109375, + -0.5300672650337219 ] }, "cluster_id": 0, @@ -321324,12 +321474,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -321782,46 +321932,46 @@ "cited_by_count": 6, "projections": { "pca": [ - 0.2031734138727188, - 0.10703694820404053 + 0.20344051718711853, + 0.10690266638994217 ], "tsne": [ - 25.825227737426758, - 6.956718444824219 + 35.44178771972656, + -13.414689064025879 ], "umap": [ - 6.898738861083984, - 8.039149284362793 + 7.151446342468262, + 7.718166351318359 ], "pca3d": [ - 0.20317299664020538, - 0.1070321798324585, - -0.10397683084011078 + 0.20344014465808868, + 0.10689748078584671, + -0.10413938760757446 ], "tsne3d": [ - 11.238164901733398, - -13.341690063476562, - -14.421031951904297 + 11.385747909545898, + 20.890735626220703, + 1.145624041557312 ], "umap3d": [ - 6.533921718597412, - 3.133298397064209, - 2.7492666244506836 + 7.002406597137451, + 3.4431817531585693, + 2.252167224884033 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 26, + "id": 33, "label": "Genetic Disease" } ], @@ -322249,31 +322399,31 @@ "doi": "10.1038/s41467-026-68742-4", "projections": { "pca": [ - 0.19847707450389862, - -0.07045980542898178 + 0.19828712940216064, + -0.07121361047029495 ], "tsne": [ - 39.487510681152344, - -12.71268081665039 + 34.62874984741211, + 18.58059310913086 ], "umap": [ - 9.156818389892578, - 8.644679069519043 + 9.420053482055664, + 8.214750289916992 ], "pca3d": [ - 0.19847698509693146, - -0.07044384628534317, - -0.148228257894516 + 0.19828712940216064, + -0.07119705528020859, + -0.14820559322834015 ], "tsne3d": [ - 18.838056564331055, - -5.269797325134277, - 5.669440269470215 + 17.913232803344727, + 4.732587814331055, + -12.048406600952148 ], "umap3d": [ - 6.7543230056762695, - 3.976865291595459, - 1.1239460706710815 + 6.383402347564697, + 3.6712429523468018, + 0.47206634283065796 ] }, "cluster_id": 0, @@ -322284,12 +322434,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -322708,47 +322858,47 @@ "doi": "10.64898/2026.01.16.699948", "projections": { "pca": [ - 0.23889681696891785, - 0.052670516073703766 + 0.23901259899139404, + 0.05220606178045273 ], "tsne": [ - 30.9267520904541, - -14.039429664611816 + 24.96859359741211, + 19.697481155395508 ], "umap": [ - 8.740355491638184, - 8.973395347595215 + 8.821322441101074, + 8.581045150756836 ], "pca3d": [ - 0.23889662325382233, - 0.05265576019883156, - -0.07985784113407135 + 0.2390124350786209, + 0.05219133570790291, + -0.08004292100667953 ], "tsne3d": [ - 19.87904930114746, - 8.694493293762207, - 8.550610542297363 + 13.980818748474121, + -1.5626386404037476, + -14.499629974365234 ], "umap3d": [ - 6.0206298828125, - 4.273301124572754, - 1.0836372375488281 + 5.627955913543701, + 3.9779107570648193, + 0.6880760788917542 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -323156,46 +323306,46 @@ ], "projections": { "pca": [ - 0.14552004635334015, - 0.10781341791152954 + 0.14611828327178955, + 0.10739720612764359 ], "tsne": [ - 0.5913987755775452, - -6.503361225128174 + 0.7266154885292053, + 5.358913421630859 ], "umap": [ - 5.2220540046691895, - 7.318737506866455 + 5.3607707023620605, + 6.558716297149658 ], "pca3d": [ - 0.14551955461502075, - 0.10781805962324142, - -0.005329273175448179 + 0.14611782133579254, + 0.10740223526954651, + -0.0053575714118778706 ], "tsne3d": [ - -0.6506737470626831, - -1.1904253959655762, - -18.78141212463379 + 2.4592394828796387, + 23.309131622314453, + -3.4041192531585693 ], "umap3d": [ - 4.7664103507995605, - 3.457402229309082, - 4.935146808624268 + 5.786491870880127, + 4.680479049682617, + 4.952656269073486 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, + "id": 13, "label": "Genomics Papers" } ], @@ -323614,31 +323764,31 @@ "doi": "10.64898/2026.01.17.700094", "projections": { "pca": [ - 0.15741075575351715, - 0.04840648174285889 + 0.15770412981510162, + 0.04846853390336037 ], "tsne": [ - 3.115905284881592, - 2.8306612968444824 + 5.790571689605713, + -2.125873565673828 ], "umap": [ - 6.54146146774292, - 5.8435845375061035 + 6.774996280670166, + 5.510852336883545 ], "pca3d": [ - 0.1574108749628067, - 0.04838300868868828, - 0.07348154485225677 + 0.1577042043209076, + 0.04844516143202782, + 0.07347731292247772 ], "tsne3d": [ - 5.543447971343994, - 1.5815157890319824, - 1.344118595123291 + 4.102268218994141, + -5.380533695220947, + -6.549472332000732 ], "umap3d": [ - 5.984757423400879, - 5.015815734863281, - 3.9531805515289307 + 6.397100448608398, + 5.7093963623046875, + 2.7599613666534424 ] }, "cluster_id": 4, @@ -323649,11 +323799,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -324077,31 +324227,31 @@ "doi": "10.1101/2025.08.17.670761", "projections": { "pca": [ - 0.1969916671514511, - -0.06578294932842255 + 0.19677773118019104, + -0.06616039574146271 ], "tsne": [ - 38.81096267700195, - -2.1880555152893066 + 32.80662536621094, + 10.180817604064941 ], "umap": [ - 9.211532592773438, - 7.176117420196533 + 9.312017440795898, + 6.78226375579834 ], "pca3d": [ - 0.1969916820526123, - -0.06578855961561203, - -0.034226901829242706 + 0.19677776098251343, + -0.06616617739200592, + -0.03427167981863022 ], "tsne3d": [ - 20.028139114379883, - 3.2715940475463867, - -1.4707099199295044 + 22.494848251342773, + 0.3281611204147339, + -4.123824596405029 ], "umap3d": [ - 7.286815166473389, - 4.754824161529541, - 1.606619954109192 + 6.980712413787842, + 4.7090067863464355, + 0.4716707170009613 ] }, "cluster_id": 0, @@ -324112,11 +324262,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -324523,31 +324673,31 @@ ], "projections": { "pca": [ - -0.16788522899150848, - 0.10022997856140137 + -0.16740462183952332, + 0.10088805854320526 ], "tsne": [ - -28.987539291381836, - 6.289779186248779 + -31.113325119018555, + -10.452259063720703 ], "umap": [ - 1.4991388320922852, - 6.1229963302612305 + 1.9939168691635132, + 5.649821758270264 ], "pca3d": [ - -0.16788505017757416, - 0.10021360963582993, - 0.006505581550300121 + -0.16740457713603973, + 0.10087137669324875, + 0.006559324450790882 ], "tsne3d": [ - -25.421709060668945, - 9.644380569458008, - 11.951629638671875 + -23.775617599487305, + -8.931286811828613, + -15.96384048461914 ], "umap3d": [ - 1.108740210533142, - 6.121970176696777, - 4.5931572914123535 + 1.8370858430862427, + 6.5352702140808105, + 4.949100017547607 ] }, "cluster_id": 2, @@ -324558,12 +324708,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -324993,31 +325143,31 @@ "abstract": "The molecular details governing transcription factor (TF) binding and the formation of accessible chromatin are not yet quantitatively understood - including how sequence context modulates affinity, how TFs search DNA, the kinetics of TF occupancy, and how motif grammars coordinate binding. To resolve these questions for a human TF, erythroid Kruppel-like factor (eKLF/KLF1), we quantitatively compare, in high throughput, in vitro TF binding rates and affinities with in vivo single molecule TF and nucleosome occupancies across engineered DNA sequences. We find that 40-fold flanking sequence effects on affinity are consistent with distal flanks tuning TF search parameters and captured by a linear energy model. Motif recognition probability, rather than time in the bound state, drives affinity changes, and in vitro and in nuclei measurements exhibit consistent, minutes-long TF residence times. Finally, pairing in vitro biophysical parameters with thermodynamic models accurately predicts in vivo single-molecule chromatin states for unseen motif grammars.", "projections": { "pca": [ - 0.2613847255706787, - -0.013898449018597603 + 0.2613036334514618, + -0.014369402080774307 ], "tsne": [ - 56.92695999145508, - 7.266585826873779 + 53.483116149902344, + 19.600868225097656 ], "umap": [ - 10.368109703063965, - 6.748775959014893 + 10.52384090423584, + 6.316493988037109 ], "pca3d": [ - 0.26138463616371155, - -0.013889937661588192, - 0.04540715366601944 + 0.261303573846817, + -0.014360902830958366, + 0.045162804424762726 ], "tsne3d": [ - 28.076366424560547, - 7.315672874450684, - -1.1366468667984009 + 29.55455780029297, + -3.130509614944458, + -3.0529797077178955 ], "umap3d": [ - 8.156037330627441, - 5.4088134765625, - 1.1949869394302368 + 7.5204386711120605, + 5.230612754821777, + -0.4506944417953491 ] }, "cluster_id": 0, @@ -325028,12 +325178,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -325455,46 +325605,46 @@ "cited_by_count": 2, "projections": { "pca": [ - 0.06109900400042534, - -0.10232651978731155 + 0.06086912006139755, + -0.102414570748806 ], "tsne": [ - 23.532180786132812, - -26.331972122192383 + 14.77999210357666, + 26.927722930908203 ], "umap": [ - 7.556423664093018, - 9.458284378051758 + 7.722838401794434, + 9.114957809448242 ], "pca3d": [ - 0.06109907105565071, - -0.10232258588075638, - -0.11836934834718704 + 0.06086926534771919, + -0.10241027921438217, + -0.11817523837089539 ], "tsne3d": [ - 13.538501739501953, - -12.302803039550781, - 8.04812240600586 + 7.015397548675537, + 1.4988807439804077, + -18.207019805908203 ], "umap3d": [ - 5.430044174194336, - 3.6939539909362793, - 1.4223321676254272 + 5.264747142791748, + 3.3676860332489014, + 1.4091525077819824 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -325919,31 +326069,31 @@ "cited_by_count": 77, "projections": { "pca": [ - 0.02488044649362564, - -0.10950450599193573 + 0.024808892980217934, + -0.10917333513498306 ], "tsne": [ - 10.09329891204834, - 5.253803253173828 + 14.369672775268555, + -10.458081245422363 ], "umap": [ - 6.702807426452637, - 6.032917022705078 + 7.003193378448486, + 5.752317428588867 ], "pca3d": [ - 0.024880381301045418, - -0.10948667675256729, - 0.045416660606861115 + 0.024808842688798904, + -0.10915456712245941, + 0.04584256187081337 ], "tsne3d": [ - 4.801112651824951, - 2.483375310897827, - -6.750576496124268 + 6.651347637176514, + 2.8758344650268555, + -0.4815734028816223 ], "umap3d": [ - 5.716431617736816, - 5.163139820098877, - 3.513854503631592 + 5.9905548095703125, + 5.382062911987305, + 2.428452253341675 ] }, "cluster_id": 4, @@ -325954,11 +326104,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -326391,31 +326541,31 @@ "cited_by_count": 6, "projections": { "pca": [ - 0.044849976897239685, - 0.044260457158088684 + 0.04516557604074478, + 0.04443942755460739 ], "tsne": [ - 39.13413619995117, - 21.730422973632812 + 22.227214813232422, + 12.366388320922852 ], "umap": [ - 8.503175735473633, - 7.137186527252197 + 8.861783027648926, + 6.7085442543029785 ], "pca3d": [ - 0.04484982788562775, - 0.04424087703227997, - 0.06392423063516617 + 0.04516546055674553, + 0.04442001134157181, + 0.06395699828863144 ], "tsne3d": [ - 25.81171989440918, - 16.51824378967285, - 4.853748321533203 + 16.84983253479004, + -12.55214786529541, + -13.446378707885742 ], "umap3d": [ - 6.518898010253906, - 5.061662197113037, - 1.6738557815551758 + 6.119785308837891, + 4.887582778930664, + 0.7742088437080383 ] }, "cluster_id": 0, @@ -326426,12 +326576,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -326837,31 +326987,31 @@ ], "projections": { "pca": [ - -0.00393719645217061, - -0.09103679656982422 + -0.004081896971911192, + -0.09065207093954086 ], "tsne": [ - 10.779397964477539, - 4.925909519195557 + 15.422399520874023, + -10.506586074829102 ], "umap": [ - 6.831909656524658, - 6.148618698120117 + 7.089749336242676, + 5.869271278381348 ], "pca3d": [ - -0.0039372495375573635, - -0.09101925790309906, - 0.005770394578576088 + -0.004081913735717535, + -0.09063387662172318, + 0.006134374532848597 ], "tsne3d": [ - 5.655248641967773, - 1.0928477048873901, - -7.585778713226318 + 6.833972930908203, + 4.469446182250977, + 0.07963520288467407 ], "umap3d": [ - 5.763610363006592, - 5.032031536102295, - 3.4983432292938232 + 6.049802780151367, + 5.20734977722168, + 2.4000868797302246 ] }, "cluster_id": 4, @@ -326872,11 +327022,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -327302,47 +327452,47 @@ "cited_by_count": 37, "projections": { "pca": [ - 0.05668725445866585, - 0.004269142169505358 + 0.056755416095256805, + 0.004934191703796387 ], "tsne": [ - -1.3853336572647095, - -23.56753158569336 + -4.080251216888428, + 19.957712173461914 ], "umap": [ - 4.613150119781494, - 8.646330833435059 + 5.072033882141113, + 7.943169593811035 ], "pca3d": [ - 0.056687310338020325, - 0.0042571742087602615, - 0.05403357371687889 + 0.05675545707345009, + 0.004922038875520229, + 0.05418187752366066 ], "tsne3d": [ - -1.274349331855774, - -5.224789619445801, - -4.380030155181885 + -2.082972526550293, + 11.138952255249023, + 3.097177505493164 ], "umap3d": [ - 3.850292444229126, - 3.8940558433532715, - 2.8726654052734375 + 4.40010404586792, + 4.199698448181152, + 3.001608371734619 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, - "label": "Medical AI" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -327748,46 +327898,46 @@ ], "projections": { "pca": [ - 0.13075068593025208, - 0.15006186068058014 + 0.13148841261863708, + 0.14919377863407135 ], "tsne": [ - 0.7847806811332703, - -8.509265899658203 + 0.4893440306186676, + 7.240604877471924 ], "umap": [ - 5.2766008377075195, - 7.392367839813232 + 5.4798054695129395, + 6.666558265686035 ], "pca3d": [ - 0.13075001537799835, - 0.15008145570755005, - -0.055633001029491425 + 0.13148781657218933, + 0.14921385049819946, + -0.055717431008815765 ], "tsne3d": [ - -2.257520914077759, - -3.80877685546875, - -20.282272338867188 + -0.3191732168197632, + 25.109764099121094, + -3.4064102172851562 ], "umap3d": [ - 4.801314830780029, - 3.3540096282958984, - 4.998805522918701 + 5.846329212188721, + 4.664694786071777, + 4.913363456726074 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, + "id": 13, "label": "Genomics Papers" } ], @@ -328251,31 +328401,31 @@ "doi": "10.64898/2026.01.12.699009", "projections": { "pca": [ - 0.18746769428253174, - 0.010080792009830475 + 0.18750812113285065, + 0.010207593441009521 ], "tsne": [ - -5.0877885818481445, - 11.209195137023926 + 0.5163675546646118, + 13.410684585571289 ], "umap": [ - 5.53857421875, - 5.64052677154541 + 5.817620754241943, + 5.234255790710449 ], "pca3d": [ - 0.1874677985906601, - 0.0100552374497056, - 0.10540368407964706 + 0.1875082403421402, + 0.01018142607063055, + 0.10531456768512726 ], "tsne3d": [ - -2.2324094772338867, - -3.925633430480957, - -7.70611047744751 + -0.4149598181247711, + 1.204706072807312, + 12.717101097106934 ], "umap3d": [ - 5.51705265045166, - 6.237717628479004, - 3.6968212127685547 + 5.393801689147949, + 6.330655574798584, + 2.5433008670806885 ] }, "cluster_id": 3, @@ -328286,12 +328436,12 @@ "label": "Protein Design" }, { - "id": 11, - "label": "Genomic Models" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, - "label": "Protein Design" + "id": 32, + "label": "Genome Protein" } ], "_embedding": [ @@ -328713,46 +328863,46 @@ "doi": "10.64898/2026.01.09.698608", "projections": { "pca": [ - 0.11001315712928772, - -0.08416063338518143 + 0.1097584068775177, + -0.08395285159349442 ], "tsne": [ - 22.079957962036133, - -22.60671615600586 + 14.255507469177246, + 23.37306785583496 ], "umap": [ - 7.616836071014404, - 9.415421485900879 + 7.699063301086426, + 9.049779891967773 ], "pca3d": [ - 0.11001309752464294, - -0.08414387702941895, - -0.13414599001407623 + 0.10975833237171173, + -0.0839359387755394, + -0.1339324712753296 ], "tsne3d": [ - 11.471256256103516, - -9.298287391662598, - 9.690102577209473 + 8.338704109191895, + 3.537015199661255, + -15.003643989562988 ], "umap3d": [ - 5.26713228225708, - 3.7832436561584473, - 1.4700982570648193 + 5.210186958312988, + 3.537180185317993, + 1.370274305343628 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -329179,31 +329329,31 @@ "openalex_url": "https://openalex.org/W7118514966", "projections": { "pca": [ - 0.034236714243888855, - -0.012606281787157059 + 0.03418240696191788, + -0.012132122181355953 ], "tsne": [ - 20.122880935668945, - 0.36738401651382446 + 21.52008819580078, + 0.7422469854354858 ], "umap": [ - 7.697062015533447, - 6.825595855712891 + 7.917957782745361, + 6.381191730499268 ], "pca3d": [ - 0.034236472100019455, - -0.012581595219671726, - -0.010036257095634937 + 0.03418216109275818, + -0.012107133865356445, + -0.00996717344969511 ], "tsne3d": [ - 11.560185432434082, - 2.4076039791107178, - -12.030596733093262 + 11.143698692321777, + 3.347141742706299, + 5.9053263664245605 ], "umap3d": [ - 6.378165245056152, - 4.667758464813232, - 2.679004192352295 + 6.448710918426514, + 4.8196120262146, + 1.7742246389389038 ] }, "cluster_id": 4, @@ -329214,12 +329364,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -329684,31 +329834,31 @@ "cited_by_count": 25, "projections": { "pca": [ - 0.2621249854564667, - 0.09944722056388855 + 0.2622973322868347, + 0.09873432666063309 ], "tsne": [ - 39.47807693481445, - 0.9242983460426331 + 39.26266098022461, + 5.977053165435791 ], "umap": [ - 9.269136428833008, - 7.8832292556762695 + 9.607108116149902, + 7.537966251373291 ], "pca3d": [ - 0.26212456822395325, - 0.09944010525941849, - -0.1485002040863037 + 0.2622970938682556, + 0.09872698038816452, + -0.14879100024700165 ], "tsne3d": [ - 21.57451820373535, - -3.6825811862945557, - -3.5573930740356445 + 20.512929916381836, + 8.683042526245117, + -1.8978883028030396 ], "umap3d": [ - 7.214608192443848, - 4.456892013549805, - 1.31796133518219 + 6.692859172821045, + 4.221481800079346, + 0.31395450234413147 ] }, "cluster_id": 0, @@ -329719,12 +329869,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -330163,31 +330313,31 @@ "doi": "10.64898/2025.12.22.695963", "projections": { "pca": [ - 0.17215849459171295, - -0.17716005444526672 + 0.17183789610862732, + -0.17721211910247803 ], "tsne": [ - 12.813653945922852, - 10.595027923583984 + 13.638378143310547, + -3.988387107849121 ], "umap": [ - 7.252025604248047, - 6.02102518081665 + 7.528459072113037, + 5.708915710449219 ], "pca3d": [ - 0.17215895652770996, - -0.1771780550479889, - 0.046265508979558945 + 0.17183828353881836, + -0.1772300899028778, + 0.04659152030944824 ], "tsne3d": [ - 9.131688117980957, - 6.4191203117370605, - -7.977973461151123 + 9.929049491882324, + -3.3357415199279785, + 4.015621662139893 ], "umap3d": [ - 6.191165447235107, - 5.118263244628906, - 3.3629744052886963 + 6.447577953338623, + 5.4850006103515625, + 2.2612385749816895 ] }, "cluster_id": 4, @@ -330198,11 +330348,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -330620,31 +330770,31 @@ "doi": "10.64898/2025.12.28.696482", "projections": { "pca": [ - 0.2325453907251358, - -0.057773251086473465 + 0.2324034422636032, + -0.05812574923038483 ], "tsne": [ - 35.11103439331055, - -3.382882595062256 + 29.71084976196289, + 8.937057495117188 ], "umap": [ - 8.95751667022705, - 7.402120590209961 + 9.18969440460205, + 7.047049045562744 ], "pca3d": [ - 0.23254531621932983, - -0.057783834636211395, - -0.11199550330638885 + 0.23240362107753754, + -0.058136310428380966, + -0.1119285374879837 ], "tsne3d": [ - 17.1702823638916, - -1.194790244102478, - -3.2138354778289795 + 18.83076286315918, + 4.416731834411621, + -3.069960594177246 ], "umap3d": [ - 6.9301228523254395, - 4.597127437591553, - 1.6870843172073364 + 6.642192840576172, + 4.475926399230957, + 0.6464425325393677 ] }, "cluster_id": 0, @@ -330655,11 +330805,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -331075,47 +331225,47 @@ ], "projections": { "pca": [ - 0.08084403723478317, - 0.0541057363152504 + 0.08112434297800064, + 0.0540633462369442 ], "tsne": [ - 13.923994064331055, - 4.048585414886475 + 21.136425018310547, + -9.503936767578125 ], "umap": [ - 6.749452590942383, - 7.375721454620361 + 6.8541388511657715, + 7.024870872497559 ], "pca3d": [ - 0.08084402978420258, - 0.054088473320007324, - -0.014558854512870312 + 0.08112436532974243, + 0.05404574051499367, + -0.014471651054918766 ], "tsne3d": [ - 5.661884307861328, - -6.726596832275391, - -10.403355598449707 + 4.946437358856201, + 10.749299049377441, + 0.4959769546985626 ], "umap3d": [ - 6.060506820678711, - 3.871058702468872, - 3.2589399814605713 + 6.57856559753418, + 4.325745105743408, + 2.6370317935943604 ] }, - "cluster_id": 4, - "cluster_label": "RNA Sequence Models", + "cluster_id": 6, + "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 4, - "label": "RNA Sequence Models" + "id": 6, + "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -331539,31 +331689,31 @@ ], "projections": { "pca": [ - 0.10504370182752609, - -0.07012397050857544 + 0.10502991825342178, + -0.07003986090421677 ], "tsne": [ - 12.893454551696777, - 3.8224546909332275 + 20.26338005065918, + -8.676603317260742 ], "umap": [ - 6.653446674346924, - 7.132004261016846 + 6.775914669036865, + 6.717540740966797 ], "pca3d": [ - 0.10504381358623505, - -0.07013432681560516, - -0.010690396651625633 + 0.10503000020980835, + -0.07005031406879425, + -0.010368294082581997 ], "tsne3d": [ - 4.837122440338135, - -5.043506622314453, - -10.878771781921387 + 4.90756368637085, + 9.010100364685059, + 1.155667781829834 ], "umap3d": [ - 5.880951881408691, - 4.129859447479248, - 3.3020951747894287 + 6.338053226470947, + 4.490887641906738, + 2.6604862213134766 ] }, "cluster_id": 4, @@ -331574,12 +331724,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -331989,31 +332139,31 @@ "abstract": "\ud83e\uddec gget enables efficient querying of genomic reference databases - scverse/gget", "projections": { "pca": [ - 0.12623895704746246, - 0.21958522498607635 + 0.12698297202587128, + 0.2189949005842209 ], "tsne": [ - 4.623981952667236, - -0.08263847231864929 + 4.837749481201172, + 0.5333952903747559 ], "umap": [ - 6.4842023849487305, - 6.185431003570557 + 6.809811115264893, + 5.807531833648682 ], "pca3d": [ - 0.12623851001262665, - 0.21958531439304352, - 0.018565811216831207 + 0.12698249518871307, + 0.21899591386318207, + 0.01821218430995941 ], "tsne3d": [ - 7.238650321960449, - -2.0911192893981934, - 1.4332654476165771 + 4.238048076629639, + -3.2698915004730225, + -3.761310338973999 ], "umap3d": [ - 5.846405506134033, - 4.7637434005737305, - 4.001316070556641 + 6.287623882293701, + 5.341951847076416, + 2.9530625343322754 ] }, "cluster_id": 4, @@ -332024,11 +332174,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -332533,31 +332683,31 @@ "cited_by_count": 26, "projections": { "pca": [ - 0.29808881878852844, - 0.01410854421555996 + 0.29813143610954285, + 0.013056722469627857 ], "tsne": [ - 44.814632415771484, - 4.834688186645508 + 44.88645553588867, + 0.4860730469226837 ], "umap": [ - 9.012654304504395, - 7.210819244384766 + 9.302800178527832, + 6.760935306549072 ], "pca3d": [ - 0.298088937997818, - 0.014098734594881535, - -0.07018769532442093 + 0.2981313467025757, + 0.013047409243881702, + -0.07040438055992126 ], "tsne3d": [ - 21.215370178222656, - 2.151962995529175, - 6.095649719238281 + 25.160749435424805, + 0.2480003386735916, + -10.763176918029785 ], "umap3d": [ - 7.891389846801758, - 4.379594802856445, - 1.7373374700546265 + 7.6140642166137695, + 4.3425164222717285, + 0.48398178815841675 ] }, "cluster_id": 0, @@ -332568,11 +332718,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -332994,31 +333144,31 @@ "doi": "10.64898/2025.12.22.695948", "projections": { "pca": [ - 0.15601776540279388, - -0.018860846757888794 + 0.15584313869476318, + -0.019110754132270813 ], "tsne": [ - 48.060462951660156, - -9.635128021240234 + 45.309410095214844, + 10.873892784118652 ], "umap": [ - 10.03056812286377, - 8.006484031677246 + 10.18759536743164, + 7.647360801696777 ], "pca3d": [ - 0.15601786971092224, - -0.01886540651321411, - -0.10206149518489838 + 0.155843123793602, + -0.01911608688533306, + -0.10217539221048355 ], "tsne3d": [ - 25.737043380737305, - -3.4963815212249756, - -10.168351173400879 + 26.088525772094727, + 8.24304485321045, + 2.664933443069458 ], "umap3d": [ - 6.972097396850586, - 4.9482831954956055, - 0.7268157601356506 + 6.3520941734313965, + 4.530984878540039, + -0.25817614793777466 ] }, "cluster_id": 0, @@ -333029,12 +333179,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -333444,31 +333594,31 @@ "journal": "Nature Genetics", "projections": { "pca": [ - 0.07477367669343948, - 0.0174908135086298 + 0.07495901733636856, + 0.01768973097205162 ], "tsne": [ - 40.42420959472656, - 5.072301387786865 + 35.42481231689453, + 3.5183157920837402 ], "umap": [ - 9.140721321105957, - 7.026952743530273 + 9.125940322875977, + 6.7225165367126465 ], "pca3d": [ - 0.07477352023124695, - 0.01749013178050518, - 0.12785860896110535 + 0.07495895773172379, + 0.017688605934381485, + 0.12783463299274445 ], "tsne3d": [ - 23.996912002563477, - 1.5144795179367065, - -5.305797576904297 + 17.12364959716797, + -4.349483013153076, + -3.230487585067749 ], "umap3d": [ - 6.644340515136719, - 5.334423065185547, - 1.506633996963501 + 6.194833278656006, + 4.98940896987915, + 0.5159384608268738 ] }, "cluster_id": 0, @@ -333479,11 +333629,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -333910,31 +334060,31 @@ "doi": "10.64898/2025.12.18.694875", "projections": { "pca": [ - 0.14724868535995483, - -0.08380767703056335 + 0.14709077775478363, + -0.08359062671661377 ], "tsne": [ - 17.065561294555664, - -0.5524426102638245 + 22.03987693786621, + 4.4720988273620605 ], "umap": [ - 8.174501419067383, - 6.684695720672607 + 8.370332717895508, + 6.17164945602417 ], "pca3d": [ - 0.14724889397621155, - -0.08381801098585129, - 0.011701597832143307 + 0.14709092676639557, + -0.08360090851783752, + 0.011882147751748562 ], "tsne3d": [ - 15.832771301269531, - 4.5035786628723145, - -7.761935710906982 + 17.18252182006836, + 1.9504308700561523, + 2.2465100288391113 ], "umap3d": [ - 6.461021423339844, - 5.137582302093506, - 2.403449058532715 + 6.354976654052734, + 5.169546127319336, + 1.408402442932129 ] }, "cluster_id": 4, @@ -333945,12 +334095,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -334375,31 +334525,31 @@ "cited_by_count": 76, "projections": { "pca": [ - 0.29459047317504883, - -0.0068619041703641415 + 0.29452618956565857, + -0.007295440882444382 ], "tsne": [ - 55.74930191040039, - 4.162420749664307 + 52.80034637451172, + 15.523000717163086 ], "umap": [ - 10.174688339233398, - 6.92857551574707 + 10.380560874938965, + 6.47560453414917 ], "pca3d": [ - 0.29459068179130554, - -0.006868593394756317, - -0.06246139481663704 + 0.2945261597633362, + -0.007302409037947655, + -0.06262838840484619 ], "tsne3d": [ - 29.06272315979004, - 3.9989495277404785, - -4.370411396026611 + 28.585655212402344, + 0.24255603551864624, + 0.23217320442199707 ], "umap3d": [ - 7.951364517211914, - 5.334003925323486, - 1.1648809909820557 + 7.283186435699463, + 5.105710983276367, + -0.34561508893966675 ] }, "cluster_id": 0, @@ -334410,12 +334560,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -334833,47 +334983,47 @@ "doi": "10.1101/2025.10.26.684578", "projections": { "pca": [ - 0.039210837334394455, - -0.1224091425538063 + 0.0388910248875618, + -0.12227515131235123 ], "tsne": [ - 24.337514877319336, - 1.669620394706726 + 27.20661735534668, + -11.557355880737305 ], "umap": [ - 7.374337673187256, - 7.6861395835876465 + 7.538713455200195, + 7.2601847648620605 ], "pca3d": [ - 0.03921111300587654, - -0.12242097407579422, - -0.1553926318883896 + 0.03889134153723717, + -0.12228697538375854, + -0.15504983067512512 ], "tsne3d": [ - 9.48863697052002, - -13.492379188537598, - -5.548407554626465 + 10.187580108642578, + 12.030261993408203, + -0.6623918414115906 ], "umap3d": [ - 6.2717719078063965, - 3.860926389694214, - 2.609809160232544 + 6.547511100769043, + 4.103583812713623, + 2.046257734298706 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -335279,47 +335429,47 @@ ], "projections": { "pca": [ - -0.14331237971782684, - 0.36216145753860474 + -0.14210081100463867, + 0.36179354786872864 ], "tsne": [ - -11.081080436706543, - -8.529632568359375 + -10.817809104919434, + 3.4635376930236816 ], "umap": [ - 4.234227180480957, - 7.390836238861084 + 4.436038970947266, + 6.659779071807861 ], "pca3d": [ - -0.14331302046775818, - 0.36217978596687317, - 0.0024351838510483503 + -0.14210155606269836, + 0.36181211471557617, + 0.0019273729994893074 ], "tsne3d": [ - -9.179023742675781, - -5.884396076202393, - 11.424187660217285 + -12.413491249084473, + 0.7675228714942932, + -14.175437927246094 ], "umap3d": [ - 3.756693124771118, - 3.793088674545288, - 4.540747165679932 + 4.771159648895264, + 4.701623916625977, + 4.652292251586914 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -335748,46 +335898,46 @@ "cited_by_count": 1, "projections": { "pca": [ - 0.18067596852779388, - 0.028408410027623177 + 0.18075430393218994, + 0.0285594891756773 ], "tsne": [ - 26.62214469909668, - 6.494345664978027 + 33.415340423583984, + -11.718755722045898 ], "umap": [ - 7.050176620483398, - 7.907942295074463 + 7.318446636199951, + 7.50219202041626 ], "pca3d": [ - 0.18067581951618195, - 0.028399139642715454, - -0.11386772990226746 + 0.18075412511825562, + 0.02855006791651249, + -0.1138552874326706 ], "tsne3d": [ - 12.160351753234863, - -14.40377426147461, - -13.140571594238281 + 11.959543228149414, + 19.05429458618164, + 1.557245135307312 ], "umap3d": [ - 6.629526615142822, - 3.415182113647461, - 2.8891139030456543 + 7.110172748565674, + 3.676995038986206, + 2.239264488220215 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 26, + "id": 33, "label": "Genetic Disease" } ], @@ -336198,47 +336348,47 @@ "journal": "Nature Machine Intelligence", "projections": { "pca": [ - -0.04030246287584305, - -0.023668760433793068 + -0.04027975723147392, + -0.023517174646258354 ], "tsne": [ - 32.833641052246094, - -25.791919708251953 + 18.769962310791016, + 16.878070831298828 ], "umap": [ - 8.085578918457031, - 8.961196899414062 + 8.11882495880127, + 8.669333457946777 ], "pca3d": [ - -0.04030264914035797, - -0.02364017441868782, - 0.005875422619283199 + -0.04027996584773064, + -0.023488789796829224, + 0.005935232155025005 ], "tsne3d": [ - 6.618831634521484, - -3.197268009185791, - 14.169632911682129 + 6.070929527282715, + -3.708010196685791, + -19.81870460510254 ], "umap3d": [ - 5.438623905181885, - 4.33733606338501, - 1.5574617385864258 + 5.400096893310547, + 4.117308616638184, + 1.1361299753189087 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -336646,47 +336796,47 @@ "journal": "Nature Methods", "projections": { "pca": [ - -0.10735997557640076, - 0.07070215791463852 + -0.10699999332427979, + 0.071342334151268 ], "tsne": [ - 4.7014946937561035, - -17.087940216064453 + -18.57743263244629, + 27.200307846069336 ], "umap": [ - 4.400153636932373, - 8.456934928894043 + 4.5688157081604, + 7.7738823890686035 ], "pca3d": [ - -0.10736004263162613, - 0.07070198655128479, - 0.08866642415523529 + -0.10700016468763351, + 0.07134246826171875, + 0.08877693861722946 ], "tsne3d": [ - 8.36689281463623, - 16.582277297973633, - -17.73063087463379 + -2.2233564853668213, + 7.768607139587402, + 3.885892391204834 ], "umap3d": [ - 3.399963140487671, - 4.0287346839904785, - 3.218853235244751 + 4.089151382446289, + 4.3087286949157715, + 3.448713779449463 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, - "label": "Medical AI" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -337094,47 +337244,47 @@ "journal": "Nature Methods", "projections": { "pca": [ - -0.07862395793199539, - -0.07016628980636597 + -0.07851055264472961, + -0.06999082118272781 ], "tsne": [ - 9.28437328338623, - -38.3160514831543 + -1.3568007946014404, + 38.994895935058594 ], "umap": [ - 6.710273265838623, - 9.46457290649414 + 6.577261924743652, + 9.00888442993164 ], "pca3d": [ - -0.07862380892038345, - -0.070188507437706, - -0.07909157127141953 + -0.0785103589296341, + -0.07001296430826187, + -0.07866334170103073 ], "tsne3d": [ - 3.4706060886383057, - -21.840206146240234, - 4.0270915031433105 + -5.914631366729736, + 12.34007453918457, + -18.461633682250977 ], "umap3d": [ - 4.89603328704834, - 3.2922723293304443, - 2.007253646850586 + 5.056366443634033, + 3.284287691116333, + 2.216407299041748 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 6, + "label": "Single Cell Data" }, { "id": 5, - "label": "Single Cell Data" + "label": "Clinical Cancer" } ], "_embedding": [ @@ -337544,47 +337694,47 @@ "journal": "Nature Methods", "projections": { "pca": [ - 0.12771955132484436, - 0.053261611610651016 + 0.12811580300331116, + 0.05287761986255646 ], "tsne": [ - 16.296113967895508, - -17.079959869384766 + 6.967581272125244, + 13.456731796264648 ], "umap": [ - 6.100680828094482, - 7.458896160125732 + 6.252091884613037, + 6.981686592102051 ], "pca3d": [ - 0.1277192234992981, - 0.053273532539606094, - 0.0034222572576254606 + 0.12811556458473206, + 0.052890099585056305, + 0.003468827111646533 ], "tsne3d": [ - 9.918259620666504, - -6.285531520843506, - 17.385791778564453 + 5.162343978881836, + -1.2337439060211182, + -8.245596885681152 ], "umap3d": [ - 5.455854892730713, - 4.163605213165283, - 3.2505300045013428 + 5.6618266105651855, + 4.371590614318848, + 2.586869478225708 ] }, - "cluster_id": 5, - "cluster_label": "Single-Cell Data", + "cluster_id": 1, + "cluster_label": "AI in Science", "cluster_levels": [ { - "id": 5, - "label": "Single-Cell Data" + "id": 1, + "label": "AI in Science" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 11, - "label": "Genomic Language" + "id": 13, + "label": "Genomics Papers" } ], "_embedding": [ @@ -338007,47 +338157,47 @@ "doi": "10.64898/2025.12.03.25341562", "projections": { "pca": [ - 0.16762089729309082, - -0.0033016998786479235 + 0.1676238626241684, + -0.003786737099289894 ], "tsne": [ - 28.313173294067383, - 7.429251670837402 + 32.243534088134766, + -8.733004570007324 ], "umap": [ - 7.450535297393799, - 8.100462913513184 + 7.6869049072265625, + 7.679098606109619 ], "pca3d": [ - 0.16762056946754456, - -0.003306223079562187, - -0.19786806404590607 + 0.16762374341487885, + -0.003791524562984705, + -0.19782795011997223 ], "tsne3d": [ - 12.323375701904297, - -11.495777130126953, - -10.002827644348145 + 12.68154525756836, + 15.387879371643066, + 0.6979948878288269 ], "umap3d": [ - 6.753281593322754, - 3.435457706451416, - 2.600079298019409 + 7.094117641448975, + 3.6785435676574707, + 1.9488455057144165 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -338473,31 +338623,31 @@ "cited_by_count": 20, "projections": { "pca": [ - 0.16231341660022736, - -0.12158270180225372 + 0.16213542222976685, + -0.12161891907453537 ], "tsne": [ - 12.81706428527832, - 12.837547302246094 + 15.598560333251953, + -6.752102851867676 ], "umap": [ - 6.947075843811035, - 6.130987644195557 + 7.228817939758301, + 5.9177656173706055 ], "pca3d": [ - 0.16231383383274078, - -0.12159820646047592, - 0.030267680063843727 + 0.16213564574718475, + -0.12163440138101578, + 0.030511464923620224 ], "tsne3d": [ - 8.000738143920898, - 3.3941547870635986, - -6.773723602294922 + 5.569114685058594, + 0.27893659472465515, + 6.228986740112305 ], "umap3d": [ - 5.937719821929932, - 5.0178656578063965, - 3.339332103729248 + 6.223955154418945, + 5.3021745681762695, + 2.4133965969085693 ] }, "cluster_id": 4, @@ -338508,11 +338658,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -338928,31 +339078,31 @@ "doi": "10.1016/s0168-9525(97)01132-3", "projections": { "pca": [ - 0.1676025092601776, - 0.07690874487161636 + 0.16794715821743011, + 0.07653895020484924 ], "tsne": [ - 27.858333587646484, - 21.402454376220703 + 33.7945671081543, + -25.70025634765625 ], "umap": [ - 8.497345924377441, - 5.685297012329102 + 8.772988319396973, + 5.3606648445129395 ], "pca3d": [ - 0.16760224103927612, - 0.07690313458442688, - -0.1065157800912857 + 0.1679469496011734, + 0.07653366774320602, + -0.10652714967727661 ], "tsne3d": [ - 15.559026718139648, - 25.707460403442383, - 5.010443210601807 + 20.601686477661133, + -10.803126335144043, + 12.222283363342285 ], "umap3d": [ - 7.58857536315918, - 4.8254852294921875, - 3.1895313262939453 + 7.699181079864502, + 5.315101623535156, + 1.5990841388702393 ] }, "cluster_id": 4, @@ -338963,11 +339113,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -339378,47 +339528,47 @@ "doi": "10.1016/j.ajhg.2025.11.002", "projections": { "pca": [ - 0.15193599462509155, - 0.11107727885246277 + 0.1521623581647873, + 0.11102598160505295 ], "tsne": [ - 27.747814178466797, - 11.518533706665039 + 31.132335662841797, + -5.081753253936768 ], "umap": [ - 7.493360996246338, - 7.714907646179199 + 7.626315593719482, + 7.345571994781494 ], "pca3d": [ - 0.15193577110767365, - 0.1110537052154541, - -0.12180150300264359 + 0.15216219425201416, + 0.11100157350301743, + -0.12200446426868439 ], "tsne3d": [ - 12.595977783203125, - -7.646862983703613, - -8.734720230102539 + 11.175415992736816, + 15.218697547912598, + 6.127419471740723 ], "umap3d": [ - 6.924901962280273, - 3.6424238681793213, - 2.9132566452026367 + 7.2805399894714355, + 3.9792349338531494, + 2.0620627403259277 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -339842,47 +339992,47 @@ "cited_by_count": 9, "projections": { "pca": [ - 0.2185848355293274, - 0.04191441088914871 + 0.21856732666492462, + 0.04182016849517822 ], "tsne": [ - 29.30269432067871, - 6.480713367462158 + 33.946800231933594, + -6.3866963386535645 ], "umap": [ - 7.54427433013916, - 8.057517051696777 + 7.79758358001709, + 7.673569679260254 ], "pca3d": [ - 0.21858438849449158, - 0.041914600878953934, - -0.18351595103740692 + 0.21856708824634552, + 0.04181995987892151, + -0.18363505601882935 ], "tsne3d": [ - 16.49198341369629, - -10.032832145690918, - -9.969293594360352 + 16.548755645751953, + 14.899270057678223, + 1.5886915922164917 ], "umap3d": [ - 6.957043647766113, - 3.4286890029907227, - 2.6192777156829834 + 7.270198345184326, + 3.6005265712738037, + 1.8056584596633911 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -340290,46 +340440,46 @@ "abstract": "Training models with billions or trillions of parameters demands advanced parallel computing. Researchers must decide how to combine parallelism strategies…", "projections": { "pca": [ - -0.1426529884338379, - 0.04694009944796562 + -0.1424369215965271, + 0.047925915569067 ], "tsne": [ - -20.418203353881836, - 4.2745795249938965 + -26.801809310913086, + 1.9326918125152588 ], "umap": [ - 2.0090296268463135, - 7.839235782623291 + 2.284489393234253, + 7.141834259033203 ], "pca3d": [ - -0.1426527500152588, - 0.046909306198358536, - 0.10326487571001053 + -0.1424366682767868, + 0.04789479449391365, + 0.1033109724521637 ], "tsne3d": [ - -21.21051597595215, - 2.487645149230957, - 15.738758087158203 + -16.57892417907715, + 0.8277073502540588, + 8.360295295715332 ], "umap3d": [ - 1.9884860515594482, - 4.68041467666626, - 5.018754959106445 + 3.118593692779541, + 5.2812933921813965, + 5.447652339935303 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 1, + "label": "Attention Models" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -340760,31 +340910,31 @@ "openalex_url": "https://openalex.org/W4417006781", "projections": { "pca": [ - 0.21208995580673218, - 0.17912670969963074 + 0.2124965786933899, + 0.17905645072460175 ], "tsne": [ - 38.92807388305664, - 11.58338451385498 + 46.642086029052734, + -3.2609686851501465 ], "umap": [ - 8.695882797241211, - 7.717056751251221 + 8.984050750732422, + 7.228361129760742 ], "pca3d": [ - 0.21208974719047546, - 0.17909815907478333, - 0.019540416076779366 + 0.21249637007713318, + 0.17902764678001404, + 0.0191446952521801 ], "tsne3d": [ - 20.08292579650879, - 1.4878665208816528, - 12.100930213928223 + 23.02247428894043, + 9.226096153259277, + 12.01487922668457 ], "umap3d": [ - 7.5393266677856445, - 4.1820454597473145, - 1.9504358768463135 + 7.236511707305908, + 4.279164791107178, + 0.7750409245491028 ] }, "cluster_id": 0, @@ -340795,12 +340945,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -341221,31 +341371,31 @@ "doi": "10.1101/2025.11.29.691298", "projections": { "pca": [ - 0.1343025267124176, - -0.06898815184831619 + 0.13433344662189484, + -0.06926701217889786 ], "tsne": [ - 15.303796768188477, - 31.781076431274414 + 17.661653518676758, + -29.912710189819336 ], "umap": [ - 7.745205402374268, - 4.668461322784424 + 8.065831184387207, + 4.204516887664795 ], "pca3d": [ - 0.1343025416135788, - -0.0689903125166893, - 0.24730272591114044 + 0.13433359563350677, + -0.06926876306533813, + 0.24730589985847473 ], "tsne3d": [ - -0.19325776398181915, - 24.016273498535156, - 4.074249744415283 + 9.607921600341797, + -18.55632209777832, + 3.9278368949890137 ], "umap3d": [ - 6.977905750274658, - 6.208345413208008, - 3.8932745456695557 + 7.117747783660889, + 6.706273555755615, + 2.2374839782714844 ] }, "cluster_id": 4, @@ -341256,11 +341406,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -341678,31 +341828,31 @@ ], "projections": { "pca": [ - 0.07876212894916534, - -0.05480213835835457 + 0.07871866971254349, + -0.055005501955747604 ], "tsne": [ - 20.15937614440918, - 5.376021385192871 + 25.369169235229492, + -13.375475883483887 ], "umap": [ - 6.907547950744629, - 7.461366653442383 + 6.963705062866211, + 7.008486747741699 ], "pca3d": [ - 0.07876227796077728, - -0.05481249466538429, - -0.13520215451717377 + 0.07871882617473602, + -0.05501576513051987, + -0.13498012721538544 ], "tsne3d": [ - 7.0858283042907715, - -12.33687973022461, - -11.126689910888672 + 7.540048122406006, + 16.351642608642578, + 2.1935040950775146 ], "umap3d": [ - 6.220099449157715, - 3.8065786361694336, - 3.155912160873413 + 6.705353736877441, + 4.121984958648682, + 2.4151992797851562 ] }, "cluster_id": 4, @@ -341713,12 +341863,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -342140,31 +342290,31 @@ "cited_by_count": 4, "projections": { "pca": [ - 0.24051064252853394, - -0.09432598948478699 + 0.24034759402275085, + -0.09481741487979889 ], "tsne": [ - 44.544960021972656, - -7.088702201843262 + 43.54767990112305, + 12.4221830368042 ], "umap": [ - 9.456570625305176, - 7.606685161590576 + 9.70353889465332, + 7.184492111206055 ], "pca3d": [ - 0.24051068723201752, - -0.09432587772607803, - -0.08129855245351791 + 0.24034768342971802, + -0.09481759369373322, + -0.08120307326316833 ], "tsne3d": [ - 23.325489044189453, - -0.683892011642456, - -7.1307501792907715 + 22.211214065551758, + 3.920320987701416, + -1.0295157432556152 ], "umap3d": [ - 6.908867359161377, - 4.993109226226807, - 1.275344967842102 + 6.384625434875488, + 4.747927188873291, + 0.17840725183486938 ] }, "cluster_id": 0, @@ -342175,12 +342325,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 20, - "label": "Chromatin Regulation" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -342618,31 +342768,31 @@ "openalex_url": "https://openalex.org/W4416438918", "projections": { "pca": [ - 0.2453281581401825, - 0.03754226490855217 + 0.24545487761497498, + 0.03675983473658562 ], "tsne": [ - 46.47261428833008, - 5.6769938468933105 + 46.77754211425781, + 1.8337619304656982 ], "umap": [ - 9.494067192077637, - 7.225122451782227 + 9.68702507019043, + 6.817135334014893 ], "pca3d": [ - 0.24532809853553772, - 0.037534862756729126, - -0.029704371467232704 + 0.24545469880104065, + 0.03675251081585884, + -0.029991183429956436 ], "tsne3d": [ - 22.702024459838867, - 3.8683667182922363, - 4.4838151931762695 + 26.87396812438965, + -1.2075896263122559, + -10.246752738952637 ], "umap3d": [ - 7.982131481170654, - 4.448987007141113, - 1.5157814025878906 + 7.615505695343018, + 4.391609191894531, + 0.13347122073173523 ] }, "cluster_id": 0, @@ -342653,11 +342803,11 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -343077,31 +343227,31 @@ "doi": "10.1101/2025.11.20.689553", "projections": { "pca": [ - 0.1856687068939209, - -0.13668526709079742 + 0.1852312833070755, + -0.13699787855148315 ], "tsne": [ - 57.41868209838867, - 1.4510958194732666 + 46.23600387573242, + 20.028392791748047 ], "umap": [ - 9.27116870880127, - 6.575046539306641 + 9.516843795776367, + 6.179271221160889 ], "pca3d": [ - 0.1856689155101776, - -0.13668479025363922, - -0.011217036284506321 + 0.18523134291172028, + -0.1369975507259369, + -0.011173599399626255 ], "tsne3d": [ - 22.942670822143555, - 4.702449321746826, - -6.039263725280762 + 23.313552856445312, + -0.4336182177066803, + 1.6935040950775146 ], "umap3d": [ - 7.149153709411621, - 5.4999284744262695, - 1.7298614978790283 + 6.610769271850586, + 5.322354316711426, + 0.471708744764328 ] }, "cluster_id": 0, @@ -343112,12 +343262,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 2, + "label": "DNA Regulation" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -343538,31 +343688,31 @@ "doi": "10.1101/2025.11.18.689149", "projections": { "pca": [ - 0.19721660017967224, - -0.02719181589782238 + 0.19716483354568481, + -0.02792455442249775 ], "tsne": [ - 37.294677734375, - -3.23803973197937 + 31.213878631591797, + 10.945387840270996 ], "umap": [ - 8.944113731384277, - 7.209427356719971 + 9.134800910949707, + 6.898898124694824 ], "pca3d": [ - 0.19721649587154388, - -0.027189919725060463, - -0.12354785203933716 + 0.19716481864452362, + -0.02792244218289852, + -0.12358104437589645 ], "tsne3d": [ - 19.52565574645996, - 0.16497567296028137, - -2.027012348175049 + 20.600046157836914, + 2.1673083305358887, + -4.714909553527832 ], "umap3d": [ - 7.142629623413086, - 4.673583984375, - 1.6366963386535645 + 6.87805700302124, + 4.540909767150879, + 0.631256639957428 ] }, "cluster_id": 0, @@ -343573,11 +343723,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -344006,31 +344156,31 @@ "cited_by_count": 2, "projections": { "pca": [ - 0.2883518934249878, - 0.07931644469499588 + 0.2885415852069855, + 0.07837003469467163 ], "tsne": [ - 40.60589599609375, - -9.684638023376465 + 38.210018157958984, + 16.445226669311523 ], "umap": [ - 9.241232872009277, - 8.448041915893555 + 9.49989128112793, + 7.891325950622559 ], "pca3d": [ - 0.2883519232273102, - 0.07930229604244232, - -0.19208231568336487 + 0.28854143619537354, + 0.07835585623979568, + -0.19231218099594116 ], "tsne3d": [ - 23.13766860961914, - -3.747784376144409, - 6.245316982269287 + 22.057846069335938, + 6.766048431396484, + -10.537611961364746 ], "umap3d": [ - 7.291889190673828, - 3.9606781005859375, - 1.2259771823883057 + 6.913773059844971, + 3.709869861602783, + 0.32429543137550354 ] }, "cluster_id": 0, @@ -344041,12 +344191,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -344462,47 +344612,47 @@ "doi": "10.1101/2025.11.21.689655", "projections": { "pca": [ - 0.17005537450313568, - -0.1029646098613739 + 0.16973504424095154, + -0.1031971201300621 ], "tsne": [ - 29.118499755859375, - -17.51041030883789 + 22.219432830810547, + 22.049814224243164 ], "umap": [ - 8.500638961791992, - 9.409136772155762 + 8.651653289794922, + 8.942962646484375 ], "pca3d": [ - 0.17005537450313568, - -0.10295205563306808, - -0.10194389522075653 + 0.16973504424095154, + -0.10318415611982346, + -0.1018252819776535 ], "tsne3d": [ - 15.255805969238281, - 11.091399192810059, - 6.88694429397583 + 10.088269233703613, + -4.491720199584961, + -14.959945678710938 ], "umap3d": [ - 5.428375720977783, - 4.540603160858154, - 1.125779628753662 + 5.041470527648926, + 4.2218475341796875, + 0.8701131343841553 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -344920,46 +345070,46 @@ "doi": "10.1101/2025.11.20.689462", "projections": { "pca": [ - 0.02754291333258152, - -0.10855875164270401 + 0.027382226660847664, + -0.10848215222358704 ], "tsne": [ - 21.178314208984375, - -31.098649978637695 + 8.09350872039795, + 26.982601165771484 ], "umap": [ - 7.213953018188477, - 9.50568962097168 + 7.270104885101318, + 9.133465766906738 ], "pca3d": [ - 0.027543075382709503, - -0.10855814814567566, - -0.08696906268596649 + 0.02738235704600811, + -0.10848172754049301, + -0.08668030053377151 ], "tsne3d": [ - 9.036164283752441, - -1.2619036436080933, - 10.095519065856934 + 1.2635095119476318, + 2.7302489280700684, + -19.205020904541016 ], "umap3d": [ - 5.13355827331543, - 3.3686532974243164, - 1.594706416130066 + 5.231407165527344, + 3.271681070327759, + 1.7390415668487549 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -345391,31 +345541,31 @@ "cited_by_count": 4, "projections": { "pca": [ - 0.25185832381248474, - 0.17187544703483582 + 0.2523375153541565, + 0.17129752039909363 ], "tsne": [ - 39.19540023803711, - -7.421769142150879 + 39.345237731933594, + 9.069989204406738 ], "umap": [ - 9.112303733825684, - 8.28498649597168 + 9.414567947387695, + 7.839531421661377 ], "pca3d": [ - 0.25185778737068176, - 0.17187821865081787, - -0.07803790271282196 + 0.2523370385169983, + 0.17130045592784882, + -0.07837079465389252 ], "tsne3d": [ - 22.28294563293457, - -9.196298599243164, - 0.5211575031280518 + 22.88681983947754, + 11.942655563354492, + -8.212937355041504 ], "umap3d": [ - 7.147432804107666, - 3.9088733196258545, - 1.2298450469970703 + 6.738050937652588, + 3.714407444000244, + 0.4795171022415161 ] }, "cluster_id": 0, @@ -345426,12 +345576,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -345842,31 +345992,31 @@ "journal": "Nature", "projections": { "pca": [ - 0.28494590520858765, - 0.05983910709619522 + 0.28521960973739624, + 0.058654673397541046 ], "tsne": [ - 52.189144134521484, - 7.869218349456787 + 58.3322868347168, + 17.988658905029297 ], "umap": [ - 10.176129341125488, - 6.966397285461426 + 10.205883026123047, + 6.43742036819458 ], "pca3d": [ - 0.2849454879760742, - 0.05985448136925697, - -0.05063747987151146 + 0.2852191925048828, + 0.058670200407505035, + -0.05092936009168625 ], "tsne3d": [ - 24.476436614990234, - 5.893415927886963, - 1.3583885431289673 + 27.33970832824707, + -2.5883634090423584, + -6.4916605949401855 ], "umap3d": [ - 8.167363166809082, - 5.027506351470947, - 1.221423864364624 + 7.597561836242676, + 4.895737171173096, + -0.25864508748054504 ] }, "cluster_id": 0, @@ -345877,12 +346027,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -346306,31 +346456,31 @@ "cited_by_count": 1, "projections": { "pca": [ - 0.18809565901756287, - -0.09528203308582306 + 0.18795739114284515, + -0.09565351158380508 ], "tsne": [ - 34.756683349609375, - -3.202989101409912 + 29.416955947875977, + 8.737857818603516 ], "umap": [ - 8.543092727661133, - 6.976892471313477 + 8.842803001403809, + 6.785010814666748 ], "pca3d": [ - 0.18809576332569122, - -0.09530416131019592, - -0.0766168013215065 + 0.18795767426490784, + -0.09567580372095108, + -0.07643697410821915 ], "tsne3d": [ - 16.1112117767334, - -1.2821338176727295, - -3.4707717895507812 + 17.86511993408203, + 4.183132171630859, + -2.680535078048706 ], "umap3d": [ - 6.783900260925293, - 4.690706253051758, - 2.0013036727905273 + 6.63580846786499, + 4.627642631530762, + 1.0293339490890503 ] }, "cluster_id": 0, @@ -346341,12 +346491,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -346768,31 +346918,31 @@ "doi": "10.1101/2025.11.18.685822", "projections": { "pca": [ - 0.28698408603668213, - 0.002527003875002265 + 0.2869187295436859, + 0.0020274112466722727 ], "tsne": [ - 53.11002731323242, - 4.958951473236084 + 50.80525588989258, + 16.93876838684082 ], "umap": [ - 10.11800765991211, - 7.004575252532959 + 10.352821350097656, + 6.4571638107299805 ], "pca3d": [ - 0.2869842052459717, - 0.002512524602934718, - -0.07573941349983215 + 0.2869186997413635, + 0.0020124244038015604, + -0.07595852762460709 ], "tsne3d": [ - 26.385908126831055, - 3.1322572231292725, - -0.8853944540023804 + 27.350549697875977, + 0.6834299564361572, + -4.504176616668701 ], "umap3d": [ - 8.164414405822754, - 5.117214202880859, - 1.1568549871444702 + 7.47534704208374, + 4.927576065063477, + -0.3518557548522949 ] }, "cluster_id": 0, @@ -346803,12 +346953,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -347228,31 +347378,31 @@ "cited_by_count": 16, "projections": { "pca": [ - 0.15474191308021545, - -0.07460068166255951 + 0.15473569929599762, + -0.0741506814956665 ], "tsne": [ - 4.751895427703857, - 13.638089179992676 + 11.08461856842041, + -14.005859375 ], "umap": [ - 6.364396572113037, - 5.642624855041504 + 6.646615028381348, + 5.268526554107666 ], "pca3d": [ - 0.15474200248718262, - -0.07462017983198166, - 0.20156632363796234 + 0.15473590791225433, + -0.07417060434818268, + 0.20175814628601074 ], "tsne3d": [ - 1.2295104265213013, - 3.778306484222412, - -4.4863362312316895 + 2.1138105392456055, + -2.168757438659668, + 7.869171619415283 ], "umap3d": [ - 5.774196147918701, - 5.745391845703125, - 3.8026158809661865 + 5.853494644165039, + 6.099104881286621, + 2.5643069744110107 ] }, "cluster_id": 4, @@ -347263,12 +347413,12 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, - "label": "Genomic Models" + "id": 32, + "label": "Genome Protein" } ], "_embedding": [ @@ -347693,31 +347843,31 @@ "cited_by_count": 5, "projections": { "pca": [ - 0.18964450061321259, - -0.14324475824832916 + 0.18936342000961304, + -0.14367441833019257 ], "tsne": [ - 12.595976829528809, - 13.308494567871094 + 15.462389945983887, + -7.04831075668335 ], "umap": [ - 7.26352071762085, - 6.14528751373291 + 7.5753655433654785, + 5.683068752288818 ], "pca3d": [ - 0.1896447092294693, - -0.14324964582920074, - -0.02832620032131672 + 0.18936382234096527, + -0.14367885887622833, + -0.02812853455543518 ], "tsne3d": [ - 9.002837181091309, - 4.339871406555176, - -6.835978984832764 + 6.694350719451904, + -0.5355062484741211, + 6.800078392028809 ], "umap3d": [ - 6.372226715087891, - 5.151357650756836, - 3.293036460876465 + 6.538059711456299, + 5.328736305236816, + 2.241652250289917 ] }, "cluster_id": 4, @@ -347728,11 +347878,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -348150,31 +348300,31 @@ "doi": "10.1101/2025.11.12.688070", "projections": { "pca": [ - 0.10617036372423172, - -0.08864875882863998 + 0.10600531846284866, + -0.08869121968746185 ], "tsne": [ - 23.122955322265625, - 11.134882926940918 + 26.448816299438477, + -7.5287556648254395 ], "umap": [ - 7.421036243438721, - 7.183929443359375 + 7.575175762176514, + 6.82409143447876 ], "pca3d": [ - 0.10617062449455261, - -0.0886642262339592, - -0.09343776851892471 + 0.10600556433200836, + -0.08870681375265121, + -0.09318479895591736 ], "tsne3d": [ - 11.001919746398926, - -5.233785629272461, - -17.480344772338867 + 10.21097183227539, + 11.898268699645996, + 9.54813289642334 ], "umap3d": [ - 6.649686336517334, - 4.08843994140625, - 3.074455976486206 + 7.000243663787842, + 4.46904182434082, + 2.2575161457061768 ] }, "cluster_id": 4, @@ -348185,12 +348335,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -348598,31 +348748,31 @@ "journal": "Nature", "projections": { "pca": [ - 0.1731424778699875, - 0.22001959383487701 + 0.17372174561023712, + 0.21996663510799408 ], "tsne": [ - 41.23737335205078, - 13.958096504211426 + 47.78623580932617, + -6.2354326248168945 ], "umap": [ - 9.357527732849121, - 7.806899547576904 + 9.595599174499512, + 7.284082889556885 ], "pca3d": [ - 0.17314234375953674, - 0.21998287737369537, - -0.013694091700017452 + 0.1737215220928192, + 0.21992969512939453, + -0.014108066447079182 ], "tsne3d": [ - 24.181026458740234, - -14.400432586669922, - -1.4689258337020874 + 23.419607162475586, + 12.131682395935059, + 5.63726806640625 ], "umap3d": [ - 7.354135513305664, - 4.415363311767578, - 1.5324807167053223 + 6.897350788116455, + 4.3553924560546875, + 0.2216830551624298 ] }, "cluster_id": 0, @@ -348633,12 +348783,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 20, - "label": "Chromatin Regulation" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -349046,46 +349196,46 @@ "journal": "Nature", "projections": { "pca": [ - 0.03847537934780121, - 0.05977791175246239 + 0.03874564543366432, + 0.05979945883154869 ], "tsne": [ - 25.915576934814453, - 5.290429592132568 + 31.823753356933594, + -15.455572128295898 ], "umap": [ - 6.974390506744385, - 7.953727722167969 + 7.1545867919921875, + 7.505612850189209 ], "pca3d": [ - 0.03847533091902733, - 0.05974875018000603, - -0.20496594905853271 + 0.03874558210372925, + 0.0597703717648983, + -0.20482617616653442 ], "tsne3d": [ - 8.181137084960938, - -16.4976749420166, - -12.993586540222168 + 11.641376495361328, + 19.681713104248047, + 5.849470138549805 ], "umap3d": [ - 6.651967525482178, - 3.2494161128997803, - 2.998765707015991 + 7.1066741943359375, + 3.688711404800415, + 2.2948122024536133 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 26, + "id": 33, "label": "Genetic Disease" } ], @@ -349517,31 +349667,31 @@ "doi": "10.1101/2025.11.05.686847", "projections": { "pca": [ - 0.19607752561569214, - -0.07337260991334915 + 0.195904940366745, + -0.0736427754163742 ], "tsne": [ - 23.61846923828125, - 22.1204833984375 + 36.91940689086914, + 1.4864997863769531 ], "umap": [ - 8.850810050964355, - 6.252804756164551 + 9.076966285705566, + 6.000747203826904 ], "pca3d": [ - 0.19607752561569214, - -0.07336261123418808, - -0.02193603292107582 + 0.1959049254655838, + -0.07363259792327881, + -0.021888967603445053 ], "tsne3d": [ - 13.971001625061035, - 18.525197982788086, - 2.3638343811035156 + 17.63402557373047, + -9.63124942779541, + 3.716010332107544 ], "umap3d": [ - 7.384760856628418, - 5.197086811065674, - 2.62506103515625 + 7.033021926879883, + 5.269052982330322, + 0.9575539231300354 ] }, "cluster_id": 0, @@ -349552,11 +349702,11 @@ "label": "Chromatin Regulation" }, { - "id": 3, - "label": "RNA Splicing" + "id": 2, + "label": "DNA Regulation" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -349986,46 +350136,46 @@ "cited_by_count": 27, "projections": { "pca": [ - 0.10591928660869598, - -0.04403170198202133 + 0.10599808394908905, + -0.043986931443214417 ], "tsne": [ - 18.800167083740234, - -23.40939712524414 + 10.431374549865723, + 23.484386444091797 ], "umap": [ - 7.238482475280762, - 9.293756484985352 + 7.304485321044922, + 8.930323600769043 ], "pca3d": [ - 0.10591909289360046, - -0.04401247575879097, - -0.044115614145994186 + 0.10599792748689651, + -0.043967653065919876, + -0.04384950175881386 ], "tsne3d": [ - 7.53264856338501, - -11.083925247192383, - 10.752132415771484 + 6.36913537979126, + 7.367222785949707, + -13.873028755187988 ], "umap3d": [ - 4.982382297515869, - 3.7117040157318115, - 1.69853675365448 + 5.084283351898193, + 3.490680456161499, + 1.7772369384765625 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -350450,31 +350600,31 @@ "doi": "10.1126/science.1205438", "projections": { "pca": [ - -0.15777745842933655, - 0.016979532316327095 + -0.15766452252864838, + 0.01757715828716755 ], "tsne": [ - -42.14008331298828, - -29.790388107299805 + -45.574893951416016, + 25.253314971923828 ], "umap": [ - 1.7944504022598267, - 6.48136568069458 + 2.2896835803985596, + 5.760426044464111 ], "pca3d": [ - -0.1577775627374649, - 0.016997432336211205, - -0.11764171719551086 + -0.15766461193561554, + 0.017595546320080757, + -0.11747623980045319 ], "tsne3d": [ - -26.485713958740234, - -13.516152381896973, - -11.214787483215332 + -19.64303970336914, + -3.850558042526245, + -14.407172203063965 ], "umap3d": [ - 1.299800157546997, - 5.4839677810668945, - 3.370798110961914 + 1.8512645959854126, + 5.482501983642578, + 3.938236713409424 ] }, "cluster_id": 2, @@ -350485,12 +350635,12 @@ "label": "Generative Models" }, { - "id": 1, - "label": "Network Learning" + "id": 5, + "label": "Diffusion Models" }, { - "id": 33, - "label": "Neural Networks" + "id": 19, + "label": "Graph Neural Nets" } ], "_embedding": [ @@ -350912,31 +351062,31 @@ "openalex_url": "https://openalex.org/W2149350210", "projections": { "pca": [ - -0.13345101475715637, - 0.06704993546009064 + -0.1333007514476776, + 0.06766794621944427 ], "tsne": [ - -42.14965057373047, - -29.783098220825195 + -45.58980941772461, + 25.243730545043945 ], "umap": [ - 1.7854925394058228, - 6.500993251800537 + 2.2873528003692627, + 5.754396915435791 ], "pca3d": [ - -0.13345114886760712, - 0.06706098467111588, - -0.10826817899942398 + -0.13330087065696716, + 0.06767916679382324, + -0.10828420519828796 ], "tsne3d": [ - -26.767620086669922, - -13.330202102661133, - -11.15051555633545 + -19.933202743530273, + -3.8123724460601807, + -14.410911560058594 ], "umap3d": [ - 1.2795554399490356, - 5.455079555511475, - 3.3665144443511963 + 1.844512939453125, + 5.406966209411621, + 3.9307990074157715 ] }, "cluster_id": 2, @@ -350947,12 +351097,12 @@ "label": "Generative Models" }, { - "id": 1, - "label": "Network Learning" + "id": 5, + "label": "Diffusion Models" }, { - "id": 33, - "label": "Neural Networks" + "id": 19, + "label": "Graph Neural Nets" } ], "_embedding": [ @@ -351367,31 +351517,31 @@ ], "projections": { "pca": [ - 0.050128210335969925, - -0.15656642615795135 + 0.04983067512512207, + -0.15615279972553253 ], "tsne": [ - 5.750490188598633, - 20.85181427001953 + -2.937894821166992, + -11.231888771057129 ], "umap": [ - 6.297599792480469, - 5.151699066162109 + 6.736647129058838, + 4.768675804138184 ], "pca3d": [ - 0.05012862756848335, - -0.1565847247838974, - 0.11117523908615112 + 0.04983107000589371, + -0.15617090463638306, + 0.111491858959198 ], "tsne3d": [ - -2.469348430633545, - 8.243071556091309, - -5.158294200897217 + -0.25677546858787537, + -6.326380729675293, + -4.425840854644775 ], "umap3d": [ - 5.255153179168701, - 5.597786903381348, - 4.5193305015563965 + 5.872653484344482, + 6.294284343719482, + 3.3815040588378906 ] }, "cluster_id": 3, @@ -351402,12 +351552,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, - "label": "Protein Design" + "id": 32, + "label": "Genome Protein" } ], "_embedding": [ @@ -351826,46 +351976,46 @@ "cited_by_count": 118, "projections": { "pca": [ - 0.17584535479545593, - 0.05130103975534439 + 0.17602884769439697, + 0.051211077719926834 ], "tsne": [ - 24.41659927368164, - 6.836671829223633 + 33.53709411621094, + -13.724161148071289 ], "umap": [ - 6.834516525268555, - 7.741636276245117 + 6.991922855377197, + 7.300833702087402 ], "pca3d": [ - 0.17584539949893951, - 0.051267143338918686, - -0.09129882603883743 + 0.17602892220020294, + 0.05117741599678993, + -0.09131687134504318 ], "tsne3d": [ - 8.541961669921875, - -12.730317115783691, - -13.936890602111816 + 9.110501289367676, + 19.20088005065918, + 2.6697239875793457 ], "umap3d": [ - 6.46765661239624, - 3.413778066635132, - 3.1588454246520996 + 7.0153021812438965, + 3.799703359603882, + 2.540083408355713 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 26, + "id": 33, "label": "Genetic Disease" } ], @@ -352295,47 +352445,47 @@ "cited_by_count": 2, "projections": { "pca": [ - 0.11996469646692276, - 0.14080429077148438 + 0.12023551762104034, + 0.14053378999233246 ], "tsne": [ - 37.23442077636719, - -7.880681991577148 + 39.0148811340332, + -5.895675182342529 ], "umap": [ - 8.003154754638672, - 8.336535453796387 + 8.30337142944336, + 7.843367099761963 ], "pca3d": [ - 0.1199645847082138, - 0.14077726006507874, - -0.18724265694618225 + 0.1202353909611702, + 0.1405062973499298, + -0.1874942034482956 ], "tsne3d": [ - 19.291805267333984, - -10.34279727935791, - -1.9076597690582275 + 21.33976936340332, + 14.045760154724121, + -5.876079559326172 ], "umap3d": [ - 6.800124168395996, - 3.558260917663574, - 1.800511121749878 + 6.716063976287842, + 3.504016399383545, + 1.1127161979675293 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { "id": 13, - "label": "Genetic Variants" + "label": "Chromatin Regulation" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -352753,31 +352903,31 @@ "doi": "10.1101/2025.10.31.685860", "projections": { "pca": [ - 0.06794615834951401, - -0.10286294668912888 + 0.06782090663909912, + -0.10262937098741531 ], "tsne": [ - 7.36773681640625, - 6.682590961456299 + 8.335468292236328, + -6.181041717529297 ], "umap": [ - 6.297868251800537, - 6.030517101287842 + 6.699425220489502, + 5.877464294433594 ], "pca3d": [ - 0.06794646382331848, - -0.10287623852491379, - 0.018391121178865433 + 0.0678211972117424, + -0.10264290869235992, + 0.018687469884753227 ], "tsne3d": [ - 1.6505353450775146, - 2.674376964569092, - -9.764700889587402 + 2.0108659267425537, + 0.9601914286613464, + 0.29547449946403503 ], "umap3d": [ - 5.350005149841309, - 4.8737053871154785, - 3.857827663421631 + 5.887771129608154, + 5.410454273223877, + 3.1666059494018555 ] }, "cluster_id": 4, @@ -352788,11 +352938,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -353211,31 +353361,31 @@ "doi": "10.1038/s41576-025-00914-2", "projections": { "pca": [ - -0.007343965116888285, - 0.06497397273778915 + -0.007278969511389732, + 0.06493733823299408 ], "tsne": [ - 22.449769973754883, - -1.4254659414291382 + 24.182950973510742, + 3.6923813819885254 ], "umap": [ - 8.03530502319336, - 6.774857997894287 + 8.248213768005371, + 6.352996826171875 ], "pca3d": [ - -0.0073442403227090836, - 0.06498998403549194, - -0.051543932408094406 + -0.007279285695403814, + 0.06495342403650284, + -0.051728807389736176 ], "tsne3d": [ - 17.032838821411133, - 2.6933650970458984, - -11.881739616394043 + 14.437809944152832, + 4.775169849395752, + 2.0518338680267334 ], "umap3d": [ - 6.497681617736816, - 4.911600112915039, - 2.508211135864258 + 6.452878952026367, + 5.005748271942139, + 1.4718607664108276 ] }, "cluster_id": 4, @@ -353246,12 +353396,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -353676,31 +353826,31 @@ "doi": "10.1101/2025.10.31.685862", "projections": { "pca": [ - 0.17962729930877686, - -0.08710798621177673 + 0.1794464886188507, + -0.08731047064065933 ], "tsne": [ - 21.735694885253906, - 8.082134246826172 + 25.550424575805664, + -4.172163009643555 ], "umap": [ - 7.376205921173096, - 7.155939102172852 + 7.6420488357543945, + 6.828336238861084 ], "pca3d": [ - 0.1796274483203888, - -0.0871308296918869, - -0.08684195578098297 + 0.17944669723510742, + -0.08733346313238144, + -0.08663515001535416 ], "tsne3d": [ - 10.491464614868164, - -3.5256361961364746, - -9.933375358581543 + 10.046895980834961, + 8.277017593383789, + 2.0783605575561523 ], "umap3d": [ - 6.663957118988037, - 4.141751766204834, - 2.9738245010375977 + 6.941857814788818, + 4.538716793060303, + 2.1341989040374756 ] }, "cluster_id": 4, @@ -353711,12 +353861,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -354142,46 +354292,46 @@ "cited_by_count": 43, "projections": { "pca": [ - 0.09460048377513885, - -0.03585357591509819 + 0.09460361301898956, + -0.03583667054772377 ], "tsne": [ - 17.755767822265625, - -29.291645050048828 + 9.773601531982422, + 30.274545669555664 ], "umap": [ - 6.997328281402588, - 9.652336120605469 + 7.05103063583374, + 9.259237289428711 ], "pca3d": [ - 0.09460057318210602, - -0.035856880247592926, - -0.1034923642873764 + 0.09460359811782837, + -0.03584003821015358, + -0.1033424660563469 ], "tsne3d": [ - 13.45676040649414, - -16.74848747253418, - 5.93086576461792 + 5.043828964233398, + 7.436268329620361, + -19.944597244262695 ], "umap3d": [ - 5.22892951965332, - 3.189877510070801, - 1.5442793369293213 + 5.282246112823486, + 2.936976909637451, + 1.844321846961975 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -354601,31 +354751,31 @@ ], "projections": { "pca": [ - 0.14018085598945618, - -0.1207624226808548 + 0.1400696337223053, + -0.12059599161148071 ], "tsne": [ - 11.013333320617676, - 13.161331176757812 + 14.227479934692383, + -7.917420864105225 ], "umap": [ - 6.964667797088623, - 6.06664514541626 + 7.211061954498291, + 5.680737018585205 ], "pca3d": [ - 0.14018118381500244, - -0.12076549977064133, - 0.01469921413809061 + 0.1400698721408844, + -0.12059878557920456, + 0.015058612450957298 ], "tsne3d": [ - 4.117625713348389, - 6.799764156341553, - -9.62834358215332 + 7.0803632736206055, + -2.307999849319458, + 5.504549980163574 ], "umap3d": [ - 5.875636577606201, - 5.155754089355469, - 3.5458734035491943 + 6.059093475341797, + 5.459905624389648, + 2.4990134239196777 ] }, "cluster_id": 4, @@ -354636,11 +354786,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -355057,46 +355207,46 @@ "doi": "10.14309/01.ajg.0001045852.91899.8f", "projections": { "pca": [ - 0.15266023576259613, - 0.1949848234653473 + 0.15320517122745514, + 0.1945326328277588 ], "tsne": [ - 12.986597061157227, - -12.666434288024902 + 40.7739143371582, + -13.50913143157959 ], "umap": [ - 6.969028949737549, - 8.36855697631836 + 7.1278204917907715, + 7.985514163970947 ], "pca3d": [ - 0.15265998244285583, - 0.19497919082641602, - -0.08281594514846802 + 0.15320482850074768, + 0.1945265531539917, + -0.08312307298183441 ], "tsne3d": [ - 14.819880485534668, - -16.382925033569336, - -3.764061212539673 + 11.950756072998047, + 20.43057632446289, + -4.599193096160889 ], "umap3d": [ - 6.3564839363098145, - 3.1414036750793457, - 2.4959332942962646 + 6.6816301345825195, + 3.296168088912964, + 2.105743408203125 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 26, + "id": 33, "label": "Genetic Disease" } ], @@ -355518,31 +355668,31 @@ "doi": "10.1038/s41467-025-64506-8", "projections": { "pca": [ - 0.16426962614059448, - -0.11914706975221634 + 0.1640961766242981, + -0.11921854317188263 ], "tsne": [ - 22.124553680419922, - -8.539472579956055 + 20.321025848388672, + 10.181154251098633 ], "umap": [ - 7.716214656829834, - 6.663304328918457 + 7.832226276397705, + 6.320230007171631 ], "pca3d": [ - 0.1642695516347885, - -0.11913953721523285, - -0.03421108424663544 + 0.16409629583358765, + -0.11921070516109467, + -0.03393726050853729 ], "tsne3d": [ - 12.030198097229004, - 3.820841073989868, - -0.8621202111244202 + 11.778120994567871, + -0.6021368503570557, + -4.856851577758789 ], "umap3d": [ - 6.2657151222229, - 4.76826810836792, - 2.7174649238586426 + 6.260312080383301, + 4.930224895477295, + 1.730665683746338 ] }, "cluster_id": 4, @@ -355553,12 +355703,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -355968,47 +356118,47 @@ "abstract": "On-policy, dense supervision is a useful tool for distillation", "projections": { "pca": [ - -0.18413829803466797, - 0.18163999915122986 + -0.18363690376281738, + 0.18234704434871674 ], "tsne": [ - -34.12981414794922, - -23.037063598632812 + -29.366195678710938, + 3.1102848052978516 ], "umap": [ - 1.7010213136672974, - 7.0217604637146 + 1.7094805240631104, + 6.185596942901611 ], "pca3d": [ - -0.18413838744163513, - 0.1816416233778, - 0.013174162246286869 + -0.18363714218139648, + 0.18234841525554657, + 0.012945239432156086 ], "tsne3d": [ - -18.435523986816406, - -11.756387710571289, - -5.779233455657959 + -22.440711975097656, + 13.628674507141113, + -6.390445709228516 ], "umap3d": [ - 1.2909241914749146, - 5.060393810272217, - 3.878225326538086 + 2.152214765548706, + 5.236420154571533, + 4.417511940002441 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -356418,31 +356568,31 @@ "journal": "Nature Biotechnology", "projections": { "pca": [ - 0.19615879654884338, - -0.02374308556318283 + 0.19635431468486786, + -0.024274751543998718 ], "tsne": [ - 52.669071197509766, - -12.278908729553223 + 45.3585090637207, + 14.023872375488281 ], "umap": [ - 10.022909164428711, - 7.760350704193115 + 10.199908256530762, + 7.424554347991943 ], "pca3d": [ - 0.19615869224071503, - -0.02374666929244995, - 0.010780912823975086 + 0.19635425508022308, + -0.024278732016682625, + 0.010828415863215923 ], "tsne3d": [ - 28.598976135253906, - 10.655016899108887, - 5.784833908081055 + 27.500164031982422, + 6.076223850250244, + 6.61305046081543 ], "umap3d": [ - 6.903289794921875, - 5.207927227020264, - 0.957730770111084 + 6.288356304168701, + 4.762208938598633, + -0.15672540664672852 ] }, "cluster_id": 0, @@ -356453,12 +356603,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -356864,46 +357014,46 @@ ], "projections": { "pca": [ - 0.14109843969345093, - 0.20733782649040222 + 0.14209303259849548, + 0.2060086727142334 ], "tsne": [ - -1.3602091073989868, - -8.083032608032227 + -1.4528037309646606, + 6.980701446533203 ], "umap": [ - 4.949873447418213, - 7.3135504722595215 + 5.198065280914307, + 6.606300354003906 ], "pca3d": [ - 0.14109770953655243, - 0.20736077427864075, - -0.04838917776942253 + 0.1420922726392746, + 0.206031933426857, + -0.04860438406467438 ], "tsne3d": [ - -0.929100751876831, - -0.010163758881390095, - -22.057964324951172 + 0.7116590142250061, + 23.42228126525879, + -6.924530029296875 ], "umap3d": [ - 4.5274271965026855, - 3.5242996215820312, - 4.908454895019531 + 5.606730937957764, + 4.678898334503174, + 4.888310432434082 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, + "id": 13, "label": "Genomics Papers" } ], @@ -357353,31 +357503,31 @@ "openalex_url": "https://openalex.org/W4415482059", "projections": { "pca": [ - 0.10508107393980026, - -0.06745390594005585 + 0.10488810390233994, + -0.06747426837682724 ], "tsne": [ - -18.177078247070312, - 18.473228454589844 + -14.785473823547363, + -25.836942672729492 ], "umap": [ - 4.768453598022461, - 4.652385711669922 + 5.267767429351807, + 4.023984432220459 ], "pca3d": [ - 0.10508105903863907, - -0.06743406504392624, - -0.013143675401806831 + 0.10488803684711456, + -0.0674542710185051, + -0.01308925449848175 ], "tsne3d": [ - 3.659644842147827, - 10.413219451904297, - 17.39885139465332 + 1.6482629776000977, + -13.492470741271973, + 15.53274154663086 ], "umap3d": [ - 4.576394081115723, - 6.793569087982178, - 4.1950154304504395 + 4.744650840759277, + 7.180582046508789, + 3.064176321029663 ] }, "cluster_id": 3, @@ -357388,12 +357538,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -357818,47 +357968,47 @@ "cited_by_count": 5, "projections": { "pca": [ - 0.04576072469353676, - -0.12538357079029083 + 0.04539824277162552, + -0.12509898841381073 ], "tsne": [ - 30.130544662475586, - -19.4929141998291 + 22.305978775024414, + 24.230897903442383 ], "umap": [ - 8.471354484558105, - 9.523978233337402 + 8.51746654510498, + 9.051385879516602 ], "pca3d": [ - 0.045760735869407654, - -0.12535840272903442, - 0.01664350926876068 + 0.04539819061756134, + -0.12507355213165283, + 0.016820166260004044 ], "tsne3d": [ - 15.566605567932129, - 12.991218566894531, - 4.666299819946289 + 9.813620567321777, + -5.65701961517334, + -12.159614562988281 ], "umap3d": [ - 5.195713996887207, - 4.642794132232666, - 1.2540217638015747 + 4.897248268127441, + 4.270533561706543, + 1.0471619367599487 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -358290,31 +358440,31 @@ "openalex_url": "https://openalex.org/W4404919853", "projections": { "pca": [ - 0.2543657720088959, - 0.06551238894462585 + 0.25437724590301514, + 0.06525074690580368 ], "tsne": [ - 53.23541259765625, - -1.4150869846343994 + 54.65273666381836, + 10.32923412322998 ], "umap": [ - 10.596433639526367, - 7.525521755218506 + 10.721881866455078, + 7.146424293518066 ], "pca3d": [ - 0.2543656527996063, - 0.06549263745546341, - -0.07683895528316498 + 0.2543770372867584, + 0.06523048877716064, + -0.07718814164400101 ], "tsne3d": [ - 32.54175567626953, - -2.51043963432312, - -2.1830549240112305 + 33.14922332763672, + 6.19652795791626, + -1.9950509071350098 ], "umap3d": [ - 7.843216896057129, - 4.940587520599365, - 0.5490198135375977 + 6.9913716316223145, + 4.532312393188477, + -0.7878628969192505 ] }, "cluster_id": 0, @@ -358325,12 +358475,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -358769,31 +358919,31 @@ "openalex_url": "https://openalex.org/W4412196454", "projections": { "pca": [ - 0.24163293838500977, - -0.03926066681742668 + 0.24154411256313324, + -0.03990347683429718 ], "tsne": [ - 37.33417892456055, - 0.05462094023823738 + 33.00838851928711, + 7.38573694229126 ], "umap": [ - 8.938675880432129, - 7.24306583404541 + 9.13012409210205, + 6.854928016662598 ], "pca3d": [ - 0.24163304269313812, - -0.03926747292280197, - -0.04800970107316971 + 0.2415441870689392, + -0.039910029619932175, + -0.04806690290570259 ], "tsne3d": [ - 19.66374969482422, - -2.422779083251953, - -6.725771903991699 + 20.97928237915039, + 6.880031108856201, + 3.0562846660614014 ], "umap3d": [ - 7.12133264541626, - 4.743922233581543, - 1.7719236612319946 + 6.844594955444336, + 4.68538761138916, + 0.5312408804893494 ] }, "cluster_id": 0, @@ -358804,11 +358954,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -359232,47 +359382,47 @@ "cited_by_count": 13, "projections": { "pca": [ - 0.27284595370292664, - -0.017759110778570175 + 0.2728816270828247, + -0.018293065950274467 ], "tsne": [ - 24.107221603393555, - -16.37914276123047 + 23.42518424987793, + 15.998231887817383 ], "umap": [ - 8.409749031066895, - 8.51661205291748 + 8.627509117126465, + 8.21847152709961 ], "pca3d": [ - 0.27284568548202515, - -0.01774679869413376, - -0.10734052211046219 + 0.2728814482688904, + -0.018280163407325745, + -0.10732011497020721 ], "tsne3d": [ - 14.430720329284668, - -3.614778995513916, - 7.721737384796143 + 13.155640602111816, + 3.1305835247039795, + -10.75243091583252 ], "umap3d": [ - 6.140573978424072, - 4.072755336761475, - 1.3958072662353516 + 5.955595016479492, + 3.8426904678344727, + 0.9003166556358337 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 12, - "label": "Genetic Variants" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -359693,47 +359843,47 @@ "doi": "10.1101/2025.10.20.683304", "projections": { "pca": [ - 0.05260833352804184, - -0.12733489274978638 + 0.052099552005529404, + -0.12717604637145996 ], "tsne": [ - 28.643720626831055, - -19.206388473510742 + 21.033620834350586, + 23.290599822998047 ], "umap": [ - 8.469120979309082, - 9.43094253540039 + 8.546394348144531, + 9.03568172454834 ], "pca3d": [ - 0.05260840803384781, - -0.127327561378479, - -0.06613796949386597 + 0.052099671214818954, + -0.12716828286647797, + -0.06603153795003891 ], "tsne3d": [ - 13.196569442749023, - 11.682721138000488, - 6.321106910705566 + 8.252935409545898, + -6.823169231414795, + -14.308812141418457 ], "umap3d": [ - 5.383012771606445, - 4.594422340393066, - 1.1726669073104858 + 5.068749904632568, + 4.214085102081299, + 0.9104003310203552 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -360148,31 +360298,31 @@ "doi": "10.1101/2025.06.25.661447", "projections": { "pca": [ - 0.223969504237175, - -0.05279216915369034 + 0.22379405796527863, + -0.05312114208936691 ], "tsne": [ - 35.82241439819336, - -2.0690839290618896 + 30.0487060546875, + 10.488630294799805 ], "umap": [ - 8.966167449951172, - 7.446794509887695 + 9.135496139526367, + 7.11196756362915 ], "pca3d": [ - 0.22396951913833618, - -0.0527929812669754, - -0.08079284429550171 + 0.2237941175699234, + -0.05312196537852287, + -0.08078370988368988 ], "tsne3d": [ - 17.110502243041992, - 1.2122524976730347, - -1.594743013381958 + 18.361125946044922, + 1.4556097984313965, + -3.941746950149536 ], "umap3d": [ - 6.909562110900879, - 4.641856670379639, - 1.6371004581451416 + 6.684720516204834, + 4.524415493011475, + 0.6449118256568909 ] }, "cluster_id": 0, @@ -360183,11 +360333,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -360606,31 +360756,31 @@ "doi": "10.1101/2025.10.16.682871", "projections": { "pca": [ - 0.19857442378997803, - -0.03028380684554577 + 0.19844917953014374, + -0.0306470338255167 ], "tsne": [ - 37.619956970214844, - 0.09252314269542694 + 33.26697540283203, + 7.602558135986328 ], "umap": [ - 8.965620040893555, - 7.276196479797363 + 9.219637870788574, + 6.810982704162598 ], "pca3d": [ - 0.19857437908649445, - -0.030289383605122566, - -0.0661475658416748 + 0.19844919443130493, + -0.030652420595288277, + -0.06619179993867874 ], "tsne3d": [ - 20.00448226928711, - -3.509512186050415, - -7.4811577796936035 + 21.47764015197754, + 7.968315601348877, + 3.574169397354126 ], "umap3d": [ - 7.217932224273682, - 4.783378601074219, - 1.7658756971359253 + 6.847300052642822, + 4.600372314453125, + 0.4800304174423218 ] }, "cluster_id": 0, @@ -360641,11 +360791,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -361069,47 +361219,47 @@ "doi": "10.1101/2024.10.24.620074", "projections": { "pca": [ - 0.18743455410003662, - -0.002824166789650917 + 0.18733999133110046, + -0.002770874183624983 ], "tsne": [ - 27.44826889038086, - -0.6913079619407654 + 29.482423782348633, + -0.450226753950119 ], "umap": [ - 8.02367115020752, - 8.07366943359375 + 8.206729888916016, + 7.671695232391357 ], "pca3d": [ - 0.18743440508842468, - -0.0028271805495023727, - -0.11567122489213943 + 0.18733997642993927, + -0.0027737172786146402, + -0.11568635702133179 ], "tsne3d": [ - 8.86142635345459, - -9.737669944763184, - -2.4077036380767822 + 11.352834701538086, + 13.636805534362793, + -12.93193531036377 ], "umap3d": [ - 6.89962911605835, - 3.8142340183258057, - 2.278829336166382 + 7.024501323699951, + 3.9778823852539062, + 1.4620256423950195 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -361549,46 +361699,46 @@ "cited_by_count": 28, "projections": { "pca": [ - 0.09582344442605972, - -0.06606760621070862 + 0.09570363163948059, + -0.06603467464447021 ], "tsne": [ - 19.323339462280273, - -21.796663284301758 + 11.354905128479004, + 21.619972229003906 ], "umap": [ - 7.377093315124512, - 9.34813117980957 + 7.335470676422119, + 8.983494758605957 ], "pca3d": [ - 0.09582343697547913, - -0.06605017185211182, - -0.05692845210433006 + 0.09570353478193283, + -0.06601681560277939, + -0.056727513670921326 ], "tsne3d": [ - 8.090944290161133, - -8.735099792480469, - 9.085441589355469 + 6.428767681121826, + 4.949884414672852, + -12.808453559875488 ], "umap3d": [ - 5.088039398193359, - 3.77392315864563, - 1.592029094696045 + 5.086083889007568, + 3.54966402053833, + 1.6561323404312134 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -362013,31 +362163,31 @@ "cited_by_count": 12, "projections": { "pca": [ - 0.17334847152233124, - 0.0014347718097269535 + 0.17361192405223846, + 0.0011261265026405454 ], "tsne": [ - 4.467556476593018, - 2.2316713333129883 + 6.3401055335998535, + -1.0215580463409424 ], "umap": [ - 6.618386268615723, - 6.005322456359863 + 6.833976745605469, + 5.706754684448242 ], "pca3d": [ - 0.17334847152233124, - 0.0014240618329495192, - 0.11879929155111313 + 0.17361199855804443, + 0.0011158233974128962, + 0.11885625869035721 ], "tsne3d": [ - 7.267263412475586, - 1.0590004920959473, - -0.1913832426071167 + 6.464666366577148, + -4.377530574798584, + -5.433076858520508 ], "umap3d": [ - 5.915307998657227, - 4.889086723327637, - 3.846482515335083 + 6.32785701751709, + 5.488678455352783, + 2.860496997833252 ] }, "cluster_id": 4, @@ -362048,11 +362198,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -362486,47 +362636,47 @@ "doi": "10.1101/2025.08.12.25333547", "projections": { "pca": [ - 0.1750539243221283, - 0.06088169291615486 + 0.1751171052455902, + 0.06025984138250351 ], "tsne": [ - 26.391427993774414, - 13.208142280578613 + 29.091407775878906, + -6.70665979385376 ], "umap": [ - 7.272651195526123, - 7.7019500732421875 + 7.503504276275635, + 7.197802543640137 ], "pca3d": [ - 0.17505407333374023, - 0.06084759533405304, - -0.14382046461105347 + 0.17511723935604095, + 0.06022542715072632, + -0.14399243891239166 ], "tsne3d": [ - 13.581951141357422, - -7.3233723640441895, - -15.506206512451172 + 13.2708740234375, + 13.016499519348145, + 7.345277786254883 ], "umap3d": [ - 6.7888593673706055, - 3.5729918479919434, - 2.9590744972229004 + 7.238314151763916, + 3.978182792663574, + 2.082385540008545 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -362934,47 +363084,47 @@ ], "projections": { "pca": [ - 0.046517856419086456, - 0.17718380689620972 + 0.04724707454442978, + 0.17688001692295074 ], "tsne": [ - 14.685729026794434, - 1.2701297998428345 + 13.486428260803223, + -36.08658218383789 ], "umap": [ - 6.127502918243408, - 6.800280570983887 + 5.96803617477417, + 6.492990970611572 ], "pca3d": [ - 0.04651753604412079, - 0.177191361784935, - 0.09364547580480576 + 0.04724668711423874, + 0.17688775062561035, + 0.09340828657150269 ], "tsne3d": [ - 11.915188789367676, - 10.522422790527344, - -2.5583529472351074 + 10.016788482666016, + 2.9400746822357178, + -3.015652656555176 ], "umap3d": [ - 5.489842414855957, - 4.436185359954834, - 3.778418779373169 + 6.056942939758301, + 5.045100212097168, + 2.9634666442871094 ] }, - "cluster_id": 4, - "cluster_label": "RNA Sequence Models", + "cluster_id": 1, + "cluster_label": "AI in Science", "cluster_levels": [ { - "id": 4, - "label": "RNA Sequence Models" + "id": 1, + "label": "AI in Science" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 11, - "label": "Genomic Language" + "id": 13, + "label": "Genomics Papers" } ], "_embedding": [ @@ -363397,31 +363547,31 @@ "cited_by_count": 1, "projections": { "pca": [ - 0.19693653285503387, - -0.07894451916217804 + 0.19665589928627014, + -0.07964502274990082 ], "tsne": [ - 41.426910400390625, - -11.345559120178223 + 38.70964431762695, + 18.454341888427734 ], "umap": [ - 9.545830726623535, - 7.592431545257568 + 9.857732772827148, + 6.915153980255127 ], "pca3d": [ - 0.19693681597709656, - -0.07894577831029892, - -0.09626780450344086 + 0.19665607810020447, + -0.07964600622653961, + -0.09630387276411057 ], "tsne3d": [ - 21.32750701904297, - -3.4550063610076904, - 3.972810745239258 + 21.38752555847168, + 3.6469385623931885, + -10.602716445922852 ], "umap3d": [ - 7.418144702911377, - 4.619087219238281, - 1.371795654296875 + 7.048964500427246, + 4.409475326538086, + 0.15491589903831482 ] }, "cluster_id": 0, @@ -363432,12 +363582,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 20, - "label": "Chromatin Regulation" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -363864,31 +364014,31 @@ "doi": "10.1101/2020.09.30.321323", "projections": { "pca": [ - 0.20485731959342957, - 0.0907905250787735 + 0.20505554974079132, + 0.09036830067634583 ], "tsne": [ - 47.784950256347656, - -2.999786615371704 + 50.48781967163086, + 4.791363716125488 ], "umap": [ - 10.07710075378418, - 7.647400856018066 + 10.079275131225586, + 7.276985168457031 ], "pca3d": [ - 0.20485703647136688, - 0.09077902883291245, - -0.12337334454059601 + 0.20505529642105103, + 0.09035594761371613, + -0.12364658713340759 ], "tsne3d": [ - 27.482378005981445, - -6.57916784286499, - -2.3203277587890625 + 30.717945098876953, + 11.233073234558105, + -3.409926176071167 ], "umap3d": [ - 7.387142658233643, - 4.799376010894775, - 0.9638360738754272 + 6.8435821533203125, + 4.542342185974121, + -0.1720947027206421 ] }, "cluster_id": 0, @@ -363899,12 +364049,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -364329,31 +364479,31 @@ "cited_by_count": 10, "projections": { "pca": [ - 0.22410468757152557, - -0.08157461881637573 + 0.22401347756385803, + -0.08189879357814789 ], "tsne": [ - 19.257633209228516, - 23.827301025390625 + 23.935169219970703, + -24.34345817565918 ], "umap": [ - 7.807605266571045, - 5.369956016540527 + 8.12484359741211, + 4.972492694854736 ], "pca3d": [ - 0.2241045981645584, - -0.08156706392765045, - 0.06334441155195236 + 0.2240133434534073, + -0.08189059793949127, + 0.06342632323503494 ], "tsne3d": [ - 8.650687217712402, - 18.00263786315918, - -0.20962657034397125 + 11.301041603088379, + -8.573307037353516, + 5.627284049987793 ], "umap3d": [ - 6.962963581085205, - 5.589918613433838, - 3.5588178634643555 + 7.0304856300354, + 5.960774898529053, + 2.0554122924804688 ] }, "cluster_id": 4, @@ -364364,11 +364514,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -364804,31 +364954,31 @@ "openalex_url": "https://openalex.org/W4415033441", "projections": { "pca": [ - 0.21593067049980164, - 0.032960157841444016 + 0.2159854620695114, + 0.032773829996585846 ], "tsne": [ - 32.025997161865234, - 27.485294342041016 + 57.00565719604492, + -2.7514255046844482 ], "umap": [ - 9.018386840820312, - 5.739560127258301 + 9.206244468688965, + 5.25017786026001 ], "pca3d": [ - 0.21593038737773895, - 0.032972320914268494, - -0.014601421542465687 + 0.21598517894744873, + 0.03278648853302002, + -0.014793855138123035 ], "tsne3d": [ - 13.554279327392578, - 20.206113815307617, - -1.3156414031982422 + 22.316905975341797, + -12.746735572814941, + 5.115062236785889 ], "umap3d": [ - 7.666314125061035, - 5.548852443695068, - 2.884535074234009 + 7.467806816101074, + 5.782929420471191, + 1.1076158285140991 ] }, "cluster_id": 4, @@ -364839,11 +364989,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -365266,31 +365416,31 @@ "cited_by_count": 14, "projections": { "pca": [ - 0.19910182058811188, - -0.08800587803125381 + 0.19890347123146057, + -0.08851108700037003 ], "tsne": [ - 50.850364685058594, - -9.523258209228516 + 47.85472106933594, + 12.538631439208984 ], "umap": [ - 10.269851684570312, - 7.750076770782471 + 10.418051719665527, + 7.496451377868652 ], "pca3d": [ - 0.19910188019275665, - -0.08800962567329407, - -0.03989364579319954 + 0.19890357553958893, + -0.08851531893014908, + -0.03989259526133537 ], "tsne3d": [ - 27.82875633239746, - 0.3424333333969116, - -8.639917373657227 + 26.98365020751953, + 3.846773386001587, + 1.249963641166687 ], "umap3d": [ - 7.1621785163879395, - 5.194353103637695, - 0.6831691265106201 + 6.427032470703125, + 4.736147403717041, + -0.4301551282405853 ] }, "cluster_id": 0, @@ -365301,12 +365451,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -365727,31 +365877,31 @@ "doi": "10.1101/2025.10.07.681052", "projections": { "pca": [ - 0.1918967068195343, - -0.055321644991636276 + 0.19165365397930145, + -0.05540795251727104 ], "tsne": [ - 33.731868743896484, - 0.5848886370658875 + 29.660905838012695, + 5.3291425704956055 ], "umap": [ - 8.674723625183105, - 6.9497456550598145 + 8.930968284606934, + 6.585499286651611 ], "pca3d": [ - 0.19189690053462982, - -0.05533478036522865, - -0.026736943051218987 + 0.1916537880897522, + -0.055421218276023865, + -0.026781214401125908 ], "tsne3d": [ - 17.66221809387207, - 3.051832437515259, - -7.43034553527832 + 18.523042678833008, + 3.451535940170288, + 3.3804309368133545 ], "umap3d": [ - 6.82982873916626, - 4.95737886428833, - 2.024127244949341 + 6.585024356842041, + 4.937127590179443, + 0.8981484174728394 ] }, "cluster_id": 0, @@ -365762,12 +365912,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -366182,31 +366332,31 @@ ], "projections": { "pca": [ - 0.136876180768013, - -0.18833591043949127 + 0.13644163310527802, + -0.18843677639961243 ], "tsne": [ - 21.966426849365234, - -4.0407185554504395 + 24.745580673217773, + 6.910773277282715 ], "umap": [ - 8.54935073852539, - 6.5333123207092285 + 8.759876251220703, + 6.230861186981201 ], "pca3d": [ - 0.13687659800052643, - -0.18833501636981964, - -0.08711990714073181 + 0.13644199073314667, + -0.18843580782413483, + -0.08679161965847015 ], "tsne3d": [ - 18.786136627197266, - 8.939323425292969, - -10.248451232910156 + 18.451488494873047, + -0.26804590225219727, + 0.6243146657943726 ], "umap3d": [ - 6.793140411376953, - 5.28107213973999, - 2.0453076362609863 + 6.497714996337891, + 5.167742729187012, + 0.9777296185493469 ] }, "cluster_id": 4, @@ -366217,12 +366367,12 @@ "label": "RNA Sequence Models" }, { - "id": 3, - "label": "RNA Splicing" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -366647,31 +366797,31 @@ "doi": "10.1101/2025.09.27.678816", "projections": { "pca": [ - 0.2621133327484131, - 0.011482409201562405 + 0.2621719539165497, + 0.010267253033816814 ], "tsne": [ - 43.981224060058594, - 3.448894739151001 + 35.18791961669922, + 10.97082233428955 ], "umap": [ - 9.192633628845215, - 7.3129048347473145 + 9.384857177734375, + 6.948055744171143 ], "pca3d": [ - 0.26211318373680115, - 0.0114698251709342, - -0.05150170624256134 + 0.26217180490493774, + 0.010254607535898685, + -0.05174088105559349 ], "tsne3d": [ - 19.895708084106445, - 2.136983871459961, - 4.499814033508301 + 23.701526641845703, + -1.09454345703125, + -10.530366897583008 ], "umap3d": [ - 7.843788146972656, - 4.323049545288086, - 1.6310017108917236 + 7.556588649749756, + 4.228826522827148, + 0.3587146997451782 ] }, "cluster_id": 0, @@ -366682,11 +366832,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -367095,47 +367245,47 @@ "abstract": "We\u2019re on a journey to advance and democratize artificial intelligence through open source and open science.", "projections": { "pca": [ - -0.05687012895941734, - 0.22840695083141327 + -0.056306686252355576, + 0.22882044315338135 ], "tsne": [ - -20.556663513183594, - -4.77931547164917 + -20.7899112701416, + -4.504142761230469 ], "umap": [ - 3.3792223930358887, - 7.7735981941223145 + 3.707047700881958, + 7.308191299438477 ], "pca3d": [ - -0.056870218366384506, - 0.22838716208934784, - -0.026795102283358574 + -0.0563068687915802, + 0.22880010306835175, + -0.02715066634118557 ], "tsne3d": [ - -9.297181129455566, - 2.1231861114501953, - -10.975116729736328 + -8.30639362335205, + 2.140669822692871, + 7.730135917663574 ], "umap3d": [ - 3.1600282192230225, - 4.457669258117676, - 3.819021224975586 + 3.9457480907440186, + 5.025223255157471, + 4.031761646270752 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { "id": 0, - "label": "LLM Language Learning" + "label": "Math AI" } ], "_embedding": [ @@ -367567,47 +367717,47 @@ "doi": "10.1101/2024.07.04.24309942", "projections": { "pca": [ - 0.20682458579540253, - 0.031166788190603256 + 0.20689819753170013, + 0.030852532014250755 ], "tsne": [ - 32.82035446166992, - 7.460648536682129 + 37.15943908691406, + -10.40949535369873 ], "umap": [ - 7.539648056030273, - 8.251520156860352 + 7.839644908905029, + 7.809734344482422 ], "pca3d": [ - 0.20682455599308014, - 0.031155992299318314, - -0.0817055031657219 + 0.20689807832241058, + 0.030841074883937836, + -0.08182549476623535 ], "tsne3d": [ - 17.74629020690918, - -14.420211791992188, - -9.973969459533691 + 16.573163986206055, + 18.984580993652344, + -0.9865100979804993 ], "umap3d": [ - 6.897793769836426, - 3.2650160789489746, - 2.3825523853302 + 7.142265796661377, + 3.445401906967163, + 1.8026123046875 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -368030,46 +368180,46 @@ "doi": "10.1101/2025.05.05.25326794", "projections": { "pca": [ - -0.012127113528549671, - -0.03324327990412712 + -0.012249539606273174, + -0.03327568247914314 ], "tsne": [ - 24.47153091430664, - 4.743192672729492 + 30.641651153564453, + -13.283360481262207 ], "umap": [ - 7.189774990081787, - 7.853163242340088 + 7.377941131591797, + 7.444620132446289 ], "pca3d": [ - -0.012127029709517956, - -0.0332459919154644, - -0.14810322225093842 + -0.012249454855918884, + -0.03327861800789833, + -0.14798986911773682 ], "tsne3d": [ - 9.994643211364746, - -14.553159713745117, - -9.948907852172852 + 10.24451732635498, + 16.993478775024414, + -0.3691581189632416 ], "umap3d": [ - 6.569596767425537, - 3.4391393661499023, - 2.8881421089172363 + 6.925014495849609, + 3.752985715866089, + 2.1815786361694336 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 26, + "id": 33, "label": "Genetic Disease" } ], @@ -368493,31 +368643,31 @@ "doi": "10.1101/2025.09.30.679565", "projections": { "pca": [ - 0.2198513001203537, - -0.02232794277369976 + 0.21975474059581757, + -0.022831259295344353 ], "tsne": [ - 48.20713806152344, - -8.516145706176758 + 45.536712646484375, + 10.182121276855469 ], "umap": [ - 9.926692008972168, - 7.96903133392334 + 10.11401653289795, + 7.610724449157715 ], "pca3d": [ - 0.21985134482383728, - -0.022339902818202972, - -0.12845532596111298 + 0.21975474059581757, + -0.0228441059589386, + -0.1285172551870346 ], "tsne3d": [ - 25.523717880249023, - -2.962050676345825, - -7.832007884979248 + 26.001733779907227, + 7.528363227844238, + 0.7954074740409851 ], "umap3d": [ - 6.986542701721191, - 4.788959503173828, - 0.8804749250411987 + 6.4523773193359375, + 4.436418056488037, + -0.1783379763364792 ] }, "cluster_id": 0, @@ -368528,12 +368678,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -368954,31 +369104,31 @@ "doi": "10.1101/2025.09.25.678665", "projections": { "pca": [ - 0.20129749178886414, - 0.032268039882183075 + 0.2014319747686386, + 0.031824711710214615 ], "tsne": [ - 42.008174896240234, - 19.63462257385254 + 13.939342498779297, + -23.073068618774414 ], "umap": [ - 5.815843105316162, - 5.539585113525391 + 6.186573505401611, + 5.146638870239258 ], "pca3d": [ - 0.20129738748073578, - 0.03226594999432564, - -0.02633604221045971 + 0.20143181085586548, + 0.03182285651564598, + -0.026462582871317863 ], "tsne3d": [ - 23.396360397338867, - 9.077372550964355, - 10.975702285766602 + 17.145841598510742, + 12.865504264831543, + -6.992984294891357 ], "umap3d": [ - 5.8567304611206055, - 6.228394985198975, - 3.6293795108795166 + 5.6350932121276855, + 6.369052886962891, + 2.25508189201355 ] }, "cluster_id": 3, @@ -368989,12 +369139,12 @@ "label": "Protein Design" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 24, - "label": "Protein Design" + "id": 32, + "label": "Genome Protein" } ], "_embedding": [ @@ -369401,47 +369551,47 @@ "abstract": "Distinguishing the rare \u201cdriver\u201d mutations that fuel cancer progression from the vast background of \u201cpassenger\u201d mutations in the non-coding genome is a funda- mental challenge in cancer biology. A primary mechanism that non-coding driver mutations contribute to cancer is by affecting gene expression, potentially from millions of nucleotides away. However, existing predictors of gene expression from mutations are unable to simultaneously handle interactions spanning mil- lions of base pairs, the extreme sparsity of somatic mutations, and generalize to unseen genes. To overcome these limitations, we introduce GenVarFormer (GVF), a novel transformer-based architecture designed to learn mutation representations and their impact on gene expression. GVF efficiently predicts the effect of muta- tions up to 8 million base pairs away from a gene by only considering mutations and their local DNA context, while omitting the vast intermediate sequence. Us- ing data from 864 breast cancer samples from The Cancer Genome Atlas, we demonstrate that GVF predicts gene expression with 26-fold higher correlation across samples than current models. In addition, GVF is the first model of its kind to generalize to unseen genes and samples simultaneously. Finally, we find that GVF patient embeddings are more informative than ground-truth gene expression for predicting overall patient survival in the most prevalent breast cancer subtype, luminal A. GVF embeddings and gene expression yielded concordance indices of 0.706\u00b10.136 and 0.573\u00b10.234, respectively. Our work establishes a new state-of- the-art for modeling the functional impact of non-coding mutations in cancer and provides a powerful new tool for identifying potential driver events and prognostic biomarkers.", "projections": { "pca": [ - 0.16562476754188538, - -0.09980988502502441 + 0.16538715362548828, + -0.09988561272621155 ], "tsne": [ - 27.83823013305664, - -0.9790716767311096 + 29.666486740112305, + -0.03270441293716431 ], "umap": [ - 7.931700229644775, - 7.738029956817627 + 8.222522735595703, + 7.3792524337768555 ], "pca3d": [ - 0.1656249612569809, - -0.09982304275035858, - -0.10205767303705215 + 0.16538739204406738, + -0.09989885985851288, + -0.1018681675195694 ], "tsne3d": [ - 9.700424194335938, - -8.892256736755371, - -1.9664534330368042 + 10.952986717224121, + 13.906293869018555, + -11.77789306640625 ], "umap3d": [ - 6.9631476402282715, - 3.9588608741760254, - 2.4734575748443604 + 7.150137901306152, + 4.114587306976318, + 1.498680830001831 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -369862,31 +370012,31 @@ "cited_by_count": 2, "projections": { "pca": [ - -0.05454688519239426, - 0.0026045709382742643 + -0.05459605157375336, + 0.003209040965884924 ], "tsne": [ - 13.104072570800781, - -1.7612898349761963 + 14.270918846130371, + 3.8430416584014893 ], "umap": [ - 7.985996723175049, - 6.214083194732666 + 8.288808822631836, + 5.7847771644592285 ], "pca3d": [ - -0.05454683303833008, - 0.002609949791803956, - -0.05558173730969429 + -0.054596010595560074, + 0.003214356955140829, + -0.05553128570318222 ], "tsne3d": [ - 14.927865028381348, - 11.539283752441406, - -15.279450416564941 + 17.51132583618164, + -3.926791191101074, + 9.137988090515137 ], "umap3d": [ - 6.219455718994141, - 5.387662410736084, - 2.5072202682495117 + 5.871490001678467, + 5.415472030639648, + 1.402787685394287 ] }, "cluster_id": 4, @@ -369897,12 +370047,12 @@ "label": "RNA Sequence Models" }, { - "id": 3, - "label": "RNA Splicing" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -370313,47 +370463,47 @@ "journal": "Nature Computational Science", "projections": { "pca": [ - 0.20379391312599182, - 0.007545364089310169 + 0.20383353531360626, + 0.006971336435526609 ], "tsne": [ - 32.80508041381836, - 12.596437454223633 + 34.36007308959961, + -1.0594487190246582 ], "umap": [ - 8.002586364746094, - 7.56563663482666 + 8.246910095214844, + 7.191218852996826 ], "pca3d": [ - 0.2037937194108963, - 0.007533794268965721, - -0.25396478176116943 + 0.2038334310054779, + 0.006959871388971806, + -0.25392305850982666 ], "tsne3d": [ - 18.054975509643555, - -4.789942264556885, - -10.695073127746582 + 17.59722137451172, + 10.647255897521973, + 4.069892406463623 ], "umap3d": [ - 7.017473220825195, - 4.0301713943481445, - 2.587136745452881 + 7.164904594421387, + 4.209545135498047, + 1.5165541172027588 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 31, - "label": "Genome Cancer" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -370772,31 +370922,31 @@ "cited_by_count": 45, "projections": { "pca": [ - 0.0013466280652210116, - -0.07428087294101715 + 0.001165602938272059, + -0.07387810200452805 ], "tsne": [ - 18.582130432128906, - 5.728305339813232 + 23.490966796875, + -13.635746002197266 ], "umap": [ - 6.754119873046875, - 7.3521728515625 + 6.857663154602051, + 6.8131422996521 ], "pca3d": [ - 0.0013468704419210553, - -0.07429207116365433, - -0.058215998113155365 + 0.001165846362709999, + -0.07388901710510254, + -0.05798608437180519 ], "tsne3d": [ - 5.192829608917236, - -10.505489349365234, - -13.07664966583252 + 5.365289211273193, + 17.0230712890625, + 3.827605724334717 ], "umap3d": [ - 6.156589984893799, - 3.933257579803467, - 3.3414254188537598 + 6.682729721069336, + 4.327652454376221, + 2.6433115005493164 ] }, "cluster_id": 4, @@ -370807,12 +370957,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -371235,47 +371385,47 @@ "doi": "10.1101/2025.05.21.655285", "projections": { "pca": [ - 0.13884294033050537, - 0.005004230421036482 + 0.13895125687122345, + 0.0044052922166883945 ], "tsne": [ - 26.287235260009766, - 13.980542182922363 + 28.620054244995117, + -7.001983165740967 ], "umap": [ - 7.380068302154541, - 7.551173210144043 + 7.640661239624023, + 7.121140480041504 ], "pca3d": [ - 0.13884320855140686, - 0.004968101624399424, - -0.10586177557706833 + 0.13895156979560852, + 0.004368967842310667, + -0.10585007816553116 ], "tsne3d": [ - 13.719606399536133, - -7.402135848999023, - -16.813671112060547 + 13.371834754943848, + 12.959897994995117, + 8.64587116241455 ], "umap3d": [ - 6.856025695800781, - 3.8100860118865967, - 3.028687000274658 + 7.247923374176025, + 4.114617824554443, + 2.0793497562408447 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -371695,31 +371845,31 @@ "doi": "10.1101/2025.09.20.677345", "projections": { "pca": [ - 0.1908269226551056, - -0.0581500418484211 + 0.19079430401325226, + -0.058391645550727844 ], "tsne": [ - 16.2972354888916, - 1.9796992540359497 + 22.388036727905273, + 7.849447250366211 ], "umap": [ - 8.124536514282227, - 6.681609630584717 + 8.34289264678955, + 6.331197261810303 ], "pca3d": [ - 0.19082684814929962, - -0.05814184620976448, - 0.05980057641863823 + 0.19079437851905823, + -0.05838270112872124, + 0.059876471757888794 ], "tsne3d": [ - 13.107878684997559, - 8.233575820922852, - -4.516913890838623 + 11.931788444519043, + 1.2858444452285767, + -1.3317923545837402 ], "umap3d": [ - 6.526521682739258, - 5.004769325256348, - 2.461517572402954 + 6.547493934631348, + 5.102285861968994, + 1.6072776317596436 ] }, "cluster_id": 4, @@ -371730,12 +371880,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -372143,46 +372293,46 @@ "journal": "Nature Genetics", "projections": { "pca": [ - 0.14492036402225494, - 0.13278992474079132 + 0.14534856379032135, + 0.13277794420719147 ], "tsne": [ - 24.659120559692383, - 6.6895670890808105 + 33.52777862548828, + -13.5680570602417 ], "umap": [ - 6.824975967407227, - 7.851092338562012 + 6.917267799377441, + 7.442816734313965 ], "pca3d": [ - 0.14492030441761017, - 0.1327611654996872, - -0.18005752563476562 + 0.14534839987754822, + 0.13274909555912018, + -0.1801074743270874 ], "tsne3d": [ - 8.717292785644531, - -14.071426391601562, - -14.169046401977539 + 10.143951416015625, + 19.380041122436523, + 3.4183852672576904 ], "umap3d": [ - 6.430807113647461, - 3.3467509746551514, - 3.134054660797119 + 6.997325897216797, + 3.75661039352417, + 2.5406503677368164 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 26, + "id": 33, "label": "Genetic Disease" } ], @@ -372606,31 +372756,31 @@ "doi": "10.1101/2025.09.21.677619", "projections": { "pca": [ - 0.1174258142709732, - -0.07793410867452621 + 0.11720198392868042, + -0.0776682123541832 ], "tsne": [ - 16.6970157623291, - 10.316133499145508 + 18.873584747314453, + -6.140690803527832 ], "umap": [ - 6.772481918334961, - 6.720874309539795 + 7.031534671783447, + 6.345768451690674 ], "pca3d": [ - 0.11742601543664932, - -0.07795587927103043, - 0.051575012505054474 + 0.11720218509435654, + -0.07768973708152771, + 0.05171208828687668 ], "tsne3d": [ - 5.4417595863342285, - 0.4377578794956207, - -12.289384841918945 + 3.529104232788086, + 5.535338401794434, + 3.379194974899292 ], "umap3d": [ - 6.098522663116455, - 4.394514083862305, - 3.550597667694092 + 6.649498462677002, + 4.785104274749756, + 2.683361530303955 ] }, "cluster_id": 4, @@ -372641,12 +372791,12 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 11, - "label": "Genomic Language" + "id": 17, + "label": "Genomic Models" } ], "_embedding": [ @@ -373065,31 +373215,31 @@ "doi": "10.1101/2025.09.19.677475", "projections": { "pca": [ - 0.18183599412441254, - -0.12179414927959442 + 0.18162712454795837, + -0.12191804498434067 ], "tsne": [ - 16.615690231323242, - 2.356736898422241 + 21.843225479125977, + 7.392181873321533 ], "umap": [ - 8.081876754760742, - 6.588352680206299 + 8.401013374328613, + 6.251801013946533 ], "pca3d": [ - 0.1818361133337021, - -0.12178919464349747, - 0.030818015336990356 + 0.18162719905376434, + -0.12191294878721237, + 0.031012801453471184 ], "tsne3d": [ - 13.613994598388672, - 7.957017421722412, - -5.5763750076293945 + 12.054031372070312, + 0.31923621892929077, + -0.6489596962928772 ], "umap3d": [ - 6.434504508972168, - 5.1403326988220215, - 2.558696985244751 + 6.523192882537842, + 5.172176837921143, + 1.5546023845672607 ] }, "cluster_id": 4, @@ -373100,12 +373250,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -373515,31 +373665,31 @@ "abstract": "Scientists used artificial intelligence to write coherent viral genomes, using them to synthesize bacteriophages capable of killing resistant strains of bacteria.", "projections": { "pca": [ - 0.002486881334334612, - 0.230060875415802 + 0.0030716198962181807, + 0.23119193315505981 ], "tsne": [ - 0.5085886716842651, - 12.93872356414795 + 12.394360542297363, + -17.105249404907227 ], "umap": [ - 5.933093070983887, - 5.589940547943115 + 6.236259460449219, + 5.224271297454834 ], "pca3d": [ - 0.0024865015875548124, - 0.23006132245063782, - 0.22422081232070923 + 0.0030712056905031204, + 0.23119202256202698, + 0.22384113073349 ], "tsne3d": [ - -2.5971310138702393, - 1.2378355264663696, - -4.353697776794434 + 2.293628454208374, + -2.9502341747283936, + 12.324808120727539 ], "umap3d": [ - 5.680501461029053, - 5.975590705871582, - 3.8126587867736816 + 5.607970714569092, + 6.276092052459717, + 2.55979061126709 ] }, "cluster_id": 3, @@ -373550,12 +373700,12 @@ "label": "Protein Design" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 24, - "label": "Protein Design" + "id": 32, + "label": "Genome Protein" } ], "_embedding": [ @@ -373976,31 +374126,31 @@ "doi": "10.1101/2025.09.12.675911", "projections": { "pca": [ - 0.13814495503902435, - -0.019015690311789513 + 0.13830147683620453, + -0.01875981315970421 ], "tsne": [ - 1.4773459434509277, - 12.826746940612793 + 12.49207878112793, + -16.163503646850586 ], "umap": [ - 5.994415760040283, - 5.558404922485352 + 6.378271579742432, + 5.259243488311768 ], "pca3d": [ - 0.13814514875411987, - -0.019039079546928406, - 0.2855599522590637 + 0.13830161094665527, + -0.018783368170261383, + 0.2855653166770935 ], "tsne3d": [ - -1.2627736330032349, - 1.770809292793274, - -5.052823066711426 + 3.0030133724212646, + -2.201507329940796, + 11.156328201293945 ], "umap3d": [ - 5.735642433166504, - 5.911031246185303, - 3.7989120483398438 + 5.689407825469971, + 6.2484846115112305, + 2.5118277072906494 ] }, "cluster_id": 3, @@ -374011,12 +374161,12 @@ "label": "Protein Design" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 24, - "label": "Protein Design" + "id": 32, + "label": "Genome Protein" } ], "_embedding": [ @@ -374426,47 +374576,47 @@ "journal": "Nature Reviews Genetics", "projections": { "pca": [ - 0.16404497623443604, - -0.01180967316031456 + 0.16398778557777405, + -0.012001154012978077 ], "tsne": [ - 32.030235290527344, - 0.040682706981897354 + 27.897512435913086, + 3.8626461029052734 ], "umap": [ - 8.506261825561523, - 6.9548845291137695 + 8.520389556884766, + 6.470849990844727 ], "pca3d": [ - 0.16404463350772858, - -0.011784477159380913, - -0.05387769639492035 + 0.163987398147583, + -0.011975149624049664, + -0.05386727303266525 ], "tsne3d": [ - 14.886615753173828, - 4.0835957527160645, - -5.005129814147949 + 17.056779861450195, + 4.854465961456299, + 4.984723091125488 ], "umap3d": [ - 6.6339497566223145, - 4.915843963623047, - 2.2237510681152344 + 6.3593621253967285, + 4.943122386932373, + 1.1316689252853394 ] }, - "cluster_id": 0, - "cluster_label": "Chromatin Regulation", + "cluster_id": 4, + "cluster_label": "RNA Sequence Models", "cluster_levels": [ { - "id": 0, - "label": "Chromatin Regulation" + "id": 4, + "label": "RNA Sequence Models" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -374892,46 +375042,46 @@ "doi": "10.1101/2025.09.09.25335438", "projections": { "pca": [ - 0.16495679318904877, - 0.05487598106265068 + 0.16514673829078674, + 0.05480572208762169 ], "tsne": [ - 26.341981887817383, - 8.503521919250488 + 30.71600341796875, + -9.103751182556152 ], "umap": [ - 6.890722274780273, - 7.772192478179932 + 7.116936206817627, + 7.386207580566406 ], "pca3d": [ - 0.16495680809020996, - 0.05484771355986595, - -0.10153920203447342 + 0.1651468276977539, + 0.05477695167064667, + -0.10153952986001968 ], "tsne3d": [ - 10.195924758911133, - -10.254371643066406, - -12.003006935119629 + 10.536596298217773, + 15.681676864624023, + 2.9921388626098633 ], "umap3d": [ - 6.493611812591553, - 3.44557523727417, - 3.0892539024353027 + 7.027990341186523, + 3.882155418395996, + 2.4051125049591064 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 26, + "id": 33, "label": "Genetic Disease" } ], @@ -375342,31 +375492,31 @@ "journal": "Nature Biotechnology", "projections": { "pca": [ - 0.02668333239853382, - -0.010764138773083687 + 0.02694203518331051, + -0.011166675016283989 ], "tsne": [ - 21.410123825073242, - 31.649160385131836 + 24.977497100830078, + -30.05074119567871 ], "umap": [ - 8.188693046569824, - 4.863539695739746 + 8.45301342010498, + 4.4388346672058105 ], "pca3d": [ - 0.026683149859309196, - -0.010741880163550377, - 0.010091165080666542 + 0.026941848918795586, + -0.011143442243337631, + 0.010220465250313282 ], "tsne3d": [ - 5.538662910461426, - 26.736351013183594, - -2.3134543895721436 + 14.155783653259277, + -17.54267120361328, + 7.935007572174072 ], "umap3d": [ - 7.364809989929199, - 5.919316291809082, - 3.7854340076446533 + 7.480057716369629, + 6.463244438171387, + 1.9795925617218018 ] }, "cluster_id": 4, @@ -375377,11 +375527,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -375817,31 +375967,31 @@ "cited_by_count": 15, "projections": { "pca": [ - 0.19102662801742554, - 0.06028752401471138 + 0.19107434153556824, + 0.06031041964888573 ], "tsne": [ - 29.538928985595703, - -1.5883831977844238 + 31.254581451416016, + 1.159425139427185 ], "umap": [ - 8.303664207458496, - 7.882137775421143 + 8.474849700927734, + 7.391171455383301 ], "pca3d": [ - 0.19102655351161957, - 0.060278646647930145, - -0.0764685645699501 + 0.19107423722743988, + 0.06030144914984703, + -0.07663480937480927 ], "tsne3d": [ - 12.763772964477539, - -8.714669227600098, - -0.395751029253006 + 11.563875198364258, + 12.143983840942383, + -9.81816291809082 ], "umap3d": [ - 7.096818923950195, - 3.9899709224700928, - 2.197667360305786 + 7.205807685852051, + 4.029656410217285, + 1.1996748447418213 ] }, "cluster_id": 0, @@ -375852,12 +376002,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -376282,31 +376432,31 @@ "openalex_url": "https://openalex.org/W4414055741", "projections": { "pca": [ - 0.08981849253177643, - -0.019651412963867188 + 0.08981598913669586, + -0.019416285678744316 ], "tsne": [ - 21.354101181030273, - 12.248475074768066 + 23.095182418823242, + -7.087857246398926 ], "umap": [ - 7.122889995574951, - 7.234582424163818 + 7.2676591873168945, + 6.876057147979736 ], "pca3d": [ - 0.08981864154338837, - -0.019670365378260612, - -0.03848257288336754 + 0.0898161455988884, + -0.01943523809313774, + -0.038362760096788406 ], "tsne3d": [ - 7.357689380645752, - -5.940515041351318, - -13.84499454498291 + 6.340116500854492, + 11.771685600280762, + 5.146728992462158 ], "umap3d": [ - 6.7546162605285645, - 3.9025042057037354, - 3.3522891998291016 + 7.166258811950684, + 4.466037750244141, + 2.511317014694214 ] }, "cluster_id": 4, @@ -376317,12 +376467,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -376730,31 +376880,31 @@ "journal": "Nature", "projections": { "pca": [ - 0.2441818118095398, - 0.024991028010845184 + 0.24409325420856476, + 0.02451404184103012 ], "tsne": [ - 56.28927993774414, - 5.845945835113525 + 55.18086624145508, + 17.37509536743164 ], "umap": [ - 10.304421424865723, - 6.750064373016357 + 10.514139175415039, + 6.306680202484131 ], "pca3d": [ - 0.24418149888515472, - 0.025000855326652527, - -0.012167438864707947 + 0.24409295618534088, + 0.024523906409740448, + -0.012517492286860943 ], "tsne3d": [ - 29.86849594116211, - 6.479996204376221, - -2.8951730728149414 + 30.188377380371094, + -2.185525417327881, + -0.2925117015838623 ], "umap3d": [ - 8.103681564331055, - 5.372955799102783, - 1.2096312046051025 + 7.401597023010254, + 5.156047344207764, + -0.40683266520500183 ] }, "cluster_id": 0, @@ -376765,12 +376915,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -377194,47 +377344,47 @@ "doi": "10.1101/2024.12.19.629561", "projections": { "pca": [ - 0.15991522371768951, - -0.07111562788486481 + 0.15974988043308258, + -0.07141885906457901 ], "tsne": [ - 31.622312545776367, - -15.82005500793457 + 24.673622131347656, + 21.968734741210938 ], "umap": [ - 8.601241111755371, - 9.29495620727539 + 8.66557788848877, + 8.882229804992676 ], "pca3d": [ - 0.15991532802581787, - -0.07111731916666031, - -0.10066182911396027 + 0.15974998474121094, + -0.07142069935798645, + -0.10058454424142838 ], "tsne3d": [ - 19.373743057250977, - 11.822966575622559, - 6.163285255432129 + 11.782334327697754, + -3.885324001312256, + -12.846003532409668 ], "umap3d": [ - 5.5769877433776855, - 4.485254764556885, - 1.0189422369003296 + 5.154493808746338, + 4.14225959777832, + 0.8137977719306946 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -377652,31 +377802,31 @@ "cited_by_count": 17, "projections": { "pca": [ - 0.07346681505441666, - -0.08279811590909958 + 0.07327869534492493, + -0.08251650631427765 ], "tsne": [ - -11.724936485290527, - 16.208322525024414 + -9.35576343536377, + -22.27859115600586 ], "umap": [ - 5.900913715362549, - 4.958837509155273 + 5.9055280685424805, + 4.218302249908447 ], "pca3d": [ - 0.0734666958451271, - -0.08277367800474167, - 0.15320084989070892 + 0.07327863574028015, + -0.08249207586050034, + 0.15322281420230865 ], "tsne3d": [ - 1.0746228694915771, - -0.14165347814559937, - 13.048233985900879 + -0.9929019212722778, + -19.782926559448242, + -7.898548126220703 ], "umap3d": [ - 5.064884185791016, - 6.518430709838867, - 4.248044013977051 + 5.554696559906006, + 6.7516093254089355, + 2.815936326980591 ] }, "cluster_id": 3, @@ -377687,11 +377837,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -378112,31 +378262,31 @@ "doi": "10.1101/2024.07.30.605881", "projections": { "pca": [ - 0.1984277218580246, - 0.0680781751871109 + 0.19867081940174103, + 0.06818859279155731 ], "tsne": [ - 4.014899253845215, - 1.95640230178833 + 5.80470609664917, + -1.068128228187561 ], "umap": [ - 6.49799108505249, - 6.174191951751709 + 6.799892902374268, + 5.784675598144531 ], "pca3d": [ - 0.19842778146266937, - 0.06805716454982758, - 0.10310333222150803 + 0.19867081940174103, + 0.06816833466291428, + 0.10297345370054245 ], "tsne3d": [ - 6.6757893562316895, - 0.31758245825767517, - -0.4610752463340759 + 6.279891490936279, + -5.040066719055176, + -5.901131629943848 ], "umap3d": [ - 5.947829246520996, - 4.774141788482666, - 3.918916940689087 + 6.313699245452881, + 5.39140510559082, + 2.892314910888672 ] }, "cluster_id": 4, @@ -378147,11 +378297,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -378584,47 +378734,47 @@ "doi": "10.1101/2025.08.28.25334614", "projections": { "pca": [ - 0.2581706941127777, - 0.04775450751185417 + 0.2582331895828247, + 0.04703773558139801 ], "tsne": [ - 29.80388641357422, - 5.196094036102295 + 35.33099365234375, + -6.835019111633301 ], "umap": [ - 7.547518730163574, - 8.16589641571045 + 7.801701068878174, + 7.738794326782227 ], "pca3d": [ - 0.25817060470581055, - 0.04774612560868263, - -0.24136586487293243 + 0.25823304057121277, + 0.04702894762158394, + -0.24149005115032196 ], "tsne3d": [ - 13.973447799682617, - -11.525979042053223, - -8.531877517700195 + 14.24035930633545, + 14.90484619140625, + -0.6439681649208069 ], "umap3d": [ - 6.941096305847168, - 3.31160044670105, - 2.486248254776001 + 7.199669361114502, + 3.5179145336151123, + 1.840897798538208 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -379034,31 +379184,31 @@ "journal": "Nature", "projections": { "pca": [ - 0.2961544394493103, - 0.07400519400835037 + 0.2963830232620239, + 0.07288595288991928 ], "tsne": [ - 52.16770935058594, - 7.962291717529297 + 58.38977813720703, + 17.993661880493164 ], "umap": [ - 10.199366569519043, - 7.009645938873291 + 10.224443435668945, + 6.456045150756836 ], "pca3d": [ - 0.2961540222167969, - 0.0740109235048294, - -0.05377546325325966 + 0.29638269543647766, + 0.07289186865091324, + -0.054156702011823654 ], "tsne3d": [ - 24.67235565185547, - 6.437020778656006, - 1.9552725553512573 + 27.736682891845703, + -2.950364112854004, + -7.079090595245361 ], "umap3d": [ - 8.20014762878418, - 5.0839385986328125, - 1.1710671186447144 + 7.592762470245361, + 4.924753189086914, + -0.3427228629589081 ] }, "cluster_id": 0, @@ -379069,12 +379219,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -379489,31 +379639,31 @@ "doi": "10.1101/2025.08.25.672121", "projections": { "pca": [ - 0.2215113788843155, - 0.04725881665945053 + 0.22167128324508667, + 0.04735340178012848 ], "tsne": [ - 45.264644622802734, - 11.224893569946289 + 52.164669036865234, + 0.793806254863739 ], "umap": [ - 9.00480842590332, - 6.762157917022705 + 9.151433944702148, + 6.461423873901367 ], "pca3d": [ - 0.22151102125644684, - 0.04725915193557739, - 0.10543357580900192 + 0.22167104482650757, + 0.047353681176900864, + 0.1052444726228714 ], "tsne3d": [ - 18.39411163330078, - 7.30654764175415, - -1.4218076467514038 + 21.726224899291992, + 2.707697629928589, + 7.9335198402404785 ], "umap3d": [ - 7.3045878410339355, - 5.166471481323242, - 2.0652270317077637 + 6.901199817657471, + 5.077640533447266, + 0.6494932174682617 ] }, "cluster_id": 0, @@ -379524,11 +379674,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -379951,47 +380101,47 @@ "cited_by_count": 29, "projections": { "pca": [ - 0.19873544573783875, - -0.059257395565509796 + 0.19848297536373138, + -0.05927220731973648 ], "tsne": [ - 28.97705078125, - -18.094205856323242 + 21.85512924194336, + 22.510177612304688 ], "umap": [ - 8.497092247009277, - 9.40628719329834 + 8.663743019104004, + 8.97097396850586 ], "pca3d": [ - 0.19873549044132233, - -0.059248752892017365, - -0.09908771514892578 + 0.19848282635211945, + -0.05926334485411644, + -0.09905914217233658 ], "tsne3d": [ - 15.210850715637207, - 10.072687149047852, - 5.807426452636719 + 9.567946434020996, + -5.871461391448975, + -15.285377502441406 ], "umap3d": [ - 5.452889919281006, - 4.602549076080322, - 1.1519855260849 + 5.051079750061035, + 4.213654518127441, + 0.8869574069976807 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -380411,31 +380561,31 @@ "cited_by_count": 3, "projections": { "pca": [ - 0.14526017010211945, - -0.12644915282726288 + 0.14510148763656616, + -0.12637750804424286 ], "tsne": [ - 10.22848892211914, - 8.772998809814453 + 12.352640151977539, + -6.1664838790893555 ], "umap": [ - 7.18256950378418, - 6.235877513885498 + 7.4021735191345215, + 6.006181716918945 ], "pca3d": [ - 0.14526034891605377, - -0.1264488846063614, - 0.03400449454784393 + 0.14510159194469452, + -0.12637721002101898, + 0.03434706851840019 ], "tsne3d": [ - 6.778283596038818, - 2.490745782852173, - -9.490852355957031 + 6.99072790145874, + 2.461550712585449, + 2.9791672229766846 ], "umap3d": [ - 6.040268421173096, - 4.91966438293457, - 3.364706039428711 + 6.301377773284912, + 5.255459308624268, + 2.3827736377716064 ] }, "cluster_id": 4, @@ -380446,11 +380596,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -380868,31 +381018,31 @@ "cited_by_count": 2, "projections": { "pca": [ - 0.14418689906597137, - -0.14887645840644836 + 0.14393067359924316, + -0.14912280440330505 ], "tsne": [ - 12.615646362304688, - 9.363121032714844 + 14.4775390625, + -3.549398899078369 ], "umap": [ - 7.172569274902344, - 6.1034088134765625 + 7.472249984741211, + 5.749165058135986 ], "pca3d": [ - 0.1441870629787445, - -0.14888300001621246, - -0.04042525589466095 + 0.14393097162246704, + -0.14912931621074677, + -0.040109481662511826 ], "tsne3d": [ - 7.865084171295166, - 6.90789270401001, - -9.925664901733398 + 9.885695457458496, + -1.566816806793213, + 4.786519527435303 ], "umap3d": [ - 6.071280002593994, - 5.034282207489014, - 3.339392900466919 + 6.345505714416504, + 5.392948150634766, + 2.2765183448791504 ] }, "cluster_id": 4, @@ -380903,11 +381053,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -381326,31 +381476,31 @@ "doi": "10.1101/2025.07.25.666754", "projections": { "pca": [ - 0.08574481308460236, - -0.1637435406446457 + 0.08531051874160767, + -0.16319233179092407 ], "tsne": [ - 14.906407356262207, - -2.212414503097534 + 16.04428482055664, + 4.314966201782227 ], "umap": [ - 8.2052640914917, - 6.169278621673584 + 8.446222305297852, + 5.836061000823975 ], "pca3d": [ - 0.08574505150318146, - -0.16374850273132324, - 0.01766316406428814 + 0.08531074970960617, + -0.1631973683834076, + 0.017961839213967323 ], "tsne3d": [ - 15.335752487182617, - 10.029461860656738, - -12.908586502075195 + 17.281789779663086, + -3.0606861114501953, + 6.513537883758545 ], "umap3d": [ - 6.549169540405273, - 5.4571638107299805, - 2.4761359691619873 + 6.306035995483398, + 5.562438011169434, + 1.2284406423568726 ] }, "cluster_id": 4, @@ -381361,12 +381511,12 @@ "label": "RNA Sequence Models" }, { - "id": 3, - "label": "RNA Splicing" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -381784,31 +381934,31 @@ "cited_by_count": 10, "projections": { "pca": [ - 0.07295691221952438, - -0.052484180778265 + 0.07292405515909195, + -0.052428003400564194 ], "tsne": [ - 18.565567016601562, - -6.133534908294678 + 17.455345153808594, + 1.7298482656478882 ], "umap": [ - 8.0153226852417, - 6.443413257598877 + 8.218449592590332, + 6.132270812988281 ], "pca3d": [ - 0.07295692712068558, - -0.05248021334409714, - -0.035454366356134415 + 0.07292409241199493, + -0.052424218505620956, + -0.035287220031023026 ], "tsne3d": [ - 12.041728973388672, - 14.576059341430664, - -11.555442810058594 + 13.715784072875977, + -2.1848368644714355, + 3.3753609657287598 ], "umap3d": [ - 6.332770824432373, - 5.238438606262207, - 2.5243630409240723 + 6.266886234283447, + 5.2930145263671875, + 1.399067759513855 ] }, "cluster_id": 4, @@ -381819,12 +381969,12 @@ "label": "RNA Sequence Models" }, { - "id": 3, - "label": "RNA Splicing" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -382242,31 +382392,31 @@ "doi": "10.1101/2025.08.04.668453", "projections": { "pca": [ - 0.18527239561080933, - -0.06505776196718216 + 0.1852150708436966, + -0.06586533039808273 ], "tsne": [ - 43.33440399169922, - -0.9289140701293945 + 34.296878814697266, + 14.399227142333984 ], "umap": [ - 9.164999961853027, - 7.498049736022949 + 9.320961952209473, + 7.223442077636719 ], "pca3d": [ - 0.18527254462242126, - -0.0650719478726387, - -0.12810038030147552 + 0.18521519005298615, + -0.06587953120470047, + -0.12804864346981049 ], "tsne3d": [ - 17.994884490966797, - 0.3975335955619812, - 4.968233108520508 + 20.331052780151367, + -0.4289655089378357, + -11.272608757019043 ], "umap3d": [ - 7.740915775299072, - 4.202732563018799, - 1.5604348182678223 + 7.508266448974609, + 4.105052947998047, + 0.3612157106399536 ] }, "cluster_id": 0, @@ -382277,11 +382427,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -382701,31 +382851,31 @@ "doi": "10.1101/2025.05.21.655414", "projections": { "pca": [ - 0.07692492753267288, - -0.12283483147621155 + 0.07679227739572525, + -0.12318100780248642 ], "tsne": [ - 63.91043472290039, - -3.067551374435425 + 46.21943283081055, + 28.595491409301758 ], "umap": [ - 9.553773880004883, - 6.346034526824951 + 9.862342834472656, + 5.8278608322143555 ], "pca3d": [ - 0.07692497968673706, - -0.12282454967498779, - 0.019975094124674797 + 0.07679229974746704, + -0.12317044287919998, + 0.02018727920949459 ], "tsne3d": [ - 24.428895950317383, - 13.822986602783203, - -8.070845603942871 + 26.98256492614746, + -8.228462219238281, + 3.308950424194336 ], "umap3d": [ - 7.631783962249756, - 5.711434364318848, - 1.8321704864501953 + 7.083484172821045, + 5.61539363861084, + 0.31038978695869446 ] }, "cluster_id": 0, @@ -382736,12 +382886,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -383158,31 +383308,31 @@ "cited_by_count": 7, "projections": { "pca": [ - 0.18695761263370514, - -0.08295393735170364 + 0.18686608970165253, + -0.08305846899747849 ], "tsne": [ - 10.547216415405273, - 11.60567855834961 + 15.229839324951172, + -5.310182094573975 ], "umap": [ - 6.956401348114014, - 6.076633453369141 + 7.292906284332275, + 5.8217082023620605 ], "pca3d": [ - 0.18695785105228424, - -0.08296028524637222, - 0.05482345446944237 + 0.1868661493062973, + -0.0830649808049202, + 0.05498969182372093 ], "tsne3d": [ - 3.981241226196289, - 10.690296173095703, - -8.537598609924316 + 9.079750061035156, + -1.8581863641738892, + 1.3553352355957031 ], "umap3d": [ - 5.854022026062012, - 5.099081039428711, - 3.462918758392334 + 6.155743598937988, + 5.4569292068481445, + 2.4672276973724365 ] }, "cluster_id": 4, @@ -383193,11 +383343,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -383618,47 +383768,47 @@ "cited_by_count": 87, "projections": { "pca": [ - 0.07557018101215363, - -0.1349218189716339 + 0.07507868111133575, + -0.13478031754493713 ], "tsne": [ - 28.188932418823242, - -18.858999252319336 + 20.864118576049805, + 22.681610107421875 ], "umap": [ - 8.417760848999023, - 9.43139934539795 + 8.516358375549316, + 9.038454055786133 ], "pca3d": [ - 0.07557009160518646, - -0.13488972187042236, - -0.14290836453437805 + 0.07507860660552979, + -0.1347477287054062, + -0.14270509779453278 ], "tsne3d": [ - 13.670351028442383, - 10.953332901000977, - 5.427250385284424 + 8.212873458862305, + -5.444295406341553, + -14.212352752685547 ], "umap3d": [ - 5.352094650268555, - 4.544369220733643, - 1.2435402870178223 + 5.059788703918457, + 4.185954570770264, + 0.9878627061843872 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -384142,31 +384292,31 @@ "cited_by_count": 76, "projections": { "pca": [ - 0.252936989068985, - 0.07120837271213531 + 0.25316470861434937, + 0.07074984163045883 ], "tsne": [ - 37.49068069458008, - 12.74716567993164 + 44.170433044433594, + -4.922170162200928 ], "umap": [ - 8.455327987670898, - 7.502602577209473 + 8.66331672668457, + 7.120173931121826 ], "pca3d": [ - 0.2529371678829193, - 0.0711657851934433, - -0.05537014082074165 + 0.25316476821899414, + 0.07070748507976532, + -0.055517811328172684 ], "tsne3d": [ - 17.41499137878418, - 2.6918559074401855, - 11.092377662658691 + 21.005496978759766, + 7.286986827850342, + 12.79706859588623 ], "umap3d": [ - 7.555478096008301, - 4.1597161293029785, - 2.267181873321533 + 7.388293266296387, + 4.2990336418151855, + 1.0608720779418945 ] }, "cluster_id": 0, @@ -384177,12 +384327,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -384614,31 +384764,31 @@ "cited_by_count": 11, "projections": { "pca": [ - 0.24729926884174347, - -0.010802239179611206 + 0.2473268210887909, + -0.011268777772784233 ], "tsne": [ - 24.056867599487305, - 30.265119552612305 + 27.313941955566406, + -29.350831985473633 ], "umap": [ - 8.464667320251465, - 5.029900550842285 + 8.770103454589844, + 4.6165571212768555 ], "pca3d": [ - 0.24729914963245392, - -0.010790936648845673, - 0.09235977381467819 + 0.24732664227485657, + -0.011256789788603783, + 0.09220699220895767 ], "tsne3d": [ - 8.219380378723145, - 24.219463348388672, - -0.5954083204269409 + 18.181507110595703, + -17.331104278564453, + 8.3721342086792 ], "umap3d": [ - 7.572070598602295, - 5.845412254333496, - 3.5133469104766846 + 7.5659871101379395, + 6.311849117279053, + 1.5822244882583618 ] }, "cluster_id": 4, @@ -384649,12 +384799,12 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, - "label": "RNA Splicing" + "id": 8, + "label": "RNA Structure" } ], "_embedding": [ @@ -385064,47 +385214,47 @@ "journal": "Nature Reviews Genetics", "projections": { "pca": [ - -0.05577929690480232, - -0.00883602537214756 + -0.05562068149447441, + -0.009084084071218967 ], "tsne": [ - 27.868541717529297, - 9.107611656188965 + 31.341964721679688, + -7.482929706573486 ], "umap": [ - 7.448729038238525, - 7.9026970863342285 + 7.5957465171813965, + 7.442526340484619 ], "pca3d": [ - -0.055779170244932175, - -0.008848671801388264, - -0.15088415145874023 + -0.05562059208750725, + -0.009096658788621426, + -0.15067735314369202 ], "tsne3d": [ - 12.834866523742676, - -10.238012313842773, - -13.563068389892578 + 13.637222290039062, + 16.820209503173828, + 5.058773040771484 ], "umap3d": [ - 6.858047962188721, - 3.535933017730713, - 2.799290180206299 + 7.283308029174805, + 3.8797340393066406, + 2.086599588394165 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -385536,47 +385686,47 @@ "cited_by_count": 9, "projections": { "pca": [ - 0.11542098969221115, - -0.000810732482932508 + 0.11537514626979828, + -0.001221911166794598 ], "tsne": [ - 25.158037185668945, - 1.3059278726577759 + 27.761125564575195, + -12.145212173461914 ], "umap": [ - 7.331571102142334, - 8.066670417785645 + 7.544785022735596, + 7.706496238708496 ], "pca3d": [ - 0.11542095988988876, - -0.0008038037922233343, - -0.15801659226417542 + 0.11537504941225052, + -0.0012147472007200122, + -0.15803289413452148 ], "tsne3d": [ - 11.38443374633789, - -13.732166290283203, - -6.488413333892822 + 11.53327465057373, + 13.556787490844727, + -1.4187495708465576 ], "umap3d": [ - 6.436324119567871, - 3.491173028945923, - 2.4951059818267822 + 6.733602523803711, + 3.692162275314331, + 1.9905270338058472 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -386017,31 +386167,31 @@ "openalex_url": "https://openalex.org/W4412570628", "projections": { "pca": [ - 0.2292439341545105, - 0.026435786858201027 + 0.22942975163459778, + 0.025690432637929916 ], "tsne": [ - 25.61264991760254, - 30.19734764099121 + 28.651325225830078, + -30.344776153564453 ], "umap": [ - 8.521795272827148, - 5.050833225250244 + 8.790059089660645, + 4.616715908050537 ], "pca3d": [ - 0.2292437106370926, - 0.026448704302310944, - 0.0799226313829422 + 0.2294294238090515, + 0.02570389397442341, + 0.07970401644706726 ], "tsne3d": [ - 9.490748405456543, - 26.801673889160156, - -0.8773443698883057 + 20.23541831970215, + -16.2149715423584, + 7.703327655792236 ], "umap3d": [ - 7.631503582000732, - 5.802278995513916, - 3.4772706031799316 + 7.615046977996826, + 6.312195301055908, + 1.611317753791809 ] }, "cluster_id": 4, @@ -386052,12 +386202,12 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, - "label": "RNA Splicing" + "id": 8, + "label": "RNA Structure" } ], "_embedding": [ @@ -386479,31 +386629,31 @@ ], "projections": { "pca": [ - 0.2133721262216568, - -0.07108926773071289 + 0.21343085169792175, + -0.07124942541122437 ], "tsne": [ - 11.074678421020508, - 14.288243293762207 + 15.225722312927246, + -8.58339786529541 ], "umap": [ - 6.8518266677856445, - 5.878767967224121 + 7.282506465911865, + 5.534759998321533 ], "pca3d": [ - 0.21337221562862396, - -0.07110864669084549, - 0.14281176030635834 + 0.21343107521533966, + -0.07126865535974503, + 0.1429406851530075 ], "tsne3d": [ - 2.267195224761963, - 8.30093002319336, - -9.989269256591797 + 6.585493564605713, + -3.068939685821533, + 7.622966289520264 ], "umap3d": [ - 6.074869632720947, - 5.250990867614746, - 3.451500415802002 + 6.219226360321045, + 5.61906099319458, + 2.451871395111084 ] }, "cluster_id": 4, @@ -386514,11 +386664,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -386940,31 +387090,31 @@ "doi": "10.1101/2025.07.24.666684", "projections": { "pca": [ - 0.27192556858062744, - -0.003886668710038066 + 0.271923303604126, + -0.0048227435909211636 ], "tsne": [ - 41.19831848144531, - -10.39931583404541 + 38.99825668334961, + 17.27947425842285 ], "umap": [ - 9.445046424865723, - 8.245750427246094 + 9.732994079589844, + 7.654913902282715 ], "pca3d": [ - 0.27192580699920654, - -0.0038846551906317472, - -0.14050935208797455 + 0.2719232738018036, + -0.004820706322789192, + -0.14065784215927124 ], "tsne3d": [ - 22.79875373840332, - -3.0062379837036133, - 4.846532821655273 + 22.311742782592773, + 5.123744010925293, + -10.402213096618652 ], "umap3d": [ - 7.495185375213623, - 4.230741024017334, - 1.1552510261535645 + 7.105020999908447, + 3.990532875061035, + 0.10785773396492004 ] }, "cluster_id": 0, @@ -386975,12 +387125,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -387457,46 +387607,46 @@ "openalex_url": "https://openalex.org/W4412671504", "projections": { "pca": [ - 0.076270692050457, - -0.006692884489893913 + 0.07630324363708496, + -0.006292026024311781 ], "tsne": [ - 14.86752700805664, - -23.039960861206055 + 9.132146835327148, + 19.966209411621094 ], "umap": [ - 7.041520595550537, - 9.186936378479004 + 7.104122638702393, + 8.751550674438477 ], "pca3d": [ - 0.07627046853303909, - -0.006676481571048498, - 0.057357221841812134 + 0.07630304247140884, + -0.006275660824030638, + 0.057442937046289444 ], "tsne3d": [ - 6.255061626434326, - -7.064016342163086, - 9.615564346313477 + 4.727248668670654, + 4.9726080894470215, + -11.042667388916016 ], "umap3d": [ - 4.876912593841553, - 3.808699131011963, - 1.8809806108474731 + 4.91497802734375, + 3.780282735824585, + 1.955622911453247 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -387910,31 +388060,31 @@ "doi": "10.1038/s41587-025-02718-5", "projections": { "pca": [ - 0.19290141761302948, - -0.03433474153280258 + 0.1927444487810135, + -0.03428349643945694 ], "tsne": [ - 30.058828353881836, - 29.72439193725586 + 25.194231033325195, + -19.05516815185547 ], "umap": [ - 8.639922142028809, - 5.849987030029297 + 8.841158866882324, + 5.393104076385498 ], "pca3d": [ - 0.19290128350257874, - -0.03432359918951988, - 0.0013740332797169685 + 0.19274424016475677, + -0.034272123128175735, + 0.0013459265464916825 ], "tsne3d": [ - 9.659127235412598, - 20.003353118896484, - -5.511234760284424 + 20.14109992980957, + -13.757476806640625, + 0.7368025779724121 ], "umap3d": [ - 7.232458591461182, - 5.461658954620361, - 2.812840223312378 + 7.144120693206787, + 5.712419509887695, + 1.280381202697754 ] }, "cluster_id": 4, @@ -387945,11 +388095,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -388384,31 +388534,31 @@ "doi": "10.1101/2025.07.16.665231", "projections": { "pca": [ - 0.2186519205570221, - 0.024338582530617714 + 0.21866834163665771, + 0.024005426093935966 ], "tsne": [ - 33.479434967041016, - -6.292356491088867 + 34.46639633178711, + 8.496628761291504 ], "umap": [ - 8.67618179321289, - 7.881429672241211 + 8.967071533203125, + 7.5052170753479 ], "pca3d": [ - 0.21865199506282806, - 0.024315647780895233, - -0.10748818516731262 + 0.21866852045059204, + 0.023982323706150055, + -0.1075592190027237 ], "tsne3d": [ - 15.870902061462402, - -5.055745601654053, - 0.4328671991825104 + 15.314738273620605, + 7.074024677276611, + -6.781401634216309 ], "umap3d": [ - 6.910831451416016, - 4.174946308135986, - 1.6283832788467407 + 6.675938606262207, + 4.015231609344482, + 0.8243184089660645 ] }, "cluster_id": 0, @@ -388419,12 +388569,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { "id": 31, - "label": "Genome Cancer" + "label": "Single Cell Seq" } ], "_embedding": [ @@ -388847,31 +388997,31 @@ "doi": "10.1101/2025.07.16.665169", "projections": { "pca": [ - 0.18466638028621674, - -0.005265810992568731 + 0.18454812467098236, + -0.005331332329660654 ], "tsne": [ - 28.231884002685547, - 24.42405891418457 + 31.37382698059082, + -23.84394645690918 ], "umap": [ - 8.646432876586914, - 5.726321220397949 + 8.956342697143555, + 5.35152530670166 ], "pca3d": [ - 0.1846659779548645, - -0.0052391462959349155, - -0.04303417354822159 + 0.1845478117465973, + -0.005303642712533474, + -0.04314286261796951 ], "tsne3d": [ - 14.310378074645996, - 22.362031936645508, - 1.9241387844085693 + 20.242572784423828, + -10.967527389526367, + 7.307583332061768 ], "umap3d": [ - 7.64290189743042, - 5.164401054382324, - 3.0900471210479736 + 7.629944801330566, + 5.539180755615234, + 1.3926459550857544 ] }, "cluster_id": 4, @@ -388882,11 +389032,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -389308,47 +389458,47 @@ "doi": "10.1101/2025.07.09.663936", "projections": { "pca": [ - 0.162620410323143, - -0.01795387826859951 + 0.1625247448682785, + -0.018382418900728226 ], "tsne": [ - 28.735523223876953, - 9.105746269226074 + 32.264984130859375, + -6.948714256286621 ], "umap": [ - 7.506965160369873, - 7.942506790161133 + 7.7495646476745605, + 7.408394813537598 ], "pca3d": [ - 0.162620410323143, - -0.017966965213418007, - -0.1628788411617279 + 0.1625247299671173, + -0.018395379185676575, + -0.1628936231136322 ], "tsne3d": [ - 13.950831413269043, - -9.766257286071777, - -12.174394607543945 + 14.044610977172852, + 15.698142051696777, + 3.6405935287475586 ], "umap3d": [ - 6.962406635284424, - 3.5335793495178223, - 2.7618086338043213 + 7.290528297424316, + 3.821927309036255, + 2.012436628341675 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -389776,31 +389926,31 @@ "doi": "10.1101/2025.07.09.662874", "projections": { "pca": [ - 0.24818620085716248, - -0.07580957561731339 + 0.247992604970932, + -0.0761803537607193 ], "tsne": [ - 56.830440521240234, - 2.565329074859619 + 49.20245361328125, + 18.55784034729004 ], "umap": [ - 9.929159164428711, - 6.854057312011719 + 10.17004680633545, + 6.338870525360107 ], "pca3d": [ - 0.24818632006645203, - -0.07581663131713867, - -0.016114529222249985 + 0.24799269437789917, + -0.07618771493434906, + -0.016138361766934395 ], "tsne3d": [ - 25.344541549682617, - 4.380175590515137, - -4.748377323150635 + 25.080448150634766, + -0.7633987069129944, + 0.21714745461940765 ], "umap3d": [ - 7.630082607269287, - 5.474595069885254, - 1.3307712078094482 + 6.972471237182617, + 5.168248653411865, + -0.13970014452934265 ] }, "cluster_id": 0, @@ -389811,12 +389961,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -390233,31 +390383,31 @@ ], "projections": { "pca": [ - 0.08522593975067139, - -0.1438717544078827 + 0.08484155684709549, + -0.14380314946174622 ], "tsne": [ - 62.175392150878906, - 0.06630662828683853 + 47.595333099365234, + 25.244333267211914 ], "umap": [ - 9.716418266296387, - 6.370376110076904 + 9.939156532287598, + 5.922204494476318 ], "pca3d": [ - 0.08522608876228333, - -0.1438576579093933, - -0.05069481208920479 + 0.08484171330928802, + -0.1437886655330658, + -0.05048038437962532 ], "tsne3d": [ - 24.925432205200195, - 8.839884757995605, - -7.893433094024658 + 24.216611862182617, + -5.193511962890625, + 0.9955439567565918 ], "umap3d": [ - 7.67658805847168, - 5.761415481567383, - 1.6682223081588745 + 7.0535969734191895, + 5.5691819190979, + 0.13434866070747375 ] }, "cluster_id": 0, @@ -390268,12 +390418,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -390696,46 +390846,46 @@ "doi": "10.1101/2025.06.03.657578", "projections": { "pca": [ - 0.064813032746315, - -0.05607841908931732 + 0.06465965509414673, + -0.05622422695159912 ], "tsne": [ - 20.97458839416504, - -27.96251678466797 + 12.082427024841309, + 28.37529945373535 ], "umap": [ - 7.397772789001465, - 9.609614372253418 + 7.336340427398682, + 9.26234245300293 ], "pca3d": [ - 0.06481306999921799, - -0.05606802552938461, - -0.12789244949817657 + 0.06465968489646912, + -0.05621390417218208, + -0.1278013288974762 ], "tsne3d": [ - 11.530024528503418, - -14.553050994873047, - 7.648848533630371 + 4.924018383026123, + 4.4573893547058105, + -18.71776580810547 ], "umap3d": [ - 5.1760358810424805, - 3.4519729614257812, - 1.438501238822937 + 5.185020923614502, + 3.079817056655884, + 1.6939853429794312 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -391157,31 +391307,31 @@ "openalex_url": "https://openalex.org/W4387966979", "projections": { "pca": [ - 0.15950517356395721, - -0.10632466524839401 + 0.15923593938350677, + -0.10641057044267654 ], "tsne": [ - 17.666780471801758, - 10.225456237792969 + 20.104496002197266, + -5.5203375816345215 ], "umap": [ - 6.994771480560303, - 6.887180805206299 + 7.1840925216674805, + 6.4897356033325195 ], "pca3d": [ - 0.1595052182674408, - -0.10633141547441483, - -0.0424560010433197 + 0.15923607349395752, + -0.10641700029373169, + -0.04225485771894455 ], "tsne3d": [ - 7.225210189819336, - -0.1919214129447937, - -11.771814346313477 + 4.97509765625, + 6.092582702636719, + 4.597458362579346 ], "umap3d": [ - 6.337221145629883, - 4.235661506652832, - 3.387686252593994 + 6.80648136138916, + 4.684670925140381, + 2.489713191986084 ] }, "cluster_id": 4, @@ -391192,12 +391342,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 7, + "label": "Genomic Models" }, { - "id": 22, - "label": "Genetic Variants" + "id": 17, + "label": "Genomic Models" } ], "_embedding": [ @@ -391619,31 +391769,31 @@ "openalex_url": "https://openalex.org/W4391065837", "projections": { "pca": [ - 0.05751100927591324, - -0.0973895788192749 + 0.057391539216041565, + -0.09733116626739502 ], "tsne": [ - 5.545024394989014, - 34.58863067626953 + 3.19032621383667, + -11.274683952331543 ], "umap": [ - 6.977306365966797, - 5.522946357727051 + 7.231247425079346, + 5.170473575592041 ], "pca3d": [ - 0.057511232793331146, - -0.09739137440919876, - 0.06092621758580208 + 0.057391732931137085, + -0.09733294695615768, + 0.061128027737140656 ], "tsne3d": [ - 7.845380783081055, - 12.641217231750488, - -3.0441782474517822 + 8.38141918182373, + -7.170116424560547, + 1.715837001800537 ], "umap3d": [ - 5.850541114807129, - 5.261260032653809, - 3.732682466506958 + 6.361611366271973, + 5.852561950683594, + 2.743448257446289 ] }, "cluster_id": 4, @@ -391654,11 +391804,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -392072,47 +392222,47 @@ "doi": "10.18653/v1/2024.langmol-1.13", "projections": { "pca": [ - 0.08931703865528107, - -0.029433155432343483 + 0.08920934796333313, + -0.029047280550003052 ], "tsne": [ - 17.253456115722656, - 7.692321300506592 + 21.190576553344727, + -10.555678367614746 ], "umap": [ - 6.953968048095703, - 7.452019691467285 + 7.0809102058410645, + 7.158829212188721 ], "pca3d": [ - 0.0893171951174736, - -0.029442090541124344, - -0.02861947938799858 + 0.08920947462320328, + -0.029056547209620476, + -0.02849445305764675 ], "tsne3d": [ - 5.625054359436035, - -7.853592395782471, - -8.753204345703125 + 5.287410259246826, + 12.6629056930542, + 0.06338696926832199 ], "umap3d": [ - 6.30623722076416, - 3.919006824493408, - 3.100445508956909 + 6.687146186828613, + 4.277200698852539, + 2.476619243621826 ] }, - "cluster_id": 4, - "cluster_label": "RNA Sequence Models", + "cluster_id": 6, + "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 4, - "label": "RNA Sequence Models" + "id": 6, + "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -392530,46 +392680,46 @@ "doi": "10.1101/2024.10.03.24314824", "projections": { "pca": [ - 0.08940305560827255, - -0.019160840660333633 + 0.08942104130983353, + -0.019226472824811935 ], "tsne": [ - 27.287752151489258, - 7.965690612792969 + 31.31611442565918, + -8.946239471435547 ], "umap": [ - 6.973939895629883, - 7.716781139373779 + 7.0755438804626465, + 7.384093761444092 ], "pca3d": [ - 0.08940306305885315, - -0.019172431901097298, - -0.10904280096292496 + 0.08942117542028427, + -0.019238417968153954, + -0.10887593775987625 ], "tsne3d": [ - 10.6798095703125, - -10.852263450622559, - -10.491593360900879 + 11.04648494720459, + 14.939290046691895, + 1.4981316328048706 ], "umap3d": [ - 6.36660623550415, - 3.577251434326172, - 3.038809299468994 + 6.803072929382324, + 3.9955246448516846, + 2.4416632652282715 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 26, + "id": 33, "label": "Genetic Disease" } ], @@ -392992,47 +393142,47 @@ "doi": "10.1101/2025.07.03.663009", "projections": { "pca": [ - 0.1245485469698906, - -0.155500128865242 + 0.12409904599189758, + -0.1557026207447052 ], "tsne": [ - 36.42892074584961, - -18.691062927246094 + 29.36868667602539, + 25.293704986572266 ], "umap": [ - 8.500581741333008, - 8.424361228942871 + 8.704665184020996, + 8.154927253723145 ], "pca3d": [ - 0.12454859167337418, - -0.15547993779182434, - -0.08582257479429245 + 0.12409909814596176, + -0.15568208694458008, + -0.0856151431798935 ], "tsne3d": [ - 18.637365341186523, - 13.691817283630371, - -2.3230020999908447 + 14.893265724182129, + -5.558577537536621, + -7.198581218719482 ], "umap3d": [ - 6.188656330108643, - 4.796204566955566, - 1.566328525543213 + 5.875519752502441, + 4.58793306350708, + 0.902722954750061 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -393448,47 +393598,47 @@ "cited_by_count": 3, "projections": { "pca": [ - 0.09845196455717087, - -0.15179739892482758 + 0.09792482107877731, + -0.15217573940753937 ], "tsne": [ - 36.462852478027344, - -18.642478942871094 + 29.425342559814453, + 25.232135772705078 ], "umap": [ - 8.529219627380371, - 8.413151741027832 + 8.772392272949219, + 8.1083345413208 ], "pca3d": [ - 0.09845205396413803, - -0.15178745985031128, - -0.14890128374099731 + 0.09792499989271164, + -0.15216536819934845, + -0.1487446427345276 ], "tsne3d": [ - 19.107471466064453, - 13.299692153930664, - -2.0764241218566895 + 15.187129020690918, + -5.485490322113037, + -7.716174602508545 ], "umap3d": [ - 6.191197872161865, - 4.807432174682617, - 1.5454405546188354 + 5.859816551208496, + 4.568231582641602, + 0.8590538501739502 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -393936,31 +394086,31 @@ "doi": "10.1101/2025.07.04.663250", "projections": { "pca": [ - 0.14373013377189636, - -0.013930275104939938 + 0.14380745589733124, + -0.013727418147027493 ], "tsne": [ - 8.322673797607422, - 1.2373764514923096 + 10.775714874267578, + -0.30755549669265747 ], "umap": [ - 6.731821537017822, - 6.40830659866333 + 7.065633296966553, + 6.105861186981201 ], "pca3d": [ - 0.14373011887073517, - -0.013935865834355354, - 0.05355819687247276 + 0.14380759000778198, + -0.013732701539993286, + 0.053658626973629 ], "tsne3d": [ - 3.7830495834350586, - -1.3095676898956299, - -8.664590835571289 + 6.825584411621094, + 2.3337550163269043, + -5.701542854309082 ], "umap3d": [ - 5.862374305725098, - 4.8559136390686035, - 3.3565804958343506 + 6.226882457733154, + 5.072574138641357, + 2.5549991130828857 ] }, "cluster_id": 4, @@ -393971,11 +394121,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -394384,46 +394534,46 @@ "journal": "Nature Biotechnology", "projections": { "pca": [ - -0.005145540926605463, - 0.014438671059906483 + -0.004861718043684959, + 0.013979182578623295 ], "tsne": [ - 27.737211227416992, - -24.600927352905273 + 18.33913803100586, + 28.806188583374023 ], "umap": [ - 7.793663024902344, - 9.416025161743164 + 7.863103866577148, + 9.146580696105957 ], "pca3d": [ - -0.0051459502428770065, - 0.01447715237736702, - -0.17484119534492493 + -0.004862117115408182, + 0.014018830843269825, + -0.1746857613325119 ], "tsne3d": [ - 14.42530345916748, - -9.55665111541748, - 13.990278244018555 + 11.004371643066406, + 1.6900123357772827, + -18.858476638793945 ], "umap3d": [ - 5.364526748657227, - 3.7919578552246094, - 1.2778199911117554 + 5.279639720916748, + 3.3766181468963623, + 1.3481746912002563 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -394846,31 +394996,31 @@ "doi": "10.1101/2025.06.27.662080", "projections": { "pca": [ - 0.2968994677066803, - 0.0631660670042038 + 0.29713401198387146, + 0.06191207095980644 ], "tsne": [ - 49.133827209472656, - 2.976290702819824 + 40.966766357421875, + 15.83515453338623 ], "umap": [ - 9.556705474853516, - 8.213714599609375 + 9.869046211242676, + 7.497207164764404 ], "pca3d": [ - 0.2968995273113251, - 0.06316127628087997, - -0.13665951788425446 + 0.29713374376296997, + 0.06190740689635277, + -0.13692456483840942 ], "tsne3d": [ - 25.494426727294922, - -2.0332725048065186, - 4.113442420959473 + 25.007780075073242, + 5.32351541519165, + -9.516711235046387 ], "umap3d": [ - 7.684162616729736, - 4.295550346374512, - 1.11961030960083 + 7.122276782989502, + 3.950943946838379, + 0.08260626345872879 ] }, "cluster_id": 0, @@ -394881,12 +395031,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -395320,31 +395470,31 @@ "cited_by_count": 22, "projections": { "pca": [ - 0.24781781435012817, - 0.06998684257268906 + 0.24794532358646393, + 0.06947170943021774 ], "tsne": [ - 53.534122467041016, - -0.8714912533760071 + 55.44491195678711, + 10.795591354370117 ], "umap": [ - 10.617082595825195, - 7.530745029449463 + 10.736332893371582, + 7.146749019622803 ], "pca3d": [ - 0.24781768023967743, - 0.06995511054992676, - -0.10656692832708359 + 0.2479453682899475, + 0.06943919509649277, + -0.10687118023633957 ], "tsne3d": [ - 34.30063247680664, - -3.0906825065612793, - -2.3155059814453125 + 34.90855026245117, + 6.752077102661133, + -2.335832357406616 ], "umap3d": [ - 7.797639846801758, - 4.966005325317383, - 0.5235990285873413 + 6.992814540863037, + 4.5417256355285645, + -0.7703242301940918 ] }, "cluster_id": 0, @@ -395355,12 +395505,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -395770,47 +395920,47 @@ "journal": "Nature Genetics", "projections": { "pca": [ - 0.08282109349966049, - 0.18443214893341064 + 0.08342120796442032, + 0.18468086421489716 ], "tsne": [ - 35.04352569580078, - 7.562997817993164 + 39.42048645019531, + -11.149557113647461 ], "umap": [ - 7.898017883300781, - 8.23895263671875 + 8.370410919189453, + 7.560602188110352 ], "pca3d": [ - 0.08282081037759781, - 0.1844138652086258, - -0.00474573764950037 + 0.08342088758945465, + 0.1846621036529541, + -0.004938716534525156 ], "tsne3d": [ - 19.819971084594727, - -15.669926643371582, - -13.300982475280762 + 18.610261917114258, + 22.20905303955078, + -0.37313321232795715 ], "umap3d": [ - 7.091943740844727, - 3.5463173389434814, - 2.199795722961426 + 7.136137962341309, + 3.5288383960723877, + 1.4346033334732056 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { "id": 13, - "label": "Genetic Variants" + "label": "Chromatin Regulation" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -396231,31 +396381,31 @@ "doi": "10.1101/2025.06.23.661015", "projections": { "pca": [ - 0.17447927594184875, - -0.06566635519266129 + 0.17437590658664703, + -0.06641805917024612 ], "tsne": [ - 16.2889347076416, - 35.275936126708984 + 18.823579788208008, + -32.73845291137695 ], "umap": [ - 7.882413864135742, - 4.367342948913574 + 8.271098136901855, + 3.9279561042785645 ], "pca3d": [ - 0.1744791716337204, - -0.06565339863300323, - 0.1310754120349884 + 0.17437587678432465, + -0.06640440970659256, + 0.13092000782489777 ], "tsne3d": [ - 3.378584146499634, - 21.85175323486328, - 7.815688610076904 + 8.97330093383789, + -19.579444885253906, + 7.032711029052734 ], "umap3d": [ - 7.203384876251221, - 6.392798900604248, - 4.119170665740967 + 7.434347152709961, + 6.92013692855835, + 2.2709872722625732 ] }, "cluster_id": 4, @@ -396266,11 +396416,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -396708,31 +396858,31 @@ "doi": "10.1101/2025.06.25.661622", "projections": { "pca": [ - 0.12269372493028641, - -0.11453761905431747 + 0.12251292914152145, + -0.11444824188947678 ], "tsne": [ - 6.604901313781738, - 6.625883102416992 + 8.900102615356445, + -4.661538124084473 ], "umap": [ - 6.595545768737793, - 6.258086204528809 + 7.024341106414795, + 5.929303169250488 ], "pca3d": [ - 0.12269376218318939, - -0.11454394459724426, - 0.0256256815046072 + 0.12251319736242294, + -0.1144542321562767, + 0.025911586359143257 ], "tsne3d": [ - 3.2330093383789062, - 2.200084686279297, - -10.974925994873047 + 2.216662883758545, + 3.116848945617676, + 1.1393176317214966 ], "umap3d": [ - 5.697177886962891, - 4.745364665985107, - 3.5685086250305176 + 6.1694254875183105, + 5.193731784820557, + 2.7622058391571045 ] }, "cluster_id": 4, @@ -396743,11 +396893,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -397163,31 +397313,31 @@ "doi": "10.1101/2025.06.25.661447", "projections": { "pca": [ - 0.22139334678649902, - -0.07043672353029251 + 0.22118115425109863, + -0.07071413844823837 ], "tsne": [ - 35.78276062011719, - -2.0477945804595947 + 30.050514221191406, + 10.416128158569336 ], "umap": [ - 8.836081504821777, - 7.300198554992676 + 9.01521110534668, + 6.932270050048828 ], "pca3d": [ - 0.22139334678649902, - -0.07044029235839844, - -0.07006911188364029 + 0.22118118405342102, + -0.07071785628795624, + -0.07002206891775131 ], "tsne3d": [ - 17.07038688659668, - 1.3178023099899292, - -1.865017056465149 + 18.380271911621094, + 1.5349208116531372, + -3.7114837169647217 ], "umap3d": [ - 6.960120677947998, - 4.690618515014648, - 1.7809454202651978 + 6.687296390533447, + 4.5775465965271, + 0.6834730505943298 ] }, "cluster_id": 0, @@ -397198,12 +397348,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -397625,31 +397775,31 @@ "doi": "10.1101/2025.06.26.661544", "projections": { "pca": [ - 0.07927572727203369, - -0.12748213112354279 + 0.07907557487487793, + -0.12695109844207764 ], "tsne": [ - 14.391945838928223, - 7.863513469696045 + 14.302006721496582, + 0.23095476627349854 ], "umap": [ - 7.361610412597656, - 5.906195640563965 + 7.771772384643555, + 5.643766403198242 ], "pca3d": [ - 0.07927579432725906, - -0.12749598920345306, - -0.06630004942417145 + 0.07907570153474808, + -0.12696559727191925, + -0.0659017488360405 ], "tsne3d": [ - 10.738436698913574, - 10.597640037536621, - -10.377151489257812 + 13.2288179397583, + -3.6818554401397705, + 7.289943218231201 ], "umap3d": [ - 6.248457908630371, - 5.256590843200684, - 3.1755154132843018 + 6.399606704711914, + 5.573221683502197, + 2.0665206909179688 ] }, "cluster_id": 4, @@ -397660,11 +397810,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -398083,31 +398233,31 @@ "doi": "10.1101/2025.06.20.660785", "projections": { "pca": [ - 0.17829617857933044, - -0.087772436439991 + 0.17820285260677338, + -0.08806150406599045 ], "tsne": [ - 15.384376525878906, - 24.08419418334961 + 15.964949607849121, + -26.42829704284668 ], "umap": [ - 7.4077253341674805, - 5.125812530517578 + 7.7081170082092285, + 4.819979667663574 ], "pca3d": [ - 0.17829620838165283, - -0.08777053654193878, - 0.12094610184431076 + 0.17820295691490173, + -0.08805891126394272, + 0.1210022047162056 ], "tsne3d": [ - -2.025164842605591, - 23.863000869750977, - 4.788719177246094 + 8.450258255004883, + -5.993436813354492, + 9.82253360748291 ], "umap3d": [ - 6.5081329345703125, - 6.043848037719727, - 3.7588560581207275 + 6.603470802307129, + 6.3888258934021, + 2.2370078563690186 ] }, "cluster_id": 4, @@ -398118,11 +398268,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -398551,31 +398701,31 @@ "doi": "10.1101/2024.12.30.630826", "projections": { "pca": [ - 0.2017960548400879, - -0.07132576406002045 + 0.20173119008541107, + -0.07165083289146423 ], "tsne": [ - 16.66803741455078, - 29.821929931640625 + 20.143415451049805, + -28.434370040893555 ], "umap": [ - 7.861636161804199, - 4.790132999420166 + 8.278902053833008, + 4.339296340942383 ], "pca3d": [ - 0.2017960101366043, - -0.07130808383226395, - 0.12201946973800659 + 0.20173108577728271, + -0.07163237780332565, + 0.12204477936029434 ], "tsne3d": [ - 1.363391399383545, - 22.1957950592041, - 1.734346866607666 + 11.619873046875, + -15.281105995178223, + 3.6668598651885986 ], "umap3d": [ - 7.116833686828613, - 6.072381019592285, - 3.7956008911132812 + 7.183714389801025, + 6.532413482666016, + 2.1505439281463623 ] }, "cluster_id": 4, @@ -398586,11 +398736,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -399001,31 +399151,31 @@ "journal": "Nature", "projections": { "pca": [ - 0.24223800003528595, - 0.09687148779630661 + 0.24253436923027039, + 0.09615153074264526 ], "tsne": [ - 49.48981857299805, - -5.951093673706055 + 47.23725509643555, + 7.392468452453613 ], "umap": [ - 10.248676300048828, - 8.03877067565918 + 10.420633316040039, + 7.698250770568848 ], "pca3d": [ - 0.242237851023674, - 0.0968501940369606, - -0.14018456637859344 + 0.2425341010093689, + 0.0961296558380127, + -0.14041143655776978 ], "tsne3d": [ - 29.333173751831055, - -6.703231334686279, - -9.838544845581055 + 29.348159790039062, + 11.44355297088623, + 3.3356049060821533 ], "umap3d": [ - 7.125735282897949, - 4.827195644378662, - 0.6052773594856262 + 6.527961730957031, + 4.380101203918457, + -0.4821119010448456 ] }, "cluster_id": 0, @@ -399036,12 +399186,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -399474,31 +399624,31 @@ "cited_by_count": 23, "projections": { "pca": [ - 0.1139005571603775, - -0.07133299857378006 + 0.11387185752391815, + -0.07083163410425186 ], "tsne": [ - 7.157268047332764, - 15.852556228637695 + 7.297854423522949, + -10.538294792175293 ], "umap": [ - 6.622708320617676, - 5.725683212280273 + 6.799903869628906, + 5.410915851593018 ], "pca3d": [ - 0.11390043050050735, - -0.07131672650575638, - 0.1784444898366928 + 0.11387181282043457, + -0.07081533223390579, + 0.17863169312477112 ], "tsne3d": [ - 0.1932474821805954, - 5.758024215698242, - -9.209433555603027 + 1.9489414691925049, + -0.5039077401161194, + 3.9169580936431885 ], "umap3d": [ - 5.5225090980529785, - 5.2101898193359375, - 3.7680089473724365 + 5.895069599151611, + 5.58367919921875, + 2.8467795848846436 ] }, "cluster_id": 4, @@ -399509,11 +399659,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -399941,31 +400091,31 @@ "doi": "10.1101/2025.06.16.660049", "projections": { "pca": [ - 0.14830608665943146, - -0.08362064510583878 + 0.1482265144586563, + -0.0839436873793602 ], "tsne": [ - 17.02263832092285, - 31.36768913269043 + 20.292558670043945, + -29.872791290283203 ], "umap": [ - 7.906782627105713, - 4.62607479095459 + 8.275124549865723, + 4.185300827026367 ], "pca3d": [ - 0.14830592274665833, - -0.08359462767839432, - 0.16323012113571167 + 0.14822648465633392, + -0.08391626179218292, + 0.163271963596344 ], "tsne3d": [ - 2.3724827766418457, - 23.413402557373047, - 3.1344597339630127 + 11.860860824584961, + -16.85753631591797, + 5.131875038146973 ], "umap3d": [ - 7.19346284866333, - 6.1933465003967285, - 3.927675485610962 + 7.3279266357421875, + 6.684525012969971, + 2.1334545612335205 ] }, "cluster_id": 4, @@ -399976,11 +400126,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -400391,31 +400541,31 @@ "journal": "Nature", "projections": { "pca": [ - 0.13288350403308868, - 0.17451538145542145 + 0.13330192863941193, + 0.17431247234344482 ], "tsne": [ - 53.27725601196289, - -6.498652458190918 + 50.521392822265625, + 9.28287410736084 ], "umap": [ - 10.463088035583496, - 7.689169883728027 + 10.594463348388672, + 7.358835220336914 ], "pca3d": [ - 0.13288290798664093, - 0.17452892661094666, - -0.0970146656036377 + 0.13330131769180298, + 0.17432530224323273, + -0.09742458909749985 ], "tsne3d": [ - 32.79438781738281, - -4.320012092590332, - -6.549139022827148 + 33.26678466796875, + 7.885046482086182, + 2.0325961112976074 ], "umap3d": [ - 7.611630916595459, - 4.996761322021484, - 0.5588300824165344 + 6.791318416595459, + 4.569594860076904, + -0.7012603282928467 ] }, "cluster_id": 0, @@ -400426,12 +400576,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -400849,31 +400999,31 @@ "cited_by_count": 6, "projections": { "pca": [ - 0.12031254172325134, - -0.1431431919336319 + 0.11994627863168716, + -0.1435517966747284 ], "tsne": [ - 16.31800079345703, - 34.459049224853516 + 19.67579460144043, + -32.69525909423828 ], "umap": [ - 7.93229341506958, - 4.423440933227539 + 8.27271842956543, + 3.918647527694702 ], "pca3d": [ - 0.12031242251396179, - -0.1431007981300354, - 0.17965158820152283 + 0.1199461966753006, + -0.1435079425573349, + 0.17963261902332306 ], "tsne3d": [ - 1.7729719877243042, - 21.48253631591797, - 7.240860462188721 + 8.408904075622559, + -18.09274673461914, + 7.713866710662842 ], "umap3d": [ - 7.216350078582764, - 6.366166114807129, - 4.096099376678467 + 7.441282749176025, + 6.910349369049072, + 2.256404161453247 ] }, "cluster_id": 4, @@ -400884,11 +401034,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -401310,31 +401460,31 @@ "doi": "10.1101/2025.03.01.641000", "projections": { "pca": [ - 0.12062143534421921, - -0.16433820128440857 + 0.12023234367370605, + -0.16493478417396545 ], "tsne": [ - 36.53183364868164, - -10.943992614746094 + 30.873493194580078, + 13.450166702270508 ], "umap": [ - 8.42597484588623, - 7.288734436035156 + 8.736963272094727, + 6.973743915557861 ], "pca3d": [ - 0.12062143534421921, - -0.16433818638324738, - -0.1726892739534378 + 0.12023259699344635, + -0.1649341732263565, + -0.17241667211055756 ], "tsne3d": [ - 15.111198425292969, - 2.8182616233825684, - 1.8615251779556274 + 18.59415626525879, + 2.8277673721313477, + -8.842806816101074 ], "umap3d": [ - 6.935362339019775, - 4.303162574768066, - 1.8760753870010376 + 6.676071643829346, + 4.30361795425415, + 1.0002704858779907 ] }, "cluster_id": 0, @@ -401345,12 +401495,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -401769,31 +401919,31 @@ "doi": "10.1101/2025.06.10.658961", "projections": { "pca": [ - 0.19977284967899323, - -0.03198571875691414 + 0.19971467554569244, + -0.032402344048023224 ], "tsne": [ - 26.473844528198242, - -8.527156829833984 + 23.638723373413086, + 8.86229419708252 ], "umap": [ - 8.225030899047852, - 7.326308250427246 + 8.375507354736328, + 7.056078910827637 ], "pca3d": [ - 0.1997726410627365, - -0.031975045800209045, - -0.1460302770137787 + 0.19971449673175812, + -0.032391373068094254, + -0.1459628790616989 ], "tsne3d": [ - 12.624287605285645, - 7.5147809982299805, - -2.599217653274536 + 12.653095245361328, + 3.5582258701324463, + -1.9537638425827026 ], "umap3d": [ - 6.317877769470215, - 4.522111892700195, - 2.0164337158203125 + 6.268350601196289, + 4.459918975830078, + 1.3041841983795166 ] }, "cluster_id": 0, @@ -401804,12 +401954,12 @@ "label": "Chromatin Regulation" }, { - "id": 13, - "label": "Genetic Variants" + "id": 2, + "label": "DNA Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -402219,31 +402369,31 @@ "doi": "10.21203/rs.3.rs-6363545/v1", "projections": { "pca": [ - 0.2064160853624344, - 0.10725950449705124 + 0.2065657377243042, + 0.10698071122169495 ], "tsne": [ - 53.185211181640625, - -5.032927513122559 + 51.54853439331055, + 7.108224391937256 ], "umap": [ - 10.43856143951416, - 7.689795970916748 + 10.54750919342041, + 7.315530776977539 ], "pca3d": [ - 0.2064157873392105, - 0.10726473480463028, - -0.12580768764019012 + 0.20656535029411316, + 0.10698527097702026, + -0.1261715143918991 ], "tsne3d": [ - 31.594884872436523, - -5.629117488861084, - -3.9501969814300537 + 33.124000549316406, + 9.149407386779785, + -0.7890562415122986 ], "umap3d": [ - 7.569295406341553, - 4.956263065338135, - 0.6129084229469299 + 6.8339009284973145, + 4.555735111236572, + -0.5855091214179993 ] }, "cluster_id": 0, @@ -402254,12 +402404,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -402684,47 +402834,47 @@ "doi": "10.1101/2025.06.05.658079", "projections": { "pca": [ - 0.16509974002838135, - -0.04425802454352379 + 0.16498541831970215, + -0.04479868710041046 ], "tsne": [ - 24.90793228149414, - 10.496018409729004 + 29.716947555541992, + -5.656923770904541 ], "umap": [ - 7.395436763763428, - 7.453393459320068 + 7.600811958312988, + 7.101500511169434 ], "pca3d": [ - 0.1650998890399933, - -0.04426936060190201, - -0.1849033087491989 + 0.16498546302318573, + -0.0448099710047245, + -0.1848137527704239 ], "tsne3d": [ - 11.794530868530273, - -6.580607891082764, - -11.223993301391602 + 11.539168357849121, + 12.421789169311523, + 3.7504994869232178 ], "umap3d": [ - 6.826676368713379, - 3.8791921138763428, - 2.971742630004883 + 7.171173572540283, + 4.189745903015137, + 2.0818915367126465 ] }, - "cluster_id": 4, - "cluster_label": "RNA Sequence Models", + "cluster_id": 6, + "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 4, - "label": "RNA Sequence Models" + "id": 6, + "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -403132,46 +403282,46 @@ ], "projections": { "pca": [ - -0.1541322022676468, - -0.14275917410850525 + -0.1543121635913849, + -0.1425269991159439 ], "tsne": [ - -29.11639404296875, - -1.4985977411270142 + -38.89592742919922, + 5.505931377410889 ], "umap": [ - 0.9626325964927673, - 8.31362247467041 + 1.1280990839004517, + 7.5597381591796875 ], "pca3d": [ - -0.15413181483745575, - -0.14275266230106354, - -0.0564294308423996 + -0.15431180596351624, + -0.14251995086669922, + -0.05601723492145538 ], "tsne3d": [ - -19.930761337280273, - -6.835913181304932, - 14.570680618286133 + -21.939359664916992, + 10.242630958557129, + 6.423901557922363 ], "umap3d": [ - 1.0870174169540405, - 4.636155128479004, - 5.266719341278076 + 2.3528897762298584, + 5.130053997039795, + 5.88040018081665 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -403607,31 +403757,31 @@ "doi": "10.1101/2025.06.05.657370", "projections": { "pca": [ - 0.1798926740884781, - 0.11675811558961868 + 0.1800646185874939, + 0.11662536859512329 ], "tsne": [ - 19.350444793701172, - -13.547450065612793 + 29.70911979675293, + 19.9905948638916 ], "umap": [ - 8.96352767944336, - 8.087000846862793 + 9.01073169708252, + 7.894546031951904 ], "pca3d": [ - 0.17989252507686615, - 0.11675062030553818, - -0.10962692648172379 + 0.1800643354654312, + 0.11661730706691742, + -0.10990944504737854 ], "tsne3d": [ - 18.438465118408203, - -16.679479598999023, - 1.5488238334655762 + 16.101314544677734, + 18.238849639892578, + -10.672046661376953 ], "umap3d": [ - 6.820430755615234, - 4.433450698852539, - 1.0378146171569824 + 6.289506435394287, + 4.21654748916626, + 0.3432501554489136 ] }, "cluster_id": 0, @@ -403642,12 +403792,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -404066,47 +404216,47 @@ "cited_by_count": 1, "projections": { "pca": [ - 0.1379833221435547, - -0.09207582473754883 + 0.13755029439926147, + -0.09220495074987411 ], "tsne": [ - 29.821727752685547, - -18.186735153198242 + 22.553966522216797, + 22.90986442565918 ], "umap": [ - 8.510095596313477, - 9.514861106872559 + 8.64968204498291, + 9.032611846923828 ], "pca3d": [ - 0.13798312842845917, - -0.09205687046051025, - -0.13657696545124054 + 0.13755013048648834, + -0.09218587726354599, + -0.13655789196491241 ], "tsne3d": [ - 16.159910202026367, - 10.851404190063477, - 5.433072566986084 + 10.588961601257324, + -6.053885459899902, + -14.585455894470215 ], "umap3d": [ - 5.398489952087402, - 4.571918487548828, - 1.0747010707855225 + 4.9603424072265625, + 4.207871437072754, + 0.908117413520813 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -404514,47 +404664,47 @@ "abstract": "When impressive AI biology results are full of errors", "projections": { "pca": [ - -0.17748650908470154, - 0.18067212402820587 + -0.1771189570426941, + 0.1819433569908142 ], "tsne": [ - -13.07781982421875, - -16.393789291381836 + -9.516206741333008, + 8.544206619262695 ], "umap": [ - 4.104633331298828, - 7.961982250213623 + 4.6478190422058105, + 7.090096473693848 ], "pca3d": [ - -0.17748703062534332, - 0.18070830404758453, - 0.03513292595744133 + -0.17711950838565826, + 0.18197982013225555, + 0.03494476526975632 ], "tsne3d": [ - -8.117927551269531, - -7.424241542816162, - 6.953264236450195 + -9.035445213317871, + 1.7662279605865479, + -2.684390068054199 ], "umap3d": [ - 3.6277170181274414, - 3.735084056854248, - 3.959408760070801 + 4.522780895233154, + 4.389584064483643, + 4.152846336364746 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -404970,31 +405120,31 @@ "doi": "10.1101/2025.05.27.656386", "projections": { "pca": [ - 0.042037200182676315, - -0.00918445736169815 + 0.04218123108148575, + -0.00903614703565836 ], "tsne": [ - 4.296950817108154, - 3.902726173400879 + 8.082599639892578, + -0.1647564321756363 ], "umap": [ - 6.687610149383545, - 6.085336685180664 + 7.101281642913818, + 5.657448768615723 ], "pca3d": [ - 0.04203750938177109, - -0.009217736311256886, - -0.013802433386445045 + 0.04218157008290291, + -0.009069430641829967, + -0.013652043417096138 ], "tsne3d": [ - 9.17027473449707, - 0.6673667430877686, - -1.9169855117797852 + 9.259115219116211, + -6.27729606628418, + -5.3478288650512695 ], "umap3d": [ - 5.954377174377441, - 4.878493785858154, - 3.791398048400879 + 6.368427276611328, + 5.37881326675415, + 2.724985122680664 ] }, "cluster_id": 4, @@ -405005,11 +405155,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -405432,47 +405582,47 @@ ], "projections": { "pca": [ - 0.06993414461612701, - -0.028165623545646667 + 0.06992312520742416, + -0.027831068262457848 ], "tsne": [ - 0.4815783202648163, - -22.303600311279297 + -2.0673041343688965, + 18.941871643066406 ], "umap": [ - 4.676496505737305, - 8.594857215881348 + 4.989625930786133, + 7.854859352111816 ], "pca3d": [ - 0.06993418186903, - -0.028163643553853035, - 0.10386840254068375 + 0.06992319226264954, + -0.027829190716147423, + 0.1039716824889183 ], "tsne3d": [ - 1.4095388650894165, - -6.628434658050537, - -5.688736915588379 + 0.5295792818069458, + 10.908055305480957, + 1.9299157857894897 ], "umap3d": [ - 3.916569471359253, - 3.9227659702301025, - 3.042337656021118 + 4.537962436676025, + 4.304527282714844, + 3.0950675010681152 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, - "label": "Medical AI" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -405887,47 +406037,47 @@ ], "projections": { "pca": [ - 0.08136259764432907, - -0.0385020487010479 + 0.08100410550832748, + -0.03845439851284027 ], "tsne": [ - 33.1983757019043, - -20.117963790893555 + 25.317825317382812, + 26.189722061157227 ], "umap": [ - 8.343510627746582, - 10.087382316589355 + 8.738153457641602, + 9.408101081848145 ], "pca3d": [ - 0.08136250823736191, - -0.038480933755636215, - -0.09353713691234589 + 0.08100400120019913, + -0.03843342512845993, + -0.09366609901189804 ], "tsne3d": [ - 17.48056411743164, - 13.63228702545166, - 2.463127851486206 + 11.970383644104004, + -7.2333760261535645, + -10.47473430633545 ], "umap3d": [ - 4.6687235832214355, - 4.869985580444336, - 1.1981549263000488 + 4.328499794006348, + 4.38948392868042, + 1.0942152738571167 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -406393,31 +406543,31 @@ "openalex_url": "https://openalex.org/W4410851723", "projections": { "pca": [ - 0.0631534531712532, - -0.06886155158281326 + 0.06293277442455292, + -0.06883932650089264 ], "tsne": [ - 20.76543426513672, - 10.47101879119873 + 24.299110412597656, + -2.5246644020080566 ], "umap": [ - 7.42245626449585, - 7.134934902191162 + 7.577522277832031, + 6.779109954833984 ], "pca3d": [ - 0.06315352767705917, - -0.06886111199855804, - -0.10027216374874115 + 0.06293285638093948, + -0.06883906573057175, + -0.10013769567012787 ], "tsne3d": [ - 12.279206275939941, - -1.55442214012146, - -13.757144927978516 + 11.458284378051758, + 8.482572555541992, + 6.172196388244629 ], "umap3d": [ - 6.623214244842529, - 4.1627516746521, - 3.034090518951416 + 6.952267646789551, + 4.491616725921631, + 2.1727488040924072 ] }, "cluster_id": 4, @@ -406428,12 +406578,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -406852,31 +407002,31 @@ ], "projections": { "pca": [ - 0.04785018414258957, - -0.14960519969463348 + 0.047660354524850845, + -0.14924611151218414 ], "tsne": [ - 14.960041999816895, - 5.876429557800293 + 16.190908432006836, + -2.2090582847595215 ], "umap": [ - 7.169133186340332, - 6.2282257080078125 + 7.425131797790527, + 5.684835433959961 ], "pca3d": [ - 0.0478505864739418, - -0.1496247798204422, - 0.04929327592253685 + 0.04766080901026726, + -0.1492658108472824, + 0.04970085248351097 ], "tsne3d": [ - 7.0072407722473145, - 6.20869255065918, - -12.459177017211914 + 9.103439331054688, + 0.4770621061325073, + 7.454831123352051 ], "umap3d": [ - 5.744213581085205, - 5.135526180267334, - 3.2012393474578857 + 5.934971332550049, + 5.387355804443359, + 2.2163381576538086 ] }, "cluster_id": 4, @@ -406887,11 +407037,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -407310,31 +407460,31 @@ "doi": "10.1101/2025.05.26.656171", "projections": { "pca": [ - 0.16471517086029053, - -0.11600350588560104 + 0.16439029574394226, + -0.1163635104894638 ], "tsne": [ - 21.12193489074707, - -2.4596118927001953 + 23.553363800048828, + 6.245394229888916 ], "umap": [ - 8.239214897155762, - 6.864894390106201 + 8.50606918334961, + 6.487067699432373 ], "pca3d": [ - 0.16471520066261292, - -0.11599425971508026, - -0.11077789962291718 + 0.16439037024974823, + -0.11635395139455795, + -0.11062823235988617 ], "tsne3d": [ - 17.20458221435547, - 5.494759559631348, - -9.643282890319824 + 12.983824729919434, + 3.0060648918151855, + 0.23144151270389557 ], "umap3d": [ - 6.604188442230225, - 4.87819766998291, - 2.3077452182769775 + 6.500186443328857, + 4.930111408233643, + 1.358996868133545 ] }, "cluster_id": 4, @@ -407345,12 +407495,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -407771,31 +407921,31 @@ "doi": "10.1101/2025.05.27.656122", "projections": { "pca": [ - 0.359427273273468, - 0.08658446371555328 + 0.3596406579017639, + 0.08557484298944473 ], "tsne": [ - 47.01536560058594, - 4.311390399932861 + 44.87016677856445, + 3.0771098136901855 ], "umap": [ - 9.58466625213623, - 7.656962871551514 + 9.812811851501465, + 6.997061252593994 ], "pca3d": [ - 0.3594270348548889, - 0.0865810215473175, - -0.06515097618103027 + 0.35964030027389526, + 0.08557158708572388, + -0.06551273167133331 ], "tsne3d": [ - 25.018661499023438, - 0.1574305146932602, - 5.7473225593566895 + 25.968990325927734, + 3.66487717628479, + -11.272435188293457 ], "umap3d": [ - 7.848641872406006, - 4.273138523101807, - 1.2438220977783203 + 7.395596981048584, + 4.173066139221191, + 0.13087314367294312 ] }, "cluster_id": 0, @@ -407806,12 +407956,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 20, - "label": "Chromatin Regulation" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -408230,31 +408380,31 @@ "doi": "10.1101/2025.05.20.655154", "projections": { "pca": [ - 0.12638770043849945, - -0.12740594148635864 + 0.12611471116542816, + -0.12709185481071472 ], "tsne": [ - -1.5139074325561523, - 21.51224136352539 + 5.761439323425293, + -23.99287223815918 ], "umap": [ - 5.850643157958984, - 4.734012126922607 + 6.352926254272461, + 4.520252704620361 ], "pca3d": [ - 0.12638792395591736, - -0.12742061913013458, - 0.1559678465127945 + 0.12611496448516846, + -0.12710650265216827, + 0.15616154670715332 ], "tsne3d": [ - -0.13182131946086884, - 3.8437492847442627, - 1.617618441581726 + -3.979243040084839, + -10.447199821472168, + 1.856844425201416 ], "umap3d": [ - 5.8378586769104, - 6.3934197425842285, - 4.48809814453125 + 6.0448174476623535, + 7.134015083312988, + 2.7519707679748535 ] }, "cluster_id": 3, @@ -408265,11 +408415,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -408695,46 +408845,46 @@ ], "projections": { "pca": [ - 0.08128013461828232, - 0.04217864200472832 + 0.08138706535100937, + 0.04213201254606247 ], "tsne": [ - 11.712449073791504, - -21.57168197631836 + 5.351327896118164, + 20.549821853637695 ], "umap": [ - 6.747649669647217, - 9.182079315185547 + 6.895505428314209, + 8.728909492492676 ], "pca3d": [ - 0.08127990365028381, - 0.04219595342874527, - 0.011934063397347927 + 0.08138681948184967, + 0.04214894026517868, + 0.011803803965449333 ], "tsne3d": [ - 2.5450472831726074, - -8.756404876708984, - 10.47366714477539 + 4.298366069793701, + 8.546456336975098, + -9.025863647460938 ], "umap3d": [ - 4.614645004272461, - 3.925123691558838, - 2.0914523601531982 + 4.764446258544922, + 3.9247586727142334, + 2.157050371170044 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -409143,46 +409293,46 @@ "abstract": "Single-cell dimensionality reduction toolkit. Contribute to saeyslab/ViVAE development by creating an account on GitHub.", "projections": { "pca": [ - 0.03612959384918213, - 0.1306866854429245 + 0.03668665140867233, + 0.1304732859134674 ], "tsne": [ - 22.755996704101562, - -28.154088973999023 + 13.72950267791748, + 28.53626823425293 ], "umap": [ - 7.558495998382568, - 9.432863235473633 + 7.491235733032227, + 9.236294746398926 ], "pca3d": [ - 0.03612933307886124, - 0.13069026172161102, - -0.09002840518951416 + 0.03668637201189995, + 0.1304769217967987, + -0.090119369328022 ], "tsne3d": [ - 14.196903228759766, - -14.267578125, - 10.586610794067383 + 6.282904624938965, + 3.6049387454986572, + -21.009519577026367 ], "umap3d": [ - 5.303732872009277, - 3.5445754528045654, - 1.4576659202575684 + 5.295802116394043, + 3.326852321624756, + 1.514840006828308 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -409597,31 +409747,31 @@ "journal": "Nature Reviews Genetics", "projections": { "pca": [ - 0.16386713087558746, - 0.07894328236579895 + 0.16408832371234894, + 0.07980417460203171 ], "tsne": [ - -1.031164288520813, - 20.592885971069336 + 7.281221389770508, + -23.074831008911133 ], "umap": [ - 5.781591415405273, - 4.385625839233398 + 6.329709053039551, + 4.1942572593688965 ], "pca3d": [ - 0.1638670414686203, - 0.07891853898763657, - 0.15385788679122925 + 0.16408824920654297, + 0.07977860420942307, + 0.15373358130455017 ], "tsne3d": [ - 4.114383220672607, - 1.6805003881454468, - 7.004797458648682 + -4.636110305786133, + -13.800537109375, + -4.9632062911987305 ], "umap3d": [ - 5.856538772583008, - 6.562410831451416, - 4.765118598937988 + 6.176679611206055, + 7.38458251953125, + 2.930812358856201 ] }, "cluster_id": 3, @@ -409632,12 +409782,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, - "label": "Protein Language" + "id": 3, + "label": "Protein Design" } ], "_embedding": [ @@ -410056,31 +410206,31 @@ "doi": "10.1101/2025.05.14.654102", "projections": { "pca": [ - 0.23628123104572296, - -0.08603141456842422 + 0.2361714243888855, + -0.0863431841135025 ], "tsne": [ - 60.46302795410156, - 8.053543090820312 + 53.59221267700195, + 23.131025314331055 ], "umap": [ - 10.14897632598877, - 6.347693920135498 + 10.3563871383667, + 5.959734916687012 ], "pca3d": [ - 0.23628132045269012, - -0.0860351100564003, - 0.18038083612918854 + 0.23617145419120789, + -0.0863468199968338, + 0.18036597967147827 ], "tsne3d": [ - 27.068254470825195, - 11.316751480102539, - -2.900411367416382 + 29.928421020507812, + -6.616860866546631, + -0.15913602709770203 ], "umap3d": [ - 8.03449821472168, - 5.731192111968994, - 1.5264219045639038 + 7.428044319152832, + 5.549930572509766, + -0.25960999727249146 ] }, "cluster_id": 0, @@ -410091,12 +410241,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -410532,31 +410682,31 @@ "doi": "10.1101/2025.05.13.653733", "projections": { "pca": [ - 0.23498252034187317, - -0.005756300874054432 + 0.23480767011642456, + -0.006484431214630604 ], "tsne": [ - 34.79851531982422, - -13.505471229553223 + 31.349355697631836, + 21.668710708618164 ], "umap": [ - 9.191299438476562, - 8.046192169189453 + 9.473419189453125, + 7.470583915710449 ], "pca3d": [ - 0.23498238623142242, - -0.0057475813664495945, - -0.15895429253578186 + 0.2348075658082962, + -0.00647570937871933, + -0.15917016565799713 ], "tsne3d": [ - 19.013607025146484, - -3.355539560317993, - 1.3590283393859863 + 22.437541961669922, + 3.8584446907043457, + -6.8786301612854 ], "umap3d": [ - 7.110377311706543, - 4.194902420043945, - 1.2446749210357666 + 6.791874885559082, + 4.078691005706787, + 0.39573758840560913 ] }, "cluster_id": 0, @@ -410567,12 +410717,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -410988,31 +411138,31 @@ "doi": "10.1101/2024.03.08.584160", "projections": { "pca": [ - 0.15909652411937714, - 0.004426896572113037 + 0.1589554101228714, + 0.004058390855789185 ], "tsne": [ - 28.623136520385742, - -2.4435741901397705 + 29.616405487060547, + 1.3867696523666382 ], "umap": [ - 8.393198013305664, - 7.687812805175781 + 8.554279327392578, + 7.26688814163208 ], "pca3d": [ - 0.1590965986251831, - 0.004417985677719116, - -0.14439882338047028 + 0.15895549952983856, + 0.00404987670481205, + -0.1445203572511673 ], "tsne3d": [ - 10.399359703063965, - -6.489998817443848, - -0.3078554570674896 + 8.779007911682129, + 14.756128311157227, + -10.265997886657715 ], "umap3d": [ - 7.173944473266602, - 4.0186944007873535, - 2.184314489364624 + 7.270949840545654, + 4.131814479827881, + 1.0965826511383057 ] }, "cluster_id": 0, @@ -411023,12 +411173,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -411458,46 +411608,46 @@ ], "projections": { "pca": [ - 0.11188936233520508, - -0.04249626770615578 + 0.11184021830558777, + -0.04251115024089813 ], "tsne": [ - 21.923460006713867, - -21.878368377685547 + 14.33027458190918, + 22.551441192626953 ], "umap": [ - 7.611303806304932, - 9.452078819274902 + 7.674596309661865, + 9.103643417358398 ], "pca3d": [ - 0.1118893250823021, - -0.04248012974858284, - -0.060195568948984146 + 0.11184021085500717, + -0.04249504953622818, + -0.060059595853090286 ], "tsne3d": [ - 11.428984642028809, - -8.131143569946289, - 9.403657913208008 + 8.87951946258545, + 3.1517744064331055, + -14.011513710021973 ], "umap3d": [ - 5.180340766906738, - 3.7690176963806152, - 1.485830545425415 + 5.1250834465026855, + 3.541783332824707, + 1.5017839670181274 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -411908,31 +412058,31 @@ "journal": "Nature Reviews Genetics", "projections": { "pca": [ - 0.043557971715927124, - -0.2143600434064865 + 0.04312020167708397, + -0.2145327925682068 ], "tsne": [ - 20.456417083740234, - -0.8390549421310425 + 21.385417938232422, + 1.9134618043899536 ], "umap": [ - 7.897828102111816, - 6.573216438293457 + 8.114970207214355, + 6.273500442504883 ], "pca3d": [ - 0.043557893484830856, - -0.21431735157966614, - -0.03144591674208641 + 0.04312020540237427, + -0.21448899805545807, + -0.03101247362792492 ], "tsne3d": [ - 13.970587730407715, - 2.72514271736145, - -12.352458953857422 + 11.895047187805176, + 2.714231014251709, + 3.4956319332122803 ], "umap3d": [ - 6.392610549926758, - 5.042747497558594, - 2.547609567642212 + 6.34220027923584, + 5.161248683929443, + 1.6371397972106934 ] }, "cluster_id": 4, @@ -411943,12 +412093,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -412370,31 +412520,31 @@ "doi": "10.1101/2025.05.06.652459", "projections": { "pca": [ - 0.1929972767829895, - -0.02682279609143734 + 0.1930694282054901, + -0.02766217105090618 ], "tsne": [ - 15.92890453338623, - 35.90244674682617 + 18.453344345092773, + -33.48563766479492 ], "umap": [ - 7.882672309875488, - 4.2508544921875 + 8.31583023071289, + 3.899545431137085 ], "pca3d": [ - 0.1929970234632492, - -0.026801496744155884, - 0.1346675604581833 + 0.19306929409503937, + -0.027640005573630333, + 0.13451409339904785 ], "tsne3d": [ - 3.116884231567383, - 21.20623207092285, - 9.047606468200684 + 7.958046913146973, + -20.24629783630371, + 7.7194671630859375 ], "umap3d": [ - 7.198672294616699, - 6.433976173400879, - 4.160643577575684 + 7.453955173492432, + 6.964912414550781, + 2.3213064670562744 ] }, "cluster_id": 4, @@ -412405,11 +412555,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -412838,31 +412988,31 @@ "openalex_url": "https://openalex.org/W4410206253", "projections": { "pca": [ - 0.24464286863803864, - 0.027347030118107796 + 0.24470923840999603, + 0.026829324662685394 ], "tsne": [ - 52.36674499511719, - -9.048137664794922 + 48.46197509765625, + 11.28200912475586 ], "umap": [ - 10.074080467224121, - 7.830019950866699 + 10.349315643310547, + 7.555792331695557 ], "pca3d": [ - 0.24464263021945953, - 0.027341153472661972, - -0.034037552773952484 + 0.24470916390419006, + 0.026822656393051147, + -0.03425423428416252 ], "tsne3d": [ - 27.91594886779785, - -0.8290805816650391, - -6.440903186798096 + 27.006277084350586, + 5.131956100463867, + -0.8204619884490967 ], "umap3d": [ - 7.0980939865112305, - 5.109184741973877, - 0.7254763245582581 + 6.489705562591553, + 4.600088119506836, + -0.35761094093322754 ] }, "cluster_id": 0, @@ -412873,12 +413023,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -413308,31 +413458,31 @@ "doi": "10.1101/2023.03.16.532704", "projections": { "pca": [ - 0.17341087758541107, - -0.03129146248102188 + 0.17334505915641785, + -0.031243344768881798 ], "tsne": [ - 39.14146041870117, - 21.713186264038086 + 22.25429916381836, + 12.348885536193848 ], "umap": [ - 8.648656845092773, - 7.045382499694824 + 8.878486633300781, + 6.661353588104248 ], "pca3d": [ - 0.17341087758541107, - -0.03131028264760971, - 0.06049090996384621 + 0.17334513366222382, + -0.03126213699579239, + 0.06045754253864288 ], "tsne3d": [ - 25.42694664001465, - 16.277311325073242, - 4.894723892211914 + 16.707149505615234, + -12.202363014221191, + -13.509208679199219 ], "umap3d": [ - 6.5844221115112305, - 5.146204948425293, - 1.6981592178344727 + 6.18963623046875, + 4.92881965637207, + 0.7591274380683899 ] }, "cluster_id": 0, @@ -413343,12 +413493,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -413771,47 +413921,47 @@ "doi": "10.1101/2025.05.02.651939", "projections": { "pca": [ - 0.26094505190849304, - -0.026753418147563934 + 0.2607438564300537, + -0.027307961136102676 ], "tsne": [ - 31.05710792541504, - -15.705643653869629 + 24.675565719604492, + 21.339336395263672 ], "umap": [ - 8.610268592834473, - 9.311139106750488 + 8.733207702636719, + 8.829544067382812 ], "pca3d": [ - 0.2609449028968811, - -0.026749590411782265, - -0.11029858142137527 + 0.26074373722076416, + -0.027304133400321007, + -0.11046458035707474 ], "tsne3d": [ - 19.12405014038086, - 10.550215721130371, - 6.831879615783691 + 13.060758590698242, + -3.7202999591827393, + -13.615882873535156 ], "umap3d": [ - 5.590085506439209, - 4.470112323760986, - 1.0263752937316895 + 5.175565242767334, + 4.118337154388428, + 0.79185950756073 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -414233,31 +414383,31 @@ "doi": "10.1101/2025.05.03.652028", "projections": { "pca": [ - 0.06396672874689102, - -0.1306864470243454 + 0.06372716277837753, + -0.13099783658981323 ], "tsne": [ - 17.096439361572266, - 34.3880615234375 + 20.538175582885742, + -32.89393615722656 ], "umap": [ - 7.921926975250244, - 4.3949971199035645 + 8.278401374816895, + 3.9335339069366455 ], "pca3d": [ - 0.06396666914224625, - -0.13065482676029205, - 0.11816191673278809 + 0.06372717022895813, + -0.13096508383750916, + 0.11826910823583603 ], "tsne3d": [ - 1.2119289636611938, - 22.935091018676758, - 7.621603965759277 + 9.523280143737793, + -17.825254440307617, + 8.812803268432617 ], "umap3d": [ - 7.274120807647705, - 6.4486165046691895, - 4.1857476234436035 + 7.458302021026611, + 6.936445713043213, + 2.28196120262146 ] }, "cluster_id": 4, @@ -414268,11 +414418,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -414700,31 +414850,31 @@ "doi": "10.1101/2025.04.27.650894", "projections": { "pca": [ - 0.1817140430212021, - -0.017608223482966423 + 0.18163758516311646, + -0.01817788928747177 ], "tsne": [ - 33.12394714355469, - -4.183767318725586 + 37.17045974731445, + 7.723648548126221 ], "umap": [ - 8.819649696350098, - 8.27126407623291 + 9.24077033996582, + 7.904836654663086 ], "pca3d": [ - 0.1817139834165573, - -0.017593860626220703, - -0.17053842544555664 + 0.18163740634918213, + -0.01816355623304844, + -0.17056909203529358 ], "tsne3d": [ - 20.200098037719727, - -5.995230197906494, - -3.906750440597534 + 21.111045837402344, + 11.406754493713379, + -2.3173975944519043 ], "umap3d": [ - 6.756576061248779, - 4.243195533752441, - 1.369947075843811 + 6.469417572021484, + 4.015110492706299, + 0.57832270860672 ] }, "cluster_id": 0, @@ -414735,12 +414885,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -415175,31 +415325,31 @@ "doi": "10.1101/2025.04.30.651381", "projections": { "pca": [ - 0.27508485317230225, - 0.012056965380907059 + 0.27506303787231445, + 0.011266565881669521 ], "tsne": [ - 38.63029098510742, - -4.801149368286133 + 39.294471740722656, + 11.374391555786133 ], "umap": [ - 9.375146865844727, - 7.987269878387451 + 9.605942726135254, + 7.581395626068115 ], "pca3d": [ - 0.27508488297462463, - 0.012048114091157913, - -0.17768287658691406 + 0.27506303787231445, + 0.011257334612309933, + -0.17779676616191864 ], "tsne3d": [ - 20.384977340698242, - -4.772256851196289, - 0.16884367167949677 + 22.77863883972168, + 6.047466278076172, + -5.822476387023926 ], "umap3d": [ - 7.278881549835205, - 4.201404571533203, - 1.2400916814804077 + 6.852360248565674, + 3.9654345512390137, + 0.2947160601615906 ] }, "cluster_id": 0, @@ -415210,12 +415360,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -415623,31 +415773,31 @@ ], "projections": { "pca": [ - -0.06315740942955017, - -0.15500836074352264 + -0.0633901059627533, + -0.15483808517456055 ], "tsne": [ - 12.621922492980957, - -5.029190540313721 + 13.004464149475098, + 6.456897735595703 ], "umap": [ - 7.989947319030762, - 6.473074436187744 + 7.964361667633057, + 6.178061485290527 ], "pca3d": [ - -0.0631573349237442, - -0.15499229729175568, - -0.09392715990543365 + -0.06339000165462494, + -0.15482105314731598, + -0.0934278666973114 ], "tsne3d": [ - 15.847525596618652, - 3.609879732131958, - -15.849291801452637 + 13.866851806640625, + 3.3961832523345947, + 9.92280101776123 ], "umap3d": [ - 6.194719314575195, - 5.060673713684082, - 2.447145938873291 + 6.076139450073242, + 5.098148345947266, + 1.6502660512924194 ] }, "cluster_id": 4, @@ -415658,12 +415808,12 @@ "label": "RNA Sequence Models" }, { - "id": 3, - "label": "RNA Splicing" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -416086,31 +416236,31 @@ "doi": "10.1101/2025.03.29.646077", "projections": { "pca": [ - 0.16875754296779633, - -0.10292593389749527 + 0.16845078766345978, + -0.10331414639949799 ], "tsne": [ - 37.066383361816406, - -2.3535048961639404 + 31.408885955810547, + 9.753363609313965 ], "umap": [ - 8.932945251464844, - 7.318758010864258 + 9.215895652770996, + 6.921962738037109 ], "pca3d": [ - 0.16875745356082916, - -0.10291123390197754, - -0.10950072109699249 + 0.16845068335533142, + -0.10329935699701309, + -0.1093948483467102 ], "tsne3d": [ - 18.7796688079834, - 2.63946795463562, - -2.7457518577575684 + 20.4803466796875, + 1.585931658744812, + -2.4888246059417725 ], "umap3d": [ - 6.847146987915039, - 4.7844014167785645, - 1.547471523284912 + 6.525322437286377, + 4.708215713500977, + 0.5520519018173218 ] }, "cluster_id": 0, @@ -416121,11 +416271,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -416547,31 +416697,31 @@ "doi": "10.1101/2025.04.22.650035", "projections": { "pca": [ - 0.17473390698432922, - -0.037329498678445816 + 0.17475788295269012, + -0.037647806107997894 ], "tsne": [ - 52.1291389465332, - -11.494429588317871 + 45.494075775146484, + 13.269554138183594 ], "umap": [ - 10.041879653930664, - 7.775743007659912 + 10.236239433288574, + 7.469379901885986 ], "pca3d": [ - 0.17473389208316803, - -0.03733757883310318, - 0.02697526104748249 + 0.17475804686546326, + -0.03765617683529854, + 0.02697870321571827 ], "tsne3d": [ - 25.010889053344727, - 0.7388896346092224, - -8.12079906463623 + 26.187204360961914, + 5.5911102294921875, + 5.320153713226318 ], "umap3d": [ - 6.96521520614624, - 5.191296100616455, - 0.8736556768417358 + 6.3372368812561035, + 4.693178653717041, + -0.20924457907676697 ] }, "cluster_id": 0, @@ -416582,12 +416732,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -417011,31 +417161,31 @@ "doi": "10.1101/2025.03.27.645711", "projections": { "pca": [ - 0.2041492611169815, - -0.07260629534721375 + 0.2041035294532776, + -0.07272317260503769 ], "tsne": [ - 8.07185173034668, - 9.296926498413086 + 10.418740272521973, + -6.732170104980469 ], "umap": [ - 6.936285495758057, - 6.096404552459717 + 7.092633247375488, + 5.9281792640686035 ], "pca3d": [ - 0.20414945483207703, - -0.0726209282875061, - 0.006112559698522091 + 0.2041037529706955, + -0.07273782044649124, + 0.006307800766080618 ], "tsne3d": [ - 8.236602783203125, - 3.527273178100586, - -9.810786247253418 + 6.438414096832275, + 3.005364418029785, + 4.772919178009033 ], "umap3d": [ - 5.74678897857666, - 4.949676036834717, - 3.5763955116271973 + 5.9975996017456055, + 5.218147277832031, + 2.562903881072998 ] }, "cluster_id": 4, @@ -417046,11 +417196,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -417476,47 +417626,47 @@ "doi": "10.1101/2025.04.16.648420", "projections": { "pca": [ - 0.19099237024784088, - -0.04148535430431366 + 0.19088096916675568, + -0.0418705977499485 ], "tsne": [ - 25.91008186340332, - 11.149434089660645 + 29.55763816833496, + -3.7481720447540283 ], "umap": [ - 7.6318864822387695, - 7.501392364501953 + 7.896028995513916, + 7.149990081787109 ], "pca3d": [ - 0.19099248945713043, - -0.041497282683849335, - -0.1354307383298874 + 0.19088110327720642, + -0.04188261926174164, + -0.13538329303264618 ], "tsne3d": [ - 14.861250877380371, - -5.187538146972656, - -12.422289848327637 + 13.73632526397705, + 11.250374794006348, + 4.111941337585449 ], "umap3d": [ - 6.94552755355835, - 3.92690110206604, - 2.8222951889038086 + 7.2318220138549805, + 4.176456451416016, + 1.8502085208892822 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -417948,31 +418098,31 @@ "doi": "10.1101/2025.04.09.647946", "projections": { "pca": [ - 0.06479194760322571, - -0.14543676376342773 + 0.06444890797138214, + -0.14520405232906342 ], "tsne": [ - 14.17418384552002, - 9.919892311096191 + 13.569607734680176, + -2.3769257068634033 ], "umap": [ - 7.089777946472168, - 6.0320024490356445 + 7.532224178314209, + 5.5617828369140625 ], "pca3d": [ - 0.06479218602180481, - -0.14543406665325165, - 0.004648794885724783 + 0.06444910913705826, + -0.14520099759101868, + 0.004926660098135471 ], "tsne3d": [ - 8.710566520690918, - 9.3910551071167, - -8.267622947692871 + 10.049416542053223, + -4.424440860748291, + 6.62224006652832 ], "umap3d": [ - 6.179501533508301, - 5.060583591461182, - 3.4207205772399902 + 6.488319396972656, + 5.450174331665039, + 2.3085856437683105 ] }, "cluster_id": 4, @@ -417983,11 +418133,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -418414,46 +418564,46 @@ "openalex_url": "https://openalex.org/W4409566559", "projections": { "pca": [ - 0.010507379658520222, - 0.07067631930112839 + 0.010785910300910473, + 0.07084028422832489 ], "tsne": [ - 23.17229461669922, - -20.10321617126465 + 16.193241119384766, + 21.702255249023438 ], "umap": [ - 7.750037670135498, - 9.510180473327637 + 7.66975736618042, + 9.109298706054688 ], "pca3d": [ - 0.010507259517908096, - 0.07068179547786713, - -0.08827687054872513 + 0.010785704478621483, + 0.0708455815911293, + -0.08829940110445023 ], "tsne3d": [ - 10.76831340789795, - -4.759570598602295, - 11.332130432128906 + 7.684540271759033, + 0.09387629479169846, + -12.536955833435059 ], "umap3d": [ - 5.253966331481934, - 3.837433099746704, - 1.3940013647079468 + 5.159715175628662, + 3.5904181003570557, + 1.4632738828659058 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -418887,46 +419037,46 @@ "cited_by_count": 30, "projections": { "pca": [ - 0.14885187149047852, - -0.017061885446310043 + 0.14883281290531158, + -0.017167512327432632 ], "tsne": [ - 24.796974182128906, - -18.854808807373047 + 18.24565887451172, + 20.89382553100586 ], "umap": [ - 7.72061824798584, - 9.324318885803223 + 7.673536777496338, + 8.972848892211914 ], "pca3d": [ - 0.14885178208351135, - -0.01706063188612461, - -0.06882679462432861 + 0.14883269369602203, + -0.017166439443826675, + -0.06882789731025696 ], "tsne3d": [ - 7.418605327606201, - -6.730252742767334, - 13.0173921585083 + 10.961921691894531, + -0.4437359869480133, + -12.827781677246094 ], "umap3d": [ - 5.184013366699219, - 3.975449323654175, - 1.4069409370422363 + 5.112587928771973, + 3.715764284133911, + 1.3143203258514404 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -419337,31 +419487,31 @@ "journal": "Nature", "projections": { "pca": [ - 0.2841639518737793, - 0.08377241343259811 + 0.28426626324653625, + 0.08286089450120926 ], "tsne": [ - 56.22222900390625, - 5.9206414222717285 + 55.566131591796875, + 17.248104095458984 ], "umap": [ - 10.25313949584961, - 6.760647773742676 + 10.496034622192383, + 6.365265369415283 ], "pca3d": [ - 0.2841634750366211, - 0.08378888666629791, - -0.12562903761863708 + 0.2842658758163452, + 0.08287753164768219, + -0.12603875994682312 ], "tsne3d": [ - 31.023778915405273, - 6.760660171508789, - -2.860973596572876 + 31.19767951965332, + -2.223267078399658, + 0.33406949043273926 ], "umap3d": [ - 8.120802879333496, - 5.4640350341796875, - 1.2502577304840088 + 7.434512138366699, + 5.148902416229248, + -0.4200999140739441 ] }, "cluster_id": 0, @@ -419372,12 +419522,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -419787,46 +419937,46 @@ "journal": "Nature", "projections": { "pca": [ - 0.028926976025104523, - 0.0034075197763741016 + 0.029097244143486023, + 0.003432617988437414 ], "tsne": [ - 15.708489418029785, - -24.205747604370117 + 12.170238494873047, + 20.163837432861328 ], "umap": [ - 7.318711757659912, - 9.437734603881836 + 7.158202171325684, + 9.01173210144043 ], "pca3d": [ - 0.028926774859428406, - 0.0034247329458594322, - 0.06588350981473923 + 0.029097046703100204, + 0.0034497915767133236, + 0.0659412294626236 ], "tsne3d": [ - 7.741794109344482, - -5.4535980224609375, - 9.813190460205078 + 5.3345770835876465, + 2.772751808166504, + -11.186531066894531 ], "umap3d": [ - 5.013652324676514, - 3.638457775115967, - 1.6949927806854248 + 4.9623541831970215, + 3.5019993782043457, + 1.875332236289978 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -420248,47 +420398,47 @@ "doi": "10.1101/2025.04.09.648060", "projections": { "pca": [ - 0.19478441774845123, - -0.05108662322163582 + 0.1945958137512207, + -0.05114162713289261 ], "tsne": [ - 32.35460662841797, - 5.434560298919678 + 33.947044372558594, + -8.619169235229492 ], "umap": [ - 7.9546589851379395, - 7.927846908569336 + 7.983591556549072, + 7.487796783447266 ], "pca3d": [ - 0.1947845220565796, - -0.051107387989759445, - -0.13838458061218262 + 0.1945960372686386, + -0.05116264894604683, + -0.1382846087217331 ], "tsne3d": [ - 17.705575942993164, - -11.219785690307617, - -13.221898078918457 + 17.60413360595703, + 17.554689407348633, + 3.3170394897460938 ], "umap3d": [ - 6.952733516693115, - 3.692060947418213, - 2.3906233310699463 + 7.1287360191345215, + 3.768120527267456, + 1.6367031335830688 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -420706,31 +420856,31 @@ "doi": "10.1101/2025.04.07.647656", "projections": { "pca": [ - 0.11230386048555374, - -0.1297125369310379 + 0.11199343204498291, + -0.12979398667812347 ], "tsne": [ - 22.342899322509766, - 9.44544506072998 + 26.35872459411621, + -2.69808292388916 ], "umap": [ - 7.587644577026367, - 7.214663505554199 + 7.702794551849365, + 6.845244407653809 ], "pca3d": [ - 0.11230402439832687, - -0.1297309845685959, - -0.12688308954238892 + 0.111993707716465, + -0.12981273233890533, + -0.12660934031009674 ], "tsne3d": [ - 13.144089698791504, - -3.5843448638916016, - -11.51707935333252 + 12.179391860961914, + 9.341938018798828, + 3.752375602722168 ], "umap3d": [ - 6.694929122924805, - 4.135826587677002, - 2.91943359375 + 7.003366947174072, + 4.466249942779541, + 2.02903151512146 ] }, "cluster_id": 4, @@ -420741,12 +420891,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -421173,31 +421323,31 @@ "doi": "10.1101/2025.04.10.648102", "projections": { "pca": [ - 0.22331613302230835, - 0.010966225527226925 + 0.22329817712306976, + 0.010532238520681858 ], "tsne": [ - 53.7700080871582, - 12.229973793029785 + 57.74488830566406, + 15.180749893188477 ], "umap": [ - 9.20194149017334, - 6.376965045928955 + 9.39725399017334, + 6.024964809417725 ], "pca3d": [ - 0.22331605851650238, - 0.010967991314828396, - -0.06908612698316574 + 0.22329801321029663, + 0.010534258559346199, + -0.0692308321595192 ], "tsne3d": [ - 34.18379211425781, - 7.764627933502197, - -4.561158180236816 + 30.620901107788086, + -3.185997486114502, + 3.9398720264434814 ], "umap3d": [ - 7.709233283996582, - 4.933714866638184, - 2.118053913116455 + 7.3799614906311035, + 5.126734733581543, + 0.7715413570404053 ] }, "cluster_id": 0, @@ -421208,12 +421358,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 2, + "label": "DNA Regulation" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -421630,31 +421780,31 @@ "doi": "10.1101/2025.04.02.646906", "projections": { "pca": [ - 0.07368489354848862, - -0.11348041146993637 + 0.0735272616147995, + -0.11343695223331451 ], "tsne": [ - -13.412114143371582, - 32.53557586669922 + -9.52507495880127, + -27.33369255065918 ], "umap": [ - 5.030889987945557, - 3.9017884731292725 + 5.626529693603516, + 3.387967586517334 ], "pca3d": [ - 0.07368491590023041, - -0.11347484588623047, - 0.1035623773932457 + 0.07352735102176666, + -0.11343146115541458, + 0.10369836539030075 ], "tsne3d": [ - -0.30414244532585144, - 8.061442375183105, - 14.00770378112793 + 0.7126742005348206, + -15.561559677124023, + 11.500367164611816 ], "umap3d": [ - 4.715142250061035, - 7.170287609100342, - 4.701396465301514 + 5.0997233390808105, + 7.694270133972168, + 3.3350472450256348 ] }, "cluster_id": 3, @@ -421665,11 +421815,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -422093,31 +422243,31 @@ "doi": "10.1101/2025.04.07.647613", "projections": { "pca": [ - 0.21214690804481506, - -0.08450280129909515 + 0.21207734942436218, + -0.08501715213060379 ], "tsne": [ - 35.90386199951172, - -0.6338273882865906 + 31.275047302246094, + 7.462975978851318 ], "umap": [ - 8.785868644714355, - 6.864049911499023 + 9.036166191101074, + 6.436059474945068 ], "pca3d": [ - 0.2121470421552658, - -0.0845039039850235, - -0.026350343599915504 + 0.21207739412784576, + -0.08501818776130676, + -0.026247192174196243 ], "tsne3d": [ - 20.24701690673828, - 2.9609439373016357, - -5.302219390869141 + 21.769062042236328, + 1.197962999343872, + 0.5223361849784851 ], "umap3d": [ - 7.118919849395752, - 4.9123406410217285, - 1.9948747158050537 + 6.882907867431641, + 4.869072437286377, + 0.83653324842453 ] }, "cluster_id": 0, @@ -422128,12 +422278,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -422546,46 +422696,46 @@ ], "projections": { "pca": [ - -0.22625893354415894, - -0.19028155505657196 + -0.22661234438419342, + -0.18955276906490326 ], "tsne": [ - -34.3747444152832, - -10.508207321166992 + -39.6523551940918, + 9.988038063049316 ], "umap": [ - 0.9706008434295654, - 8.132372856140137 + 1.2097433805465698, + 7.272585391998291 ], "pca3d": [ - -0.22625850141048431, - -0.19028900563716888, - -0.04876957833766937 + -0.2266118973493576, + -0.18956005573272705, + -0.048188041895627975 ], "tsne3d": [ - -22.245458602905273, - -8.721393585205078, - 9.722627639770508 + -22.351421356201172, + 6.245030403137207, + 7.7456464767456055 ], "umap3d": [ - 0.9633399844169617, - 4.617991924285889, - 5.092116832733154 + 2.175769090652466, + 4.986215591430664, + 5.738149642944336 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -422992,46 +423142,46 @@ ], "projections": { "pca": [ - 0.09297960996627808, - 0.0571218766272068 + 0.09347673505544662, + 0.05703318864107132 ], "tsne": [ - 1.669929027557373, - -7.112166881561279 + 1.6344594955444336, + 6.200404167175293 ], "umap": [ - 5.289020538330078, - 7.374168395996094 + 5.5110039710998535, + 6.650047779083252 ], "pca3d": [ - 0.0929793044924736, - 0.057129453867673874, - 0.030729997903108597 + 0.09347638487815857, + 0.057041291147470474, + 0.030859965831041336 ], "tsne3d": [ - -0.4592832028865814, - -3.370656728744507, - -18.293115615844727 + 1.9180610179901123, + 25.33687973022461, + -2.4627230167388916 ], "umap3d": [ - 4.861249923706055, - 3.348252773284912, - 5.024611949920654 + 5.902985095977783, + 4.674795627593994, + 4.903954029083252 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, + "id": 13, "label": "Genomics Papers" } ], @@ -423451,31 +423601,31 @@ "doi": "10.1101/2025.04.01.646034", "projections": { "pca": [ - 0.19871705770492554, - -0.16091856360435486 + 0.19834579527378082, + -0.16145487129688263 ], "tsne": [ - 60.02684783935547, - 0.09774719178676605 + 46.14748764038086, + 23.455005645751953 ], "umap": [ - 9.15382194519043, - 6.45770263671875 + 9.323258399963379, + 6.046472549438477 ], "pca3d": [ - 0.19871735572814941, - -0.16091014444828033, - -0.04218686744570732 + 0.1983458250761032, + -0.16144612431526184, + -0.04202284291386604 ], "tsne3d": [ - 21.826019287109375, - 7.166589736938477, - -8.9395112991333 + 21.510448455810547, + -3.185819387435913, + 0.9370898008346558 ], "umap3d": [ - 7.307417869567871, - 5.518113136291504, - 1.8830161094665527 + 6.768510818481445, + 5.38883113861084, + 0.6823386549949646 ] }, "cluster_id": 0, @@ -423486,12 +423636,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 2, + "label": "DNA Regulation" }, { - "id": 29, - "label": "Transcription Binding" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -423918,31 +424068,31 @@ "doi": "10.1101/2025.04.02.646792", "projections": { "pca": [ - 0.19626876711845398, - -0.04416610300540924 + 0.19622381031513214, + -0.045250024646520615 ], "tsne": [ - 39.570716857910156, - -12.730177879333496 + 34.383567810058594, + 18.2131290435791 ], "umap": [ - 9.497476577758789, - 8.183265686035156 + 9.724294662475586, + 7.7532477378845215 ], "pca3d": [ - 0.19626864790916443, - -0.04415786266326904, - -0.12941874563694 + 0.19622372090816498, + -0.045241594314575195, + -0.12945543229579926 ], "tsne3d": [ - 18.320960998535156, - -5.0891289710998535, - 4.924726963043213 + 17.484210968017578, + 4.293082237243652, + -11.442730903625488 ], "umap3d": [ - 6.896286487579346, - 4.331117630004883, - 1.0926238298416138 + 6.468034267425537, + 4.02222204208374, + 0.24070198833942413 ] }, "cluster_id": 0, @@ -423953,12 +424103,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -424374,31 +424524,31 @@ "doi": "10.1101/2025.03.31.646349", "projections": { "pca": [ - 0.26858946681022644, - 0.004822215996682644 + 0.2684747874736786, + 0.004540770314633846 ], "tsne": [ - 45.1062126159668, - 2.529496669769287 + 36.350955963134766, + 11.463274955749512 ], "umap": [ - 9.592559814453125, - 7.32869291305542 + 9.786760330200195, + 7.013595104217529 ], "pca3d": [ - 0.2685893476009369, - 0.004814663901925087, - -0.1283324509859085 + 0.2684747278690338, + 0.004533060360699892, + -0.12846972048282623 ], "tsne3d": [ - 21.982389450073242, - 0.5692976713180542, - 2.961031198501587 + 23.700681686401367, + 0.8961557745933533, + -7.93094539642334 ], "umap3d": [ - 7.7196855545043945, - 4.483433723449707, - 1.4061181545257568 + 7.3056321144104, + 4.289433479309082, + 0.14272752404212952 ] }, "cluster_id": 0, @@ -424409,12 +424559,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 20, - "label": "Chromatin Regulation" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -424853,31 +425003,31 @@ "openalex_url": "https://openalex.org/W4409176397", "projections": { "pca": [ - 0.2987138032913208, - 0.00034372927621006966 + 0.2986612915992737, + -0.00040774038643576205 ], "tsne": [ - 34.75522994995117, - 3.958238363265991 + 40.28515625, + 3.0074000358581543 ], "umap": [ - 8.924337387084961, - 7.874700546264648 + 9.1383638381958, + 7.480724334716797 ], "pca3d": [ - 0.2987138032913208, - 0.00032913038739934564, - -0.1745719015598297 + 0.2986612021923065, + -0.00042262117494829, + -0.17470039427280426 ], "tsne3d": [ - 18.146953582763672, - -7.691049098968506, - -6.9918646812438965 + 18.163393020629883, + 11.67790412902832, + -0.6432387232780457 ], "umap3d": [ - 7.099575996398926, - 4.207439422607422, - 1.7261884212493896 + 6.865072727203369, + 4.130855560302734, + 0.7278380393981934 ] }, "cluster_id": 0, @@ -424888,12 +425038,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -425301,47 +425451,47 @@ "journal": "Nature Genetics", "projections": { "pca": [ - 0.03489817678928375, - -0.07412398606538773 + 0.034742385149002075, + -0.0742129236459732 ], "tsne": [ - 28.84994888305664, - -24.430145263671875 + 19.419960021972656, + 29.117958068847656 ], "umap": [ - 7.977565765380859, - 9.540175437927246 + 8.059840202331543, + 9.16753101348877 ], "pca3d": [ - 0.03489788621664047, - -0.07408158481121063, - -0.1640201210975647 + 0.03474210947751999, + -0.07416955381631851, + -0.16379451751708984 ], "tsne3d": [ - 15.28508472442627, - -11.331626892089844, - 15.364974975585938 + 11.24101448059082, + 0.5037559866905212, + -20.873891830444336 ], "umap3d": [ - 5.337017059326172, - 3.9315693378448486, - 1.2333427667617798 + 5.352634429931641, + 3.540133237838745, + 1.1450237035751343 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -425766,31 +425916,31 @@ "doi": "10.1101/2024.11.22.624813", "projections": { "pca": [ - 0.24277877807617188, - -0.021736806258559227 + 0.24273458123207092, + -0.02253388985991478 ], "tsne": [ - 44.89079666137695, - -3.3906078338623047 + 31.76860237121582, + 15.719221115112305 ], "umap": [ - 9.128305435180664, - 7.833571434020996 + 9.400174140930176, + 7.486602306365967 ], "pca3d": [ - 0.24277888238430023, - -0.02173980511724949, - -0.1436934918165207 + 0.2427346408367157, + -0.02253694087266922, + -0.14376790821552277 ], "tsne3d": [ - 15.647031784057617, - -1.0981981754302979, - 1.023943305015564 + 17.327762603759766, + -0.02927670255303383, + -7.209168434143066 ], "umap3d": [ - 6.894931793212891, - 4.4237565994262695, - 1.2834391593933105 + 6.720759868621826, + 4.224701404571533, + 0.2595769762992859 ] }, "cluster_id": 0, @@ -425801,12 +425951,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -426234,31 +426384,31 @@ "cited_by_count": 13, "projections": { "pca": [ - 0.1670280545949936, - -0.060757726430892944 + 0.1668270081281662, + -0.06121373176574707 ], "tsne": [ - 45.969879150390625, - -9.706635475158691 + 43.655303955078125, + 10.998594284057617 ], "umap": [ - 9.860773086547852, - 8.113554000854492 + 10.146662712097168, + 7.722526550292969 ], "pca3d": [ - 0.16702806949615479, - -0.06076705828309059, - -0.1051306501030922 + 0.16682714223861694, + -0.0612235963344574, + -0.10511673986911774 ], "tsne3d": [ - 22.73587417602539, - -2.9560976028442383, - -8.944784164428711 + 23.603309631347656, + 7.17518949508667, + 1.395944595336914 ], "umap3d": [ - 6.94045352935791, - 4.780853748321533, - 0.8475321531295776 + 6.3466691970825195, + 4.381827354431152, + -0.15650559961795807 ] }, "cluster_id": 0, @@ -426269,12 +426419,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -426693,31 +426843,31 @@ "doi": "10.1101/2024.01.24.577115", "projections": { "pca": [ - 0.2621198892593384, - -0.09294439852237701 + 0.26193472743034363, + -0.09344116598367691 ], "tsne": [ - 59.264835357666016, - 7.101406097412109 + 52.90060806274414, + 21.82291030883789 ], "umap": [ - 10.179600715637207, - 6.426300525665283 + 10.401166915893555, + 6.057382106781006 ], "pca3d": [ - 0.26212000846862793, - -0.0929446816444397, - 0.028721513226628304 + 0.2619347870349884, + -0.09344158321619034, + 0.028720956295728683 ], "tsne3d": [ - 27.83611488342285, - 9.650666236877441, - -2.93951678276062 + 29.24201011657715, + -5.467097759246826, + -1.3750336170196533 ], "umap3d": [ - 8.072513580322266, - 5.6814141273498535, - 1.4632744789123535 + 7.432296276092529, + 5.493476867675781, + -0.30170562863349915 ] }, "cluster_id": 0, @@ -426728,12 +426878,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -427143,31 +427293,31 @@ "journal": "Nature Machine Intelligence", "projections": { "pca": [ - 0.07578635215759277, - -0.006267329677939415 + 0.07603134214878082, + -0.006228220649063587 ], "tsne": [ - 19.714900970458984, - 12.694470405578613 + 22.60381507873535, + -5.643984794616699 ], "umap": [ - 7.022085189819336, - 7.169344902038574 + 7.234666347503662, + 6.829337120056152 ], "pca3d": [ - 0.07578632235527039, - -0.006278724875301123, - -0.006014859303832054 + 0.07603131234645844, + -0.0062399073503911495, + -0.005726704839617014 ], "tsne3d": [ - 7.438892364501953, - -2.9005439281463623, - -14.711675643920898 + 8.040106773376465, + 8.096302032470703, + 5.531830787658691 ], "umap3d": [ - 6.633856296539307, - 3.9551236629486084, - 3.4165709018707275 + 7.097179412841797, + 4.5344085693359375, + 2.5399725437164307 ] }, "cluster_id": 4, @@ -427178,12 +427328,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -427593,31 +427743,31 @@ "journal": "Nature", "projections": { "pca": [ - 0.1409498006105423, - -0.035749442875385284 + 0.14119476079940796, + -0.03647947683930397 ], "tsne": [ - 23.991071701049805, - 34.28570556640625 + 26.199813842773438, + -33.13100051879883 ], "umap": [ - 8.365992546081543, - 4.904980659484863 + 8.734135627746582, + 4.5081658363342285 ], "pca3d": [ - 0.14094944298267365, - -0.0357256755232811, - 0.13919256627559662 + 0.1411944329738617, + -0.036454468965530396, + 0.13922196626663208 ], "tsne3d": [ - 4.6590471267700195, - 27.217531204223633, - 2.5697226524353027 + 14.902449607849121, + -19.650461196899414, + 4.662178993225098 ], "umap3d": [ - 7.505064487457275, - 5.934605121612549, - 3.6167800426483154 + 7.482071876525879, + 6.436799049377441, + 1.7183831930160522 ] }, "cluster_id": 4, @@ -427628,11 +427778,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -428051,31 +428201,31 @@ "doi": "10.1101/2025.03.11.642630", "projections": { "pca": [ - 0.15858812630176544, - -0.0992201715707779 + 0.15842920541763306, + -0.0997714027762413 ], "tsne": [ - 33.03400421142578, - -1.2626715898513794 + 28.332408905029297, + 6.319699287414551 ], "umap": [ - 8.05799674987793, - 6.761198043823242 + 8.140937805175781, + 6.490768909454346 ], "pca3d": [ - 0.158588245511055, - -0.09920480102300644, - -0.0019425138598307967 + 0.15842927992343903, + -0.09975563734769821, + -0.0018416844541206956 ], "tsne3d": [ - 15.444993019104004, - 1.3285738229751587, - -6.281073093414307 + 16.893688201904297, + 3.1843302249908447, + -0.004382474347949028 ], "umap3d": [ - 6.595684051513672, - 4.793227672576904, - 2.435802698135376 + 6.538412570953369, + 4.922174453735352, + 1.3366018533706665 ] }, "cluster_id": 4, @@ -428086,12 +428236,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -428512,46 +428662,46 @@ "doi": "10.1101/2025.03.07.25323578", "projections": { "pca": [ - 0.08518107235431671, - -0.038537852466106415 + 0.08509901911020279, + -0.03898004814982414 ], "tsne": [ - 27.65896224975586, - 8.043052673339844 + 31.532533645629883, + -8.706149101257324 ], "umap": [ - 7.2264251708984375, - 7.903782367706299 + 7.454341411590576, + 7.530714511871338 ], "pca3d": [ - 0.08518118411302567, - -0.03855537995696068, - -0.19158722460269928 + 0.08509915322065353, + -0.03899814933538437, + -0.19148056209087372 ], "tsne3d": [ - 11.646413803100586, - -10.979602813720703, - -10.746262550354004 + 11.982095718383789, + 15.15909194946289, + 1.521204948425293 ], "umap3d": [ - 6.700510025024414, - 3.4789531230926514, - 2.8381402492523193 + 7.100890636444092, + 3.8371472358703613, + 2.1607322692871094 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 26, + "id": 33, "label": "Genetic Disease" } ], @@ -428968,47 +429118,47 @@ "cited_by_count": 104, "projections": { "pca": [ - 0.1365196853876114, - -0.10647061467170715 + 0.1360163539648056, + -0.10708572715520859 ], "tsne": [ - 35.48817443847656, - -18.1982421875 + 28.363258361816406, + 25.038875579833984 ], "umap": [ - 8.54628849029541, - 8.453405380249023 + 8.726625442504883, + 8.243462562561035 ], "pca3d": [ - 0.1365196406841278, - -0.10644364356994629, - -0.17303045094013214 + 0.13601632416248322, + -0.1070580706000328, + -0.17305636405944824 ], "tsne3d": [ - 17.276525497436523, - 12.336816787719727, - -1.860479474067688 + 13.524718284606934, + -4.192427158355713, + -7.510490894317627 ], "umap3d": [ - 6.182318210601807, - 4.772034645080566, - 1.5249969959259033 + 5.818483829498291, + 4.486814498901367, + 0.8708502650260925 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -429425,31 +429575,31 @@ ], "projections": { "pca": [ - 0.09407516568899155, - -0.096316859126091 + 0.093986377120018, + -0.09589121490716934 ], "tsne": [ - 5.977198600769043, - 8.09509563446045 + 18.2496395111084, + -6.40261697769165 ], "umap": [ - 6.538986682891846, - 6.555657386779785 + 6.805825710296631, + 6.122612953186035 ], "pca3d": [ - 0.09407542645931244, - -0.09633199125528336, - 0.1357540339231491 + 0.09398660808801651, + -0.09590598195791245, + 0.13604223728179932 ], "tsne3d": [ - 4.483155250549316, - 0.8177756071090698, - -13.008747100830078 + 2.507661819458008, + 5.069425106048584, + 2.8620691299438477 ], "umap3d": [ - 5.830475330352783, - 4.583309650421143, - 3.6860294342041016 + 6.313559055328369, + 5.02907133102417, + 2.91386342048645 ] }, "cluster_id": 4, @@ -429460,12 +429610,12 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 11, - "label": "Genomic Language" + "id": 17, + "label": "Genomic Models" } ], "_embedding": [ @@ -429871,46 +430021,46 @@ ], "projections": { "pca": [ - 0.18352800607681274, - 0.16223390400409698 + 0.18439005315303802, + 0.16103293001651764 ], "tsne": [ - -1.3842425346374512, - -8.211374282836914 + -1.109375, + 7.185136795043945 ], "umap": [ - 5.076158046722412, - 7.322590351104736 + 5.278065204620361, + 6.605445384979248 ], "pca3d": [ - 0.18352720141410828, - 0.162260040640831, - -0.08071145415306091 + 0.18438932299613953, + 0.16105948388576508, + -0.08081840723752975 ], "tsne3d": [ - -0.3019387125968933, - 0.5550644397735596, - -21.4277286529541 + 1.7251008749008179, + 23.70821762084961, + -6.982336521148682 ], "umap3d": [ - 4.682248115539551, - 3.4392292499542236, - 4.964138031005859 + 5.744366645812988, + 4.657161235809326, + 4.910592079162598 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, + "id": 13, "label": "Genomics Papers" } ], @@ -430339,31 +430489,31 @@ "openalex_url": "https://openalex.org/W4386636808", "projections": { "pca": [ - 0.15377739071846008, - -0.0625835657119751 + 0.15363110601902008, + -0.06280268728733063 ], "tsne": [ - 26.435230255126953, - -8.062528610229492 + 24.352590560913086, + 9.39690113067627 ], "umap": [ - 7.775459289550781, - 7.307857513427734 + 8.022215843200684, + 7.0762553215026855 ], "pca3d": [ - 0.15377752482891083, - -0.06258942186832428, - -0.08788082748651505 + 0.15363115072250366, + -0.06280862540006638, + -0.0877414345741272 ], "tsne3d": [ - 12.035049438476562, - -2.4783883094787598, - -6.907016277313232 + 12.649128913879395, + 5.534736156463623, + -2.0064303874969482 ], "umap3d": [ - 6.244063854217529, - 4.376005172729492, - 2.2252767086029053 + 6.245106220245361, + 4.369889259338379, + 1.5350432395935059 ] }, "cluster_id": 4, @@ -430374,12 +430524,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -430797,31 +430947,31 @@ ], "projections": { "pca": [ - 0.029223350808024406, - -0.09510024636983871 + 0.028949758037924767, + -0.09474693238735199 ], "tsne": [ - -32.11121368408203, - 16.404233932495117 + -26.96753692626953, + -19.88534927368164 ], "umap": [ - 3.381758689880371, - 4.8509063720703125 + 3.8485429286956787, + 4.3194780349731445 ], "pca3d": [ - 0.029223468154668808, - -0.0950956717133522, - 0.0961160883307457 + 0.02894992008805275, + -0.09474272280931473, + 0.09615843743085861 ], "tsne3d": [ - -11.015267372131348, - 9.854463577270508, - 2.259432792663574 + -10.678396224975586, + -17.920486450195312, + 3.472766876220703 ], "umap3d": [ - 3.2503833770751953, - 6.849455833435059, - 4.726362228393555 + 3.5974161624908447, + 7.2754130363464355, + 4.136105537414551 ] }, "cluster_id": 2, @@ -430832,12 +430982,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 16, - "label": "Diffusion Models" + "id": 7, + "label": "Diffusion Gen" } ], "_embedding": [ @@ -431255,31 +431405,31 @@ "doi": "10.1101/2025.02.25.639942", "projections": { "pca": [ - 0.09344057738780975, - -0.11677851527929306 + 0.09338130056858063, + -0.11676736176013947 ], "tsne": [ - 14.628738403320312, - 29.43224334716797 + 18.67320442199707, + -28.056385040283203 ], "umap": [ - 7.738020420074463, - 4.722887992858887 + 8.07217025756836, + 4.288337707519531 ], "pca3d": [ - 0.0934406965970993, - -0.11676764488220215, - 0.15950120985507965 + 0.09338144212961197, + -0.11675623059272766, + 0.1597372442483902 ], "tsne3d": [ - -0.6608229279518127, - 21.592811584472656, - 1.0907765626907349 + 11.12529468536377, + -15.845401763916016, + 1.820717215538025 ], "umap3d": [ - 6.967382907867432, - 6.1285929679870605, - 3.8419125080108643 + 7.022844314575195, + 6.567729949951172, + 2.2531027793884277 ] }, "cluster_id": 4, @@ -431290,11 +431440,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -431703,31 +431853,31 @@ ], "projections": { "pca": [ - 0.04935170337557793, - -0.004182647913694382 + 0.04938650131225586, + -0.003950559534132481 ], "tsne": [ - 20.26572608947754, - 3.3731658458709717 + 22.72888946533203, + -1.548732042312622 ], "umap": [ - 7.461630821228027, - 6.879828453063965 + 7.674533367156982, + 6.56889533996582 ], "pca3d": [ - 0.04935160279273987, - -0.004180751740932465, - -0.09633851796388626 + 0.04938637465238571, + -0.003948678728193045, + -0.09619744122028351 ], "tsne3d": [ - 10.233427047729492, - -0.8795952200889587, - -9.998202323913574 + 9.784433364868164, + 5.904499053955078, + 3.2682406902313232 ], "umap3d": [ - 6.5101213455200195, - 4.460577964782715, - 2.9356677532196045 + 6.686619281768799, + 4.7386932373046875, + 2.02571964263916 ] }, "cluster_id": 4, @@ -431738,12 +431888,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 1, + "label": "Genomic Regulation" } ], "_embedding": [ @@ -432162,31 +432312,31 @@ "doi": "10.1101/2025.03.01.641000", "projections": { "pca": [ - 0.11300798505544662, - -0.1541801244020462 + 0.11262442171573639, + -0.15462027490139008 ], "tsne": [ - 36.561214447021484, - -10.928112983703613 + 30.912206649780273, + 13.450088500976562 ], "umap": [ - 8.432452201843262, - 7.302867412567139 + 8.816108703613281, + 7.162243843078613 ], "pca3d": [ - 0.11300817131996155, - -0.1541832834482193, - -0.16560646891593933 + 0.11262461543083191, + -0.15462298691272736, + -0.16534900665283203 ], "tsne3d": [ - 15.182435989379883, - 2.7157812118530273, - 1.8904595375061035 + 18.666072845458984, + 2.853074550628662, + -8.888274192810059 ], "umap3d": [ - 6.941440105438232, - 4.305593490600586, - 1.8479509353637695 + 6.7819952964782715, + 4.201533794403076, + 0.9479341506958008 ] }, "cluster_id": 0, @@ -432197,12 +432347,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -432623,31 +432773,31 @@ "cited_by_count": 85, "projections": { "pca": [ - 0.16372723877429962, - -0.09127001464366913 + 0.16355864703655243, + -0.09159450232982635 ], "tsne": [ - 50.82038116455078, - -10.892992973327637 + 47.11262893676758, + 13.657022476196289 ], "umap": [ - 10.158349990844727, - 7.737190246582031 + 10.381481170654297, + 7.493646621704102 ], "pca3d": [ - 0.16372719407081604, - -0.09126702696084976, - 0.04720843583345413 + 0.1635587066411972, + -0.09159163385629654, + 0.0472332201898098 ], "tsne3d": [ - 26.45452308654785, - 1.519883394241333, - -10.190164566040039 + 25.850133895874023, + 3.4709057807922363, + 3.0277228355407715 ], "umap3d": [ - 7.000203609466553, - 5.281743049621582, - 0.8548004031181335 + 6.338954925537109, + 4.855776786804199, + -0.3023361563682556 ] }, "cluster_id": 0, @@ -432658,12 +432808,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -433073,31 +433223,31 @@ "journal": "Nature", "projections": { "pca": [ - 0.196055069565773, - 0.022403238341212273 + 0.1963295042514801, + 0.021681690588593483 ], "tsne": [ - 50.557098388671875, - -8.02086067199707 + 47.61781311035156, + 9.63839054107666 ], "umap": [ - 10.239781379699707, - 7.98734188079834 + 10.483769416809082, + 7.641286849975586 ], "pca3d": [ - 0.19605478644371033, - 0.022407574579119682, - 0.0011269684182479978 + 0.19632914662361145, + 0.021685300394892693, + 0.0010381313040852547 ], "tsne3d": [ - 28.85013771057129, - -3.2263121604919434, - -9.4544095993042 + 28.655391693115234, + 8.064736366271973, + 3.6422932147979736 ], "umap3d": [ - 7.077571868896484, - 5.0304083824157715, - 0.5613001585006714 + 6.397418022155762, + 4.560967445373535, + -0.4830479025840759 ] }, "cluster_id": 0, @@ -433108,12 +433258,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -433574,31 +433724,31 @@ "doi": "10.1101/2025.02.18.638918", "projections": { "pca": [ - 0.204893097281456, - -0.06021944433450699 + 0.20481273531913757, + -0.06035821884870529 ], "tsne": [ - 5.133896350860596, - 14.260891914367676 + 11.306884765625, + -13.464171409606934 ], "umap": [ - 6.465768337249756, - 5.671409606933594 + 6.825531482696533, + 5.429666996002197 ], "pca3d": [ - 0.20489314198493958, - -0.06023715063929558, - 0.07610049843788147 + 0.2048128992319107, + -0.060376010835170746, + 0.07614807784557343 ], "tsne3d": [ - 2.8163764476776123, - 3.279332399368286, - -3.536194086074829 + 3.4170796871185303, + -1.0679757595062256, + 7.4873223304748535 ], "umap3d": [ - 5.839447498321533, - 5.608222007751465, - 3.78347110748291 + 6.048593521118164, + 6.002658367156982, + 2.5905508995056152 ] }, "cluster_id": 4, @@ -433609,11 +433759,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -434058,47 +434208,47 @@ "doi": "10.1101/2025.02.20.639398", "projections": { "pca": [ - 0.14689384400844574, - -0.07255738228559494 + 0.14666639268398285, + -0.07307733595371246 ], "tsne": [ - 29.55097198486328, - -15.153014183044434 + 23.386938095092773, + 20.102380752563477 ], "umap": [ - 8.190033912658691, - 9.284653663635254 + 8.253429412841797, + 8.906110763549805 ], "pca3d": [ - 0.14689384400844574, - -0.07253849506378174, - -0.08234690874814987 + 0.14666630327701569, + -0.073057621717453, + -0.08232904225587845 ], "tsne3d": [ - 14.389876365661621, - -10.23165225982666, - 6.213043212890625 + 11.779524803161621, + -0.8709092736244202, + -15.298503875732422 ], "umap3d": [ - 5.501517295837402, - 4.068259239196777, - 1.254354476928711 + 5.246349811553955, + 3.7355310916900635, + 1.0923070907592773 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -434506,47 +434656,47 @@ "journal": "Nature", "projections": { "pca": [ - -0.05413525179028511, - -0.041623108088970184 + -0.05420371890068054, + -0.04154578596353531 ], "tsne": [ - 28.549165725708008, - -24.49533462524414 + 19.124475479125977, + 29.052047729492188 ], "umap": [ - 7.916357040405273, - 9.486217498779297 + 7.969415664672852, + 9.18644905090332 ], "pca3d": [ - -0.0541355162858963, - -0.041582562029361725, - -0.2352367788553238 + -0.05420401692390442, + -0.0415043868124485, + -0.23497603833675385 ], "tsne3d": [ - 14.864944458007812, - -10.592561721801758, - 14.73209285736084 + 11.188736915588379, + 1.0249736309051514, + -19.99444007873535 ], "umap3d": [ - 5.343647003173828, - 3.842381000518799, - 1.2470465898513794 + 5.313514232635498, + 3.448530673980713, + 1.205728530883789 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { "id": 28, - "label": "Causal Cell Perturbation" + "label": "Single Cell Data" } ], "_embedding": [ @@ -434965,31 +435115,31 @@ "doi": "10.1101/2025.02.19.638643", "projections": { "pca": [ - 0.18362797796726227, - -0.09368881583213806 + 0.1834961175918579, + -0.09402111172676086 ], "tsne": [ - 36.846832275390625, - -9.843816757202148 + 30.364425659179688, + 12.445764541625977 ], "umap": [ - 8.514856338500977, - 7.301157474517822 + 8.935834884643555, + 6.984922885894775 ], "pca3d": [ - 0.18362808227539062, - -0.09369993954896927, - -0.07741938531398773 + 0.18349634110927582, + -0.09403221309185028, + -0.07722286880016327 ], "tsne3d": [ - 16.47464370727539, - 2.9814798831939697, - 0.0823059156537056 + 18.874229431152344, + 3.1426472663879395, + -6.821150779724121 ], "umap3d": [ - 7.0517659187316895, - 4.4948344230651855, - 1.8005101680755615 + 6.833054542541504, + 4.371777057647705, + 0.9172478914260864 ] }, "cluster_id": 0, @@ -435000,12 +435150,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -435424,31 +435574,31 @@ "doi": "10.1101/2025.02.13.638190", "projections": { "pca": [ - 0.03323260322213173, - -0.1327155977487564 + 0.03294137865304947, + -0.13260816037654877 ], "tsne": [ - 13.212092399597168, - 7.267557144165039 + 12.602686882019043, + -1.0197978019714355 ], "umap": [ - 7.20341157913208, - 5.946391582489014 + 7.3829450607299805, + 5.652407169342041 ], "pca3d": [ - 0.033232804387807846, - -0.13271881639957428, - -0.06272606551647186 + 0.03294165059924126, + -0.1326112151145935, + -0.06242772564291954 ], "tsne3d": [ - 10.112939834594727, - 8.381155014038086, - -6.4771037101745605 + 11.908950805664062, + -5.229936599731445, + 4.719644069671631 ], "umap3d": [ - 6.052395820617676, - 5.058351516723633, - 3.373124599456787 + 6.355939865112305, + 5.331249237060547, + 2.413849115371704 ] }, "cluster_id": 4, @@ -435459,11 +435609,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -435886,31 +436036,31 @@ "doi": "10.1101/2025.02.21.639494", "projections": { "pca": [ - 0.18464961647987366, - -0.10620808601379395 + 0.1843854784965515, + -0.10625960677862167 ], "tsne": [ - 21.441577911376953, - 1.6280431747436523 + 23.433847427368164, + 0.8548569083213806 ], "umap": [ - 7.731196403503418, - 6.970926284790039 + 7.931990146636963, + 6.502676486968994 ], "pca3d": [ - 0.18464972078800201, - -0.10621145367622375, - -0.09840463101863861 + 0.18438567221164703, + -0.1062626838684082, + -0.09818396717309952 ], "tsne3d": [ - 12.824159622192383, - 0.5371060371398926, - -8.797149658203125 + 13.63607406616211, + 5.026851654052734, + 5.020108699798584 ], "umap3d": [ - 6.432081699371338, - 4.5621819496154785, - 2.6725199222564697 + 6.500751972198486, + 4.696809768676758, + 1.7616442441940308 ] }, "cluster_id": 4, @@ -435921,12 +436071,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -436339,47 +436489,47 @@ ], "projections": { "pca": [ - 0.012769848108291626, - 0.10328291356563568 + 0.013099973089993, + 0.10421212017536163 ], "tsne": [ - 2.361694812774658, - -20.127361297607422 + -0.09658409655094147, + 16.435840606689453 ], "umap": [ - 5.245417594909668, - 8.230658531188965 + 5.376883506774902, + 7.6321868896484375 ], "pca3d": [ - 0.012769795022904873, - 0.10327047854661942, - 0.1636447012424469 + 0.013099884614348412, + 0.10419908910989761, + 0.16355200111865997 ], "tsne3d": [ - -2.3485589027404785, - -5.531778812408447, - -10.014693260192871 + -0.2700553834438324, + 12.690488815307617, + 5.874818325042725 ], "umap3d": [ - 4.345520496368408, - 4.204456806182861, - 3.012075901031494 + 4.823840141296387, + 4.547260284423828, + 2.838334083557129 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 18, - "label": "Genomics Papers" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -436789,31 +436939,31 @@ "doi": "10.1101/2024.12.03.626631", "projections": { "pca": [ - 0.14736074209213257, - 0.06712736934423447 + 0.14772525429725647, + 0.06716535985469818 ], "tsne": [ - 6.810323238372803, - 9.862817764282227 + 8.474625587463379, + -8.130300521850586 ], "umap": [ - 6.853701114654541, - 5.8270792961120605 + 7.0336737632751465, + 5.535743713378906 ], "pca3d": [ - 0.1473606824874878, - 0.0671151652932167, - 0.021865637972950935 + 0.1477251797914505, + 0.06715326756238937, + 0.02183888480067253 ], "tsne3d": [ - 7.686023235321045, - 8.893115043640137, - -4.066450119018555 + 5.246864318847656, + -3.615446090698242, + -1.1989936828613281 ], "umap3d": [ - 5.708305835723877, - 5.062496185302734, - 3.8869590759277344 + 6.089062213897705, + 5.5455827713012695, + 2.7693593502044678 ] }, "cluster_id": 4, @@ -436824,11 +436974,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -437259,31 +437409,31 @@ ], "projections": { "pca": [ - 0.1856454461812973, - -0.07495470345020294 + 0.1857152134180069, + -0.07505117356777191 ], "tsne": [ - 4.595571041107178, - 11.555970191955566 + 13.547493934631348, + -13.307689666748047 ], "umap": [ - 6.500953674316406, - 5.88151216506958 + 6.843692302703857, + 5.611344814300537 ], "pca3d": [ - 0.18564578890800476, - -0.07497703284025192, - 0.08266948908567429 + 0.18571531772613525, + -0.07507358491420746, + 0.08289970457553864 ], "tsne3d": [ - 1.2218856811523438, - 1.6042611598968506, - -6.582083225250244 + 5.019166469573975, + -0.2949020266532898, + 9.256772994995117 ], "umap3d": [ - 5.871502876281738, - 5.30031156539917, - 3.5242207050323486 + 6.042745113372803, + 5.685209274291992, + 2.540651798248291 ] }, "cluster_id": 4, @@ -437294,11 +437444,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -437715,31 +437865,31 @@ "doi": "10.1101/2025.02.11.637758", "projections": { "pca": [ - 0.14368979632854462, - -0.10990622639656067 + 0.14343325793743134, + -0.10999307781457901 ], "tsne": [ - 26.043621063232422, - 10.383575439453125 + 28.163183212280273, + -4.848263740539551 ], "umap": [ - 7.546216011047363, - 7.440850257873535 + 7.689086437225342, + 7.019185543060303 ], "pca3d": [ - 0.14369015395641327, - -0.10992900282144547, - -0.09668242186307907 + 0.1434335857629776, + -0.11001604795455933, + -0.09644782543182373 ], "tsne3d": [ - 13.698518753051758, - -6.448299407958984, - -12.370464324951172 + 13.867669105529785, + 12.783867835998535, + 4.5018720626831055 ], "umap3d": [ - 6.8985915184021, - 3.957002639770508, - 2.9393370151519775 + 7.172036647796631, + 4.239265441894531, + 2.033600091934204 ] }, "cluster_id": 4, @@ -437750,12 +437900,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -438168,47 +438318,47 @@ ], "projections": { "pca": [ - 0.026665780693292618, - -0.06195823475718498 + 0.026649456471204758, + -0.06183743104338646 ], "tsne": [ - 13.677926063537598, - 28.77448272705078 + -24.346853256225586, + -20.741886138916016 ], "umap": [ - 3.5398995876312256, - 4.8600335121154785 + 4.062406063079834, + 4.343299865722656 ], "pca3d": [ - 0.026665804907679558, - -0.061945293098688126, - 0.08276849240064621 + 0.026649480685591698, + -0.061824291944503784, + 0.08292602002620697 ], "tsne3d": [ - -2.191124439239502, - 20.18202018737793, - 0.11093516647815704 + -8.948686599731445, + -19.97791290283203, + 0.6381493210792542 ], "umap3d": [ - 3.3696811199188232, - 6.7897491455078125, - 4.700827121734619 + 3.8122775554656982, + 7.246783256530762, + 4.085789680480957 ] }, - "cluster_id": 2, - "cluster_label": "Generative Models", + "cluster_id": 3, + "cluster_label": "Protein Design", "cluster_levels": [ { - "id": 2, - "label": "Generative Models" + "id": 3, + "label": "Protein Design" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 16, - "label": "Diffusion Models" + "id": 7, + "label": "Diffusion Gen" } ], "_embedding": [ @@ -438629,31 +438779,31 @@ "openalex_url": "https://openalex.org/W4407306192", "projections": { "pca": [ - 0.06352968513965607, - -0.05313307046890259 + 0.0634893998503685, + -0.05244862660765648 ], "tsne": [ - 3.6894309520721436, - 13.601919174194336 + 9.578606605529785, + -13.993361473083496 ], "umap": [ - 6.342093467712402, - 5.7597761154174805 + 6.633227825164795, + 5.414196014404297 ], "pca3d": [ - 0.06352982670068741, - -0.0531555712223053, - 0.0951235219836235 + 0.06348954141139984, + -0.052471376955509186, + 0.09532570838928223 ], "tsne3d": [ - 2.4972681999206543, - 0.8900213837623596, - -4.218523025512695 + 0.8255647420883179, + -0.4118382930755615, + 7.211906433105469 ], "umap3d": [ - 5.850818157196045, - 5.588286399841309, - 3.9046950340270996 + 5.9640631675720215, + 5.975836753845215, + 2.6324119567871094 ] }, "cluster_id": 4, @@ -438664,12 +438814,12 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, - "label": "Genomic Models" + "id": 32, + "label": "Genome Protein" } ], "_embedding": [ @@ -439088,46 +439238,46 @@ "doi": "10.1101/2025.02.05.636714", "projections": { "pca": [ - 0.11529777199029922, - -0.01985296607017517 + 0.1152586042881012, + -0.0201709121465683 ], "tsne": [ - 17.715768814086914, - -29.53819465637207 + 9.77960205078125, + 30.56045913696289 ], "umap": [ - 6.936739921569824, - 9.689024925231934 + 6.984953880310059, + 9.280428886413574 ], "pca3d": [ - 0.11529763042926788, - -0.01983933337032795, - -0.15530197322368622 + 0.11525844782590866, + -0.020156696438789368, + -0.15524494647979736 ], "tsne3d": [ - 13.927574157714844, - -16.55261993408203, - 5.050696849822998 + 4.3431010246276855, + 7.755066394805908, + -20.602956771850586 ], "umap3d": [ - 5.248103618621826, - 3.1321868896484375, - 1.5445491075515747 + 5.292895317077637, + 2.8626041412353516, + 1.8808549642562866 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -439581,31 +439731,31 @@ "openalex_url": "https://openalex.org/W4407199675", "projections": { "pca": [ - 0.25987309217453003, - 0.036143913865089417 + 0.26001089811325073, + 0.035819802433252335 ], "tsne": [ - 44.55934524536133, - 8.106861114501953 + 48.96538162231445, + 0.4275132417678833 ], "umap": [ - 9.463921546936035, - 7.1746015548706055 + 9.4845552444458, + 6.675228118896484 ], "pca3d": [ - 0.2598729729652405, - 0.036137957125902176, - 0.05036959797143936 + 0.260010689496994, + 0.03581376373767853, + 0.050155967473983765 ], "tsne3d": [ - 19.695436477661133, - 5.865683555603027, - 1.8487707376480103 + 25.03138542175293, + -4.713564872741699, + -11.385673522949219 ], "umap3d": [ - 7.742506504058838, - 4.700446605682373, - 1.633131504058838 + 7.3156609535217285, + 4.533347129821777, + 0.19444875419139862 ] }, "cluster_id": 0, @@ -439616,11 +439766,11 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -440036,31 +440186,31 @@ "doi": "10.1101/2025.02.04.636130", "projections": { "pca": [ - 0.14164529740810394, - -0.14352428913116455 + 0.14124678075313568, + -0.1435854732990265 ], "tsne": [ - 22.038169860839844, - -2.1242659091949463 + 23.486522674560547, + 4.504565715789795 ], "umap": [ - 8.07392692565918, - 6.754302978515625 + 8.394003868103027, + 6.359828948974609 ], "pca3d": [ - 0.1416453719139099, - -0.1435278058052063, - -0.050883062183856964 + 0.1412469744682312, + -0.1435891091823578, + -0.050683923065662384 ], "tsne3d": [ - 16.30546760559082, - 3.3241283893585205, - -9.967658042907715 + 15.032766342163086, + 2.7468767166137695, + 1.6006672382354736 ], "umap3d": [ - 6.548996448516846, - 4.94858980178833, - 2.3732995986938477 + 6.405346870422363, + 5.028787612915039, + 1.3923825025558472 ] }, "cluster_id": 4, @@ -440071,12 +440221,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -440504,31 +440654,31 @@ "doi": "10.1101/2025.01.30.635558", "projections": { "pca": [ - 0.10727477073669434, - -0.09212386608123779 + 0.10725467652082443, + -0.09202379733324051 ], "tsne": [ - 3.558201789855957, - 14.612038612365723 + 9.493277549743652, + -15.048260688781738 ], "umap": [ - 6.305215358734131, - 5.602898597717285 + 6.640504360198975, + 5.305604457855225 ], "pca3d": [ - 0.10727500915527344, - -0.09214470535516739, - 0.14438220858573914 + 0.1072549968957901, + -0.09204436838626862, + 0.14458292722702026 ], "tsne3d": [ - 3.1387367248535156, - 1.5213745832443237, - -2.6066901683807373 + 5.608292102813721, + -8.320907592773438, + -5.44003438949585 ], "umap3d": [ - 5.785080432891846, - 5.6951799392700195, - 4.168272018432617 + 5.988073348999023, + 6.14955997467041, + 2.751539945602417 ] }, "cluster_id": 4, @@ -440539,12 +440689,12 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, - "label": "Genomic Models" + "id": 32, + "label": "Genome Protein" } ], "_embedding": [ @@ -440961,31 +441111,31 @@ ], "projections": { "pca": [ - 0.006620211061090231, - -0.014953606761991978 + 0.006659755017608404, + -0.014435174874961376 ], "tsne": [ - 19.060392379760742, - 6.577365398406982 + 23.928926467895508, + -14.562583923339844 ], "umap": [ - 6.742617607116699, - 7.411612033843994 + 6.922231674194336, + 6.875258445739746 ], "pca3d": [ - 0.0066204066388309, - -0.014976581558585167, - -0.004042887594550848 + 0.006659954320639372, + -0.014458736404776573, + -0.0038719060830771923 ], "tsne3d": [ - 4.602532863616943, - -12.751343727111816, - -12.918807029724121 + 6.903817176818848, + 17.670469284057617, + 5.326692581176758 ], "umap3d": [ - 6.1761369705200195, - 3.859290361404419, - 3.230097770690918 + 6.686173439025879, + 4.235174655914307, + 2.617042303085327 ] }, "cluster_id": 4, @@ -440996,12 +441146,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -441419,31 +441569,31 @@ "cited_by_count": 11, "projections": { "pca": [ - 0.09988940507173538, - -0.04745588079094887 + 0.09994832426309586, + -0.04741427302360535 ], "tsne": [ - 21.651113510131836, - 11.333209037780762 + 21.644607543945312, + -8.148399353027344 ], "umap": [ - 6.935925006866455, - 7.137947082519531 + 7.0796284675598145, + 6.770809650421143 ], "pca3d": [ - 0.09988967329263687, - -0.04747823625802994, - -0.023696262389421463 + 0.09994857013225555, + -0.04743662476539612, + -0.023423807695508003 ], "tsne3d": [ - 5.93798303604126, - -4.865025520324707, - -11.735777854919434 + 5.41227388381958, + 9.685662269592285, + 2.4446651935577393 ], "umap3d": [ - 6.427605152130127, - 3.997565746307373, - 3.401160717010498 + 6.870391368865967, + 4.495700836181641, + 2.611358642578125 ] }, "cluster_id": 4, @@ -441454,12 +441604,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -441904,31 +442054,31 @@ "cited_by_count": 37, "projections": { "pca": [ - 0.1377612203359604, - 0.16336795687675476 + 0.138045072555542, + 0.16348470747470856 ], "tsne": [ - 38.519046783447266, - 7.418928146362305 + 38.58500671386719, + 0.8180057406425476 ], "umap": [ - 9.101293563842773, - 7.782752513885498 + 9.160918235778809, + 7.562138080596924 ], "pca3d": [ - 0.13776104152202606, - 0.16335083544254303, - -0.06874770671129227 + 0.1380448192358017, + 0.16346685588359833, + -0.06911250948905945 ], "tsne3d": [ - 24.63253402709961, - -0.9149729013442993, - 11.767007827758789 + 17.071170806884766, + 12.522354125976562, + -12.509244918823242 ], "umap3d": [ - 6.822869777679443, - 4.980682373046875, - 1.3614895343780518 + 6.734350681304932, + 4.238840103149414, + 0.6716136336326599 ] }, "cluster_id": 0, @@ -441939,12 +442089,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 20, - "label": "Chromatin Regulation" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -442364,46 +442514,46 @@ "doi": "10.1101/2025.01.28.635153", "projections": { "pca": [ - 0.103968046605587, - -0.12386849522590637 + 0.10373985767364502, + -0.12399810552597046 ], "tsne": [ - 19.94124412536621, - -23.751108169555664 + 11.439167976379395, + 24.089868545532227 ], "umap": [ - 7.407238006591797, - 9.232422828674316 + 7.403605937957764, + 8.935689926147461 ], "pca3d": [ - 0.10396818071603775, - -0.12385798990726471, - -0.08223652094602585 + 0.10373994708061218, + -0.1239873543381691, + -0.08192409574985504 ], "tsne3d": [ - 9.093417167663574, - -11.461381912231445, - 9.772153854370117 + 6.200834274291992, + 5.9567389488220215, + -15.101968765258789 ], "umap3d": [ - 5.03203821182251, - 3.8375754356384277, - 1.6285945177078247 + 5.067606449127197, + 3.5299415588378906, + 1.6690179109573364 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -442810,31 +442960,31 @@ ], "projections": { "pca": [ - -0.07921812683343887, - -0.01891685463488102 + -0.07890645414590836, + -0.01860978454351425 ], "tsne": [ - 9.684829711914062, - 22.932937622070312 + 6.427340507507324, + 3.602097511291504 ], "umap": [ - 7.198647499084473, - 4.941577911376953 + 7.464481353759766, + 4.716595649719238 ], "pca3d": [ - -0.07921837270259857, - -0.01889212056994438, - 0.08184435963630676 + -0.07890664041042328, + -0.018584441393613815, + 0.0821652039885521 ], "tsne3d": [ - 5.519613265991211, - 14.96218204498291, - -6.603847503662109 + 1.2892515659332275, + -5.6978840827941895, + -10.875433921813965 ], "umap3d": [ - 5.95604133605957, - 5.561812400817871, - 4.124349594116211 + 6.55341911315918, + 6.118878364562988, + 2.8019747734069824 ] }, "cluster_id": 4, @@ -442845,11 +442995,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -443271,31 +443421,31 @@ "openalex_url": "https://openalex.org/W4407010921", "projections": { "pca": [ - 0.1882421225309372, - -0.026765841990709305 + 0.18825526535511017, + -0.02717428281903267 ], "tsne": [ - 43.13560485839844, - 3.3375020027160645 + 34.855255126953125, + 10.69930362701416 ], "umap": [ - 9.215924263000488, - 7.300014972686768 + 9.39375114440918, + 6.955050945281982 ], "pca3d": [ - 0.18824218213558197, - -0.02677139826118946, - -0.016259847208857536 + 0.18825526535511017, + -0.027179965749382973, + -0.016284523531794548 ], "tsne3d": [ - 19.2038516998291, - 2.751091718673706, - 3.6888725757598877 + 23.111536026000977, + -2.127581834793091, + -10.5396089553833 ], "umap3d": [ - 7.747546195983887, - 4.433624267578125, - 1.6303118467330933 + 7.484126091003418, + 4.306436538696289, + 0.3842349648475647 ] }, "cluster_id": 0, @@ -443306,11 +443456,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -443732,47 +443882,47 @@ "doi": "10.1101/2025.01.29.635579", "projections": { "pca": [ - 0.12800347805023193, - -0.04356880486011505 + 0.12799975275993347, + -0.04345189779996872 ], "tsne": [ - 19.652254104614258, - 5.962090015411377 + 24.60655975341797, + -13.827232360839844 ], "umap": [ - 6.753716945648193, - 7.502525806427002 + 6.916022300720215, + 6.962642669677734 ], "pca3d": [ - 0.12800370156764984, - -0.04359544441103935, - -0.03812680393457413 + 0.12800002098083496, + -0.0434783510863781, + -0.03792789578437805 ], "tsne3d": [ - 6.4260573387146, - -12.107422828674316, - -12.536567687988281 + 7.31265115737915, + 17.37883186340332, + 3.3983144760131836 ], "umap3d": [ - 6.249426364898682, - 3.7407774925231934, - 3.262432336807251 + 6.773407459259033, + 4.113694190979004, + 2.606954574584961 ] }, - "cluster_id": 5, - "cluster_label": "Single-Cell Data", + "cluster_id": 4, + "cluster_label": "RNA Sequence Models", "cluster_levels": [ { - "id": 5, - "label": "Single-Cell Data" + "id": 4, + "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -444194,47 +444344,47 @@ "doi": "10.1101/2025.01.30.635675", "projections": { "pca": [ - 0.26136037707328796, - 0.05728321895003319 + 0.2614592909812927, + 0.05673789232969284 ], "tsne": [ - 29.760395050048828, - 5.614073753356934 + 34.77299118041992, + -6.840581893920898 ], "umap": [ - 7.552834510803223, - 8.094856262207031 + 7.7815752029418945, + 7.65688419342041 ], "pca3d": [ - 0.26136013865470886, - 0.057262104004621506, - -0.1895235925912857 + 0.26145920157432556, + 0.05671629682183266, + -0.18966573476791382 ], "tsne3d": [ - 14.707587242126465, - -10.470877647399902, - -9.111311912536621 + 14.928300857543945, + 14.369514465332031, + 0.4337686002254486 ], "umap3d": [ - 6.950436115264893, - 3.3938615322113037, - 2.6264548301696777 + 7.238229274749756, + 3.626260757446289, + 1.9346250295639038 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -444653,31 +444803,31 @@ "doi": "10.1101/2025.01.29.635607", "projections": { "pca": [ - 0.10698279738426208, - -0.14603446424007416 + 0.10664311796426773, + -0.14593186974525452 ], "tsne": [ - 10.555825233459473, - 2.8165640830993652 + 12.20284652709961, + 1.6638716459274292 ], "umap": [ - 7.011685371398926, - 6.294346332550049 + 7.314957618713379, + 5.9750590324401855 ], "pca3d": [ - 0.10698280483484268, - -0.14602233469486237, - -0.04027964174747467 + 0.1066431924700737, + -0.14591968059539795, + -0.039978496730327606 ], "tsne3d": [ - 7.379916667938232, - -0.11580054461956024, - -5.286972999572754 + 6.4096832275390625, + 5.788022518157959, + -3.2343368530273438 ], "umap3d": [ - 5.660632610321045, - 4.980441093444824, - 3.139779567718506 + 5.991111755371094, + 5.233219146728516, + 2.200742721557617 ] }, "cluster_id": 4, @@ -444688,11 +444838,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -445117,31 +445267,31 @@ "cited_by_count": 13, "projections": { "pca": [ - 0.21935532987117767, - -0.03699097782373428 + 0.21926738321781158, + -0.03734800964593887 ], "tsne": [ - 33.76227951049805, - -9.411736488342285 + 28.387081146240234, + 14.764888763427734 ], "umap": [ - 8.741501808166504, - 8.012909889221191 + 8.975882530212402, + 7.702279567718506 ], "pca3d": [ - 0.21935532987117767, - -0.03698758780956268, - -0.12702231109142303 + 0.21926741302013397, + -0.037344373762607574, + -0.12697923183441162 ], "tsne3d": [ - 15.397571563720703, - -4.057863712310791, - 3.290851593017578 + 14.837053298950195, + 5.2639241218566895, + -8.451573371887207 ], "umap3d": [ - 6.605515956878662, - 4.21907901763916, - 1.4653735160827637 + 6.370575904846191, + 3.983436346054077, + 0.7248603701591492 ] }, "cluster_id": 0, @@ -445152,12 +445302,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -445578,31 +445728,31 @@ "openalex_url": "https://openalex.org/W4406970274", "projections": { "pca": [ - 0.12636804580688477, - 0.12708772718906403 + 0.12666192650794983, + 0.12692788243293762 ], "tsne": [ - 39.84721755981445, - 11.133428573608398 + 45.78479766845703, + -3.4460132122039795 ], "umap": [ - 8.795459747314453, - 7.65300178527832 + 9.067215919494629, + 7.237649917602539 ], "pca3d": [ - 0.1263677328824997, - 0.12707901000976562, - -0.01398298516869545 + 0.12666162848472595, + 0.12691934406757355, + -0.01429507602006197 ], "tsne3d": [ - 19.294483184814453, - 0.07553457468748093, - 10.371359825134277 + 23.145856857299805, + 10.123851776123047, + 9.746158599853516 ], "umap3d": [ - 7.53594970703125, - 4.172784805297852, - 1.9012166261672974 + 7.140185356140137, + 4.2959723472595215, + 0.6597532033920288 ] }, "cluster_id": 0, @@ -445613,12 +445763,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -446050,31 +446200,31 @@ "openalex_url": "https://openalex.org/W4406970313", "projections": { "pca": [ - 0.26985645294189453, - 0.037949275225400925 + 0.26996350288391113, + 0.03742589056491852 ], "tsne": [ - 38.85908889770508, - 10.60604476928711 + 45.0427360534668, + -2.908351182937622 ], "umap": [ - 8.612083435058594, - 7.6247711181640625 + 8.73982048034668, + 7.237802982330322 ], "pca3d": [ - 0.26985612511634827, - 0.03793906792998314, - -0.08339717984199524 + 0.2699633240699768, + 0.037416066974401474, + -0.08354064077138901 ], "tsne3d": [ - 19.472740173339844, - 1.6383106708526611, - 9.412384986877441 + 21.784448623657227, + 8.93352222442627, + 9.861176490783691 ], "umap3d": [ - 7.540391445159912, - 4.141247749328613, - 2.055950880050659 + 7.289246082305908, + 4.176476001739502, + 0.8671258687973022 ] }, "cluster_id": 0, @@ -446085,12 +446235,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -446511,31 +446661,31 @@ "cited_by_count": 1, "projections": { "pca": [ - 0.22241301834583282, - -0.08270290493965149 + 0.22212199866771698, + -0.08303515613079071 ], "tsne": [ - 40.611759185791016, - -12.267426490783691 + 35.97163391113281, + 18.799144744873047 ], "umap": [ - 9.230286598205566, - 8.459460258483887 + 9.476507186889648, + 8.025009155273438 ], "pca3d": [ - 0.22241303324699402, - -0.0826987624168396, - -0.09744610637426376 + 0.22212202847003937, + -0.08303108811378479, + -0.09741578251123428 ], "tsne3d": [ - 20.656387329101562, - -5.336147785186768, - 4.827468395233154 + 19.881927490234375, + 3.9887022972106934, + -12.11634349822998 ], "umap3d": [ - 6.840173721313477, - 4.016530513763428, - 1.2522990703582764 + 6.548693656921387, + 3.7859275341033936, + 0.5247923731803894 ] }, "cluster_id": 0, @@ -446546,12 +446696,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -446972,31 +447122,31 @@ "doi": "10.1101/2025.01.22.634371", "projections": { "pca": [ - 0.16650564968585968, - -0.11889373511075974 + 0.16632188856601715, + -0.11892158538103104 ], "tsne": [ - 22.123689651489258, - -8.540399551391602 + 20.319313049316406, + 10.181779861450195 ], "umap": [ - 7.702967166900635, - 6.624972343444824 + 7.766470432281494, + 6.329078197479248 ], "pca3d": [ - 0.1665056049823761, - -0.11888204514980316, - -0.03159279376268387 + 0.16632190346717834, + -0.11890964955091476, + -0.03132093325257301 ], "tsne3d": [ - 12.016282081604004, - 3.8361153602600098, - -0.8598092794418335 + 11.770347595214844, + -0.6108666658401489, + -4.854691028594971 ], "umap3d": [ - 6.21885347366333, - 4.742763042449951, - 2.7473413944244385 + 6.307440280914307, + 4.914737224578857, + 1.7242389917373657 ] }, "cluster_id": 4, @@ -447007,12 +447157,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -447450,47 +447600,47 @@ "cited_by_count": 42, "projections": { "pca": [ - 0.21051417291164398, - 0.01758182980120182 + 0.21063953638076782, + 0.01682358980178833 ], "tsne": [ - 30.227500915527344, - -13.862480163574219 + 24.3001708984375, + 19.281015396118164 ], "umap": [ - 8.243456840515137, - 8.975114822387695 + 8.446856498718262, + 8.629471778869629 ], "pca3d": [ - 0.21051397919654846, - 0.01756994239985943, - -0.12052079290151596 + 0.21063938736915588, + 0.016811737790703773, + -0.12057815492153168 ], "tsne3d": [ - 15.996564865112305, - -10.260787963867188, - 5.995110511779785 + 13.413308143615723, + -0.49599891901016235, + -15.502859115600586 ], "umap3d": [ - 5.9087934494018555, - 3.898902416229248, - 1.310836911201477 + 5.645670413970947, + 3.6731789112091064, + 1.0035885572433472 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -447899,46 +448049,46 @@ "journal": "Nature Machine Intelligence", "projections": { "pca": [ - 0.06652898341417313, - 0.08700473606586456 + 0.06679196655750275, + 0.08673311024904251 ], "tsne": [ - 19.486787796020508, - -18.7076416015625 + 13.565469741821289, + 18.688217163085938 ], "umap": [ - 7.206228256225586, - 8.954345703125 + 7.286196708679199, + 8.662270545959473 ], "pca3d": [ - 0.06652886420488358, - 0.0870005339384079, - -0.14009052515029907 + 0.0667918473482132, + 0.08672924339771271, + -0.14013198018074036 ], "tsne3d": [ - 8.922730445861816, - -5.311953544616699, - 5.531514644622803 + 8.604546546936035, + 4.246397972106934, + -8.360918045043945 ], "umap3d": [ - 5.5119218826293945, - 3.4413740634918213, - 2.206674575805664 + 5.640067100524902, + 3.4882047176361084, + 2.053086519241333 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -448370,47 +448520,47 @@ "openalex_url": "https://openalex.org/W4406695248", "projections": { "pca": [ - 0.07691339403390884, - 0.08380872011184692 + 0.07716411352157593, + 0.08322513848543167 ], "tsne": [ - 29.296588897705078, - 12.664289474487305 + 31.75501251220703, + -3.7297191619873047 ], "umap": [ - 7.596202373504639, - 7.678298473358154 + 7.726296901702881, + 7.314604759216309 ], "pca3d": [ - 0.07691317796707153, - 0.08380835503339767, - -0.1702944040298462 + 0.07716387510299683, + 0.0832248330116272, + -0.17038896679878235 ], "tsne3d": [ - 16.915693283081055, - -8.036298751831055, - -14.31706428527832 + 16.720823287963867, + 14.672112464904785, + 5.904055595397949 ], "umap3d": [ - 6.95787239074707, - 3.698167324066162, - 2.7991769313812256 + 7.236919403076172, + 3.9258487224578857, + 1.9213159084320068 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -448829,31 +448979,31 @@ "doi": "10.1101/2025.01.22.634321", "projections": { "pca": [ - 0.14450570940971375, - -0.1434958428144455 + 0.14426344633102417, + -0.14351895451545715 ], "tsne": [ - 7.461138725280762, - 4.12542724609375 + 9.611945152282715, + -2.419409990310669 ], "umap": [ - 6.932406902313232, - 6.014374732971191 + 7.195220947265625, + 5.800992012023926 ], "pca3d": [ - 0.14450600743293762, - -0.14350229501724243, - 0.0332031287252903 + 0.14426372945308685, + -0.14352475106716156, + 0.03347063809633255 ], "tsne3d": [ - 11.077766418457031, - 3.587207317352295, - -5.514029026031494 + 9.052164077758789, + -4.151116371154785, + -1.5712013244628906 ], "umap3d": [ - 6.038092136383057, - 5.038241863250732, - 3.388930559158325 + 6.343437194824219, + 5.336406707763672, + 2.50166916847229 ] }, "cluster_id": 4, @@ -448864,11 +449014,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -449305,31 +449455,31 @@ "cited_by_count": 47, "projections": { "pca": [ - 0.3068554997444153, - 0.047948531806468964 + 0.3069697618484497, + 0.04696198180317879 ], "tsne": [ - 48.69488525390625, - 4.019475936889648 + 42.08619689941406, + 14.373262405395508 ], "umap": [ - 9.655533790588379, - 7.54521369934082 + 9.944976806640625, + 7.008688926696777 ], "pca3d": [ - 0.3068552315235138, - 0.047945164144039154, - -0.11116144806146622 + 0.30696961283683777, + 0.046958521008491516, + -0.1114364042878151 ], "tsne3d": [ - 25.870304107666016, - 0.4305640161037445, - 2.9689438343048096 + 26.527517318725586, + 3.464952230453491, + -8.198451042175293 ], "umap3d": [ - 7.843908786773682, - 4.564881324768066, - 1.2172385454177856 + 7.270369529724121, + 4.303480625152588, + 0.016277994960546494 ] }, "cluster_id": 0, @@ -449340,12 +449490,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 20, - "label": "Chromatin Regulation" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -449765,31 +449915,31 @@ ], "projections": { "pca": [ - 0.041173581033945084, - -0.15040849149227142 + 0.04109051451086998, + -0.14985710382461548 ], "tsne": [ - 4.5420732498168945, - 22.39432716369629 + -0.8129785060882568, + -14.112329483032227 ], "umap": [ - 6.312298774719238, - 4.498053073883057 + 6.71604585647583, + 4.367732048034668 ], "pca3d": [ - 0.04117375239729881, - -0.15040569007396698, - 0.18491528928279877 + 0.04109076038002968, + -0.14985431730747223, + 0.18534758687019348 ], "tsne3d": [ - -1.3751707077026367, - 8.598372459411621, - -3.380535840988159 + 0.02671804651618004, + -6.858541965484619, + -2.6238090991973877 ], "umap3d": [ - 5.194990634918213, - 5.851840019226074, - 4.7622270584106445 + 5.834231853485107, + 6.737447738647461, + 3.5991322994232178 ] }, "cluster_id": 3, @@ -449800,11 +449950,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -450227,31 +450377,31 @@ ], "projections": { "pca": [ - 0.1581806093454361, - -0.05290072783827782 + 0.15813139081001282, + -0.05287551134824753 ], "tsne": [ - 21.163476943969727, - 7.972426891326904 + 24.9965763092041, + -4.11752986907959 ], "umap": [ - 7.1046528816223145, - 7.0001325607299805 + 7.368932723999023, + 6.660613536834717 ], "pca3d": [ - 0.15818071365356445, - -0.052914466708898544, - -0.033720023930072784 + 0.15813151001930237, + -0.052889276295900345, + -0.03351622074842453 ], "tsne3d": [ - 9.478063583374023, - -3.221402168273926, - -10.01939582824707 + 9.064824104309082, + 7.941126346588135, + 2.0424880981445312 ], "umap3d": [ - 6.405257701873779, - 4.106418609619141, - 3.157731771469116 + 6.79503059387207, + 4.573641777038574, + 2.3804965019226074 ] }, "cluster_id": 4, @@ -450262,12 +450412,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -450695,31 +450845,31 @@ "doi": "10.1101/2025.01.14.632548", "projections": { "pca": [ - 0.2829257547855377, - 0.0427427664399147 + 0.2829638123512268, + 0.04230881109833717 ], "tsne": [ - 50.0787239074707, - -4.196662902832031 + 48.65391540527344, + 6.936891078948975 ], "umap": [ - 10.164145469665527, - 7.8372650146484375 + 10.43299388885498, + 7.465641021728516 ], "pca3d": [ - 0.28292569518089294, - 0.04271412268280983, - -0.08351064473390579 + 0.2829638719558716, + 0.04227964207530022, + -0.08375675231218338 ], "tsne3d": [ - 27.350366592407227, - -3.8465416431427, - -4.521908283233643 + 28.773149490356445, + 8.497492790222168, + 0.026116246357560158 ], "umap3d": [ - 7.421925067901611, - 4.807189464569092, - 0.6814871430397034 + 6.749258041381836, + 4.4329752922058105, + -0.5237297415733337 ] }, "cluster_id": 0, @@ -450730,12 +450880,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -451182,47 +451332,47 @@ "openalex_url": "https://openalex.org/W4404628441", "projections": { "pca": [ - 0.15447938442230225, - 0.103977732360363 + 0.15488062798976898, + 0.10338088870048523 ], "tsne": [ - 11.384587287902832, - -11.635522842407227 + 37.53761291503906, + -11.491280555725098 ], "umap": [ - 7.178508281707764, - 8.352611541748047 + 7.437094211578369, + 7.919463157653809 ], "pca3d": [ - 0.15447893738746643, - 0.10397946834564209, - -0.04287845268845558 + 0.15488028526306152, + 0.10338238626718521, + -0.0430455207824707 ], "tsne3d": [ - 17.8748836517334, - -16.302358627319336, - -10.015110969543457 + 15.64367389678955, + 20.3055419921875, + -2.089242696762085 ], "umap3d": [ - 6.431332111358643, - 3.134334087371826, - 2.4885549545288086 + 6.836464881896973, + 3.3761658668518066, + 2.0877935886383057 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 26, - "label": "Genetic Disease" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -451641,31 +451791,31 @@ "cited_by_count": 168, "projections": { "pca": [ - 0.23443731665611267, - -0.04968142881989479 + 0.2343032956123352, + -0.05010947212576866 ], "tsne": [ - 16.122859954833984, - 1.500311017036438 + 23.220623016357422, + 8.157538414001465 ], "umap": [ - 8.183135986328125, - 6.817429065704346 + 8.486063957214355, + 6.420523643493652 ], "pca3d": [ - 0.2344372272491455, - -0.04966318607330322, - -0.08445539325475693 + 0.23430316150188446, + -0.05009060725569725, + -0.08442793786525726 ], "tsne3d": [ - 12.730161666870117, - 8.219066619873047, - -3.281015396118164 + 12.145816802978516, + 2.6509904861450195, + -1.6137309074401855 ], "umap3d": [ - 6.580989837646484, - 4.873340129852295, - 2.359616994857788 + 6.571310520172119, + 4.974097728729248, + 1.433465600013733 ] }, "cluster_id": 4, @@ -451676,12 +451826,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -452114,31 +452264,31 @@ "cited_by_count": 96, "projections": { "pca": [ - 0.25213098526000977, - -0.04860137030482292 + 0.25195616483688354, + -0.04910430684685707 ], "tsne": [ - 36.30949020385742, - -3.5320537090301514 + 30.541027069091797, + 8.955229759216309 ], "umap": [ - 9.198753356933594, - 7.634801387786865 + 9.291152954101562, + 7.275250434875488 ], "pca3d": [ - 0.2521309554576874, - -0.04859349876642227, - -0.1251397430896759 + 0.2519559860229492, + -0.04909659922122955, + -0.1251503825187683 ], "tsne3d": [ - 17.540109634399414, - 0.7067622542381287, - -4.040741920471191 + 20.059616088867188, + 3.6266777515411377, + -1.8452805280685425 ], "umap3d": [ - 6.770305633544922, - 4.683492183685303, - 1.4526406526565552 + 6.477226734161377, + 4.458362579345703, + 0.5171933770179749 ] }, "cluster_id": 0, @@ -452149,11 +452299,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -452586,31 +452736,31 @@ "cited_by_count": 51, "projections": { "pca": [ - 0.25753140449523926, - 0.08288561552762985 + 0.257796049118042, + 0.08235608041286469 ], "tsne": [ - 40.91220474243164, - 1.984093189239502 + 38.73604202270508, + 4.464231014251709 ], "umap": [ - 9.450201034545898, - 7.4848151206970215 + 9.592256546020508, + 7.204518795013428 ], "pca3d": [ - 0.25753122568130493, - 0.08288383483886719, - -0.06029243394732475 + 0.2577957212924957, + 0.08235379308462143, + -0.0605333186686039 ], "tsne3d": [ - 24.417095184326172, - -2.707430124282837, - -2.239732027053833 + 22.582860946655273, + 6.243113040924072, + -2.4524483680725098 ], "umap3d": [ - 7.056437015533447, - 4.881275177001953, - 1.3534001111984253 + 6.611765384674072, + 4.575650691986084, + 0.2558889091014862 ] }, "cluster_id": 0, @@ -452621,12 +452771,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 20, - "label": "Chromatin Regulation" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -453051,46 +453201,46 @@ "openalex_url": "https://openalex.org/W4404905434", "projections": { "pca": [ - 0.0683826208114624, - -0.014609663747251034 + 0.06850305199623108, + -0.014041132293641567 ], "tsne": [ - 11.385149002075195, - -23.522335052490234 + 6.499861240386963, + 18.802778244018555 ], "umap": [ - 6.8053998947143555, - 9.085517883300781 + 6.886728286743164, + 8.68448257446289 ], "pca3d": [ - 0.0683823898434639, - -0.014598150737583637, - 0.12542644143104553 + 0.06850283592939377, + -0.014029940590262413, + 0.1255253553390503 ], "tsne3d": [ - 3.0330917835235596, - -5.693765163421631, - 9.711938858032227 + 1.8702428340911865, + 6.145598888397217, + -9.28251838684082 ], "umap3d": [ - 4.706131935119629, - 3.940643072128296, - 2.168321371078491 + 4.839951038360596, + 3.9897689819335938, + 2.1282005310058594 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -453501,31 +453651,31 @@ "journal": "Nature Biotechnology", "projections": { "pca": [ - 0.10181530565023422, - 0.00512972567230463 + 0.10197488218545914, + 0.005326372105628252 ], "tsne": [ - 17.604244232177734, - 11.384730339050293 + 20.193227767944336, + -6.409473419189453 ], "umap": [ - 6.8964362144470215, - 6.845582008361816 + 7.028684139251709, + 6.441232204437256 ], "pca3d": [ - 0.10181526839733124, - 0.005120106507092714, - 0.09364563971757889 + 0.10197485983371735, + 0.005317243747413158, + 0.09377025812864304 ], "tsne3d": [ - 5.784205913543701, - -1.7975924015045166, - -12.075615882873535 + 5.787328243255615, + 6.600329875946045, + 2.877910614013672 ], "umap3d": [ - 6.262670993804932, - 4.292328357696533, - 3.491945505142212 + 6.771413326263428, + 4.730006217956543, + 2.649519205093384 ] }, "cluster_id": 4, @@ -453536,12 +453686,12 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 22, - "label": "Genetic Variants" + "id": 17, + "label": "Genomic Models" } ], "_embedding": [ @@ -453967,31 +454117,31 @@ "doi": "10.1101/2024.12.31.630783", "projections": { "pca": [ - 0.15147674083709717, - 0.01714453287422657 + 0.15163224935531616, + 0.017022790387272835 ], "tsne": [ - 23.2413272857666, - 21.971210479736328 + 37.410804748535156, + 1.5389517545700073 ], "umap": [ - 9.249277114868164, - 7.30807638168335 + 9.422507286071777, + 6.951960563659668 ], "pca3d": [ - 0.15147662162780762, - 0.017144793644547462, - 0.02911156788468361 + 0.151632159948349, + 0.017022626474499702, + 0.029083557426929474 ], "tsne3d": [ - 13.739112854003906, - 17.37216567993164, - 2.288574695587158 + 16.852087020874023, + -10.001169204711914, + 2.586165189743042 ], "umap3d": [ - 6.806556224822998, - 5.2417731285095215, - 1.469946026802063 + 6.448993682861328, + 4.971289157867432, + 0.6178749799728394 ] }, "cluster_id": 0, @@ -454002,11 +454152,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -454442,31 +454592,31 @@ "cited_by_count": 13, "projections": { "pca": [ - 0.18750937283039093, - -0.05775878205895424 + 0.18745824694633484, + -0.058090198785066605 ], "tsne": [ - 16.729141235351562, - 29.728029251098633 + 20.23030662536621, + -28.354433059692383 ], "umap": [ - 7.824236869812012, - 4.801460266113281 + 8.165897369384766, + 4.353328227996826 ], "pca3d": [ - 0.18750914931297302, - -0.0577409490942955, - 0.1185179352760315 + 0.1874583512544632, + -0.05807178467512131, + 0.11850782483816147 ], "tsne3d": [ - 1.7025762796401978, - 21.88160514831543, - 1.6150057315826416 + 11.574618339538574, + -14.874592781066895, + 3.8291022777557373 ], "umap3d": [ - 7.076693534851074, - 6.012434005737305, - 3.7600314617156982 + 7.211785793304443, + 6.487086296081543, + 2.1690564155578613 ] }, "cluster_id": 4, @@ -454477,11 +454627,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -454910,31 +455060,31 @@ "doi": "10.1101/2024.12.25.630221", "projections": { "pca": [ - 0.2499072551727295, - -0.0972500815987587 + 0.24966542422771454, + -0.09808354079723358 ], "tsne": [ - 37.694122314453125, - -2.1697757244110107 + 32.139347076416016, + 9.78678035736084 ], "umap": [ - 9.005260467529297, - 7.139787197113037 + 9.21687126159668, + 6.832173824310303 ], "pca3d": [ - 0.24990737438201904, - -0.09725844115018845, - -0.08640096336603165 + 0.2496655285358429, + -0.09809183329343796, + -0.08637332171201706 ], "tsne3d": [ - 20.174667358398438, - 2.030264139175415, - -2.8369498252868652 + 21.778478622436523, + 1.3943291902542114, + -3.0398833751678467 ], "umap3d": [ - 7.2358503341674805, - 4.6891093254089355, - 1.686580777168274 + 6.8732404708862305, + 4.601813316345215, + 0.5941672325134277 ] }, "cluster_id": 0, @@ -454945,11 +455095,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -455358,46 +455508,46 @@ ], "projections": { "pca": [ - 0.0260220505297184, - 0.147012859582901 + 0.026697272434830666, + 0.14637327194213867 ], "tsne": [ - 17.52370834350586, - -17.73412322998047 + 8.086512565612793, + 14.788328170776367 ], "umap": [ - 7.822037696838379, - 9.1583833694458 + 7.762913703918457, + 8.792003631591797 ], "pca3d": [ - 0.026021765545010567, - 0.14700691401958466, - -0.052562370896339417 + 0.02669697068631649, + 0.14636756479740143, + -0.05271010100841522 ], "tsne3d": [ - 12.603691101074219, - -7.521115779876709, - 16.705867767333984 + 4.270533084869385, + -1.2240605354309082, + -11.11345100402832 ], "umap3d": [ - 5.63400936126709, - 3.741839647293091, - 1.5558834075927734 + 5.451268672943115, + 3.511373519897461, + 1.4953758716583252 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -455817,31 +455967,31 @@ "cited_by_count": 8, "projections": { "pca": [ - 0.17954248189926147, - -0.0459272637963295 + 0.17954425513744354, + -0.04618218541145325 ], "tsne": [ - 15.458431243896484, - 1.382322907447815 + 23.178918838500977, + 8.07957935333252 ], "umap": [ - 8.113964080810547, - 6.8009443283081055 + 8.36994743347168, + 6.401957988739014 ], "pca3d": [ - 0.17954261600971222, - -0.0459366999566555, - -0.013284826651215553 + 0.1795443892478943, + -0.046191420406103134, + -0.01320933923125267 ], "tsne3d": [ - 12.353808403015137, - 9.379805564880371, - -2.995884656906128 + 11.038679122924805, + 2.69816517829895, + -2.2354896068573 ], "umap3d": [ - 6.523622989654541, - 4.855741024017334, - 2.391572952270508 + 6.5220441818237305, + 4.94862699508667, + 1.4779480695724487 ] }, "cluster_id": 4, @@ -455852,12 +456002,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -456279,31 +456429,31 @@ "doi": "10.1101/2024.12.19.629312", "projections": { "pca": [ - 0.20286092162132263, - -0.036639660596847534 + 0.20278921723365784, + -0.036976784467697144 ], "tsne": [ - 33.759952545166016, - -9.432206153869629 + 28.375293731689453, + 14.769023895263672 ], "umap": [ - 8.65165901184082, - 8.256927490234375 + 9.022065162658691, + 7.811732769012451 ], "pca3d": [ - 0.2028609961271286, - -0.036634158343076706, - -0.13489484786987305 + 0.20278921723365784, + -0.036971043795347214, + -0.1348176896572113 ], "tsne3d": [ - 15.3464994430542, - -4.0835957527160645, - 3.4218578338623047 + 14.794024467468262, + 5.222064018249512, + -8.50775146484375 ], "umap3d": [ - 6.544620037078857, - 4.171355247497559, - 1.4425050020217896 + 6.315273284912109, + 3.8997504711151123, + 0.8211881518363953 ] }, "cluster_id": 0, @@ -456314,12 +456464,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -456743,31 +456893,31 @@ "doi": "10.1101/2024.12.17.628768", "projections": { "pca": [ - 0.3260914385318756, - 0.022746430709958076 + 0.32618680596351624, + 0.021593227982521057 ], "tsne": [ - 41.69768142700195, - -9.70193862915039 + 39.364646911621094, + 16.42238426208496 ], "umap": [ - 9.401656150817871, - 8.458474159240723 + 9.639655113220215, + 7.846058368682861 ], "pca3d": [ - 0.32609128952026367, - 0.022738495841622353, - -0.1485999971628189 + 0.3261866569519043, + 0.02158564142882824, + -0.14879372715950012 ], "tsne3d": [ - 24.400907516479492, - -3.1234686374664307, - 5.210775375366211 + 23.738218307495117, + 5.893082618713379, + -10.71732234954834 ], "umap3d": [ - 7.511743545532227, - 4.052554130554199, - 1.1281450986862183 + 7.048968315124512, + 3.7990975379943848, + 0.12843546271324158 ] }, "cluster_id": 0, @@ -456778,12 +456928,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -457208,31 +457358,31 @@ "doi": "10.1101/2024.12.17.628791", "projections": { "pca": [ - 0.3478807806968689, - 0.08208876848220825 + 0.34811145067214966, + 0.0806988999247551 ], "tsne": [ - 42.10459518432617, - -9.325458526611328 + 39.67310333251953, + 15.97877025604248 ], "umap": [ - 9.321764945983887, - 8.429120063781738 + 9.560246467590332, + 7.848977565765381 ], "pca3d": [ - 0.34788039326667786, - 0.08207368850708008, - -0.1538606733083725 + 0.3481113016605377, + 0.08068379759788513, + -0.15418855845928192 ], "tsne3d": [ - 25.421863555908203, - -3.3402252197265625, - 5.416368007659912 + 24.760860443115234, + 5.98483419418335, + -11.21282958984375 ], "umap3d": [ - 7.5584869384765625, - 4.0239787101745605, - 1.1464338302612305 + 6.995296478271484, + 3.7927639484405518, + 0.22235620021820068 ] }, "cluster_id": 0, @@ -457243,12 +457393,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -457668,31 +457818,31 @@ "doi": "10.1101/2024.12.18.628606", "projections": { "pca": [ - 0.05156713351607323, - -0.13542889058589935 + 0.051274389028549194, + -0.13500334322452545 ], "tsne": [ - 8.745491981506348, - 8.520878791809082 + 11.046063423156738, + -5.8157877922058105 ], "umap": [ - 6.846161842346191, - 6.339520454406738 + 7.163097381591797, + 5.996547698974609 ], "pca3d": [ - 0.051567383110523224, - -0.1354304552078247, - -0.02236214280128479 + 0.051274675875902176, + -0.13500462472438812, + -0.021990640088915825 ], "tsne3d": [ - 6.64967155456543, - 3.4598922729492188, - -10.666845321655273 + 5.268299102783203, + 2.2900748252868652, + 3.802406072616577 ], "umap3d": [ - 5.66790246963501, - 4.873298645019531, - 3.421361207962036 + 6.00333833694458, + 5.165911674499512, + 2.5542380809783936 ] }, "cluster_id": 4, @@ -457703,11 +457853,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -458137,31 +458287,31 @@ "cited_by_count": 16, "projections": { "pca": [ - 0.1955113708972931, - 0.10087727010250092 + 0.19587112963199615, + 0.09993135184049606 ], "tsne": [ - 42.68751525878906, - -8.606409072875977 + 39.017364501953125, + 15.03939151763916 ], "umap": [ - 9.127487182617188, - 8.598321914672852 + 9.425336837768555, + 8.062621116638184 ], "pca3d": [ - 0.19551102817058563, - 0.10087741166353226, - -0.16085846722126007 + 0.19587083160877228, + 0.09993188083171844, + -0.16104693710803986 ], "tsne3d": [ - 26.17659568786621, - -5.649420738220215, - 5.086835861206055 + 25.65580177307129, + 7.811081886291504, + -12.373764038085938 ], "umap3d": [ - 7.286981582641602, - 3.9844462871551514, - 1.177077293395996 + 6.829398155212402, + 3.662109136581421, + 0.31609803438186646 ] }, "cluster_id": 0, @@ -458172,12 +458322,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -458592,46 +458742,46 @@ "doi": "10.1101/2023.11.11.566161", "projections": { "pca": [ - 0.09374293684959412, - -0.052303943783044815 + 0.09363352507352829, + -0.05274641886353493 ], "tsne": [ - 24.080759048461914, - -22.111553192138672 + 16.301319122314453, + 23.888511657714844 ], "umap": [ - 7.740931034088135, - 9.428508758544922 + 7.676082134246826, + 9.10055923461914 ], "pca3d": [ - 0.09374303370714188, - -0.05229559168219566, - -0.09923364967107773 + 0.09363359212875366, + -0.052737656980752945, + -0.09918338805437088 ], "tsne3d": [ - 13.584779739379883, - -6.562971591949463, - 8.752846717834473 + 11.523613929748535, + 3.5239782333374023, + -13.485796928405762 ], "umap3d": [ - 5.386959552764893, - 3.6197190284729004, - 1.3757710456848145 + 5.179131507873535, + 3.452895402908325, + 1.3366656303405762 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -459056,31 +459206,31 @@ "openalex_url": "https://openalex.org/W4405529257", "projections": { "pca": [ - -0.08449552208185196, - -0.06117667257785797 + -0.08449770510196686, + -0.060084033757448196 ], "tsne": [ - 3.1632559299468994, - 6.130351543426514 + 6.173403263092041, + -6.443670272827148 ], "umap": [ - 6.064115524291992, - 6.498182773590088 + 6.38514518737793, + 6.180320739746094 ], "pca3d": [ - -0.08449512720108032, - -0.061209164559841156, - 0.018945127725601196 + -0.08449731767177582, + -0.06011759117245674, + 0.01935897208750248 ], "tsne3d": [ - 2.4374043941497803, - 5.6853532791137695, - -13.301604270935059 + 0.6254538297653198, + 4.741439342498779, + -1.7949318885803223 ], "umap3d": [ - 5.010015487670898, - 4.6461405754089355, - 3.727935552597046 + 5.532066822052002, + 5.086420059204102, + 3.103306770324707 ] }, "cluster_id": 4, @@ -459091,12 +459241,12 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 11, - "label": "Genomic Language" + "id": 13, + "label": "Genomics Papers" } ], "_embedding": [ @@ -459522,31 +459672,31 @@ ], "projections": { "pca": [ - 0.1586618721485138, - -0.03934822604060173 + 0.15862712264060974, + -0.03949815407395363 ], "tsne": [ - 8.075789451599121, - 2.0829455852508545 + 9.947266578674316, + -0.11609397083520889 ], "umap": [ - 6.664285182952881, - 6.491528511047363 + 6.988781452178955, + 6.130685329437256 ], "pca3d": [ - 0.15866173803806305, - -0.039339736104011536, - 0.002245127223432064 + 0.15862710773944855, + -0.03948880732059479, + 0.002343930536881089 ], "tsne3d": [ - 3.428161859512329, - 0.3833654522895813, - -9.658509254455566 + 7.650656223297119, + 2.2081027030944824, + -7.85788631439209 ], "umap3d": [ - 5.701744556427002, - 4.643636226654053, - 3.365515947341919 + 6.018218040466309, + 4.9450883865356445, + 2.6258809566497803 ] }, "cluster_id": 4, @@ -459557,12 +459707,12 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 11, - "label": "Genomic Language" + "id": 17, + "label": "Genomic Models" } ], "_embedding": [ @@ -459999,31 +460149,31 @@ "cited_by_count": 28, "projections": { "pca": [ - 0.31866973638534546, - 0.002621665131300688 + 0.31867969036102295, + 0.0016433238051831722 ], "tsne": [ - 53.314064025878906, - 5.485140323638916 + 51.50982666015625, + 17.117557525634766 ], "umap": [ - 10.094067573547363, - 6.9398298263549805 + 10.347082138061523, + 6.494290828704834 ], "pca3d": [ - 0.31866976618766785, - 0.0026087418664246798, - -0.05978603661060333 + 0.318679541349411, + 0.0016299851704388857, + -0.06005576625466347 ], "tsne3d": [ - 26.604251861572266, - 4.202740669250488, - -0.031158361583948135 + 27.705793380737305, + -0.42227473855018616, + -5.401536464691162 ], "umap3d": [ - 8.160263061523438, - 5.123217582702637, - 1.128757119178772 + 7.499120235443115, + 4.917204856872559, + -0.39116308093070984 ] }, "cluster_id": 0, @@ -460034,12 +460184,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -460456,31 +460606,31 @@ "doi": "10.1101/2024.12.17.628962", "projections": { "pca": [ - 0.1479393094778061, - -0.09076128154993057 + 0.1478915512561798, + -0.0901801660656929 ], "tsne": [ - 4.768380165100098, - 13.617281913757324 + 10.979284286499023, + -14.023006439208984 ], "umap": [ - 6.434116363525391, - 5.51923131942749 + 6.738316059112549, + 5.233800888061523 ], "pca3d": [ - 0.14793945848941803, - -0.0907789021730423, - 0.21266688406467438 + 0.14789171516895294, + -0.09019804745912552, + 0.21290427446365356 ], "tsne3d": [ - 1.4119945764541626, - 4.3555684089660645, - -4.26607084274292 + 1.8881715536117554, + -2.589848756790161, + 7.394432544708252 ], "umap3d": [ - 5.717097759246826, - 5.7159743309021, - 3.854778289794922 + 5.853689670562744, + 6.083693981170654, + 2.5719380378723145 ] }, "cluster_id": 4, @@ -460491,12 +460641,12 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, - "label": "Genomic Models" + "id": 32, + "label": "Genome Protein" } ], "_embedding": [ @@ -460922,47 +461072,47 @@ "cited_by_count": 72, "projections": { "pca": [ - 0.0942220687866211, - 0.1322888284921646 + 0.0944356918334961, + 0.1321653127670288 ], "tsne": [ - 17.785234451293945, - -13.436003684997559 + 29.981178283691406, + 19.08647918701172 ], "umap": [ - 6.899792194366455, - 8.598012924194336 + 7.200508117675781, + 8.299066543579102 ], "pca3d": [ - 0.09422164410352707, - 0.1323041468858719, - -0.20120462775230408 + 0.09443521499633789, + 0.13218045234680176, + -0.2014530599117279 ], "tsne3d": [ - 17.321044921875, - -15.886308670043945, - 0.1463606059551239 + 15.688767433166504, + 18.702491760253906, + -8.826848030090332 ], "umap3d": [ - 6.121959686279297, - 3.0983967781066895, - 2.0452756881713867 + 6.330475330352783, + 3.1267547607421875, + 1.869356393814087 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 6, + "label": "Single Cell Data" }, { - "id": 26, - "label": "Genetic Disease" + "id": 28, + "label": "Single Cell Data" } ], "_embedding": [ @@ -461386,31 +461536,31 @@ ], "projections": { "pca": [ - 0.10821942985057831, - -0.1603614240884781 + 0.10795605182647705, + -0.16009747982025146 ], "tsne": [ - 11.60073471069336, - 7.659120082855225 + 13.209814071655273, + -7.465226173400879 ], "umap": [ - 7.068594932556152, - 6.137905597686768 + 7.227349758148193, + 5.86335563659668 ], "pca3d": [ - 0.10821958631277084, - -0.16036507487297058, - 0.03620914742350578 + 0.10795629769563675, + -0.16010156273841858, + 0.036573201417922974 ], "tsne3d": [ - 7.770810127258301, - 10.225981712341309, - -13.358656883239746 + 7.022549629211426, + -0.042700737714767456, + -2.2610650062561035 ], "umap3d": [ - 5.900160789489746, - 5.0058698654174805, - 3.2399919033050537 + 6.160829544067383, + 5.311054229736328, + 2.33683180809021 ] }, "cluster_id": 4, @@ -461421,11 +461571,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -461842,47 +461992,47 @@ "doi": "10.1101/2023.10.29.564479", "projections": { "pca": [ - 0.1170828714966774, - 0.07389983534812927 + 0.11750248819589615, + 0.07429999113082886 ], "tsne": [ - -2.2507729530334473, - -21.024362564086914 + -4.858516693115234, + 17.339645385742188 ], "umap": [ - 4.731234550476074, - 8.624640464782715 + 5.031685829162598, + 7.866063594818115 ], "pca3d": [ - 0.11708272248506546, - 0.0738995149731636, - 0.15134209394454956 + 0.1175023689866066, + 0.07429992407560349, + 0.15138491988182068 ], "tsne3d": [ - -2.5692970752716064, - -8.703932762145996, - -8.044209480285645 + -1.6371957063674927, + 16.2520694732666, + 2.510805606842041 ], "umap3d": [ - 3.785007953643799, - 3.785952568054199, - 2.946168899536133 + 4.419186115264893, + 4.130287170410156, + 3.134512186050415 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, - "label": "Medical AI" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -462300,47 +462450,47 @@ "doi": "10.1101/2024.12.03.24318432", "projections": { "pca": [ - 0.14466628432273865, - -0.048021748661994934 + 0.14443698525428772, + -0.048137057572603226 ], "tsne": [ - 27.447528839111328, - 5.796751976013184 + 33.92112350463867, + -11.265860557556152 ], "umap": [ - 7.117043972015381, - 7.686405181884766 + 7.316513538360596, + 7.2844343185424805 ], "pca3d": [ - 0.14466631412506104, - -0.04803154245018959, - -0.11855221539735794 + 0.14443697035312653, + -0.048146530985832214, + -0.11851600557565689 ], "tsne3d": [ - 13.931577682495117, - -14.482048988342285, - -14.128411293029785 + 13.323453903198242, + 19.77063751220703, + 2.8748674392700195 ], "umap3d": [ - 6.516361236572266, - 3.535555362701416, - 3.021702527999878 + 7.012063980102539, + 3.8945040702819824, + 2.3607845306396484 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -462759,47 +462909,47 @@ ], "projections": { "pca": [ - 0.10262320190668106, - -0.10066261142492294 + 0.10229871422052383, + -0.10073094815015793 ], "tsne": [ - 27.933719635009766, - -18.8148193359375 + 20.779708862304688, + 22.38361358642578 ], "umap": [ - 8.437756538391113, - 9.468792915344238 + 8.555133819580078, + 8.975988388061523 ], "pca3d": [ - 0.1026230901479721, - -0.10063053667545319, - -0.07985110580921173 + 0.10229862481355667, + -0.10069825500249863, + -0.07971889525651932 ], "tsne3d": [ - 13.929231643676758, - 10.800336837768555, - 4.399435997009277 + 8.24132251739502, + -4.561001300811768, + -13.762738227844238 ], "umap3d": [ - 5.331300258636475, - 4.592897891998291, - 1.2701287269592285 + 4.984707355499268, + 4.287431716918945, + 1.0132231712341309 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -463221,31 +463371,31 @@ "cited_by_count": 34, "projections": { "pca": [ - 0.18344935774803162, - -0.02919723466038704 + 0.18351761996746063, + -0.029454639181494713 ], "tsne": [ - 16.658668518066406, - 27.325586318969727 + 20.6397762298584, + -26.498613357543945 ], "umap": [ - 7.824676036834717, - 4.979635715484619 + 8.149236679077148, + 4.5224995613098145 ], "pca3d": [ - 0.1834491789340973, - -0.02917923964560032, - 0.07103925198316574 + 0.18351738154888153, + -0.02943602204322815, + 0.07105746120214462 ], "tsne3d": [ - 4.8767170906066895, - 20.148662567138672, - 0.9682939052581787 + 10.278429985046387, + -12.040223121643066, + 4.85850191116333 ], "umap3d": [ - 7.020401954650879, - 5.860692501068115, - 3.7863879203796387 + 7.132452964782715, + 6.335189342498779, + 2.1997807025909424 ] }, "cluster_id": 4, @@ -463256,11 +463406,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -463676,46 +463826,46 @@ "doi": "10.1101/2024.11.06.622266", "projections": { "pca": [ - 0.07217586785554886, - -0.15317152440547943 + 0.07177311927080154, + -0.15368032455444336 ], "tsne": [ - 21.028766632080078, - -27.911935806274414 + 12.11141586303711, + 28.268159866333008 ], "umap": [ - 7.496970176696777, - 9.506214141845703 + 7.649175643920898, + 9.1537446975708 ], "pca3d": [ - 0.07217609882354736, - -0.1531701236963272, - -0.13114823400974274 + 0.07177340984344482, + -0.1536789834499359, + -0.13093602657318115 ], "tsne3d": [ - 10.55685806274414, - -14.127320289611816, - 8.070687294006348 + 4.717336654663086, + 3.5054147243499756, + -18.20472526550293 ], "umap3d": [ - 5.189563751220703, - 3.6602301597595215, - 1.3929529190063477 + 5.208281517028809, + 3.251667022705078, + 1.5070205926895142 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -464133,31 +464283,31 @@ "doi": "10.1101/2024.12.08.627375", "projections": { "pca": [ - -0.0600871779024601, - -0.14502325654029846 + -0.06037361919879913, + -0.1448214203119278 ], "tsne": [ - -33.10287857055664, - 1.8450055122375488 + -32.16789245605469, + -4.598503112792969 ], "umap": [ - 2.425872802734375, - 6.723651885986328 + 2.8685495853424072, + 5.9875969886779785 ], "pca3d": [ - -0.06008684262633324, - -0.14502643048763275, - -0.004418953787535429 + -0.06037328019738197, + -0.14482411742210388, + -0.004073213320225477 ], "tsne3d": [ - -16.581262588500977, - 1.4361836910247803, - -10.018153190612793 + -13.512002944946289, + -9.452646255493164, + -6.263664245605469 ], "umap3d": [ - 2.1842658519744873, - 5.348771572113037, - 3.8249733448028564 + 2.7855570316314697, + 5.673458576202393, + 4.144713878631592 ] }, "cluster_id": 2, @@ -464168,12 +464318,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 33, - "label": "Neural Networks" + "id": 19, + "label": "Graph Neural Nets" } ], "_embedding": [ @@ -464673,46 +464823,46 @@ "openalex_url": "https://openalex.org/W4403405969", "projections": { "pca": [ - 0.02587970905005932, - 0.07784947007894516 + 0.026056883856654167, + 0.07799328863620758 ], "tsne": [ - 11.260642051696777, - -23.085872650146484 + 6.100555896759033, + 19.10541534423828 ], "umap": [ - 6.699612140655518, - 9.093138694763184 + 6.840157508850098, + 8.687549591064453 ], "pca3d": [ - 0.025879500433802605, - 0.07785262912511826, - 0.06156287342309952 + 0.026056678965687752, + 0.07799588888883591, + 0.06135817617177963 ], "tsne3d": [ - 2.5719754695892334, - -6.510185718536377, - 10.190770149230957 + 2.301602602005005, + 7.04060697555542, + -8.992582321166992 ], "umap3d": [ - 4.559874534606934, - 3.9397776126861572, - 2.1431941986083984 + 4.743518352508545, + 3.9876441955566406, + 2.1919901371002197 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -465135,31 +465285,31 @@ "doi": "10.1101/2024.12.11.627935", "projections": { "pca": [ - 0.16835741698741913, - -0.07698796689510345 + 0.16800247132778168, + -0.07728791981935501 ], "tsne": [ - 35.22162628173828, - -16.345972061157227 + 29.088525772094727, + 23.037029266357422 ], "umap": [ - 8.745266914367676, - 8.214177131652832 + 9.038253784179688, + 7.941290378570557 ], "pca3d": [ - 0.16835734248161316, - -0.07697521150112152, - -0.08634829521179199 + 0.1680024415254593, + -0.077274851500988, + -0.08639735728502274 ], "tsne3d": [ - 20.533306121826172, - 11.834773063659668, - -0.6748263239860535 + 15.407612800598145, + -3.84134578704834, + -9.613737106323242 ], "umap3d": [ - 6.359787940979004, - 4.812611103057861, - 1.4174294471740723 + 5.9572296142578125, + 4.433347702026367, + 0.5825660228729248 ] }, "cluster_id": 0, @@ -465170,12 +465320,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -465600,31 +465750,31 @@ "openalex_url": "https://openalex.org/W4405137525", "projections": { "pca": [ - 0.06687113642692566, - -0.13567586243152618 + 0.0664999857544899, + -0.135525643825531 ], "tsne": [ - 15.622390747070312, - 5.437533855438232 + 16.813522338867188, + -1.6957372426986694 ], "umap": [ - 7.34658670425415, - 6.373166561126709 + 7.612754821777344, + 5.88124942779541 ], "pca3d": [ - 0.06687138974666595, - -0.13567230105400085, - 0.007418079301714897 + 0.06650030612945557, + -0.1355218142271042, + 0.007631630636751652 ], "tsne3d": [ - 7.9313273429870605, - 6.2366557121276855, - -13.8018217086792 + 9.829971313476562, + 0.7909546494483948, + 8.770578384399414 ], "umap3d": [ - 5.879703521728516, - 5.056572914123535, - 3.0489509105682373 + 6.0610551834106445, + 5.296218395233154, + 2.0696346759796143 ] }, "cluster_id": 4, @@ -465635,11 +465785,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -466073,46 +466223,46 @@ "doi": "10.1101/2024.12.03.24318375", "projections": { "pca": [ - 0.12573948502540588, - 0.018306436017155647 + 0.12572140991687775, + 0.018006974831223488 ], "tsne": [ - 26.58477210998535, - 3.2747347354888916 + 29.76728630065918, + -11.175996780395508 ], "umap": [ - 7.0435566902160645, - 7.917975902557373 + 7.274761199951172, + 7.503198623657227 ], "pca3d": [ - 0.1257394552230835, - 0.018299851566553116, - -0.1598828285932541 + 0.12572142481803894, + 0.018000677227973938, + -0.15991178154945374 ], "tsne3d": [ - 7.781538486480713, - -11.46466064453125, - -7.793923854827881 + 8.051817893981934, + 15.184863090515137, + -2.6696956157684326 ], "umap3d": [ - 6.40205717086792, - 3.488621950149536, - 2.8356847763061523 + 6.800095558166504, + 3.8371810913085938, + 2.3411967754364014 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 26, + "id": 33, "label": "Genetic Disease" } ], @@ -466538,31 +466688,31 @@ "doi": "10.1101/2024.12.01.625444", "projections": { "pca": [ - 0.12632769346237183, - -0.12158621102571487 + 0.12616059184074402, + -0.12119807302951813 ], "tsne": [ - 7.9123029708862305, - 14.839920043945312 + 9.404988288879395, + -11.493209838867188 ], "umap": [ - 6.56203031539917, - 5.85836935043335 + 6.988954067230225, + 5.504758834838867 ], "pca3d": [ - 0.12632812559604645, - -0.12159942090511322, - 0.14947865903377533 + 0.1261608898639679, + -0.12121150642633438, + 0.14973977208137512 ], "tsne3d": [ - 1.195499300956726, - 6.921198844909668, - -7.999782085418701 + 4.426458358764648, + -3.407729148864746, + 6.361535549163818 ], "umap3d": [ - 5.688719749450684, - 5.377671241760254, - 3.6548242568969727 + 5.943546295166016, + 5.704688549041748, + 2.6058437824249268 ] }, "cluster_id": 4, @@ -466573,11 +466723,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -466989,46 +467139,46 @@ "journal": "Nature Biomedical Engineering", "projections": { "pca": [ - 0.001768804620951414, - -0.06308828294277191 + 0.0018296600319445133, + -0.06315477937459946 ], "tsne": [ - 18.39183807373047, - -24.225814819335938 + 10.220610618591309, + 24.42568588256836 ], "umap": [ - 7.347173690795898, - 9.347497940063477 + 7.430144786834717, + 8.994307518005371 ], "pca3d": [ - 0.0017684746999293566, - -0.06305244565010071, - -0.0880088284611702 + 0.0018293469911441207, + -0.06311842054128647, + -0.0876794159412384 ], "tsne3d": [ - 7.805739402770996, - -12.54593276977539, - 11.794803619384766 + 6.18207311630249, + 8.337175369262695, + -15.329106330871582 ], "umap3d": [ - 5.043676853179932, - 3.693403959274292, - 1.607753038406372 + 5.077387809753418, + 3.4816417694091797, + 1.6915132999420166 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -467445,31 +467595,31 @@ "doi": "10.1101/2024.02.06.579067", "projections": { "pca": [ - 0.1183163970708847, - -0.17308717966079712 + 0.11794713884592056, + -0.17270401120185852 ], "tsne": [ - 19.27634620666504, - -1.2217278480529785 + 20.604917526245117, + 2.878343343734741 ], "umap": [ - 7.786668300628662, - 6.315524101257324 + 8.00153636932373, + 5.926801681518555 ], "pca3d": [ - 0.11831643432378769, - -0.17307108640670776, - 0.0808631032705307 + 0.11794721335172653, + -0.1726876199245453, + 0.08116141706705093 ], "tsne3d": [ - 11.77912712097168, - 4.405617713928223, - -9.775341987609863 + 12.370165824890137, + 0.4036436975002289, + 2.772249460220337 ], "umap3d": [ - 6.379484176635742, - 5.039018154144287, - 2.6596367359161377 + 6.346179962158203, + 5.335237979888916, + 1.669508934020996 ] }, "cluster_id": 4, @@ -467480,12 +467630,12 @@ "label": "RNA Sequence Models" }, { - "id": 3, - "label": "RNA Splicing" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -467910,46 +468060,46 @@ "doi": "10.1101/2024.11.28.625303", "projections": { "pca": [ - 0.05750485509634018, - -0.12619167566299438 + 0.057191748172044754, + -0.12619896233081818 ], "tsne": [ - 20.952484130859375, - -23.326692581176758 + 12.996302604675293, + 23.576889038085938 ], "umap": [ - 7.462890148162842, - 9.302793502807617 + 7.623208999633789, + 8.945369720458984 ], "pca3d": [ - 0.05750491842627525, - -0.12618428468704224, - -0.09153895080089569 + 0.057191845029592514, + -0.12619158625602722, + -0.0912875235080719 ], "tsne3d": [ - 9.668063163757324, - -10.897942543029785, - 7.840302467346191 + 5.307291030883789, + 3.7943148612976074, + -15.077194213867188 ], "umap3d": [ - 5.22109842300415, - 3.87213134765625, - 1.5759222507476807 + 5.264801502227783, + 3.6629233360290527, + 1.6121402978897095 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -468373,31 +468523,31 @@ "doi": "10.1101/2024.11.28.625345", "projections": { "pca": [ - 0.1354384422302246, - -0.09683144092559814 + 0.13528293371200562, + -0.09676282852888107 ], "tsne": [ - 16.293779373168945, - 27.9034423828125 + 20.145469665527344, + -26.82352638244629 ], "umap": [ - 7.6426873207092285, - 4.967350959777832 + 8.012571334838867, + 4.490085124969482 ], "pca3d": [ - 0.1354382336139679, - -0.09680517762899399, - 0.16689549386501312 + 0.1352827548980713, + -0.09673605114221573, + 0.16699592769145966 ], "tsne3d": [ - 3.7983458042144775, - 19.824827194213867, - 2.1839449405670166 + 10.349676132202148, + -13.133907318115234, + 6.070216178894043 ], "umap3d": [ - 6.923467636108398, - 5.9442667961120605, - 3.8687219619750977 + 7.051107883453369, + 6.402656555175781, + 2.2569642066955566 ] }, "cluster_id": 4, @@ -468408,11 +468558,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -468830,31 +468980,31 @@ "doi": "10.1101/2024.11.19.624283", "projections": { "pca": [ - 0.19179199635982513, - -0.08486619591712952 + 0.19160926342010498, + -0.0853123888373375 ], "tsne": [ - 22.44304084777832, - 30.632688522338867 + 26.3804874420166, + -30.08770179748535 ], "umap": [ - 8.356740951538086, - 4.942948818206787 + 8.625088691711426, + 4.5424323081970215 ], "pca3d": [ - 0.19179180264472961, - -0.0848270058631897, - 0.04549971595406532 + 0.19160902500152588, + -0.08527220040559769, + 0.04549386352300644 ], "tsne3d": [ - 6.377453327178955, - 24.12824058532715, - -1.9789141416549683 + 16.568805694580078, + -16.572599411010742, + 7.024450778961182 ], "umap3d": [ - 7.493566036224365, - 5.839541435241699, - 3.602806329727173 + 7.499124526977539, + 6.436582088470459, + 1.7610417604446411 ] }, "cluster_id": 4, @@ -468865,11 +469015,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -469283,31 +469433,31 @@ "doi": "10.1038/s41576-024-00788-w", "projections": { "pca": [ - 0.1925729215145111, - 0.1402786523103714 + 0.19299523532390594, + 0.1400037258863449 ], "tsne": [ - 41.383426666259766, - 7.643364429473877 + 38.91318893432617, + 3.404045343399048 ], "umap": [ - 9.222970008850098, - 7.264967441558838 + 9.155323028564453, + 6.794612884521484 ], "pca3d": [ - 0.19257231056690216, - 0.1402907520532608, - -0.021666226908564568 + 0.19299468398094177, + 0.14001601934432983, + -0.02193034626543522 ], "tsne3d": [ - 25.22909164428711, - -2.6101691722869873, - -0.9369739890098572 + 23.68702507019043, + -5.327818870544434, + -14.674728393554688 ], "umap3d": [ - 6.972358226776123, - 4.981501579284668, - 1.7143925428390503 + 6.534686088562012, + 4.834475040435791, + 0.6422697901725769 ] }, "cluster_id": 0, @@ -469318,11 +469468,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -469754,31 +469904,31 @@ "cited_by_count": 368, "projections": { "pca": [ - 0.1458071619272232, - -0.09936751425266266 + 0.14569437503814697, + -0.09940099716186523 ], "tsne": [ - 13.686174392700195, - 9.474040031433105 + 14.026317596435547, + -2.8358733654022217 ], "umap": [ - 7.17071008682251, - 6.103760242462158 + 7.5342607498168945, + 5.822722434997559 ], "pca3d": [ - 0.14580747485160828, - -0.09938846528530121, - 0.010449601337313652 + 0.14569465816020966, + -0.09942221641540527, + 0.010689867660403252 ], "tsne3d": [ - 8.973265647888184, - 8.22419548034668, - -9.396720886230469 + 10.263358116149902, + -2.873941659927368, + 6.1921563148498535 ], "umap3d": [ - 6.14939546585083, - 4.991349220275879, - 3.292741298675537 + 6.475312232971191, + 5.326749801635742, + 2.286606550216675 ] }, "cluster_id": 4, @@ -469789,11 +469939,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -470228,47 +470378,47 @@ "cited_by_count": 38, "projections": { "pca": [ - 0.0737638920545578, - -0.09101256728172302 + 0.07354617863893509, + -0.09136713296175003 ], "tsne": [ - 24.842769622802734, - 2.4157774448394775 + 27.16840362548828, + -10.873132705688477 ], "umap": [ - 7.299681186676025, - 7.745510578155518 + 7.300300598144531, + 7.303013324737549 ], "pca3d": [ - 0.07376419007778168, - -0.09102457761764526, - -0.15123803913593292 + 0.07354645431041718, + -0.09137953072786331, + -0.1510522961616516 ], "tsne3d": [ - 8.738118171691895, - -13.730741500854492, - -7.495810031890869 + 9.255753517150879, + 12.24114990234375, + -2.3456687927246094 ], "umap3d": [ - 6.402163028717041, - 3.608027219772339, - 2.771686553955078 + 6.762321949005127, + 3.9684810638427734, + 2.244727373123169 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -470676,31 +470826,31 @@ "journal": "Nature Genetics", "projections": { "pca": [ - 0.1922660917043686, - 0.11393918097019196 + 0.19257239997386932, + 0.11369242519140244 ], "tsne": [ - 61.06516647338867, - 3.6904709339141846 + 48.54882049560547, + 22.22852897644043 ], "umap": [ - 9.731953620910645, - 6.894131183624268 + 9.940885543823242, + 6.342822551727295 ], "pca3d": [ - 0.19226565957069397, - 0.1139523833990097, - 0.015510451048612595 + 0.1925719976425171, + 0.11370569467544556, + 0.015191611833870411 ], "tsne3d": [ - 31.111209869384766, - 6.971433162689209, - -9.770289421081543 + 23.534502029418945, + -5.13469123840332, + -3.9714527130126953 ], "umap3d": [ - 7.641136169433594, - 5.132936477661133, - 1.4702541828155518 + 7.153234481811523, + 5.073495864868164, + 0.11405909061431885 ] }, "cluster_id": 0, @@ -470711,12 +470861,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -471139,31 +471289,31 @@ "doi": "10.1101/2024.11.26.625483", "projections": { "pca": [ - 0.25736090540885925, - -0.02171916700899601 + 0.2573239803314209, + -0.021949267014861107 ], "tsne": [ - 30.009069442749023, - 29.853187561035156 + 25.364221572875977, + -19.19324493408203 ], "umap": [ - 8.807035446166992, - 5.834964275360107 + 8.9699125289917, + 5.39715576171875 ], "pca3d": [ - 0.25736042857170105, - -0.021701758727431297, - -0.015260646119713783 + 0.2573234736919403, + -0.021931692957878113, + -0.015295648016035557 ], "tsne3d": [ - 10.41958236694336, - 20.517303466796875, - -5.272305011749268 + 19.735431671142578, + -14.267229080200195, + 1.3355827331542969 ], "umap3d": [ - 7.367908000946045, - 5.451614856719971, - 2.8154261112213135 + 7.265049457550049, + 5.6963958740234375, + 1.2084319591522217 ] }, "cluster_id": 4, @@ -471174,11 +471324,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -471610,31 +471760,31 @@ "doi": "10.1101/2024.11.23.624931", "projections": { "pca": [ - 0.2341601550579071, - -0.03368120640516281 + 0.23402459919452667, + -0.03444143757224083 ], "tsne": [ - 46.474510192871094, - -5.621949672698975 + 44.727638244628906, + 7.309097766876221 ], "umap": [ - 9.530228614807129, - 8.152377128601074 + 9.767507553100586, + 7.758205413818359 ], "pca3d": [ - 0.23415999114513397, - -0.03367938473820686, - -0.21785622835159302 + 0.2340245097875595, + -0.03443979471921921, + -0.2178800255060196 ], "tsne3d": [ - 25.59758949279785, - -6.967677116394043, - -7.6232733726501465 + 26.15839958190918, + 11.340686798095703, + 0.3180520832538605 ], "umap3d": [ - 6.871866226196289, - 4.44284725189209, - 1.0549534559249878 + 6.487377643585205, + 4.122692584991455, + 0.04748065397143364 ] }, "cluster_id": 0, @@ -471645,12 +471795,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -472076,46 +472226,46 @@ "doi": "10.1101/2024.11.22.624786", "projections": { "pca": [ - 0.15350696444511414, - -0.04466748982667923 + 0.1535336971282959, + -0.044992610812187195 ], "tsne": [ - 20.788721084594727, - -20.34902572631836 + 14.045506477355957, + 20.495384216308594 ], "umap": [ - 7.73892879486084, - 9.271044731140137 + 7.683910846710205, + 8.966071128845215 ], "pca3d": [ - 0.15350700914859772, - -0.04465772584080696, - -0.03664851188659668 + 0.1535336673259735, + -0.04498250409960747, + -0.036524347960948944 ], "tsne3d": [ - 10.205509185791016, - -6.2527995109558105, - 7.859793663024902 + 8.643750190734863, + 3.41489839553833, + -10.967414855957031 ], "umap3d": [ - 5.454122066497803, - 3.8512251377105713, - 1.603339433670044 + 5.253085136413574, + 3.6063923835754395, + 1.4614757299423218 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -472526,46 +472676,46 @@ "journal": "Nature Reviews Genetics", "projections": { "pca": [ - 0.10056497156620026, - 0.18163703382015228 + 0.10113508254289627, + 0.18177208304405212 ], "tsne": [ - 8.922026634216309, - -11.237628936767578 + 27.298603057861328, + -14.957842826843262 ], "umap": [ - 6.5210065841674805, - 8.087308883666992 + 6.741133213043213, + 7.618588447570801 ], "pca3d": [ - 0.10056467354297638, - 0.18161822855472565, - -0.07595782727003098 + 0.10113481432199478, + 0.1817532330751419, + -0.07608060538768768 ], "tsne3d": [ - 5.29754638671875, - -15.341242790222168, - -9.189562797546387 + 6.562914848327637, + 18.78163719177246, + -1.188205599784851 ], "umap3d": [ - 6.102118015289307, - 3.182279348373413, - 3.1106274127960205 + 6.648872375488281, + 3.5729308128356934, + 2.730893135070801 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 26, + "id": 33, "label": "Genetic Disease" } ], @@ -472977,46 +473127,46 @@ "journal": "Nature", "projections": { "pca": [ - 0.017375711351633072, - 0.011533872224390507 + 0.017582546919584274, + 0.01112605631351471 ], "tsne": [ - 25.196285247802734, - -23.780061721801758 + 16.75253677368164, + 26.098922729492188 ], "umap": [ - 7.685210704803467, - 9.298356056213379 + 7.74471378326416, + 9.121108055114746 ], "pca3d": [ - 0.01737564615905285, - 0.011537564918398857, - -0.10872819274663925 + 0.017582431435585022, + 0.011130393482744694, + -0.10860306769609451 ], "tsne3d": [ - 16.08645248413086, - -9.983141899108887, - 9.357309341430664 + 12.137504577636719, + 3.182157278060913, + -16.740589141845703 ], "umap3d": [ - 5.43290901184082, - 3.5840423107147217, - 1.4426701068878174 + 5.374245643615723, + 3.3850507736206055, + 1.4312154054641724 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -473448,31 +473598,31 @@ "cited_by_count": 185, "projections": { "pca": [ - 0.1092756986618042, - -0.14963895082473755 + 0.10903645306825638, + -0.15008588135242462 ], "tsne": [ - 16.74105453491211, - 33.299781799316406 + 19.97477912902832, + -31.65567398071289 ], "umap": [ - 7.902980327606201, - 4.459181308746338 + 8.262872695922852, + 4.002457141876221 ], "pca3d": [ - 0.1092757135629654, - -0.14962156116962433, - 0.16266073286533356 + 0.10903659462928772, + -0.15006744861602783, + 0.16279236972332 ], "tsne3d": [ - 2.5570600032806396, - 22.45123863220215, - 5.836791515350342 + 9.873348236083984, + -17.747255325317383, + 6.787508487701416 ], "umap3d": [ - 7.174307346343994, - 6.313897132873535, - 4.047277450561523 + 7.383347034454346, + 6.84001350402832, + 2.2322587966918945 ] }, "cluster_id": 4, @@ -473483,11 +473633,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -473903,31 +474053,31 @@ ], "projections": { "pca": [ - 0.1265321522951126, - -0.1495831310749054 + 0.12620022892951965, + -0.1497122198343277 ], "tsne": [ - 20.763704299926758, - 9.849166870117188 + 25.38974952697754, + -1.4778188467025757 ], "umap": [ - 7.55686092376709, - 7.174742221832275 + 7.781895637512207, + 6.747795581817627 ], "pca3d": [ - 0.12653230130672455, - -0.14958518743515015, - -0.07889527827501297 + 0.1262003779411316, + -0.14971406757831573, + -0.07857351005077362 ], "tsne3d": [ - 13.85441780090332, - -2.0221664905548096, - -12.447638511657715 + 13.104390144348145, + 8.376120567321777, + 4.931431293487549 ], "umap3d": [ - 6.7153544425964355, - 4.214599132537842, - 2.9034523963928223 + 6.943050861358643, + 4.533580303192139, + 2.0207972526550293 ] }, "cluster_id": 4, @@ -473938,12 +474088,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 1, + "label": "Genomic Regulation" } ], "_embedding": [ @@ -474370,47 +474520,47 @@ "doi": "10.1101/2024.11.16.623974", "projections": { "pca": [ - 0.1206631064414978, - -0.04539408162236214 + 0.12059792876243591, + -0.045383110642433167 ], "tsne": [ - 23.605785369873047, - -15.115805625915527 + 21.676597595214844, + 16.034774780273438 ], "umap": [ - 8.289661407470703, - 8.551139831542969 + 8.523430824279785, + 8.236270904541016 ], "pca3d": [ - 0.12066297233104706, - -0.04537909850478172, - -0.0012780597899109125 + 0.12059786915779114, + -0.045368075370788574, + -0.0012163944775238633 ], "tsne3d": [ - 13.166788101196289, - -1.7456531524658203, - 9.676136016845703 + 3.01224946975708, + -0.6544775366783142, + -17.531152725219727 ], "umap3d": [ - 5.880588054656982, - 4.189159393310547, - 1.5248781442642212 + 5.764054775238037, + 4.203863620758057, + 1.1026636362075806 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 13, + "label": "Chromatin Regulation" }, { - "id": 12, - "label": "Genetic Variants" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -474820,31 +474970,31 @@ "journal": "Nature", "projections": { "pca": [ - 0.2535003423690796, - -0.004062768537551165 + 0.25346726179122925, + -0.004690969828516245 ], "tsne": [ - 55.97529220581055, - 8.043631553649902 + 54.493629455566406, + 19.19788360595703 ], "umap": [ - 10.391332626342773, - 6.767922401428223 + 10.572356224060059, + 6.384476661682129 ], "pca3d": [ - 0.253499835729599, - -0.004035273101180792, - -0.02793629840016365 + 0.253466933965683, + -0.004663269035518169, + -0.028143759816884995 ], "tsne3d": [ - 27.504323959350586, - 7.509382724761963, - 1.365674376487732 + 30.170753479003906, + -3.5183568000793457, + -5.223333835601807 ], "umap3d": [ - 8.301836967468262, - 5.398466110229492, - 1.2527722120285034 + 7.550753593444824, + 5.182394981384277, + -0.5252521634101868 ] }, "cluster_id": 0, @@ -474855,12 +475005,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -475282,31 +475432,31 @@ "cited_by_count": 10, "projections": { "pca": [ - 0.1894855946302414, - -0.024779055267572403 + 0.18948021531105042, + -0.0252826027572155 ], "tsne": [ - 27.228593826293945, - -5.047875881195068 + 27.079893112182617, + 10.134641647338867 ], "umap": [ - 8.319432258605957, - 7.359498023986816 + 8.525065422058105, + 6.904461860656738 ], "pca3d": [ - 0.18948550522327423, - -0.024779167026281357, - -0.21384483575820923 + 0.18948020040988922, + -0.025282900780439377, + -0.21375133097171783 ], "tsne3d": [ - 17.523591995239258, - -0.8900772333145142, - -11.42300796508789 + 14.126091003417969, + 7.87491512298584, + 0.7292091846466064 ], "umap3d": [ - 6.65324592590332, - 4.5075273513793945, - 2.0483832359313965 + 6.538687705993652, + 4.490162372589111, + 1.1717040538787842 ] }, "cluster_id": 0, @@ -475317,12 +475467,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -475749,47 +475899,47 @@ "doi": "10.1101/2024.11.11.623049", "projections": { "pca": [ - 0.14778181910514832, - -0.0557464174926281 + 0.1476217806339264, + -0.056101564317941666 ], "tsne": [ - 25.847206115722656, - 1.7535361051559448 + 28.1119441986084, + -11.118815422058105 ], "umap": [ - 7.335542678833008, - 7.94633674621582 + 7.4763102531433105, + 7.518171787261963 ], "pca3d": [ - 0.1477818340063095, - -0.055739931762218475, - -0.16698303818702698 + 0.1476217657327652, + -0.05609501153230667, + -0.1668727546930313 ], "tsne3d": [ - 10.534818649291992, - -12.268115997314453, - -7.3191962242126465 + 11.277287483215332, + 12.131317138671875, + -2.5201096534729004 ], "umap3d": [ - 6.4189605712890625, - 3.605433225631714, - 2.5153467655181885 + 6.742151260375977, + 3.787761688232422, + 2.062410354614258 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 12, - "label": "Genetic Variants" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -476214,31 +476364,31 @@ "doi": "10.1101/2024.11.11.623015", "projections": { "pca": [ - 0.18189890682697296, - -0.03044065274298191 + 0.18182377517223358, + -0.03068498522043228 ], "tsne": [ - 22.75551414489746, - 9.591668128967285 + 26.31983184814453, + -3.630011558532715 ], "umap": [ - 7.452184677124023, - 7.304654121398926 + 7.609194278717041, + 6.947100639343262 ], "pca3d": [ - 0.1818990856409073, - -0.030464110895991325, - -0.06779242306947708 + 0.1818239837884903, + -0.03070884197950363, + -0.06773993372917175 ], "tsne3d": [ - 11.77372932434082, - -4.20544958114624, - -11.067781448364258 + 10.669108390808105, + 9.61288833618164, + 3.5898685455322266 ], "umap3d": [ - 6.711197376251221, - 4.07710075378418, - 2.9613804817199707 + 7.127145767211914, + 4.381528854370117, + 2.130948066711426 ] }, "cluster_id": 4, @@ -476249,12 +476399,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -476674,46 +476824,46 @@ "openalex_url": "https://openalex.org/W4404373866", "projections": { "pca": [ - 0.02195615880191326, - 0.20225846767425537 + 0.022443536669015884, + 0.20255276560783386 ], "tsne": [ - 13.822294235229492, - -24.26766586303711 + 8.257859230041504, + 18.827735900878906 ], "umap": [ - 6.997859001159668, - 9.132438659667969 + 7.015758514404297, + 8.730049133300781 ], "pca3d": [ - 0.021955423057079315, - 0.20228849351406097, - 0.01214055810123682 + 0.022442808374762535, + 0.20258334279060364, + 0.011780362576246262 ], "tsne3d": [ - 4.95580530166626, - -5.244262218475342, - 8.880013465881348 + 2.5589120388031006, + 4.490647792816162, + -10.527968406677246 ], "umap3d": [ - 4.833425998687744, - 3.853963613510132, - 1.9933950901031494 + 4.899271011352539, + 3.842130184173584, + 1.966330885887146 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -477159,31 +477309,31 @@ "openalex_url": "https://openalex.org/W4404349982", "projections": { "pca": [ - 0.14793135225772858, - -0.08326385915279388 + 0.14792266488075256, + -0.08303312957286835 ], "tsne": [ - 4.868398666381836, - 14.050565719604492 + 11.168807029724121, + -13.770983695983887 ], "umap": [ - 6.417539119720459, - 5.6244916915893555 + 6.730953693389893, + 5.326788902282715 ], "pca3d": [ - 0.1479315310716629, - -0.0832800567150116, - 0.19519491493701935 + 0.1479228287935257, + -0.08304920047521591, + 0.19537125527858734 ], "tsne3d": [ - 1.836501121520996, - 2.9692931175231934, - -3.8193867206573486 + 3.384019374847412, + -2.089146137237549, + 7.740348815917969 ], "umap3d": [ - 5.805734157562256, - 5.660803318023682, - 3.8260698318481445 + 5.924724578857422, + 6.057840347290039, + 2.5627856254577637 ] }, "cluster_id": 4, @@ -477194,12 +477344,12 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, - "label": "Genomic Models" + "id": 32, + "label": "Genome Protein" } ], "_embedding": [ @@ -477647,31 +477797,31 @@ "doi": "10.1101/2024.11.11.619379", "projections": { "pca": [ - 0.2780206501483917, - -0.025053609162569046 + 0.27802374958992004, + -0.02545618638396263 ], "tsne": [ - 57.256492614746094, - 4.183663368225098 + 50.604759216308594, + 19.473556518554688 ], "umap": [ - 10.067902565002441, - 6.671016693115234 + 10.246359825134277, + 6.203961372375488 ], "pca3d": [ - 0.2780206799507141, - -0.02506694756448269, - 0.04534793272614479 + 0.27802398800849915, + -0.025469809770584106, + 0.0452570803463459 ], "tsne3d": [ - 26.87964630126953, - 5.467050075531006, - -4.328304290771484 + 27.03917694091797, + -1.7994552850723267, + -0.03286043927073479 ], "umap3d": [ - 7.894451141357422, - 5.5482892990112305, - 1.4038461446762085 + 7.233316421508789, + 5.334320068359375, + -0.2470880001783371 ] }, "cluster_id": 0, @@ -477682,12 +477832,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -478112,31 +478262,31 @@ "doi": "10.1101/2024.11.11.618478", "projections": { "pca": [ - 0.26953125, - -0.0069689033553004265 + 0.2695320248603821, + -0.007518523372709751 ], "tsne": [ - 57.45852279663086, - 4.057374954223633 + 50.56325912475586, + 19.682849884033203 ], "umap": [ - 10.178670883178711, - 6.695125579833984 + 10.37834358215332, + 6.236729621887207 ], "pca3d": [ - 0.2695314586162567, - -0.0069885547272861, - -0.05994407460093498 + 0.26953214406967163, + -0.007538540288805962, + -0.06005566939711571 ], "tsne3d": [ - 28.061458587646484, - 6.105155944824219, - -5.148326396942139 + 26.835237503051758, + -0.8512012362480164, + -1.0858557224273682 ], "umap3d": [ - 7.931441783905029, - 5.5311994552612305, - 1.3398191928863525 + 7.295550346374512, + 5.2708964347839355, + -0.3260868787765503 ] }, "cluster_id": 0, @@ -478147,12 +478297,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -478594,31 +478744,31 @@ "doi": "10.1101/2024.11.11.622097", "projections": { "pca": [ - 0.26173532009124756, - 0.010300527326762676 + 0.26181668043136597, + 0.00984436646103859 ], "tsne": [ - 57.028900146484375, - 4.096412658691406 + 50.61543655395508, + 19.366487503051758 ], "umap": [ - 10.075570106506348, - 6.651092529296875 + 10.295835494995117, + 6.292759418487549 ], "pca3d": [ - 0.26173529028892517, - 0.010294097475707531, - -0.0532536543905735 + 0.26181650161743164, + 0.009837426245212555, + -0.05335135757923126 ], "tsne3d": [ - 27.74214744567871, - 5.086973667144775, - -4.488101005554199 + 27.50867462158203, + -1.0408365726470947, + 0.010135561227798462 ], "umap3d": [ - 7.9284491539001465, - 5.4516682624816895, - 1.3402692079544067 + 7.226162910461426, + 5.291468620300293, + -0.279056191444397 ] }, "cluster_id": 0, @@ -478629,12 +478779,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -479042,31 +479192,31 @@ "journal": "Nature Genetics", "projections": { "pca": [ - 0.20018793642520905, - -0.014649467542767525 + 0.20034581422805786, + -0.015168357640504837 ], "tsne": [ - 32.93020248413086, - 16.112712860107422 + 39.46141052246094, + -3.0098423957824707 ], "umap": [ - 8.090386390686035, - 7.083950996398926 + 8.296074867248535, + 6.874373435974121 ], "pca3d": [ - 0.20018793642520905, - -0.014669525437057018, - 0.034239813685417175 + 0.20034575462341309, + -0.015187880024313927, + 0.03425941988825798 ], "tsne3d": [ - 12.937251091003418, - 5.178675174713135, - 13.092511177062988 + 17.29550552368164, + 4.412999629974365, + 15.366311073303223 ], "umap3d": [ - 7.53397274017334, - 4.2601637840271, - 2.6218037605285645 + 7.507496356964111, + 4.4013543128967285, + 1.460831880569458 ] }, "cluster_id": 4, @@ -479077,12 +479227,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -479510,31 +479660,31 @@ "openalex_url": "https://openalex.org/W4401224836", "projections": { "pca": [ - 0.06783334910869598, - -0.0709187462925911 + 0.06765198707580566, + -0.07113801687955856 ], "tsne": [ - 28.018957138061523, - -5.6971001625061035 + 26.844295501708984, + 9.287745475769043 ], "umap": [ - 8.190051078796387, - 7.086653709411621 + 8.446175575256348, + 6.733153343200684 ], "pca3d": [ - 0.06783328950405121, - -0.07090559601783752, - -0.1456962674856186 + 0.06765198707580566, + -0.07112472504377365, + -0.14555484056472778 ], "tsne3d": [ - 18.83521270751953, - 0.23719853162765503, - -12.920051574707031 + 15.319829940795898, + 6.378915786743164, + -0.27428436279296875 ], "umap3d": [ - 6.728131294250488, - 4.488025665283203, - 2.1181159019470215 + 6.513111591339111, + 4.5571818351745605, + 1.214436411857605 ] }, "cluster_id": 4, @@ -479545,12 +479695,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -479991,47 +480141,47 @@ "doi": "10.1101/2024.11.07.622307", "projections": { "pca": [ - 0.16360075771808624, - -0.010536082088947296 + 0.16358153522014618, + -0.011178725399076939 ], "tsne": [ - 27.632225036621094, - 10.289251327514648 + 30.969820022583008, + -6.384673595428467 ], "umap": [ - 7.449137210845947, - 7.666409015655518 + 7.6179962158203125, + 7.29248571395874 ], "pca3d": [ - 0.163600891828537, - -0.010554947890341282, - -0.16769525408744812 + 0.16358168423175812, + -0.011197855696082115, + -0.1677052080631256 ], "tsne3d": [ - 13.0983304977417, - -8.31991958618164, - -11.929293632507324 + 13.101570129394531, + 14.379636764526367, + 4.160129547119141 ], "umap3d": [ - 6.966687202453613, - 3.7159740924835205, - 2.9805173873901367 + 7.317564487457275, + 4.001064777374268, + 2.068748950958252 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -480451,31 +480601,31 @@ "doi": "10.1101/2024.11.07.622410", "projections": { "pca": [ - 0.14167609810829163, - -0.04594190791249275 + 0.14165960252285004, + -0.04594054073095322 ], "tsne": [ - 11.76108169555664, - 18.356969833374023 + 41.04521179199219, + -20.772865295410156 ], "umap": [ - 7.527522563934326, - 5.832902908325195 + 7.653487205505371, + 5.395724296569824 ], "pca3d": [ - 0.14167605340480804, - -0.04594019800424576, - 0.13515585660934448 + 0.14165958762168884, + -0.04593903571367264, + 0.13519896566867828 ], "tsne3d": [ - 13.404388427734375, - 11.980428695678711, - -6.990418910980225 + 13.198823928833008, + -3.1510422229766846, + -1.4851487874984741 ], "umap3d": [ - 5.937806606292725, - 5.6878485679626465, - 3.3265466690063477 + 6.025798797607422, + 5.921331882476807, + 1.9458591938018799 ] }, "cluster_id": 4, @@ -480486,11 +480636,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, - "label": "RNA Splicing" + "id": 7, + "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -480921,47 +481071,47 @@ "openalex_url": "https://openalex.org/W4404125138", "projections": { "pca": [ - 0.17978806793689728, - 0.015353972092270851 + 0.17990368604660034, + 0.015957316383719444 ], "tsne": [ - 1.0927685499191284, - 16.26706886291504 + 10.807257652282715, + -27.008005142211914 ], "umap": [ - 6.2880473136901855, - 5.388790607452393 + 6.832204341888428, + 5.079715728759766 ], "pca3d": [ - 0.1797880381345749, - 0.015332052484154701, - 0.1934312880039215 + 0.17990367114543915, + 0.015935508534312248, + 0.19336050748825073 ], "tsne3d": [ - 0.47973155975341797, - 2.828786611557007, - -1.3099597692489624 + 3.128309488296509, + -14.14881420135498, + -7.559055328369141 ], "umap3d": [ - 5.924658298492432, - 6.113124847412109, - 4.047287464141846 + 6.085441589355469, + 6.489416599273682, + 2.4579319953918457 ] }, - "cluster_id": 3, - "cluster_label": "Protein Design", + "cluster_id": 4, + "cluster_label": "RNA Sequence Models", "cluster_levels": [ { - "id": 3, - "label": "Protein Design" + "id": 4, + "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, - "label": "Genomic Models" + "id": 32, + "label": "Genome Protein" } ], "_embedding": [ @@ -481381,31 +481531,31 @@ ], "projections": { "pca": [ - 0.2274588793516159, - -0.03197287768125534 + 0.22730985283851624, + -0.032305117696523666 ], "tsne": [ - 35.55796813964844, - 0.8799018263816833 + 32.43966293334961, + 5.7635674476623535 ], "umap": [ - 8.769664764404297, - 7.382676601409912 + 9.016210556030273, + 6.899569988250732 ], "pca3d": [ - 0.22745880484580994, - -0.03198345750570297, - -0.10088831186294556 + 0.22730982303619385, + -0.03231565281748772, + -0.1009216234087944 ], "tsne3d": [ - 19.117063522338867, - 0.08102526515722275, - -6.53013277053833 + 19.27939796447754, + 5.346836566925049, + 1.3794077634811401 ], "umap3d": [ - 7.03963565826416, - 4.644314289093018, - 1.8680319786071777 + 6.7201738357543945, + 4.541202545166016, + 0.8132502436637878 ] }, "cluster_id": 0, @@ -481416,12 +481566,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -481829,31 +481979,31 @@ "journal": "Nature Computational Science", "projections": { "pca": [ - 0.10602258890867233, - -0.05095575749874115 + 0.1062450110912323, + -0.05119597539305687 ], "tsne": [ - 13.107327461242676, - 31.494550704956055 + 16.670663833618164, + -28.671995162963867 ], "umap": [ - 7.744696140289307, - 4.698133945465088 + 8.072259902954102, + 4.27001953125 ], "pca3d": [ - 0.10602246224880219, - -0.050936415791511536, - 0.2340555042028427 + 0.10624482482671738, + -0.05117582529783249, + 0.2341604083776474 ], "tsne3d": [ - -1.5518558025360107, - 23.928373336791992, - 1.9195713996887207 + 10.728452682495117, + -18.385303497314453, + 1.7807896137237549 ], "umap3d": [ - 6.9511566162109375, - 6.178611755371094, - 3.875051736831665 + 7.079336643218994, + 6.7188544273376465, + 2.2505974769592285 ] }, "cluster_id": 4, @@ -481864,11 +482014,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -482290,31 +482440,31 @@ "openalex_url": "https://openalex.org/W4403945222", "projections": { "pca": [ - 0.27636200189590454, - -0.0032642483711242676 + 0.2764473557472229, + -0.003727972973138094 ], "tsne": [ - 59.11305236816406, - 6.410176753997803 + 52.32427978515625, + 21.586679458618164 ], "umap": [ - 10.194967269897461, - 6.544239044189453 + 10.411087989807129, + 6.123807907104492 ], "pca3d": [ - 0.2763618230819702, - -0.003269513603299856, - -0.017220279201865196 + 0.27644726634025574, + -0.0037334044463932514, + -0.017297621816396713 ], "tsne3d": [ - 28.497156143188477, - 8.440508842468262, - -3.796926259994507 + 28.549930572509766, + -4.3719482421875, + -0.6846359968185425 ], "umap3d": [ - 8.085277557373047, - 5.623509407043457, - 1.4073365926742554 + 7.419713497161865, + 5.423940181732178, + -0.3063294291496277 ] }, "cluster_id": 0, @@ -482325,12 +482475,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -482748,31 +482898,31 @@ "doi": "10.1101/2024.11.02.620823", "projections": { "pca": [ - 0.3257863521575928, - 0.01699151284992695 + 0.32574114203453064, + 0.0159369558095932 ], "tsne": [ - 51.24124526977539, - 5.719686508178711 + 52.17135238647461, + 16.943140029907227 ], "umap": [ - 10.113224029541016, - 7.10573673248291 + 10.398041725158691, + 6.5913166999816895 ], "pca3d": [ - 0.32578641176223755, - 0.016988230869174004, - -0.10733985155820847 + 0.3257409334182739, + 0.015933675691485405, + -0.10765901952981949 ], "tsne3d": [ - 26.187847137451172, - 3.726715087890625, - 1.8627735376358032 + 26.917587280273438, + -0.06481271982192993, + -7.476863384246826 ], "umap3d": [ - 8.204801559448242, - 4.9062981605529785, - 1.1152997016906738 + 7.52549409866333, + 4.792720317840576, + -0.396405428647995 ] }, "cluster_id": 0, @@ -482783,12 +482933,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -483226,31 +483376,31 @@ "cited_by_count": 21, "projections": { "pca": [ - 0.18515872955322266, - -0.04733172431588173 + 0.18508602678775787, + -0.04810081049799919 ], "tsne": [ - 43.732513427734375, - -0.614837110042572 + 33.901371002197266, + 13.858709335327148 ], "umap": [ - 9.100152015686035, - 7.484608173370361 + 9.330202102661133, + 7.21583366394043 ], "pca3d": [ - 0.18515872955322266, - -0.047331444919109344, - -0.162953719496727 + 0.18508605659008026, + -0.04810025915503502, + -0.1629384160041809 ], "tsne3d": [ - 17.317243576049805, - 0.88575279712677, - 3.6701810359954834 + 19.93320655822754, + -0.39571061730384827, + -9.625886917114258 ], "umap3d": [ - 7.732853889465332, - 4.253118991851807, - 1.5567102432250977 + 7.499334335327148, + 4.1086320877075195, + 0.3803215026855469 ] }, "cluster_id": 0, @@ -483261,11 +483411,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -483685,46 +483835,46 @@ "doi": "10.1101/2024.10.31.619327", "projections": { "pca": [ - 0.062048446387052536, - -0.04976819083094597 + 0.06186295300722122, + -0.04981289058923721 ], "tsne": [ - 23.03748321533203, - -24.40915298461914 + 14.50727367401123, + 25.30328369140625 ], "umap": [ - 7.663076877593994, - 9.370763778686523 + 7.736678600311279, + 9.116253852844238 ], "pca3d": [ - 0.06204834207892418, - -0.04975443333387375, - -0.22182388603687286 + 0.06186283752322197, + -0.04979926720261574, + -0.22166749835014343 ], "tsne3d": [ - 13.180736541748047, - -10.867491722106934, - 10.468363761901855 + 8.6630859375, + 3.7770943641662598, + -17.070545196533203 ], "umap3d": [ - 5.2931389808654785, - 3.755423069000244, - 1.4844290018081665 + 5.276241779327393, + 3.4506988525390625, + 1.3948625326156616 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -484133,31 +484283,31 @@ "journal": "Nature Biotechnology", "projections": { "pca": [ - 0.1609155237674713, - -0.01688145101070404 + 0.1610335111618042, + -0.017119716852903366 ], "tsne": [ - 52.228851318359375, - -15.737296104431152 + 35.31702423095703, + 28.545307159423828 ], "umap": [ - 9.582653999328613, - 7.421846866607666 + 9.852370262145996, + 7.080244064331055 ], "pca3d": [ - 0.16091512143611908, - -0.016862118616700172, - -0.06740908324718475 + 0.16103315353393555, + -0.017100011929869652, + -0.0673154667019844 ], "tsne3d": [ - 29.4420108795166, - 9.054478645324707, - 8.06421947479248 + 29.971603393554688, + 4.248191833496094, + 7.541415691375732 ], "umap3d": [ - 6.731984615325928, - 5.014715194702148, - 1.2673542499542236 + 6.192413806915283, + 4.761922359466553, + 0.2616572380065918 ] }, "cluster_id": 0, @@ -484168,12 +484318,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 20, - "label": "Chromatin Regulation" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -484591,46 +484741,46 @@ "cited_by_count": 7, "projections": { "pca": [ - 0.14480037987232208, - -0.0028022972401231527 + 0.14475950598716736, + -0.003151576267555356 ], "tsne": [ - 21.639827728271484, - -19.924768447875977 + 14.970170974731445, + 20.859094619750977 ], "umap": [ - 7.612720489501953, - 9.429230690002441 + 7.5829949378967285, + 9.099998474121094 ], "pca3d": [ - 0.14480027556419373, - -0.0027685267850756645, - -0.07761776447296143 + 0.14475931227207184, + -0.003116987179964781, + -0.07766643166542053 ], "tsne3d": [ - 11.674335479736328, - -7.368567943572998, - 7.445069789886475 + 9.754507064819336, + 4.414149284362793, + -12.001070022583008 ], "umap3d": [ - 5.367453575134277, - 3.6270275115966797, - 1.4988538026809692 + 5.189571380615234, + 3.5176377296447754, + 1.4550024271011353 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -485063,47 +485213,47 @@ "openalex_url": "https://openalex.org/W4403925918", "projections": { "pca": [ - -0.03773113712668419, - 0.1965019851922989 + -0.03729550167918205, + 0.19799070060253143 ], "tsne": [ - -5.637635231018066, - -24.76722526550293 + -8.679728507995605, + 20.09827995300293 ], "umap": [ - 4.34170389175415, - 8.720796585083008 + 4.4965949058532715, + 8.053692817687988 ], "pca3d": [ - -0.03773149102926254, - 0.19650787115097046, - 0.1272863894701004 + -0.0372958704829216, + 0.1979958862066269, + 0.12707574665546417 ], "tsne3d": [ - -2.8563272953033447, - -9.195393562316895, - -1.1226943731307983 + -4.643177032470703, + 13.589299201965332, + -1.999396800994873 ], "umap3d": [ - 3.494234800338745, - 3.5262062549591064, - 3.1305527687072754 + 4.097968578338623, + 3.902837038040161, + 3.552016019821167 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, - "label": "Medical AI" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -485523,31 +485673,31 @@ "doi": "10.1101/2024.10.29.620880", "projections": { "pca": [ - 0.20283742249011993, - -0.0221159178763628 + 0.20285320281982422, + -0.02216646820306778 ], "tsne": [ - 32.063785552978516, - 23.98693084716797 + 53.6963005065918, + -3.770444393157959 ], "umap": [ - 8.94273853302002, - 5.996784210205078 + 9.17788028717041, + 5.636513710021973 ], "pca3d": [ - 0.2028369903564453, - -0.022108368575572968, - -0.009869922883808613 + 0.2028530091047287, + -0.022158920764923096, + -0.009855148382484913 ], "tsne3d": [ - 17.049741744995117, - 22.420785903930664, - 1.558072566986084 + 22.61902618408203, + -9.768898963928223, + 7.378174781799316 ], "umap3d": [ - 7.680724143981934, - 5.079994201660156, - 2.7181906700134277 + 7.555685520172119, + 5.35987663269043, + 1.0795369148254395 ] }, "cluster_id": 4, @@ -485558,11 +485708,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, - "label": "RNA Splicing" + "id": 2, + "label": "DNA Regulation" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -485985,31 +486135,31 @@ "cited_by_count": 29, "projections": { "pca": [ - 0.14423954486846924, - -0.0037559892516583204 + 0.1442275196313858, + -0.003996845334768295 ], "tsne": [ - 30.58837890625, - -8.252768516540527 + 25.52630043029785, + 13.490015983581543 ], "umap": [ - 8.498236656188965, - 7.938736438751221 + 8.730759620666504, + 7.510992050170898 ], "pca3d": [ - 0.1442396342754364, - -0.003757564816623926, - -0.10289369523525238 + 0.1442275494337082, + -0.003998784348368645, + -0.10289424657821655 ], "tsne3d": [ - 14.105569839477539, - -6.775449752807617, - 1.9808622598648071 + 12.419059753417969, + 7.883119106292725, + -6.814565181732178 ], "umap3d": [ - 6.657719135284424, - 4.185236930847168, - 1.8817899227142334 + 6.621626377105713, + 4.0249738693237305, + 1.0062990188598633 ] }, "cluster_id": 0, @@ -486020,12 +486170,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { "id": 31, - "label": "Genome Cancer" + "label": "Single Cell Seq" } ], "_embedding": [ @@ -486445,31 +486595,31 @@ "doi": "10.1101/2024.10.28.620517", "projections": { "pca": [ - 0.27813029289245605, - 0.0184298325330019 + 0.27818605303764343, + 0.01809236966073513 ], "tsne": [ - 48.087188720703125, - -0.6608258485794067 + 44.49790954589844, + 16.700159072875977 ], "umap": [ - 9.703522682189941, - 7.551412582397461 + 9.869958877563477, + 7.177535057067871 ], "pca3d": [ - 0.2781301438808441, - 0.018415115773677826, - -0.03460888937115669 + 0.27818602323532104, + 0.018077390268445015, + -0.034746695309877396 ], "tsne3d": [ - 28.43712615966797, - 5.578351020812988, - 6.272534370422363 + 31.082504272460938, + 1.8864421844482422, + 5.506918907165527 ], "umap3d": [ - 7.095122337341309, - 4.935666561126709, - 1.0361779928207397 + 6.4298529624938965, + 4.618346214294434, + 0.14069539308547974 ] }, "cluster_id": 0, @@ -486480,12 +486630,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 20, - "label": "Chromatin Regulation" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -486902,46 +487052,46 @@ ], "projections": { "pca": [ - 0.08836030215024948, - -0.10971500724554062 + 0.08819855749607086, + -0.10957071930170059 ], "tsne": [ - 9.43132495880127, - 25.913352966308594 + 17.702930450439453, + -22.776546478271484 ], "umap": [ - 6.936433792114258, - 4.589300155639648 + 7.388854026794434, + 4.3994646072387695 ], "pca3d": [ - 0.08836029469966888, - -0.10970449447631836, - 0.10498077422380447 + 0.08819861710071564, + -0.10955997556447983, + 0.10520970821380615 ], "tsne3d": [ - 4.5810465812683105, - 17.00708770751953, - -3.8121566772460938 + 8.722640037536621, + -10.412243843078613, + 8.293394088745117 ], "umap3d": [ - 6.238744258880615, - 5.953260898590088, - 4.516688346862793 + 6.65454626083374, + 6.773451328277588, + 2.843613386154175 ] }, - "cluster_id": 3, - "cluster_label": "Protein Design", + "cluster_id": 4, + "cluster_label": "RNA Sequence Models", "cluster_levels": [ { - "id": 3, - "label": "Protein Design" + "id": 4, + "label": "RNA Sequence Models" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -487390,31 +487540,31 @@ "cited_by_count": 49, "projections": { "pca": [ - 0.23255378007888794, - 0.0982687696814537 + 0.23285652697086334, + 0.0974934846162796 ], "tsne": [ - 36.84036636352539, - 4.1639227867126465 + 40.62151336669922, + 4.9780354499816895 ], "umap": [ - 8.937150955200195, - 8.281462669372559 + 9.205577850341797, + 7.907375812530518 ], "pca3d": [ - 0.23255379498004913, - 0.09824968129396439, - -0.1375063806772232 + 0.2328563928604126, + 0.09747428447008133, + -0.13773643970489502 ], "tsne3d": [ - 21.736650466918945, - 6.453673362731934, - 9.307096481323242 + 17.842313766479492, + 9.899110794067383, + -4.543112754821777 ], "umap3d": [ - 6.9107666015625, - 4.165440082550049, - 1.3933101892471313 + 6.672400951385498, + 3.9992423057556152, + 0.5652329921722412 ] }, "cluster_id": 0, @@ -487425,12 +487575,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -487845,46 +487995,46 @@ "doi": "10.1101/2024.10.24.620111", "projections": { "pca": [ - 0.10976564139127731, - -0.022552186623215675 + 0.10973881185054779, + -0.02287174202501774 ], "tsne": [ - 23.844860076904297, - -22.94980812072754 + 15.878002166748047, + 24.630977630615234 ], "umap": [ - 7.655090808868408, - 9.355098724365234 + 7.5729756355285645, + 9.130002975463867 ], "pca3d": [ - 0.10976545512676239, - -0.02253376878798008, - -0.15664613246917725 + 0.10973875969648361, + -0.022852890193462372, + -0.1565472036600113 ], "tsne3d": [ - 13.859107971191406, - -8.209869384765625, - 8.601059913635254 + 11.099637985229492, + 4.824956893920898, + -14.34288501739502 ], "umap3d": [ - 5.369174957275391, - 3.5432753562927246, - 1.4663119316101074 + 5.230705261230469, + 3.363633394241333, + 1.3628113269805908 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -488300,31 +488450,31 @@ "doi": "10.1101/2024.10.22.619701", "projections": { "pca": [ - 0.10940216481685638, - -0.1524737924337387 + 0.10915374755859375, + -0.1525532454252243 ], "tsne": [ - 19.75794792175293, - -3.4492604732513428 + 20.669851303100586, + 5.445252895355225 ], "umap": [ - 7.938617706298828, - 6.411500930786133 + 8.278841972351074, + 6.072901725769043 ], "pca3d": [ - 0.10940220952033997, - -0.15246382355690002, - 0.035633351653814316 + 0.10915391147136688, + -0.15254336595535278, + 0.0359017588198185 ], "tsne3d": [ - 14.832279205322266, - 9.609006881713867, - -7.4369893074035645 + 13.9558744430542, + -1.2282243967056274, + 0.08631043881177902 ], "umap3d": [ - 6.309540748596191, - 5.285614013671875, - 2.5558359622955322 + 6.302350044250488, + 5.352640628814697, + 1.4924612045288086 ] }, "cluster_id": 4, @@ -488335,12 +488485,12 @@ "label": "RNA Sequence Models" }, { - "id": 3, - "label": "RNA Splicing" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -488754,31 +488904,31 @@ "doi": "10.1038/d41586-024-03170-2", "projections": { "pca": [ - 0.13553354144096375, - -0.02745955064892769 + 0.1355481892824173, + -0.027251742780208588 ], "tsne": [ - 40.151004791259766, - 3.71038818359375 + 36.76286315917969, + 3.815971612930298 ], "umap": [ - 9.384637832641602, - 7.327784061431885 + 9.531051635742188, + 7.018579006195068 ], "pca3d": [ - 0.13553348183631897, - -0.02745012566447258, - 0.0466233491897583 + 0.13554812967777252, + -0.027242543175816536, + 0.046685297042131424 ], "tsne3d": [ - 24.754220962524414, - -1.2335492372512817, - -5.275974750518799 + 16.9367618560791, + -1.989013433456421, + -2.2628448009490967 ], "umap3d": [ - 6.778515815734863, - 5.2474822998046875, - 1.3643710613250732 + 6.315290451049805, + 4.838437080383301, + 0.2663242816925049 ] }, "cluster_id": 0, @@ -488789,11 +488939,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -489222,31 +489372,31 @@ "cited_by_count": 123, "projections": { "pca": [ - 0.22517496347427368, - 0.003842179197818041 + 0.22514431178569794, + 0.003481027903035283 ], "tsne": [ - 40.08355712890625, - 2.0534420013427734 + 37.89546203613281, + 5.020637035369873 ], "umap": [ - 9.33720588684082, - 7.455933570861816 + 9.671375274658203, + 7.3765387535095215 ], "pca3d": [ - 0.22517479956150055, - 0.0038312922697514296, - -0.030101226642727852 + 0.22514426708221436, + 0.0034695365466177464, + -0.03025093302130699 ], "tsne3d": [ - 23.515640258789062, - -2.2750799655914307, - -4.307563304901123 + 21.7263240814209, + 6.9722185134887695, + -1.039353370666504 ], "umap3d": [ - 6.8078742027282715, - 5.119738578796387, - 1.2652089595794678 + 6.373697280883789, + 4.706532955169678, + 0.2734455466270447 ] }, "cluster_id": 0, @@ -489257,12 +489407,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 20, - "label": "Chromatin Regulation" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -489678,31 +489828,31 @@ "doi": "10.1101/2024.08.01.606099", "projections": { "pca": [ - 0.2043410837650299, - -0.10511056333780289 + 0.20407234132289886, + -0.10562156140804291 ], "tsne": [ - 23.68962860107422, - -1.6448283195495605 + 25.438222885131836, + 3.3590025901794434 ], "umap": [ - 8.462355613708496, - 7.368335723876953 + 8.713055610656738, + 6.782373428344727 ], "pca3d": [ - 0.20434126257896423, - -0.10510649532079697, - -0.11789223551750183 + 0.20407244563102722, + -0.10561773926019669, + -0.11779630184173584 ], "tsne3d": [ - 16.494470596313477, - 0.9071827530860901, - -9.93168830871582 + 12.006872177124023, + 6.527520179748535, + 0.8832066059112549 ], "umap3d": [ - 6.662234306335449, - 4.762025356292725, - 1.965466856956482 + 6.4123921394348145, + 4.718506813049316, + 0.9606067538261414 ] }, "cluster_id": 0, @@ -489713,12 +489863,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -490138,31 +490288,31 @@ "openalex_url": "https://openalex.org/W4318027593", "projections": { "pca": [ - 0.14165399968624115, - 0.07269922643899918 + 0.14191332459449768, + 0.07239921391010284 ], "tsne": [ - 35.997169494628906, - 1.0867469310760498 + 32.767818450927734, + 5.6334404945373535 ], "umap": [ - 8.850685119628906, - 7.392685890197754 + 9.082155227661133, + 6.802539825439453 ], "pca3d": [ - 0.1416536569595337, - 0.0727061778306961, - -0.03501433506608009 + 0.14191298186779022, + 0.07240623235702515, + -0.035157110542058945 ], "tsne3d": [ - 20.09705352783203, - -0.23147150874137878, - -5.638793468475342 + 20.1118221282959, + 6.085981369018555, + 0.8875982761383057 ], "umap3d": [ - 6.997832775115967, - 4.736265182495117, - 1.7506775856018066 + 6.653595447540283, + 4.598052024841309, + 0.6899827122688293 ] }, "cluster_id": 0, @@ -490173,12 +490323,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -490602,31 +490752,31 @@ "doi": "10.1101/2024.08.26.609813", "projections": { "pca": [ - 0.20675943791866302, - -0.11841924488544464 + 0.20651525259017944, + -0.11856061220169067 ], "tsne": [ - 21.524477005004883, - 25.669878005981445 + 25.854595184326172, + -25.703813552856445 ], "umap": [ - 8.165810585021973, - 5.293933868408203 + 8.42258358001709, + 4.885955333709717 ], "pca3d": [ - 0.20675942301750183, - -0.11840903759002686, - 0.015115968883037567 + 0.2065153270959854, + -0.11854942888021469, + 0.01529189758002758 ], "tsne3d": [ - 8.132757186889648, - 20.778911590576172, - 3.394151210784912 + 14.267131805419922, + -11.89421272277832, + 6.744316577911377 ], "umap3d": [ - 7.255692481994629, - 5.626844882965088, - 3.484071731567383 + 7.2688493728637695, + 6.087896347045898, + 1.881291151046753 ] }, "cluster_id": 4, @@ -490637,11 +490787,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -491093,31 +491243,31 @@ "cited_by_count": 15, "projections": { "pca": [ - 0.21555042266845703, - -0.02517426759004593 + 0.21547831594944, + -0.02554517239332199 ], "tsne": [ - 49.65116882324219, - -9.8308744430542 + 46.587127685546875, + 11.65096378326416 ], "umap": [ - 10.197229385375977, - 7.946410179138184 + 10.391565322875977, + 7.593314170837402 ], "pca3d": [ - 0.21555042266845703, - -0.02518927864730358, - -0.004158221650868654 + 0.21547839045524597, + -0.025560984387993813, + -0.004278029780834913 ], "tsne3d": [ - 25.841022491455078, - -0.8569334149360657, - -9.631421089172363 + 26.50025749206543, + 5.724730491638184, + 2.9094111919403076 ], "umap3d": [ - 7.026772975921631, - 5.08105993270874, - 0.6311877369880676 + 6.367603778839111, + 4.650623321533203, + -0.4042147397994995 ] }, "cluster_id": 0, @@ -491128,12 +491278,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -491557,31 +491707,31 @@ "doi": "10.1101/2024.10.18.619134", "projections": { "pca": [ - 0.2481720745563507, - -0.042933765798807144 + 0.2481531947851181, + -0.04370678961277008 ], "tsne": [ - 58.376914978027344, - 4.864304065704346 + 50.8259391784668, + 20.89157485961914 ], "umap": [ - 9.98208999633789, - 6.62216329574585 + 10.24575138092041, + 6.204940319061279 ], "pca3d": [ - 0.24817192554473877, - -0.04292130842804909, - 0.07678111642599106 + 0.2481531798839569, + -0.04369382560253143, + 0.07665194571018219 ], "tsne3d": [ - 25.266511917114258, - 6.905323028564453, - -3.9437191486358643 + 27.382465362548828, + -3.220670700073242, + 1.4902087450027466 ], "umap3d": [ - 7.822566509246826, - 5.506906032562256, - 1.4501826763153076 + 7.228495121002197, + 5.338517189025879, + -0.12257173657417297 ] }, "cluster_id": 0, @@ -491592,12 +491742,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -492017,47 +492167,47 @@ ], "projections": { "pca": [ - 0.03348309546709061, - -0.11975474655628204 + 0.033216435462236404, + -0.11913810670375824 ], "tsne": [ - -31.635759353637695, - 16.413599014282227 + -26.97525405883789, + -20.2852783203125 ], "umap": [ - 3.4959096908569336, - 4.828383922576904 + 4.010293483734131, + 4.245345115661621 ], "pca3d": [ - 0.03348330035805702, - -0.11975658684968948, - 0.16147635877132416 + 0.033216673880815506, + -0.1191403716802597, + 0.16163542866706848 ], "tsne3d": [ - -11.045905113220215, - 11.143793106079102, - 2.0642995834350586 + -10.473137855529785, + -19.017993927001953, + 2.944408416748047 ], "umap3d": [ - 3.3708791732788086, - 6.8596978187561035, - 4.744736194610596 + 3.7669594287872314, + 7.29045295715332, + 4.044399261474609 ] }, - "cluster_id": 2, - "cluster_label": "Generative Models", + "cluster_id": 3, + "cluster_label": "Protein Design", "cluster_levels": [ { - "id": 2, - "label": "Generative Models" + "id": 3, + "label": "Protein Design" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 16, - "label": "Diffusion Models" + "id": 7, + "label": "Diffusion Gen" } ], "_embedding": [ @@ -492475,31 +492625,31 @@ "doi": "10.1101/2024.10.15.618510", "projections": { "pca": [ - 0.07535666972398758, - -0.13693195581436157 + 0.07509105652570724, + -0.13654108345508575 ], "tsne": [ - 20.43259048461914, - 1.8675917387008667 + 23.3531436920166, + -0.49311119318008423 ], "umap": [ - 7.4836907386779785, - 7.028993606567383 + 7.7069830894470215, + 6.677987575531006 ], "pca3d": [ - 0.07535675168037415, - -0.13693436980247498, - -0.07654910534620285 + 0.07509114593267441, + -0.13654354214668274, + -0.07614390552043915 ], "tsne3d": [ - 12.696823120117188, - -0.4925198554992676, - -11.420190811157227 + 12.063673973083496, + 6.3769965171813965, + 5.060455799102783 ], "umap3d": [ - 6.49740743637085, - 4.4321465492248535, - 2.8479738235473633 + 6.7248992919921875, + 4.598984241485596, + 1.9987077713012695 ] }, "cluster_id": 4, @@ -492510,12 +492660,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 1, + "label": "Genomic Regulation" } ], "_embedding": [ @@ -492942,46 +493092,46 @@ "doi": "10.1101/2024.10.15.618501", "projections": { "pca": [ - 0.10367526859045029, - -0.05203193426132202 + 0.10369905084371567, + -0.05212193727493286 ], "tsne": [ - 18.80600929260254, - -23.33846664428711 + 10.41457748413086, + 23.433101654052734 ], "umap": [ - 7.280134201049805, - 9.287897109985352 + 7.301660537719727, + 8.897543907165527 ], "pca3d": [ - 0.10367507487535477, - -0.052004434168338776, - -0.07466065138578415 + 0.10369886457920074, + -0.0520942285656929, + -0.0744062140583992 ], "tsne3d": [ - 7.320619106292725, - -10.934359550476074, - 10.466899871826172 + 6.4660844802856445, + 7.430875778198242, + -13.546270370483398 ], "umap3d": [ - 5.0048394203186035, - 3.7285537719726562, - 1.7158923149108887 + 5.045289039611816, + 3.515394449234009, + 1.7669599056243896 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -493399,31 +493549,31 @@ ], "projections": { "pca": [ - -0.24020113050937653, - -0.06210292503237724 + -0.24019521474838257, + -0.061593033373355865 ], "tsne": [ - -36.326171875, - 11.591352462768555 + -32.834442138671875, + -17.924503326416016 ], "umap": [ - 2.4200193881988525, - 5.258152008056641 + 2.725695848464966, + 4.673816204071045 ], "pca3d": [ - -0.24020084738731384, - -0.06210116669535637, - -0.02067936770617962 + -0.24019499123096466, + -0.06159159541130066, + -0.02038813941180706 ], "tsne3d": [ - -17.296525955200195, - 9.064169883728027, - 5.533905029296875 + -16.89383316040039, + -18.6492977142334, + 6.890720844268799 ], "umap3d": [ - 2.1357364654541016, - 6.5889410972595215, - 4.98719596862793 + 2.784146785736084, + 7.046386241912842, + 4.8093132972717285 ] }, "cluster_id": 2, @@ -493434,11 +493584,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -493864,31 +494014,31 @@ "doi": "10.1101/2024.10.08.616922", "projections": { "pca": [ - 0.23868781328201294, - 0.06999324262142181 + 0.2387261986732483, + 0.06945361196994781 ], "tsne": [ - 43.968048095703125, - -5.582208633422852 + 43.172996520996094, + 7.566561222076416 ], "umap": [ - 9.631620407104492, - 8.178071022033691 + 10.022802352905273, + 7.6723504066467285 ], "pca3d": [ - 0.2386876344680786, - 0.0699847936630249, - -0.1827273815870285 + 0.23872604966163635, + 0.06944464892148972, + -0.1830092817544937 ], "tsne3d": [ - 24.22332000732422, - -8.270712852478027, - -5.481728553771973 + 26.139196395874023, + 11.04432487487793, + -2.693988084793091 ], "umap3d": [ - 7.124550819396973, - 4.430327892303467, - 0.8308632373809814 + 6.630535125732422, + 3.9621989727020264, + -0.01614690199494362 ] }, "cluster_id": 0, @@ -493899,12 +494049,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -494322,47 +494472,47 @@ "doi": "10.1101/2023.07.28.551039", "projections": { "pca": [ - 0.054434023797512054, - -0.09224152565002441 + 0.05416083708405495, + -0.09197835624217987 ], "tsne": [ - 27.01239013671875, - -18.339391708374023 + 20.00066566467285, + 21.8392391204834 ], "umap": [ - 8.330408096313477, - 9.261991500854492 + 8.531652450561523, + 8.941888809204102 ], "pca3d": [ - 0.05443383753299713, - -0.09220677614212036, - -0.05190863087773323 + 0.05416065827012062, + -0.09194301813840866, + -0.051724568009376526 ], "tsne3d": [ - 13.771801948547363, - 9.39340591430664, - 3.600916862487793 + 7.445775985717773, + -3.3648841381073, + -14.396895408630371 ], "umap3d": [ - 5.426026344299316, - 4.588577747344971, - 1.3662350177764893 + 5.153321743011475, + 4.298912525177002, + 1.0745093822479248 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -494786,31 +494936,31 @@ "doi": "10.1101/2024.10.10.617568", "projections": { "pca": [ - 0.1998891681432724, - -0.12408927083015442 + 0.19966693222522736, + -0.12407797574996948 ], "tsne": [ - -0.26040950417518616, - 22.138383865356445 + 6.05709171295166, + -22.169029235839844 ], "umap": [ - 6.3967485427856445, - 4.581211090087891 + 6.972341537475586, + 4.343554496765137 ], "pca3d": [ - 0.19988925755023956, - -0.12409470975399017, - 0.14649219810962677 + 0.19966714084148407, + -0.12408363074064255, + 0.1466447412967682 ], "tsne3d": [ - 2.7887985706329346, - 5.078896522521973, - 0.3488633632659912 + 2.182267904281616, + -5.454214572906494, + 3.869525194168091 ], "umap3d": [ - 5.957597732543945, - 6.2463154792785645, - 4.610230922698975 + 6.400278091430664, + 6.95605993270874, + 2.954014301300049 ] }, "cluster_id": 3, @@ -494821,11 +494971,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -495246,31 +495396,31 @@ "doi": "10.1101/2024.10.10.617658", "projections": { "pca": [ - 0.1278379261493683, - -0.1296670138835907 + 0.12758496403694153, + -0.129632368683815 ], "tsne": [ - 18.958717346191406, - 26.847244262695312 + 23.018388748168945, + -26.67059326171875 ], "umap": [ - 7.847021102905273, - 4.950346946716309 + 8.229562759399414, + 4.601078033447266 ], "pca3d": [ - 0.12783797085285187, - -0.1296481043100357, - 0.09443406760692596 + 0.12758494913578033, + -0.129612535238266, + 0.09464021772146225 ], "tsne3d": [ - 5.981136798858643, - 20.33123207092285, - 4.304889678955078 + 12.536181449890137, + -13.119129180908203, + 7.957819938659668 ], "umap3d": [ - 7.067010879516602, - 5.850503921508789, - 3.7122960090637207 + 7.155841827392578, + 6.401432991027832, + 2.133866310119629 ] }, "cluster_id": 4, @@ -495281,11 +495431,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -495802,31 +495952,31 @@ "cited_by_count": 31, "projections": { "pca": [ - 0.07341451942920685, - -0.12167378515005112 + 0.07308002561330795, + -0.12129287421703339 ], "tsne": [ - 18.243282318115234, - -2.260293960571289 + 19.695220947265625, + 4.277838230133057 ], "umap": [ - 7.704894542694092, - 6.419171333312988 + 8.029739379882812, + 6.107816696166992 ], "pca3d": [ - 0.07341469079256058, - -0.12167878448963165, - 0.004921273328363895 + 0.07308027148246765, + -0.12129776924848557, + 0.005130596458911896 ], "tsne3d": [ - 13.920668601989746, - 6.24749755859375, - -9.210465431213379 + 15.48853874206543, + 0.11725852638483047, + 2.8431005477905273 ], "umap3d": [ - 6.205918312072754, - 5.192450046539307, - 2.710120439529419 + 6.262247562408447, + 5.286679744720459, + 1.6509053707122803 ] }, "cluster_id": 4, @@ -495837,12 +495987,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -496256,31 +496406,31 @@ ], "projections": { "pca": [ - 0.13371139764785767, - -0.1536751091480255 + 0.13336201012134552, + -0.15335875749588013 ], "tsne": [ - 12.096846580505371, - 5.89849853515625 + 18.1205997467041, + -4.029092788696289 ], "umap": [ - 7.346214771270752, - 6.433154106140137 + 7.438555717468262, + 6.100029468536377 ], "pca3d": [ - 0.133711576461792, - -0.1536761224269867, - -0.01806822046637535 + 0.133362278342247, + -0.15335950255393982, + -0.017696358263492584 ], "tsne3d": [ - 8.284956932067871, - 0.9982786774635315, - -8.61293888092041 + 8.235260963439941, + 3.919696569442749, + 1.7961530685424805 ], "umap3d": [ - 6.084181785583496, - 4.907470703125, - 3.0888309478759766 + 6.286230564117432, + 5.084733486175537, + 2.1255409717559814 ] }, "cluster_id": 4, @@ -496291,11 +496441,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -496723,31 +496873,31 @@ "doi": "10.1101/2024.10.07.616676", "projections": { "pca": [ - 0.12158674001693726, - -0.07521466910839081 + 0.12146841734647751, + -0.0752531886100769 ], "tsne": [ - 20.576444625854492, - 29.02117347717285 + 20.577049255371094, + -23.45705795288086 ], "umap": [ - 7.526925086975098, - 4.722875595092773 + 8.145096778869629, + 4.5214104652404785 ], "pca3d": [ - 0.12158667296171188, - -0.07520049065351486, - 0.11486764252185822 + 0.1214684247970581, + -0.07523880898952484, + 0.11492961645126343 ], "tsne3d": [ - 4.794590950012207, - 21.532556533813477, - -3.4875376224517822 + 15.789193153381348, + -11.386214256286621, + 2.4195404052734375 ], "umap3d": [ - 6.998301029205322, - 5.9062957763671875, - 4.063199996948242 + 7.0517425537109375, + 6.610044479370117, + 2.298576593399048 ] }, "cluster_id": 4, @@ -496758,11 +496908,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -497200,46 +497350,46 @@ "cited_by_count": 157, "projections": { "pca": [ - 0.12057047337293625, - 0.208255797624588 + 0.12092297524213791, + 0.20848694443702698 ], "tsne": [ - 16.27652931213379, - -12.233185768127441 + 39.92554473876953, + -15.256877899169922 ], "umap": [ - 6.915780544281006, - 8.403605461120605 + 7.113028526306152, + 8.035016059875488 ], "pca3d": [ - 0.12056994438171387, - 0.20825627446174622, - -0.13001491129398346 + 0.12092246860265732, + 0.20848727226257324, + -0.13039037585258484 ], "tsne3d": [ - 17.922534942626953, - -16.797622680664062, - -2.7515320777893066 + 14.932271003723145, + 20.962244033813477, + -6.762669563293457 ], "umap3d": [ - 6.3775248527526855, - 3.0610721111297607, - 2.3918063640594482 + 6.678091526031494, + 3.219324827194214, + 2.0475292205810547 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 26, + "id": 33, "label": "Genetic Disease" } ], @@ -497663,31 +497813,31 @@ "cited_by_count": 16, "projections": { "pca": [ - 0.3162434995174408, - 0.020039770752191544 + 0.31618672609329224, + 0.01934504508972168 ], "tsne": [ - 53.367889404296875, - 0.38125288486480713 + 52.81163024902344, + 12.02272891998291 ], "umap": [ - 10.418973922729492, - 7.315978527069092 + 10.625397682189941, + 7.002918243408203 ], "pca3d": [ - 0.31624335050582886, - 0.020020458847284317, - -0.12675748765468597 + 0.3161866068840027, + 0.019325030967593193, + -0.12700898945331573 ], "tsne3d": [ - 30.484970092773438, - 0.18398204445838928, - -1.961053490638733 + 30.753576278686523, + 3.682464361190796, + -2.324941635131836 ], "umap3d": [ - 7.90785026550293, - 5.002059459686279, - 0.7374901175498962 + 7.148865222930908, + 4.6771769523620605, + -0.6096377968788147 ] }, "cluster_id": 0, @@ -497698,12 +497848,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -498135,31 +498285,31 @@ "doi": "10.1101/2024.10.04.615095", "projections": { "pca": [ - 0.29023095965385437, - 0.04726283624768257 + 0.2902572751045227, + 0.046597838401794434 ], "tsne": [ - 52.488990783691406, - -2.378847360610962 + 53.77462387084961, + 8.810049057006836 ], "umap": [ - 10.587310791015625, - 7.485258102416992 + 10.745722770690918, + 7.105975151062012 ], "pca3d": [ - 0.29023075103759766, - 0.047251809388399124, - -0.11338715255260468 + 0.290257066488266, + 0.04658609256148338, + -0.1136971265077591 ], "tsne3d": [ - 30.218069076538086, - -2.9593141078948975, - -1.3546656370162964 + 32.380455017089844, + 5.344501495361328, + -4.293841361999512 ], "umap3d": [ - 7.887363910675049, - 4.901096820831299, - 0.5903927683830261 + 7.091832160949707, + 4.573028087615967, + -0.7332684397697449 ] }, "cluster_id": 0, @@ -498170,12 +498320,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -498596,31 +498746,31 @@ "cited_by_count": 17, "projections": { "pca": [ - 0.2953431010246277, - 0.04349573701620102 + 0.29538556933403015, + 0.04291025176644325 ], "tsne": [ - 53.802303314208984, - -2.76090145111084 + 52.920711517333984, + 10.094747543334961 ], "umap": [ - 10.569927215576172, - 7.511712074279785 + 10.730697631835938, + 7.121906757354736 ], "pca3d": [ - 0.29534298181533813, - 0.04347798228263855, - -0.12785571813583374 + 0.2953855097293854, + 0.04289188235998154, + -0.12810686230659485 ], "tsne3d": [ - 30.595308303833008, - -1.7866415977478027, - -3.213263511657715 + 30.801925659179688, + 5.928491592407227, + -2.7061524391174316 ], "umap3d": [ - 7.829782009124756, - 4.9157280921936035, - 0.595332682132721 + 7.00416898727417, + 4.543948173522949, + -0.7339788675308228 ] }, "cluster_id": 0, @@ -498631,12 +498781,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -499057,31 +499207,31 @@ "doi": "10.1101/2024.08.14.607850", "projections": { "pca": [ - 0.09556546807289124, - -0.10253002494573593 + 0.0955057144165039, + -0.1024782806634903 ], "tsne": [ - 10.139846801757812, - 7.4848222732543945 + 12.466033935546875, + -8.02530288696289 ], "umap": [ - 6.818606853485107, - 6.094470977783203 + 6.927759647369385, + 5.691618919372559 ], "pca3d": [ - 0.09556557983160019, - -0.10253001749515533, - 0.02076919935643673 + 0.09550584107637405, + -0.10247812420129776, + 0.021099066361784935 ], "tsne3d": [ - 3.901881217956543, - 3.9805614948272705, - -8.99737548828125 + 4.734528541564941, + 0.2545897662639618, + -1.1215583086013794 ], "umap3d": [ - 5.660733699798584, - 5.001523017883301, - 3.5923705101013184 + 6.08223819732666, + 5.441944122314453, + 2.7108354568481445 ] }, "cluster_id": 4, @@ -499092,11 +499242,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -499525,31 +499675,31 @@ "cited_by_count": 33, "projections": { "pca": [ - 0.24649938941001892, - 0.031048158183693886 + 0.24663788080215454, + 0.03059173934161663 ], "tsne": [ - 25.365856170654297, - 28.970584869384766 + 29.34639549255371, + -29.265499114990234 ], "umap": [ - 8.554713249206543, - 5.191925525665283 + 8.823773384094238, + 4.773174285888672 ], "pca3d": [ - 0.2464991807937622, - 0.031051047146320343, - 0.036975231021642685 + 0.24663753807544708, + 0.03059491328895092, + 0.03678696230053902 ], "tsne3d": [ - 10.653114318847656, - 26.025205612182617, - 0.6967705488204956 + 19.129619598388672, + -14.69136905670166, + 8.668495178222656 ], "umap3d": [ - 7.683704376220703, - 5.606044292449951, - 3.4187209606170654 + 7.651128768920898, + 6.154232501983643, + 1.5824447870254517 ] }, "cluster_id": 4, @@ -499560,11 +499710,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -499984,31 +500134,31 @@ "doi": "10.1101/2024.09.23.614632", "projections": { "pca": [ - 0.08384719491004944, - -0.10241120308637619 + 0.08356830477714539, + -0.10216885805130005 ], "tsne": [ - 20.458175659179688, - 2.212230682373047 + 22.715763092041016, + -0.32049107551574707 ], "umap": [ - 7.612661838531494, - 6.902615547180176 + 7.819476127624512, + 6.534870624542236 ], "pca3d": [ - 0.083847276866436, - -0.10240736603736877, - -0.12198694050312042 + 0.08356845378875732, + -0.10216489434242249, + -0.1217123419046402 ], "tsne3d": [ - 11.45036792755127, - 0.02262973040342331, - -10.392515182495117 + 11.293561935424805, + 5.557693958282471, + 3.580721139907837 ], "umap3d": [ - 6.442237377166748, - 4.520203590393066, - 2.7757515907287598 + 6.537036895751953, + 4.734204292297363, + 1.8447154760360718 ] }, "cluster_id": 4, @@ -500019,12 +500169,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 2, + "label": "DNA Regulation" }, { - "id": 22, - "label": "Genetic Variants" + "id": 1, + "label": "Genomic Regulation" } ], "_embedding": [ @@ -500447,31 +500597,31 @@ "cited_by_count": 5, "projections": { "pca": [ - 0.1230921819806099, - -0.050913818180561066 + 0.12289342284202576, + -0.05124581232666969 ], "tsne": [ - 26.321563720703125, - 25.090299606323242 + 30.048603057861328, + -23.924606323242188 ], "umap": [ - 8.552277565002441, - 5.670863628387451 + 8.817217826843262, + 5.35653018951416 ], "pca3d": [ - 0.12309204041957855, - -0.05089324340224266, - -0.11701637506484985 + 0.1228933110833168, + -0.05122391879558563, + -0.11697050929069519 ], "tsne3d": [ - 12.264466285705566, - 20.57386016845703, - 4.498439788818359 + 17.764190673828125, + -10.954560279846191, + 7.0587334632873535 ], "umap3d": [ - 7.544803142547607, - 5.170355319976807, - 3.183436155319214 + 7.583291053771973, + 5.6030659675598145, + 1.495840072631836 ] }, "cluster_id": 4, @@ -500482,11 +500632,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -500909,47 +501059,47 @@ "doi": "10.1101/2024.09.19.613754", "projections": { "pca": [ - 0.2682686746120453, - -0.04507724568247795 + 0.2681915760040283, + -0.04558027908205986 ], "tsne": [ - 24.108386993408203, - -16.351781845092773 + 23.393709182739258, + 16.001163482666016 ], "umap": [ - 8.44614315032959, - 8.505525588989258 + 8.619491577148438, + 8.20717716217041 ], "pca3d": [ - 0.26826849579811096, - -0.045067835599184036, - -0.11378791183233261 + 0.26819148659706116, + -0.04557030647993088, + -0.11374375224113464 ], "tsne3d": [ - 14.387693405151367, - -3.6772892475128174, - 7.817282676696777 + 13.140040397644043, + 3.2024025917053223, + -10.824932098388672 ], "umap3d": [ - 6.073736190795898, - 4.082087993621826, - 1.4233245849609375 + 5.926609516143799, + 3.8733956813812256, + 0.948758602142334 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -501369,31 +501519,31 @@ "openalex_url": "https://openalex.org/W4402717588", "projections": { "pca": [ - 0.1381663680076599, - -0.07753206789493561 + 0.1381421685218811, + -0.07766158878803253 ], "tsne": [ - 11.509618759155273, - 12.03100299835205 + 16.13351058959961, + -4.405543804168701 ], "umap": [ - 7.025559902191162, - 6.1551666259765625 + 7.410444259643555, + 5.8609795570373535 ], "pca3d": [ - 0.1381666660308838, - -0.07755468785762787, - 0.07386307418346405 + 0.13814248144626617, + -0.07768461108207703, + 0.07401665300130844 ], "tsne3d": [ - 2.3435425758361816, - 10.83793830871582, - -9.771085739135742 + 10.547194480895996, + -3.2917802333831787, + 1.516782283782959 ], "umap3d": [ - 5.957942485809326, - 5.132546901702881, - 3.3092458248138428 + 6.130799770355225, + 5.461953639984131, + 2.399916410446167 ] }, "cluster_id": 4, @@ -501404,11 +501554,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -501833,31 +501983,31 @@ "openalex_url": "https://openalex.org/W4384155135", "projections": { "pca": [ - 0.27187153697013855, - 0.040630143135786057 + 0.27188840508461, + 0.0400981605052948 ], "tsne": [ - 54.402347564697266, - -2.4414303302764893 + 52.989234924316406, + 10.610238075256348 ], "umap": [ - 10.573149681091309, - 7.507620334625244 + 10.734142303466797, + 7.118427276611328 ], "pca3d": [ - 0.27187153697013855, - 0.04061786085367203, - -0.13046295940876007 + 0.2718883156776428, + 0.04008504003286362, + -0.13073736429214478 ], "tsne3d": [ - 29.360902786254883, - -1.436341643333435, - -3.1783454418182373 + 30.330373764038086, + 5.3365478515625, + -3.7968852519989014 ], "umap3d": [ - 7.874578475952148, - 4.918722152709961, - 0.6104529500007629 + 7.057468414306641, + 4.57905912399292, + -0.7277406454086304 ] }, "cluster_id": 0, @@ -501868,12 +502018,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -502281,46 +502431,46 @@ "journal": "Nature Computational Science", "projections": { "pca": [ - 0.08220463246107101, - -0.011817486956715584 + 0.08230173587799072, + -0.012472979724407196 ], "tsne": [ - 26.05032730102539, - -26.146215438842773 + 16.138587951660156, + 29.79854965209961 ], "umap": [ - 7.493288516998291, - 9.195382118225098 + 7.6134490966796875, + 9.05117416381836 ], "pca3d": [ - 0.08220429718494415, - -0.011796386912465096, - -0.1397014558315277 + 0.08230147510766983, + -0.012451322749257088, + -0.1396074891090393 ], "tsne3d": [ - 16.44040298461914, - -8.489357948303223, - 11.828801155090332 + 11.631226539611816, + 5.2662787437438965, + -18.80583381652832 ], "umap3d": [ - 5.246387481689453, - 3.671508312225342, - 1.5784865617752075 + 5.322275638580322, + 3.519437551498413, + 1.5636303424835205 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -502741,31 +502891,31 @@ "doi": "10.1101/2024.09.18.612131", "projections": { "pca": [ - 0.09904071688652039, - -0.04092171788215637 + 0.09927470237016678, + -0.04057895019650459 ], "tsne": [ - 7.932867527008057, - 12.570225715637207 + 10.32511043548584, + -9.951977729797363 ], "umap": [ - 6.77825927734375, - 5.625360488891602 + 6.98220157623291, + 5.328977108001709 ], "pca3d": [ - 0.09904089570045471, - -0.04093888774514198, - 0.15147313475608826 + 0.09927485883235931, + -0.0405961237847805, + 0.15176686644554138 ], "tsne3d": [ - 2.995910167694092, - 7.658145904541016, - -4.998319149017334 + 3.958503246307373, + -2.443091630935669, + 2.911426305770874 ], "umap3d": [ - 5.75817346572876, - 5.381036281585693, - 3.8590683937072754 + 6.081457614898682, + 5.759937763214111, + 2.672116756439209 ] }, "cluster_id": 4, @@ -502776,11 +502926,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -503189,31 +503339,31 @@ ], "projections": { "pca": [ - -0.0890032947063446, - -0.08595936000347137 + -0.08929035067558289, + -0.0853235051035881 ], "tsne": [ - 27.107019424438477, - -20.210002899169922 + 16.629718780517578, + 14.25136661529541 ], "umap": [ - 7.375481128692627, - 6.620284557342529 + 7.351662635803223, + 6.171499729156494 ], "pca3d": [ - -0.08900332450866699, - -0.08592962473630905, - -0.011992689222097397 + -0.08929036557674408, + -0.08529309183359146, + -0.011785889975726604 ], "tsne3d": [ - 12.381279945373535, - 11.718177795410156, - 3.0239081382751465 + 6.300642013549805, + -5.128294467926025, + -12.442395210266113 ], "umap3d": [ - 5.562379360198975, - 4.9011383056640625, - 2.5589168071746826 + 5.527768135070801, + 4.879331588745117, + 1.826923131942749 ] }, "cluster_id": 4, @@ -503224,12 +503374,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 7, + "label": "Genomic Models" }, { - "id": 22, - "label": "Genetic Variants" + "id": 17, + "label": "Genomic Models" } ], "_embedding": [ @@ -503643,46 +503793,46 @@ "cited_by_count": 773, "projections": { "pca": [ - 0.06808673590421677, - 0.14119230210781097 + 0.06877002865076065, + 0.1406026929616928 ], "tsne": [ - 4.798856258392334, - -9.565322875976562 + 5.955635070800781, + 12.471782684326172 ], "umap": [ - 5.758453845977783, - 7.483757972717285 + 6.010561466217041, + 6.910420894622803 ], "pca3d": [ - 0.06808605045080185, - 0.1412295699119568, - -0.005596132483333349 + 0.06876926869153976, + 0.1406407505273819, + -0.005649343132972717 ], "tsne3d": [ - 8.599332809448242, - -5.6129279136657715, - 18.42620277404785 + 3.751765251159668, + -0.6747808456420898, + -7.259117126464844 ], "umap3d": [ - 5.352616310119629, - 4.026094913482666, - 3.567600727081299 + 5.66287899017334, + 4.4146728515625, + 2.8388543128967285 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, + "id": 13, "label": "Genomics Papers" } ], @@ -504106,47 +504256,47 @@ "doi": "10.1101/2024.08.12.607533", "projections": { "pca": [ - 0.14638328552246094, - -0.09418631345033646 + 0.1461183875799179, + -0.09412633627653122 ], "tsne": [ - 28.361806869506836, - -14.557024955749512 + 22.22881507873535, + 19.082481384277344 ], "umap": [ - 8.45181941986084, - 9.141846656799316 + 8.637153625488281, + 8.624828338623047 ], "pca3d": [ - 0.14638331532478333, - -0.09418575465679169, - -0.057081542909145355 + 0.14611846208572388, + -0.09412585198879242, + -0.05693841353058815 ], "tsne3d": [ - 20.326351165771484, - 9.263079643249512, - 5.152901649475098 + 12.517484664916992, + -1.891945481300354, + -11.928826332092285 ], "umap3d": [ - 5.699604511260986, - 4.328818321228027, - 1.1819952726364136 + 5.292898178100586, + 4.0609588623046875, + 0.8614000678062439 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -504562,31 +504712,31 @@ ], "projections": { "pca": [ - 0.04797211289405823, - -0.12329208105802536 + 0.04780266061425209, + -0.12323562800884247 ], "tsne": [ - 14.025659561157227, - 29.056652069091797 + 18.58970069885254, + -27.892879486083984 ], "umap": [ - 3.7119107246398926, - 4.865441799163818 + 4.249963760375977, + 4.345017910003662 ], "pca3d": [ - 0.047972116619348526, - -0.12327206134796143, - 0.10550352931022644 + 0.04780273512005806, + -0.12321509420871735, + 0.10571950674057007 ], "tsne3d": [ - -1.3914295434951782, - 20.71500015258789, - 0.5549106597900391 + 11.419641494750977, + -15.127488136291504, + 1.0615307092666626 ], "umap3d": [ - 6.841658592224121, - 6.10863733291626, - 3.79982328414917 + 6.921746253967285, + 6.558332920074463, + 2.284163475036621 ] }, "cluster_id": 3, @@ -504597,12 +504747,12 @@ "label": "Protein Design" }, { - "id": 7, - "label": "Diffusion Models" + "id": 9, + "label": "Protein Design" }, { - "id": 16, - "label": "Diffusion Models" + "id": 7, + "label": "Diffusion Gen" } ], "_embedding": [ @@ -505021,31 +505171,31 @@ "doi": "10.1101/2024.09.09.612081", "projections": { "pca": [ - 0.06959857791662216, - -0.10876969248056412 + 0.06948217004537582, + -0.1084306538105011 ], "tsne": [ - 7.5956339836120605, - 10.611077308654785 + 9.56884765625, + -8.263641357421875 ], "umap": [ - 6.867766380310059, - 5.6449079513549805 + 7.061333656311035, + 5.387681484222412 ], "pca3d": [ - 0.0695987269282341, - -0.10877043753862381, - 0.03761705756187439 + 0.06948236376047134, + -0.10843110084533691, + 0.037977248430252075 ], "tsne3d": [ - 5.678369045257568, - 8.56609058380127, - -4.133357048034668 + 4.982066631317139, + -3.531744956970215, + 0.8138843178749084 ], "umap3d": [ - 5.729293346405029, - 5.206690311431885, - 3.951542377471924 + 6.116684913635254, + 5.648560047149658, + 2.804702043533325 ] }, "cluster_id": 4, @@ -505056,11 +505206,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -505479,47 +505629,47 @@ "doi": "10.1101/2024.09.12.612709", "projections": { "pca": [ - 0.1524215191602707, - -0.04384512081742287 + 0.1522315889596939, + -0.04398902505636215 ], "tsne": [ - 27.088211059570312, - -15.950767517089844 + 20.68181800842285, + 19.843536376953125 ], "umap": [ - 8.380724906921387, - 9.218992233276367 + 8.5399808883667, + 8.774843215942383 ], "pca3d": [ - 0.15242120623588562, - -0.043808549642562866, - -0.002765279496088624 + 0.15223123133182526, + -0.04395216703414917, + -0.0028570282738655806 ], "tsne3d": [ - 15.608475685119629, - 13.06372356414795, - 8.913630485534668 + 11.573779106140137, + -4.351568698883057, + -17.965757369995117 ], "umap3d": [ - 5.653505802154541, - 4.547600746154785, - 1.2441813945770264 + 5.316530704498291, + 4.254312515258789, + 0.9446398615837097 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -505937,46 +506087,46 @@ ], "projections": { "pca": [ - 0.028612390160560608, - 0.017646541818976402 + 0.02870883047580719, + 0.018057217821478844 ], "tsne": [ - 2.013035535812378, - -26.495941162109375 + -3.752601385116577, + 26.11268424987793 ], "umap": [ - 5.327062129974365, - 8.249388694763184 + 5.600379467010498, + 7.487490653991699 ], "pca3d": [ - 0.028612403199076653, - 0.017648978158831596, - 0.10313690453767776 + 0.02870883420109749, + 0.018059059977531433, + 0.10317155718803406 ], "tsne3d": [ - -2.2289364337921143, - -17.708755493164062, - 4.014973163604736 + 0.6941084861755371, + 8.680914878845215, + -1.1301358938217163 ], "umap3d": [ - 4.492259979248047, - 4.167558193206787, - 3.3371899127960205 + 5.015470504760742, + 4.5070109367370605, + 3.167304039001465 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 18, + "id": 13, "label": "Genomics Papers" } ], @@ -506405,31 +506555,31 @@ "cited_by_count": 3, "projections": { "pca": [ - 0.03997746855020523, - -0.15693746507167816 + 0.039595432579517365, + -0.15685279667377472 ], "tsne": [ - 17.033830642700195, - -2.242858648300171 + 18.44740867614746, + 4.51688289642334 ], "umap": [ - 7.797762870788574, - 6.353764533996582 + 8.0665283203125, + 6.062011241912842 ], "pca3d": [ - 0.039977747946977615, - -0.15694276988506317, - 0.06070110201835632 + 0.03959573060274124, + -0.1568577140569687, + 0.06094213202595711 ], "tsne3d": [ - 13.57306957244873, - 7.83714485168457, - -10.28532600402832 + 16.66322898864746, + -1.1150261163711548, + 3.724853515625 ], "umap3d": [ - 6.311360836029053, - 5.2707390785217285, - 2.606916666030884 + 6.211699485778809, + 5.324126720428467, + 1.561135172843933 ] }, "cluster_id": 4, @@ -506440,12 +506590,12 @@ "label": "RNA Sequence Models" }, { - "id": 3, - "label": "RNA Splicing" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -506870,46 +507020,46 @@ "openalex_url": "https://openalex.org/W4403585782", "projections": { "pca": [ - -0.0634346455335617, - 0.3701743185520172 + -0.062405090779066086, + 0.37009936571121216 ], "tsne": [ - 9.104118347167969, - -13.539793968200684 + 2.4796271324157715, + 25.282512664794922 ], "umap": [ - 6.421971797943115, - 8.19400405883789 + 6.615167617797852, + 7.804620742797852 ], "pca3d": [ - -0.06343518942594528, - 0.37015533447265625, - -0.147381991147995 + -0.06240561604499817, + 0.37007951736450195, + -0.14787302911281586 ], "tsne3d": [ - 8.277837753295898, - -18.471004486083984, - -6.309600830078125 + 9.899492263793945, + 18.432891845703125, + -5.466915607452393 ], "umap3d": [ - 5.851653099060059, - 3.148956298828125, - 2.9108638763427734 + 6.4662861824035645, + 3.475586414337158, + 2.626917839050293 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 3, + "label": "Genetic Disease" }, { - "id": 26, + "id": 33, "label": "Genetic Disease" } ], @@ -507327,31 +507477,31 @@ "doi": "10.1101/2024.09.03.611114", "projections": { "pca": [ - 0.18411311507225037, - -0.06008388474583626 + 0.1840023696422577, + -0.060372333973646164 ], "tsne": [ - 26.874479293823242, - -3.677145481109619 + 27.15542221069336, + -0.5813146829605103 ], "umap": [ - 7.882152080535889, - 7.349182605743408 + 8.114665985107422, + 6.993875980377197 ], "pca3d": [ - 0.18411315977573395, - -0.06008173152804375, - -0.0731893852353096 + 0.1840023696422577, + -0.06036997586488724, + -0.07309803366661072 ], "tsne3d": [ - 15.340664863586426, - -2.4985737800598145, - -11.059469223022461 + 14.659523010253906, + 8.55031681060791, + 3.626317024230957 ], "umap3d": [ - 6.76593542098999, - 4.217110633850098, - 2.5936412811279297 + 6.916003227233887, + 4.393803596496582, + 1.6187680959701538 ] }, "cluster_id": 4, @@ -507362,12 +507512,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -507784,31 +507934,31 @@ "doi": "10.1101/2024.09.06.611573", "projections": { "pca": [ - 0.2722614109516144, - 0.0515616312623024 + 0.27228349447250366, + 0.050913792103528976 ], "tsne": [ - 32.52324676513672, - -11.837182998657227 + 27.67247200012207, + 17.579492568969727 ], "umap": [ - 8.84922981262207, - 8.659039497375488 + 9.00903606414795, + 8.264852523803711 ], "pca3d": [ - 0.27226120233535767, - 0.05156430974602699, - -0.1258683055639267 + 0.2722832262516022, + 0.05091670900583267, + -0.12609942257404327 ], "tsne3d": [ - 20.185937881469727, - 7.376378536224365, - 10.250984191894531 + 17.96173667907715, + 9.7982816696167, + -6.757004737854004 ], "umap3d": [ - 6.448600769042969, - 4.2402849197387695, - 1.179642677307129 + 6.026949405670166, + 3.925934314727783, + 0.5722173452377319 ] }, "cluster_id": 0, @@ -507819,12 +507969,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -508240,46 +508390,46 @@ ], "projections": { "pca": [ - 0.005466606933623552, - -0.13670197129249573 + 0.005086875986307859, + -0.13666288554668427 ], "tsne": [ - 22.051223754882812, - -26.89638900756836 + 13.101588249206543, + 27.252662658691406 ], "umap": [ - 7.3714070320129395, - 9.436015129089355 + 7.549886226654053, + 9.25052547454834 ], "pca3d": [ - 0.00546666607260704, - -0.1366739124059677, - -0.061453960835933685 + 0.005086925812065601, + -0.13663437962532043, + -0.061241257935762405 ], "tsne3d": [ - 11.930713653564453, - -15.258760452270508, - 10.349385261535645 + 8.075995445251465, + 4.254719257354736, + -19.740619659423828 ], "umap3d": [ - 5.108058452606201, - 3.8441591262817383, - 1.5258148908615112 + 4.9573564529418945, + 3.695276975631714, + 1.5486758947372437 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -508717,47 +508867,47 @@ "doi": "10.1101/2024.05.05.592437", "projections": { "pca": [ - 0.27377814054489136, - -0.009369340725243092 + 0.2737022042274475, + -0.009719080291688442 ], "tsne": [ - 29.003612518310547, - 9.999139785766602 + 32.61468505859375, + -5.652891635894775 ], "umap": [ - 7.658932685852051, - 7.784807205200195 + 7.907690048217773, + 7.392534255981445 ], "pca3d": [ - 0.2737779915332794, - -0.009390599094331264, - -0.14843329787254333 + 0.2737022042274475, + -0.009740718640387058, + -0.14847318828105927 ], "tsne3d": [ - 15.088593482971191, - -8.228235244750977, - -11.127676010131836 + 15.088242530822754, + 14.005749702453613, + 3.3908615112304688 ], "umap3d": [ - 7.0834455490112305, - 3.683720588684082, - 2.686981678009033 + 7.2875165939331055, + 3.8896358013153076, + 1.8366124629974365 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -509165,31 +509315,31 @@ "journal": "Nature", "projections": { "pca": [ - 0.06081096827983856, - 0.062132250517606735 + 0.06082237884402275, + 0.06219666451215744 ], "tsne": [ - 27.793975830078125, - -10.988972663879395 + 28.168874740600586, + 7.701487064361572 ], "umap": [ - 8.58873462677002, - 7.069494247436523 + 8.714295387268066, + 6.674138069152832 ], "pca3d": [ - 0.060810547322034836, - 0.06216868385672569, - -0.10862741619348526 + 0.06082192808389664, + 0.06223379448056221, + -0.10876040905714035 ], "tsne3d": [ - 15.060178756713867, - 1.5524951219558716, - -3.8068578243255615 + 17.415042877197266, + 1.574344515800476, + -1.367594838142395 ], "umap3d": [ - 6.593539237976074, - 4.909939765930176, - 1.8857334852218628 + 6.347238063812256, + 4.7472429275512695, + 1.0626262426376343 ] }, "cluster_id": 0, @@ -509200,12 +509350,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -509636,31 +509786,31 @@ "doi": "10.1101/2024.05.26.595809", "projections": { "pca": [ - 0.2703820765018463, - 0.054947033524513245 + 0.2704523205757141, + 0.054383985698223114 ], "tsne": [ - 53.505950927734375, - -0.9078742265701294 + 55.40269088745117, + 10.769054412841797 ], "umap": [ - 10.625664710998535, - 7.543684959411621 + 10.750187873840332, + 7.169785022735596 ], "pca3d": [ - 0.27038198709487915, - 0.054911453276872635, - -0.09581686556339264 + 0.27045249938964844, + 0.05434764549136162, + -0.09612468630075455 ], "tsne3d": [ - 34.01565933227539, - -3.2057650089263916, - -2.3915131092071533 + 34.67630386352539, + 6.7306413650512695, + -2.381629467010498 ], "umap3d": [ - 7.791542053222656, - 4.912288188934326, - 0.48345115780830383 + 6.963241100311279, + 4.513785362243652, + -0.7827508449554443 ] }, "cluster_id": 0, @@ -509671,12 +509821,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -510094,31 +510244,31 @@ "doi": "10.1101/2024.08.26.609360", "projections": { "pca": [ - 0.25382739305496216, - 0.024382196366786957 + 0.25388550758361816, + 0.024015963077545166 ], "tsne": [ - 51.74076843261719, - -0.5052956938743591 + 55.0352783203125, + 9.018779754638672 ], "umap": [ - 10.444579124450684, - 7.494345188140869 + 10.623628616333008, + 7.0513834953308105 ], "pca3d": [ - 0.25382715463638306, - 0.024360142648220062, - -0.08573059737682343 + 0.25388553738594055, + 0.02399330586194992, + -0.08590467274188995 ], "tsne3d": [ - 30.29152488708496, - -1.1014796495437622, - 0.4153347611427307 + 33.238311767578125, + 3.553635835647583, + -2.781621217727661 ], "umap3d": [ - 7.900478839874268, - 4.856884479522705, - 0.6555607914924622 + 7.067824840545654, + 4.523377418518066, + -0.6931499242782593 ] }, "cluster_id": 0, @@ -510129,12 +510279,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -510562,31 +510712,31 @@ "cited_by_count": 5, "projections": { "pca": [ - 0.19386553764343262, - -0.11165085434913635 + 0.19364269077777863, + -0.11172418296337128 ], "tsne": [ - 21.488780975341797, - 25.638675689697266 + 25.827234268188477, + -25.663536071777344 ], "umap": [ - 8.174699783325195, - 5.44122838973999 + 8.408031463623047, + 5.001983165740967 ], "pca3d": [ - 0.19386552274227142, - -0.11164497584104538, - 0.02576346881687641 + 0.1936427801847458, + -0.11171744018793106, + 0.02593551017343998 ], "tsne3d": [ - 8.13259220123291, - 20.555179595947266, - 3.282771348953247 + 14.214982986450195, + -11.862009048461914, + 6.615080833435059 ], "umap3d": [ - 7.211308479309082, - 5.544445991516113, - 3.4545788764953613 + 7.257724761962891, + 5.998202323913574, + 1.874727725982666 ] }, "cluster_id": 4, @@ -510597,11 +510747,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -511022,31 +511172,31 @@ "doi": "10.1101/2024.08.26.609645", "projections": { "pca": [ - 0.2707805931568146, - 0.02930394932627678 + 0.2708589732646942, + 0.02867230214178562 ], "tsne": [ - 52.37849044799805, - -9.020371437072754 + 48.49308395385742, + 11.294351577758789 ], "umap": [ - 10.155233383178711, - 7.8015594482421875 + 10.337071418762207, + 7.499766826629639 ], "pca3d": [ - 0.2707805931568146, - 0.029295632615685463, - -0.030164407566189766 + 0.27085888385772705, + 0.02866315096616745, + -0.03041534125804901 ], "tsne3d": [ - 27.807552337646484, - -0.6035139560699463, - -6.190019130706787 + 27.255861282348633, + 5.014845848083496, + -0.8360612392425537 ], "umap3d": [ - 7.131206512451172, - 5.1194748878479, - 0.6998838782310486 + 6.490126609802246, + 4.6587419509887695, + -0.4217323064804077 ] }, "cluster_id": 0, @@ -511057,12 +511207,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -511468,47 +511618,47 @@ ], "projections": { "pca": [ - 0.08555372804403305, - 0.03676120191812515 + 0.0857413038611412, + 0.03675258159637451 ], "tsne": [ - 9.491544723510742, - -17.184473037719727 + 11.059078216552734, + 16.227203369140625 ], "umap": [ - 6.438910484313965, - 7.13366174697876 + 7.682241916656494, + 8.233512878417969 ], "pca3d": [ - 0.08555340021848679, - 0.03677820786833763, - -0.01737740822136402 + 0.08574097603559494, + 0.03676987811923027, + -0.017392771318554878 ], "tsne3d": [ - 8.502832412719727, - -11.143218994140625, - 6.1871256828308105 + 3.3975932598114014, + 3.1423237323760986, + -14.757566452026367 ], "umap3d": [ - 5.548764705657959, - 4.254526615142822, - 2.0898683071136475 + 5.532272815704346, + 4.116838455200195, + 1.7686922550201416 ] }, - "cluster_id": 4, - "cluster_label": "RNA Sequence Models", + "cluster_id": 6, + "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 4, - "label": "RNA Sequence Models" + "id": 6, + "label": "Single-Cell Data" }, { - "id": 11, - "label": "Genomic Models" + "id": 6, + "label": "Single Cell Data" }, { - "id": 11, - "label": "Genomic Language" + "id": 28, + "label": "Single Cell Data" } ], "_embedding": [ @@ -511927,46 +512077,46 @@ "openalex_url": "https://openalex.org/W4401208777", "projections": { "pca": [ - 0.09447159618139267, - 0.09530659019947052 + 0.09480206668376923, + 0.0949082002043724 ], "tsne": [ - 17.39890480041504, - -17.676820755004883 + 7.9834160804748535, + 14.649100303649902 ], "umap": [ - 7.8043389320373535, - 9.198358535766602 + 7.721862316131592, + 8.850074768066406 ], "pca3d": [ - 0.0944710299372673, - 0.09533374011516571, - -0.10495351254940033 + 0.09480153769254684, + 0.09493592381477356, + -0.10506889969110489 ], "tsne3d": [ - 11.841254234313965, - -7.235171794891357, - 16.708158493041992 + 4.726573467254639, + -1.1546740531921387, + -10.465328216552734 ], "umap3d": [ - 5.544485569000244, - 3.803741693496704, - 1.6374924182891846 + 5.427033424377441, + 3.5095012187957764, + 1.5930613279342651 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -512391,31 +512541,31 @@ "doi": "10.1101/2024.06.04.596709", "projections": { "pca": [ - 0.11739721149206161, - -0.06847083568572998 + 0.11739248037338257, + -0.06820537149906158 ], "tsne": [ - 14.694841384887695, - 15.874565124511719 + 36.162540435791016, + -20.70335578918457 ], "umap": [ - 7.001171112060547, - 6.362772464752197 + 7.23961067199707, + 6.078368663787842 ], "pca3d": [ - 0.11739735305309296, - -0.06848224252462387, - 0.1055181473493576 + 0.1173926293849945, + -0.06821645051240921, + 0.10572166740894318 ], "tsne3d": [ - 3.810730457305908, - 12.621549606323242, - -12.947016716003418 + 3.365459680557251, + 5.07163143157959, + 7.069843292236328 ], "umap3d": [ - 6.311271667480469, - 4.739872455596924, - 3.549478054046631 + 6.637462139129639, + 5.1287336349487305, + 2.542764186859131 ] }, "cluster_id": 4, @@ -512426,11 +512576,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -512853,47 +513003,47 @@ "doi": "10.1101/2024.08.16.607277", "projections": { "pca": [ - 0.13557007908821106, - -0.0019029214745387435 + 0.13545718789100647, + -0.002057109959423542 ], "tsne": [ - 32.94676971435547, - -18.24180793762207 + 25.53143310546875, + 24.500646591186523 ], "umap": [ - 8.58436393737793, - 9.57703971862793 + 8.819684982299805, + 9.021510124206543 ], "pca3d": [ - 0.13557003438472748, - -0.0019011915428563952, - -0.08443908393383026 + 0.1354571431875229, + -0.0020557953976094723, + -0.08458372205495834 ], "tsne3d": [ - 19.806774139404297, - 12.085841178894043, - 3.4461467266082764 + 13.991377830505371, + -6.048696994781494, + -12.078680038452148 ], "umap3d": [ - 5.357729911804199, - 4.650087356567383, - 1.0205345153808594 + 4.944446563720703, + 4.182659149169922, + 0.8519386053085327 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -513301,47 +513451,47 @@ "journal": "Nature Biotechnology", "projections": { "pca": [ - 0.09150263667106628, - -0.012311100959777832 + 0.09160280227661133, + -0.01263415813446045 ], "tsne": [ - 37.94221878051758, - -23.145904541015625 + 27.705808639526367, + 30.146650314331055 ], "umap": [ - 8.643365859985352, - 8.986387252807617 + 8.796735763549805, + 8.610969543457031 ], "pca3d": [ - 0.09150253981351852, - -0.012308357283473015, - -0.06337635219097137 + 0.09160271286964417, + -0.012630507349967957, + -0.06326647102832794 ], "tsne3d": [ - 22.87287712097168, - 15.113219261169434, - 6.476418495178223 + 15.25600528717041, + -9.911578178405762, + -15.380972862243652 ], "umap3d": [ - 5.881227970123291, - 4.5628557205200195, - 1.2294635772705078 + 5.575956344604492, + 4.4317474365234375, + 0.8366575241088867 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -513765,31 +513915,31 @@ "doi": "10.1101/2024.08.05.606702", "projections": { "pca": [ - 0.3168535530567169, - -0.00547007704153657 + 0.31682366132736206, + -0.006075344979763031 ], "tsne": [ - 52.663997650146484, - 5.440473556518555 + 51.10483932495117, + 17.601022720336914 ], "umap": [ - 9.998710632324219, - 7.0082316398620605 + 10.250679016113281, + 6.665343761444092 ], "pca3d": [ - 0.3168533146381378, - -0.005474735517054796, - -0.09643451124429703 + 0.3168235719203949, + -0.006080294959247112, + -0.09660598635673523 ], "tsne3d": [ - 27.26388931274414, - 2.3933322429656982, - 0.39466339349746704 + 28.083942413330078, + 1.3375098705291748, + -5.812561511993408 ], "umap3d": [ - 8.085505485534668, - 5.022042751312256, - 1.181540846824646 + 7.458127498626709, + 4.844529151916504, + -0.30820712447166443 ] }, "cluster_id": 0, @@ -513800,12 +513950,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -514226,31 +514376,31 @@ "cited_by_count": 109, "projections": { "pca": [ - 0.26305025815963745, - 0.02354891039431095 + 0.26296788454055786, + 0.022845763713121414 ], "tsne": [ - 45.28092575073242, - 17.34449577331543 + 24.223630905151367, + 15.681306838989258 ], "umap": [ - 8.957329750061035, - 8.275053024291992 + 9.13623046875, + 8.096405982971191 ], "pca3d": [ - 0.2630499303340912, - 0.023577963933348656, - -0.10883632302284241 + 0.26296746730804443, + 0.022875549271702766, + -0.10911543667316437 ], "tsne3d": [ - 15.58681583404541, - -2.20843505859375, - 7.546949863433838 + 14.323583602905273, + 1.961177110671997, + -9.992201805114746 ], "umap3d": [ - 6.60037088394165, - 4.2423906326293945, - 1.1517366170883179 + 6.285703182220459, + 3.9957973957061768, + 0.44717735052108765 ] }, "cluster_id": 0, @@ -514261,12 +514411,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -514676,47 +514826,47 @@ "journal": "Nature Methods", "projections": { "pca": [ - -0.092339888215065, - 0.21444149315357208 + -0.09159351885318756, + 0.21544231474399567 ], "tsne": [ - -4.243995189666748, - -26.288475036621094 + -8.196619987487793, + 22.144214630126953 ], "umap": [ - 4.409281253814697, - 8.3172025680542 + 4.463983535766602, + 7.714232921600342 ], "pca3d": [ - -0.09234046190977097, - 0.21445325016975403, - 0.13581407070159912 + -0.09159406274557114, + 0.21545368432998657, + 0.13568103313446045 ], "tsne3d": [ - -0.34767261147499084, - -10.445612907409668, - -1.3314640522003174 + -2.133679151535034, + 18.411081314086914, + -2.429098129272461 ], "umap3d": [ - 3.546391725540161, - 3.457289695739746, - 3.5244030952453613 + 4.230816841125488, + 3.9926609992980957, + 3.817481756210327 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 17, - "label": "Medical AI" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -515145,47 +515295,47 @@ "openalex_url": "https://openalex.org/W4400002042", "projections": { "pca": [ - 0.12493699043989182, - 0.07499044388532639 + 0.1252906769514084, + 0.07424484193325043 ], "tsne": [ - 32.23445510864258, - 19.737367630004883 + 26.58992576599121, + 21.53921127319336 ], "umap": [ - 8.412129402160645, - 8.864168167114258 + 8.565338134765625, + 8.409332275390625 ], "pca3d": [ - 0.12493643909692764, - 0.07502533495426178, - -0.007755257189273834 + 0.1252901256084442, + 0.07428015768527985, + -0.007956952787935734 ], "tsne3d": [ - 19.671110153198242, - 12.99222183227539, - 9.463196754455566 + 15.445791244506836, + -5.17489767074585, + -15.236478805541992 ], "umap3d": [ - 6.022619724273682, - 4.581894397735596, - 1.35873544216156 + 5.7159247398376465, + 4.382878303527832, + 0.8776450753211975 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -515608,31 +515758,31 @@ "openalex_url": "https://openalex.org/W4394688742", "projections": { "pca": [ - 0.21809208393096924, - 0.12418899685144424 + 0.21848008036613464, + 0.12405091524124146 ], "tsne": [ - 38.90637969970703, - 7.156428337097168 + 38.37547302246094, + 1.4731733798980713 ], "umap": [ - 9.128762245178223, - 7.5540595054626465 + 9.348348617553711, + 7.336879253387451 ], "pca3d": [ - 0.2180916965007782, - 0.12418366968631744, - 0.03256798908114433 + 0.21847975254058838, + 0.12404491752386093, + 0.032308608293533325 ], "tsne3d": [ - 23.9569149017334, - -0.469640851020813, - 13.174264907836914 + 17.083038330078125, + -8.923141479492188, + 0.7933375835418701 ], "umap3d": [ - 6.736395835876465, - 5.204294681549072, - 1.3993114233016968 + 6.406650066375732, + 4.738048553466797, + 0.528040885925293 ] }, "cluster_id": 0, @@ -515643,12 +515793,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 20, - "label": "Chromatin Regulation" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -516058,47 +516208,47 @@ "journal": "Nature Methods", "projections": { "pca": [ - -0.030072897672653198, - 0.034338705241680145 + -0.029672447592020035, + 0.035043928772211075 ], "tsne": [ - 0.3960941433906555, - -25.678592681884766 + -10.814166069030762, + 23.464988708496094 ], "umap": [ - 5.041461944580078, - 8.51750373840332 + 5.288166522979736, + 7.689499855041504 ], "pca3d": [ - -0.030073238536715508, - 0.034358132630586624, - 0.12856769561767578 + -0.02967277355492115, + 0.03506350517272949, + 0.12884606420993805 ], "tsne3d": [ - -0.5724824070930481, - -12.482518196105957, - -6.009075164794922 + -0.5164570808410645, + 7.726726055145264, + 0.26622089743614197 ], "umap3d": [ - 4.206599235534668, - 3.8459694385528564, - 3.228867292404175 + 4.790698051452637, + 4.198309898376465, + 3.272249460220337 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, - "label": "Medical AI" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -516504,47 +516654,47 @@ ], "projections": { "pca": [ - -0.07029779255390167, - 0.36044973134994507 + -0.0691763162612915, + 0.36028677225112915 ], "tsne": [ - -7.850741863250732, - -5.131627082824707 + -6.368161678314209, + 4.504364967346191 ], "umap": [ - 4.49447774887085, - 7.416210174560547 + 4.761609077453613, + 6.572603225708008 ], "pca3d": [ - -0.07029859721660614, - 0.3604816198348999, - 0.0490177758038044 + -0.06917719542980194, + 0.3603188097476959, + 0.04846707731485367 ], "tsne3d": [ - -7.3350067138671875, - -3.0570156574249268, - 15.982093811035156 + -4.5613484382629395, + 0.2604016065597534, + -8.769940376281738 ], "umap3d": [ - 4.042205810546875, - 3.6673262119293213, - 4.627348899841309 + 5.052605152130127, + 4.679777145385742, + 4.714163780212402 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -516967,31 +517117,31 @@ "openalex_url": "https://openalex.org/W3111795212", "projections": { "pca": [ - 0.013367016799747944, - -0.02501458115875721 + 0.013187042437493801, + -0.02498444728553295 ], "tsne": [ - 21.309608459472656, - -16.585927963256836 + 16.66025161743164, + 18.296180725097656 ], "umap": [ - 3.2587103843688965, - 5.495765686035156 + 3.367563247680664, + 5.1072540283203125 ], "pca3d": [ - 0.013366933912038803, - -0.024996306747198105, - -0.12975569069385529 + 0.013186979107558727, + -0.024965310469269753, + -0.12973245978355408 ], "tsne3d": [ - 10.549190521240234, - -2.563194513320923, - 6.827822208404541 + 10.33245849609375, + 0.3263702690601349, + -9.238664627075195 ], "umap3d": [ - 5.124515056610107, - 4.393894195556641, - 2.136120319366455 + 5.196556568145752, + 4.093475341796875, + 1.8212116956710815 ] }, "cluster_id": 2, @@ -517002,12 +517152,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 16, - "label": "Diffusion Models" + "id": 7, + "label": "Diffusion Gen" } ], "_embedding": [ @@ -517420,31 +517570,31 @@ "journal": "Journal of Machine Learning Research", "projections": { "pca": [ - -0.2073948234319687, - -0.02630491554737091 + -0.2074657529592514, + -0.0261578056961298 ], "tsne": [ - -38.10235595703125, - 6.211740970611572 + -33.50217056274414, + -11.46379280090332 ], "umap": [ - 2.262711763381958, - 5.991304874420166 + 2.7989437580108643, + 5.38363790512085 ], "pca3d": [ - -0.20739522576332092, - -0.02626819722354412, - -0.1339627206325531 + -0.2074659764766693, + -0.026119545102119446, + -0.13379134237766266 ], "tsne3d": [ - -20.78290367126465, - 1.5630037784576416, - -12.48361873626709 + -15.770153045654297, + -15.46278190612793, + -6.569095611572266 ], "umap3d": [ - 2.0497732162475586, - 5.878270149230957, - 3.788447856903076 + 2.5162253379821777, + 5.970498085021973, + 4.056696891784668 ] }, "cluster_id": 2, @@ -517455,12 +517605,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 33, - "label": "Neural Networks" + "id": 19, + "label": "Graph Neural Nets" } ], "_embedding": [ @@ -517866,46 +518016,46 @@ ], "projections": { "pca": [ - 0.07528044283390045, - 0.1079249382019043 + 0.07582477480173111, + 0.10778692364692688 ], "tsne": [ - 2.1768248081207275, - -7.077445983886719 + 2.1124320030212402, + 6.164126873016357 ], "umap": [ - 5.308789253234863, - 7.423943042755127 + 5.5429277420043945, + 6.694122791290283 ], "pca3d": [ - 0.07527998089790344, - 0.1079430803656578, - -0.04992096871137619 + 0.0758243128657341, + 0.10780556499958038, + -0.0499119907617569 ], "tsne3d": [ - 0.4895974099636078, - -4.212190628051758, - -19.51557159423828 + 3.6384084224700928, + 25.23711585998535, + -3.026621103286743 ], "umap3d": [ - 4.853817939758301, - 3.338785409927368, - 5.014185428619385 + 5.915887355804443, + 4.632984161376953, + 4.9109930992126465 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, + "id": 13, "label": "Genomics Papers" } ], @@ -518327,31 +518477,31 @@ "doi": "10.1101/2024.08.07.605876", "projections": { "pca": [ - 0.21815679967403412, - -0.07091698795557022 + 0.21794089674949646, + -0.07143586874008179 ], "tsne": [ - 38.23221206665039, - -3.648732900619507 + 31.94390296936035, + 11.532767295837402 ], "umap": [ - 9.154600143432617, - 7.567747592926025 + 9.342309951782227, + 7.088354587554932 ], "pca3d": [ - 0.218156635761261, - -0.0709003135561943, - -0.1396397352218628 + 0.2179408073425293, + -0.07141873240470886, + -0.1396082043647766 ], "tsne3d": [ - 19.261871337890625, - -2.1168129444122314, - -0.9721392393112183 + 22.035083770751953, + 3.796320676803589, + -4.248519420623779 ], "umap3d": [ - 7.102226257324219, - 4.462069988250732, - 1.4989715814590454 + 6.775835037231445, + 4.353561878204346, + 0.5656934380531311 ] }, "cluster_id": 0, @@ -518362,11 +518512,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -518790,31 +518940,31 @@ "doi": "10.1101/2024.07.27.605418", "projections": { "pca": [ - 0.26517027616500854, - -0.07922405749559402 + 0.2650413513183594, + -0.07968320697546005 ], "tsne": [ - 19.305709838867188, - 23.812231063842773 + 23.9852294921875, + -24.32547378540039 ], "umap": [ - 7.9228715896606445, - 5.464452266693115 + 8.255389213562012, + 5.039036750793457 ], "pca3d": [ - 0.2651701867580414, - -0.07921735942363739, - 0.05315924808382988 + 0.26504141092300415, + -0.07967600971460342, + 0.053183961659669876 ], "tsne3d": [ - 8.792181968688965, - 18.060495376586914, - -0.1101890280842781 + 11.439224243164062, + -8.557764053344727, + 5.664312362670898 ], "umap3d": [ - 7.041402339935303, - 5.50631856918335, - 3.4201178550720215 + 7.05359411239624, + 5.911952972412109, + 1.953076958656311 ] }, "cluster_id": 4, @@ -518825,11 +518975,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -519245,31 +519395,31 @@ ], "projections": { "pca": [ - 0.04424808546900749, - -0.08595465123653412 + 0.04406195506453514, + -0.0861150398850441 ], "tsne": [ - 23.127071380615234, - 11.424799919128418 + 26.37441635131836, + -7.619313716888428 ], "umap": [ - 7.31797456741333, - 7.4168806076049805 + 7.4009480476379395, + 7.025789737701416 ], "pca3d": [ - 0.044248465448617935, - -0.08597781509160995, - -0.11458303779363632 + 0.044062353670597076, + -0.08613843470811844, + -0.11436262726783752 ], "tsne3d": [ - 10.837040901184082, - -5.79641056060791, - -16.718812942504883 + 10.123943328857422, + 12.232634544372559, + 8.706537246704102 ], "umap3d": [ - 6.701170444488525, - 3.8636794090270996, - 3.1950995922088623 + 7.1567840576171875, + 4.319609642028809, + 2.306036949157715 ] }, "cluster_id": 4, @@ -519280,12 +519430,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -519706,31 +519856,31 @@ "doi": "10.1101/2024.07.05.602265", "projections": { "pca": [ - 0.17631180584430695, - -0.0838928371667862 + 0.1760694533586502, + -0.08445211499929428 ], "tsne": [ - 27.96775245666504, - -5.571204662322998 + 26.97873878479004, + 9.447307586669922 ], "umap": [ - 8.322600364685059, - 7.304837226867676 + 8.784440994262695, + 6.962070465087891 ], "pca3d": [ - 0.17631177604198456, - -0.08389608561992645, - -0.1834661066532135 + 0.1760694980621338, + -0.08445543795824051, + -0.18336151540279388 ], "tsne3d": [ - 18.62162971496582, - -0.22083911299705505, - -12.330236434936523 + 15.157370567321777, + 6.948497295379639, + -0.08043155819177628 ], "umap3d": [ - 6.701682090759277, - 4.493332862854004, - 1.9924591779708862 + 6.5588202476501465, + 4.437564849853516, + 1.0339535474777222 ] }, "cluster_id": 0, @@ -519741,12 +519891,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -520164,31 +520314,31 @@ "doi": "10.1101/2023.01.30.526198", "projections": { "pca": [ - 0.06045978143811226, - -0.10968291014432907 + 0.060383182018995285, + -0.10974401235580444 ], "tsne": [ - 11.326781272888184, - 25.864377975463867 + 19.089759826660156, + -21.65304183959961 ], "umap": [ - 7.2565178871154785, - 4.6069865226745605 + 7.702582359313965, + 4.4632110595703125 ], "pca3d": [ - 0.060459934175014496, - -0.10968288034200668, - 0.091899074614048 + 0.06038334220647812, + -0.10974382609128952, + 0.09213430434465408 ], "tsne3d": [ - 3.690598726272583, - 19.49419593811035, - -5.129560470581055 + 8.749397277832031, + -10.116466522216797, + 11.327893257141113 ], "umap3d": [ - 6.692436218261719, - 5.918076515197754, - 4.355993270874023 + 6.8877997398376465, + 6.770740985870361, + 2.637876033782959 ] }, "cluster_id": 4, @@ -520199,11 +520349,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -520626,31 +520776,31 @@ "doi": "10.1101/2024.06.26.600749", "projections": { "pca": [ - 0.2951112985610962, - -0.05179033800959587 + 0.29495781660079956, + -0.05231368541717529 ], "tsne": [ - 56.964420318603516, - 5.035758972167969 + 51.46699523925781, + 19.133358001708984 ], "umap": [ - 10.187270164489746, - 6.670351505279541 + 10.386112213134766, + 6.246763706207275 ], "pca3d": [ - 0.29511135816574097, - -0.051790568977594376, - -0.011321122758090496 + 0.29495769739151, + -0.05231371894478798, + -0.01144958846271038 ], "tsne3d": [ - 27.248910903930664, - 5.665809631347656, - -2.8362908363342285 + 28.20079231262207, + -2.1815996170043945, + -1.1538211107254028 ], "umap3d": [ - 8.049267768859863, - 5.454174518585205, - 1.2579975128173828 + 7.394874572753906, + 5.187405586242676, + -0.35806581377983093 ] }, "cluster_id": 0, @@ -520661,12 +520811,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -521081,31 +521231,31 @@ ], "projections": { "pca": [ - 0.07209231704473495, - -0.0447227843105793 + 0.0721968486905098, + -0.0443401075899601 ], "tsne": [ - 5.867727756500244, - 6.787514686584473 + 8.114213943481445, + -5.093456268310547 ], "umap": [ - 6.25286865234375, - 6.630695343017578 + 6.511661529541016, + 6.249366283416748 ], "pca3d": [ - 0.07209239900112152, - -0.04472466930747032, - 0.10485059767961502 + 0.07219690084457397, + -0.04434166103601456, + 0.10513302683830261 ], "tsne3d": [ - 2.763023853302002, - 2.896801233291626, - -11.750410079956055 + 1.3982611894607544, + 4.199021816253662, + 0.9922924637794495 ], "umap3d": [ - 5.314537048339844, - 4.557790279388428, - 3.632021903991699 + 5.792983055114746, + 4.944757461547852, + 3.0848100185394287 ] }, "cluster_id": 4, @@ -521116,12 +521266,12 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 11, - "label": "Genomic Language" + "id": 17, + "label": "Genomic Models" } ], "_embedding": [ @@ -521529,31 +521679,31 @@ "journal": "Nature", "projections": { "pca": [ - 0.2822486162185669, - 0.11996523290872574 + 0.28266164660453796, + 0.11892889440059662 ], "tsne": [ - 39.316619873046875, - -5.681928634643555 + 39.80275344848633, + 12.519325256347656 ], "umap": [ - 9.213391304016113, - 8.194321632385254 + 9.556724548339844, + 7.766350269317627 ], "pca3d": [ - 0.2822480797767639, - 0.11996520310640335, - -0.138950914144516 + 0.2826613187789917, + 0.1189289316534996, + -0.13922372460365295 ], "tsne3d": [ - 20.83914566040039, - -6.570479869842529, - 1.7728921175003052 + 23.308719635009766, + 7.9019269943237305, + -7.707016944885254 ], "umap3d": [ - 7.309535026550293, - 4.09152889251709, - 1.2517606019973755 + 6.811263084411621, + 3.745615005493164, + 0.3565707802772522 ] }, "cluster_id": 0, @@ -521564,12 +521714,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -521975,46 +522125,46 @@ ], "projections": { "pca": [ - 0.11030411720275879, - 0.10923957079648972 + 0.11094725131988525, + 0.10887666046619415 ], "tsne": [ - 1.348507285118103, - -7.4139275550842285 + 1.3007973432540894, + 6.345594882965088 ], "umap": [ - 5.286765098571777, - 7.383504390716553 + 5.484659671783447, + 6.646790981292725 ], "pca3d": [ - 0.11030364781618118, - 0.10925208032131195, - -0.015500780194997787 + 0.11094672232866287, + 0.10888972878456116, + -0.015472909435629845 ], "tsne3d": [ - -0.48640093207359314, - -3.112041711807251, - -19.881013870239258 + 2.459059238433838, + 24.89393424987793, + -3.8818602561950684 ], "umap3d": [ - 4.8208746910095215, - 3.335237503051758, - 5.016315937042236 + 5.893353462219238, + 4.62662410736084, + 4.959488868713379 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, + "id": 13, "label": "Genomics Papers" } ], @@ -522431,31 +522581,31 @@ "doi": "10.1145/3638529.3653990", "projections": { "pca": [ - 0.10098864883184433, - -0.14348198473453522 + 0.10067568719387054, + -0.14312462508678436 ], "tsne": [ - 6.301726341247559, - 12.503022193908691 + 12.327425003051758, + -11.663212776184082 ], "umap": [ - 7.006207466125488, - 5.698324203491211 + 7.101741790771484, + 5.431556701660156 ], "pca3d": [ - 0.10098887234926224, - -0.14347957074642181, - 0.08263539522886276 + 0.10067591816186905, + -0.1431218385696411, + 0.08292759954929352 ], "tsne3d": [ - 1.9692546129226685, - 5.424169540405273, - -5.727996349334717 + 9.260497093200684, + 1.500105857849121, + 2.0079283714294434 ], "umap3d": [ - 5.873735427856445, - 5.464244842529297, - 3.5268614292144775 + 6.030304431915283, + 5.767063140869141, + 2.2421987056732178 ] }, "cluster_id": 4, @@ -522466,11 +522616,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -522893,31 +523043,31 @@ "doi": "10.1101/2023.05.30.542889", "projections": { "pca": [ - 0.23413591086864471, - 0.03105476312339306 + 0.23418927192687988, + 0.030148688703775406 ], "tsne": [ - 39.48579025268555, - -10.547327041625977 + 36.53703689575195, + 16.83008575439453 ], "umap": [ - 9.220911979675293, - 8.449380874633789 + 9.450565338134766, + 7.943949222564697 ], "pca3d": [ - 0.23413582146167755, - 0.031058818101882935, - -0.163758784532547 + 0.23418916761875153, + 0.03015323169529438, + -0.16392864286899567 ], "tsne3d": [ - 21.476999282836914, - -3.3948662281036377, - 7.031531810760498 + 20.248226165771484, + 6.430188179016113, + -10.712373733520508 ], "umap3d": [ - 7.230136394500732, - 3.9347894191741943, - 1.3105533123016357 + 6.948493003845215, + 3.709284782409668, + 0.40417835116386414 ] }, "cluster_id": 0, @@ -522928,12 +523078,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -523356,31 +523506,31 @@ ], "projections": { "pca": [ - 0.08443289250135422, - -0.11660370975732803 + 0.08420766144990921, + -0.11675604432821274 ], "tsne": [ - 17.75127410888672, - 30.824106216430664 + 21.158288955688477, + -29.378671646118164 ], "umap": [ - 7.9944257736206055, - 4.79518461227417 + 8.249054908752441, + 4.30687952041626 ], "pca3d": [ - 0.0844329372048378, - -0.11658859997987747, - 0.040019821375608444 + 0.08420775085687637, + -0.11673999577760696, + 0.040219634771347046 ], "tsne3d": [ - 3.996946334838867, - 22.669025421142578, - 2.436434268951416 + 12.751612663269043, + -15.364079475402832, + 5.94851541519165 ], "umap3d": [ - 7.23487663269043, - 6.031946659088135, - 3.76678729057312 + 7.31132698059082, + 6.594191551208496, + 2.090552806854248 ] }, "cluster_id": 4, @@ -523391,11 +523541,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -523816,46 +523966,46 @@ "doi": "10.1101/2023.11.28.568918", "projections": { "pca": [ - 0.11351969093084335, - -0.03268640860915184 + 0.11355607211589813, + -0.03270181640982628 ], "tsne": [ - 20.773330688476562, - -25.574081420898438 + 11.934028625488281, + 25.710691452026367 ], "umap": [ - 7.394885540008545, - 9.404082298278809 + 7.520907878875732, + 9.255196571350098 ], "pca3d": [ - 0.11351964622735977, - -0.03269154950976372, - -0.0868196114897728 + 0.11355600506067276, + -0.03270731121301651, + -0.08664581179618835 ], "tsne3d": [ - 11.115279197692871, - -12.599411964416504, - 11.241291046142578 + 7.727946758270264, + 6.5772881507873535, + -17.642311096191406 ], "umap3d": [ - 5.330411911010742, - 3.5263307094573975, - 1.5119273662567139 + 5.3307294845581055, + 3.344362258911133, + 1.5465697050094604 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -524278,31 +524428,31 @@ "cited_by_count": 25, "projections": { "pca": [ - 0.26026543974876404, - 0.10025220364332199 + 0.2605254650115967, + 0.09982025623321533 ], "tsne": [ - 47.27326965332031, - 6.272809982299805 + 47.808902740478516, + 2.4659693241119385 ], "umap": [ - 9.678730010986328, - 7.240497589111328 + 9.91581916809082, + 6.896711826324463 ], "pca3d": [ - 0.26026517152786255, - 0.10025665163993835, - -0.06491952389478683 + 0.26052504777908325, + 0.0998242124915123, + -0.06523953378200531 ], "tsne3d": [ - 23.834062576293945, - 5.0072174072265625, - 5.122115612030029 + 28.467256546020508, + -1.2690023183822632, + -10.983595848083496 ], "umap3d": [ - 7.887779235839844, - 4.529207706451416, - 1.4068423509597778 + 7.452056884765625, + 4.427984237670898, + 0.024631567299365997 ] }, "cluster_id": 0, @@ -524313,12 +524463,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 20, - "label": "Chromatin Regulation" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -524740,31 +524890,31 @@ "cited_by_count": 10, "projections": { "pca": [ - 0.1669922024011612, - -0.07083750516176224 + 0.16681832075119019, + -0.07098369300365448 ], "tsne": [ - 49.65207290649414, - -8.995622634887695 + 46.681121826171875, + 10.893569946289062 ], "umap": [ - 10.158622741699219, - 7.9582085609436035 + 10.361302375793457, + 7.578617095947266 ], "pca3d": [ - 0.16699229180812836, - -0.07084084302186966, - -0.047020211815834045 + 0.16681841015815735, + -0.07098782062530518, + -0.046991799026727676 ], "tsne3d": [ - 26.280569076538086, - -1.353430151939392, - -8.019000053405762 + 26.327049255371094, + 5.989590644836426, + 1.2296085357666016 ], "umap3d": [ - 7.029076099395752, - 5.065485954284668, - 0.6556960940361023 + 6.361203193664551, + 4.634838104248047, + -0.3852859437465668 ] }, "cluster_id": 0, @@ -524775,12 +524925,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -525196,31 +525346,31 @@ "doi": "10.1101/2024.05.20.594948", "projections": { "pca": [ - 0.22527208924293518, - 0.04115181416273117 + 0.22532260417938232, + 0.0411897636950016 ], "tsne": [ - 45.45873260498047, - 11.719442367553711 + 53.034080505371094, + 1.2502726316452026 ], "umap": [ - 8.900463104248047, - 6.786039352416992 + 9.116106986999512, + 6.523347854614258 ], "pca3d": [ - 0.22527195513248444, - 0.04113871976733208, - 0.08254241943359375 + 0.22532247006893158, + 0.041176218539476395, + 0.08227208256721497 ], "tsne3d": [ - 17.422779083251953, - 7.359810829162598, - -2.5560169219970703 + 21.415002822875977, + 3.484304904937744, + 6.682875156402588 ], "umap3d": [ - 7.324831008911133, - 5.150422096252441, - 2.0601887702941895 + 6.935815334320068, + 5.020700454711914, + 0.729110598564148 ] }, "cluster_id": 0, @@ -525231,11 +525381,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -525656,31 +525806,31 @@ "doi": "10.1101/2024.06.04.597340", "projections": { "pca": [ - 0.18563780188560486, - -0.0739838108420372 + 0.18530529737472534, + -0.07445727288722992 ], "tsne": [ - 44.751068115234375, - 1.510837197303772 + 33.59388732910156, + 11.210838317871094 ], "umap": [ - 9.293489456176758, - 7.445401191711426 + 9.588592529296875, + 6.951005458831787 ], "pca3d": [ - 0.18563774228096008, - -0.0739734023809433, - -0.10984068363904953 + 0.18530523777008057, + -0.07444638013839722, + -0.10988064855337143 ], "tsne3d": [ - 21.02065658569336, - 0.9099997878074646, - 0.04432089999318123 + 21.855316162109375, + 0.610203206539154, + -6.639406681060791 ], "umap3d": [ - 7.630092144012451, - 4.55465030670166, - 1.5655537843704224 + 7.235215187072754, + 4.396949291229248, + 0.33126479387283325 ] }, "cluster_id": 0, @@ -525691,11 +525841,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -526125,46 +526275,46 @@ "doi": "10.1101/2024.06.04.597369", "projections": { "pca": [ - 0.11013311892747879, - -0.03646368533372879 + 0.1101125106215477, + -0.036427803337574005 ], "tsne": [ - 20.872011184692383, - -21.43579864501953 + 13.499876022338867, + 21.755701065063477 ], "umap": [ - 7.5144500732421875, - 9.42569637298584 + 7.443458557128906, + 8.976906776428223 ], "pca3d": [ - 0.11013299971818924, - -0.03645009547472, - -0.05449889600276947 + 0.11011242866516113, + -0.036413803696632385, + -0.05434723198413849 ], "tsne3d": [ - 9.876639366149902, - -7.814085006713867, - 8.8294095993042 + 8.041648864746094, + 4.045990467071533, + -12.740861892700195 ], "umap3d": [ - 5.20353889465332, - 3.7901506423950195, - 1.5017048120498657 + 5.081513404846191, + 3.5615298748016357, + 1.586156964302063 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -526573,46 +526723,46 @@ "journal": "Nature Methods", "projections": { "pca": [ - 0.0794028714299202, - -0.08575087785720825 + 0.07928824424743652, + -0.08601413667201996 ], "tsne": [ - 22.79189109802246, - -21.635202407836914 + 15.111087799072266, + 22.800254821777344 ], "umap": [ - 7.698861598968506, - 9.499971389770508 + 7.7223920822143555, + 9.241073608398438 ], "pca3d": [ - 0.07940264046192169, - -0.0857125073671341, - -0.11970604956150055 + 0.07928794622421265, + -0.08597459644079208, + -0.11943832784891129 ], "tsne3d": [ - 12.448594093322754, - -7.468932151794434, - 11.603203773498535 + 9.252689361572266, + 1.4768255949020386, + -15.21717357635498 ], "umap3d": [ - 5.252169609069824, - 3.6832242012023926, - 1.3493318557739258 + 5.155506134033203, + 3.388023853302002, + 1.3803805112838745 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -527021,31 +527171,31 @@ "journal": "Nature", "projections": { "pca": [ - 0.12114731222391129, - 0.08441456407308578 + 0.12136862426996231, + 0.08388382941484451 ], "tsne": [ - 64.7269058227539, - 1.0730173587799072 + 49.354148864746094, + 27.210407257080078 ], "umap": [ - 9.620094299316406, - 6.64619779586792 + 9.811222076416016, + 6.092353820800781 ], "pca3d": [ - 0.12114691734313965, - 0.08443710207939148, - -0.10862944275140762 + 0.12136814743280411, + 0.0839066430926323, + -0.1088118925690651 ], "tsne3d": [ - 27.59321403503418, - 9.48533821105957, - -10.854975700378418 + 24.26822853088379, + -8.60285758972168, + -1.5757014751434326 ], "umap3d": [ - 7.497657775878906, - 5.344752788543701, - 1.5687793493270874 + 7.039709091186523, + 5.055263996124268, + 0.4199196696281433 ] }, "cluster_id": 0, @@ -527056,12 +527206,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -527480,31 +527630,31 @@ "doi": "10.1101/2024.05.28.596138", "projections": { "pca": [ - 0.21219390630722046, - -0.1301327645778656 + 0.21179412305355072, + -0.13069285452365875 ], "tsne": [ - 50.346580505371094, - 11.688301086425781 + 45.318946838378906, + 20.1519718170166 ], "umap": [ - 9.417099952697754, - 6.62302303314209 + 9.549560546875, + 6.230255603790283 ], "pca3d": [ - 0.21219392120838165, - -0.1301220953464508, - -0.044330764561891556 + 0.2117941826581955, + -0.1306820511817932, + -0.04431008920073509 ], "tsne3d": [ - 21.84513282775879, - 5.732702255249023, - -5.684988498687744 + 23.61164665222168, + 0.24857421219348907, + 3.1436729431152344 ], "umap3d": [ - 7.282402038574219, - 5.456749439239502, - 1.6449825763702393 + 6.679654598236084, + 5.324043273925781, + 0.37622591853141785 ] }, "cluster_id": 0, @@ -527515,12 +527665,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -527944,47 +528094,47 @@ "openalex_url": "https://openalex.org/W4399123386", "projections": { "pca": [ - 0.09845803678035736, - 0.007501510437577963 + 0.09839680790901184, + 0.00676185917109251 ], "tsne": [ - 27.880277633666992, - 12.702184677124023 + 28.658611297607422, + -5.223423957824707 ], "umap": [ - 7.51745080947876, - 7.626134872436523 + 7.656394958496094, + 7.185708522796631 ], "pca3d": [ - 0.09845808893442154, - 0.007477405946701765, - -0.20988181233406067 + 0.09839693456888199, + 0.0067375339567661285, + -0.20994815230369568 ], "tsne3d": [ - 14.612386703491211, - -7.560312747955322, - -13.634225845336914 + 14.891963005065918, + 13.521303176879883, + 5.790855407714844 ], "umap3d": [ - 6.932928562164307, - 3.7442352771759033, - 2.939812183380127 + 7.294186592102051, + 4.1099629402160645, + 2.058429718017578 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -528404,31 +528554,31 @@ "doi": "10.1101/2024.05.28.596078", "projections": { "pca": [ - 0.22433336079120636, - -0.10487136244773865 + 0.22399412095546722, + -0.10556820780038834 ], "tsne": [ - 52.20867919921875, - 4.119187355041504 + 49.320709228515625, + 16.86419677734375 ], "umap": [ - 9.943737030029297, - 6.932889938354492 + 10.128232955932617, + 6.572616100311279 ], "pca3d": [ - 0.22433334589004517, - -0.1048642098903656, - -0.11693811416625977 + 0.22399429976940155, + -0.10556123405694962, + -0.11697769910097122 ], "tsne3d": [ - 24.397846221923828, - 3.804405689239502, - -1.014451265335083 + 25.575225830078125, + -0.31123194098472595, + -4.114933967590332 ], "umap3d": [ - 7.9387407302856445, - 5.084591388702393, - 1.3111815452575684 + 7.383419036865234, + 4.837162017822266, + -0.12227693200111389 ] }, "cluster_id": 0, @@ -528439,12 +528589,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -528869,31 +529019,31 @@ "openalex_url": "https://openalex.org/W4394579804", "projections": { "pca": [ - 0.21823807060718536, - 0.004366384819149971 + 0.21839872002601624, + 0.0036518541164696217 ], "tsne": [ - 24.40049171447754, - 33.96471405029297 + 26.68247413635254, + -32.92953109741211 ], "umap": [ - 8.435269355773926, - 4.957940578460693 + 8.822649002075195, + 4.586902141571045 ], "pca3d": [ - 0.2182374894618988, - 0.004410842899233103, - 0.047355469316244125 + 0.2183981090784073, + 0.0036979203578084707, + 0.04727359488606453 ], "tsne3d": [ - 5.720442295074463, - 27.321880340576172, - 2.267336130142212 + 15.951979637145996, + -19.658266067504883, + 4.830325126647949 ], "umap3d": [ - 7.579568862915039, - 5.884613990783691, - 3.529928207397461 + 7.513695240020752, + 6.396801948547363, + 1.6641038656234741 ] }, "cluster_id": 4, @@ -528904,12 +529054,12 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, - "label": "RNA Splicing" + "id": 8, + "label": "RNA Structure" } ], "_embedding": [ @@ -529331,47 +529481,47 @@ "openalex_url": "https://openalex.org/W4398231714", "projections": { "pca": [ - 0.10818985849618912, - 0.050995200872421265 + 0.10836711525917053, + 0.05085194483399391 ], "tsne": [ - 26.12406349182129, - -29.66024398803711 + 40.80644226074219, + 1.166974663734436 ], "umap": [ - 7.787517547607422, - 8.834157943725586 + 7.905757904052734, + 8.450582504272461 ], "pca3d": [ - 0.10818959027528763, - 0.05099239945411682, - -0.03159106522798538 + 0.10836692154407501, + 0.05084867775440216, + -0.03172510489821434 ], "tsne3d": [ - 18.958566665649414, - -2.5694472789764404, - 14.0303955078125 + 21.25984001159668, + -4.120135307312012, + -14.815164566040039 ], "umap3d": [ - 5.916004180908203, - 3.8165602684020996, - 1.5653462409973145 + 5.819459438323975, + 3.733567237854004, + 1.175937533378601 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 12, - "label": "Genetic Variants" + "id": 28, + "label": "Single Cell Data" } ], "_embedding": [ @@ -530024,31 +530174,31 @@ "openalex_url": "https://openalex.org/W4398250017", "projections": { "pca": [ - 0.22948884963989258, - 0.037028536200523376 + 0.22955334186553955, + 0.03621307387948036 ], "tsne": [ - 41.835777282714844, - -5.1214919090271 + 41.408905029296875, + 8.372416496276855 ], "umap": [ - 9.438326835632324, - 8.199527740478516 + 9.686058044433594, + 7.752580165863037 ], "pca3d": [ - 0.22948864102363586, - 0.037022851407527924, - -0.17275598645210266 + 0.2295532077550888, + 0.036206964403390884, + -0.1729327142238617 ], "tsne3d": [ - 22.44893455505371, - -8.660585403442383, - -4.039679050445557 + 24.428728103637695, + 11.582651138305664, + -4.068084239959717 ], "umap3d": [ - 7.147928237915039, - 4.1433258056640625, - 0.9934587478637695 + 6.675397872924805, + 3.7907047271728516, + 0.2120923548936844 ] }, "cluster_id": 0, @@ -530059,12 +530209,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -530482,31 +530632,31 @@ ], "projections": { "pca": [ - 0.06967607885599136, - -0.10183099657297134 + 0.06959348917007446, + -0.10189556330442429 ], "tsne": [ - 7.40472412109375, - 10.674458503723145 + 9.537020683288574, + -8.090761184692383 ], "umap": [ - 6.88380765914917, - 5.739223003387451 + 7.029582977294922, + 5.454679489135742 ], "pca3d": [ - 0.06967628747224808, - -0.10184062272310257, - 0.04308612272143364 + 0.06959379464387894, + -0.10190480202436447, + 0.04335631802678108 ], "tsne3d": [ - 5.909195423126221, - 7.26282262802124, - -4.255361080169678 + 4.305089950561523, + -2.418759822845459, + 0.41849371790885925 ], "umap3d": [ - 5.7115020751953125, - 5.108328819274902, - 3.8254354000091553 + 6.104996204376221, + 5.537448883056641, + 2.7682862281799316 ] }, "cluster_id": 4, @@ -530517,11 +530667,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -530945,31 +531095,31 @@ "cited_by_count": 12, "projections": { "pca": [ - 0.1330406218767166, - -0.08903180062770844 + 0.13276880979537964, + -0.08908098936080933 ], "tsne": [ - 25.675189971923828, - -12.708269119262695 + 19.987239837646484, + 13.83703327178955 ], "umap": [ - 8.522415161132812, - 7.578350067138672 + 8.909335136413574, + 7.3864264488220215 ], "pca3d": [ - 0.1330406218767166, - -0.08901618421077728, - -0.046634044498205185 + 0.13276877999305725, + -0.08906583487987518, + -0.046564627438783646 ], "tsne3d": [ - 14.62025260925293, - 11.804529190063477, - 12.912485122680664 + 15.845280647277832, + -7.5941386222839355, + -1.9460346698760986 ], "umap3d": [ - 6.288801670074463, - 4.919687747955322, - 1.5357383489608765 + 5.897654056549072, + 4.621625900268555, + 0.863979160785675 ] }, "cluster_id": 0, @@ -530980,12 +531130,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -531402,31 +531552,31 @@ ], "projections": { "pca": [ - 0.06601007282733917, - -0.15047535300254822 + 0.06557314097881317, + -0.15026240050792694 ], "tsne": [ - 15.575129508972168, - 5.475574970245361 + 16.77644157409668, + -1.7378462553024292 ], "umap": [ - 7.350610733032227, - 6.355731964111328 + 7.553300380706787, + 5.863218307495117 ], "pca3d": [ - 0.06601019948720932, - -0.15046335756778717, - 0.0017516475636512041 + 0.06557333469390869, + -0.15024995803833008, + 0.0019819142762571573 ], "tsne3d": [ - 7.6392717361450195, - 6.45789098739624, - -13.71542739868164 + 9.600208282470703, + 0.689992368221283, + 8.662117004394531 ], "umap3d": [ - 5.900947570800781, - 5.092629909515381, - 3.1195387840270996 + 6.037493705749512, + 5.330923080444336, + 2.041529893875122 ] }, "cluster_id": 4, @@ -531437,11 +531587,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -531868,31 +532018,31 @@ "openalex_url": "https://openalex.org/W4394681169", "projections": { "pca": [ - 0.14011512696743011, - -0.1212674155831337 + 0.13978850841522217, + -0.12127036601305008 ], "tsne": [ - 21.61469268798828, - -1.6031478643417358 + 23.992237091064453, + 4.357194423675537 ], "umap": [ - 8.12125301361084, - 6.685743808746338 + 8.355657577514648, + 6.329550266265869 ], "pca3d": [ - 0.14011524617671967, - -0.12126386165618896, - -0.07926201075315475 + 0.13978853821754456, + -0.12126702070236206, + -0.07905517518520355 ], "tsne3d": [ - 18.118558883666992, - 3.369951009750366, - -10.349150657653809 + 14.374554634094238, + 3.061624050140381, + 2.8062949180603027 ], "umap3d": [ - 6.616555213928223, - 4.912554740905762, - 2.417263984680176 + 6.516078948974609, + 5.044363498687744, + 1.3392202854156494 ] }, "cluster_id": 4, @@ -531903,12 +532053,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -532324,31 +532474,31 @@ "journal": "OpenReview", "projections": { "pca": [ - 0.11138857156038284, - -0.1634323000907898 + 0.11094009876251221, + -0.1633642017841339 ], "tsne": [ - 15.72846794128418, - -2.4896585941314697 + 16.848163604736328, + 4.769667148590088 ], "umap": [ - 8.32922649383545, - 6.28480339050293 + 8.496952056884766, + 5.972018718719482 ], "pca3d": [ - 0.1113886684179306, - -0.16341887414455414, - -0.01664598658680916 + 0.11094020307064056, + -0.16335050761699677, + -0.016423065215349197 ], "tsne3d": [ - 15.862338066101074, - 8.884114265441895, - -12.497894287109375 + 17.28658103942871, + -1.8886525630950928, + 6.05053186416626 ], "umap3d": [ - 6.578847885131836, - 5.407268524169922, - 2.3380112648010254 + 6.353898048400879, + 5.4573974609375, + 1.1757924556732178 ] }, "cluster_id": 4, @@ -532359,12 +532509,12 @@ "label": "RNA Sequence Models" }, { - "id": 3, - "label": "RNA Splicing" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -532792,47 +532942,47 @@ "cited_by_count": 21, "projections": { "pca": [ - 0.07225699722766876, - -0.013689951971173286 + 0.07237685471773148, + -0.013102887198328972 ], "tsne": [ - -0.803889274597168, - -22.433008193969727 + -3.544680595397949, + 18.813655853271484 ], "umap": [ - 4.71652364730835, - 8.552709579467773 + 5.034461498260498, + 7.863958835601807 ], "pca3d": [ - 0.07225701957941055, - -0.013695472851395607, - 0.15112338960170746 + 0.07237687706947327, + -0.013108652085065842, + 0.15128830075263977 ], "tsne3d": [ - -0.028679076582193375, - -8.27059555053711, - -5.525556564331055 + -0.4301000237464905, + 12.641683578491211, + 1.5778653621673584 ], "umap3d": [ - 3.961059808731079, - 3.9939026832580566, - 2.9249696731567383 + 4.465022087097168, + 4.231926441192627, + 3.1050221920013428 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, - "label": "Medical AI" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -533252,31 +533402,31 @@ "openalex_url": "https://openalex.org/W4395447528", "projections": { "pca": [ - 0.11356200277805328, - -0.035865359008312225 + 0.11348667740821838, + -0.03595641255378723 ], "tsne": [ - 49.536922454833984, - 12.936565399169922 + 43.82600784301758, + 20.941865921020508 ], "umap": [ - 9.389704704284668, - 6.605952739715576 + 9.526674270629883, + 6.139881610870361 ], "pca3d": [ - 0.11356177181005478, - -0.035839010030031204, - -0.0016214693896472454 + 0.1134863868355751, + -0.03592996299266815, + -0.0016335654072463512 ], "tsne3d": [ - 20.426851272583008, - 8.206503868103027, - -6.122145175933838 + 24.805654525756836, + -0.34820857644081116, + 5.981143474578857 ], "umap3d": [ - 7.305383682250977, - 5.440766334533691, - 1.7099822759628296 + 6.7318854331970215, + 5.3474555015563965, + 0.40289804339408875 ] }, "cluster_id": 0, @@ -533287,12 +533437,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 2, + "label": "DNA Regulation" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -533716,31 +533866,31 @@ "openalex_url": "https://openalex.org/W4395447546", "projections": { "pca": [ - 0.210007444024086, - -0.05042065307497978 + 0.20980918407440186, + -0.05059406906366348 ], "tsne": [ - 49.23445510864258, - 12.207489013671875 + 44.109554290771484, + 20.226913452148438 ], "umap": [ - 9.391752243041992, - 6.644833564758301 + 9.69375991821289, + 6.286413669586182 ], "pca3d": [ - 0.2100072205066681, - -0.050411567091941833, - -0.0273544080555439 + 0.2098091095685959, + -0.05058486387133598, + -0.027402374893426895 ], "tsne3d": [ - 20.45758056640625, - 7.500551700592041, - -4.726743698120117 + 24.12909507751465, + 0.9255524277687073, + 5.427682876586914 ], "umap3d": [ - 7.351994514465332, - 5.390656471252441, - 1.6230992078781128 + 6.760889530181885, + 5.304361343383789, + 0.3434905707836151 ] }, "cluster_id": 0, @@ -533751,12 +533901,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -534185,31 +534335,31 @@ "doi": "10.1101/2024.04.17.589795", "projections": { "pca": [ - 0.14155642688274384, - 0.002076858188956976 + 0.14151014387607574, + 0.0015187272801995277 ], "tsne": [ - 45.49049758911133, - -8.454535484313965 + 43.19741439819336, + 9.732048034667969 ], "umap": [ - 9.596587181091309, - 8.17412281036377 + 9.921443939208984, + 7.710399150848389 ], "pca3d": [ - 0.14155633747577667, - 0.0020759354811161757, - -0.1552852988243103 + 0.1415100395679474, + 0.001517581520602107, + -0.15537534654140472 ], "tsne3d": [ - 22.87235450744629, - -5.361326694488525, - -8.081581115722656 + 23.590015411376953, + 9.77355670928955, + 0.5429719686508179 ], "umap3d": [ - 6.856687068939209, - 4.557159900665283, - 0.9483271241188049 + 6.356240749359131, + 4.2105865478515625, + -0.03823179751634598 ] }, "cluster_id": 0, @@ -534220,12 +534370,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -534648,31 +534798,31 @@ "cited_by_count": 76, "projections": { "pca": [ - 0.2382582724094391, - 0.018918968737125397 + 0.23826384544372559, + 0.01814458891749382 ], "tsne": [ - 45.78205490112305, - 1.323384404182434 + 36.4256706237793, + 12.75466537475586 ], "umap": [ - 9.582109451293945, - 7.596811771392822 + 9.843567848205566, + 7.093433380126953 ], "pca3d": [ - 0.23825806379318237, - 0.018919318914413452, - -0.12054941803216934 + 0.23826350271701813, + 0.018144827336072922, + -0.12075390666723251 ], "tsne3d": [ - 21.290283203125, - -0.749247133731842, - 1.4115488529205322 + 23.405847549438477, + -1.9051213264465332, + -7.563158988952637 ], "umap3d": [ - 7.766948699951172, - 4.386441707611084, - 1.3801236152648926 + 7.425167083740234, + 4.266192436218262, + 0.08677885681390762 ] }, "cluster_id": 0, @@ -534683,12 +534833,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 20, - "label": "Chromatin Regulation" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -535111,31 +535261,31 @@ "doi": "10.1101/2023.10.11.561938", "projections": { "pca": [ - 0.10830330848693848, - -0.09813365340232849 + 0.10818509757518768, + -0.09832268953323364 ], "tsne": [ - 11.691011428833008, - 25.90838050842285 + 19.38630485534668, + -21.995574951171875 ], "umap": [ - 7.297567367553711, - 4.608688831329346 + 7.723250389099121, + 4.457716941833496 ], "pca3d": [ - 0.10830320417881012, - -0.09812022745609283, - 0.09750614315271378 + 0.10818513482809067, + -0.09830904006958008, + 0.09766305983066559 ], "tsne3d": [ - 4.927971839904785, - 19.44886589050293, - -5.0152411460876465 + 9.650696754455566, + -10.773775100708008, + 10.895672798156738 ], "umap3d": [ - 6.7130446434021, - 5.925178050994873, - 4.337560176849365 + 6.906088829040527, + 6.760852813720703, + 2.622378349304199 ] }, "cluster_id": 4, @@ -535146,11 +535296,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -535581,31 +535731,31 @@ "cited_by_count": 11, "projections": { "pca": [ - 0.1398233324289322, - -0.1207982674241066 + 0.139583557844162, + -0.12096182256937027 ], "tsne": [ - 16.71509552001953, - 31.972278594970703 + 19.975008010864258, + -30.276288986206055 ], "umap": [ - 7.871044158935547, - 4.607296466827393 + 8.235661506652832, + 4.208075523376465 ], "pca3d": [ - 0.1398233026266098, - -0.120781809091568, - 0.1839590072631836 + 0.13958357274532318, + -0.12094439566135406, + 0.18404029309749603 ], "tsne3d": [ - 2.4552202224731445, - 23.740915298461914, - 4.4295735359191895 + 10.577800750732422, + -16.94792366027832, + 5.5345964431762695 ], "umap3d": [ - 7.165791034698486, - 6.213996410369873, - 3.963689088821411 + 7.30352258682251, + 6.715194225311279, + 2.208618402481079 ] }, "cluster_id": 4, @@ -535616,11 +535766,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -536045,47 +536195,47 @@ "doi": "10.1101/2024.04.04.588111", "projections": { "pca": [ - 0.16506427526474, - 0.05134989693760872 + 0.16526912152767181, + 0.05071774497628212 ], "tsne": [ - 22.66958236694336, - -17.225322723388672 + 17.258806228637695, + 19.142391204833984 ], "umap": [ - 8.046445846557617, - 9.031909942626953 + 8.080219268798828, + 8.799539566040039 ], "pca3d": [ - 0.16506390273571014, - 0.05137147754430771, - -0.13731645047664642 + 0.16526876389980316, + 0.050740037113428116, + -0.13739320635795593 ], "tsne3d": [ - 12.12478256225586, - -4.048205852508545, - 7.58181619644165 + 11.12134075164795, + 1.8174012899398804, + -10.76673412322998 ], "umap3d": [ - 5.761590480804443, - 3.9333603382110596, - 1.4393121004104614 + 5.459105968475342, + 3.640761137008667, + 1.2813490629196167 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -536503,31 +536653,31 @@ ], "projections": { "pca": [ - 0.07578995078802109, - -0.11280228197574615 + 0.07567339390516281, + -0.11318617314100266 ], "tsne": [ - 15.41835880279541, - 32.12842559814453 + 17.847396850585938, + -30.058679580688477 ], "umap": [ - 7.807351589202881, - 4.5383782386779785 + 8.181427001953125, + 4.096284866333008 ], "pca3d": [ - 0.07579018920660019, - -0.11280135810375214, - 0.17304807901382446 + 0.07567358762025833, + -0.11318451166152954, + 0.17311924695968628 ], "tsne3d": [ - -0.06442191451787949, - 23.476139068603516, - 4.485315322875977 + 9.541467666625977, + -18.457260131835938, + 4.523942947387695 ], "umap3d": [ - 7.090920448303223, - 6.333521842956543, - 4.054035186767578 + 7.340427875518799, + 6.847134590148926, + 2.2433087825775146 ] }, "cluster_id": 4, @@ -536538,11 +536688,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -536951,31 +537101,31 @@ "abstract": "Biological sequence analysis for the modern age. Contribute to jmschrei/tangermeme development by creating an account on GitHub.", "projections": { "pca": [ - 0.016121409833431244, - 0.14750556647777557 + 0.01686844788491726, + 0.14764773845672607 ], "tsne": [ - 18.547000885009766, - -6.4789042472839355 + 17.133960723876953, + 1.449055552482605 ], "umap": [ - 6.719117164611816, - 5.486441612243652 + 7.027618408203125, + 5.0514302253723145 ], "pca3d": [ - 0.016121087595820427, - 0.14750467240810394, - 0.10161934047937393 + 0.01686815731227398, + 0.14764642715454102, + 0.10157686471939087 ], "tsne3d": [ - 11.602156639099121, - 15.397090911865234, - -11.396782875061035 + 13.492687225341797, + -3.171351671218872, + 3.2801451683044434 ], "umap3d": [ - 5.635469436645508, - 5.335717678070068, - 4.1338701248168945 + 6.249687194824219, + 5.679794788360596, + 2.8520469665527344 ] }, "cluster_id": 4, @@ -536986,11 +537136,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -537414,47 +537564,47 @@ "doi": "10.1101/2024.03.23.586383", "projections": { "pca": [ - 0.09552688896656036, - -0.019619131460785866 + 0.09556356817483902, + -0.01926153525710106 ], "tsne": [ - 15.179117202758789, - -20.578630447387695 + 9.879173278808594, + 20.545652389526367 ], "umap": [ - 5.921154499053955, - 7.9408063888549805 + 6.158733367919922, + 7.329543113708496 ], "pca3d": [ - 0.09552670270204544, - -0.01960509456694126, - 0.037952929735183716 + 0.0955633893609047, + -0.019247692078351974, + 0.038116879761219025 ], "tsne3d": [ - 6.101564884185791, - -8.398426055908203, - 7.2741570472717285 + 1.1003775596618652, + 6.972886085510254, + -0.3253055810928345 ], "umap3d": [ - 4.88487434387207, - 4.2492241859436035, - 3.1496732234954834 + 5.2525858879089355, + 4.487723350524902, + 2.9047107696533203 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 3, + "label": "Genetic Disease" }, { - "id": 26, - "label": "Genetic Disease" + "id": 13, + "label": "Genomics Papers" } ], "_embedding": [ @@ -537872,31 +538022,31 @@ "doi": "10.1101/2024.03.29.587323", "projections": { "pca": [ - 0.24461285769939423, - -0.05025608092546463 + 0.24440336227416992, + -0.05087995156645775 ], "tsne": [ - 45.13994216918945, - 0.1109858974814415 + 33.286231994628906, + 12.316167831420898 ], "umap": [ - 9.331477165222168, - 7.431659698486328 + 9.730195045471191, + 6.881497383117676 ], "pca3d": [ - 0.24461275339126587, - -0.05023887753486633, - -0.15099672973155975 + 0.2444031685590744, + -0.05086272582411766, + -0.15106579661369324 ], "tsne3d": [ - 20.795881271362305, - 3.0640060901641846, - 1.1472254991531372 + 20.70096778869629, + -1.50442636013031, + -5.983344554901123 ], "umap3d": [ - 7.678740501403809, - 4.542006492614746, - 1.496780276298523 + 7.321818828582764, + 4.336575031280518, + 0.1911430060863495 ] }, "cluster_id": 0, @@ -537907,11 +538057,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -538320,46 +538470,46 @@ "journal": "Nature Reviews Genetics", "projections": { "pca": [ - 0.024805806577205658, - 0.3200041353702545 + 0.02560575120151043, + 0.3200104534626007 ], "tsne": [ - 24.67470359802246, - 8.206700325012207 + 35.118797302246094, + -14.845322608947754 ], "umap": [ - 6.6430816650390625, - 7.772653102874756 + 6.841952323913574, + 7.426489353179932 ], "pca3d": [ - 0.024805111810564995, - 0.31999853253364563, - -0.08835349977016449 + 0.025605060160160065, + 0.3200043737888336, + -0.08882241696119308 ], "tsne3d": [ - 10.303312301635742, - -12.991867065429688, - -16.520124435424805 + 9.63711166381836, + 22.106491088867188, + 2.5615861415863037 ], "umap3d": [ - 6.44394588470459, - 3.2356531620025635, - 3.13100528717041 + 7.01572847366333, + 3.661832809448242, + 2.5723352432250977 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 26, + "id": 33, "label": "Genetic Disease" } ], @@ -538781,31 +538931,31 @@ "cited_by_count": 4, "projections": { "pca": [ - 0.09716449677944183, - -0.049332596361637115 + 0.0971992164850235, + -0.04879285395145416 ], "tsne": [ - 2.3373725414276123, - 18.15056610107422 + 9.558670043945312, + -30.442583084106445 ], "umap": [ - 5.53448486328125, - 4.732705593109131 + 6.021336555480957, + 4.21298360824585 ], "pca3d": [ - 0.09716452658176422, - -0.04932961240410805, - 0.23521415889263153 + 0.09719928354024887, + -0.04879006743431091, + 0.2353021800518036 ], "tsne3d": [ - -1.8283944129943848, - 4.941247463226318, - -2.040372371673584 + 2.713423490524292, + -17.292020797729492, + -5.645465850830078 ], "umap3d": [ - 5.426511287689209, - 6.543115139007568, - 4.319645404815674 + 5.645341873168945, + 7.087138652801514, + 2.7944867610931396 ] }, "cluster_id": 3, @@ -538816,11 +538966,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -539239,31 +539389,31 @@ "doi": "10.1101/2024.03.22.586363", "projections": { "pca": [ - 0.16536261141300201, - -0.07472318410873413 + 0.16506430506706238, + -0.07475406676530838 ], "tsne": [ - 26.810754776000977, - 22.399137496948242 + 32.31917953491211, + -25.25895118713379 ], "umap": [ - 8.503223419189453, - 5.667198657989502 + 8.777036666870117, + 5.347949504852295 ], "pca3d": [ - 0.1653626263141632, - -0.07472403347492218, - -0.03858676180243492 + 0.16506437957286835, + -0.07475445419549942, + -0.038549911230802536 ], "tsne3d": [ - 13.208109855651855, - 24.529682159423828, - 4.700766563415527 + 18.957563400268555, + -10.61286449432373, + 10.266829490661621 ], "umap3d": [ - 7.608355522155762, - 5.045425891876221, - 3.2268176078796387 + 7.688333034515381, + 5.443094253540039, + 1.5804122686386108 ] }, "cluster_id": 4, @@ -539274,11 +539424,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -539699,31 +539849,31 @@ "doi": "10.1101/2024.03.20.585793", "projections": { "pca": [ - 0.13945244252681732, - -0.1347162425518036 + 0.1391606479883194, + -0.13464488089084625 ], "tsne": [ - 22.526718139648438, - 25.21477508544922 + 27.097652435302734, + -25.525541305541992 ], "umap": [ - 8.38833236694336, - 5.555366039276123 + 8.51128101348877, + 5.151436805725098 ], "pca3d": [ - 0.13945242762565613, - -0.1347009241580963, - 0.02202921360731125 + 0.13916079699993134, + -0.13462898135185242, + 0.0222505871206522 ], "tsne3d": [ - 9.33967113494873, - 21.515798568725586, - 4.244794845581055 + 15.259666442871094, + -11.002264976501465, + 7.726497173309326 ], "umap3d": [ - 7.4211225509643555, - 5.272127628326416, - 3.307037353515625 + 7.460026741027832, + 5.758123397827148, + 1.6746909618377686 ] }, "cluster_id": 4, @@ -539734,11 +539884,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -540153,31 +540303,31 @@ "doi": "10.1101/2024.03.19.585716", "projections": { "pca": [ - 0.049873244017362595, - -0.06224740296602249 + 0.050000160932540894, + -0.06217609718441963 ], "tsne": [ - 2.179030656814575, - 12.45514965057373 + 12.786434173583984, + -15.46541976928711 ], "umap": [ - 6.211454391479492, - 5.672220706939697 + 6.476217269897461, + 5.381675720214844 ], "pca3d": [ - 0.04987350106239319, - -0.06226951256394386, - 0.138799250125885 + 0.05000042915344238, + -0.0621982142329216, + 0.1390148550271988 ], "tsne3d": [ - -1.241186261177063, - 3.047556161880493, - -6.170960903167725 + 4.57818078994751, + -2.4661307334899902, + 10.805943489074707 ], "umap3d": [ - 5.7179460525512695, - 5.736208915710449, - 3.838684320449829 + 5.715039253234863, + 6.100365161895752, + 2.5816619396209717 ] }, "cluster_id": 4, @@ -540188,12 +540338,12 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, - "label": "Genomic Models" + "id": 32, + "label": "Genome Protein" } ], "_embedding": [ @@ -540609,31 +540759,31 @@ "cited_by_count": 16, "projections": { "pca": [ - 0.2124936431646347, - -0.09905871003866196 + 0.21219250559806824, + -0.09955599904060364 ], "tsne": [ - 50.21897506713867, - 11.766609191894531 + 45.157936096191406, + 20.174137115478516 ], "umap": [ - 9.455069541931152, - 6.591745376586914 + 9.628544807434082, + 6.183199405670166 ], "pca3d": [ - 0.21249352395534515, - -0.09904500097036362, - -0.004023800138384104 + 0.21219255030155182, + -0.09954213351011276, + -0.004059505183249712 ], "tsne3d": [ - 21.81529998779297, - 6.457830429077148, - -5.457147121429443 + 24.097505569458008, + 0.35114073753356934, + 3.688675880432129 ], "umap3d": [ - 7.350349426269531, - 5.469185829162598, - 1.5948700904846191 + 6.725518226623535, + 5.335237979888916, + 0.2700916528701782 ] }, "cluster_id": 0, @@ -540644,12 +540794,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -541065,31 +541215,31 @@ ], "projections": { "pca": [ - 0.1369248330593109, - -0.10224032402038574 + 0.13692152500152588, + -0.10225679725408554 ], "tsne": [ - 11.336344718933105, - 14.108677864074707 + 15.076004028320312, + -8.317753791809082 ], "umap": [ - 6.975492000579834, - 5.9360456466674805 + 7.318485736846924, + 5.606293201446533 ], "pca3d": [ - 0.13692516088485718, - -0.10225971043109894, - 0.10194769501686096 + 0.1369219273328781, + -0.10227600485086441, + 0.1022031307220459 ], "tsne3d": [ - 3.1674997806549072, - 7.763648509979248, - -10.365814208984375 + 7.0533013343811035, + -2.4870517253875732, + 6.896214008331299 ], "umap3d": [ - 6.139684200286865, - 5.1778340339660645, - 3.5197594165802 + 6.300248146057129, + 5.555878639221191, + 2.4655282497406006 ] }, "cluster_id": 4, @@ -541100,11 +541250,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -541519,31 +541669,31 @@ "doi": "10.1038/d41586-024-00575-x", "projections": { "pca": [ - 0.2172483652830124, - 0.15803897380828857 + 0.21772406995296478, + 0.1579226702451706 ], "tsne": [ - 43.91519546508789, - 8.59405517578125 + 49.27256393432617, + -0.47226881980895996 ], "umap": [ - 9.15533447265625, - 6.969923973083496 + 9.302870750427246, + 6.587015628814697 ], "pca3d": [ - 0.21724769473075867, - 0.15805184841156006, - -0.016057517379522324 + 0.21772347390651703, + 0.15793564915657043, + -0.016355736181139946 ], "tsne3d": [ - 18.397388458251953, - 6.841969966888428, - 2.0841996669769287 + 23.93143653869629, + -6.220421314239502, + -11.528511047363281 ], "umap3d": [ - 7.314934730529785, - 4.905239582061768, - 1.9406073093414307 + 6.868180751800537, + 4.775257110595703, + 0.5811474919319153 ] }, "cluster_id": 0, @@ -541554,11 +541704,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -541969,31 +542119,31 @@ "journal": "Nature", "projections": { "pca": [ - 0.17843478918075562, - 0.24149273335933685 + 0.1790757179260254, + 0.2412496656179428 ], "tsne": [ - 43.720455169677734, - 9.222939491271973 + 49.86630630493164, + -0.05910059064626694 ], "umap": [ - 9.205770492553711, - 7.088203430175781 + 9.405261993408203, + 6.592358589172363 ], "pca3d": [ - 0.17843416333198547, - 0.2415056675672531, - 0.031684961169958115 + 0.17907504737377167, + 0.2412622720003128, + 0.03115350939333439 ], "tsne3d": [ - 19.6123046875, - 7.77382230758667, - 0.9649065732955933 + 25.58515167236328, + -6.813635349273682, + -12.311205863952637 ], "umap3d": [ - 7.279262542724609, - 4.952932357788086, - 1.902961015701294 + 6.8403544425964355, + 4.759715557098389, + 0.5341991782188416 ] }, "cluster_id": 0, @@ -542004,11 +542154,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -542423,46 +542573,46 @@ ], "projections": { "pca": [ - -0.12339048832654953, - -0.15955369174480438 + -0.12367549538612366, + -0.1588074117898941 ], "tsne": [ - -32.18708801269531, - -2.602180004119873 + -40.41869354248047, + 3.930720806121826 ], "umap": [ - 0.967681884765625, - 8.24018669128418 + 1.0861529111862183, + 7.504771709442139 ], "pca3d": [ - -0.12338994443416595, - -0.15956304967403412, - 0.04693576693534851 + -0.12367496639490128, + -0.15881659090518951, + 0.047370824962854385 ], "tsne3d": [ - -19.225568771362305, - -2.3876452445983887, - 12.64696979522705 + -24.335193634033203, + 9.80114459991455, + 2.310760259628296 ], "umap3d": [ - 1.1406561136245728, - 4.749835968017578, - 5.220435619354248 + 2.3504137992858887, + 5.359752655029297, + 5.970181465148926 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -542882,47 +543032,47 @@ "doi": "10.1101/2024.02.27.582086", "projections": { "pca": [ - 0.0640033483505249, - -0.06046035513281822 + 0.06374573707580566, + -0.060340218245983124 ], "tsne": [ - 22.67950439453125, - 3.9058985710144043 + 32.54355239868164, + -11.415643692016602 ], "umap": [ - 7.160306930541992, - 7.430864334106445 + 7.301361083984375, + 7.106357574462891 ], "pca3d": [ - 0.0640033483505249, - -0.060459550470113754, - -0.10537797957658768 + 0.06374572217464447, + -0.06033938750624657, + -0.10528935492038727 ], "tsne3d": [ - 12.672239303588867, - -16.230791091918945, - -14.09164810180664 + 6.09221076965332, + 14.4310302734375, + 0.3184128701686859 ], "umap3d": [ - 6.410579204559326, - 3.7880430221557617, - 3.001452684402466 + 6.81752872467041, + 4.0732421875, + 2.289008855819702 ] }, - "cluster_id": 4, - "cluster_label": "RNA Sequence Models", + "cluster_id": 6, + "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 4, - "label": "RNA Sequence Models" + "id": 6, + "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -543341,31 +543491,31 @@ "doi": "10.1101/2024.02.25.581982", "projections": { "pca": [ - -0.03358643129467964, - -0.14124277234077454 + -0.033869192004203796, + -0.14090657234191895 ], "tsne": [ - 6.917640209197998, - 5.305434226989746 + 9.039337158203125, + -3.635073661804199 ], "umap": [ - 6.628553867340088, - 6.005838394165039 + 6.9801859855651855, + 5.849888324737549 ], "pca3d": [ - -0.03358616679906845, - -0.14123313128948212, - 0.04184719920158386 + -0.03386890888214111, + -0.14089643955230713, + 0.04214375838637352 ], "tsne3d": [ - 4.585187911987305, - 3.410332202911377, - -15.105692863464355 + 3.2308762073516846, + 3.221529483795166, + -0.21533754467964172 ], "umap3d": [ - 5.6268110275268555, - 4.946986675262451, - 3.376542329788208 + 6.160846710205078, + 5.248114585876465, + 2.75142765045166 ] }, "cluster_id": 4, @@ -543376,11 +543526,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -543806,46 +543956,46 @@ "openalex_url": "https://openalex.org/W4392242857", "projections": { "pca": [ - 0.08505380153656006, - -0.07565760612487793 + 0.08491400629281998, + -0.0759635716676712 ], "tsne": [ - 23.59821891784668, - -26.783004760742188 + 14.837272644042969, + 27.372671127319336 ], "umap": [ - 7.486156463623047, - 9.502005577087402 + 7.653918266296387, + 9.1317720413208 ], "pca3d": [ - 0.08505380153656006, - -0.07564696669578552, - -0.1337052881717682 + 0.08491403609514236, + -0.07595221698284149, + -0.13355538249015808 ], "tsne3d": [ - 14.35252571105957, - -13.192721366882324, - 7.810581684112549 + 6.804617404937744, + 1.4152414798736572, + -19.40476417541504 ], "umap3d": [ - 5.38655424118042, - 3.633007526397705, - 1.4109452962875366 + 5.257975101470947, + 3.2403969764709473, + 1.5173546075820923 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -544266,31 +544416,31 @@ "cited_by_count": 1, "projections": { "pca": [ - 0.17031165957450867, - -0.07248330116271973 + 0.16999322175979614, + -0.07247978448867798 ], "tsne": [ - 43.9037971496582, - -19.003833770751953 + 40.84843444824219, + 23.718212127685547 ], "umap": [ - 8.82613468170166, - 6.661185264587402 + 9.20336627960205, + 6.410024642944336 ], "pca3d": [ - 0.17031165957450867, - -0.0724794864654541, - -0.06341071426868439 + 0.16999328136444092, + -0.07247619330883026, + -0.06341950595378876 ], "tsne3d": [ - 21.560863494873047, - 3.0232768058776855, - -10.774130821228027 + 21.933185577392578, + -2.588745355606079, + 5.409409046173096 ], "umap3d": [ - 6.930295467376709, - 5.25722599029541, - 1.7213935852050781 + 6.540314674377441, + 5.109469413757324, + 0.583170473575592 ] }, "cluster_id": 0, @@ -544301,12 +544451,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 1, - "label": "Regulatory Genomics" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -544723,31 +544873,31 @@ "doi": "10.1101/2024.02.15.580353", "projections": { "pca": [ - 0.04756724834442139, - -0.08933708816766739 + 0.047286611050367355, + -0.08877985179424286 ], "tsne": [ - 14.02687931060791, - -1.9281352758407593 + 15.163778305053711, + 4.048883438110352 ], "umap": [ - 8.112688064575195, - 6.200243949890137 + 8.39097785949707, + 5.825129508972168 ], "pca3d": [ - 0.04756729677319527, - -0.08933635801076889, - -0.06722559779882431 + 0.047286730259656906, + -0.08877956122159958, + -0.06704510003328323 ], "tsne3d": [ - 15.16038990020752, - 10.689942359924316, - -14.056836128234863 + 17.357213973999023, + -3.502397060394287, + 7.731316566467285 ], "umap3d": [ - 6.461451053619385, - 5.452433109283447, - 2.533980131149292 + 6.219301700592041, + 5.520458698272705, + 1.2803497314453125 ] }, "cluster_id": 4, @@ -544758,12 +544908,12 @@ "label": "RNA Sequence Models" }, { - "id": 3, - "label": "RNA Splicing" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -545307,31 +545457,31 @@ "openalex_url": "https://openalex.org/W4367173587", "projections": { "pca": [ - 0.15445104241371155, - 0.027441781014204025 + 0.1544286608695984, + 0.027159519493579865 ], "tsne": [ - 46.452457427978516, - -7.948395729064941 + 44.35483169555664, + 9.109305381774902 ], "umap": [ - 9.797775268554688, - 8.048354148864746 + 10.03918743133545, + 7.668727874755859 ], "pca3d": [ - 0.154450923204422, - 0.027429167181253433, - -0.14898240566253662 + 0.15442854166030884, + 0.027146195992827415, + -0.1490977555513382 ], "tsne3d": [ - 24.553606033325195, - -5.445019721984863, - -9.324075698852539 + 25.004119873046875, + 10.042060852050781, + 1.872087836265564 ], "umap3d": [ - 6.948066234588623, - 4.677234649658203, - 0.8544374704360962 + 6.423347473144531, + 4.318556308746338, + -0.15085655450820923 ] }, "cluster_id": 0, @@ -545342,12 +545492,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -545763,31 +545913,31 @@ "doi": "10.1101/2023.12.18.572218", "projections": { "pca": [ - 0.08465282618999481, - -0.09709493815898895 + 0.08463083207607269, + -0.09686065465211868 ], "tsne": [ - 2.186976909637451, - 12.418374061584473 + 12.794812202453613, + -15.483356475830078 ], "umap": [ - 6.2757110595703125, - 5.719480037689209 + 6.577236652374268, + 5.399112224578857 ], "pca3d": [ - 0.0846530944108963, - -0.09710973501205444, - 0.15356168150901794 + 0.08463102579116821, + -0.09687530249357224, + 0.1538340300321579 ], "tsne3d": [ - -0.711943507194519, - 2.352038860321045, - -6.0627031326293945 + 4.1150593757629395, + -1.7839633226394653, + 10.750868797302246 ], "umap3d": [ - 5.724017143249512, - 5.593475818634033, - 3.765979051589966 + 5.766791343688965, + 6.047671318054199, + 2.560486316680908 ] }, "cluster_id": 4, @@ -545798,12 +545948,12 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, - "label": "Genomic Models" + "id": 32, + "label": "Genome Protein" } ], "_embedding": [ @@ -546230,31 +546380,31 @@ "doi": "10.1101/2024.02.02.578660", "projections": { "pca": [ - 0.3172808885574341, - 0.02475774474442005 + 0.3172733187675476, + 0.0238445233553648 ], "tsne": [ - 54.73887634277344, - 6.328232288360596 + 53.424476623535156, + 17.847017288208008 ], "umap": [ - 10.40658187866211, - 6.923723220825195 + 10.509876251220703, + 6.453595161437988 ], "pca3d": [ - 0.317280650138855, - 0.024766525253653526, - -0.08884776383638382 + 0.3172733187675476, + 0.023853251710534096, + -0.08918001502752304 ], "tsne3d": [ - 27.9549560546875, - 5.497071743011475, - -0.2515306770801544 + 29.22836685180664, + -1.5215356349945068, + -4.165031909942627 ], "umap3d": [ - 8.148992538452148, - 5.255697727203369, - 1.1259968280792236 + 7.470607757568359, + 5.0567731857299805, + -0.4672706425189972 ] }, "cluster_id": 0, @@ -546265,12 +546415,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -546703,31 +546853,31 @@ "doi": "10.1101/2024.02.01.578352", "projections": { "pca": [ - 0.2279043197631836, - -0.026037096977233887 + 0.2279026359319687, + -0.026517486199736595 ], "tsne": [ - 40.87350845336914, - 4.46527624130249 + 35.786468505859375, + 4.106517791748047 ], "umap": [ - 9.34907341003418, - 7.261320114135742 + 9.310966491699219, + 6.897515773773193 ], "pca3d": [ - 0.22790412604808807, - -0.026044372469186783, - 0.025967178866267204 + 0.22790268063545227, + -0.02652563340961933, + 0.025871919468045235 ], "tsne3d": [ - 23.831031799316406, - 0.5033584833145142, - -4.420905113220215 + 18.191198348999023, + -3.6065833568573, + -2.9289968013763428 ], "umap3d": [ - 6.731987476348877, - 5.266537666320801, - 1.4764147996902466 + 6.292145252227783, + 4.876984596252441, + 0.4266546964645386 ] }, "cluster_id": 0, @@ -546738,11 +546888,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -547151,46 +547301,46 @@ "abstract": "The study is one of the first in Asia to examine links between the genomes of mothers, babies and their health.", "projections": { "pca": [ - 0.12420924752950668, - 0.3194773495197296 + 0.12492699921131134, + 0.31952202320098877 ], "tsne": [ - 25.019927978515625, - 8.617586135864258 + 35.66856002807617, + -14.571793556213379 ], "umap": [ - 6.975227355957031, - 7.82333517074585 + 7.123895645141602, + 7.432989120483398 ], "pca3d": [ - 0.12420879304409027, - 0.3194429576396942, - -0.11662666499614716 + 0.12492654472589493, + 0.3194868862628937, + -0.11714417487382889 ], "tsne3d": [ - 11.63532543182373, - -12.157000541687012, - -16.558931350708008 + 10.5582914352417, + 22.996381759643555, + 1.9096273183822632 ], "umap3d": [ - 6.599140167236328, - 3.30281138420105, - 3.00307559967041 + 7.135126113891602, + 3.6591269969940186, + 2.304246425628662 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 26, + "id": 33, "label": "Genetic Disease" } ], @@ -547610,31 +547760,31 @@ "cited_by_count": 5, "projections": { "pca": [ - 0.2248595654964447, - 0.0876447856426239 + 0.22515267133712769, + 0.08685421943664551 ], "tsne": [ - 40.88985061645508, - -14.341158866882324 + 36.15498733520508, + 20.56032943725586 ], "umap": [ - 9.175378799438477, - 8.58818244934082 + 9.451597213745117, + 8.159192085266113 ], "pca3d": [ - 0.2248592972755432, - 0.08766010403633118, - -0.11220291256904602 + 0.22515226900577545, + 0.0868699848651886, + -0.11241725087165833 ], "tsne3d": [ - 21.821752548217773, - -5.964566230773926, - 7.968084812164307 + 20.482290267944336, + 6.882012844085693, + -13.609564781188965 ], "umap3d": [ - 7.074916362762451, - 3.846590518951416, - 1.1766293048858643 + 6.802433490753174, + 3.5661745071411133, + 0.4854589104652405 ] }, "cluster_id": 0, @@ -547645,12 +547795,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -548065,31 +548215,31 @@ "doi": "10.1101/2024.01.27.577533", "projections": { "pca": [ - 0.09297618269920349, - -0.15087080001831055 + 0.09273641556501389, + -0.1511450558900833 ], "tsne": [ - 16.971969604492188, - 33.722206115722656 + 20.697891235351562, + -32.142696380615234 ], "umap": [ - 7.903072834014893, - 4.4415693283081055 + 8.258365631103516, + 3.9755027294158936 ], "pca3d": [ - 0.09297627955675125, - -0.15084579586982727, - 0.12978433072566986 + 0.09273650497198105, + -0.15111880004405975, + 0.12997087836265564 ], "tsne3d": [ - 1.4492733478546143, - 23.521728515625, - 6.373945236206055 + 10.331001281738281, + -16.84518051147461, + 8.129798889160156 ], "umap3d": [ - 7.2186174392700195, - 6.362170696258545, - 4.086497783660889 + 7.422173976898193, + 6.886213302612305, + 2.267728328704834 ] }, "cluster_id": 4, @@ -548100,11 +548250,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -548529,31 +548679,31 @@ "doi": "10.1101/2024.01.27.577521", "projections": { "pca": [ - 0.0989580824971199, - -0.04804021120071411 + 0.09889114648103714, + -0.04811849445104599 ], "tsne": [ - 12.890478134155273, - 3.240610122680664 + 19.971818923950195, + -8.637433052062988 ], "umap": [ - 6.680253505706787, - 7.3073410987854 + 6.801064491271973, + 6.836739540100098 ], "pca3d": [ - 0.09895797818899155, - -0.048033084720373154, - -0.005388944409787655 + 0.09889116883277893, + -0.048111628741025925, + -0.005284650716930628 ], "tsne3d": [ - 3.638688325881958, - -5.104394435882568, - -11.268346786499023 + 3.8707826137542725, + 8.603883743286133, + 0.8321684002876282 ], "umap3d": [ - 5.73213005065918, - 4.244090557098389, - 2.8407623767852783 + 6.005687713623047, + 4.449830532073975, + 2.3685944080352783 ] }, "cluster_id": 4, @@ -548564,12 +548714,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -548995,47 +549145,47 @@ "doi": "10.1101/2024.01.29.576933", "projections": { "pca": [ - 0.22520993649959564, - -0.014625120908021927 + 0.22507208585739136, + -0.014972273260354996 ], "tsne": [ - 30.55034637451172, - -16.27509117126465 + 24.028200149536133, + 21.465991973876953 ], "umap": [ - 8.576700210571289, - 9.30366325378418 + 8.69982624053955, + 8.863682746887207 ], "pca3d": [ - 0.22520963847637177, - -0.014609444886446, - -0.11760412901639938 + 0.22507178783416748, + -0.014956372790038586, + -0.11769787222146988 ], "tsne3d": [ - 17.98871612548828, - 11.124083518981934, - 7.659875869750977 + 12.705304145812988, + -3.8588767051696777, + -15.049461364746094 ], "umap3d": [ - 5.592191219329834, - 4.485264301300049, - 1.0439717769622803 + 5.111671447753906, + 4.186061859130859, + 0.8535761833190918 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -549462,31 +549612,31 @@ "doi": "10.1101/2023.09.18.558350", "projections": { "pca": [ - 0.31574851274490356, - 0.0437123104929924 + 0.31583595275878906, + 0.04274658486247063 ], "tsne": [ - 36.98588562011719, - 5.189210891723633 + 42.36614227294922, + 17.560333251953125 ], "umap": [ - 8.972441673278809, - 8.636819839477539 + 9.201967239379883, + 8.190961837768555 ], "pca3d": [ - 0.3157486021518707, - 0.043696146458387375, - -0.04481148719787598 + 0.3158356845378876, + 0.042730577290058136, + -0.045117080211639404 ], "tsne3d": [ - 21.79608726501465, - 8.224358558654785, - 8.687082290649414 + 16.440532684326172, + 10.834158897399902, + -5.402720928192139 ], "umap3d": [ - 6.451506614685059, - 4.3509345054626465, - 1.049985408782959 + 6.096141815185547, + 3.9785337448120117, + 0.5212643146514893 ] }, "cluster_id": 0, @@ -549497,12 +549647,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -549929,31 +550079,31 @@ "doi": "10.1101/2023.10.24.563624", "projections": { "pca": [ - 0.10555504262447357, - -0.07368209213018417 + 0.1054561510682106, + -0.07332884520292282 ], "tsne": [ - 15.006579399108887, - 15.933547973632812 + 36.31376266479492, + -20.535612106323242 ], "umap": [ - 7.021714687347412, - 6.6162238121032715 + 7.195469379425049, + 6.28168249130249 ], "pca3d": [ - 0.10555514693260193, - -0.07368754595518112, - 0.038238655775785446 + 0.10545624047517776, + -0.07333355396986008, + 0.03845905512571335 ], "tsne3d": [ - 3.887166976928711, - 13.375212669372559, - -13.248357772827148 + 3.2769484519958496, + 5.825466632843018, + 7.125434875488281 ], "umap3d": [ - 6.323632717132568, - 4.587470054626465, - 3.528970718383789 + 6.745380401611328, + 4.941864967346191, + 2.5392963886260986 ] }, "cluster_id": 4, @@ -549964,12 +550114,12 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 22, - "label": "Genetic Variants" + "id": 17, + "label": "Genomic Models" } ], "_embedding": [ @@ -550393,31 +550543,31 @@ "doi": "10.1101/2024.01.22.576745", "projections": { "pca": [ - 0.26429301500320435, - 0.0531194806098938 + 0.26447731256484985, + 0.05289167910814285 ], "tsne": [ - 39.23064041137695, - 10.900948524475098 + 45.65384292602539, + -2.9716129302978516 ], "umap": [ - 8.697866439819336, - 7.625975608825684 + 9.000419616699219, + 7.318436145782471 ], "pca3d": [ - 0.26429304480552673, - 0.05308438837528229, - 0.005606611724942923 + 0.26447734236717224, + 0.05285649374127388, + 0.005458970554172993 ], "tsne3d": [ - 19.575119018554688, - 1.422558307647705, - 10.61707878112793 + 22.81683349609375, + 8.96916675567627, + 10.563591957092285 ], "umap3d": [ - 7.599656105041504, - 4.170469284057617, - 1.9663723707199097 + 7.282279014587402, + 4.279052257537842, + 0.8145171403884888 ] }, "cluster_id": 0, @@ -550428,12 +550578,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -550873,31 +551023,31 @@ "openalex_url": "https://openalex.org/W4391112869", "projections": { "pca": [ - 0.2945845425128937, - 0.0491308718919754 + 0.29461541771888733, + 0.04856696352362633 ], "tsne": [ - 53.87614822387695, - 4.231276512145996 + 51.32666778564453, + 16.225614547729492 ], "umap": [ - 10.210587501525879, - 6.965356826782227 + 10.442658424377441, + 6.553845405578613 ], "pca3d": [ - 0.2945846915245056, - 0.04909755662083626, - -0.08335345983505249 + 0.29461541771888733, + 0.048532869666814804, + -0.08365882188081741 ], "tsne3d": [ - 27.43199348449707, - 3.04293155670166, - -2.172363519668579 + 28.489744186401367, + 1.633947730064392, + -3.366001605987549 ], "umap3d": [ - 8.101428031921387, - 5.214607238769531, - 1.1188937425613403 + 7.426137924194336, + 4.9547858238220215, + -0.41826730966567993 ] }, "cluster_id": 0, @@ -550908,12 +551058,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -551323,31 +551473,31 @@ "doi": "10.21203/rs.3.rs-3676579/v1", "projections": { "pca": [ - -0.005808776710182428, - -0.016152875497937202 + -0.005801323801279068, + -0.015791030600667 ], "tsne": [ - 11.046700477600098, - 0.051712121814489365 + 18.286022186279297, + -12.134845733642578 ], "umap": [ - 7.018369674682617, - 6.967042922973633 + 7.248692989349365, + 6.54929256439209 ], "pca3d": [ - -0.005808657966554165, - -0.01615985482931137, - 0.0002269370452268049 + -0.005801226012408733, + -0.015798328444361687, + 0.00033943564631044865 ], "tsne3d": [ - 5.850158214569092, - -5.395848274230957, - -4.618569374084473 + 5.000055313110352, + 8.97632122039795, + 11.588249206542969 ], "umap3d": [ - 5.852230072021484, - 4.395717144012451, - 3.114798069000244 + 6.4015984535217285, + 4.629897594451904, + 2.3889148235321045 ] }, "cluster_id": 4, @@ -551358,12 +551508,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -551790,31 +551940,31 @@ "openalex_url": "https://openalex.org/W4390918072", "projections": { "pca": [ - 0.11070901155471802, - -0.09118697792291641 + 0.11057555675506592, + -0.09111972898244858 ], "tsne": [ - 24.036487579345703, - 28.993186950683594 + 28.122934341430664, + -28.509048461914062 ], "umap": [ - 8.46298599243164, - 5.185818195343018 + 8.72762393951416, + 4.7714314460754395 ], "pca3d": [ - 0.11070883274078369, - -0.09116720408201218, - 0.09800941497087479 + 0.11057548224925995, + -0.09109940379858017, + 0.09812244772911072 ], "tsne3d": [ - 9.26506233215332, - 24.365684509277344, - 1.0519887208938599 + 17.41880989074707, + -15.734134674072266, + 9.059237480163574 ], "umap3d": [ - 7.58554744720459, - 5.660552978515625, - 3.467890501022339 + 7.61195707321167, + 6.1745076179504395, + 1.6395074129104614 ] }, "cluster_id": 4, @@ -551825,11 +551975,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -552261,47 +552411,47 @@ "openalex_url": "https://openalex.org/W4390885420", "projections": { "pca": [ - 0.19218432903289795, - 0.04450799152255058 + 0.19231538474559784, + 0.04378940165042877 ], "tsne": [ - 30.45958709716797, - -11.705655097961426 + 25.890701293945312, + 17.74271583557129 ], "umap": [ - 8.629319190979004, - 8.74055004119873 + 8.769437789916992, + 8.411887168884277 ], "pca3d": [ - 0.19218401610851288, - 0.04451965168118477, - -0.13155052065849304 + 0.19231510162353516, + 0.04380161315202713, + -0.13166412711143494 ], "tsne3d": [ - 17.471389770507812, - 7.016263008117676, - 9.239262580871582 + 14.93446159362793, + 0.692686915397644, + -13.046746253967285 ], "umap3d": [ - 6.149925231933594, - 4.2447075843811035, - 1.283028483390808 + 5.810611248016357, + 3.994788885116577, + 0.8002028465270996 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 13, + "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -552716,31 +552866,31 @@ "doi": "10.1101/2024.01.14.575543", "projections": { "pca": [ - 0.12967045605182648, - -0.11562323570251465 + 0.12952986359596252, + -0.11550964415073395 ], "tsne": [ - 13.042464256286621, - 11.077176094055176 + 12.67956829071045, + -4.018128395080566 ], "umap": [ - 7.1336469650268555, - 5.9713239669799805 + 7.419057369232178, + 5.563387393951416 ], "pca3d": [ - 0.12967050075531006, - -0.11562053859233856, - 0.08563904464244843 + 0.12952998280525208, + -0.11550647765398026, + 0.08590056747198105 ], "tsne3d": [ - 7.212739944458008, - 6.392306327819824, - -7.523220062255859 + 8.606879234313965, + -4.650717258453369, + 4.62556791305542 ], "umap3d": [ - 6.181098937988281, - 5.047953128814697, - 3.4952356815338135 + 6.51164436340332, + 5.529865741729736, + 2.372992515563965 ] }, "cluster_id": 4, @@ -552751,11 +552901,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -553175,31 +553325,31 @@ "cited_by_count": 2, "projections": { "pca": [ - 0.08537404984235764, - 0.0005502525018528104 + 0.08547591418027878, + 0.00037200411316007376 ], "tsne": [ - 24.734601974487305, - -5.779519557952881 + 27.319679260253906, + 11.962041854858398 ], "umap": [ - 8.428022384643555, - 7.399629592895508 + 8.601957321166992, + 6.829122066497803 ], "pca3d": [ - 0.08537404239177704, - 0.000554505328182131, - -0.0949002355337143 + 0.08547590672969818, + 0.0003765348228625953, + -0.09484843909740448 ], "tsne3d": [ - 12.50690746307373, - -2.4928719997406006, - 2.983928680419922 + 13.60634708404541, + 4.130361080169678, + -6.239015579223633 ], "umap3d": [ - 6.614385604858398, - 4.579059600830078, - 2.0266706943511963 + 6.401394367218018, + 4.585098743438721, + 1.101097583770752 ] }, "cluster_id": 0, @@ -553210,12 +553360,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -553644,31 +553794,31 @@ "cited_by_count": 75, "projections": { "pca": [ - 0.1963805854320526, - 0.003020737785845995 + 0.19628813862800598, + 0.002628025831654668 ], "tsne": [ - 46.092037200927734, - -12.4599027633667 + 40.69917297363281, + 20.357402801513672 ], "umap": [ - 9.80246639251709, - 7.872575759887695 + 10.025757789611816, + 7.551429271697998 ], "pca3d": [ - 0.1963803470134735, - 0.0030240791384130716, - -0.1477995663881302 + 0.19628795981407166, + 0.002631062874570489, + -0.1479356586933136 ], "tsne3d": [ - 28.312314987182617, - -9.357858657836914, - 1.7525360584259033 + 29.451940536499023, + 9.918207168579102, + -8.284503936767578 ], "umap3d": [ - 7.173088550567627, - 4.656387805938721, - 0.9190812706947327 + 6.600381374359131, + 4.284372329711914, + -0.07756776362657547 ] }, "cluster_id": 0, @@ -553679,12 +553829,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 13, + "label": "Chromatin Regulation" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -554102,31 +554252,31 @@ "doi": "10.1101/2024.01.03.574022", "projections": { "pca": [ - 0.05907474830746651, - -0.08182142674922943 + 0.05898052453994751, + -0.08217322826385498 ], "tsne": [ - -14.696516990661621, - 12.861136436462402 + -8.52938461303711, + -12.982460021972656 ], "umap": [ - 6.13695764541626, - 5.17997407913208 + 6.464326858520508, + 4.807483196258545 ], "pca3d": [ - 0.059074766933918, - -0.08180591464042664, - 0.10173599421977997 + 0.05898062139749527, + -0.08215714991092682, + 0.10180685669183731 ], "tsne3d": [ - 1.1099669933319092, - -9.966477394104004, - -24.207876205444336 + 5.453649997711182, + 9.982950210571289, + -4.438381671905518 ], "umap3d": [ - 5.518253326416016, - 6.200399398803711, - 3.864285469055176 + 5.740631580352783, + 6.614127159118652, + 2.470670461654663 ] }, "cluster_id": 3, @@ -554137,12 +554287,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, - "label": "Protein Design" + "id": 32, + "label": "Genome Protein" } ], "_embedding": [ @@ -554562,31 +554712,31 @@ "doi": "10.1101/2023.12.27.573416", "projections": { "pca": [ - 0.09245550632476807, - -0.05186957120895386 + 0.09248469769954681, + -0.05169021338224411 ], "tsne": [ - 11.749300003051758, - 18.351303100585938 + 41.04515075683594, + -20.775297164916992 ], "umap": [ - 7.559753894805908, - 5.849734306335449 + 7.677116870880127, + 5.474401950836182 ], "pca3d": [ - 0.09245538711547852, - -0.0518636591732502, - 0.1621292531490326 + 0.09248463064432144, + -0.05168459936976433, + 0.16221360862255096 ], "tsne3d": [ - 13.62242603302002, - 11.623514175415039, - -7.0562238693237305 + 13.309386253356934, + -2.9155914783477783, + -1.2670859098434448 ], "umap3d": [ - 6.0210041999816895, - 5.660917282104492, - 3.2642409801483154 + 6.072129249572754, + 5.884806156158447, + 1.9088603258132935 ] }, "cluster_id": 4, @@ -554597,11 +554747,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, - "label": "RNA Splicing" + "id": 7, + "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -555035,47 +555185,47 @@ "openalex_url": "https://openalex.org/W4390063959", "projections": { "pca": [ - 0.2563483715057373, - 0.0852210745215416 + 0.2566398084163666, + 0.08446573466062546 ], "tsne": [ - 29.598482131958008, - 10.672404289245605 + 32.85526657104492, + -4.811372756958008 ], "umap": [ - 7.825265407562256, - 7.93779182434082 + 8.062580108642578, + 7.474628925323486 ], "pca3d": [ - 0.2563481628894806, - 0.08520124107599258, - -0.12536458671092987 + 0.2566397488117218, + 0.08444564044475555, + -0.12554337084293365 ], "tsne3d": [ - 15.791837692260742, - -6.955672264099121, - -10.072456359863281 + 16.102949142456055, + 12.620246887207031, + 2.8258419036865234 ], "umap3d": [ - 7.130484104156494, - 3.748443841934204, - 2.5858044624328613 + 7.300662994384766, + 3.9643590450286865, + 1.645050287246704 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -555493,31 +555643,31 @@ "doi": "10.1101/2023.12.21.572730", "projections": { "pca": [ - 0.1707911640405655, - -0.06995797157287598 + 0.170512393116951, + -0.06998398900032043 ], "tsne": [ - 18.11683464050293, - 0.35284650325775146 + 22.59425926208496, + 2.791811466217041 ], "umap": [ - 8.11647891998291, - 6.724364757537842 + 8.09727668762207, + 6.2023186683654785 ], "pca3d": [ - 0.17079110443592072, - -0.06994878500699997, - 0.007642912212759256 + 0.17051231861114502, + -0.06997447460889816, + 0.007645391393452883 ], "tsne3d": [ - 12.829248428344727, - 2.9340789318084717, - -6.711798191070557 + 15.661484718322754, + 2.8674521446228027, + 5.367563247680664 ], "umap3d": [ - 6.406185150146484, - 5.024728298187256, - 2.630552291870117 + 6.403109073638916, + 4.972634315490723, + 1.5103673934936523 ] }, "cluster_id": 4, @@ -555528,12 +555678,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -555966,31 +556116,31 @@ "doi": "10.1101/2023.12.13.571579", "projections": { "pca": [ - 0.14699704945087433, - -0.08284105360507965 + 0.14682438969612122, + -0.08302612602710724 ], "tsne": [ - 16.863910675048828, - 29.182485580444336 + 20.715744018554688, + -28.135723114013672 ], "umap": [ - 7.866866111755371, - 4.717137336730957 + 8.157397270202637, + 4.3581414222717285 ], "pca3d": [ - 0.1469968557357788, - -0.08280672132968903, - 0.08747982978820801 + 0.14682424068450928, + -0.08299103379249573, + 0.08750122040510178 ], "tsne3d": [ - 1.8606963157653809, - 20.564029693603516, - 2.7172749042510986 + 10.01649284362793, + -14.512532234191895, + 4.476888656616211 ], "umap3d": [ - 7.0763840675354, - 6.087934494018555, - 3.8500988483428955 + 7.221303939819336, + 6.578856468200684, + 2.1750500202178955 ] }, "cluster_id": 4, @@ -556001,11 +556151,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -556414,31 +556564,31 @@ "journal": "Nature Computational Science", "projections": { "pca": [ - 0.014791964553296566, - -0.05302100256085396 + 0.014751282520592213, + -0.05287608131766319 ], "tsne": [ - -5.610675811767578, - 14.238997459411621 + -8.826626777648926, + -18.0692138671875 ], "umap": [ - 5.720399856567383, - 4.997155666351318 + 6.115656852722168, + 4.651183605194092 ], "pca3d": [ - 0.014791836962103844, - -0.05299947410821915, - 0.07550876587629318 + 0.014751126989722252, + -0.052854422479867935, + 0.07559720426797867 ], "tsne3d": [ - 1.2459797859191895, - -3.7162296772003174, - 2.9733362197875977 + -3.4717915058135986, + -3.3360939025878906, + 9.618252754211426 ], "umap3d": [ - 5.537980079650879, - 6.4671220779418945, - 4.234455108642578 + 5.632375717163086, + 6.860265254974365, + 2.785917043685913 ] }, "cluster_id": 3, @@ -556449,12 +556599,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, - "label": "Protein Design" + "id": 32, + "label": "Genome Protein" } ], "_embedding": [ @@ -556876,31 +557026,31 @@ "cited_by_count": 90, "projections": { "pca": [ - 0.1560118943452835, - -0.03535522520542145 + 0.1558123528957367, + -0.035758282989263535 ], "tsne": [ - 48.12038803100586, - -9.640372276306152 + 45.34398651123047, + 10.925948143005371 ], "umap": [ - 10.051007270812988, - 8.05689811706543 + 10.249917030334473, + 7.712916374206543 ], "pca3d": [ - 0.15601187944412231, - -0.03535917401313782, - -0.09640379995107651 + 0.15581229329109192, + -0.03576282039284706, + -0.09650958329439163 ], "tsne3d": [ - 25.107526779174805, - -2.8445322513580322, - -9.972086906433105 + 25.59404754638672, + 7.563902854919434, + 2.8209431171417236 ], "umap3d": [ - 6.927626132965088, - 4.974486351013184, - 0.7426784038543701 + 6.314767360687256, + 4.534892559051514, + -0.31130537390708923 ] }, "cluster_id": 0, @@ -556911,12 +557061,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -557345,31 +557495,31 @@ "cited_by_count": 156, "projections": { "pca": [ - 0.20106273889541626, - -0.04354269430041313 + 0.20090730488300323, + -0.04393976926803589 ], "tsne": [ - 49.138511657714844, - -9.168603897094727 + 46.29841995239258, + 10.851153373718262 ], "umap": [ - 10.15723991394043, - 7.936644554138184 + 10.337713241577148, + 7.610783576965332 ], "pca3d": [ - 0.20106278359889984, - -0.043548669666051865, - -0.06473448127508163 + 0.20090727508068085, + -0.04394643381237984, + -0.06482259929180145 ], "tsne3d": [ - 26.66980743408203, - -2.1792378425598145, - -9.221413612365723 + 27.15322494506836, + 6.862626552581787, + 2.0971641540527344 ], "umap3d": [ - 7.01508903503418, - 4.975639343261719, - 0.6353887319564819 + 6.351123809814453, + 4.590728759765625, + -0.3934224843978882 ] }, "cluster_id": 0, @@ -557380,12 +557530,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -557811,31 +557961,31 @@ "openalex_url": "https://openalex.org/W4389296032", "projections": { "pca": [ - 0.1420232355594635, - 0.012000568211078644 + 0.142054945230484, + 0.01176771242171526 ], "tsne": [ - 26.02106475830078, - 18.354557037353516 + 26.23049545288086, + -4.903391361236572 ], "umap": [ - 7.446364879608154, - 7.1208624839782715 + 7.579607009887695, + 6.909182548522949 ], "pca3d": [ - 0.14202336966991425, - 0.011989661492407322, - -0.14705580472946167 + 0.14205493032932281, + 0.011756573803722858, + -0.1470288336277008 ], "tsne3d": [ - 10.782059669494629, - -4.829143047332764, - -8.640040397644043 + 9.258572578430176, + 10.573844909667969, + 2.124412775039673 ], "umap3d": [ - 7.027747631072998, - 4.1394195556640625, - 3.114962100982666 + 7.3758769035339355, + 4.645788669586182, + 2.081206798553467 ] }, "cluster_id": 4, @@ -557846,12 +557996,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -558270,31 +558420,31 @@ "cited_by_count": 65, "projections": { "pca": [ - 0.13898205757141113, - 0.0929870754480362 + 0.13918037712574005, + 0.09248977899551392 ], "tsne": [ - 40.747920989990234, - -14.30739688873291 + 35.967689514160156, + 20.52994155883789 ], "umap": [ - 9.191781997680664, - 8.646697998046875 + 9.484013557434082, + 8.175105094909668 ], "pca3d": [ - 0.1389818787574768, - 0.09299616515636444, - -0.15394392609596252 + 0.13918015360832214, + 0.09249912202358246, + -0.15417461097240448 ], "tsne3d": [ - 21.235095977783203, - -5.462021350860596, - 8.557783126831055 + 19.801462173461914, + 6.45700740814209, + -14.09585952758789 ], "umap3d": [ - 6.974520683288574, - 3.9066708087921143, - 1.1627202033996582 + 6.739636421203613, + 3.5535335540771484, + 0.48625412583351135 ] }, "cluster_id": 0, @@ -558305,12 +558455,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -558726,31 +558876,31 @@ "doi": "10.3390/ijms23137131", "projections": { "pca": [ - 0.17237083613872528, - 0.1879015415906906 + 0.1728799045085907, + 0.18712885677814484 ], "tsne": [ - 42.84194564819336, - 6.503543376922607 + 43.24476623535156, + 2.2454819679260254 ], "umap": [ - 9.246378898620605, - 7.97169828414917 + 9.437651634216309, + 7.6091628074646 ], "pca3d": [ - 0.17237035930156708, - 0.18790379166603088, - -0.050921641290187836 + 0.17287948727607727, + 0.18713083863258362, + -0.05139801278710365 ], "tsne3d": [ - 24.419082641601562, - 0.41437697410583496, - 9.460407257080078 + 18.864999771118164, + 11.199538230895996, + -11.434723854064941 ], "umap3d": [ - 7.482779502868652, - 4.310160160064697, - 1.26298189163208 + 7.119600296020508, + 4.022316932678223, + 0.36865493655204773 ] }, "cluster_id": 0, @@ -558761,12 +558911,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -559181,47 +559331,47 @@ "doi": "10.1101/2023.11.29.569083", "projections": { "pca": [ - 0.020810481160879135, - -0.06685278564691544 + 0.020450012758374214, + -0.06664738059043884 ], "tsne": [ - 26.270545959472656, - -0.9164493083953857 + 18.040077209472656, + 11.88868522644043 ], "umap": [ - 7.832221984863281, - 8.263489723205566 + 7.873208522796631, + 7.677451133728027 ], "pca3d": [ - 0.020810579881072044, - -0.06684082746505737, - -0.16931314766407013 + 0.020450089126825333, + -0.06663475185632706, + -0.16920503973960876 ], "tsne3d": [ - 8.929762840270996, - -11.900736808776855, - -1.6734042167663574 + 8.786590576171875, + 13.872289657592773, + -13.396476745605469 ], "umap3d": [ - 6.487965106964111, - 3.8386826515197754, - 2.314079999923706 + 6.651829242706299, + 3.8966012001037598, + 1.859157681465149 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -559729,31 +559879,31 @@ "cited_by_count": 113, "projections": { "pca": [ - 0.25942254066467285, - 0.07879716902971268 + 0.25953349471092224, + 0.07858836650848389 ], "tsne": [ - 42.459903717041016, - 13.601186752319336 + 47.21752166748047, + -7.287088871002197 ], "umap": [ - 9.088875770568848, - 7.566540718078613 + 9.212501525878906, + 7.066190719604492 ], "pca3d": [ - 0.2594224512577057, - 0.07877282053232193, - -0.03811001777648926 + 0.2595333755016327, + 0.0785638764500618, + -0.0383642204105854 ], "tsne3d": [ - 21.983455657958984, - -14.59732437133789, - -2.7952675819396973 + 22.226787567138672, + 10.11886215209961, + 5.384729385375977 ], "umap3d": [ - 7.333164691925049, - 4.404994487762451, - 1.8425836563110352 + 6.952401638031006, + 4.395174980163574, + 0.4947925806045532 ] }, "cluster_id": 0, @@ -559764,11 +559914,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -560197,31 +560347,31 @@ "cited_by_count": 59, "projections": { "pca": [ - 0.21023713052272797, - -0.019451623782515526 + 0.2101650983095169, + -0.02017238736152649 ], "tsne": [ - 28.234689712524414, - 31.642698287963867 + 26.062639236450195, + -21.625850677490234 ], "umap": [ - 8.631288528442383, - 5.186188220977783 + 8.963011741638184, + 4.829291343688965 ], "pca3d": [ - 0.21023666858673096, - -0.01942022517323494, - 0.02132619358599186 + 0.21016471087932587, + -0.020139949396252632, + 0.021143628284335136 ], "tsne3d": [ - 11.35981273651123, - 23.083480834960938, - -3.6047587394714355 + 18.358474731445312, + -16.63225746154785, + 3.1575605869293213 ], "umap3d": [ - 7.616158485412598, - 5.772778034210205, - 3.3243885040283203 + 7.439961910247803, + 6.201197624206543, + 1.4637356996536255 ] }, "cluster_id": 4, @@ -560232,11 +560382,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -560654,31 +560804,31 @@ "doi": "10.1101/2023.11.23.568470", "projections": { "pca": [ - 0.2161816656589508, - 0.008475028909742832 + 0.21624834835529327, + 0.007958514615893364 ], "tsne": [ - 12.0665922164917, - 25.457035064697266 + 19.89242935180664, + -21.594179153442383 ], "umap": [ - 7.432347297668457, - 4.692983627319336 + 7.848830699920654, + 4.513336181640625 ], "pca3d": [ - 0.21618156135082245, - 0.008476654067635536, - 0.010375403799116611 + 0.21624813973903656, + 0.007960135117173195, + 0.010243813507258892 ], "tsne3d": [ - 5.534400939941406, - 20.120573043823242, - -6.0864434242248535 + 10.828394889831543, + -10.334955215454102, + 11.359189987182617 ], "umap3d": [ - 6.781558513641357, - 5.903240203857422, - 4.312500953674316 + 6.950377941131592, + 6.710345268249512, + 2.5164361000061035 ] }, "cluster_id": 4, @@ -560689,11 +560839,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -561112,46 +561262,46 @@ ], "projections": { "pca": [ - -0.17369194328784943, - -0.06358667463064194 + -0.1739424467086792, + -0.06217540055513382 ], "tsne": [ - -31.419803619384766, - -15.047781944274902 + -33.516822814941406, + 11.647701263427734 ], "umap": [ - 1.7118486166000366, - 7.867576599121094 + 1.9212950468063354, + 7.074397563934326 ], "pca3d": [ - -0.1736917495727539, - -0.06359170377254486, - -0.07628436386585236 + -0.17394223809242249, + -0.06218075752258301, + -0.07599665224552155 ], "tsne3d": [ - -18.295368194580078, - -10.68104362487793, - 2.5386106967926025 + -17.614322662353516, + 0.547943115234375, + -0.2569427490234375 ], "umap3d": [ - 1.594934344291687, - 4.60902214050293, - 4.44431209564209 + 2.539611577987671, + 4.948712348937988, + 4.9986748695373535 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -561570,31 +561720,31 @@ "doi": "10.1101/2023.11.27.568764", "projections": { "pca": [ - 0.22750519216060638, - 0.07467657327651978 + 0.2278282344341278, + 0.07405034452676773 ], "tsne": [ - 41.491363525390625, - 4.559889316558838 + 35.511138916015625, + 4.7465949058532715 ], "umap": [ - 9.30670166015625, - 7.244157314300537 + 9.449197769165039, + 6.885540008544922 ], "pca3d": [ - 0.22750525176525116, - 0.07466545701026917, - 0.07788564264774323 + 0.22782813012599945, + 0.07403852790594101, + 0.07762668281793594 ], "tsne3d": [ - 23.76885414123535, - 0.24487267434597015, - -2.9307241439819336 + 19.70539665222168, + -3.832005262374878, + -2.692399501800537 ], "umap3d": [ - 6.859377861022949, - 5.225220680236816, - 1.4802944660186768 + 6.4527974128723145, + 4.881854057312012, + 0.31653648614883423 ] }, "cluster_id": 0, @@ -561605,11 +561755,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -562030,31 +562180,31 @@ "doi": "10.1101/2023.11.13.566946", "projections": { "pca": [ - 0.2813321053981781, - -0.04274255409836769 + 0.28128790855407715, + -0.04303695261478424 ], "tsne": [ - 58.447715759277344, - 7.535489559173584 + 53.457115173339844, + 21.100126266479492 ], "umap": [ - 10.29934024810791, - 6.509164333343506 + 10.472692489624023, + 6.120364665985107 ], "pca3d": [ - 0.2813321352005005, - -0.04275105148553848, - 0.14570483565330505 + 0.2812880277633667, + -0.04304560646414757, + 0.14553582668304443 ], "tsne3d": [ - 28.378658294677734, - 8.960079193115234, - -1.617863655090332 + 30.20238494873047, + -4.393987655639648, + -1.9803509712219238 ], "umap3d": [ - 8.145781517028809, - 5.655619144439697, - 1.3633177280426025 + 7.485395908355713, + 5.442610740661621, + -0.379027783870697 ] }, "cluster_id": 0, @@ -562065,12 +562215,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -562478,31 +562628,31 @@ "journal": "Nature", "projections": { "pca": [ - 0.18450525403022766, - 0.16084961593151093 + 0.18484878540039062, + 0.1610516756772995 ], "tsne": [ - 45.62480163574219, - 12.982769012451172 + 53.81307601928711, + 0.7215707302093506 ], "umap": [ - 8.766960144042969, - 7.110607147216797 + 8.499443054199219, + 7.198655605316162 ], "pca3d": [ - 0.18450485169887543, - 0.16085229814052582, - -0.0673665925860405 + 0.18484824895858765, + 0.16105376183986664, + -0.06765774637460709 ], "tsne3d": [ - 21.96664047241211, - -3.135594367980957, - -17.088056564331055 + 17.27910041809082, + 9.690311431884766, + 11.08655834197998 ], "umap3d": [ - 7.2659592628479, - 4.606441497802734, - 2.232003688812256 + 7.121052265167236, + 4.541952133178711, + 1.0610090494155884 ] }, "cluster_id": 0, @@ -562513,12 +562663,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -562936,47 +563086,47 @@ "cited_by_count": 53, "projections": { "pca": [ - -0.08632227033376694, - -0.11221564561128616 + -0.08682881295681, + -0.11172541230916977 ], "tsne": [ - -34.448612213134766, - -30.587825775146484 + -38.849037170410156, + 26.390262603759766 ], "umap": [ - 8.279576301574707, - 10.226698875427246 + 8.746150970458984, + 9.582474708557129 ], "pca3d": [ - -0.08632218837738037, - -0.11219392716884613, - -0.08236151188611984 + -0.08682874590158463, + -0.11170370876789093, + -0.08225291222333908 ], "tsne3d": [ - -13.883126258850098, - 0.38472333550453186, - -16.054935455322266 + -13.572010040283203, + -7.69819450378418, + -11.27785587310791 ], "umap3d": [ - 4.441935062408447, - 4.951040267944336, - 1.2685301303863525 + 4.072941303253174, + 4.432879447937012, + 1.2019259929656982 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -563396,31 +563546,31 @@ "cited_by_count": 19, "projections": { "pca": [ - 0.2673951983451843, - 0.07782064378261566 + 0.26758888363838196, + 0.07689502090215683 ], "tsne": [ - 39.89950180053711, - -5.011218070983887 + 38.57451248168945, + 10.939482688903809 ], "umap": [ - 9.22640323638916, - 8.25879955291748 + 9.560053825378418, + 7.7807512283325195 ], "pca3d": [ - 0.26739510893821716, - 0.0778101235628128, - -0.21035602688789368 + 0.26758867502212524, + 0.07688431441783905, + -0.2105608731508255 ], "tsne3d": [ - 19.89388084411621, - -6.364355087280273, - -0.8047964572906494 + 21.721166610717773, + 7.727467060089111, + -6.460148811340332 ], "umap3d": [ - 7.20934534072876, - 4.040689468383789, - 1.156125783920288 + 6.7908806800842285, + 3.7623088359832764, + 0.31905195116996765 ] }, "cluster_id": 0, @@ -563431,12 +563581,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -563867,31 +564017,31 @@ "openalex_url": "https://openalex.org/W4388929106", "projections": { "pca": [ - 0.004171763081103563, - -0.0791475772857666 + 0.004043084103614092, + -0.07891959697008133 ], "tsne": [ - 15.92741870880127, - -6.986870288848877 + 16.66636085510254, + 7.292620658874512 ], "umap": [ - 8.591863632202148, - 6.435755252838135 + 8.763543128967285, + 6.1056976318359375 ], "pca3d": [ - 0.00417178962379694, - -0.07914546877145767, - -0.048802152276039124 + 0.004043077118694782, + -0.07891778647899628, + -0.04858916997909546 ], "tsne3d": [ - 18.887174606323242, - 7.601256370544434, - -14.301288604736328 + 18.59719467163086, + 0.34204158186912537, + 8.152853965759277 ], "umap3d": [ - 6.674010753631592, - 5.4498162269592285, - 2.181718349456787 + 6.354565143585205, + 5.413431644439697, + 0.9796320796012878 ] }, "cluster_id": 4, @@ -563902,12 +564052,12 @@ "label": "RNA Sequence Models" }, { - "id": 3, - "label": "RNA Splicing" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -564332,31 +564482,31 @@ "doi": "10.1101/2023.11.19.567742", "projections": { "pca": [ - 0.15836437046527863, - 0.049800146371126175 + 0.15849050879478455, + 0.0491708405315876 ], "tsne": [ - 30.184816360473633, - 13.489131927490234 + 31.271591186523438, + -2.421800136566162 ], "umap": [ - 7.859273910522461, - 7.249149322509766 + 7.964565753936768, + 6.871639251708984 ], "pca3d": [ - 0.15836431086063385, - 0.04979703947901726, - -0.06618395447731018 + 0.1584904044866562, + 0.04916784167289734, + -0.06638000160455704 ], "tsne3d": [ - 18.646224975585938, - -6.2008233070373535, - -14.814824104309082 + 18.77549171447754, + 13.973963737487793, + 6.771900177001953 ], "umap3d": [ - 6.8602399826049805, - 4.376638889312744, - 2.4435248374938965 + 6.929208755493164, + 4.53248405456543, + 1.3855305910110474 ] }, "cluster_id": 4, @@ -564367,12 +564517,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -564789,46 +564939,46 @@ "cited_by_count": 1, "projections": { "pca": [ - 0.09180392324924469, - -0.01620800979435444 + 0.09181410819292068, + -0.016428232192993164 ], "tsne": [ - 24.455341339111328, - -22.097135543823242 + 16.634471893310547, + 24.029390335083008 ], "umap": [ - 7.693251132965088, - 9.433178901672363 + 7.722253799438477, + 9.15693187713623 ], "pca3d": [ - 0.09180384874343872, - -0.01619892194867134, - -0.12791338562965393 + 0.0918140858411789, + -0.016418907791376114, + -0.12783080339431763 ], "tsne3d": [ - 14.9612455368042, - -6.810328006744385, - 8.987628936767578 + 12.535284042358398, + 4.615673065185547, + -13.379999160766602 ], "umap3d": [ - 5.4342756271362305, - 3.633922576904297, - 1.360022783279419 + 5.276616096496582, + 3.4528160095214844, + 1.2524833679199219 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -565245,46 +565395,46 @@ "doi": "10.1101/2023.11.10.566588", "projections": { "pca": [ - 0.14678126573562622, - -0.12280433624982834 + 0.14637050032615662, + -0.12257665395736694 ], "tsne": [ - 4.344821929931641, - 18.892223358154297 + 16.772197723388672, + -16.330169677734375 ], "umap": [ - 6.345357894897461, - 5.240318298339844 + 7.429402828216553, + 5.2005510330200195 ], "pca3d": [ - 0.1467815339565277, - -0.12280246615409851, - 0.1136946752667427 + 0.1463707983493805, + -0.12257429957389832, + 0.11373328417539597 ], "tsne3d": [ - -5.619548320770264, - 5.765806674957275, - -2.9044790267944336 + 12.13032054901123, + -6.822734832763672, + 0.7385662794113159 ], "umap3d": [ - 6.072408199310303, - 5.682016372680664, - 3.3829309940338135 + 6.214524745941162, + 6.136114120483398, + 2.111701250076294 ] }, - "cluster_id": 3, - "cluster_label": "Protein Design", + "cluster_id": 4, + "cluster_label": "RNA Sequence Models", "cluster_levels": [ { - "id": 3, - "label": "Protein Design" + "id": 4, + "label": "RNA Sequence Models" }, { - "id": 2, - "label": "Protein Language" + "id": 7, + "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -565709,31 +565859,31 @@ "cited_by_count": 21, "projections": { "pca": [ - 0.11891750246286392, - -0.12618638575077057 + 0.11866899579763412, + -0.12641467154026031 ], "tsne": [ - 17.854206085205078, - -3.3404319286346436 + 18.949663162231445, + 3.343670606613159 ], "umap": [ - 7.983705997467041, - 6.432539463043213 + 8.223590850830078, + 6.071832180023193 ], "pca3d": [ - 0.11891761422157288, - -0.12618152797222137, - -0.013125198893249035 + 0.11866912245750427, + -0.12640953063964844, + -0.012925388291478157 ], "tsne3d": [ - 14.748229026794434, - 5.917898654937744, - -11.603038787841797 + 14.655431747436523, + 0.05403057858347893, + 4.623507976531982 ], "umap3d": [ - 6.385374546051025, - 5.282536029815674, - 2.4266772270202637 + 6.189373970031738, + 5.3171820640563965, + 1.427220106124878 ] }, "cluster_id": 4, @@ -565744,12 +565894,12 @@ "label": "RNA Sequence Models" }, { - "id": 3, - "label": "RNA Splicing" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -566167,31 +566317,31 @@ "cited_by_count": 221, "projections": { "pca": [ - 0.2146388292312622, - -0.033728014677762985 + 0.2146054059267044, + -0.03422116860747337 ], "tsne": [ - 26.79677963256836, - 32.581199645996094 + 24.281103134155273, + -28.295297622680664 ], "umap": [ - 8.396306037902832, - 4.9566545486450195 + 8.73215389251709, + 4.542961120605469 ], "pca3d": [ - 0.21463841199874878, - -0.03369585797190666, - 0.043996378779411316 + 0.21460503339767456, + -0.03418805077672005, + 0.043904274702072144 ], "tsne3d": [ - 10.142305374145508, - 21.96848487854004, - -2.3468027114868164 + 16.292226791381836, + -16.68416976928711, + 4.008575916290283 ], "umap3d": [ - 7.548567295074463, - 5.904168128967285, - 3.510155439376831 + 7.411368370056152, + 6.384005546569824, + 1.6632965803146362 ] }, "cluster_id": 4, @@ -566202,12 +566352,12 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, - "label": "RNA Splicing" + "id": 8, + "label": "RNA Structure" } ], "_embedding": [ @@ -566697,31 +566847,31 @@ "openalex_url": "https://openalex.org/W4388658080", "projections": { "pca": [ - 0.3077218234539032, - 0.03790922462940216 + 0.3077969551086426, + 0.03679444640874863 ], "tsne": [ - 40.09578323364258, - -0.3166200518608093 + 38.760799407958984, + 7.177834987640381 ], "umap": [ - 9.209046363830566, - 8.042868614196777 + 9.48950481414795, + 7.72874641418457 ], "pca3d": [ - 0.30772122740745544, - 0.03790838643908501, - -0.22376716136932373 + 0.3077965974807739, + 0.03679344058036804, + -0.22394168376922607 ], "tsne3d": [ - 22.288856506347656, - -5.10215425491333, - -2.349263906478882 + 21.440208435058594, + 9.017354965209961, + -3.478825330734253 ], "umap3d": [ - 7.059681415557861, - 4.24014949798584, - 1.2737674713134766 + 6.634755611419678, + 3.997502565383911, + 0.2900700569152832 ] }, "cluster_id": 0, @@ -566732,12 +566882,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -567158,31 +567308,31 @@ ], "projections": { "pca": [ - -0.032286178320646286, - -0.1396213173866272 + -0.03246252238750458, + -0.13905802369117737 ], "tsne": [ - 4.035698890686035, - 6.181802272796631 + 6.999876022338867, + -5.837635517120361 ], "umap": [ - 6.1305155754089355, - 6.612759590148926 + 6.443891525268555, + 6.226936340332031 ], "pca3d": [ - -0.0322858951985836, - -0.13963687419891357, - 0.007040634751319885 + -0.03246218338608742, + -0.1390736997127533, + 0.007547733839601278 ], "tsne3d": [ - 2.6752495765686035, - 4.445798873901367, - -12.300341606140137 + 0.8453496098518372, + 5.031158924102783, + -0.17375919222831726 ], "umap3d": [ - 5.1122541427612305, - 4.589999198913574, - 3.6368021965026855 + 5.571937084197998, + 4.9796013832092285, + 3.1096084117889404 ] }, "cluster_id": 4, @@ -567193,12 +567343,12 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 11, - "label": "Genomic Language" + "id": 17, + "label": "Genomic Models" } ], "_embedding": [ @@ -567652,31 +567802,31 @@ "doi": "10.1101/2023.11.09.563812", "projections": { "pca": [ - 0.26817288994789124, - -0.026495205238461494 + 0.2680027186870575, + -0.027317604050040245 ], "tsne": [ - 46.7608757019043, - -5.763301849365234 + 44.967227935791016, + 7.530152320861816 ], "umap": [ - 9.96037483215332, - 7.98743200302124 + 10.080389976501465, + 7.648889064788818 ], "pca3d": [ - 0.26817262172698975, - -0.026503097265958786, - -0.19688625633716583 + 0.2680027186870575, + -0.02732594683766365, + -0.19698144495487213 ], "tsne3d": [ - 26.004505157470703, - -6.328060626983643, - -7.457860946655273 + 26.657377243041992, + 10.756650924682617, + 0.40860822796821594 ], "umap3d": [ - 7.1483964920043945, - 4.687524318695068, - 0.8785579204559326 + 6.612504959106445, + 4.308992385864258, + -0.21828563511371613 ] }, "cluster_id": 0, @@ -567687,12 +567837,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 13, + "label": "Chromatin Regulation" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -568111,31 +568261,31 @@ "doi": "10.1101/2023.11.02.565416", "projections": { "pca": [ - 0.24277101457118988, - -0.08262225240468979 + 0.24256689846515656, + -0.08337616175413132 ], "tsne": [ - 59.460262298583984, - 5.824778079986572 + 51.67448043823242, + 22.033937454223633 ], "umap": [ - 10.108123779296875, - 6.468527317047119 + 10.254591941833496, + 6.081577301025391 ], "pca3d": [ - 0.24277126789093018, - -0.08260779827833176, - -0.05189022794365883 + 0.24256698787212372, + -0.08336109668016434, + -0.05190945416688919 ], "tsne3d": [ - 27.352754592895508, - 8.841665267944336, - -4.775277137756348 + 27.231670379638672, + -4.870997428894043, + -1.322685718536377 ], "umap3d": [ - 7.964836120605469, - 5.583580017089844, - 1.4395641088485718 + 7.356490612030029, + 5.413825511932373, + -0.20355981588363647 ] }, "cluster_id": 0, @@ -568146,12 +568296,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -568576,31 +568726,31 @@ "openalex_url": "https://openalex.org/W4388212864", "projections": { "pca": [ - 0.10140268504619598, - -0.09526778012514114 + 0.10103026032447815, + -0.09525836259126663 ], "tsne": [ - -12.147873878479004, - 15.806490898132324 + -8.767932891845703, + -22.334230422973633 ], "umap": [ - 5.7483744621276855, - 4.432506561279297 + 6.3627753257751465, + 4.064201831817627 ], "pca3d": [ - 0.10140267759561539, - -0.09525428712368011, - 0.06292294710874557 + 0.10103022307157516, + -0.09524472057819366, + 0.06286515295505524 ], "tsne3d": [ - 2.386439085006714, - 0.6692588925361633, - 14.121557235717773 + -1.4448204040527344, + -20.976451873779297, + -6.597369194030762 ], "umap3d": [ - 5.017744541168213, - 6.407784461975098, - 4.777270317077637 + 5.639789581298828, + 7.022463798522949, + 3.21205735206604 ] }, "cluster_id": 3, @@ -568611,12 +568761,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, - "label": "Protein Language" + "id": 3, + "label": "Protein Design" } ], "_embedding": [ @@ -569033,47 +569183,47 @@ "doi": "10.1101/2023.10.13.562293", "projections": { "pca": [ - 0.021694211289286613, - -0.0696115493774414 + 0.02133151888847351, + -0.06944767385721207 ], "tsne": [ - 33.20488357543945, - -20.93507957458496 + 25.049116134643555, + 26.96367073059082 ], "umap": [ - 8.361072540283203, - 10.077174186706543 + 8.749490737915039, + 9.434242248535156 ], "pca3d": [ - 0.021694287657737732, - -0.06960239261388779, - -0.0946580171585083 + 0.021331602707505226, + -0.0694384053349495, + -0.09462297707796097 ], "tsne3d": [ - 17.598670959472656, - 15.182424545288086, - 2.521486520767212 + 11.510862350463867, + -8.612228393554688, + -10.103424072265625 ], "umap3d": [ - 4.632831573486328, - 4.893062591552734, - 1.2033175230026245 + 4.323735237121582, + 4.395644187927246, + 1.0924580097198486 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -569490,47 +569640,47 @@ "doi": "10.1101/2023.08.01.551575", "projections": { "pca": [ - 0.13110633194446564, - -0.10241249948740005 + 0.13062867522239685, + -0.10289734601974487 ], "tsne": [ - 35.42005920410156, - -18.18143081665039 + 28.28610610961914, + 25.032634735107422 ], "umap": [ - 8.462158203125, - 8.608377456665039 + 8.635757446289062, + 8.301806449890137 ], "pca3d": [ - 0.13110633194446564, - -0.10239126533269882, - -0.15655285120010376 + 0.13062867522239685, + -0.10287550836801529, + -0.15654844045639038 ], "tsne3d": [ - 16.975067138671875, - 12.20807933807373, - -1.916472315788269 + 13.314105033874512, + -3.9978368282318115, + -7.441757678985596 ], "umap3d": [ - 6.002226829528809, - 4.669608116149902, - 1.539899230003357 + 5.71684455871582, + 4.402224063873291, + 0.9196107983589172 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 13, + "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -569938,47 +570088,47 @@ "journal": "Nature", "projections": { "pca": [ - 0.11130064725875854, - -0.02901841141283512 + 0.11118574440479279, + -0.02940395288169384 ], "tsne": [ - 37.94257354736328, - -23.138328552246094 + 27.707136154174805, + 30.136388778686523 ], "umap": [ - 8.66113567352295, - 8.961596488952637 + 8.773567199707031, + 8.551663398742676 ], "pca3d": [ - 0.11130055040121078, - -0.029004467651247978, - -0.0680890753865242 + 0.11118562519550323, + -0.02938927710056305, + -0.06812310218811035 ], "tsne3d": [ - 23.06144142150879, - 15.18713665008545, - 6.326446533203125 + 15.337197303771973, + -10.025737762451172, + -15.264191627502441 ], "umap3d": [ - 5.897428512573242, - 4.635706901550293, - 1.2395731210708618 + 5.6533379554748535, + 4.53021240234375, + 0.8018370270729065 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -570393,47 +570543,47 @@ ], "projections": { "pca": [ - 0.06604953110218048, - -0.1284533143043518 + 0.06542892754077911, + -0.12862268090248108 ], "tsne": [ - 34.21296691894531, - -20.133014678955078 + 26.27323341369629, + 26.46857261657715 ], "umap": [ - 8.405473709106445, - 9.982344627380371 + 8.803032875061035, + 9.306502342224121 ], "pca3d": [ - 0.06604961305856705, - -0.1284330040216446, - -0.07351836562156677 + 0.06542899459600449, + -0.12860167026519775, + -0.07354665547609329 ], "tsne3d": [ - 18.22294807434082, - 13.971303939819336, - 0.7083030939102173 + 13.235200881958008, + -7.328874588012695, + -9.070585250854492 ], "umap3d": [ - 4.740678310394287, - 4.897610187530518, - 1.2269169092178345 + 4.438488483428955, + 4.421411037445068, + 1.0709912776947021 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -570858,31 +571008,31 @@ "doi": "10.1101/2023.10.04.560808", "projections": { "pca": [ - 0.27913767099380493, - -0.016240013763308525 + 0.27899369597435, + -0.016827039420604706 ], "tsne": [ - 52.521305084228516, - 4.68595027923584 + 52.281436920166016, + 17.61956214904785 ], "umap": [ - 10.228031158447266, - 7.06557035446167 + 10.404641151428223, + 6.605063438415527 ], "pca3d": [ - 0.27913767099380493, - -0.01623672805726528, - -0.11532462388277054 + 0.27899372577667236, + -0.016823839396238327, + -0.11551766842603683 ], "tsne3d": [ - 28.304975509643555, - 3.7375526428222656, - 1.0363363027572632 + 29.607582092285156, + 0.268181711435318, + -5.053553104400635 ], "umap3d": [ - 8.137491226196289, - 5.120214939117432, - 1.076311707496643 + 7.457033157348633, + 4.873441219329834, + -0.38010290265083313 ] }, "cluster_id": 0, @@ -570893,12 +571043,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -571316,46 +571466,46 @@ "doi": "10.1101/2023.10.19.563100", "projections": { "pca": [ - 0.09336060285568237, - -0.0924587994813919 + 0.09313920140266418, + -0.09266074001789093 ], "tsne": [ - 21.938512802124023, - -21.007139205932617 + 14.662256240844727, + 21.82825469970703 ], "umap": [ - 7.695391654968262, - 9.4287748336792 + 7.65197229385376, + 9.076163291931152 ], "pca3d": [ - 0.09336057305335999, - -0.09242754429578781, - -0.12507294118404388 + 0.0931391492486, + -0.09262875467538834, + -0.1248822808265686 ], "tsne3d": [ - 11.327754974365234, - -6.603721618652344, - 9.255777359008789 + 9.068520545959473, + 2.6339430809020996, + -12.71349048614502 ], "umap3d": [ - 5.368133544921875, - 3.728968381881714, - 1.458364486694336 + 5.214405059814453, + 3.5388343334198, + 1.414809226989746 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -571769,31 +571919,31 @@ ], "projections": { "pca": [ - 0.13348229229450226, - -0.06561598926782608 + 0.13336682319641113, + -0.06582978367805481 ], "tsne": [ - -15.344948768615723, - 19.900537490844727 + -11.582155227661133, + -24.936403274536133 ], "umap": [ - 5.004364967346191, - 4.64067268371582 + 5.4129815101623535, + 3.816706657409668 ], "pca3d": [ - 0.13348238170146942, - -0.06560920178890228, - 0.10751447081565857 + 0.1333668828010559, + -0.06582283228635788, + 0.10750424861907959 ], "tsne3d": [ - 3.701890468597412, - 8.123555183410645, - 14.817583084106445 + 4.434633731842041, + -13.85543441772461, + 12.464831352233887 ], "umap3d": [ - 4.719327449798584, - 6.857624053955078, - 4.298672199249268 + 5.034823417663574, + 7.147181987762451, + 2.9766058921813965 ] }, "cluster_id": 3, @@ -571804,12 +571954,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { "id": 24, - "label": "Protein Design" + "label": "Protein Diffusion" } ], "_embedding": [ @@ -572233,31 +572383,31 @@ "openalex_url": "https://openalex.org/W4387362445", "projections": { "pca": [ - 0.06946658343076706, - -0.1060996726155281 + 0.06917358934879303, + -0.1058158427476883 ], "tsne": [ - 24.408275604248047, - 26.028427124023438 + 29.37491798400879, + -26.648420333862305 ], "umap": [ - 8.303950309753418, - 5.721353054046631 + 8.558907508850098, + 5.290829181671143 ], "pca3d": [ - 0.06946642696857452, - -0.10606292635202408, - 0.04663465544581413 + 0.06917344778776169, + -0.1057782918214798, + 0.04676537960767746 ], "tsne3d": [ - 10.832818031311035, - 22.502246856689453, - 1.9778181314468384 + 16.109237670898438, + -7.913468360900879, + 7.308176517486572 ], "umap3d": [ - 7.392815113067627, - 5.317739009857178, - 3.2272748947143555 + 7.402886867523193, + 5.770412445068359, + 1.5850164890289307 ] }, "cluster_id": 4, @@ -572268,11 +572418,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -572690,46 +572840,46 @@ "doi": "10.1101/2023.10.16.561085", "projections": { "pca": [ - 0.07099742442369461, - -0.04807070642709732 + 0.07094816118478775, + -0.04801715537905693 ], "tsne": [ - 22.902545928955078, - -20.353118896484375 + 15.8508939743042, + 21.77198600769043 ], "umap": [ - 7.75584077835083, - 9.401515007019043 + 7.746059894561768, + 9.086992263793945 ], "pca3d": [ - 0.07099739462137222, - -0.048060499131679535, - -0.07814380526542664 + 0.07094811648130417, + -0.04800683632493019, + -0.07800830155611038 ], "tsne3d": [ - 10.784116744995117, - -5.3857574462890625, - 10.784113883972168 + 7.901867866516113, + 0.8463945388793945, + -12.657066345214844 ], "umap3d": [ - 5.274508476257324, - 3.8334312438964844, - 1.4197068214416504 + 5.212924480438232, + 3.590762138366699, + 1.400555968284607 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -573158,31 +573308,31 @@ "openalex_url": "https://openalex.org/W4387672313", "projections": { "pca": [ - 0.24046590924263, - 0.1515534669160843 + 0.24078309535980225, + 0.15146830677986145 ], "tsne": [ - 46.51591873168945, - 9.086502075195312 + 54.78133773803711, + 3.8539552688598633 ], "umap": [ - 9.171926498413086, - 7.021974563598633 + 9.282881736755371, + 6.551307678222656 ], "pca3d": [ - 0.24046577513217926, - 0.15152499079704285, - -0.0020500023383647203 + 0.2407827228307724, + 0.15143917500972748, + -0.002458908362314105 ], "tsne3d": [ - 15.658684730529785, - 6.580016136169434, - -0.2012767642736435 + 21.850841522216797, + 6.057363986968994, + 6.21144437789917 ], "umap3d": [ - 7.430176734924316, - 4.889917373657227, - 1.8387075662612915 + 7.028848171234131, + 4.787396430969238, + 0.417224258184433 ] }, "cluster_id": 0, @@ -573193,11 +573343,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -573619,31 +573769,31 @@ "doi": "10.1101/2023.10.11.561938", "projections": { "pca": [ - 0.11043406277894974, - -0.09324458241462708 + 0.11031873524188995, + -0.0934167429804802 ], "tsne": [ - 11.703301429748535, - 25.917804718017578 + 19.393661499023438, + -22.021215438842773 ], "umap": [ - 7.322422981262207, - 4.600455284118652 + 7.751719951629639, + 4.461078643798828 ], "pca3d": [ - 0.1104341521859169, - -0.0932314544916153, - 0.09202694147825241 + 0.11031875014305115, + -0.09340327233076096, + 0.09217299520969391 ], "tsne3d": [ - 4.992753982543945, - 19.738718032836914, - -4.7788262367248535 + 9.694559097290039, + -10.684091567993164, + 10.591032981872559 ], "umap3d": [ - 6.729622840881348, - 5.930164337158203, - 4.344343662261963 + 6.873188018798828, + 6.751870632171631, + 2.6256213188171387 ] }, "cluster_id": 4, @@ -573654,11 +573804,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -574081,31 +574231,31 @@ ], "projections": { "pca": [ - 0.06172029674053192, - -0.12595556676387787 + 0.06151548773050308, + -0.12541033327579498 ], "tsne": [ - 9.467244148254395, - 9.617609024047852 + 11.648999214172363, + -6.800743579864502 ], "umap": [ - 7.000220775604248, - 6.003014087677002 + 7.268074035644531, + 5.613075256347656 ], "pca3d": [ - 0.061720483005046844, - -0.12595270574092865, - 0.03254358097910881 + 0.061515677720308304, + -0.12540745735168457, + 0.03291216492652893 ], "tsne3d": [ - 5.808837413787842, - 5.674308776855469, - -10.367216110229492 + 6.055697917938232, + 0.6399410963058472, + 3.2982521057128906 ], "umap3d": [ - 5.734446048736572, - 5.204766750335693, - 3.613668441772461 + 5.995314598083496, + 5.469935417175293, + 2.6383633613586426 ] }, "cluster_id": 4, @@ -574116,11 +574266,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -574541,31 +574691,31 @@ "cited_by_count": 3, "projections": { "pca": [ - 0.2766484022140503, - 0.06129869073629379 + 0.2767164409160614, + 0.06084569916129112 ], "tsne": [ - 53.81776809692383, - -2.7147326469421387 + 52.97650146484375, + 10.1076021194458 ], "umap": [ - 10.552001953125, - 7.565395355224609 + 10.720860481262207, + 7.159750461578369 ], "pca3d": [ - 0.27664828300476074, - 0.06128298491239548, - -0.12087500840425491 + 0.27671629190444946, + 0.06082935258746147, + -0.12114902585744858 ], "tsne3d": [ - 30.936660766601562, - -1.5613081455230713, - -2.984323501586914 + 31.154436111450195, + 5.896195411682129, + -2.593454360961914 ], "umap3d": [ - 7.828477382659912, - 4.934649467468262, - 0.5732203722000122 + 7.01109504699707, + 4.5425801277160645, + -0.7590053081512451 ] }, "cluster_id": 0, @@ -574576,12 +574726,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -575004,31 +575154,31 @@ "cited_by_count": 182, "projections": { "pca": [ - 0.0021380558609962463, - 0.07721630483865738 + 0.0022157959174364805, + 0.07762067019939423 ], "tsne": [ - -20.40337562561035, - 22.58738136291504 + 4.3704094886779785, + -40.596370697021484 ], "umap": [ - 4.283897876739502, - 3.6518537998199463 + 4.813769817352295, + 3.178961992263794 ], "pca3d": [ - 0.0021381513215601444, - 0.07720435410737991, - 0.058137860149145126 + 0.002215844113379717, + 0.07760873436927795, + 0.05790812894701958 ], "tsne3d": [ - -5.003394603729248, - 9.338201522827148, - -17.384496688842773 + 2.454308032989502, + -5.350701808929443, + 18.51968002319336 ], "umap3d": [ - 4.9976091384887695, - 7.775677680969238, - 4.089545726776123 + 4.574321746826172, + 7.982223987579346, + 2.6971187591552734 ] }, "cluster_id": 3, @@ -575039,12 +575189,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -575465,31 +575615,31 @@ "cited_by_count": 29, "projections": { "pca": [ - 0.12174423038959503, - -0.09312967211008072 + 0.12160549312829971, + -0.09319094568490982 ], "tsne": [ - 17.68308448791504, - 11.258928298950195 + 20.233287811279297, + -6.3043012619018555 ], "umap": [ - 6.952050685882568, - 6.924201965332031 + 7.111668586730957, + 6.49821138381958 ], "pca3d": [ - 0.12174442410469055, - -0.09314528852701187, - 0.045152515172958374 + 0.1216057613492012, + -0.09320627897977829, + 0.045370131731033325 ], "tsne3d": [ - 6.420381546020508, - -1.6218295097351074, - -11.78627872467041 + 5.711623668670654, + 6.8982157707214355, + 3.462371587753296 ], "umap3d": [ - 6.340482711791992, - 4.235945701599121, - 3.4586219787597656 + 6.802273750305176, + 4.711263656616211, + 2.59356689453125 ] }, "cluster_id": 4, @@ -575500,12 +575650,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 7, + "label": "Genomic Models" }, { - "id": 22, - "label": "Genetic Variants" + "id": 17, + "label": "Genomic Models" } ], "_embedding": [ @@ -575917,31 +576067,31 @@ "cited_by_count": 1228, "projections": { "pca": [ - -0.0916968360543251, - 0.04868854954838753 + -0.09158148616552353, + 0.049113716930150986 ], "tsne": [ - 10.254892349243164, - -4.760861873626709 + 11.664725303649902, + 5.409701347351074 ], "umap": [ - 5.9334797859191895, - 6.8424601554870605 + 6.2052106857299805, + 6.446776390075684 ], "pca3d": [ - -0.09169706702232361, - 0.04871088266372681, - 0.03516806289553642 + -0.09158172458410263, + 0.049136508256196976, + 0.03512084111571312 ], "tsne3d": [ - 14.529894828796387, - 1.933692455291748, - -17.027862548828125 + 11.708719253540039, + 4.231091499328613, + 11.253159523010254 ], "umap3d": [ - 4.897446632385254, - 4.336427211761475, - 3.5220515727996826 + 5.319322109222412, + 4.813461780548096, + 3.1826224327087402 ] }, "cluster_id": 4, @@ -575952,12 +576102,12 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 11, - "label": "Genomic Language" + "id": 13, + "label": "Genomics Papers" } ], "_embedding": [ @@ -576375,31 +576525,31 @@ "doi": "10.1101/2023.09.26.559463", "projections": { "pca": [ - 0.11032474786043167, - -0.1385282576084137 + 0.11000437289476395, + -0.13886868953704834 ], "tsne": [ - 16.06354331970215, - 34.4154167175293 + 19.501996994018555, + -32.510990142822266 ], "umap": [ - 7.925472736358643, - 4.445328235626221 + 8.306421279907227, + 3.966874599456787 ], "pca3d": [ - 0.11032462865114212, - -0.13849163055419922, - 0.10142491012811661 + 0.11000433564186096, + -0.13883085548877716, + 0.10148139297962189 ], "tsne3d": [ - 2.3944945335388184, - 20.82292938232422, - 6.584356307983398 + 8.120006561279297, + -17.975040435791016, + 6.698336124420166 ], "umap3d": [ - 7.250281810760498, - 6.320901870727539, - 4.058143615722656 + 7.414647102355957, + 6.874330520629883, + 2.248013496398926 ] }, "cluster_id": 4, @@ -576410,11 +576560,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -576821,47 +576971,47 @@ ], "projections": { "pca": [ - 0.11024879664182663, - 0.008710040710866451 + 0.11041584610939026, + 0.008754915557801723 ], "tsne": [ - 20.113876342773438, - 20.59003257751465 + 21.364337921142578, + -18.128183364868164 ], "umap": [ - 8.66505241394043, - 6.702706336975098 + 8.826583862304688, + 6.205566883087158 ], "pca3d": [ - 0.11024881899356842, - 0.008697541430592537, - 0.06739901006221771 + 0.11041584610939026, + 0.008742943406105042, + 0.06740450114011765 ], "tsne3d": [ - 10.677265167236328, - 7.102489471435547, - 11.27777099609375 + 18.265291213989258, + -0.9205785393714905, + 14.412310600280762 ], "umap3d": [ - 7.615086078643799, - 4.615963459014893, - 2.419663429260254 + 7.508456230163574, + 4.794331073760986, + 1.1512113809585571 ] }, - "cluster_id": 0, - "cluster_label": "Chromatin Regulation", + "cluster_id": 4, + "cluster_label": "RNA Sequence Models", "cluster_levels": [ { - "id": 0, - "label": "Chromatin Regulation" + "id": 4, + "label": "RNA Sequence Models" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -577320,31 +577470,31 @@ "doi": "10.1101/2023.09.20.558508", "projections": { "pca": [ - 0.16569222509860992, - -0.010460239835083485 + 0.16563951969146729, + -0.010663537308573723 ], "tsne": [ - 17.611703872680664, - 28.294416427612305 + 21.28827667236328, + -27.272310256958008 ], "umap": [ - 7.966073513031006, - 5.055471420288086 + 8.254927635192871, + 4.538961410522461 ], "pca3d": [ - 0.16569207608699799, - -0.010439155623316765, - 0.0064933523535728455 + 0.16563937067985535, + -0.010641815140843391, + 0.006441962439566851 ], "tsne3d": [ - 2.6892974376678467, - 20.75871467590332, - 0.4299169182777405 + 12.237924575805664, + -13.107097625732422, + 4.380915641784668 ], "umap3d": [ - 7.137460708618164, - 5.866323947906494, - 3.6866846084594727 + 7.255520343780518, + 6.323543071746826, + 2.071538209915161 ] }, "cluster_id": 4, @@ -577355,11 +577505,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -577800,31 +577950,31 @@ "openalex_url": "https://openalex.org/W4386917535", "projections": { "pca": [ - 0.2655085027217865, - 0.020207563415169716 + 0.2654991149902344, + 0.019657853990793228 ], "tsne": [ - 53.90152359008789, - 11.99585247039795 + 57.545631408691406, + 15.33041000366211 ], "umap": [ - 10.34228229522705, - 6.722382545471191 + 10.440120697021484, + 6.348935127258301 ], "pca3d": [ - 0.2655082643032074, - 0.02020503208041191, - -0.07399718463420868 + 0.2654990553855896, + 0.01965511217713356, + -0.0742512121796608 ], "tsne3d": [ - 33.57291030883789, - 7.497452259063721, - -4.229205131530762 + 30.572153091430664, + -2.9149208068847656, + 3.278900623321533 ], "umap3d": [ - 8.115994453430176, - 5.355719089508057, - 1.261630654335022 + 7.430313587188721, + 5.167238712310791, + -0.37021419405937195 ] }, "cluster_id": 0, @@ -577835,12 +577985,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -578260,31 +578410,31 @@ "cited_by_count": 637, "projections": { "pca": [ - 0.22104039788246155, - -0.032579757273197174 + 0.22093746066093445, + -0.03314993157982826 ], "tsne": [ - 34.91240692138672, - -16.086299896240234 + 28.952255249023438, + 22.720855712890625 ], "umap": [ - 8.999762535095215, - 7.932530879974365 + 9.220717430114746, + 7.669389724731445 ], "pca3d": [ - 0.22104030847549438, - -0.03256802260875702, - -0.13069333136081696 + 0.2209373563528061, + -0.033138129860162735, + -0.1307508498430252 ], "tsne3d": [ - 21.08060646057129, - 11.531542778015137, - 0.29340165853500366 + 15.31921100616455, + -3.55220365524292, + -10.684837341308594 ], "umap3d": [ - 6.531547546386719, - 4.722513198852539, - 1.332152009010315 + 6.091364860534668, + 4.416685581207275, + 0.4875298738479614 ] }, "cluster_id": 0, @@ -578295,12 +578445,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -578722,47 +578872,47 @@ "cited_by_count": 127, "projections": { "pca": [ - 0.0731521025300026, - -0.11271385848522186 + 0.0727749913930893, + -0.11312459409236908 ], "tsne": [ - 11.85249137878418, - 29.235565185546875 + 15.079497337341309, + 16.382976531982422 ], "umap": [ - 7.908191680908203, - 8.805930137634277 + 8.264464378356934, + 5.519351959228516 ], "pca3d": [ - 0.0731520727276802, - -0.11268655955791473, - 0.0014608076307922602 + 0.0727749764919281, + -0.1130962148308754, + 0.0014630467630922794 ], "tsne3d": [ - 14.018939971923828, - -2.294292449951172, - 13.201217651367188 + 12.848365783691406, + -14.538033485412598, + -1.0159364938735962 ], "umap3d": [ - 6.833564758300781, - 5.869528770446777, - 3.3727316856384277 + 6.928231239318848, + 6.144406318664551, + 1.908738613128662 ] }, - "cluster_id": 5, - "cluster_label": "Single-Cell Data", + "cluster_id": 4, + "cluster_label": "RNA Sequence Models", "cluster_levels": [ { - "id": 5, - "label": "Single-Cell Data" + "id": 4, + "label": "RNA Sequence Models" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 14, + "label": "RNA Splicing" }, { - "id": 12, - "label": "Genetic Variants" + "id": 21, + "label": "RNA Splicing" } ], "_embedding": [ @@ -579186,46 +579336,46 @@ "doi": "10.1101/2023.09.11.557287", "projections": { "pca": [ - 0.08770426362752914, - -0.038842473179101944 + 0.08779116719961166, + -0.038760777562856674 ], "tsne": [ - 18.9372501373291, - -21.758935928344727 + 10.991168975830078, + 21.45401382446289 ], "umap": [ - 7.207982063293457, - 9.30761432647705 + 7.305892467498779, + 8.879008293151855 ], "pca3d": [ - 0.08770409971475601, - -0.03882231563329697, - 0.0044523365795612335 + 0.08779098838567734, + -0.03874041885137558, + 0.004685778636485338 ], "tsne3d": [ - 7.394994735717773, - -8.506631851196289, - 9.258672714233398 + 6.073192596435547, + 5.262627124786377, + -12.297670364379883 ], "umap3d": [ - 4.93057918548584, - 3.7703068256378174, - 1.790145993232727 + 4.991535186767578, + 3.652777910232544, + 1.8272713422775269 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -579643,31 +579793,31 @@ "doi": "10.1101/2023.07.27.550754", "projections": { "pca": [ - 0.1284676045179367, - -0.11390434205532074 + 0.12819990515708923, + -0.11420201510190964 ], "tsne": [ - 25.212182998657227, - 24.79692268371582 + 29.718074798583984, + -25.07229232788086 ], "umap": [ - 8.456077575683594, - 5.612366199493408 + 8.667232513427734, + 5.229802131652832 ], "pca3d": [ - 0.1284676492214203, - -0.11389219760894775, - -0.013747517950832844 + 0.12819990515708923, + -0.11418881267309189, + -0.013619550503790379 ], "tsne3d": [ - 11.168099403381348, - 21.979846954345703, - 4.327378749847412 + 16.776588439941406, + -10.138534545898438, + 8.1281156539917 ], "umap3d": [ - 7.455753326416016, - 5.211784362792969, - 3.273803949356079 + 7.567405700683594, + 5.663582801818848, + 1.5783981084823608 ] }, "cluster_id": 4, @@ -579678,11 +579828,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -580105,31 +580255,31 @@ "cited_by_count": 22, "projections": { "pca": [ - 0.0261832382529974, - -0.14440228044986725 + 0.025863541290163994, + -0.14435523748397827 ], "tsne": [ - 16.165233612060547, - 5.044402122497559 + 17.315439224243164, + -1.262473702430725 ], "umap": [ - 7.440629959106445, - 6.563660621643066 + 7.727064609527588, + 6.186362266540527 ], "pca3d": [ - 0.02618337981402874, - -0.14438575506210327, - -0.07259316742420197 + 0.02586366795003414, + -0.14433805644512177, + -0.07225819677114487 ], "tsne3d": [ - 8.944809913635254, - 5.848883628845215, - -14.627130508422852 + 10.631308555603027, + 1.2055076360702515, + 9.723308563232422 ], "umap3d": [ - 6.078576564788818, - 4.8469929695129395, - 2.9103784561157227 + 6.187005519866943, + 5.069876670837402, + 1.9785102605819702 ] }, "cluster_id": 4, @@ -580140,12 +580290,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 7, + "label": "Genomic Models" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -580582,31 +580732,31 @@ "doi": "10.1101/2023.09.09.556981", "projections": { "pca": [ - 0.0706472098827362, - -0.08627413213253021 + 0.07058838754892349, + -0.08598417043685913 ], "tsne": [ - 8.314167022705078, - 25.831188201904297 + 6.111294746398926, + -20.580312728881836 ], "umap": [ - 6.7887654304504395, - 4.581689834594727 + 7.271263599395752, + 4.383230686187744 ], "pca3d": [ - 0.07064720243215561, - -0.08626671135425568, - 0.18498864769935608 + 0.07058844715356827, + -0.08597664535045624, + 0.18520872294902802 ], "tsne3d": [ - 4.4405198097229, - 15.289318084716797, - -3.7466883659362793 + 3.506258487701416, + -6.686861991882324, + 3.5437073707580566 ], "umap3d": [ - 5.936954021453857, - 5.862657070159912, - 4.609821796417236 + 6.504837989807129, + 6.734302997589111, + 3.027376890182495 ] }, "cluster_id": 3, @@ -580617,11 +580767,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -581030,46 +581180,46 @@ "journal": "Nature", "projections": { "pca": [ - -0.15531085431575775, - -0.133752703666687 + -0.15543542802333832, + -0.13363675773143768 ], "tsne": [ - -29.9528865814209, - -2.2478525638580322 + -39.52069091796875, + 5.864147663116455 ], "umap": [ - 0.9122365713119507, - 8.265074729919434 + 1.1331191062927246, + 7.490285396575928 ], "pca3d": [ - -0.15531045198440552, - -0.13376064598560333, - -0.010685042478144169 + -0.1554349660873413, + -0.1336439549922943, + -0.010276052169501781 ], "tsne3d": [ - -20.551677703857422, - -5.525531768798828, - 13.447404861450195 + -22.792356491088867, + 9.145068168640137, + 5.2775044441223145 ], "umap3d": [ - 1.0337826013565063, - 4.666193962097168, - 5.2657694816589355 + 2.2766339778900146, + 5.130190849304199, + 5.942975997924805 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -581492,31 +581642,31 @@ "doi": "10.1101/2023.08.21.554146", "projections": { "pca": [ - 0.16110044717788696, - -0.07598093897104263 + 0.16086001694202423, + -0.07651698589324951 ], "tsne": [ - 56.38566589355469, - -4.944791316986084 + 50.1428108215332, + 12.383888244628906 ], "umap": [ - 10.063949584960938, - 7.556334018707275 + 10.292202949523926, + 7.220118522644043 ], "pca3d": [ - 0.16110053658485413, - -0.07597538083791733, - -0.11057218909263611 + 0.16085998713970184, + -0.07651179283857346, + -0.11059047281742096 ], "tsne3d": [ - 31.693269729614258, - 0.757633626461029, - -7.5670084953308105 + 30.33456802368164, + 4.195502758026123, + 1.5064510107040405 ], "umap3d": [ - 7.343721866607666, - 5.125471591949463, - 0.9138261079788208 + 6.737450122833252, + 4.82130241394043, + -0.30802538990974426 ] }, "cluster_id": 0, @@ -581527,12 +581677,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -581953,31 +582103,31 @@ "doi": "10.1101/2023.08.21.554109", "projections": { "pca": [ - 0.24501082301139832, - 0.012689835391938686 + 0.24506685137748718, + 0.012129014357924461 ], "tsne": [ - 25.73883819580078, - 22.9962158203125 + 31.845195770263672, + -26.910768508911133 ], "umap": [ - 8.57382583618164, - 5.4507060050964355 + 8.8212308883667, + 5.197731971740723 ], "pca3d": [ - 0.24501067399978638, - 0.012684481218457222, - 0.00019831555255223066 + 0.2450668066740036, + 0.012123766355216503, + 2.6941230316879228e-05 ], "tsne3d": [ - 12.318099975585938, - 26.010404586791992, - 2.614332437515259 + 18.83687973022461, + -12.770156860351562, + 9.44774341583252 ], "umap3d": [ - 7.640356063842773, - 5.194541931152344, - 3.2031350135803223 + 7.658589839935303, + 5.624721050262451, + 1.4976613521575928 ] }, "cluster_id": 4, @@ -581988,11 +582138,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -582411,46 +582561,46 @@ "cited_by_count": 87, "projections": { "pca": [ - 0.0643506571650505, - -0.02376631647348404 + 0.06426358968019485, + -0.0239811260253191 ], "tsne": [ - 18.489255905151367, - -29.97493553161621 + 10.561495780944824, + 31.035398483276367 ], "umap": [ - 6.944433689117432, - 9.71925163269043 + 7.007246494293213, + 9.319355964660645 ], "pca3d": [ - 0.06435064226388931, - -0.023768965154886246, - -0.11113052815198898 + 0.06426360458135605, + -0.023982936516404152, + -0.11112114787101746 ], "tsne3d": [ - 15.004059791564941, - -15.341574668884277, - 5.785903453826904 + 3.3675220012664795, + 6.349987030029297, + -21.21324348449707 ], "umap3d": [ - 5.256310939788818, - 3.0817577838897705, - 1.5391435623168945 + 5.300637722015381, + 2.8541629314422607, + 1.8608100414276123 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -582859,31 +583009,31 @@ "journal": "Nature Genetics", "projections": { "pca": [ - 0.22048266232013702, - -0.0012966720387339592 + 0.22053726017475128, + -0.002122635720297694 ], "tsne": [ - 50.57960510253906, - -1.633711338043213 + 56.81974792480469, + 8.768839836120605 ], "umap": [ - 10.56855297088623, - 7.509448051452637 + 10.792387008666992, + 7.023690700531006 ], "pca3d": [ - 0.22048233449459076, - -0.0012821723939850926, - -0.03701891750097275 + 0.2205369919538498, + -0.0021083219908177853, + -0.037214431911706924 ], "tsne3d": [ - 34.23537826538086, - -0.3359140455722809, - 0.2735000252723694 + 35.34531021118164, + 4.330790042877197, + 0.3284680247306824 ], "umap3d": [ - 7.795229911804199, - 4.925742149353027, - 0.5371262431144714 + 7.09213924407959, + 4.521666049957275, + -0.6812479496002197 ] }, "cluster_id": 0, @@ -582894,12 +583044,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -583317,31 +583467,31 @@ "doi": "10.1101/2023.07.19.549677", "projections": { "pca": [ - 0.17121273279190063, - -0.09469767659902573 + 0.1711142659187317, + -0.09441982954740524 ], "tsne": [ - 8.623802185058594, - 11.493072509765625 + 10.958024978637695, + -9.109540939331055 ], "umap": [ - 6.790661334991455, - 5.810913562774658 + 7.116209506988525, + 5.478919982910156 ], "pca3d": [ - 0.17121288180351257, - -0.09470324218273163, - 0.0891198217868805 + 0.17111435532569885, + -0.09442509710788727, + 0.0893828496336937 ], "tsne3d": [ - 4.283539772033691, - 6.996491432189941, - -6.173967361450195 + 5.343482971191406, + -1.4966466426849365, + 2.2372074127197266 ], "umap3d": [ - 5.739806175231934, - 5.189948558807373, - 3.739938974380493 + 6.03712797164917, + 5.605329513549805, + 2.6137161254882812 ] }, "cluster_id": 4, @@ -583352,11 +583502,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -583791,31 +583941,31 @@ "openalex_url": "https://openalex.org/W4385666895", "projections": { "pca": [ - 0.26371636986732483, - -0.004633749835193157 + 0.2636907398700714, + -0.005419885739684105 ], "tsne": [ - 52.465091705322266, - 3.423485040664673 + 49.69041061401367, + 15.99726676940918 ], "umap": [ - 10.072809219360352, - 7.212807655334473 + 10.274423599243164, + 6.733514308929443 ], "pca3d": [ - 0.2637164890766144, - -0.004651818424463272, - -0.12434076517820358 + 0.2636907696723938, + -0.005438500549644232, + -0.12451618164777756 ], "tsne3d": [ - 24.866790771484375, - 2.3692445755004883, - -0.8061023950576782 + 25.788665771484375, + 1.1904313564300537, + -3.9297423362731934 ], "umap3d": [ - 7.892587184906006, - 4.921268463134766, - 1.0834379196166992 + 7.320057392120361, + 4.7402167320251465, + -0.31171926856040955 ] }, "cluster_id": 0, @@ -583826,12 +583976,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -584258,31 +584408,31 @@ "openalex_url": "https://openalex.org/W4385576708", "projections": { "pca": [ - 0.2072618305683136, - -0.08416914194822311 + 0.2071601301431656, + -0.08464550971984863 ], "tsne": [ - 52.080448150634766, - -15.592360496520996 + 35.470115661621094, + 28.376026153564453 ], "umap": [ - 9.560493469238281, - 7.450315475463867 + 9.73011302947998, + 7.047837257385254 ], "pca3d": [ - 0.20726165175437927, - -0.08415382355451584, - -0.03752611577510834 + 0.20715995132923126, + -0.08462996780872345, + -0.03743273392319679 ], "tsne3d": [ - 29.334989547729492, - 8.453417778015137, - 8.363072395324707 + 29.88730239868164, + 3.7515251636505127, + 7.9181060791015625 ], "umap3d": [ - 6.788751125335693, - 4.973440647125244, - 1.3107786178588867 + 6.31050443649292, + 4.718599319458008, + 0.2679173946380615 ] }, "cluster_id": 0, @@ -584293,12 +584443,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 20, - "label": "Chromatin Regulation" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -584718,31 +584868,31 @@ ], "projections": { "pca": [ - -0.27310147881507874, - -0.06578278541564941 + -0.27328428626060486, + -0.06490638107061386 ], "tsne": [ - -39.0335693359375, - 12.720720291137695 + -35.157466888427734, + -19.41851043701172 ], "umap": [ - 2.0904929637908936, - 5.257990837097168 + 2.5325798988342285, + 4.625045299530029 ], "pca3d": [ - -0.2731013000011444, - -0.06579213589429855, - -0.03912755474448204 + -0.27328386902809143, + -0.06491637974977493, + -0.03893795236945152 ], "tsne3d": [ - -20.207111358642578, - 8.237937927246094, - 3.123661518096924 + -19.506263732910156, + -16.675395965576172, + 5.0130414962768555 ], "umap3d": [ - 1.9428280591964722, - 6.688587665557861, - 4.8448805809021 + 2.543668508529663, + 7.028625011444092, + 4.75460958480835 ] }, "cluster_id": 2, @@ -584753,11 +584903,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -585181,31 +585331,31 @@ "cited_by_count": 46, "projections": { "pca": [ - 0.08016733825206757, - -0.12235424667596817 + 0.07970219105482101, + -0.12253139913082123 ], "tsne": [ - 20.888227462768555, - -6.121032238006592 + 19.157350540161133, + 7.190570831298828 ], "umap": [ - 7.8803486824035645, - 6.513860702514648 + 8.028792381286621, + 6.131243705749512 ], "pca3d": [ - 0.08016742020845413, - -0.12234188616275787, - -0.04083755984902382 + 0.07970234751701355, + -0.12251859903335571, + -0.04077724367380142 ], "tsne3d": [ - 17.234834671020508, - 12.88688850402832, - -8.10196304321289 + 15.848816871643066, + -4.2484025955200195, + 1.130456805229187 ], "umap3d": [ - 6.268119812011719, - 5.139261245727539, - 2.620574712753296 + 6.292058944702148, + 5.214951038360596, + 1.513267993927002 ] }, "cluster_id": 4, @@ -585216,12 +585366,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -585632,47 +585782,47 @@ ], "projections": { "pca": [ - 0.24551677703857422, - 0.03164868429303169 + 0.24555738270282745, + 0.031258389353752136 ], "tsne": [ - 27.763898849487305, - 10.836995124816895 + 31.132471084594727, + -5.684350967407227 ], "umap": [ - 7.526247978210449, - 7.667474269866943 + 7.6746063232421875, + 7.3708038330078125 ], "pca3d": [ - 0.2455165982246399, - 0.03163960203528404, - -0.1080385223031044 + 0.24555721879005432, + 0.031249240040779114, + -0.10815546661615372 ], "tsne3d": [ - 13.011286735534668, - -7.670329570770264, - -10.314229011535645 + 11.609509468078613, + 14.231633186340332, + 4.789281845092773 ], "umap3d": [ - 7.013183116912842, - 3.7410168647766113, - 2.9545788764953613 + 7.3988356590271, + 4.003310680389404, + 1.9913392066955566 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -586090,47 +586240,47 @@ "doi": "10.1101/2023.07.26.550634", "projections": { "pca": [ - 0.23949404060840607, - -0.1026497632265091 + 0.23920838534832, + -0.10300679504871368 ], "tsne": [ - 27.46175765991211, - -9.034246444702148 + 24.35211181640625, + 10.592696189880371 ], "umap": [ - 8.292088508605957, - 8.298121452331543 + 8.530784606933594, + 7.999013423919678 ], "pca3d": [ - 0.23949377238750458, - -0.10263609141111374, - -0.15774695575237274 + 0.23920829594135284, + -0.10299285501241684, + -0.1575690656900406 ], "tsne3d": [ - 13.229314804077148, - -0.5749512910842896, - -6.448583602905273 + 13.943938255310059, + 4.294281959533691, + -2.9969279766082764 ], "umap3d": [ - 6.148867130279541, - 4.387779712677002, - 1.686707615852356 + 5.930052280426025, + 4.164040565490723, + 1.0869708061218262 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 12, - "label": "Genetic Variants" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -586557,46 +586707,46 @@ "doi": "10.1101/2023.07.18.549537", "projections": { "pca": [ - 0.1400556117296219, - -0.0012434959644451737 + 0.14004048705101013, + -0.001628149184398353 ], "tsne": [ - 23.937833786010742, - -23.394737243652344 + 15.954864501953125, + 25.16396713256836 ], "umap": [ - 7.579549789428711, - 9.263569831848145 + 7.647895812988281, + 9.055882453918457 ], "pca3d": [ - 0.14005544781684875, - -0.0012345516588538885, - -0.12093797326087952 + 0.1400403529405594, + -0.0016189218731597066, + -0.12093015015125275 ], "tsne3d": [ - 14.432697296142578, - -9.360681533813477, - 8.700237274169922 + 11.466574668884277, + 4.713057518005371, + -15.59915828704834 ], "umap3d": [ - 5.491844177246094, - 3.480295181274414, - 1.5066872835159302 + 5.375786781311035, + 3.339719533920288, + 1.4261409044265747 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -587041,46 +587191,46 @@ ], "projections": { "pca": [ - -0.11290668696165085, - 0.028058724477887154 + -0.11271115392446518, + 0.02873033657670021 ], "tsne": [ - 1.3691813945770264, - -33.124385833740234 + -6.407094478607178, + 31.144323348999023 ], "umap": [ - 4.905982494354248, - 9.408320426940918 + 5.107511520385742, + 8.720419883728027 ], "pca3d": [ - -0.11290645599365234, - 0.028038540855050087, - -0.009588360786437988 + -0.11271090060472488, + 0.028709517791867256, + -0.009378854185342789 ], "tsne3d": [ - 0.18355891108512878, - -17.727380752563477, - -0.3874322772026062 + -6.208317756652832, + 15.366063117980957, + -10.407657623291016 ], "umap3d": [ - 3.986560583114624, - 2.724069118499756, - 2.9697985649108887 + 4.712046146392822, + 3.046393394470215, + 3.487137794494629 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, + "id": 26, "label": "Medical AI" } ], @@ -587514,31 +587664,31 @@ "doi": "10.1101/2023.03.28.533945", "projections": { "pca": [ - 0.3018437325954437, - 0.03399863466620445 + 0.30194705724716187, + 0.03289920836687088 ], "tsne": [ - 48.651371002197266, - 4.030444145202637 + 42.11943435668945, + 14.283731460571289 ], "umap": [ - 9.591219902038574, - 7.537562370300293 + 9.952982902526855, + 6.915452480316162 ], "pca3d": [ - 0.30184346437454224, - 0.03400375694036484, - -0.11551046371459961 + 0.30194661021232605, + 0.03290444239974022, + -0.1157708540558815 ], "tsne3d": [ - 26.114103317260742, - 0.5405104756355286, - 2.6530117988586426 + 26.7445068359375, + 3.5085206031799316, + -7.9102253913879395 ], "umap3d": [ - 7.796391487121582, - 4.664956569671631, - 1.2150256633758545 + 7.185216426849365, + 4.329010963439941, + 0.06095883622765541 ] }, "cluster_id": 0, @@ -587549,12 +587699,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 20, - "label": "Chromatin Regulation" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -587962,31 +588112,31 @@ "journal": "Nature", "projections": { "pca": [ - 0.21303512156009674, - 0.030305538326501846 + 0.21325331926345825, + 0.029731379821896553 ], "tsne": [ - 52.205169677734375, - -6.615407466888428 + 49.6916389465332, + 8.922296524047852 ], "umap": [ - 10.450958251953125, - 7.828690052032471 + 10.608718872070312, + 7.46641206741333 ], "pca3d": [ - 0.21303509175777435, - 0.030290862545371056, - -0.01382507849484682 + 0.2132532000541687, + 0.029715878888964653, + -0.013970091007649899 ], "tsne3d": [ - 31.43926429748535, - -4.227676868438721, - -7.5464253425598145 + 31.790109634399414, + 8.431559562683105, + 2.6234030723571777 ], "umap3d": [ - 7.4508843421936035, - 5.014195442199707, - 0.4929670989513397 + 6.626637935638428, + 4.559024333953857, + -0.7043800950050354 ] }, "cluster_id": 0, @@ -587997,12 +588147,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -588420,31 +588570,31 @@ "doi": "10.1101/2023.07.08.548206", "projections": { "pca": [ - 0.08188135921955109, - -0.14253424108028412 + 0.08144591748714447, + -0.14234310388565063 ], "tsne": [ - 12.247376441955566, - 26.575057983398438 + 19.21254539489746, + -22.74297332763672 ], "umap": [ - 7.410472869873047, - 4.576901435852051 + 7.807168960571289, + 4.464676380157471 ], "pca3d": [ - 0.0818813145160675, - -0.14251074194908142, - 0.06349220871925354 + 0.08144593238830566, + -0.14231880009174347, + 0.0636504739522934 ], "tsne3d": [ - 6.186734676361084, - 19.07942771911621, - -3.9094367027282715 + 9.546085357666016, + -12.201831817626953, + 10.112078666687012 ], "umap3d": [ - 6.788025379180908, - 5.93589448928833, - 4.289891719818115 + 6.990382671356201, + 6.769927978515625, + 2.5578815937042236 ] }, "cluster_id": 4, @@ -588455,11 +588605,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -588884,46 +589034,46 @@ "openalex_url": "https://openalex.org/W4367602258", "projections": { "pca": [ - 0.08389414846897125, - -0.08240506798028946 + 0.08380795270204544, + -0.08218519389629364 ], "tsne": [ - 21.532503128051758, - -21.11931610107422 + 14.176592826843262, + 21.696430206298828 ], "umap": [ - 7.564270973205566, - 9.453495979309082 + 7.483166694641113, + 9.081366539001465 ], "pca3d": [ - 0.08389423042535782, - -0.08239711821079254, - -0.034009918570518494 + 0.08380796015262604, + -0.08217709511518478, + -0.03372916206717491 ], "tsne3d": [ - 9.75218677520752, - -6.3948235511779785, - 9.831075668334961 + 7.631568908691406, + 2.509718894958496, + -12.19085693359375 ], "umap3d": [ - 5.244311332702637, - 3.6997132301330566, - 1.557509183883667 + 5.06278657913208, + 3.5714900493621826, + 1.600800633430481 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -589347,31 +589497,31 @@ ], "projections": { "pca": [ - 0.14586593210697174, - -0.11757440865039825 + 0.1456136405467987, + -0.11758895218372345 ], "tsne": [ - 13.187012672424316, - 11.038899421691895 + 13.062776565551758, + -3.9649131298065186 ], "umap": [ - 7.2021403312683105, - 6.081915378570557 + 7.487566947937012, + 5.723654747009277 ], "pca3d": [ - 0.14586618542671204, - -0.11758480221033096, - 0.012672873213887215 + 0.14561383426189423, + -0.11759892106056213, + 0.012886968441307545 ], "tsne3d": [ - 8.212864875793457, - 6.41970157623291, - -7.134319305419922 + 9.43636703491211, + -4.550094127655029, + 4.18030309677124 ], "umap3d": [ - 6.286572456359863, - 4.9793477058410645, - 3.3774871826171875 + 6.534692764282227, + 5.3774542808532715, + 2.335484743118286 ] }, "cluster_id": 4, @@ -589382,11 +589532,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -589803,31 +589953,31 @@ "doi": "10.1101/2023.06.27.546584", "projections": { "pca": [ - 0.2713838815689087, - -0.011471621692180634 + 0.271269291639328, + -0.011740616522729397 ], "tsne": [ - 49.19617462158203, - 12.128154754638672 + 44.13726806640625, + 20.144248962402344 ], "umap": [ - 9.529415130615234, - 6.7392168045043945 + 9.72470760345459, + 6.315127849578857 ], "pca3d": [ - 0.2713836431503296, - -0.011464782059192657, - -0.04119735211133957 + 0.27126917243003845, + -0.01173380296677351, + -0.0413389652967453 ], "tsne3d": [ - 20.532934188842773, - 7.374078273773193, - -4.263849258422852 + 24.114845275878906, + 1.3365436792373657, + 5.2957658767700195 ], "umap3d": [ - 7.4291205406188965, - 5.365314960479736, - 1.5456548929214478 + 6.842010974884033, + 5.266960144042969, + 0.2345467507839203 ] }, "cluster_id": 0, @@ -589838,12 +589988,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -590267,31 +590417,31 @@ "cited_by_count": 203, "projections": { "pca": [ - 0.2735965847969055, - 0.0954911932349205 + 0.27402037382125854, + 0.09480056166648865 ], "tsne": [ - 14.527359962463379, - 21.53545570373535 + 15.183349609375, + -21.306957244873047 ], "umap": [ - 5.956758499145508, - 5.492086887359619 + 6.26981782913208, + 5.135523796081543 ], "pca3d": [ - 0.27359649538993835, - 0.09547857195138931, - 0.12721920013427734 + 0.27402013540267944, + 0.0947878509759903, + 0.12691707909107208 ], "tsne3d": [ - 9.223682403564453, - 21.12207794189453, - 11.842650413513184 + 6.719607830047607, + -4.101674556732178, + 14.221447944641113 ], "umap3d": [ - 6.057528972625732, - 6.1790995597839355, - 3.6872782707214355 + 5.710726737976074, + 6.421480178833008, + 2.300267219543457 ] }, "cluster_id": 3, @@ -590302,12 +590452,12 @@ "label": "Protein Design" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 24, - "label": "Protein Design" + "id": 32, + "label": "Genome Protein" } ], "_embedding": [ @@ -590715,46 +590865,46 @@ ], "projections": { "pca": [ - 0.12154825776815414, - 0.15981371700763702 + 0.12224317342042923, + 0.15914548933506012 ], "tsne": [ - -0.24843730032444, - -6.980254650115967 + -0.18678230047225952, + 5.645984172821045 ], "umap": [ - 5.118963241577148, - 7.332535743713379 + 5.352444648742676, + 6.64094877243042 ], "pca3d": [ - 0.1215476393699646, - 0.15983043611049652, - -0.01702055148780346 + 0.12224254757165909, + 0.15916258096694946, + -0.017188388854265213 ], "tsne3d": [ - -2.6870601177215576, - -1.7451590299606323, - -20.26310920715332 + 2.0020525455474854, + 22.761571884155273, + -4.718338489532471 ], "umap3d": [ - 4.67409610748291, - 3.42962908744812, - 4.920374870300293 + 5.749908924102783, + 4.663074493408203, + 4.94488525390625 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, + "id": 13, "label": "Genomics Papers" } ], @@ -591173,31 +591323,31 @@ "doi": "10.1101/2022.10.01.510472", "projections": { "pca": [ - 0.07434750348329544, - -0.10221997648477554 + 0.07404119521379471, + -0.10188008099794388 ], "tsne": [ - 24.398622512817383, - 26.03289794921875 + 29.370685577392578, + -26.653270721435547 ], "umap": [ - 8.311098098754883, - 5.691878795623779 + 8.522289276123047, + 5.280821323394775 ], "pca3d": [ - 0.07434739917516708, - -0.10218121111392975, - 0.04980895668268204 + 0.07404101639986038, + -0.10184027999639511, + 0.049926530569791794 ], "tsne3d": [ - 10.474289894104004, - 23.08597183227539, - 2.3458101749420166 + 16.522205352783203, + -7.652259826660156, + 7.803732395172119 ], "umap3d": [ - 7.379651069641113, - 5.344608306884766, - 3.2574408054351807 + 7.4232330322265625, + 5.76442289352417, + 1.5805259943008423 ] }, "cluster_id": 4, @@ -591208,11 +591358,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -591630,31 +591780,31 @@ "cited_by_count": 1, "projections": { "pca": [ - 0.16910098493099213, - 0.07722387462854385 + 0.16928716003894806, + 0.07708104699850082 ], "tsne": [ - 36.70762634277344, - -5.454303741455078 + 41.686702728271484, + 11.835362434387207 ], "umap": [ - 9.449058532714844, - 7.8278422355651855 + 9.654556274414062, + 7.291551113128662 ], "pca3d": [ - 0.1691007763147354, - 0.07720915228128433, - -0.06206457316875458 + 0.16928698122501373, + 0.0770658627152443, + -0.06225861236453056 ], "tsne3d": [ - 23.736797332763672, - -12.54371166229248, - 2.5903701782226562 + 25.339017868041992, + 5.514033317565918, + -4.025099277496338 ], "umap3d": [ - 7.36204195022583, - 4.475456237792969, - 1.3015576601028442 + 6.832859992980957, + 4.218313694000244, + 0.09500617533922195 ] }, "cluster_id": 0, @@ -591665,12 +591815,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 20, - "label": "Chromatin Regulation" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -592092,31 +592242,31 @@ "doi": "10.1101/2023.06.05.543769", "projections": { "pca": [ - 0.245159313082695, - 0.010437067598104477 + 0.2452823668718338, + 0.009710513986647129 ], "tsne": [ - 25.537038803100586, - 30.24816131591797 + 28.54907989501953, + -30.305999755859375 ], "umap": [ - 8.50870132446289, - 5.002172946929932 + 8.853374481201172, + 4.652964115142822 ], "pca3d": [ - 0.24515916407108307, - 0.010452660731971264, - 0.04355233162641525 + 0.24528203904628754, + 0.00972678791731596, + 0.043388232588768005 ], "tsne3d": [ - 9.265935897827148, - 26.405624389648438, - -0.9169542789459229 + 19.85772132873535, + -16.30040168762207, + 7.561459064483643 ], "umap3d": [ - 7.642301082611084, - 5.786643028259277, - 3.453622579574585 + 7.5955810546875, + 6.31649923324585, + 1.5785117149353027 ] }, "cluster_id": 4, @@ -592127,11 +592277,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -592540,31 +592690,31 @@ "journal": "Nature Biotechnology", "projections": { "pca": [ - 0.21189555525779724, - 0.1436987966299057 + 0.21211853623390198, + 0.1431119441986084 ], "tsne": [ - 54.30524826049805, - 8.121673583984375 + 54.93185806274414, + 15.782463073730469 ], "umap": [ - 10.300590515136719, - 6.874927043914795 + 10.56126594543457, + 6.3500542640686035 ], "pca3d": [ - 0.2118951380252838, - 0.14370781183242798, - -0.1032361388206482 + 0.21211813390254974, + 0.14312119781970978, + -0.10372234135866165 ], "tsne3d": [ - 31.999906539916992, - 5.112479209899902, - 0.08515966683626175 + 31.341552734375, + -1.4689135551452637, + -6.689422607421875 ], "umap3d": [ - 8.111058235168457, - 5.306425094604492, - 1.1255744695663452 + 7.474967956542969, + 5.068645477294922, + -0.4922451078891754 ] }, "cluster_id": 0, @@ -592575,12 +592725,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -592988,46 +593138,46 @@ "journal": "Nature", "projections": { "pca": [ - -0.06104401871562004, - -0.10965388268232346 + -0.06134922057390213, + -0.10938862711191177 ], "tsne": [ - 4.944136619567871, - -16.9699649810791 + -18.71590805053711, + 27.36204719543457 ], "umap": [ - 6.213327407836914, - 7.727333068847656 + 6.496399879455566, + 7.0993218421936035 ], "pca3d": [ - -0.06104437634348869, - -0.10960375517606735, - -0.031230177730321884 + -0.06134955957531929, + -0.10933757573366165, + -0.031034205108880997 ], "tsne3d": [ - 9.390177726745605, - 16.532073974609375, - -17.13773536682129 + -1.5112180709838867, + 7.491202354431152, + 4.8411641120910645 ], "umap3d": [ - 4.61171817779541, - 4.747138500213623, - 2.3890023231506348 + 4.978048324584961, + 4.665266036987305, + 1.7665743827819824 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 26, + "id": 33, "label": "Genetic Disease" } ], @@ -593449,31 +593599,31 @@ "cited_by_count": 90, "projections": { "pca": [ - 0.11459039151668549, - 0.046776916831731796 + 0.1146412268280983, + 0.04659884795546532 ], "tsne": [ - 27.682491302490234, - 24.153236389160156 + 31.336185455322266, + -24.147817611694336 ], "umap": [ - 8.528051376342773, - 5.6761155128479 + 8.792819023132324, + 5.263941764831543 ], "pca3d": [ - 0.11459014564752579, - 0.046796608716249466, - -0.07218071073293686 + 0.11464102566242218, + 0.046619314700365067, + -0.07233348488807678 ], "tsne3d": [ - 14.255188941955566, - 23.72368812561035, - 2.2672524452209473 + 19.91304588317871, + -9.744950294494629, + 7.837931156158447 ], "umap3d": [ - 7.5997467041015625, - 5.080302715301514, - 3.2438907623291016 + 7.643004894256592, + 5.569424629211426, + 1.5354465246200562 ] }, "cluster_id": 4, @@ -593484,11 +593634,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -593899,31 +594049,31 @@ "journal": "Nature Genetics", "projections": { "pca": [ - 0.20752236247062683, - 0.15401369333267212 + 0.20787104964256287, + 0.15374860167503357 ], "tsne": [ - 51.682106018066406, - -2.55340576171875 + 53.9714241027832, + 7.51785135269165 ], "umap": [ - 10.388609886169434, - 7.5104899406433105 + 10.563032150268555, + 7.0985260009765625 ], "pca3d": [ - 0.20752188563346863, - 0.15400168299674988, - -0.10377082973718643 + 0.207870751619339, + 0.1537359058856964, + -0.10416039079427719 ], "tsne3d": [ - 30.36405372619629, - -4.357532501220703, - -0.5079905390739441 + 33.45329666137695, + 4.919021129608154, + -5.364593029022217 ], "umap3d": [ - 7.814821243286133, - 4.795221328735352, - 0.7657256722450256 + 7.1120195388793945, + 4.487193584442139, + -0.5650717616081238 ] }, "cluster_id": 0, @@ -593934,12 +594084,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -594354,31 +594504,31 @@ ], "projections": { "pca": [ - -0.06813337653875351, - -0.03714330494403839 + -0.06800978630781174, + -0.03658398613333702 ], "tsne": [ - -30.572298049926758, - 18.383808135986328 + -25.507389068603516, + -22.222387313842773 ], "umap": [ - 3.326831102371216, - 4.8707733154296875 + 3.788508176803589, + 4.291667938232422 ], "pca3d": [ - -0.06813319027423859, - -0.03715083748102188, - 0.09898652136325836 + -0.06800956279039383, + -0.03659207373857498, + 0.09920099377632141 ], "tsne3d": [ - -11.458827018737793, - 13.508305549621582, - 1.216984748840332 + -10.717724800109863, + -21.34392547607422, + 1.9579284191131592 ], "umap3d": [ - 3.19917631149292, - 6.88474702835083, - 4.750245094299316 + 3.5868611335754395, + 7.279928684234619, + 4.200477600097656 ] }, "cluster_id": 2, @@ -594389,12 +594539,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 16, - "label": "Diffusion Models" + "id": 7, + "label": "Diffusion Gen" } ], "_embedding": [ @@ -594852,31 +595002,31 @@ "doi": "10.1101/2023.05.15.540865", "projections": { "pca": [ - 0.21268673241138458, - -0.017121251672506332 + 0.21266178786754608, + -0.017496025189757347 ], "tsne": [ - 27.77191734313965, - 26.3021240234375 + 29.412445068359375, + -21.28681182861328 ], "umap": [ - 8.702437400817871, - 5.804187297821045 + 8.876264572143555, + 5.441208839416504 ], "pca3d": [ - 0.21268640458583832, - -0.01711529865860939, - -0.09407567232847214 + 0.21266156435012817, + -0.01748957671225071, + -0.09405191987752914 ], "tsne3d": [ - 10.513466835021973, - 18.67130470275879, - 4.52742338180542 + 16.49144744873047, + -12.714044570922852, + 5.810494899749756 ], "umap3d": [ - 7.4759345054626465, - 5.2680768966674805, - 3.0487112998962402 + 7.429502964019775, + 5.5777764320373535, + 1.412340521812439 ] }, "cluster_id": 4, @@ -594887,11 +595037,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -595315,31 +595465,31 @@ "doi": "10.1101/2023.05.11.540401", "projections": { "pca": [ - 0.2071317881345749, - -0.11892899125814438 + 0.2067369818687439, + -0.11926792562007904 ], "tsne": [ - 57.960697174072266, - 6.514027118682861 + 52.50859069824219, + 20.537065505981445 ], "umap": [ - 10.063212394714355, - 6.612504005432129 + 10.255037307739258, + 6.169976234436035 ], "pca3d": [ - 0.20713181793689728, - -0.11891722679138184, - 0.007172259036451578 + 0.20673701167106628, + -0.11925611644983292, + 0.0071418918669223785 ], "tsne3d": [ - 27.14319610595703, - 7.923047065734863, - -2.3314125537872314 + 28.51886749267578, + -4.052308082580566, + -2.5652670860290527 ], "umap3d": [ - 7.950460433959961, - 5.556796073913574, - 1.389763593673706 + 7.315480709075928, + 5.39490270614624, + -0.18510060012340546 ] }, "cluster_id": 0, @@ -595350,12 +595500,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -595783,47 +595933,47 @@ "cited_by_count": 105, "projections": { "pca": [ - 0.048529013991355896, - -0.09867828339338303 + 0.04818899556994438, + -0.0987020879983902 ], "tsne": [ - 21.395408630371094, - -11.831148147583008 + 15.262627601623535, + 12.118245124816895 ], "umap": [ - 7.638200283050537, - 8.601125717163086 + 7.98552942276001, + 8.130255699157715 ], "pca3d": [ - 0.04852905124425888, - -0.0986640602350235, - -0.13455817103385925 + 0.048188965767621994, + -0.09868788719177246, + -0.13440322875976562 ], "tsne3d": [ - 8.376226425170898, - 10.90743350982666, - -14.301467895507812 + 7.830591678619385, + -0.18903684616088867, + -3.3196163177490234 ], "umap3d": [ - 5.889564514160156, - 4.815584182739258, - 2.254945993423462 + 5.77863073348999, + 4.849225044250488, + 1.6918461322784424 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -596231,31 +596381,31 @@ "journal": "Nature Genetics", "projections": { "pca": [ - 0.11178983002901077, - 0.0032475159969180822 + 0.11188089102506638, + 0.002928993199020624 ], "tsne": [ - 26.6851863861084, - 20.757179260253906 + 33.6082878112793, + -24.490177154541016 ], "umap": [ - 8.507126808166504, - 5.659280300140381 + 8.749256134033203, + 5.3082170486450195 ], "pca3d": [ - 0.11178960651159286, - 0.0032576126977801323, - -0.17307431995868683 + 0.11188067495822906, + 0.00293985684402287, + -0.1729402393102646 ], "tsne3d": [ - 14.3633394241333, - 24.70782470703125, - 6.981157302856445 + 19.563039779663086, + -8.73521614074707, + 12.028270721435547 ], "umap3d": [ - 7.551117420196533, - 4.895573139190674, - 3.2204413414001465 + 7.706762313842773, + 5.356111526489258, + 1.639007806777954 ] }, "cluster_id": 4, @@ -596266,11 +596416,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -596806,31 +596956,31 @@ "openalex_url": "https://openalex.org/W4367173723", "projections": { "pca": [ - 0.27426183223724365, - 0.0788235142827034 + 0.2743394672870636, + 0.07857349514961243 ], "tsne": [ - 42.427677154541016, - 13.447259902954102 + 47.27957534790039, + -7.148050785064697 ], "umap": [ - 9.375999450683594, - 7.507858753204346 + 9.815643310546875, + 6.804236888885498 ], "pca3d": [ - 0.27426159381866455, - 0.07880733907222748, - -0.048536743968725204 + 0.27433934807777405, + 0.07855714857578278, + -0.04883367195725441 ], "tsne3d": [ - 22.31135368347168, - -14.277434349060059, - -2.596226453781128 + 22.569793701171875, + 10.148964881896973, + 5.395811080932617 ], "umap3d": [ - 7.456835746765137, - 4.527263641357422, - 1.5895909070968628 + 7.01463508605957, + 4.467648983001709, + 0.16895167529582977 ] }, "cluster_id": 0, @@ -596841,12 +596991,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 20, - "label": "Chromatin Regulation" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -597286,31 +597436,31 @@ "doi": "10.1101/2023.04.26.538471", "projections": { "pca": [ - 0.12017844617366791, - -0.16769655048847198 + 0.11968466639518738, + -0.16761422157287598 ], "tsne": [ - 18.37816047668457, - -2.32015061378479 + 19.859800338745117, + 4.1625776290893555 ], "umap": [ - 7.917294025421143, - 6.526435375213623 + 8.16067123413086, + 6.140688896179199 ], "pca3d": [ - 0.12017867714166641, - -0.16769222915172577, - -0.04680905491113663 + 0.11968489736318588, + -0.16760972142219543, + -0.046568986028432846 ], "tsne3d": [ - 14.203875541687012, - 5.896501541137695, - -9.566802024841309 + 15.186956405639648, + 0.29045671224594116, + 3.033709764480591 ], "umap3d": [ - 6.423069477081299, - 5.1269850730896, - 2.512686014175415 + 6.262794017791748, + 5.280360698699951, + 1.5572705268859863 ] }, "cluster_id": 4, @@ -597321,12 +597471,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -597747,31 +597897,31 @@ "openalex_url": "https://openalex.org/W4366688485", "projections": { "pca": [ - 0.25117823481559753, - -0.02075255662202835 + 0.25111812353134155, + -0.020986072719097137 ], "tsne": [ - 35.46382141113281, - 0.5153924822807312 + 31.678607940673828, + 5.646047115325928 ], "umap": [ - 8.667829513549805, - 6.9629316329956055 + 8.810734748840332, + 6.542053699493408 ], "pca3d": [ - 0.25117796659469604, - -0.020748862996697426, - -0.006840042769908905 + 0.2511180341243744, + -0.020982276648283005, + -0.0068946764804422855 ], "tsne3d": [ - 18.607322692871094, - 1.136720061302185, - -6.235540390014648 + 19.068605422973633, + 4.235103607177734, + 1.4254438877105713 ], "umap3d": [ - 6.960612773895264, - 4.939778804779053, - 2.1175038814544678 + 6.682531356811523, + 4.878377437591553, + 0.8586902618408203 ] }, "cluster_id": 0, @@ -597782,12 +597932,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -598214,47 +598364,47 @@ "doi": "10.1101/2023.04.11.536361", "projections": { "pca": [ - 0.03732335567474365, - -0.06461377441883087 + 0.036917172372341156, + -0.06442221999168396 ], "tsne": [ - 34.189701080322266, - -20.118968963623047 + 26.25916290283203, + 26.447128295898438 ], "umap": [ - 8.415361404418945, - 9.95810604095459 + 8.784454345703125, + 9.342324256896973 ], "pca3d": [ - 0.037323202937841415, - -0.06458750367164612, - -0.08084037154912949 + 0.036917056888341904, + -0.06439564377069473, + -0.08087459951639175 ], "tsne3d": [ - 17.42241096496582, - 13.530329704284668, - 0.8007200360298157 + 12.555073738098145, + -6.797990322113037, + -9.00058364868164 ], "umap3d": [ - 4.757329940795898, - 4.906798362731934, - 1.2229502201080322 + 4.39771842956543, + 4.418286323547363, + 1.0675393342971802 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -598664,31 +598814,31 @@ "journal": "Nature Reviews Genetics", "projections": { "pca": [ - 0.10718066245317459, - 0.0013801617315039039 + 0.10742580145597458, + 0.0012005295138806105 ], "tsne": [ - 31.698640823364258, - 15.746442794799805 + 27.610647201538086, + -3.8732686042785645 ], "umap": [ - 7.81155252456665, - 7.269968509674072 + 7.9429192543029785, + 6.831509113311768 ], "pca3d": [ - 0.10718082636594772, - 0.0013424456119537354, - -0.01360748615115881 + 0.10742595791816711, + 0.0011634428519755602, + -0.013410016894340515 ], "tsne3d": [ - 7.342418670654297, - -3.4303030967712402, - -8.743423461914062 + 17.22325897216797, + 6.096079349517822, + 14.60750675201416 ], "umap3d": [ - 7.349173545837402, - 4.12615442276001, - 2.782341480255127 + 7.408629894256592, + 4.382946014404297, + 1.7007346153259277 ] }, "cluster_id": 4, @@ -598699,12 +598849,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -599156,31 +599306,31 @@ "cited_by_count": 17, "projections": { "pca": [ - 0.25114312767982483, - 0.08392276614904404 + 0.2513066530227661, + 0.08288557827472687 ], "tsne": [ - 39.57963562011719, - -6.681789875030518 + 39.34522247314453, + 9.800260543823242 ], "umap": [ - 9.136805534362793, - 8.363260269165039 + 9.506368637084961, + 7.931906223297119 ], "pca3d": [ - 0.25114285945892334, - 0.08392401784658432, - -0.2409992218017578 + 0.25130629539489746, + 0.0828867182135582, + -0.24123996496200562 ], "tsne3d": [ - 21.366872787475586, - -8.021615028381348, - -0.40374884009361267 + 23.10721778869629, + 10.574275016784668, + -6.860158443450928 ], "umap3d": [ - 7.1660332679748535, - 3.961414337158203, - 1.2216037511825562 + 6.7460808753967285, + 3.6908929347991943, + 0.3614262342453003 ] }, "cluster_id": 0, @@ -599191,12 +599341,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -599612,31 +599762,31 @@ "cited_by_count": 126, "projections": { "pca": [ - 0.07738125324249268, - -0.022464314475655556 + 0.07743685692548752, + -0.022384827956557274 ], "tsne": [ - 5.522850036621094, - 2.956831216812134 + 7.502697467803955, + -1.6540472507476807 ], "umap": [ - 6.716987609863281, - 5.9707794189453125 + 6.936916828155518, + 5.645108699798584 ], "pca3d": [ - 0.07738129794597626, - -0.02246204763650894, - 0.02998734824359417 + 0.0774368867278099, + -0.022382210940122604, + 0.030083179473876953 ], "tsne3d": [ - 7.808924198150635, - 2.738877534866333, - -0.7218908667564392 + 7.681766033172607, + -4.044151782989502, + -4.149089336395264 ], "umap3d": [ - 5.8699212074279785, - 4.9271368980407715, - 3.684640884399414 + 6.325056552886963, + 5.457369804382324, + 2.732895851135254 ] }, "cluster_id": 4, @@ -599647,11 +599797,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -600060,31 +600210,31 @@ "journal": "Nature", "projections": { "pca": [ - 0.21201378107070923, - 0.005852897651493549 + 0.2120445966720581, + 0.005493006203323603 ], "tsne": [ - 48.23455047607422, - -0.3902052938938141 + 44.893959045410156, + 16.644365310668945 ], "umap": [ - 9.764790534973145, - 7.5028910636901855 + 9.892481803894043, + 7.09378719329834 ], "pca3d": [ - 0.21201372146606445, - 0.005841363687068224, - -0.048624664545059204 + 0.21204447746276855, + 0.00548118120059371, + -0.04870985448360443 ], "tsne3d": [ - 30.07284927368164, - 5.332351207733154, - 6.095932483673096 + 32.57783889770508, + 2.386751651763916, + 5.7736358642578125 ], "umap3d": [ - 7.089113712310791, - 4.9703264236450195, - 1.1728593111038208 + 6.43661642074585, + 4.652548789978027, + 0.1268380582332611 ] }, "cluster_id": 0, @@ -600095,12 +600245,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 20, - "label": "Chromatin Regulation" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -600538,31 +600688,31 @@ "doi": "10.1101/2023.04.05.535726", "projections": { "pca": [ - 0.21047890186309814, - 0.06448917835950851 + 0.21055924892425537, + 0.06428226083517075 ], "tsne": [ - 36.97562026977539, - -5.57633638381958 + 41.35576629638672, + 11.437190055847168 ], "umap": [ - 9.339282035827637, - 7.8927764892578125 + 9.568251609802246, + 7.368171215057373 ], "pca3d": [ - 0.2104785293340683, - 0.06448716670274734, - -0.11394268274307251 + 0.21055898070335388, + 0.06428031623363495, + -0.11413451284170151 ], "tsne3d": [ - 22.449798583984375, - -12.683547019958496, - 1.9936250448226929 + 25.13472557067871, + 6.766406059265137, + -4.280996799468994 ], "umap3d": [ - 7.2720160484313965, - 4.3388190269470215, - 1.363041877746582 + 6.758631229400635, + 4.144216537475586, + 0.16059058904647827 ] }, "cluster_id": 0, @@ -600573,12 +600723,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -601126,47 +601276,47 @@ "openalex_url": "https://openalex.org/W4361269820", "projections": { "pca": [ - 0.20561307668685913, - 0.05658247694373131 + 0.2058255970478058, + 0.05625872686505318 ], "tsne": [ - 32.32414627075195, - 5.539720058441162 + 33.76362228393555, + -8.759024620056152 ], "umap": [ - 7.895740032196045, - 7.919687271118164 + 7.990823745727539, + 7.451474666595459 ], "pca3d": [ - 0.20561321079730988, - 0.05654554069042206, - -0.16170763969421387 + 0.20582570135593414, + 0.0562211349606514, + -0.1617143303155899 ], "tsne3d": [ - 16.849273681640625, - -11.602398872375488, - -14.156050682067871 + 16.797466278076172, + 18.274927139282227, + 4.000050067901611 ], "umap3d": [ - 6.947650909423828, - 3.708773374557495, - 2.4077107906341553 + 7.113601207733154, + 3.8191187381744385, + 1.6290959119796753 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -601590,31 +601740,31 @@ "openalex_url": "https://openalex.org/W4361001523", "projections": { "pca": [ - 0.16552039980888367, - -0.01126342173665762 + 0.16533678770065308, + -0.011120733805000782 ], "tsne": [ - 25.015907287597656, - -2.7640583515167236 + 27.219924926757812, + 5.40858268737793 ], "umap": [ - 8.377243041992188, - 6.7661452293396 + 8.532928466796875, + 6.390880107879639 ], "pca3d": [ - 0.16552023589611053, - -0.01125488430261612, - -0.023457560688257217 + 0.16533666849136353, + -0.011112086474895477, + -0.02355898730456829 ], "tsne3d": [ - 18.033432006835938, - 5.8292341232299805, - -6.385775566101074 + 19.900081634521484, + 1.1271564960479736, + 3.412642478942871 ], "umap3d": [ - 6.600192070007324, - 5.018808841705322, - 2.2597601413726807 + 6.48658561706543, + 5.056768417358398, + 1.1414424180984497 ] }, "cluster_id": 4, @@ -601625,12 +601775,12 @@ "label": "RNA Sequence Models" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -602052,31 +602202,31 @@ "doi": "10.1101/2023.03.16.532969", "projections": { "pca": [ - 0.09614535421133041, - -0.10597630590200424 + 0.0957479178905487, + -0.10574691742658615 ], "tsne": [ - 20.47496223449707, - 0.701053261756897 + 22.105575561523438, + 0.6998975872993469 ], "umap": [ - 7.6549577713012695, - 6.787095546722412 + 7.8604021072387695, + 6.4750075340271 ], "pca3d": [ - 0.09614530205726624, - -0.10596328973770142, - -0.09287180751562119 + 0.0957479327917099, + -0.10573358833789825, + -0.09269032627344131 ], "tsne3d": [ - 12.233809471130371, - 1.7936192750930786, - -10.885287284851074 + 11.67808723449707, + 4.452012538909912, + 5.283539772033691 ], "umap3d": [ - 6.3685078620910645, - 4.670706748962402, - 2.7359962463378906 + 6.482664585113525, + 4.7858123779296875, + 1.8360306024551392 ] }, "cluster_id": 4, @@ -602087,12 +602237,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -602511,31 +602661,31 @@ "cited_by_count": 22, "projections": { "pca": [ - 0.24104639887809753, - 0.02411426603794098 + 0.24107223749160767, + 0.023589683696627617 ], "tsne": [ - 32.927574157714844, - 23.695823669433594 + 52.99641418457031, + -3.4200758934020996 ], "umap": [ - 9.299186706542969, - 6.429234504699707 + 9.396655082702637, + 5.860800266265869 ], "pca3d": [ - 0.24104620516300201, - 0.02412206307053566, - -0.14729836583137512 + 0.24107196927070618, + 0.023597462102770805, + -0.14746055006980896 ], "tsne3d": [ - 17.885631561279297, - 21.01428985595703, - 1.3285064697265625 + 23.829486846923828, + -8.766217231750488, + 7.215777397155762 ], "umap3d": [ - 7.803943157196045, - 5.022268772125244, - 2.272040367126465 + 7.5244460105896, + 5.09451961517334, + 0.652880072593689 ] }, "cluster_id": 0, @@ -602546,12 +602696,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 2, + "label": "DNA Regulation" }, { - "id": 29, - "label": "Transcription Binding" + "id": 21, + "label": "RNA Splicing" } ], "_embedding": [ @@ -602975,31 +603125,31 @@ "doi": "10.1101/2023.02.24.529941", "projections": { "pca": [ - 0.13645721971988678, - -0.08713805675506592 + 0.1361788660287857, + -0.08718657493591309 ], "tsne": [ - 25.673221588134766, - -12.698505401611328 + 19.98785972595215, + 13.832640647888184 ], "umap": [ - 8.607752799987793, - 7.567231178283691 + 8.933319091796875, + 7.408633708953857 ], "pca3d": [ - 0.1364571452140808, - -0.08712030202150345, - -0.052089158445596695 + 0.13617882132530212, + -0.08716920763254166, + -0.052026696503162384 ], "tsne3d": [ - 14.64341926574707, - 11.795633316040039, - 12.990662574768066 + 15.83403491973877, + -7.5977935791015625, + -1.9449350833892822 ], "umap3d": [ - 6.348124027252197, - 4.966645240783691, - 1.5247753858566284 + 5.895517349243164, + 4.649782180786133, + 0.8582122921943665 ] }, "cluster_id": 0, @@ -603010,12 +603160,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -603434,31 +603584,31 @@ "cited_by_count": 20, "projections": { "pca": [ - 0.15161511301994324, - -0.16089555621147156 + 0.15128940343856812, + -0.16144046187400818 ], "tsne": [ - 23.342302322387695, - -3.8743696212768555 + 25.42061424255371, + 5.459985733032227 ], "umap": [ - 8.584168434143066, - 6.6188249588012695 + 8.739236831665039, + 6.157377243041992 ], "pca3d": [ - 0.15161539614200592, - -0.16089238226413727, - -0.056343208998441696 + 0.15128958225250244, + -0.16143688559532166, + -0.05611894279718399 ], "tsne3d": [ - 20.15994644165039, - 6.293859004974365, - -10.815433502197266 + 19.13913345336914, + -2.7818455696105957, + 2.0476226806640625 ], "umap3d": [ - 6.874202728271484, - 5.230960369110107, - 2.2135262489318848 + 6.587529182434082, + 5.202950954437256, + 0.9722314476966858 ] }, "cluster_id": 4, @@ -603469,12 +603619,12 @@ "label": "RNA Sequence Models" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -603903,31 +604053,31 @@ "doi": "10.1101/2023.02.15.528663", "projections": { "pca": [ - 0.19045569002628326, - 0.060281965881586075 + 0.1905384510755539, + 0.059597767889499664 ], "tsne": [ - 41.95448303222656, - -5.1353044509887695 + 41.50837707519531, + 8.33884048461914 ], "umap": [ - 9.388372421264648, - 8.278011322021484 + 9.778511047363281, + 7.777345657348633 ], "pca3d": [ - 0.19045546650886536, - 0.060282811522483826, - -0.17148444056510925 + 0.1905381828546524, + 0.05959818512201309, + -0.17170105874538422 ], "tsne3d": [ - 22.490217208862305, - -8.656610488891602, - -4.662151336669922 + 24.888916015625, + 11.673360824584961, + -3.8303091526031494 ], "umap3d": [ - 7.157416820526123, - 4.051708221435547, - 0.9732062816619873 + 6.6369829177856445, + 3.783041477203369, + 0.10128475725650787 ] }, "cluster_id": 0, @@ -603938,12 +604088,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -604363,31 +604513,31 @@ "cited_by_count": 8, "projections": { "pca": [ - 0.2189197689294815, - 0.1287088543176651 + 0.2194346785545349, + 0.128064826130867 ], "tsne": [ - 5.380138397216797, - 0.456548810005188 + 5.875426292419434, + 0.7749965190887451 ], "umap": [ - 6.507944107055664, - 6.2064900398254395 + 6.814023017883301, + 5.80622673034668 ], "pca3d": [ - 0.21891933679580688, - 0.12871834635734558, - -0.025589417666196823 + 0.21943418681621552, + 0.12807491421699524, + -0.02578887902200222 ], "tsne3d": [ - 8.42119026184082, - -0.7625938653945923, - 2.000082015991211 + 4.999673843383789, + -2.1380832195281982, + -4.8744215965271 ], "umap3d": [ - 5.918282985687256, - 4.743144989013672, - 3.895158052444458 + 6.260972023010254, + 5.266580581665039, + 2.860602378845215 ] }, "cluster_id": 4, @@ -604398,11 +604548,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -604829,47 +604979,47 @@ "openalex_url": "https://openalex.org/W4323322746", "projections": { "pca": [ - 0.18746231496334076, - -0.03069687820971012 + 0.18724516034126282, + -0.03143886476755142 ], "tsne": [ - 32.668067932128906, - -3.7842154502868652 + 34.640411376953125, + 0.6163828372955322 ], "umap": [ - 8.16730785369873, - 7.943436145782471 + 8.458656311035156, + 7.431112766265869 ], "pca3d": [ - 0.18746232986450195, - -0.03070736862719059, - -0.2076793611049652 + 0.1872452348470688, + -0.03144955635070801, + -0.2077759951353073 ], "tsne3d": [ - 19.216232299804688, - -6.40134859085083, - -4.807119369506836 + 20.4494571685791, + 12.355415344238281, + -1.6717357635498047 ], "umap3d": [ - 6.821998596191406, - 4.031652450561523, - 2.099989891052246 + 6.832905292510986, + 4.034357070922852, + 1.2212499380111694 ] }, - "cluster_id": 5, - "cluster_label": "Single-Cell Data", + "cluster_id": 0, + "cluster_label": "Chromatin Regulation", "cluster_levels": [ { - "id": 5, - "label": "Single-Cell Data" + "id": 0, + "label": "Chromatin Regulation" }, { "id": 13, - "label": "Genetic Variants" + "label": "Chromatin Regulation" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -605294,31 +605444,31 @@ "doi": "10.1101/2023.03.05.531189", "projections": { "pca": [ - 0.3015069365501404, - 0.01353457197546959 + 0.30148106813430786, + 0.012863077223300934 ], "tsne": [ - 40.12568283081055, - 1.3845690488815308 + 38.63277053833008, + 5.647966384887695 ], "umap": [ - 9.528325080871582, - 7.752213001251221 + 9.720663070678711, + 7.535710334777832 ], "pca3d": [ - 0.3015066087245941, - 0.013525852933526039, - -0.13388997316360474 + 0.3014809787273407, + 0.012853956781327724, + -0.13406512141227722 ], "tsne3d": [ - 23.336959838867188, - -3.0394539833068848, - -3.7143216133117676 + 21.578336715698242, + 7.195972919464111, + -1.8074147701263428 ], "umap3d": [ - 7.134309768676758, - 4.643172264099121, - 1.1741644144058228 + 6.5972185134887695, + 4.400481700897217, + 0.1183633804321289 ] }, "cluster_id": 0, @@ -605329,12 +605479,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 20, - "label": "Chromatin Regulation" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -605749,31 +605899,31 @@ "doi": "10.1016/s1097-2765(00)00003-4", "projections": { "pca": [ - 0.19736023247241974, - 0.10825981944799423 + 0.1978493332862854, + 0.10738833993673325 ], "tsne": [ - 36.102928161621094, - 1.9870630502700806 + 33.59490966796875, + 5.547493934631348 ], "umap": [ - 8.875289916992188, - 7.772283554077148 + 9.336261749267578, + 7.198512554168701 ], "pca3d": [ - 0.19735990464687347, - 0.10824436694383621, - 0.01625317521393299 + 0.19784902036190033, + 0.10737231373786926, + 0.015993064269423485 ], "tsne3d": [ - 20.649085998535156, - 0.8654383420944214, - -7.345410346984863 + 18.403846740722656, + 6.5475640296936035, + 1.9124743938446045 ], "umap3d": [ - 7.081620216369629, - 4.512632846832275, - 1.7934582233428955 + 6.76552677154541, + 4.342336177825928, + 0.6317897439002991 ] }, "cluster_id": 0, @@ -605784,12 +605934,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -606210,31 +606360,31 @@ "doi": "10.1101/2023.02.14.528560", "projections": { "pca": [ - 0.1505461037158966, - -0.11916106194257736 + 0.150255486369133, + -0.1196538433432579 ], "tsne": [ - 22.48908805847168, - 31.598234176635742 + 25.92884063720703, + -30.564477920532227 ], "umap": [ - 8.339048385620117, - 4.846644878387451 + 8.682535171508789, + 4.458284378051758 ], "pca3d": [ - 0.15054583549499512, - -0.11910724639892578, - 0.0134808961302042 + 0.15025515854358673, + -0.11959869414567947, + 0.013545428402721882 ], "tsne3d": [ - 5.968408584594727, - 25.270315170288086, - -1.0941746234893799 + 15.83006477355957, + -17.32352638244629, + 7.957486152648926 ], "umap3d": [ - 7.545655727386475, - 5.925590515136719, - 3.6597931385040283 + 7.583055019378662, + 6.521417140960693, + 1.802986741065979 ] }, "cluster_id": 4, @@ -606245,11 +606395,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -606669,31 +606819,31 @@ "cited_by_count": 1, "projections": { "pca": [ - 0.22242720425128937, - -0.04652402922511101 + 0.2222708910703659, + -0.047144677489995956 ], "tsne": [ - 51.25409698486328, - 4.329401016235352 + 48.76791000366211, + 16.751150131225586 ], "umap": [ - 9.801016807556152, - 7.119362831115723 + 10.044434547424316, + 6.602810382843018 ], "pca3d": [ - 0.22242720425128937, - -0.04651789367198944, - -0.044661764055490494 + 0.22227086126804352, + -0.047138333320617676, + -0.04478712007403374 ], "tsne3d": [ - 23.506389617919922, - 4.121819972991943, - -0.09730403125286102 + 25.00847625732422, + -0.9120926260948181, + -5.055233955383301 ], "umap3d": [ - 7.969712257385254, - 4.891357898712158, - 1.3779534101486206 + 7.467379093170166, + 4.662526607513428, + -0.10863376408815384 ] }, "cluster_id": 0, @@ -606704,12 +606854,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -607157,31 +607307,31 @@ "cited_by_count": 233, "projections": { "pca": [ - 0.25617700815200806, - 0.03193844109773636 + 0.2561953663825989, + 0.031229205429553986 ], "tsne": [ - 43.358985900878906, - 1.6930683851242065 + 35.741424560546875, + 12.281627655029297 ], "umap": [ - 9.256638526916504, - 7.6423563957214355 + 9.442193031311035, + 7.389344215393066 ], "pca3d": [ - 0.2561768591403961, - 0.03192410618066788, - -0.13054227828979492 + 0.2561953365802765, + 0.031214585527777672, + -0.1307680308818817 ], "tsne3d": [ - 19.804424285888672, - -0.7452013492584229, - 3.113532066345215 + 22.193330764770508, + 0.21389465034008026, + -9.504074096679688 ], "umap3d": [ - 7.737106800079346, - 4.201136589050293, - 1.5829236507415771 + 7.466089248657227, + 4.103080749511719, + 0.34544873237609863 ] }, "cluster_id": 0, @@ -607192,12 +607342,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 20, - "label": "Chromatin Regulation" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -607620,47 +607770,47 @@ "doi": "10.1101/2023.02.09.527751", "projections": { "pca": [ - 0.2212277352809906, - -0.033772896975278854 + 0.22108900547027588, + -0.03445492684841156 ], "tsne": [ - 31.222625732421875, - -16.441965103149414 + 26.014854431152344, + 21.493000030517578 ], "umap": [ - 8.562129020690918, - 9.063638687133789 + 8.67214298248291, + 8.713464736938477 ], "pca3d": [ - 0.22122764587402344, - -0.03375180438160896, - -0.10396195948123932 + 0.2210886925458908, + -0.034433506429195404, + -0.10405648499727249 ], "tsne3d": [ - 19.112464904785156, - 12.514189720153809, - 8.52961254119873 + 14.392993927001953, + -4.743463516235352, + -14.818068504333496 ], "umap3d": [ - 5.733836650848389, - 4.592545032501221, - 1.1274222135543823 + 5.267395973205566, + 4.24252462387085, + 0.81760573387146 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -608086,31 +608236,31 @@ "openalex_url": "https://openalex.org/W4319593304", "projections": { "pca": [ - 0.32037806510925293, - 0.082429900765419 + 0.3204810321331024, + 0.08150339871644974 ], "tsne": [ - 53.152435302734375, - 1.7392868995666504 + 51.84064483642578, + 13.446138381958008 ], "umap": [ - 10.254651069641113, - 7.206353664398193 + 10.4056396484375, + 6.8569183349609375 ], "pca3d": [ - 0.3203778862953186, - 0.08242689818143845, - -0.1798253357410431 + 0.32048067450523376, + 0.08149974048137665, + -0.180204376578331 ], "tsne3d": [ - 31.032228469848633, - 2.00410532951355, - -0.7352838516235352 + 30.206457138061523, + 3.35099196434021, + -6.915684700012207 ], "umap3d": [ - 7.986353874206543, - 4.897626876831055, - 0.9718414545059204 + 7.364522933959961, + 4.715859889984131, + -0.39542093873023987 ] }, "cluster_id": 0, @@ -608121,12 +608271,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -608547,31 +608697,31 @@ "doi": "10.1101/2023.01.31.526427", "projections": { "pca": [ - 0.11143341660499573, - -0.1007821336388588 + 0.11130935698747635, + -0.10047164559364319 ], "tsne": [ - 24.57779884338379, - 24.08972930908203 + 29.072036743164062, + -24.818458557128906 ], "umap": [ - 8.375301361083984, - 5.591160297393799 + 8.522446632385254, + 5.240455150604248 ], "pca3d": [ - 0.11143331974744797, - -0.10076697170734406, - 0.06547479331493378 + 0.11130938678979874, + -0.10045574605464935, + 0.06574515253305435 ], "tsne3d": [ - 10.748435974121094, - 23.642921447753906, - 5.032535552978516 + 16.37013816833496, + -10.455364227294922, + 9.833712577819824 ], "umap3d": [ - 7.488872051239014, - 5.203136920928955, - 3.3210740089416504 + 7.5770487785339355, + 5.640749454498291, + 1.6696425676345825 ] }, "cluster_id": 4, @@ -608582,11 +608732,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -609005,46 +609155,46 @@ "doi": "10.1101/2023.01.13.523995", "projections": { "pca": [ - 0.08485885709524155, - -0.09572258591651917 + 0.08467447757720947, + -0.09617216885089874 ], "tsne": [ - 24.827842712402344, - -25.572134017944336 + 15.045429229736328, + 17.189048767089844 ], "umap": [ - 7.878237247467041, - 9.052918434143066 + 7.800909996032715, + 8.852792739868164 ], "pca3d": [ - 0.08485886454582214, - -0.09570929408073425, - -0.0610872358083725 + 0.08467450737953186, + -0.0961584597826004, + -0.060963116586208344 ], "tsne3d": [ - 14.033082962036133, - -3.309824228286743, - 11.945188522338867 + 5.188995838165283, + 0.4334641396999359, + -16.448400497436523 ], "umap3d": [ - 5.52414608001709, - 3.9971933364868164, - 1.5300101041793823 + 5.30379581451416, + 3.8196563720703125, + 1.4347946643829346 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -609451,31 +609601,31 @@ ], "projections": { "pca": [ - 0.02376016415655613, - -0.13879145681858063 + 0.02339547872543335, + -0.1391015350818634 ], "tsne": [ - 13.51717472076416, - -5.344659328460693 + 13.980206489562988, + 6.817901611328125 ], "umap": [ - 8.164237976074219, - 6.450376033782959 + 8.338769912719727, + 6.127671718597412 ], "pca3d": [ - 0.023760095238685608, - -0.1387578547000885, - -0.13728348910808563 + 0.023395391181111336, + -0.13906732201576233, + -0.13704407215118408 ], "tsne3d": [ - 16.573627471923828, - 4.315861701965332, - -14.753992080688477 + 15.118505477905273, + 2.9387638568878174, + 9.290042877197266 ], "umap3d": [ - 6.377921104431152, - 5.271242618560791, - 2.3283658027648926 + 6.146360874176025, + 5.208602428436279, + 1.3805925846099854 ] }, "cluster_id": 4, @@ -609486,12 +609636,12 @@ "label": "RNA Sequence Models" }, { - "id": 3, - "label": "RNA Splicing" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -609920,31 +610070,31 @@ "doi": "10.1101/2023.01.11.523679", "projections": { "pca": [ - 0.1818939447402954, - -0.12853796780109406 + 0.18165571987628937, + -0.12859417498111725 ], "tsne": [ - 13.567020416259766, - 9.547377586364746 + 13.969841957092285, + -2.9983456134796143 ], "umap": [ - 7.119234561920166, - 6.0165019035339355 + 7.512271404266357, + 5.801239013671875 ], "pca3d": [ - 0.1818942129611969, - -0.12855948507785797, - 0.0023847937118262053 + 0.18165597319602966, + -0.12861596047878265, + 0.0026321278419345617 ], "tsne3d": [ - 8.788772583007812, - 7.890695571899414, - -8.995028495788574 + 10.155908584594727, + -3.012617349624634, + 5.683927059173584 ], "umap3d": [ - 6.247142791748047, - 4.966714382171631, - 3.2989299297332764 + 6.447144508361816, + 5.430471897125244, + 2.275085687637329 ] }, "cluster_id": 4, @@ -609955,11 +610105,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -610368,31 +610518,31 @@ "journal": "Nature Reviews Genetics", "projections": { "pca": [ - 0.16413478553295135, - -0.09831953793764114 + 0.16413193941116333, + -0.09869659692049026 ], "tsne": [ - 19.24549674987793, - 31.733293533325195 + 22.819473266601562, + -30.3211727142334 ], "umap": [ - 8.100791931152344, - 4.93835973739624 + 8.522295951843262, + 4.551830768585205 ], "pca3d": [ - 0.16413447260856628, - -0.09827475994825363, - 0.16291476786136627 + 0.16413158178329468, + -0.09865009039640427, + 0.16307561099529266 ], "tsne3d": [ - 4.1749091148376465, - 25.379924774169922, - 3.583449363708496 + 13.272499084472656, + -18.536394119262695, + 4.757232189178467 ], "umap3d": [ - 7.295571327209473, - 5.975401401519775, - 3.7255706787109375 + 7.3280415534973145, + 6.325610160827637, + 1.8883901834487915 ] }, "cluster_id": 4, @@ -610403,11 +610553,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -610816,47 +610966,47 @@ "journal": "Nature Genetics", "projections": { "pca": [ - 0.2174319624900818, - 0.06085200235247612 + 0.2175540328025818, + 0.06022539734840393 ], "tsne": [ - 29.974266052246094, - -23.856355667114258 + 20.71821403503418, + 29.071979522705078 ], "umap": [ - 8.565323829650879, - 8.814727783203125 + 8.751558303833008, + 8.397663116455078 ], "pca3d": [ - 0.21743156015872955, - 0.06088128685951233, - -0.19584256410598755 + 0.21755346655845642, + 0.06025517359375954, + -0.19599692523479462 ], "tsne3d": [ - 16.82478904724121, - -9.711324691772461, - 16.307153701782227 + 13.137848854064941, + -0.724789559841156, + -20.16112518310547 ], "umap3d": [ - 6.117450714111328, - 4.413460731506348, - 1.2325090169906616 + 5.885398864746094, + 4.106080055236816, + 0.7949786186218262 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 13, + "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -611284,31 +611434,31 @@ "cited_by_count": 126, "projections": { "pca": [ - 0.2493022084236145, - -0.07688172161579132 + 0.2491111308336258, + -0.07772030681371689 ], "tsne": [ - 43.10885238647461, - 0.7535492777824402 + 34.626708984375, + 12.884771347045898 ], "umap": [ - 9.138134002685547, - 7.391385555267334 + 9.37150764465332, + 6.9945068359375 ], "pca3d": [ - 0.24930217862129211, - -0.07688293606042862, - -0.07769794017076492 + 0.24911120533943176, + -0.07772142440080643, + -0.07773666828870773 ], "tsne3d": [ - 18.40730094909668, - 0.797208309173584, - 2.0989081859588623 + 21.033130645751953, + -1.9265215396881104, + -9.212265968322754 ], "umap3d": [ - 7.809351921081543, - 4.3023457527160645, - 1.6968697309494019 + 7.527117729187012, + 4.289804458618164, + 0.3663974404335022 ] }, "cluster_id": 0, @@ -611319,11 +611469,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -611746,31 +611896,31 @@ "doi": "10.1101/2023.01.04.522632", "projections": { "pca": [ - 0.21657302975654602, - 0.03877662867307663 + 0.216617152094841, + 0.038500312715768814 ], "tsne": [ - 30.892459869384766, - 26.782272338867188 + 56.370670318603516, + -3.914041757583618 ], "umap": [ - 9.008953094482422, - 5.721921443939209 + 9.238480567932129, + 5.289584636688232 ], "pca3d": [ - 0.21657271683216095, - 0.03878353536128998, - -0.04717744141817093 + 0.21661688387393951, + 0.03850793465971947, + -0.04739448428153992 ], "tsne3d": [ - 14.671198844909668, - 22.159801483154297, - -1.0724546909332275 + 20.269977569580078, + -12.48836612701416, + 4.598704814910889 ], "umap3d": [ - 7.724963665008545, - 5.486948490142822, - 2.86961030960083 + 7.525574684143066, + 5.699714660644531, + 1.0672893524169922 ] }, "cluster_id": 4, @@ -611781,11 +611931,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -612208,31 +612358,31 @@ "cited_by_count": 88, "projections": { "pca": [ - 0.22518093883991241, - 0.11767277866601944 + 0.22544832527637482, + 0.11696405708789825 ], "tsne": [ - 42.55260467529297, - -14.968046188354492 + 37.81365966796875, + 20.914180755615234 ], "umap": [ - 9.21721363067627, - 8.577548027038574 + 9.420709609985352, + 8.026565551757812 ], "pca3d": [ - 0.2251807302236557, - 0.11766151338815689, - -0.15460757911205292 + 0.22544808685779572, + 0.11695311218500137, + -0.154938742518425 ], "tsne3d": [ - 23.28230094909668, - -8.083222389221191, - 7.298524856567383 + 22.231985092163086, + 8.484902381896973, + -14.484880447387695 ], "umap3d": [ - 7.098055839538574, - 3.753880500793457, - 1.1939488649368286 + 6.903648376464844, + 3.4823288917541504, + 0.46566176414489746 ] }, "cluster_id": 0, @@ -612243,12 +612393,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -612693,31 +612843,31 @@ "cited_by_count": 54, "projections": { "pca": [ - 0.05195000767707825, - -0.022906526923179626 + 0.05186604708433151, + -0.02273666486144066 ], "tsne": [ - 20.254587173461914, - 29.49060821533203 + 20.820810317993164, + -24.32080841064453 ], "umap": [ - 7.835844993591309, - 4.785854816436768 + 8.245417594909668, + 4.377044677734375 ], "pca3d": [ - 0.05194980278611183, - -0.02286462113261223, - 0.11984611302614212 + 0.05186581239104271, + -0.02269374579191208, + 0.11978548765182495 ], "tsne3d": [ - 4.2845001220703125, - 22.35671043395996, - -2.389017105102539 + 14.72135066986084, + -12.463482856750488, + 2.3108677864074707 ], "umap3d": [ - 7.193679332733154, - 5.946184158325195, - 3.908109188079834 + 7.2539215087890625, + 6.588245868682861, + 2.124671697616577 ] }, "cluster_id": 4, @@ -612728,11 +612878,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -613198,31 +613348,31 @@ "cited_by_count": 618, "projections": { "pca": [ - 0.2730614244937897, - 0.11960725486278534 + 0.2733175456523895, + 0.1186584085226059 ], "tsne": [ - 32.84521484375, - -7.600266933441162 + 37.66276168823242, + 10.120687484741211 ], "umap": [ - 8.79458236694336, - 8.130829811096191 + 8.982295036315918, + 7.6730475425720215 ], "pca3d": [ - 0.273061066865921, - 0.1195991113781929, - -0.2323113977909088 + 0.2733171582221985, + 0.11865024268627167, + -0.2325872927904129 ], "tsne3d": [ - 18.034683227539062, - -7.3648529052734375, - 0.7710803151130676 + 14.350652694702148, + 9.255257606506348, + -9.104159355163574 ], "umap3d": [ - 6.958334922790527, - 4.011683464050293, - 1.5840610265731812 + 6.700344085693359, + 3.7667181491851807, + 0.7313539385795593 ] }, "cluster_id": 0, @@ -613233,12 +613383,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -613657,31 +613807,31 @@ "openalex_url": "https://openalex.org/W4312207608", "projections": { "pca": [ - 0.13389241695404053, - 0.2414986938238144 + 0.13448427617549896, + 0.24190762639045715 ], "tsne": [ - 32.81412124633789, - -7.062640190124512 + 36.15515899658203, + 9.390121459960938 ], "umap": [ - 8.696446418762207, - 8.078566551208496 + 8.963970184326172, + 7.588866710662842 ], "pca3d": [ - 0.13389189541339874, - 0.24149943888187408, - -0.02430485188961029 + 0.1344836801290512, + 0.24190792441368103, + -0.024692656472325325 ], "tsne3d": [ - 16.69894790649414, - -7.4470391273498535, - -0.42071059346199036 + 15.167573928833008, + 10.150147438049316, + -10.100154876708984 ], "umap3d": [ - 7.044008255004883, - 4.106929302215576, - 1.6202746629714966 + 6.746877193450928, + 3.9009029865264893, + 0.8550115823745728 ] }, "cluster_id": 0, @@ -613692,12 +613842,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -614119,31 +614269,31 @@ "openalex_url": "https://openalex.org/W4280508767", "projections": { "pca": [ - 0.13879679143428802, - -0.06027701124548912 + 0.1386720836162567, + -0.06047982722520828 ], "tsne": [ - 35.42122268676758, - 15.128154754638672 + 41.90130615234375, + -3.7172648906707764 ], "umap": [ - 8.47383975982666, - 7.040684223175049 + 8.78287124633789, + 6.651376247406006 ], "pca3d": [ - 0.13879692554473877, - -0.06030264496803284, - -0.03668750822544098 + 0.13867232203483582, + -0.06050537899136543, + -0.03662760928273201 ], "tsne3d": [ - 13.865341186523438, - 3.608205795288086, - 9.07300853729248 + 20.216768264770508, + 3.0292022228240967, + 13.12026596069336 ], "umap3d": [ - 7.6868791580200195, - 4.435763359069824, - 2.311528205871582 + 7.551413536071777, + 4.518192291259766, + 0.9997156262397766 ] }, "cluster_id": 0, @@ -614154,12 +614304,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -614569,47 +614719,47 @@ "doi": "10.4414/pc-d.2012.00202", "projections": { "pca": [ - -0.09728403389453888, - 0.2798272967338562 + -0.09613865613937378, + 0.2792305648326874 ], "tsne": [ - -14.123760223388672, - 0.1157752275466919 + -10.735189437866211, + -4.34744119644165 ], "umap": [ - 4.432232856750488, - 7.1316657066345215 + 4.643955230712891, + 6.266885280609131 ], "pca3d": [ - -0.09728435426950455, - 0.27981269359588623, - 0.02826203778386116 + -0.09613899141550064, + 0.27921587228775024, + 0.027948414906859398 ], "tsne3d": [ - -8.709527969360352, - 23.19573402404785, - -1.3294968605041504 + -18.10276985168457, + -2.209479570388794, + 11.812448501586914 ], "umap3d": [ - 3.9110546112060547, - 4.13145637512207, - 4.526620388031006 + 4.713497638702393, + 5.073338031768799, + 4.511800765991211 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -615019,31 +615169,31 @@ "doi": "10.1073/pnas.2113929118", "projections": { "pca": [ - 0.1200922355055809, - 0.0692819207906723 + 0.12019556760787964, + 0.06940695643424988 ], "tsne": [ - 28.928213119506836, - 33.52484130859375 + 24.424734115600586, + -21.279266357421875 ], "umap": [ - 8.610494613647461, - 5.228752613067627 + 8.97695255279541, + 4.872592449188232 ], "pca3d": [ - 0.12009170651435852, - 0.06929702311754227, - 0.023827746510505676 + 0.12019506096839905, + 0.06942194700241089, + 0.023595772683620453 ], "tsne3d": [ - 9.954794883728027, - 24.80139923095703, - -5.402052402496338 + 20.894954681396484, + -17.57652473449707, + 3.6728572845458984 ], "umap3d": [ - 7.567333698272705, - 5.711641788482666, - 3.2850029468536377 + 7.347504138946533, + 6.135724067687988, + 1.4179737567901611 ] }, "cluster_id": 4, @@ -615054,11 +615204,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -615480,31 +615630,31 @@ "doi": "10.1101/2022.12.22.521582", "projections": { "pca": [ - 0.08892378956079483, - -0.12526266276836395 + 0.08845366537570953, + -0.1253701001405716 ], "tsne": [ - 20.880950927734375, - -6.104884147644043 + 19.175352096557617, + 7.170614242553711 ], "umap": [ - 7.868744850158691, - 6.4770684242248535 + 8.082518577575684, + 6.1345744132995605 ], "pca3d": [ - 0.08892394602298737, - -0.1252506524324417, - -0.03824939951300621 + 0.0884537547826767, + -0.1253577023744583, + -0.03818462789058685 ], "tsne3d": [ - 17.142391204833984, - 12.785886764526367, - -8.066609382629395 + 15.78493881225586, + -4.174846172332764, + 1.1049492359161377 ], "umap3d": [ - 6.314600944519043, - 5.138618469238281, - 2.5966033935546875 + 6.325026988983154, + 5.191762924194336, + 1.4996167421340942 ] }, "cluster_id": 4, @@ -615515,12 +615665,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -615939,31 +616089,31 @@ "cited_by_count": 12, "projections": { "pca": [ - 0.1593509018421173, - 0.028536150231957436 + 0.1597086787223816, + 0.028162537142634392 ], "tsne": [ - 5.094242572784424, - 1.440165638923645 + 6.366822719573975, + -0.06535027176141739 ], "umap": [ - 6.514568328857422, - 6.212869167327881 + 6.793369770050049, + 5.872825622558594 ], "pca3d": [ - 0.15935058891773224, - 0.028543714433908463, - 0.0606626495718956 + 0.15970849990844727, + 0.028170621022582054, + 0.060691796243190765 ], "tsne3d": [ - 8.14180850982666, - 0.7834007740020752, - 1.2131335735321045 + 5.774712085723877, + -2.748586416244507, + -6.417944431304932 ], "umap3d": [ - 5.908749103546143, - 4.711810111999512, - 3.817317008972168 + 6.250022888183594, + 5.3070759773254395, + 2.809169054031372 ] }, "cluster_id": 4, @@ -615974,11 +616124,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -616422,47 +616572,47 @@ "openalex_url": "https://openalex.org/W4312211397", "projections": { "pca": [ - 0.13770321011543274, - 0.14745678007602692 + 0.1381913125514984, + 0.1470075100660324 ], "tsne": [ - 30.89444351196289, - -5.446427345275879 + 36.946102142333984, + -4.100277423858643 ], "umap": [ - 7.63409948348999, - 8.353078842163086 + 8.071616172790527, + 7.979895114898682 ], "pca3d": [ - 0.1377028226852417, - 0.14745767414569855, - -0.08960603177547455 + 0.13819094002246857, + 0.1470077931880951, + -0.08984703570604324 ], "tsne3d": [ - 21.050159454345703, - -13.235356330871582, - 4.1115827560424805 + 13.864114761352539, + 13.192334175109863, + -4.502561092376709 ], "umap3d": [ - 6.59340238571167, - 3.5120863914489746, - 2.075080633163452 + 6.750306606292725, + 3.5056612491607666, + 1.5807238817214966 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -616881,31 +617031,31 @@ "doi": "10.1101/2022.12.16.520785", "projections": { "pca": [ - 0.2838999330997467, - 0.0700206458568573 + 0.28402838110923767, + 0.06994079798460007 ], "tsne": [ - 44.0025634765625, - 9.204452514648438 + 49.57583236694336, + 0.1805890053510666 ], "umap": [ - 9.151205062866211, - 6.99098014831543 + 9.308664321899414, + 6.577202320098877 ], "pca3d": [ - 0.2838995158672333, - 0.07002343237400055, - 0.030561426654458046 + 0.2840280532836914, + 0.06994357705116272, + 0.030302859842777252 ], "tsne3d": [ - 19.058650970458984, - 6.709275245666504, - 0.5638324618339539 + 25.16075325012207, + -5.716100692749023, + -12.298136711120605 ], "umap3d": [ - 7.207816123962402, - 5.039987564086914, - 1.9756007194519043 + 6.775259017944336, + 4.8636274337768555, + 0.5658858418464661 ] }, "cluster_id": 0, @@ -616916,11 +617066,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -617361,31 +617511,31 @@ "cited_by_count": 89, "projections": { "pca": [ - 0.2653232216835022, - 0.11373840272426605 + 0.2655567228794098, + 0.11305876076221466 ], "tsne": [ - 39.396419525146484, - 16.420480728149414 + 43.95448303222656, + -9.161727905273438 ], "umap": [ - 8.282602310180664, - 7.686738014221191 + 8.55583381652832, + 7.309903144836426 ], "pca3d": [ - 0.26532337069511414, - 0.11371047049760818, - -0.12402498722076416 + 0.2655566334724426, + 0.11303048580884933, + -0.12434569746255875 ], "tsne3d": [ - 15.608413696289062, - -11.554170608520508, - -2.4769773483276367 + 20.508455276489258, + 12.593042373657227, + 11.092808723449707 ], "umap3d": [ - 7.431347846984863, - 4.04168176651001, - 2.324232578277588 + 7.3764166831970215, + 4.236175060272217, + 1.1065959930419922 ] }, "cluster_id": 0, @@ -617396,12 +617546,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -617811,31 +617961,31 @@ "journal": "Nature Reviews Genetics", "projections": { "pca": [ - 0.17978323996067047, - 0.0580291710793972 + 0.1799434870481491, + 0.0575815849006176 ], "tsne": [ - 46.700801849365234, - 1.45427405834198 + 36.90232467651367, + 13.438822746276855 ], "umap": [ - 9.709640502929688, - 7.507683753967285 + 10.047460556030273, + 7.01373815536499 ], "pca3d": [ - 0.17978301644325256, - 0.05801740661263466, - -0.09683026373386383 + 0.17994321882724762, + 0.05756980553269386, + -0.09700840711593628 ], "tsne3d": [ - 22.97628402709961, - -1.456815242767334, - 1.055302619934082 + 23.185012817382812, + -4.01486873626709, + -7.389277458190918 ], "umap3d": [ - 7.837553977966309, - 4.47458553314209, - 1.2089577913284302 + 7.353379249572754, + 4.317350387573242, + -0.1046634390950203 ] }, "cluster_id": 0, @@ -617846,12 +617996,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 20, - "label": "Chromatin Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -618280,31 +618430,31 @@ "cited_by_count": 5, "projections": { "pca": [ - 0.24559499323368073, - -0.006518886890262365 + 0.24551494419574738, + -0.00711845001205802 ], "tsne": [ - 46.09728240966797, - -12.445521354675293 + 40.71193313598633, + 20.346126556396484 ], "umap": [ - 9.757479667663574, - 7.978909969329834 + 10.090799331665039, + 7.5709614753723145 ], "pca3d": [ - 0.24559491872787476, - -0.006523866206407547, - -0.15522217750549316 + 0.24551481008529663, + -0.007123914081603289, + -0.1553453952074051 ], "tsne3d": [ - 28.312318801879883, - -9.330140113830566, - 1.7358731031417847 + 29.44747543334961, + 9.89970588684082, + -8.262858390808105 ], "umap3d": [ - 7.094278812408447, - 4.668035507202148, - 0.9064123630523682 + 6.5937981605529785, + 4.216556549072266, + -0.030887767672538757 ] }, "cluster_id": 0, @@ -618315,12 +618465,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -618730,31 +618880,31 @@ "journal": "Nature", "projections": { "pca": [ - 0.2396540641784668, - 0.08548044413328171 + 0.23991410434246063, + 0.08463022112846375 ], "tsne": [ - 48.5108642578125, - -5.850769996643066 + 46.403133392333984, + 7.185492038726807 ], "umap": [ - 10.260910987854004, - 8.091086387634277 + 10.416955947875977, + 7.715564250946045 ], "pca3d": [ - 0.23965363204479218, - 0.08547372370958328, - -0.2224329262971878 + 0.23991380631923676, + 0.08462272584438324, + -0.22260966897010803 ], "tsne3d": [ - 27.654016494750977, - -6.44981050491333, - -8.943267822265625 + 27.87388038635254, + 11.137410163879395, + 2.2053277492523193 ], "umap3d": [ - 7.168402671813965, - 4.822163105010986, - 0.5738762617111206 + 6.58419942855835, + 4.325798988342285, + -0.4752758741378784 ] }, "cluster_id": 0, @@ -618765,12 +618915,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -619178,31 +619328,31 @@ "abstract": "Photosynthetic factories camouflaged for transplantation into animal cells.", "projections": { "pca": [ - 0.11603953689336777, - 0.22920799255371094 + 0.1166137233376503, + 0.22935731709003448 ], "tsne": [ - 19.537540435791016, - -10.52136516571045 + 48.08792495727539, + -3.1001696586608887 ], "umap": [ - 8.547996520996094, - 7.870563507080078 + 8.921163558959961, + 7.2117133140563965 ], "pca3d": [ - 0.11603879928588867, - 0.22921957075595856, - 0.07033222168684006 + 0.1166129857301712, + 0.22936858236789703, + 0.06986849009990692 ], "tsne3d": [ - 20.57450294494629, - 0.1425858736038208, - 13.85538101196289 + 21.194644927978516, + -6.5132575035095215, + -13.325984954833984 ], "umap3d": [ - 7.3295817375183105, - 4.318434715270996, - 2.0157768726348877 + 6.472515106201172, + 4.261621475219727, + 0.8917046189308167 ] }, "cluster_id": 0, @@ -619213,12 +619363,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -619628,47 +619778,47 @@ "journal": "Nature", "projections": { "pca": [ - 0.252072811126709, - 0.026789097115397453 + 0.2521016299724579, + 0.026317331939935684 ], "tsne": [ - 34.58240509033203, - 11.2323579788208 + 37.74718475341797, + -2.0774264335632324 ], "umap": [ - 7.95707893371582, - 7.547157287597656 + 8.374497413635254, + 7.264246463775635 ], "pca3d": [ - 0.25207266211509705, - 0.02676878497004509, - -0.1394837349653244 + 0.2521015703678131, + 0.02629656344652176, + -0.1396193653345108 ], "tsne3d": [ - 18.371145248413086, - -8.04686450958252, - -11.271679878234863 + 19.0881290435791, + 13.904617309570312, + 3.4165756702423096 ], "umap3d": [ - 7.328311920166016, - 4.003824710845947, - 2.4697089195251465 + 7.4531097412109375, + 4.186978816986084, + 1.4441406726837158 ] }, - "cluster_id": 5, - "cluster_label": "Single-Cell Data", + "cluster_id": 0, + "cluster_label": "Chromatin Regulation", "cluster_levels": [ { - "id": 5, - "label": "Single-Cell Data" + "id": 0, + "label": "Chromatin Regulation" }, { - "id": 13, - "label": "Genetic Variants" + "id": 2, + "label": "DNA Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -620089,31 +620239,31 @@ "cited_by_count": 201, "projections": { "pca": [ - 0.2536095976829529, - 0.007451905403286219 + 0.25340670347213745, + 0.006707532797008753 ], "tsne": [ - 54.64507293701172, - 1.931514024734497 + 50.55890655517578, + 14.662274360656738 ], "umap": [ - 10.174132347106934, - 6.8931474685668945 + 10.39640998840332, + 6.571483135223389 ], "pca3d": [ - 0.25360944867134094, - 0.00745395990088582, - -0.11421389132738113 + 0.2534065544605255, + 0.006709157023578882, + -0.11454533785581589 ], "tsne3d": [ - 33.51055145263672, - 3.0783004760742188, - -2.1523079872131348 + 31.809133529663086, + 2.2892987728118896, + -8.553619384765625 ], "umap3d": [ - 7.976089954376221, - 5.179795742034912, - 1.1866194009780884 + 7.339180946350098, + 5.011322498321533, + -0.4261963367462158 ] }, "cluster_id": 0, @@ -620124,12 +620274,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -620549,31 +620699,31 @@ "cited_by_count": 2, "projections": { "pca": [ - 0.09603024274110794, - -0.1553073227405548 + 0.09559512883424759, + -0.15546555817127228 ], "tsne": [ - 19.02182388305664, - -2.545064687728882 + 20.133970260620117, + 3.795285701751709 ], "umap": [ - 7.874307155609131, - 6.438807010650635 + 8.155953407287598, + 6.125040531158447 ], "pca3d": [ - 0.0960303470492363, - -0.15529067814350128, - -0.043921343982219696 + 0.09559517353773117, + -0.15544849634170532, + -0.0437145009636879 ], "tsne3d": [ - 13.479268074035645, - 4.831999778747559, - -10.112401008605957 + 13.959322929382324, + -0.5413944125175476, + 2.4875872135162354 ], "umap3d": [ - 6.3285980224609375, - 5.224795818328857, - 2.593744993209839 + 6.269269943237305, + 5.3400187492370605, + 1.5022984743118286 ] }, "cluster_id": 4, @@ -620584,12 +620734,12 @@ "label": "RNA Sequence Models" }, { - "id": 3, - "label": "RNA Splicing" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -621004,31 +621154,31 @@ ], "projections": { "pca": [ - 0.11279705911874771, - -0.11089497059583664 + 0.11239071190357208, + -0.1108349859714508 ], "tsne": [ - 24.47433090209961, - 25.985431671142578 + 29.44981575012207, + -26.650299072265625 ], "umap": [ - 8.4030179977417, - 5.665983200073242 + 8.644342422485352, + 5.2347636222839355 ], "pca3d": [ - 0.11279691010713577, - -0.11086539924144745, - -0.002151916269212961 + 0.11239064484834671, + -0.11080485582351685, + -0.002095018280670047 ], "tsne3d": [ - 11.317305564880371, - 23.201509475708008, - 2.0963752269744873 + 16.890695571899414, + -8.140083312988281, + 7.114827632904053 ], "umap3d": [ - 7.480092525482178, - 5.290927886962891, - 3.2476913928985596 + 7.510166168212891, + 5.742683410644531, + 1.5347150564193726 ] }, "cluster_id": 4, @@ -621039,11 +621189,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -621468,31 +621618,31 @@ "doi": "10.1101/2022.03.05.483136", "projections": { "pca": [ - 0.21843042969703674, - -0.06347092241048813 + 0.2182496190071106, + -0.06419505178928375 ], "tsne": [ - 43.1072883605957, - 0.7722146511077881 + 34.68505859375, + 12.918662071228027 ], "umap": [ - 9.100502014160156, - 7.440755844116211 + 9.361457824707031, + 7.085880756378174 ], "pca3d": [ - 0.21843065321445465, - -0.06347698718309402, - -0.08784431219100952 + 0.2182496339082718, + -0.06420095264911652, + -0.08789617568254471 ], "tsne3d": [ - 18.456090927124023, - 0.5654195547103882, - 2.3755390644073486 + 21.118885040283203, + -1.7648481130599976, + -9.450104713439941 ], "umap3d": [ - 7.805818557739258, - 4.267501354217529, - 1.7174627780914307 + 7.539219856262207, + 4.18373966217041, + 0.4560002386569977 ] }, "cluster_id": 0, @@ -621503,11 +621653,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -621916,31 +622066,31 @@ "journal": "Nature", "projections": { "pca": [ - 0.20001386106014252, - 0.07280252128839493 + 0.20014236867427826, + 0.07210829108953476 ], "tsne": [ - 50.51522445678711, - 6.426260471343994 + 57.752052307128906, + 10.872347831726074 ], "umap": [ - 10.385764122009277, - 7.15674352645874 + 10.581111907958984, + 6.7403364181518555 ], "pca3d": [ - 0.20001359283924103, - 0.07282072305679321, - -0.04469108581542969 + 0.20014198124408722, + 0.07212627679109573, + -0.045054227113723755 ], "tsne3d": [ - 31.765947341918945, - -0.011152956634759903, - 2.6029043197631836 + 35.31131362915039, + 1.9078731536865234, + -2.484985589981079 ], "umap3d": [ - 8.076201438903809, - 4.914210319519043, - 0.850477397441864 + 7.305842399597168, + 4.692104816436768, + -0.6294327974319458 ] }, "cluster_id": 0, @@ -621951,12 +622101,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -622379,47 +622529,47 @@ "doi": "10.1101/2022.08.20.504663", "projections": { "pca": [ - 0.1836867332458496, - 0.010366244241595268 + 0.18373902142047882, + 0.009865781292319298 ], "tsne": [ - 30.049442291259766, - -16.260046005249023 + 23.56439971923828, + 21.34375 ], "umap": [ - 8.527786254882812, - 9.354392051696777 + 8.626401901245117, + 8.93442153930664 ], "pca3d": [ - 0.18368636071681976, - 0.010394085198640823, - -0.12097300589084625 + 0.18373866379261017, + 0.009894047863781452, + -0.12103783339262009 ], "tsne3d": [ - 17.318143844604492, - 10.086350440979004, - 6.904914379119873 + 11.411093711853027, + -3.0876705646514893, + -14.674518585205078 ], "umap3d": [ - 5.539729118347168, - 4.445811748504639, - 1.09536874294281 + 5.090781211853027, + 4.130717754364014, + 0.8651876449584961 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -623021,47 +623171,47 @@ "openalex_url": "https://openalex.org/W4308654581", "projections": { "pca": [ - 0.18492327630519867, - 0.06376772373914719 + 0.1850014328956604, + 0.063332699239254 ], "tsne": [ - 38.720367431640625, - 17.1777400970459 + 43.25114440917969, + -9.840164184570312 ], "umap": [ - 8.011479377746582, - 7.7718119621276855 + 8.060916900634766, + 7.409088611602783 ], "pca3d": [ - 0.1849232017993927, - 0.06374508887529373, - -0.1548519730567932 + 0.18500138819217682, + 0.06330970674753189, + -0.1550098955631256 ], "tsne3d": [ - 17.018712997436523, - -11.579048156738281, - -4.101393222808838 + 19.535430908203125, + 14.470417022705078, + 10.911730766296387 ], "umap3d": [ - 7.29688024520874, - 3.9087209701538086, - 2.5731167793273926 + 7.411282062530518, + 4.124695777893066, + 1.4915046691894531 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -623477,47 +623627,47 @@ "openalex_url": "https://openalex.org/W36335397", "projections": { "pca": [ - 0.15163761377334595, - 0.11445941030979156 + 0.15192028880119324, + 0.11432719975709915 ], "tsne": [ - 28.474803924560547, - 16.5667724609375 + 24.491975784301758, + -10.918478965759277 ], "umap": [ - 7.634047508239746, - 7.679569721221924 + 7.688638687133789, + 7.232250690460205 ], "pca3d": [ - 0.1516372561454773, - 0.11446364969015121, - -0.0682845488190651 + 0.1519199013710022, + 0.11433147639036179, + -0.06848745048046112 ], "tsne3d": [ - 14.050559043884277, - -5.424455642700195, - -6.743433952331543 + 14.868565559387207, + 17.324235916137695, + 9.240093231201172 ], "umap3d": [ - 7.055545806884766, - 4.111747741699219, - 3.0278069972991943 + 7.33819580078125, + 4.2390851974487305, + 1.9909546375274658 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -623954,47 +624104,47 @@ "doi": "10.1101/2022.11.01.514606", "projections": { "pca": [ - 0.23176485300064087, - 0.04195661470293999 + 0.23177488148212433, + 0.041254643350839615 ], "tsne": [ - 31.14115333557129, - 3.2745516300201416 + 36.6746711730957, + -5.341683387756348 ], "umap": [ - 7.77603006362915, - 8.14129638671875 + 8.246846199035645, + 7.69115686416626 ], "pca3d": [ - 0.23176492750644684, - 0.04193893074989319, - -0.18426451086997986 + 0.23177483677864075, + 0.04123643785715103, + -0.18446090817451477 ], "tsne3d": [ - 15.473662376403809, - -9.869643211364746, - -6.594428539276123 + 15.179229736328125, + 12.995424270629883, + -2.04524564743042 ], "umap3d": [ - 6.840588092803955, - 3.5847418308258057, - 2.0502474308013916 + 6.953767776489258, + 3.593812942504883, + 1.4435869455337524 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -624414,31 +624564,31 @@ "cited_by_count": 16, "projections": { "pca": [ - 0.012228616513311863, - -0.0391237810254097 + 0.012195524759590626, + -0.03867754340171814 ], "tsne": [ - 8.431976318359375, - -7.463079929351807 + 9.377839088439941, + 9.919418334960938 ], "umap": [ - 7.6961822509765625, - 6.030594825744629 + 8.032594680786133, + 5.60115909576416 ], "pca3d": [ - 0.012228491716086864, - -0.03909945487976074, - 0.0707487091422081 + 0.01219541858881712, + -0.03865305706858635, + 0.0708778128027916 ], "tsne3d": [ - 7.4015703201293945, - 5.711291313171387, - -19.666006088256836 + 1.9001028537750244, + -10.199816703796387, + -10.517684936523438 ], "umap3d": [ - 5.816641807556152, - 5.325586795806885, - 2.8072609901428223 + 5.825847625732422, + 5.280719757080078, + 1.8591928482055664 ] }, "cluster_id": 4, @@ -624449,12 +624599,12 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -624860,47 +625010,47 @@ ], "projections": { "pca": [ - -0.07457135617733002, - 0.22244581580162048 + -0.07386630773544312, + 0.22221729159355164 ], "tsne": [ - -7.533241271972656, - -6.6423115730285645 + -7.832387447357178, + 3.194486141204834 ], "umap": [ - 4.290976047515869, - 7.221208095550537 + 4.626249313354492, + 6.545466423034668 ], "pca3d": [ - -0.0745716467499733, - 0.22243979573249817, - -0.04211745411157608 + -0.07386661320924759, + 0.22221040725708008, + -0.04245825484395027 ], "tsne3d": [ - -9.298287391662598, - -2.3770601749420166, - 13.129203796386719 + -5.562460422515869, + 0.16006368398666382, + -5.528564929962158 ], "umap3d": [ - 3.8782546520233154, - 3.8127059936523438, - 4.473417282104492 + 4.808021068572998, + 4.6750006675720215, + 4.606147766113281 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -625310,47 +625460,47 @@ "journal": "Nature Biomedical Engineering", "projections": { "pca": [ - -0.05796293169260025, - 0.4359323978424072 + -0.05672614648938179, + 0.4360976815223694 ], "tsne": [ - -9.431293487548828, - -13.058552742004395 + -16.114412307739258, + 7.729562282562256 ], "umap": [ - 3.8697171211242676, - 7.59011173248291 + 4.132490158081055, + 6.846317768096924 ], "pca3d": [ - -0.057964179664850235, - 0.43597522377967834, - 0.08938931673765182 + -0.05672725662589073, + 0.4361402690410614, + 0.08869216591119766 ], "tsne3d": [ - -3.2199785709381104, - -12.902341842651367, - 13.586153984069824 + -8.68776798248291, + 5.935142517089844, + -10.822043418884277 ], "umap3d": [ - 3.380669593811035, - 3.488640546798706, - 4.491849422454834 + 4.3617401123046875, + 4.193356037139893, + 4.815173625946045 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -625782,31 +625932,31 @@ "doi": "10.1101/2022.10.12.511898", "projections": { "pca": [ - 0.13609319925308228, - 0.12831078469753265 + 0.13633866608142853, + 0.12796331942081451 ], "tsne": [ - 38.75445556640625, - -7.223872184753418 + 39.72998809814453, + 9.562260627746582 ], "umap": [ - 9.141552925109863, - 8.392995834350586 + 9.349324226379395, + 7.932002544403076 ], "pca3d": [ - 0.13609269261360168, - 0.12831652164459229, - -0.15574784576892853 + 0.13633818924427032, + 0.12796896696090698, + -0.15600229799747467 ], "tsne3d": [ - 20.81832504272461, - -9.262426376342773, - -0.750859797000885 + 22.46488380432129, + 11.676652908325195, + -6.474765777587891 ], "umap3d": [ - 6.9786481857299805, - 3.720407009124756, - 1.2473183870315552 + 6.683015823364258, + 3.596163511276245, + 0.4449087679386139 ] }, "cluster_id": 0, @@ -625817,12 +625967,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -626230,46 +626380,46 @@ "journal": "Nature Genetics", "projections": { "pca": [ - 0.047852784395217896, - -0.0625159814953804 + 0.04776803404092789, + -0.06265697628259659 ], "tsne": [ - 21.096179962158203, - -24.58448600769043 + 12.523789405822754, + 24.84503173828125 ], "umap": [ - 7.589010715484619, - 9.319866180419922 + 7.556091785430908, + 8.902161598205566 ], "pca3d": [ - 0.04785260930657387, - -0.06248989328742027, - -0.09523511677980423 + 0.04776781052350998, + -0.0626303032040596, + -0.09506890922784805 ], "tsne3d": [ - 10.822896003723145, - -12.103824615478516, - 13.60697078704834 + 9.689132690429688, + 7.890487194061279, + -17.089313507080078 ], "umap3d": [ - 5.326611518859863, - 3.6750802993774414, - 1.5029141902923584 + 5.208178997039795, + 3.4997267723083496, + 1.4554765224456787 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -626678,31 +626828,31 @@ "journal": "Nature Genetics", "projections": { "pca": [ - 0.14685426652431488, - 0.05453880503773689 + 0.1471257507801056, + 0.05381239578127861 ], "tsne": [ - 16.012893676757812, - 13.000792503356934 + 26.573217391967773, + 1.3498188257217407 ], "umap": [ - 8.139525413513184, - 7.014876842498779 + 8.29892635345459, + 6.809820652008057 ], "pca3d": [ - 0.14685402810573578, - 0.05453284829854965, - -0.06711622327566147 + 0.14712557196617126, + 0.05380670353770256, + -0.06718471646308899 ], "tsne3d": [ - 12.565254211425781, - -0.13191799819469452, - -2.5079822540283203 + 16.67449188232422, + 7.926877021789551, + 7.532885551452637 ], "umap3d": [ - 6.919862270355225, - 4.502529144287109, - 2.564492702484131 + 6.949888706207275, + 4.650357246398926, + 1.3957703113555908 ] }, "cluster_id": 4, @@ -626713,12 +626863,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -627126,46 +627276,46 @@ "journal": "Nature Machine Intelligence", "projections": { "pca": [ - 0.0138819245621562, - -0.0747675746679306 + 0.013977734372019768, + -0.07509724050760269 ], "tsne": [ - 18.12311363220215, - -22.646390914916992 + 10.208273887634277, + 22.489376068115234 ], "umap": [ - 7.337532043457031, - 9.3571138381958 + 7.379423141479492, + 8.9740571975708 ], "pca3d": [ - 0.013881607912480831, - -0.07472402602434158, - -0.07506084442138672 + 0.013977439142763615, + -0.07505285739898682, + -0.07468180358409882 ], "tsne3d": [ - 7.531418323516846, - -10.317981719970703, - 13.011445045471191 + 8.32851791381836, + 8.557927131652832, + -14.165897369384766 ], "umap3d": [ - 5.032074451446533, - 3.734816551208496, - 1.654913067817688 + 4.995940208435059, + 3.561382293701172, + 1.7305058240890503 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -627577,47 +627727,47 @@ "journal": "Nature Reviews Genetics", "projections": { "pca": [ - -0.06186385080218315, - 0.11389539390802383 + -0.06148582696914673, + 0.114128977060318 ], "tsne": [ - -5.344783782958984, - -9.017413139343262 + -8.766983985900879, + 8.050944328308105 ], "umap": [ - 5.671091079711914, - 6.907949447631836 + 5.740659713745117, + 6.447868347167969 ], "pca3d": [ - -0.06186418607831001, - 0.11390996724367142, - -0.004469077102839947 + -0.06148616597056389, + 0.1141434758901596, + -0.004567191004753113 ], "tsne3d": [ - -9.070178031921387, - -6.584648609161377, - 7.034031867980957 + -8.355718612670898, + 0.8703746199607849, + -2.1601271629333496 ], "umap3d": [ - 4.550945281982422, - 4.167506217956543, - 3.833240509033203 + 5.160377025604248, + 4.781847953796387, + 3.6884207725524902 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 11, - "label": "Genomic Models" + "id": 4, + "label": "Genomics Tools" }, { - "id": 11, - "label": "Genomic Language" + "id": 13, + "label": "Genomics Papers" } ], "_embedding": [ @@ -628036,31 +628186,31 @@ "cited_by_count": 60, "projections": { "pca": [ - 0.08264617621898651, - -0.08863301575183868 + 0.08257858455181122, + -0.08814561367034912 ], "tsne": [ - 9.498363494873047, - 6.29669189453125 + 13.240450859069824, + -10.41369342803955 ], "umap": [ - 6.720076084136963, - 5.711071491241455 + 6.879647254943848, + 5.299225330352783 ], "pca3d": [ - 0.08264593034982681, - -0.08860376477241516, - 0.12680670619010925 + 0.08257835358381271, + -0.08811576664447784, + 0.12709923088550568 ], "tsne3d": [ - 3.2977051734924316, - 3.8432486057281494, - -6.715451717376709 + 5.385501384735107, + 1.7160404920578003, + 0.638923168182373 ], "umap3d": [ - 5.797667503356934, - 5.534719944000244, - 3.6686387062072754 + 5.978344440460205, + 5.8678364753723145, + 2.4103057384490967 ] }, "cluster_id": 4, @@ -628071,11 +628221,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -628505,31 +628655,31 @@ "doi": "10.1101/2022.09.27.509776", "projections": { "pca": [ - 0.3325488567352295, - 0.029966700822114944 + 0.33263176679611206, + 0.029273951426148415 ], "tsne": [ - 55.18743133544922, - 5.698764801025391 + 53.910518646240234, + 16.97638511657715 ], "umap": [ - 10.320050239562988, - 6.8744096755981445 + 10.57140827178955, + 6.423501491546631 ], "pca3d": [ - 0.332548588514328, - 0.02998240478336811, - -0.01620570756494999 + 0.33263128995895386, + 0.029289497062563896, + -0.016471082344651222 ], "tsne3d": [ - 29.634994506835938, - 4.816821098327637, - -1.370759129524231 + 30.038599014282227, + -0.4070119261741638, + -2.0119621753692627 ], "umap3d": [ - 8.130237579345703, - 5.310898780822754, - 1.087636947631836 + 7.406005859375, + 5.104799270629883, + -0.4971252977848053 ] }, "cluster_id": 0, @@ -628540,12 +628690,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -628960,31 +629110,31 @@ "doi": "10.1101/2022.05.24.493302", "projections": { "pca": [ - 0.2608890235424042, - 0.07383561134338379 + 0.2610322833061218, + 0.07341240346431732 ], "tsne": [ - 55.0824089050293, - 10.00317668914795 + 57.2594108581543, + 21.082805633544922 ], "umap": [ - 10.277602195739746, - 6.774201393127441 + 10.423273086547852, + 6.432190895080566 ], "pca3d": [ - 0.2608887851238251, - 0.07382141053676605, - -0.04898461326956749 + 0.2610321640968323, + 0.0733981505036354, + -0.049294907599687576 ], "tsne3d": [ - 33.21316146850586, - 8.433661460876465, - -0.45023927092552185 + 34.081905364990234, + -2.98225474357605, + -5.109678268432617 ], "umap3d": [ - 8.124195098876953, - 5.3594889640808105, - 1.135871171951294 + 7.487564563751221, + 5.058160781860352, + -0.4571245014667511 ] }, "cluster_id": 0, @@ -628995,12 +629145,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -629418,31 +629568,31 @@ "doi": "10.1101/2022.09.09.507333", "projections": { "pca": [ - 0.15554633736610413, - -0.12250752747058868 + 0.15537942945957184, + -0.12317404896020889 ], "tsne": [ - 15.314802169799805, - 35.42805862426758 + 18.952091217041016, + -33.76192855834961 ], "umap": [ - 7.803483963012695, - 4.2361860275268555 + 8.234526634216309, + 3.8838698863983154 ], "pca3d": [ - 0.1555461585521698, - -0.12248186022043228, - 0.20184102654457092 + 0.15537945926189423, + -0.1231473982334137, + 0.20184780657291412 ], "tsne3d": [ - 1.6915758848190308, - 20.280916213989258, - 8.487874984741211 + 6.927024841308594, + -18.86787223815918, + 8.000131607055664 ], "umap3d": [ - 7.130787372589111, - 6.364010334014893, - 4.0916032791137695 + 7.437038421630859, + 6.9884514808654785, + 2.32501220703125 ] }, "cluster_id": 4, @@ -629453,11 +629603,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -629868,31 +630018,31 @@ "abstract": "Working out how a cell is wired lets researchers find key regulators of behaviour.", "projections": { "pca": [ - 0.08448833972215652, - 0.2553722560405731 + 0.08501365780830383, + 0.25577908754348755 ], "tsne": [ - 34.79560470581055, - 1.6949890851974487 + 32.6553955078125, + 4.481667995452881 ], "umap": [ - 8.919506072998047, - 7.527505397796631 + 9.374907493591309, + 7.235989570617676 ], "pca3d": [ - 0.08448745310306549, - 0.2553991377353668, - -0.028258146718144417 + 0.0850127786397934, + 0.255805641412735, + -0.028755951672792435 ], "tsne3d": [ - 19.119199752807617, - -0.3506350815296173, - -8.428740501403809 + 21.264060974121094, + 4.276431560516357, + 2.104870080947876 ], "umap3d": [ - 6.854643821716309, - 4.8144850730896, - 1.6330327987670898 + 6.5430908203125, + 4.588452339172363, + 0.5366258025169373 ] }, "cluster_id": 0, @@ -629903,11 +630053,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -630336,31 +630486,31 @@ "cited_by_count": 100, "projections": { "pca": [ - 0.1371191442012787, - -0.0888102799654007 + 0.13687027990818024, + -0.08899189531803131 ], "tsne": [ - 20.18381690979004, - -3.96159291267395 + 21.17812156677246, + 5.501567840576172 ], "umap": [ - 8.258731842041016, - 6.607402324676514 + 8.454522132873535, + 6.190110206604004 ], "pca3d": [ - 0.13711924850940704, - -0.08880401402711868, - -0.0046088844537734985 + 0.13687030971050262, + -0.08898591995239258, + -0.004559983033686876 ], "tsne3d": [ - 16.282176971435547, - 8.645597457885742, - -8.062830924987793 + 15.38438606262207, + -0.32666391134262085, + 0.023769721388816833 ], "umap3d": [ - 6.519824981689453, - 5.2688751220703125, - 2.229461193084717 + 6.315221309661865, + 5.222428798675537, + 1.1889585256576538 ] }, "cluster_id": 4, @@ -630371,12 +630521,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -630795,31 +630945,31 @@ "doi": "10.1101/2022.08.31.506061", "projections": { "pca": [ - 0.28203997015953064, - -0.029514048248529434 + 0.28192657232284546, + -0.029891526326537132 ], "tsne": [ - 55.466190338134766, - 3.2792065143585205 + 52.46709442138672, + 14.819260597229004 ], "umap": [ - 10.269086837768555, - 7.244899272918701 + 10.497261047363281, + 6.829846382141113 ], "pca3d": [ - 0.2820400297641754, - -0.02952781319618225, - -0.060937341302633286 + 0.28192657232284546, + -0.02990611083805561, + -0.06104772537946701 ], "tsne3d": [ - 28.787702560424805, - 2.8269362449645996, - -4.31397819519043 + 28.312824249267578, + 1.3967853784561157, + 0.47773829102516174 ], "umap3d": [ - 7.753418922424316, - 5.244492530822754, - 0.9100807309150696 + 6.970193862915039, + 4.892327308654785, + -0.5178372263908386 ] }, "cluster_id": 0, @@ -630830,12 +630980,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -631262,31 +631412,31 @@ "doi": "10.1101/2022.08.26.505496", "projections": { "pca": [ - 0.28197482228279114, - -0.0002934049698524177 + 0.2819651961326599, + -0.0007309503853321075 ], "tsne": [ - 48.24405288696289, - -0.4388146996498108 + 44.78739929199219, + 16.66809844970703 ], "umap": [ - 9.84276008605957, - 7.46335506439209 + 9.917082786560059, + 7.092611312866211 ], "pca3d": [ - 0.28197458386421204, - -0.0003046343335881829, - -0.07008581608533859 + 0.28196507692337036, + -0.0007427610107697546, + -0.07020901888608932 ], "tsne3d": [ - 29.406766891479492, - 5.430757522583008, - 5.889505386352539 + 32.055152893066406, + 2.121318817138672, + 5.438483238220215 ], "umap3d": [ - 7.187528133392334, - 5.00726842880249, - 1.0889679193496704 + 6.486598968505859, + 4.756196975708008, + 0.11055212467908859 ] }, "cluster_id": 0, @@ -631297,12 +631447,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -631721,31 +631871,31 @@ "doi": "10.1101/2022.08.22.504706", "projections": { "pca": [ - 0.1560038924217224, - -0.10399644821882248 + 0.15576790273189545, + -0.10410694032907486 ], "tsne": [ - 17.635303497314453, - 10.151289939880371 + 20.052087783813477, + -5.4158220291137695 ], "umap": [ - 7.040506362915039, - 6.846189022064209 + 7.216924667358398, + 6.403298377990723 ], "pca3d": [ - 0.15600383281707764, - -0.10400069504976273, - -0.046952053904533386 + 0.15576806664466858, + -0.10411082953214645, + -0.046750832349061966 ], "tsne3d": [ - 7.397014617919922, - 0.20395933091640472, - -11.86519718170166 + 4.731707572937012, + 5.958738327026367, + 4.964115142822266 ], "umap3d": [ - 6.324869632720947, - 4.358941078186035, - 3.380202054977417 + 6.773280620574951, + 4.775612831115723, + 2.501673460006714 ] }, "cluster_id": 4, @@ -631756,12 +631906,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 7, + "label": "Genomic Models" }, { - "id": 22, - "label": "Genetic Variants" + "id": 17, + "label": "Genomic Models" } ], "_embedding": [ @@ -632180,31 +632330,31 @@ "doi": "10.1101/2022.08.21.504696", "projections": { "pca": [ - 0.23435422778129578, - 0.023985790088772774 + 0.23430706560611725, + 0.023369912058115005 ], "tsne": [ - 29.090686798095703, - 31.210168838500977 + 26.26068878173828, + -20.881479263305664 ], "umap": [ - 8.82852554321289, - 5.585775852203369 + 9.049607276916504, + 5.042056560516357 ], "pca3d": [ - 0.23435387015342712, - 0.02401801571249962, - -0.05376579239964485 + 0.23430657386779785, + 0.02340315654873848, + -0.054011568427085876 ], "tsne3d": [ - 11.706497192382812, - 22.256441116333008, - -4.353321075439453 + 18.461488723754883, + -15.790942192077637, + 2.3041460514068604 ], "umap3d": [ - 7.515901565551758, - 5.5972137451171875, - 3.002159357070923 + 7.402430534362793, + 5.889973163604736, + 1.298222541809082 ] }, "cluster_id": 4, @@ -632215,11 +632365,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -632643,47 +632793,47 @@ "cited_by_count": 253, "projections": { "pca": [ - 0.23206734657287598, - 0.057711198925971985 + 0.2321873903274536, + 0.05712855979800224 ], "tsne": [ - 30.43271827697754, - 7.10537576675415 + 35.1846923828125, + -9.72507381439209 ], "umap": [ - 7.43168830871582, - 8.13621711730957 + 7.784667491912842, + 7.6951584815979 ], "pca3d": [ - 0.23206709325313568, - 0.057701271027326584, - -0.13630928099155426 + 0.23218722641468048, + 0.057118672877550125, + -0.13647517561912537 ], "tsne3d": [ - 15.237322807312012, - -13.322125434875488, - -11.848085403442383 + 14.872550964355469, + 18.134389877319336, + 1.1960619688034058 ], "umap3d": [ - 6.925756931304932, - 3.269073724746704, - 2.6258506774902344 + 7.265740871429443, + 3.542782783508301, + 1.9276853799819946 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -633108,31 +633258,31 @@ "openalex_url": "https://openalex.org/W4291605629", "projections": { "pca": [ - 0.07531572878360748, - -0.10724711418151855 + 0.07516615092754364, + -0.10748190432786942 ], "tsne": [ - 59.40390396118164, - -8.77839183807373 + 41.417354583740234, + 27.572959899902344 ], "umap": [ - 9.197497367858887, - 6.592902660369873 + 9.352157592773438, + 6.273024559020996 ], "pca3d": [ - 0.07531600445508957, - -0.10725893080234528, - -0.005151631310582161 + 0.07516651600599289, + -0.1074938103556633, + -0.004961489234119654 ], "tsne3d": [ - 20.26531219482422, - 11.257302284240723, - -11.190949440002441 + 20.625307083129883, + -8.389142036437988, + -4.706667423248291 ], "umap3d": [ - 7.116645336151123, - 5.402978897094727, - 1.8610007762908936 + 6.707798957824707, + 5.254234313964844, + 0.3514351546764374 ] }, "cluster_id": 0, @@ -633143,12 +633293,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 2, + "label": "DNA Regulation" }, { - "id": 29, - "label": "Transcription Binding" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -633587,31 +633737,31 @@ "openalex_url": "https://openalex.org/W4290973104", "projections": { "pca": [ - 0.19015823304653168, - -0.06998436152935028 + 0.1898605227470398, + -0.07028084248304367 ], "tsne": [ - 46.506195068359375, - -6.121557235717773 + 45.017677307128906, + 7.801105499267578 ], "umap": [ - 9.99866771697998, - 8.040952682495117 + 10.09086799621582, + 7.529773235321045 ], "pca3d": [ - 0.19015829265117645, - -0.07000292837619781, - -0.09734835475683212 + 0.18986070156097412, + -0.07029975950717926, + -0.0973753109574318 ], "tsne3d": [ - 26.006561279296875, - -6.06003475189209, - -6.171011447906494 + 27.464075088500977, + 10.597079277038574, + -0.5528794527053833 ], "umap3d": [ - 7.1511430740356445, - 4.754823684692383, - 0.8410242199897766 + 6.5719428062438965, + 4.379631519317627, + -0.3100385069847107 ] }, "cluster_id": 0, @@ -633622,12 +633772,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -634035,31 +634185,31 @@ "journal": "Nature Methods", "projections": { "pca": [ - 0.016705550253391266, - -0.07572048157453537 + 0.016575559973716736, + -0.07562956213951111 ], "tsne": [ - 37.809425354003906, - -13.63166332244873 + 33.33768081665039, + 19.922992706298828 ], "umap": [ - 8.950429916381836, - 8.84554386138916 + 9.204695701599121, + 8.405654907226562 ], "pca3d": [ - 0.01670546643435955, - -0.07569648325443268, - -0.10056206583976746 + 0.016575492918491364, + -0.07560470700263977, + -0.10031744837760925 ], "tsne3d": [ - 17.74715805053711, - -5.777054309844971, - 8.333024978637695 + 16.04343032836914, + 6.15378999710083, + -13.464303016662598 ], "umap3d": [ - 6.468688011169434, - 3.875325918197632, - 1.1792904138565063 + 6.2493085861206055, + 3.6085848808288574, + 0.6973752379417419 ] }, "cluster_id": 0, @@ -634070,12 +634220,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -634494,31 +634644,31 @@ "cited_by_count": 1, "projections": { "pca": [ - 0.17897149920463562, - -0.11611618846654892 + 0.17869682610034943, + -0.11660856008529663 ], "tsne": [ - 38.15958023071289, - -1.6801811456680298 + 32.498085021972656, + 9.433131217956543 ], "umap": [ - 9.081995964050293, - 7.074800491333008 + 9.16775894165039, + 6.7069315910339355 ], "pca3d": [ - 0.17897169291973114, - -0.11610762029886246, - -0.08006806671619415 + 0.17869693040847778, + -0.11660000681877136, + -0.07997052371501923 ], "tsne3d": [ - 21.443445205688477, - 3.2140543460845947, - -2.650642156600952 + 21.767690658569336, + -0.3383963704109192, + -2.542729377746582 ], "umap3d": [ - 7.181434631347656, - 4.849194049835205, - 1.711203694343567 + 6.938247203826904, + 4.809830188751221, + 0.6118406653404236 ] }, "cluster_id": 0, @@ -634529,11 +634679,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -634952,31 +635102,31 @@ "doi": "10.1101/2022.05.15.491755", "projections": { "pca": [ - 0.10519357770681381, - -0.1266014575958252 + 0.10498961061239243, + -0.12687568366527557 ], "tsne": [ - 16.322904586791992, - 33.79488754272461 + 20.146648406982422, + -32.52532196044922 ], "umap": [ - 7.885620594024658, - 4.389608383178711 + 8.191575050354004, + 3.91428279876709 ], "pca3d": [ - 0.1051936075091362, - -0.12658028304576874, - 0.17658796906471252 + 0.10498969256877899, + -0.1268533319234848, + 0.1766558289527893 ], "tsne3d": [ - 0.7175661325454712, - 21.871082305908203, - 6.127448081970215 + 8.702119827270508, + -16.601449966430664, + 7.76583194732666 ], "umap3d": [ - 7.201066493988037, - 6.371910572052002, - 4.109785556793213 + 7.429195880889893, + 6.928365707397461, + 2.2828528881073 ] }, "cluster_id": 4, @@ -634987,11 +635137,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -635413,31 +635563,31 @@ "doi": "10.1101/2022.07.27.500203", "projections": { "pca": [ - 0.2351074516773224, - 0.10705820471048355 + 0.23534554243087769, + 0.10623863339424133 ], "tsne": [ - 42.54837417602539, - -14.952771186828613 + 37.81602478027344, + 20.897567749023438 ], "umap": [ - 9.230741500854492, - 8.539897918701172 + 9.485774993896484, + 8.019403457641602 ], "pca3d": [ - 0.23510704934597015, - 0.10704819858074188, - -0.2042427957057953 + 0.2353452742099762, + 0.10622882843017578, + -0.2045333832502365 ], "tsne3d": [ - 23.330224990844727, - -7.911255359649658, - 7.149900913238525 + 22.26500701904297, + 8.470073699951172, + -14.33456802368164 ], "umap3d": [ - 7.213644504547119, - 3.807342290878296, - 1.1663384437561035 + 6.944958209991455, + 3.514691114425659, + 0.43475621938705444 ] }, "cluster_id": 0, @@ -635448,12 +635598,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -635871,31 +636021,31 @@ "doi": "10.1101/2022.07.26.501466", "projections": { "pca": [ - 0.21268750727176666, - -0.0714464858174324 + 0.21247059106826782, + -0.07183708995580673 ], "tsne": [ - 49.11998748779297, - -9.021367073059082 + 46.26087951660156, + 10.76807975769043 ], "umap": [ - 10.10122013092041, - 7.9402241706848145 + 10.272388458251953, + 7.633057594299316 ], "pca3d": [ - 0.21268761157989502, - -0.07145804911851883, - -0.05110746994614601 + 0.2124706506729126, + -0.07184948772192001, + -0.05114683508872986 ], "tsne3d": [ - 26.702255249023438, - -2.472100019454956, - -8.448734283447266 + 27.148054122924805, + 6.976177215576172, + 1.3139854669570923 ], "umap3d": [ - 7.003491401672363, - 5.006328105926514, - 0.690506637096405 + 6.315156936645508, + 4.613292217254639, + -0.36581143736839294 ] }, "cluster_id": 0, @@ -635906,12 +636056,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -636363,31 +636513,31 @@ "openalex_url": "https://openalex.org/W4286254555", "projections": { "pca": [ - 0.19717949628829956, - 0.10448624938726425 + 0.1974613517522812, + 0.10420792549848557 ], "tsne": [ - 59.689903259277344, - 3.414426803588867 + 48.984256744384766, + 21.031314849853516 ], "umap": [ - 9.969740867614746, - 6.94285249710083 + 10.091628074645996, + 6.390605926513672 ], "pca3d": [ - 0.19717928767204285, - 0.10448630154132843, - -0.07551994919776917 + 0.19746100902557373, + 0.10420785844326019, + -0.07576850056648254 ], "tsne3d": [ - 30.51873016357422, - 6.737729072570801, - -7.310697078704834 + 24.525463104248047, + -3.150380849838257, + -2.737297534942627 ], "umap3d": [ - 7.80479621887207, - 5.174864292144775, - 1.3156042098999023 + 7.253116130828857, + 4.989226818084717, + -0.08120367676019669 ] }, "cluster_id": 0, @@ -636398,12 +636548,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -636813,31 +636963,31 @@ "journal": "Nature Reviews Genetics", "projections": { "pca": [ - 0.29311925172805786, - 0.07989908009767532 + 0.2933095693588257, + 0.07886263728141785 ], "tsne": [ - 40.17148208618164, - -1.0888341665267944 + 38.402462005615234, + 7.551772117614746 ], "umap": [ - 9.187446594238281, - 8.166565895080566 + 9.59309196472168, + 7.7558674812316895 ], "pca3d": [ - 0.2931188642978668, - 0.07991180568933487, - -0.2384692281484604 + 0.29330921173095703, + 0.07887568324804306, + -0.23869404196739197 ], "tsne3d": [ - 23.59196662902832, - -6.06850528717041, - -1.8443001508712769 + 20.775121688842773, + 9.705857276916504, + -4.4255194664001465 ], "umap3d": [ - 6.994795799255371, - 4.233133316040039, - 1.2447751760482788 + 6.508535385131836, + 4.025032997131348, + 0.316691517829895 ] }, "cluster_id": 0, @@ -636848,12 +636998,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -637273,31 +637423,31 @@ "doi": "10.1101/2022.07.13.499982", "projections": { "pca": [ - 0.2600681483745575, - 0.029593104496598244 + 0.2601526081562042, + 0.02900419570505619 ], "tsne": [ - 45.4649543762207, - 4.463827133178711 + 45.554534912109375, + 1.170074462890625 ], "umap": [ - 9.359696388244629, - 7.249935150146484 + 9.580427169799805, + 6.813180446624756 ], "pca3d": [ - 0.2600681781768799, - 0.0295790396630764, - -0.09036193042993546 + 0.26015251874923706, + 0.028990481048822403, + -0.09055230766534805 ], "tsne3d": [ - 22.25310516357422, - 1.9310652017593384, - 4.6204833984375 + 25.556570053100586, + -0.04575727880001068, + -8.956792831420898 ], "umap3d": [ - 7.923505783081055, - 4.373458385467529, - 1.590322494506836 + 7.6006340980529785, + 4.312417030334473, + 0.2894333004951477 ] }, "cluster_id": 0, @@ -637308,11 +637458,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -637723,46 +637873,46 @@ "journal": "Nature Genetics", "projections": { "pca": [ - -0.003421397414058447, - 0.11460047215223312 + -0.003030891064554453, + 0.1150428056716919 ], "tsne": [ - -5.722243785858154, - -3.7294580936431885 + -4.895315647125244, + 1.998321294784546 ], "umap": [ - 5.242229461669922, - 6.864837169647217 + 5.612347602844238, + 6.2386908531188965 ], "pca3d": [ - -0.0034215175546705723, - 0.114600270986557, - 0.03511206805706024 + -0.0030310284346342087, + 0.11504273116588593, + 0.03502456471323967 ], "tsne3d": [ - -9.167617797851562, - -0.5824448466300964, - 17.042844772338867 + -3.5776207447052, + 0.3244631886482239, + -2.130052089691162 ], "umap3d": [ - 4.550154685974121, - 4.191232204437256, - 4.100388050079346 + 5.2913665771484375, + 4.961092948913574, + 3.80552339553833 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, + "id": 13, "label": "Genomics Papers" } ], @@ -638181,31 +638331,31 @@ "doi": "10.1038/s41576-022-00514-4", "projections": { "pca": [ - 0.13850699365139008, - 0.11812523007392883 + 0.138870507478714, + 0.11867348104715347 ], "tsne": [ - 29.023189544677734, - 23.367984771728516 + 32.005008697509766, + -22.69297981262207 ], "umap": [ - 8.548930168151855, - 5.686690330505371 + 8.833287239074707, + 5.30768346786499 ], "pca3d": [ - 0.1385066956281662, - 0.11811454594135284, - 0.06598325818777084 + 0.13887016475200653, + 0.11866289377212524, + 0.06583906710147858 ], "tsne3d": [ - 15.767004013061523, - 21.700145721435547, - 4.2384514808654785 + 21.600357055664062, + -11.73731803894043, + 9.105493545532227 ], "umap3d": [ - 7.6502790451049805, - 5.09035587310791, - 3.139139413833618 + 7.6698408126831055, + 5.5376505851745605, + 1.4488223791122437 ] }, "cluster_id": 4, @@ -638216,11 +638366,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -638632,31 +638782,31 @@ "journal": "Nature Reviews Genetics", "projections": { "pca": [ - 0.3061787188053131, - 0.04002885892987251 + 0.30623936653137207, + 0.03892103582620621 ], "tsne": [ - 54.010032653808594, - 6.797728061676025 + 52.729061126708984, + 18.57302474975586 ], "umap": [ - 10.267790794372559, - 6.756861209869385 + 10.439956665039062, + 6.304636478424072 ], "pca3d": [ - 0.30617842078208923, - 0.04003945738077164, - -0.12094778567552567 + 0.3062390089035034, + 0.038931917399168015, + -0.1212594136595726 ], "tsne3d": [ - 25.892776489257812, - 6.148834228515625, - -0.46257659792900085 + 27.493289947509766, + -2.0845720767974854, + -3.9852817058563232 ], "umap3d": [ - 8.167237281799316, - 5.323203086853027, - 1.2535067796707153 + 7.579514980316162, + 5.162089824676514, + -0.374416708946228 ] }, "cluster_id": 0, @@ -638667,12 +638817,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -639089,31 +639239,31 @@ "doi": "10.1101/2022.07.10.499405", "projections": { "pca": [ - 0.1205965131521225, - -0.094215027987957 + 0.12031257152557373, + -0.09417860954999924 ], "tsne": [ - -11.896642684936523, - 16.05615234375 + -9.039440155029297, + -22.323623657226562 ], "umap": [ - 5.688349723815918, - 4.624908447265625 + 5.970814228057861, + 4.097721576690674 ], "pca3d": [ - 0.12059647589921951, - -0.09419732540845871, - 0.16708379983901978 + 0.12031247466802597, + -0.0941610336303711, + 0.16702118515968323 ], "tsne3d": [ - 1.7299695014953613, - 0.15588338673114777, - 13.534554481506348 + -1.2094299793243408, + -20.33193016052246, + -7.279684066772461 ], "umap3d": [ - 4.936563014984131, - 6.491506099700928, - 4.605288982391357 + 5.53843355178833, + 6.96516752243042, + 3.106491804122925 ] }, "cluster_id": 3, @@ -639124,11 +639274,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -639537,31 +639687,31 @@ "journal": "Nature Genetics", "projections": { "pca": [ - 0.10247310250997543, - -0.04278960078954697 + 0.10246669501066208, + -0.04373864457011223 ], "tsne": [ - 44.10336685180664, - -2.0594351291656494 + 32.92670440673828, + 14.733301162719727 ], "umap": [ - 9.084440231323242, - 7.585022926330566 + 9.347983360290527, + 7.26198673248291 ], "pca3d": [ - 0.10247298330068588, - -0.04278525337576866, - -0.20032785832881927 + 0.10246659070253372, + -0.04373342543840408, + -0.20025275647640228 ], "tsne3d": [ - 15.524874687194824, - 0.01821312867105007, - 3.652820348739624 + 18.113975524902344, + -0.6023687720298767, + -9.640605926513672 ], "umap3d": [ - 7.684436321258545, - 4.175655364990234, - 1.4735958576202393 + 7.378593921661377, + 4.095332145690918, + 0.31968289613723755 ] }, "cluster_id": 0, @@ -639572,11 +639722,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -639985,47 +640135,47 @@ "journal": "Nature", "projections": { "pca": [ - 0.14982987940311432, - 0.009101919829845428 + 0.1499779373407364, + 0.00876740925014019 ], "tsne": [ - 31.941865921020508, - 20.452001571655273 + 27.145849227905273, + 21.350505828857422 ], "umap": [ - 8.521652221679688, - 8.777853965759277 + 8.56946086883545, + 8.329690933227539 ], "pca3d": [ - 0.14982949197292328, - 0.009129897691309452, - -0.019585605710744858 + 0.14997749030590057, + 0.008795896545052528, + -0.01959882490336895 ], "tsne3d": [ - 19.3550968170166, - 14.505659103393555, - 9.23394775390625 + 18.081562042236328, + -13.383193016052246, + -2.702371120452881 ], "umap3d": [ - 7.280874729156494, - 5.164618968963623, - 2.773430585861206 + 5.685221195220947, + 4.467525482177734, + 0.8700336217880249 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -640449,31 +640599,31 @@ "doi": "10.1101/2022.07.07.499190", "projections": { "pca": [ - 0.27800291776657104, - 0.04367273673415184 + 0.27810877561569214, + 0.042728278785943985 ], "tsne": [ - 50.72977828979492, - -1.526152491569519 + 56.63449478149414, + 8.817235946655273 ], "umap": [ - 10.604618072509766, - 7.521512031555176 + 10.748384475708008, + 7.074033260345459 ], "pca3d": [ - 0.27800270915031433, - 0.04367532208561897, - -0.05199957638978958 + 0.2781085669994354, + 0.04273046553134918, + -0.05232943966984749 ], "tsne3d": [ - 33.60367965698242, - -0.7526470422744751, - -0.23023124039173126 + 34.78468322753906, + 4.642522811889648, + -0.2613675594329834 ], "umap3d": [ - 7.791173934936523, - 4.903702259063721, - 0.5003946423530579 + 7.072384834289551, + 4.534457683563232, + -0.7493460774421692 ] }, "cluster_id": 0, @@ -640484,12 +640634,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -640895,31 +641045,31 @@ ], "projections": { "pca": [ - -0.12947334349155426, - -0.033670518547296524 + -0.1294473260641098, + -0.033222511410713196 ], "tsne": [ - 11.699416160583496, - -5.102322101593018 + 12.048026084899902, + 6.4887166023254395 ], "umap": [ - 5.889947891235352, - 6.836100101470947 + 6.292139530181885, + 6.415931224822998 ], "pca3d": [ - -0.12947344779968262, - -0.03364228829741478, - -0.07119946926832199 + -0.12944753468036652, + -0.03319339081645012, + -0.07091666013002396 ], "tsne3d": [ - 16.12735939025879, - 2.549558162689209, - -16.506107330322266 + 13.379318237304688, + 4.4500885009765625, + 10.50607967376709 ], "umap3d": [ - 4.910172939300537, - 4.354246616363525, - 3.4723918437957764 + 5.37919807434082, + 4.840021133422852, + 3.106454610824585 ] }, "cluster_id": 4, @@ -640930,12 +641080,12 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 11, - "label": "Genomic Language" + "id": 13, + "label": "Genomics Papers" } ], "_embedding": [ @@ -641356,31 +641506,31 @@ "cited_by_count": 84, "projections": { "pca": [ - 0.1775180697441101, - -0.04895039275288582 + 0.17731861770153046, + -0.049234479665756226 ], "tsne": [ - 28.587221145629883, - -1.421537160873413 + 30.20537567138672, + 0.6838899254798889 ], "umap": [ - 8.231978416442871, - 7.7251505851745605 + 8.41010570526123, + 7.313311576843262 ], "pca3d": [ - 0.17751815915107727, - -0.04895954206585884, - -0.07334434986114502 + 0.1773187220096588, + -0.04924312233924866, + -0.07336635887622833 ], "tsne3d": [ - 10.68016529083252, - -8.170893669128418, - -1.3085893392562866 + 10.45178508758545, + 13.898341178894043, + -10.564377784729004 ], "umap3d": [ - 7.113431453704834, - 3.949300527572632, - 2.295081853866577 + 7.27825403213501, + 4.100622653961182, + 1.2126604318618774 ] }, "cluster_id": 0, @@ -641391,12 +641541,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -641819,31 +641969,31 @@ "openalex_url": "https://openalex.org/W4282929864", "projections": { "pca": [ - 0.15838216245174408, - -0.042224764823913574 + 0.15829253196716309, + -0.0428096242249012 ], "tsne": [ - 42.69843292236328, - -2.2260751724243164 + 33.69807434082031, + 15.552712440490723 ], "umap": [ - 8.868735313415527, - 7.680426597595215 + 8.964997291564941, + 7.370908260345459 ], "pca3d": [ - 0.15838222205638885, - -0.04223593696951866, - -0.10833762586116791 + 0.15829263627529144, + -0.04282023012638092, + -0.10833138972520828 ], "tsne3d": [ - 11.130110740661621, - -4.9839043617248535, - 0.24293851852416992 + 18.3821964263916, + 0.740494966506958, + -11.511199951171875 ], "umap3d": [ - 7.545201301574707, - 4.079728603363037, - 1.7850701808929443 + 7.43043851852417, + 4.027090072631836, + 0.6407040357589722 ] }, "cluster_id": 0, @@ -641854,12 +642004,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -642280,47 +642430,47 @@ "cited_by_count": 18, "projections": { "pca": [ - 0.20687954127788544, - 0.11880091577768326 + 0.2072027623653412, + 0.11851442605257034 ], "tsne": [ - 17.747955322265625, - -13.372431755065918 + 30.06871795654297, + 18.87799644470215 ], "umap": [ - 7.045285701751709, - 8.584654808044434 + 7.3760085105896, + 8.250630378723145 ], "pca3d": [ - 0.20687901973724365, - 0.11881042271852493, - -0.09440499544143677 + 0.2072022706270218, + 0.11852394789457321, + -0.09461689740419388 ], "tsne3d": [ - 17.276884078979492, - -15.190171241760254, - -0.357582688331604 + 16.035736083984375, + 18.44135856628418, + -8.156673431396484 ], "umap3d": [ - 6.280811309814453, - 3.191241502761841, - 2.007401704788208 + 6.4091477394104, + 3.1861162185668945, + 1.7429161071777344 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 6, + "label": "Single Cell Data" }, { - "id": 26, - "label": "Genetic Disease" + "id": 28, + "label": "Single Cell Data" } ], "_embedding": [ @@ -642733,31 +642883,31 @@ "doi": "10.1016/s0168-9525(04)00071-x", "projections": { "pca": [ - 0.16399791836738586, - 0.276195228099823 + 0.16466116905212402, + 0.27598828077316284 ], "tsne": [ - 42.89456558227539, - 14.681607246398926 + 46.86621856689453, + -8.545951843261719 ], "umap": [ - 8.651250839233398, - 7.7999491691589355 + 9.098993301391602, + 7.079532623291016 ], "pca3d": [ - 0.16399729251861572, - 0.27620455622673035, - -0.040626294910907745 + 0.16466043889522552, + 0.27599743008613586, + -0.0411752313375473 ], "tsne3d": [ - 20.156335830688477, - -15.62429428100586, - -2.962068796157837 + 15.654845237731934, + 22.78877830505371, + -7.0568766593933105 ], "umap3d": [ - 7.219849109649658, - 4.272848129272461, - 1.926350712776184 + 6.925373077392578, + 4.19582986831665, + 0.6765982508659363 ] }, "cluster_id": 0, @@ -642768,12 +642918,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -643196,31 +643346,31 @@ "openalex_url": "https://openalex.org/W4282011047", "projections": { "pca": [ - 0.13574974238872528, - -0.050162188708782196 + 0.13559919595718384, + -0.05047917366027832 ], "tsne": [ - 62.46210861206055, - 0.9905831813812256 + 48.532955169677734, + 25.119340896606445 ], "umap": [ - 9.75916576385498, - 6.4058990478515625 + 9.933609008789062, + 5.953587532043457 ], "pca3d": [ - 0.13574948906898499, - -0.05012489855289459, - -0.043290141969919205 + 0.13559898734092712, + -0.05044109746813774, + -0.04330497980117798 ], "tsne3d": [ - 26.249271392822266, - 8.774680137634277, - -9.09593677520752 + 23.46402359008789, + -6.247830390930176, + -0.2161124348640442 ], "umap3d": [ - 7.682399272918701, - 5.71531343460083, - 1.606762409210205 + 7.0773725509643555, + 5.510742664337158, + 0.12770727276802063 ] }, "cluster_id": 0, @@ -643231,12 +643381,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -643646,31 +643796,31 @@ "journal": "Nature Biotechnology", "projections": { "pca": [ - 0.1928538829088211, - -0.010503077879548073 + 0.1930723786354065, + -0.0116142388433218 ], "tsne": [ - 43.85012435913086, - 4.284001350402832 + 43.6983757019043, + -0.49780189990997314 ], "umap": [ - 9.112635612487793, - 7.1442670822143555 + 9.288304328918457, + 6.71674108505249 ], "pca3d": [ - 0.1928539276123047, - -0.0105203902348876, - -0.046328067779541016 + 0.19307246804237366, + -0.011630880646407604, + -0.0463620088994503 ], "tsne3d": [ - 18.66526985168457, - 3.114234209060669, - 5.876046180725098 + 23.74443244934082, + -0.7450934648513794, + -12.441073417663574 ], "umap3d": [ - 7.950091361999512, - 4.370046138763428, - 1.814507246017456 + 7.666767597198486, + 4.331614017486572, + 0.48965415358543396 ] }, "cluster_id": 0, @@ -643681,11 +643831,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -644113,31 +644263,31 @@ "cited_by_count": 222, "projections": { "pca": [ - 0.3001501262187958, - 0.05106625333428383 + 0.3002007305622101, + 0.05086987465620041 ], "tsne": [ - 55.591766357421875, - 3.870723247528076 + 53.473121643066406, + 15.071136474609375 ], "umap": [ - 10.190223693847656, - 7.096362590789795 + 10.418848991394043, + 6.575401782989502 ], "pca3d": [ - 0.3001500964164734, - 0.05106613039970398, - -0.08184512704610825 + 0.30020052194595337, + 0.05086927488446236, + -0.08209680020809174 ], "tsne3d": [ - 29.868511199951172, - 3.631098985671997, - -3.151292562484741 + 29.998416900634766, + 0.6062441468238831, + 0.16596077382564545 ], "umap3d": [ - 7.750583648681641, - 5.287628650665283, - 1.0519983768463135 + 7.081336975097656, + 4.984320163726807, + -0.43493038415908813 ] }, "cluster_id": 0, @@ -644148,12 +644298,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -644610,31 +644760,31 @@ "openalex_url": "https://openalex.org/W4281564034", "projections": { "pca": [ - 0.24838422238826752, - 0.0758705586194992 + 0.24847251176834106, + 0.0750429704785347 ], "tsne": [ - 39.62568283081055, - -9.487494468688965 + 37.22550582885742, + 15.910652160644531 ], "umap": [ - 9.235482215881348, - 8.473227500915527 + 9.403487205505371, + 7.867043495178223 ], "pca3d": [ - 0.24838417768478394, - 0.07586388289928436, - -0.1359531581401825 + 0.2484724372625351, + 0.07503608614206314, + -0.13626399636268616 ], "tsne3d": [ - 23.45140838623047, - -2.9122209548950195, - 8.679276466369629 + 20.100852966308594, + 8.764150619506836, + -11.074976921081543 ], "umap3d": [ - 7.329449653625488, - 4.003742218017578, - 1.261183500289917 + 6.958301544189453, + 3.757647752761841, + 0.4022586941719055 ] }, "cluster_id": 0, @@ -644645,12 +644795,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -645077,47 +645227,47 @@ "cited_by_count": 133, "projections": { "pca": [ - 0.16843390464782715, - -0.052761148661375046 + 0.16827097535133362, + -0.05291571095585823 ], "tsne": [ - -18.66084861755371, - 19.734609603881836 + -14.423829078674316, + -27.243989944458008 ], "umap": [ - 10.323384284973145, - 6.429510116577148 + 5.4259257316589355, + 3.8115127086639404 ], "pca3d": [ - 0.16843363642692566, - -0.05272999778389931, - 0.07661477476358414 + 0.16827072203159332, + -0.05288398638367653, + 0.07651343941688538 ], "tsne3d": [ - 1.9818353652954102, - 12.360526084899902, - 17.84775161743164 + 3.6345386505126953, + -15.633124351501465, + 15.857335090637207 ], "umap3d": [ - 4.649051666259766, - 6.917718887329102, - 4.243766784667969 + 7.399287700653076, + 5.524076461791992, + -0.2786739766597748 ] }, - "cluster_id": 0, - "cluster_label": "Chromatin Regulation", + "cluster_id": 3, + "cluster_label": "Protein Design", "cluster_levels": [ { - "id": 0, - "label": "Chromatin Regulation" + "id": 3, + "label": "Protein Design" }, { - "id": 14, - "label": "Transcription Binding" + "id": 9, + "label": "Protein Design" }, { - "id": 29, - "label": "Transcription Binding" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -645536,31 +645686,31 @@ "cited_by_count": 8, "projections": { "pca": [ - 0.03919750452041626, - -0.19863149523735046 + 0.038668084889650345, + -0.19845032691955566 ], "tsne": [ - 15.010621070861816, - -2.599738359451294 + 16.266342163085938, + 4.252385139465332 ], "umap": [ - 8.120698928833008, - 6.1310133934021 + 8.380629539489746, + 5.8367438316345215 ], "pca3d": [ - 0.03919782117009163, - -0.1986195594072342, - -0.04040461406111717 + 0.03866836056113243, + -0.1984383463859558, + -0.040068063884973526 ], "tsne3d": [ - 14.261195182800293, - 9.494464874267578, - -12.759703636169434 + 16.141231536865234, + -3.0376663208007812, + 6.26848840713501 ], "umap3d": [ - 6.513343811035156, - 5.485504150390625, - 2.589555501937866 + 6.296975135803223, + 5.482632160186768, + 1.3387632369995117 ] }, "cluster_id": 4, @@ -645571,12 +645721,12 @@ "label": "RNA Sequence Models" }, { - "id": 3, - "label": "RNA Splicing" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -645984,31 +646134,31 @@ "journal": "Nature", "projections": { "pca": [ - 0.19468949735164642, - 0.06738031655550003 + 0.19491319358348846, + 0.06714383512735367 ], "tsne": [ - 51.404571533203125, - -5.643311977386475 + 49.7685546875, + 7.958941459655762 ], "umap": [ - 10.364470481872559, - 7.852794170379639 + 10.522019386291504, + 7.4972243309021 ], "pca3d": [ - 0.19468939304351807, - 0.06736116856336594, - -0.058668360114097595 + 0.1949130743741989, + 0.06712375581264496, + -0.05882110074162483 ], "tsne3d": [ - 29.855655670166016, - -5.242582321166992, - -6.774024963378906 + 30.77960205078125, + 9.438878059387207, + 1.278846025466919 ], "umap3d": [ - 7.321253299713135, - 4.988139629364014, - 0.5306732654571533 + 6.549376964569092, + 4.552157878875732, + -0.5899695158004761 ] }, "cluster_id": 0, @@ -646019,12 +646169,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -646432,31 +646582,31 @@ "journal": "Nature Genetics", "projections": { "pca": [ - 0.12982213497161865, - -0.08726373314857483 + 0.12982413172721863, + -0.08787380158901215 ], "tsne": [ - 34.840423583984375, - 16.026010513305664 + 41.13885498046875, + -3.328662395477295 ], "umap": [ - 8.58981704711914, - 6.787313938140869 + 8.886625289916992, + 6.459939002990723 ], "pca3d": [ - 0.12982240319252014, - -0.08729249238967896, - 0.014581668190658092 + 0.12982438504695892, + -0.08790184557437897, + 0.014761257916688919 ], "tsne3d": [ - 12.607563972473145, - 4.7558159828186035, - 10.24380111694336 + 18.943357467651367, + 2.1616320610046387, + 14.469062805175781 ], "umap3d": [ - 7.8325724601745605, - 4.465508460998535, - 2.3587546348571777 + 7.731031894683838, + 4.600082874298096, + 0.9625793099403381 ] }, "cluster_id": 0, @@ -646467,12 +646617,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 1, - "label": "Regulatory Genomics" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -646924,47 +647074,47 @@ "openalex_url": "https://openalex.org/W4280633014", "projections": { "pca": [ - 0.19388872385025024, - 0.06327573955059052 + 0.19400295615196228, + 0.06258989125490189 ], "tsne": [ - 35.889617919921875, - -7.561588764190674 + 37.88078308105469, + 9.336400032043457 ], "umap": [ - 8.06808853149414, - 8.578179359436035 + 8.216373443603516, + 8.13265323638916 ], "pca3d": [ - 0.1938883513212204, - 0.06329038739204407, - -0.1673387736082077 + 0.19400250911712646, + 0.06260474026203156, + -0.16751016676425934 ], "tsne3d": [ - 19.27410316467285, - -9.616395950317383, - 1.471409797668457 + 20.713319778442383, + 10.273859977722168, + -7.574946403503418 ], "umap3d": [ - 6.602519512176514, - 3.589200735092163, - 1.6401183605194092 + 6.431794166564941, + 3.4783856868743896, + 1.130872368812561 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -647372,31 +647522,31 @@ "journal": "Nature Genetics", "projections": { "pca": [ - 0.11815984547138214, - 0.07456331700086594 + 0.11830538511276245, + 0.07422610372304916 ], "tsne": [ - 49.64326858520508, - -10.881754875183105 + 46.75343322753906, + 12.463272094726562 ], "umap": [ - 10.18091869354248, - 7.979564189910889 + 10.413572311401367, + 7.626447677612305 ], "pca3d": [ - 0.11815956234931946, - 0.07456693798303604, - 0.013089303858578205 + 0.11830513924360275, + 0.07422953844070435, + 0.01279870979487896 ], "tsne3d": [ - 27.219449996948242, - -0.5565434694290161, - -11.288337707519531 + 28.177248001098633, + 4.724050521850586, + 3.418464422225952 ], "umap3d": [ - 6.991769790649414, - 5.118231773376465, - 0.6752809882164001 + 6.3033342361450195, + 4.672919750213623, + -0.33940544724464417 ] }, "cluster_id": 0, @@ -647407,12 +647557,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -647828,47 +647978,47 @@ "doi": "10.1101/2022.05.09.491198", "projections": { "pca": [ - 0.18556182086467743, - -0.050099655985832214 + 0.1853606104850769, + -0.05034903064370155 ], "tsne": [ - 28.447038650512695, - 16.52189064025879 + 24.529613494873047, + -10.8456392288208 ], "umap": [ - 7.581779479980469, - 7.610084533691406 + 7.6690754890441895, + 7.198139190673828 ], "pca3d": [ - 0.18556183576583862, - -0.05011030286550522, - -0.11119745671749115 + 0.1853606104850769, + -0.05035994574427605, + -0.111171193420887 ], "tsne3d": [ - 13.992066383361816, - -5.515230655670166, - -7.122035980224609 + 14.854278564453125, + 17.07986831665039, + 9.038087844848633 ], "umap3d": [ - 7.057817459106445, - 4.073550224304199, - 3.087345838546753 + 7.311086654663086, + 4.26809024810791, + 2.0293946266174316 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -648285,47 +648435,47 @@ "doi": "10.1101/2022.05.07.491045", "projections": { "pca": [ - 0.21782860159873962, - 0.0736125186085701 + 0.2179664820432663, + 0.07335522025823593 ], "tsne": [ - 30.674518585205078, - 6.915276527404785 + 35.406063079833984, + -9.248015403747559 ], "umap": [ - 7.50932502746582, - 8.161893844604492 + 7.750813961029053, + 7.722989082336426 ], "pca3d": [ - 0.21782836318016052, - 0.07360408455133438, - -0.15096980333328247 + 0.21796628832817078, + 0.07334652543067932, + -0.15113520622253418 ], "tsne3d": [ - 15.738341331481934, - -12.60302448272705, - -11.044096946716309 + 15.548551559448242, + 17.271724700927734, + 0.9278803467750549 ], "umap3d": [ - 6.963237285614014, - 3.252837657928467, - 2.5661211013793945 + 7.3025126457214355, + 3.52767276763916, + 1.8941775560379028 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -648733,46 +648883,46 @@ "journal": "Nature Reviews Genetics", "projections": { "pca": [ - 0.08255966007709503, - 0.17278961837291718 + 0.08308807760477066, + 0.17244082689285278 ], "tsne": [ - 4.1940507888793945, - -3.8833868503570557 + 2.453047513961792, + 1.343043327331543 ], "umap": [ - 5.640323162078857, - 7.021493434906006 + 5.938455581665039, + 6.548373222351074 ], "pca3d": [ - 0.08255919814109802, - 0.1728055477142334, - -0.05793530121445656 + 0.08308759331703186, + 0.17245717346668243, + -0.0581829771399498 ], "tsne3d": [ - 6.889627933502197, - -5.443365097045898, - 1.1426639556884766 + 6.3446502685546875, + 17.05718994140625, + -9.166778564453125 ], "umap3d": [ - 4.996358871459961, - 3.8432276248931885, - 4.366990566253662 + 5.778171062469482, + 4.757925510406494, + 4.108082294464111 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, + "id": 13, "label": "Genomics Papers" } ], @@ -649181,47 +649331,47 @@ "journal": "Nature Genetics", "projections": { "pca": [ - 0.07007922232151031, - 0.07755149900913239 + 0.0701398253440857, + 0.07728447765111923 ], "tsne": [ - 25.48855209350586, - 11.583747863769531 + 29.875423431396484, + -3.215367078781128 ], "umap": [ - 7.5184173583984375, - 7.567804336547852 + 7.7880024909973145, + 7.230820655822754 ], "pca3d": [ - 0.07007928937673569, - 0.07752886414527893, - -0.19040995836257935 + 0.07013983279466629, + 0.07726088166236877, + -0.19057710468769073 ], "tsne3d": [ - 16.430103302001953, - -5.315224647521973, - -13.304398536682129 + 13.334863662719727, + 11.583442687988281, + 2.247088670730591 ], "umap3d": [ - 6.887063026428223, - 3.7688491344451904, - 2.92987322807312 + 7.256118297576904, + 4.096305847167969, + 2.031846523284912 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -649640,31 +649790,31 @@ "doi": "10.1101/2022.04.30.490167", "projections": { "pca": [ - 0.06679242104291916, - -0.12086573988199234 + 0.06646955013275146, + -0.1208202913403511 ], "tsne": [ - 26.137264251708984, - 14.427574157714844 + 28.159204483032227, + -7.12283182144165 ], "umap": [ - 7.564999103546143, - 7.335693836212158 + 7.809824466705322, + 6.984421253204346 ], "pca3d": [ - 0.06679275631904602, - -0.12089493870735168, - -0.08157159388065338 + 0.0664699450135231, + -0.12084921449422836, + -0.08133989572525024 ], "tsne3d": [ - 13.662184715270996, - -7.198427200317383, - -17.937971115112305 + 13.24870491027832, + 12.714698791503906, + 9.734920501708984 ], "umap3d": [ - 6.854935169219971, - 3.9775640964508057, - 3.002087116241455 + 7.200312614440918, + 4.331399917602539, + 1.963991403579712 ] }, "cluster_id": 4, @@ -649675,12 +649825,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -650095,31 +650245,31 @@ "doi": "10.1101/2022.04.21.489091", "projections": { "pca": [ - 0.2782139778137207, - 0.08374880254268646 + 0.2783857583999634, + 0.08306097239255905 ], "tsne": [ - 52.68818283081055, - -5.090754508972168 + 50.975486755371094, + 7.096979141235352 ], "umap": [ - 10.327963829040527, - 7.7817230224609375 + 10.501641273498535, + 7.3362531661987305 ], "pca3d": [ - 0.27821364998817444, - 0.08374901115894318, - -0.13833779096603394 + 0.2783854603767395, + 0.08306066691875458, + -0.13863135874271393 ], "tsne3d": [ - 30.341703414916992, - -5.794796943664551, - -4.359965801239014 + 31.90485954284668, + 9.480708122253418, + -0.7961986660957336 ], "umap3d": [ - 7.468850612640381, - 4.944944381713867, - 0.6417965292930603 + 6.72517204284668, + 4.5589070320129395, + -0.5060274600982666 ] }, "cluster_id": 0, @@ -650130,12 +650280,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -650565,31 +650715,31 @@ "cited_by_count": 441, "projections": { "pca": [ - 0.2716578543186188, - -0.014331315644085407 + 0.27152854204177856, + -0.014888158068060875 ], "tsne": [ - 51.41374969482422, - -0.5797699093818665 + 55.36247634887695, + 8.870222091674805 ], "umap": [ - 10.505388259887695, - 7.3672380447387695 + 10.724837303161621, + 7.076766490936279 ], "pca3d": [ - 0.2716575562953949, - -0.014333156868815422, - -0.07884808629751205 + 0.27152836322784424, + -0.01489027589559555, + -0.07906543463468552 ], "tsne3d": [ - 31.82018280029297, - -1.4723873138427734, - 0.25822386145591736 + 33.52101516723633, + 3.5856664180755615, + -1.3072389364242554 ], "umap3d": [ - 7.864083290100098, - 4.957566261291504, - 0.5970261693000793 + 7.110808372497559, + 4.579939842224121, + -0.6897360682487488 ] }, "cluster_id": 0, @@ -650600,12 +650750,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -651011,31 +651161,31 @@ ], "projections": { "pca": [ - 0.017220281064510345, - 0.013756515458226204 + 0.017312409356236458, + 0.013588348403573036 ], "tsne": [ - 63.70603561401367, - 1.4317604303359985 + 49.45038986206055, + 26.134395599365234 ], "umap": [ - 9.693285942077637, - 6.379642963409424 + 9.874594688415527, + 5.90419340133667 ], "pca3d": [ - 0.017220064997673035, - 0.013783585280179977, - -0.013588200323283672 + 0.0173121877014637, + 0.013615862466394901, + -0.013659541495144367 ], "tsne3d": [ - 27.249229431152344, - 10.997005462646484, - -9.386874198913574 + 23.67209243774414, + -8.333168983459473, + 0.3794769048690796 ], "umap3d": [ - 7.550599098205566, - 5.699934959411621, - 1.655016541481018 + 6.973658561706543, + 5.508768558502197, + 0.21692395210266113 ] }, "cluster_id": 0, @@ -651046,12 +651196,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -651487,47 +651637,47 @@ "openalex_url": "https://openalex.org/W4224006698", "projections": { "pca": [ - 0.24904866516590118, - 0.09729350358247757 + 0.24928005039691925, + 0.0967087522149086 ], "tsne": [ - 30.147245407104492, - -0.3556079864501953 + 32.04368591308594, + -0.2370256632566452 ], "umap": [ - 8.204779624938965, - 7.813508987426758 + 8.343207359313965, + 7.4074015617370605 ], "pca3d": [ - 0.24904847145080566, - 0.09727442264556885, - -0.11972562968730927 + 0.24927999079227448, + 0.09668933600187302, + -0.11998757719993591 ], "tsne3d": [ - 11.793303489685059, - -7.511560440063477, - -2.8953750133514404 + 11.780728340148926, + 14.961625099182129, + -9.365203857421875 ], "umap3d": [ - 7.1212873458862305, - 3.9073026180267334, - 2.3098838329315186 + 7.236055374145508, + 4.045866966247559, + 1.2801357507705688 ] }, - "cluster_id": 0, - "cluster_label": "Chromatin Regulation", + "cluster_id": 6, + "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 0, - "label": "Chromatin Regulation" + "id": 6, + "label": "Single-Cell Data" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 3, + "label": "Genetic Disease" }, { - "id": 31, - "label": "Genome Cancer" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -651937,31 +652087,31 @@ "abstract": "Scientists have published the first complete, gapless sequence of a human genome, two decades after the Human Genome Project produced the first draft human genome sequence.", "projections": { "pca": [ - 0.14029604196548462, - 0.2653757333755493 + 0.14111600816249847, + 0.2656024694442749 ], "tsne": [ - 38.05657958984375, - 13.220972061157227 + 44.71279525756836, + -5.621434688568115 ], "umap": [ - 8.68702507019043, - 7.529468536376953 + 8.901589393615723, + 7.167823314666748 ], "pca3d": [ - 0.14029563963413239, - 0.2653588056564331, - 0.06458556652069092 + 0.14111557602882385, + 0.26558566093444824, + 0.06421523541212082 ], "tsne3d": [ - 17.363529205322266, - 2.906111478805542, - 13.124388694763184 + 21.509052276611328, + 8.293113708496094, + 14.407598495483398 ], "umap3d": [ - 7.637896537780762, - 4.216590404510498, - 2.066854238510132 + 7.308172702789307, + 4.3258209228515625, + 0.9098753929138184 ] }, "cluster_id": 0, @@ -651972,12 +652122,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -652412,47 +652562,47 @@ "openalex_url": "https://openalex.org/W4225777909", "projections": { "pca": [ - 0.27350422739982605, - 0.05268753692507744 + 0.27355721592903137, + 0.05212651938199997 ], "tsne": [ - 31.04131507873535, - 6.644312381744385 + 35.84433364868164, + -8.923234939575195 ], "umap": [ - 7.623847007751465, - 8.26297664642334 + 7.912540912628174, + 7.817089557647705 ], "pca3d": [ - 0.2735039293766022, - 0.05267872288823128, - -0.18269170820713043 + 0.2735569477081299, + 0.05211715027689934, + -0.18288426101207733 ], "tsne3d": [ - 16.486661911010742, - -12.535897254943848, - -10.092048645019531 + 16.53543472290039, + 16.899986267089844, + 0.3942115306854248 ], "umap3d": [ - 7.001909255981445, - 3.266981840133667, - 2.3983988761901855 + 7.2711381912231445, + 3.4846303462982178, + 1.7623189687728882 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -652871,31 +653021,31 @@ "doi": "10.1101/2022.03.30.486382", "projections": { "pca": [ - 0.20048002898693085, - 0.04967007786035538 + 0.20061789453029633, + 0.0491475835442543 ], "tsne": [ - 38.62942123413086, - -10.371055603027344 + 35.95640182495117, + 16.41956329345703 ], "umap": [ - 8.930784225463867, - 8.044235229492188 + 9.272643089294434, + 7.7888689041137695 ], "pca3d": [ - 0.20047979056835175, - 0.04967477172613144, - -0.14730198681354523 + 0.20061759650707245, + 0.04915264993906021, + -0.14743337035179138 ], "tsne3d": [ - 21.371978759765625, - -1.5370237827301025, - 7.629166126251221 + 19.350074768066406, + 6.699161529541016, + -9.171711921691895 ], "umap3d": [ - 7.254946231842041, - 4.13452672958374, - 1.5521351099014282 + 7.005289077758789, + 3.8223729133605957, + 0.4811486303806305 ] }, "cluster_id": 0, @@ -652906,12 +653056,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -653330,47 +653480,47 @@ "openalex_url": "https://openalex.org/W3082188664", "projections": { "pca": [ - -0.13230803608894348, - 0.25777387619018555 + -0.1317908614873886, + 0.25884950160980225 ], "tsne": [ - -10.254739761352539, - -31.47599220275879 + -13.10596752166748, + 24.326894760131836 ], "umap": [ - 3.7717654705047607, - 8.212639808654785 + 4.213533401489258, + 7.597560882568359 ], "pca3d": [ - -0.13230879604816437, - 0.25780025124549866, - 0.031088249757885933 + -0.13179147243499756, + 0.2588760554790497, + 0.030722057446837425 ], "tsne3d": [ - -9.118892669677734, - -14.730459213256836, - 2.7268288135528564 + -1.767935037612915, + 9.971907615661621, + -2.1415443420410156 ], "umap3d": [ - 3.078054666519165, - 4.055758476257324, - 3.3523848056793213 + 3.659057140350342, + 4.489790439605713, + 3.641655683517456 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -653785,31 +653935,31 @@ "doi": "10.1101/2022.03.18.484474", "projections": { "pca": [ - 0.14243409037590027, - -0.042925599962472916 + 0.14221222698688507, + -0.042649656534194946 ], "tsne": [ - 21.639507293701172, - 29.38164710998535 + 21.84857177734375, + -23.142793655395508 ], "umap": [ - 8.151912689208984, - 5.061952114105225 + 8.421168327331543, + 4.665915012359619 ], "pca3d": [ - 0.1424337923526764, - -0.042894769459962845, - 0.00048293991130776703 + 0.1422118991613388, + -0.042618002742528915, + 0.0004941438091918826 ], "tsne3d": [ - 6.302967548370361, - 22.716331481933594, - -3.270928144454956 + 16.60150718688965, + -13.492839813232422, + 2.6005685329437256 ], "umap3d": [ - 7.304059982299805, - 5.71160888671875, - 3.53659725189209 + 7.301933288574219, + 6.264769554138184, + 1.79534113407135 ] }, "cluster_id": 4, @@ -653820,11 +653970,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -654246,31 +654396,31 @@ "cited_by_count": 5, "projections": { "pca": [ - 0.2894677221775055, - 0.053150612860918045 + 0.2894495725631714, + 0.05245628207921982 ], "tsne": [ - 55.354312896728516, - -1.1918879747390747 + 53.36172103881836, + 11.724474906921387 ], "umap": [ - 10.52601146697998, - 7.423084735870361 + 10.662900924682617, + 6.996865272521973 ], "pca3d": [ - 0.2894675135612488, - 0.05313162878155708, - -0.08055184036493301 + 0.28944942355155945, + 0.05243679881095886, + -0.08091413229703903 ], "tsne3d": [ - 28.70572853088379, - 0.31714633107185364, - -2.6467528343200684 + 29.254592895507812, + 3.9272806644439697, + -3.506683588027954 ], "umap3d": [ - 7.9348649978637695, - 4.96051549911499, - 0.7017635703086853 + 7.16424560546875, + 4.637425422668457, + -0.6997023224830627 ] }, "cluster_id": 0, @@ -654281,12 +654431,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -654703,31 +654853,31 @@ "cited_by_count": 35, "projections": { "pca": [ - 0.12952567636966705, - -0.11551997065544128 + 0.12919558584690094, + -0.11608550697565079 ], "tsne": [ - 38.867794036865234, - -13.543985366821289 + 34.24531173706055, + 19.45915985107422 ], "umap": [ - 9.053442001342773, - 8.697936058044434 + 9.335190773010254, + 8.260281562805176 ], "pca3d": [ - 0.1295257955789566, - -0.11549989134073257, - -0.20569415390491486 + 0.12919558584690094, + -0.11606468260288239, + -0.2055363953113556 ], "tsne3d": [ - 18.86346435546875, - -6.7707648277282715, - 6.574294567108154 + 17.624313354492188, + 4.559564113616943, + -13.69270133972168 ], "umap3d": [ - 6.651162624359131, - 3.952357292175293, - 1.2037343978881836 + 6.316808223724365, + 3.627052068710327, + 0.6018415093421936 ] }, "cluster_id": 0, @@ -654738,12 +654888,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -655162,31 +655312,31 @@ "cited_by_count": 1, "projections": { "pca": [ - 0.02204733155667782, - -0.10877703130245209 + 0.021721359342336655, + -0.10862210392951965 ], "tsne": [ - 18.69219970703125, - 2.7167203426361084 + 20.152618408203125, + -0.4834108352661133 ], "umap": [ - 7.597179889678955, - 7.13505220413208 + 7.783863544464111, + 6.8462419509887695 ], "pca3d": [ - 0.022047530859708786, - -0.10878105461597443, - -0.09545408189296722 + 0.02172158658504486, + -0.10862631350755692, + -0.09524615108966827 ], "tsne3d": [ - 9.942666053771973, - 1.7515367269515991, - -14.648660659790039 + 8.512127876281738, + 4.834052562713623, + 7.126107692718506 ], "umap3d": [ - 6.394555568695068, - 4.285788059234619, - 2.8497202396392822 + 6.50832986831665, + 4.62630558013916, + 1.8624898195266724 ] }, "cluster_id": 4, @@ -655197,12 +655347,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -655622,31 +655772,31 @@ "cited_by_count": 275, "projections": { "pca": [ - 0.25063756108283997, - -0.048349153250455856 + 0.25048190355300903, + -0.048743151128292084 ], "tsne": [ - 34.63468933105469, - -0.7628389596939087 + 32.05051803588867, + 6.62816858291626 ], "umap": [ - 8.495024681091309, - 7.5370707511901855 + 8.760357856750488, + 7.194010257720947 ], "pca3d": [ - 0.2506376802921295, - -0.04836898297071457, - -0.13698989152908325 + 0.25048208236694336, + -0.04876318201422691, + -0.1369429975748062 ], "tsne3d": [ - 17.34859848022461, - -0.6795026659965515, - -5.876389503479004 + 18.325998306274414, + 5.063357353210449, + -0.3532746732234955 ], "umap3d": [ - 7.02822732925415, - 4.360942840576172, - 1.9458407163619995 + 6.898303508758545, + 4.271521091461182, + 1.0373382568359375 ] }, "cluster_id": 0, @@ -655657,12 +655807,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -656070,46 +656220,46 @@ "journal": "Nature Biotechnology", "projections": { "pca": [ - 0.08193254470825195, - 0.03869524225592613 + 0.0821753740310669, + 0.03817272186279297 ], "tsne": [ - 16.51416015625, - -17.209184646606445 + 7.1711015701293945, + 13.682879447937012 ], "umap": [ - 7.815729141235352, - 9.152268409729004 + 7.6211748123168945, + 8.793429374694824 ], "pca3d": [ - 0.08193214237689972, - 0.038725413382053375, - -0.06291569024324417 + 0.08217503875494003, + 0.038203805685043335, + -0.06293660402297974 ], "tsne3d": [ - 10.405380249023438, - -6.550012111663818, - 17.10049819946289 + 5.23756742477417, + -1.2208706140518188, + -8.824053764343262 ], "umap3d": [ - 5.523125171661377, - 3.7845964431762695, - 1.7232757806777954 + 5.394772052764893, + 3.5723283290863037, + 1.6398621797561646 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -656606,46 +656756,46 @@ "openalex_url": "https://openalex.org/W4220873173", "projections": { "pca": [ - 0.14660440385341644, - 0.1640663594007492 + 0.14715629816055298, + 0.16330258548259735 ], "tsne": [ - 4.192382335662842, - -3.9444754123687744 + 2.4783427715301514, + 1.4545884132385254 ], "umap": [ - 5.647011756896973, - 7.144033908843994 + 5.823903560638428, + 6.695789813995361 ], "pca3d": [ - 0.14660397171974182, - 0.16407501697540283, - -0.08271341025829315 + 0.14715595543384552, + 0.1633116602897644, + -0.0829658955335617 ], "tsne3d": [ - 7.265837669372559, - -5.886488914489746, - 0.7561068534851074 + 6.916435241699219, + 17.24313735961914, + -9.205743789672852 ], "umap3d": [ - 5.015986442565918, - 3.6753430366516113, - 4.535538196563721 + 5.869604110717773, + 4.687711715698242, + 4.3387227058410645 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, + "id": 13, "label": "Genomics Papers" } ], @@ -657071,31 +657221,31 @@ "openalex_url": "https://openalex.org/W4210792591", "projections": { "pca": [ - 0.31026691198349, - 0.15638794004917145 + 0.31072798371315, + 0.15533322095870972 ], "tsne": [ - 48.070430755615234, - 2.356740713119507 + 40.917640686035156, + 16.301485061645508 ], "umap": [ - 9.417712211608887, - 8.310722351074219 + 9.65662670135498, + 7.850501537322998 ], "pca3d": [ - 0.31026652455329895, - 0.15637996792793274, - -0.10424913465976715 + 0.3107275366783142, + 0.15532515943050385, + -0.10465852916240692 ], "tsne3d": [ - 27.352367401123047, - -2.5073652267456055, - 4.414633274078369 + 26.266590118408203, + 6.75834846496582, + -9.2301664352417 ], "umap3d": [ - 7.460241317749023, - 4.2280592918396, - 1.1543008089065552 + 7.036746501922607, + 3.9151105880737305, + 0.23642009496688843 ] }, "cluster_id": 0, @@ -657106,12 +657256,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -657533,31 +657683,31 @@ "cited_by_count": 180, "projections": { "pca": [ - 0.21311061084270477, - 0.04279451444745064 + 0.21320994198322296, + 0.04243214800953865 ], "tsne": [ - 57.011863708496094, - 9.49021053314209 + 55.8796501159668, + 19.872159957885742 ], "umap": [ - 10.370779991149902, - 6.781989574432373 + 10.56478214263916, + 6.34244441986084 ], "pca3d": [ - 0.21311037242412567, - 0.04279789701104164, - 0.06376106292009354 + 0.21320979297161102, + 0.04243545979261398, + 0.06344578415155411 ], "tsne3d": [ - 30.446758270263672, - 8.470488548278809, - 0.1933637410402298 + 32.51933670043945, + -2.9700732231140137, + -2.564335346221924 ], "umap3d": [ - 8.152765274047852, - 5.424046993255615, - 1.1057125329971313 + 7.50868034362793, + 5.147970199584961, + -0.5700642466545105 ] }, "cluster_id": 0, @@ -657568,12 +657718,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -657998,31 +658148,31 @@ "openalex_url": "https://openalex.org/W4221095165", "projections": { "pca": [ - 0.2471439391374588, - 0.09985212981700897 + 0.2475889027118683, + 0.09878911823034286 ], "tsne": [ - 51.4088020324707, - -15.035658836364746 + 36.03319549560547, + 27.702777862548828 ], "umap": [ - 9.630648612976074, - 7.556361198425293 + 9.740952491760254, + 7.210177898406982 ], "pca3d": [ - 0.24714314937591553, - 0.09989438951015472, - -0.06423835456371307 + 0.247588112950325, + 0.09883156418800354, + -0.06447868794202805 ], "tsne3d": [ - 29.082448959350586, - 7.0940022468566895, - 9.188904762268066 + 29.706764221191406, + 2.433177947998047, + 8.689000129699707 ], "umap3d": [ - 6.846319675445557, - 4.862607955932617, - 1.245416283607483 + 6.423487663269043, + 4.454980850219727, + 0.306812047958374 ] }, "cluster_id": 0, @@ -658033,12 +658183,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 13, + "label": "Chromatin Regulation" }, { - "id": 20, - "label": "Chromatin Regulation" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -658447,47 +658597,47 @@ "cited_by_count": 2, "projections": { "pca": [ - -0.15668293833732605, - -0.12060265243053436 + -0.15687942504882812, + -0.12009672820568085 ], "tsne": [ - 4.971456050872803, - 37.52406692504883 + -0.812761664390564, + -10.517733573913574 ], "umap": [ - 6.811867713928223, - 4.824573040008545 + 7.255951881408691, + 4.849453926086426 ], "pca3d": [ - -0.15668293833732605, - -0.12056991457939148, - 0.03415119647979736 + -0.15687942504882812, + -0.12006330490112305, + 0.03446686267852783 ], "tsne3d": [ - -1.4851903915405273, - 16.205101013183594, - -3.897413730621338 + 8.6243257522583, + -11.765140533447266, + -3.3923542499542236 ], "umap3d": [ - 5.27866792678833, - 5.333710193634033, - 3.924691915512085 + 6.006759166717529, + 6.157406806945801, + 3.4562742710113525 ] }, - "cluster_id": 3, - "cluster_label": "Protein Design", + "cluster_id": 4, + "cluster_label": "RNA Sequence Models", "cluster_levels": [ { - "id": 3, - "label": "Protein Design" + "id": 4, + "label": "RNA Sequence Models" }, { - "id": 2, - "label": "Protein Language" + "id": 7, + "label": "Genomic Models" }, { - "id": 15, - "label": "Protein Language" + "id": 17, + "label": "Genomic Models" } ], "_embedding": [ @@ -658913,31 +659063,31 @@ "openalex_url": "https://openalex.org/W4210703721", "projections": { "pca": [ - 0.21582166850566864, - -0.056231964379549026 + 0.21578143537044525, + -0.05618246644735336 ], "tsne": [ - 59.084102630615234, - 7.790204048156738 + 53.60268020629883, + 21.73206329345703 ], "umap": [ - 10.244284629821777, - 6.442211627960205 + 10.421112060546875, + 6.030296325683594 ], "pca3d": [ - 0.21582160890102386, - -0.056230101734399796, - 0.1026761457324028 + 0.21578137576580048, + -0.056180667132139206, + 0.10267045348882675 ], "tsne3d": [ - 28.393882751464844, - 10.342061042785645, - -1.416446566581726 + 30.849790573120117, + -5.600032806396484, + -1.8630958795547485 ], "umap3d": [ - 8.149864196777344, - 5.743617534637451, - 1.4545644521713257 + 7.4576239585876465, + 5.457043170928955, + -0.37065383791923523 ] }, "cluster_id": 0, @@ -658948,12 +659098,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -659361,47 +659511,47 @@ "journal": "Nature Genetics", "projections": { "pca": [ - 0.24361388385295868, - 0.12635867297649384 + 0.24390053749084473, + 0.12579500675201416 ], "tsne": [ - 31.115718841552734, - 4.579441547393799 + 36.93550491333008, + -7.1043596267700195 ], "umap": [ - 7.672435760498047, - 8.26999568939209 + 7.91667366027832, + 7.78502082824707 ], "pca3d": [ - 0.24361355602741241, - 0.12634481489658356, - -0.15441977977752686 + 0.24390029907226562, + 0.12578032910823822, + -0.1546851098537445 ], "tsne3d": [ - 16.255001068115234, - -12.832091331481934, - -7.646491050720215 + 18.060409545898438, + 15.624059677124023, + -0.7521622776985168 ], "umap3d": [ - 6.955719947814941, - 3.385470390319824, - 2.2662458419799805 + 7.1337151527404785, + 3.4651849269866943, + 1.6213549375534058 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -659819,31 +659969,31 @@ "openalex_url": "https://openalex.org/W4206302432", "projections": { "pca": [ - 0.16646821796894073, - -0.022301193326711655 + 0.16644394397735596, + -0.023276591673493385 ], "tsne": [ - 28.005733489990234, - 31.86981201171875 + 26.2296199798584, + -22.096370697021484 ], "umap": [ - 8.527106285095215, - 5.040122032165527 + 8.868232727050781, + 4.647952079772949 ], "pca3d": [ - 0.16646772623062134, - -0.022256523370742798, - -0.06663393974304199 + 0.16644345223903656, + -0.023230139166116714, + -0.06671365350484848 ], "tsne3d": [ - 11.93906021118164, - 23.93482208251953, - -3.0577070713043213 + 18.154394149780273, + -17.749452590942383, + 3.210416555404663 ], "umap3d": [ - 7.606595516204834, - 5.828401565551758, - 3.424527883529663 + 7.443105697631836, + 6.320067882537842, + 1.5475077629089355 ] }, "cluster_id": 4, @@ -659854,11 +660004,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -660268,31 +660418,31 @@ "cited_by_count": 189, "projections": { "pca": [ - 0.14502476155757904, - 0.09317699819803238 + 0.14528316259384155, + 0.09280039370059967 ], "tsne": [ - 33.14836883544922, - 1.7297238111495972 + 30.43085479736328, + 5.040893077850342 ], "umap": [ - 9.496211051940918, - 8.087545394897461 + 9.748444557189941, + 7.660107135772705 ], "pca3d": [ - 0.14502424001693726, - 0.09318771958351135, - -0.07675691694021225 + 0.14528270065784454, + 0.09281093627214432, + -0.0769578218460083 ], "tsne3d": [ - 21.769514083862305, - -6.4822893142700195, - -9.276793479919434 + 22.40166473388672, + 11.202367782592773, + 1.4164537191390991 ], "umap3d": [ - 6.666498184204102, - 4.6398491859436035, - 1.1847543716430664 + 6.248264789581299, + 4.254022598266602, + 0.19701269268989563 ] }, "cluster_id": 0, @@ -660303,12 +660453,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -660747,47 +660897,47 @@ "doi": "10.1101/2021.12.28.21267792", "projections": { "pca": [ - 0.19655703008174896, - 0.10187702625989914 + 0.19684694707393646, + 0.10114549845457077 ], "tsne": [ - 26.29634666442871, - 12.129898071289062 + 29.844554901123047, + -4.278626441955566 ], "umap": [ - 7.622810363769531, - 7.642814636230469 + 7.780884265899658, + 7.233357906341553 ], "pca3d": [ - 0.19655704498291016, - 0.10184863954782486, - -0.1079711765050888 + 0.1968468427658081, + 0.10111647844314575, + -0.1081874743103981 ], "tsne3d": [ - 14.378911018371582, - -5.11452579498291, - -14.507396697998047 + 13.780693054199219, + 11.2448091506958, + 6.127380847930908 ], "umap3d": [ - 6.9277424812316895, - 3.741915225982666, - 2.9082138538360596 + 7.227715015411377, + 4.010230541229248, + 2.0539817810058594 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -661208,31 +661358,31 @@ "doi": "10.1101/2021.12.31.474623", "projections": { "pca": [ - 0.14345277845859528, - -0.09302257001399994 + 0.14324474334716797, + -0.09341583400964737 ], "tsne": [ - 27.213359832763672, - -4.333163261413574 + 26.78864860534668, + -0.19542542099952698 ], "umap": [ - 8.051563262939453, - 7.282407283782959 + 8.253377914428711, + 6.9510016441345215 ], "pca3d": [ - 0.14345282316207886, - -0.09302336722612381, - -0.18770408630371094 + 0.14324481785297394, + -0.09341661632061005, + -0.1874948889017105 ], "tsne3d": [ - 16.185543060302734, - -1.6293847560882568, - -11.847029685974121 + 14.803677558898926, + 8.202085494995117, + 2.284775733947754 ], "umap3d": [ - 6.734694480895996, - 4.292409420013428, - 2.3589253425598145 + 6.819729804992676, + 4.42082405090332, + 1.4645172357559204 ] }, "cluster_id": 4, @@ -661243,12 +661393,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 2, + "label": "DNA Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -661654,31 +661804,31 @@ ], "projections": { "pca": [ - 0.05129336193203926, - 0.03584382310509682 + 0.051460471004247665, + 0.035909175872802734 ], "tsne": [ - 50.873741149902344, - -8.105782508850098 + 47.839752197265625, + 9.718323707580566 ], "umap": [ - 10.288890838623047, - 8.037535667419434 + 10.528034210205078, + 7.6547112464904785 ], "pca3d": [ - 0.0512930266559124, - 0.03585483133792877, - -0.040312349796295166 + 0.051460158079862595, + 0.03591980040073395, + -0.04036550596356392 ], "tsne3d": [ - 29.51787567138672, - -2.6431655883789062, - -10.429776191711426 + 28.76875114440918, + 8.0851469039917, + 4.845700263977051 ], "umap3d": [ - 7.067342758178711, - 5.107583522796631, - 0.5691577792167664 + 6.3869500160217285, + 4.637728691101074, + -0.5096132159233093 ] }, "cluster_id": 0, @@ -661689,12 +661839,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -662111,31 +662261,31 @@ "cited_by_count": 1, "projections": { "pca": [ - 0.20690487325191498, - -0.10193496197462082 + 0.20662103593349457, + -0.10253745317459106 ], "tsne": [ - 44.861328125, - 0.2254931777715683 + 33.57135772705078, + 12.436101913452148 ], "umap": [ - 9.284181594848633, - 7.412515640258789 + 9.57478141784668, + 6.867970943450928 ], "pca3d": [ - 0.20690490305423737, - -0.10193933546543121, - -0.10430561751127243 + 0.20662112534046173, + -0.10254208743572235, + -0.10428369045257568 ], "tsne3d": [ - 19.906578063964844, - 2.3246572017669678, - 1.1394264698028564 + 20.99076271057129, + -1.5136743783950806, + -6.9733967781066895 ], "umap3d": [ - 7.720193386077881, - 4.4406538009643555, - 1.568543553352356 + 7.407778739929199, + 4.31441593170166, + 0.26201578974723816 ] }, "cluster_id": 0, @@ -662146,11 +662296,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -662557,47 +662707,47 @@ ], "projections": { "pca": [ - -0.19516973197460175, - 0.3905770182609558 + -0.19398583471775055, + 0.3908061981201172 ], "tsne": [ - -15.352181434631348, - -9.39616870880127 + -12.130739212036133, + 0.9643265008926392 ], "umap": [ - 4.073983192443848, - 7.331100940704346 + 4.407930850982666, + 6.725179672241211 ], "pca3d": [ - -0.19517052173614502, - 0.3905879259109497, - 0.1073896512389183 + -0.1939866840839386, + 0.390817254781723, + 0.10682504624128342 ], "tsne3d": [ - -9.841361045837402, - -7.634787082672119, - 16.762033462524414 + -8.329181671142578, + -2.875488758087158, + -12.451401710510254 ], "umap3d": [ - 3.6870365142822266, - 3.666632652282715, - 4.675583839416504 + 4.701096534729004, + 4.467931747436523, + 4.879141807556152 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -663043,47 +663193,47 @@ "openalex_url": "https://openalex.org/W4200539320", "projections": { "pca": [ - 0.27064305543899536, - 0.027532033622264862 + 0.2706979811191559, + 0.027071375399827957 ], "tsne": [ - 30.869916915893555, - -14.026917457580566 + 24.941587448120117, + 19.64906883239746 ], "umap": [ - 8.586221694946289, - 9.09107780456543 + 8.766644477844238, + 8.599743843078613 ], "pca3d": [ - 0.2706427574157715, - 0.0275337602943182, - -0.11123031377792358 + 0.2706977427005768, + 0.027073414996266365, + -0.11133341491222382 ], "tsne3d": [ - 18.88628578186035, - 9.160616874694824, - 8.704192161560059 + 13.957462310791016, + -2.04388165473938, + -15.367501258850098 ], "umap3d": [ - 5.86552619934082, - 4.256981372833252, - 1.0697360038757324 + 5.513999938964844, + 3.947204828262329, + 0.7054321765899658 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -663491,46 +663641,46 @@ "journal": "Nature", "projections": { "pca": [ - 0.14373861253261566, - 0.07251273095607758 + 0.14397569000720978, + 0.07214221358299255 ], "tsne": [ - 18.331409454345703, - -30.431446075439453 + 10.393805503845215, + 31.46336555480957 ], "umap": [ - 6.892146587371826, - 9.73459243774414 + 6.902401447296143, + 9.331633567810059 ], "pca3d": [ - 0.14373841881752014, - 0.07249956578016281, - -0.1389043927192688 + 0.14397545158863068, + 0.07212939113378525, + -0.13897891342639923 ], "tsne3d": [ - 14.88944149017334, - -15.204450607299805, - 4.184365749359131 + 3.684400796890259, + 7.486067295074463, + -22.170530319213867 ], "umap3d": [ - 5.2265167236328125, - 3.0560460090637207, - 1.558171272277832 + 5.333137512207031, + 2.7678701877593994, + 1.8655508756637573 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -663956,31 +664106,31 @@ "openalex_url": "https://openalex.org/W4225699323", "projections": { "pca": [ - 0.2368137240409851, - -0.03586697578430176 + 0.23663564026355743, + -0.036646660417318344 ], "tsne": [ - 40.620460510253906, - -12.390157699584961 + 35.93672561645508, + 18.836437225341797 ], "umap": [ - 9.274651527404785, - 8.472874641418457 + 9.507834434509277, + 8.097755432128906 ], "pca3d": [ - 0.23681344091892242, - -0.035867080092430115, - -0.18658950924873352 + 0.23663564026355743, + -0.03664681687951088, + -0.18666084110736847 ], "tsne3d": [ - 20.665069580078125, - -6.208209991455078, - 5.053857803344727 + 19.698266983032227, + 4.081697940826416, + -12.934596061706543 ], "umap3d": [ - 6.812497615814209, - 4.004297256469727, - 1.160697102546692 + 6.49111795425415, + 3.754728078842163, + 0.41309654712677 ] }, "cluster_id": 0, @@ -663991,12 +664141,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -664404,31 +664554,31 @@ "journal": "eLife", "projections": { "pca": [ - 0.01354902982711792, - 0.04154160991311073 + 0.013600521720945835, + 0.04102325811982155 ], "tsne": [ - 47.26120376586914, - -15.093833923339844 + 36.246307373046875, + 24.04110336303711 ], "umap": [ - 9.728838920593262, - 8.286847114562988 + 9.911593437194824, + 7.958855152130127 ], "pca3d": [ - 0.01354899164289236, - 0.04155399277806282, - -0.20043706893920898 + 0.013600438833236694, + 0.04103556275367737, + -0.20055097341537476 ], "tsne3d": [ - 28.38730812072754, - -11.756428718566895, - -6.407310962677002 + 28.94440460205078, + 16.162778854370117, + -1.0986084938049316 ], "umap3d": [ - 6.732354640960693, - 4.340364933013916, - 0.9742258191108704 + 6.271659851074219, + 4.112161636352539, + 0.08254864811897278 ] }, "cluster_id": 0, @@ -664439,12 +664589,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -664881,47 +665031,47 @@ "cited_by_count": 638, "projections": { "pca": [ - 0.1144595667719841, - 0.026352228596806526 + 0.11442159861326218, + 0.026217371225357056 ], "tsne": [ - 11.594539642333984, - -30.491662979125977 + 3.834353446960449, + 31.42898941040039 ], "umap": [ - 6.362235069274902, - 9.566142082214355 + 6.396379470825195, + 8.930899620056152 ], "pca3d": [ - 0.11445943266153336, - 0.026358971372246742, - -0.11948234587907791 + 0.11442148685455322, + 0.026224134489893913, + -0.1195618286728859 ], "tsne3d": [ - 9.703057289123535, - -15.170635223388672, - -0.401410847902298 + 3.160174608230591, + 12.20560359954834, + -14.96131706237793 ], "umap3d": [ - 5.066264629364014, - 2.823513984680176, - 2.231431007385254 + 5.397436618804932, + 2.8256616592407227, + 2.5333266258239746 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -665342,31 +665492,31 @@ "doi": "10.1101/2021.12.06.471431", "projections": { "pca": [ - -0.036249902099370956, - -0.11800399422645569 + -0.036481473594903946, + -0.1180582270026207 ], "tsne": [ - -14.73105525970459, - 12.879598617553711 + -8.550162315368652, + -13.006975173950195 ], "umap": [ - 6.148130416870117, - 5.206127166748047 + 6.433351993560791, + 4.774563789367676 ], "pca3d": [ - -0.0362497940659523, - -0.1179938018321991, - 0.041307952255010605 + -0.03648129850625992, + -0.11804765462875366, + 0.041501957923173904 ], "tsne3d": [ - 0.7280107140541077, - -10.435100555419922, - -24.347211837768555 + 5.83290433883667, + 10.365827560424805, + -4.57923698425293 ], "umap3d": [ - 5.417771816253662, - 6.230991363525391, - 3.891413927078247 + 5.720507621765137, + 6.658175468444824, + 2.482667922973633 ] }, "cluster_id": 3, @@ -665377,12 +665527,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, - "label": "Protein Design" + "id": 32, + "label": "Genome Protein" } ], "_embedding": [ @@ -665803,31 +665953,31 @@ "cited_by_count": 34, "projections": { "pca": [ - 0.12228918075561523, - -0.14120110869407654 + 0.12175397574901581, + -0.14139246940612793 ], "tsne": [ - 62.480987548828125, - -0.743777871131897 + 47.02157974243164, + 25.895523071289062 ], "umap": [ - 9.624895095825195, - 6.3569793701171875 + 9.917206764221191, + 5.935620307922363 ], "pca3d": [ - 0.12228909879922867, - -0.14117445051670074, - -0.11352642625570297 + 0.12175390869379044, + -0.14136554300785065, + -0.1134587824344635 ], "tsne3d": [ - 23.682966232299805, - 10.059391021728516, - -7.723392009735107 + 24.406795501708984, + -5.491756439208984, + 2.661050796508789 ], "umap3d": [ - 7.605334281921387, - 5.775081157684326, - 1.712777018547058 + 7.0205206871032715, + 5.5903167724609375, + 0.17248669266700745 ] }, "cluster_id": 0, @@ -665838,12 +665988,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -666251,31 +666401,31 @@ "journal": "Nature Reviews Genetics", "projections": { "pca": [ - 0.005399452522397041, - -0.011180568486452103 + 0.005507984664291143, + -0.010813268832862377 ], "tsne": [ - 10.196321487426758, - -8.05136489868164 + 11.419772148132324, + 9.912609100341797 ], "umap": [ - 7.464883327484131, - 6.8629150390625 + 7.588287353515625, + 6.425652980804443 ], "pca3d": [ - 0.00539914146065712, - -0.011144793592393398, - -0.03618348017334938 + 0.005507661961019039, + -0.010776613838970661, + -0.03598370775580406 ], "tsne3d": [ - 9.420299530029297, - 3.2104012966156006, - -19.322677612304688 + 12.047152519226074, + 7.407536029815674, + 13.761797904968262 ], "umap3d": [ - 5.877226829528809, - 4.656376361846924, - 2.6828720569610596 + 6.0547194480896, + 4.813946723937988, + 1.9602842330932617 ] }, "cluster_id": 4, @@ -666286,12 +666436,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 7, + "label": "Genomic Models" }, { - "id": 22, - "label": "Genetic Variants" + "id": 1, + "label": "Genomic Regulation" } ], "_embedding": [ @@ -666722,31 +666872,31 @@ "openalex_url": "https://openalex.org/W3214158901", "projections": { "pca": [ - 0.17627082765102386, - 0.032357119023799896 + 0.176498681306839, + 0.03113899938762188 ], "tsne": [ - 40.80632781982422, - -7.457645893096924 + 39.00286865234375, + 13.420869827270508 ], "umap": [ - 9.199501991271973, - 8.417491912841797 + 9.598454475402832, + 7.980245113372803 ], "pca3d": [ - 0.17627057433128357, - 0.03236899897456169, - -0.20843379199504852 + 0.17649832367897034, + 0.031151745468378067, + -0.20845988392829895 ], "tsne3d": [ - 22.898340225219727, - -6.367293834686279, - 2.3377280235290527 + 23.433338165283203, + 9.212782859802246, + -9.257062911987305 ], "umap3d": [ - 7.292673110961914, - 3.9534339904785156, - 1.1506620645523071 + 6.839213848114014, + 3.6588504314422607, + 0.3042534291744232 ] }, "cluster_id": 0, @@ -666757,12 +666907,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -667204,47 +667354,47 @@ "cited_by_count": 80, "projections": { "pca": [ - 0.1463467925786972, - -0.021757027134299278 + 0.14636197686195374, + -0.02209697663784027 ], "tsne": [ - 27.060495376586914, - -30.03084373474121 + 41.106651306152344, + 0.2973349690437317 ], "umap": [ - 8.08115291595459, - 8.687875747680664 + 8.162782669067383, + 8.310342788696289 ], "pca3d": [ - 0.14634673297405243, - -0.02176269330084324, - -0.045321010053157806 + 0.14636193215847015, + -0.022102534770965576, + -0.04532865062355995 ], "tsne3d": [ - 18.727373123168945, - -4.476370334625244, - 13.361177444458008 + 20.227407455444336, + -2.332789897918701, + -15.244009017944336 ], "umap3d": [ - 6.160542964935303, - 3.743840456008911, - 1.47598135471344 + 6.041642665863037, + 3.629816770553589, + 1.033534049987793 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 12, - "label": "Genetic Variants" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -667664,31 +667814,31 @@ "cited_by_count": 2, "projections": { "pca": [ - -0.0049686124548316, - -0.06664087623357773 + -0.005124034360051155, + -0.06651979684829712 ], "tsne": [ - -14.668624877929688, - 12.872565269470215 + -8.527535438537598, + -12.973780632019043 ], "umap": [ - 5.970240116119385, - 5.104588031768799 + 6.30690860748291, + 4.719322681427002 ], "pca3d": [ - -0.004968584049493074, - -0.06661953777074814, - 0.07631740719079971 + -0.005123988259583712, + -0.06649839878082275, + 0.07634950429201126 ], "tsne3d": [ - 0.8721932172775269, - -9.767793655395508, - -25.074655532836914 + 4.94711971282959, + 10.677026748657227, + -4.659541606903076 ], "umap3d": [ - 5.458971977233887, - 6.325418472290039, - 3.9925570487976074 + 5.7126970291137695, + 6.8212175369262695, + 2.5697193145751953 ] }, "cluster_id": 3, @@ -667699,12 +667849,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, - "label": "Protein Design" + "id": 32, + "label": "Genome Protein" } ], "_embedding": [ @@ -668121,31 +668271,31 @@ "doi": "10.1101/2021.11.16.468742", "projections": { "pca": [ - 0.23213602602481842, - -0.04873959347605705 + 0.23201775550842285, + -0.049402058124542236 ], "tsne": [ - 57.828792572021484, - 6.242008209228516 + 52.33024978637695, + 20.153541564941406 ], "umap": [ - 10.172008514404297, - 6.621549129486084 + 10.38184928894043, + 6.270707130432129 ], "pca3d": [ - 0.2321358174085617, - -0.0487314909696579, - 0.0057603903114795685 + 0.23201774060726166, + -0.04939398169517517, + 0.005605058744549751 ], "tsne3d": [ - 25.602792739868164, - 8.254256248474121, - -2.005017042160034 + 27.77977752685547, + -4.7978997230529785, + -3.7914464473724365 ], "umap3d": [ - 7.995250225067139, - 5.533642292022705, - 1.309114933013916 + 7.401980876922607, + 5.311898231506348, + -0.3728739321231842 ] }, "cluster_id": 0, @@ -668156,12 +668306,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -668569,31 +668719,31 @@ "journal": "Nature Machine Intelligence", "projections": { "pca": [ - 0.013458942994475365, - -0.14535437524318695 + 0.013213912956416607, + -0.1460377424955368 ], "tsne": [ - 18.531475067138672, - 33.47994613647461 + 21.770721435546875, + -31.691450119018555 ], "umap": [ - 7.936044692993164, - 4.491394996643066 + 8.312150955200195, + 4.023232936859131 ], "pca3d": [ - 0.01345863938331604, - -0.1453007012605667, - -0.06666530668735504 + 0.013213647529482841, + -0.14598225057125092, + -0.0664190948009491 ], "tsne3d": [ - 4.171772480010986, - 24.61375617980957, - 6.659134387969971 + 11.863929748535156, + -19.232730865478516, + 7.858836650848389 ], "umap3d": [ - 7.2758097648620605, - 6.311616897583008, - 3.996032476425171 + 7.41709041595459, + 6.8353352546691895, + 2.223766326904297 ] }, "cluster_id": 4, @@ -668604,11 +668754,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -669030,31 +669180,31 @@ "cited_by_count": 3, "projections": { "pca": [ - 0.2152828425168991, - -0.0066779907792806625 + 0.21514029800891876, + -0.006582374684512615 ], "tsne": [ - 46.27297592163086, - 11.905241012573242 + 53.15877151489258, + 1.6536014080047607 ], "umap": [ - 9.31983757019043, - 6.77331018447876 + 9.435248374938965, + 6.516903877258301 ], "pca3d": [ - 0.2152826189994812, - -0.006679974030703306, - -0.03653934225440025 + 0.21514013409614563, + -0.006584651302546263, + -0.03665599972009659 ], "tsne3d": [ - 18.315996170043945, - 7.869966506958008, - -3.4459877014160156 + 22.48958969116211, + 2.8461601734161377, + 6.1386823654174805 ], "umap3d": [ - 7.4316582679748535, - 5.240486145019531, - 1.7497239112854004 + 6.909661293029785, + 5.124570369720459, + 0.4578959345817566 ] }, "cluster_id": 0, @@ -669065,11 +669215,11 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 2, + "label": "DNA Regulation" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -669490,31 +669640,31 @@ "openalex_url": "https://openalex.org/W3212488818", "projections": { "pca": [ - 0.2705104649066925, - -0.0066360486671328545 + 0.27046823501586914, + -0.0072874282486736774 ], "tsne": [ - 44.95418167114258, - 2.6141858100891113 + 36.311607360839844, + 11.506339073181152 ], "umap": [ - 9.153338432312012, - 7.715060234069824 + 9.421053886413574, + 7.379507064819336 ], "pca3d": [ - 0.27051037549972534, - -0.006648014299571514, - -0.11110866814851761 + 0.27046817541122437, + -0.007299251854419708, + -0.11122607439756393 ], "tsne3d": [ - 21.676637649536133, - 0.45431628823280334, - 3.572096824645996 + 23.6413631439209, + 0.8979810476303101, + -8.518747329711914 ], "umap3d": [ - 7.576855182647705, - 4.147636890411377, - 1.5860235691070557 + 7.323358535766602, + 4.050355434417725, + 0.3700779676437378 ] }, "cluster_id": 0, @@ -669525,12 +669675,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 20, - "label": "Chromatin Regulation" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -669962,31 +670112,31 @@ "doi": "10.1101/2021.10.25.465795", "projections": { "pca": [ - 0.24403275549411774, - 0.03763436898589134 + 0.24404698610305786, + 0.037032727152109146 ], "tsne": [ - 40.799373626708984, - -0.7617679834365845 + 39.30690383911133, + 7.294373989105225 ], "umap": [ - 9.435724258422852, - 8.1251859664917 + 9.766593933105469, + 7.703491687774658 ], "pca3d": [ - 0.24403242766857147, - 0.03764428570866585, - -0.20425888895988464 + 0.2440466582775116, + 0.03704238682985306, + -0.20441821217536926 ], "tsne3d": [ - 22.873062133789062, - -5.477585315704346, - -3.851569175720215 + 22.546422958374023, + 9.762016296386719, + -2.4880943298339844 ], "umap3d": [ - 7.0089826583862305, - 4.404096603393555, - 1.1180964708328247 + 6.498807907104492, + 4.132620334625244, + 0.16232039034366608 ] }, "cluster_id": 0, @@ -669997,12 +670147,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -670421,31 +670571,31 @@ "doi": "10.1101/2021.10.28.466307", "projections": { "pca": [ - 0.1084311455488205, - -0.10341329127550125 + 0.10824527591466904, + -0.1032767966389656 ], "tsne": [ - 2.0647454261779785, - 20.169889450073242 + 7.777040481567383, + -31.646814346313477 ], "umap": [ - 5.466306209564209, - 4.772945404052734 + 6.033903121948242, + 4.286003589630127 ], "pca3d": [ - 0.10843118280172348, - -0.10339286178350449, - 0.2344421148300171 + 0.10824527591466904, + -0.10325580835342407, + 0.23448173701763153 ], "tsne3d": [ - -4.330936908721924, - 6.692214012145996, - -0.2130587100982666 + 1.488538384437561, + -18.09278678894043, + -2.5560646057128906 ], "umap3d": [ - 5.23179292678833, - 6.691628932952881, - 4.384446620941162 + 5.519958972930908, + 7.240522861480713, + 2.834514617919922 ] }, "cluster_id": 3, @@ -670456,11 +670606,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -670878,31 +671028,31 @@ "doi": "10.1101/2021.10.21.465269", "projections": { "pca": [ - 0.31065046787261963, - 0.056739456951618195 + 0.31072476506233215, + 0.05606207624077797 ], "tsne": [ - 51.8709716796875, - -4.505906581878662 + 51.03244400024414, + 8.168206214904785 ], "umap": [ - 10.266633033752441, - 7.810131072998047 + 10.47283935546875, + 7.343616485595703 ], "pca3d": [ - 0.3106505274772644, - 0.05672239512205124, - -0.13007180392742157 + 0.31072452664375305, + 0.05604416877031326, + -0.13036879897117615 ], "tsne3d": [ - 29.878820419311523, - -3.756870985031128, - -4.925971984863281 + 31.22216033935547, + 7.460097312927246, + -0.04422241449356079 ], "umap3d": [ - 7.543127536773682, - 4.881802558898926, - 0.6994708776473999 + 6.783820629119873, + 4.498723030090332, + -0.5259890556335449 ] }, "cluster_id": 0, @@ -670913,12 +671063,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -671326,31 +671476,31 @@ "journal": "Nature", "projections": { "pca": [ - -0.05560961738228798, - -0.06068028509616852 + -0.05559222772717476, + -0.060530245304107666 ], "tsne": [ - 19.80841827392578, - 14.314845085144043 + 23.339651107788086, + -4.478168964385986 ], "umap": [ - 7.133769989013672, - 7.235009670257568 + 7.328617572784424, + 6.872987270355225 ], "pca3d": [ - -0.055609412491321564, - -0.06069636717438698, - -0.06274647265672684 + -0.05559202656149864, + -0.06054633855819702, + -0.062407173216342926 ], "tsne3d": [ - 9.786650657653809, - -2.567521095275879, - -16.221590042114258 + 9.280921936035156, + 8.381997108459473, + 7.978603363037109 ], "umap3d": [ - 6.753279685974121, - 3.8922555446624756, - 3.3671889305114746 + 7.129973888397217, + 4.465031623840332, + 2.4001994132995605 ] }, "cluster_id": 4, @@ -671361,12 +671511,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -671774,31 +671924,31 @@ "journal": "Nature", "projections": { "pca": [ - 0.24938446283340454, - 0.04744759574532509 + 0.24949969351291656, + 0.04704440385103226 ], "tsne": [ - 54.176605224609375, - -3.3639419078826904 + 52.43231964111328, + 9.867703437805176 ], "umap": [ - 10.555658340454102, - 7.543828010559082 + 10.736571311950684, + 7.149423599243164 ], "pca3d": [ - 0.24938420951366425, - 0.04743728041648865, - -0.1420159935951233 + 0.24949942529201508, + 0.04703313857316971, + -0.14219239354133606 ], "tsne3d": [ - 31.19568634033203, - -1.5669960975646973, - -4.529086112976074 + 30.034847259521484, + 6.935084342956543, + -2.3521742820739746 ], "umap3d": [ - 7.799901008605957, - 4.9306745529174805, - 0.5766450762748718 + 6.941980838775635, + 4.507359504699707, + -0.7206743955612183 ] }, "cluster_id": 0, @@ -671809,12 +671959,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -672238,31 +672388,31 @@ "doi": "10.1101/2021.10.23.462170", "projections": { "pca": [ - 0.27220526337623596, - 0.02462139166891575 + 0.2721399664878845, + 0.024219397455453873 ], "tsne": [ - 51.80777359008789, - -5.283413887023926 + 50.298709869384766, + 7.983523368835449 ], "umap": [ - 10.413224220275879, - 7.77044677734375 + 10.615080833435059, + 7.386762619018555 ], "pca3d": [ - 0.2722051739692688, - 0.02461223118007183, - -0.09001804143190384 + 0.2721397578716278, + 0.024209503084421158, + -0.09024853259325027 ], "tsne3d": [ - 30.085586547851562, - -4.860734939575195, - -5.775602340698242 + 31.151443481445312, + 8.795960426330566, + 0.4896049201488495 ], "umap3d": [ - 7.48240852355957, - 4.932538986206055, - 0.5754145383834839 + 6.700579643249512, + 4.512345314025879, + -0.5879983305931091 ] }, "cluster_id": 0, @@ -672273,12 +672423,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -672698,31 +672848,31 @@ "cited_by_count": 15, "projections": { "pca": [ - 0.117723748087883, - -0.14005780220031738 + 0.11728383600711823, + -0.1406102478504181 ], "tsne": [ - 61.63230895996094, - -0.8071241974830627 + 46.58023452758789, + 25.181325912475586 ], "umap": [ - 9.595233917236328, - 6.356064796447754 + 9.860709190368652, + 5.911844730377197 ], "pca3d": [ - 0.11772380769252777, - -0.14003534615039825, - -0.06729920208454132 + 0.1172839105129242, + -0.1405869573354721, + -0.06722065061330795 ], "tsne3d": [ - 23.864898681640625, - 9.67639446258545, - -9.82748031616211 + 22.556346893310547, + -6.193981170654297, + 2.1646671295166016 ], "umap3d": [ - 7.5837883949279785, - 5.772144794464111, - 1.7414476871490479 + 6.9743876457214355, + 5.5914716720581055, + 0.2350430190563202 ] }, "cluster_id": 0, @@ -672733,12 +672883,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -673152,31 +673302,31 @@ "doi": "10.1038/s41587-021-01088-y", "projections": { "pca": [ - 0.07304529845714569, - 0.08456142246723175 + 0.07341866195201874, + 0.08487125486135483 ], "tsne": [ - 0.9541741609573364, - 9.116908073425293 + 8.254194259643555, + -13.473466873168945 ], "umap": [ - 6.126689910888672, - 6.1263909339904785 + 6.446329593658447, + 5.608882427215576 ], "pca3d": [ - 0.0730452835559845, - 0.08453849703073502, - 0.05216626450419426 + 0.07341860979795456, + 0.08484780788421631, + 0.05209383741021156 ], "tsne3d": [ - -2.4923770427703857, - 1.716677188873291, - -12.533951759338379 + -0.05171293020248413, + -1.6447728872299194, + -2.0572762489318848 ], "umap3d": [ - 6.0792412757873535, - 5.3244805335998535, - 3.848358631134033 + 6.009462356567383, + 5.848991394042969, + 2.637157678604126 ] }, "cluster_id": 4, @@ -673187,12 +673337,12 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 11, - "label": "Genomic Language" + "id": 32, + "label": "Genome Protein" } ], "_embedding": [ @@ -673613,31 +673763,31 @@ "doi": "10.1101/2021.10.20.465152", "projections": { "pca": [ - 0.21146023273468018, - 0.007620003540068865 + 0.2114175260066986, + 0.007043071091175079 ], "tsne": [ - 27.253005981445312, - 31.351282119750977 + 25.273332595825195, + -22.073949813842773 ], "umap": [ - 8.637332916259766, - 5.159551620483398 + 8.93372917175293, + 4.808169841766357 ], "pca3d": [ - 0.21145986020565033, - 0.007637310307472944, - -0.05780107527971268 + 0.21141725778579712, + 0.007060962729156017, + -0.05800022929906845 ], "tsne3d": [ - 9.905713081359863, - 24.47583770751953, - -3.2752349376678467 + 19.16936492919922, + -16.90771484375, + 4.943114280700684 ], "umap3d": [ - 7.618484020233154, - 5.743627071380615, - 3.354008674621582 + 7.449211597442627, + 6.196646213531494, + 1.427885890007019 ] }, "cluster_id": 4, @@ -673648,11 +673798,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -674068,31 +674218,31 @@ "doi": "10.1101/2021.08.27.457995", "projections": { "pca": [ - 0.28993314504623413, - 0.030420424416661263 + 0.28991514444351196, + 0.029642583802342415 ], "tsne": [ - 54.73879623413086, - 1.9772653579711914 + 50.61234664916992, + 14.694953918457031 ], "umap": [ - 10.152717590332031, - 6.749998092651367 + 10.428126335144043, + 6.490096092224121 ], "pca3d": [ - 0.28993287682533264, - 0.03043260984122753, - -0.07580150663852692 + 0.2899150252342224, + 0.029654666781425476, + -0.0760970264673233 ], "tsne3d": [ - 33.156558990478516, - 3.458948850631714, - -2.5164597034454346 + 31.913442611694336, + 1.7418230772018433, + -8.459056854248047 ], "umap3d": [ - 7.9633073806762695, - 5.321645736694336, - 1.214394211769104 + 7.349390506744385, + 5.135363578796387, + -0.3692057430744171 ] }, "cluster_id": 0, @@ -674103,12 +674253,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -674524,31 +674674,31 @@ "cited_by_count": 69, "projections": { "pca": [ - 0.08720256388187408, - -0.1445261538028717 + 0.08703333139419556, + -0.1446278989315033 ], "tsne": [ - 22.249460220336914, - 31.876161575317383 + 25.55483627319336, + -30.7954044342041 ], "umap": [ - 8.189897537231445, - 4.828996658325195 + 8.417511940002441, + 4.4294352531433105 ], "pca3d": [ - 0.08720257133245468, - -0.14450496435165405, - 0.1043313592672348 + 0.08703336864709854, + -0.1446063071489334, + 0.10461024194955826 ], "tsne3d": [ - 4.602143287658691, - 25.28603172302246, - -1.1109368801116943 + 15.472816467285156, + -16.474246978759766, + 9.03138256072998 ], "umap3d": [ - 7.437298774719238, - 5.886200904846191, - 3.7322888374328613 + 7.565547943115234, + 6.516159534454346, + 1.8625702857971191 ] }, "cluster_id": 4, @@ -674559,11 +674709,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -674981,31 +675131,31 @@ "cited_by_count": 185, "projections": { "pca": [ - 0.13267315924167633, - -0.0502045676112175 + 0.13249747455120087, + -0.05082814767956734 ], "tsne": [ - 43.08271789550781, - -1.1157991886138916 + 34.40066146850586, + 14.652708053588867 ], "umap": [ - 9.19049072265625, - 7.669871807098389 + 9.273167610168457, + 7.273702621459961 ], "pca3d": [ - 0.13267309963703156, - -0.05019890144467354, - -0.17897385358810425 + 0.13249745965003967, + -0.05082201585173607, + -0.17898106575012207 ], "tsne3d": [ - 17.706565856933594, - -0.2671186327934265, - 5.507910251617432 + 20.084732055664062, + -0.42950740456581116, + -12.05321979522705 ], "umap3d": [ - 7.7350969314575195, - 4.164492607116699, - 1.5960943698883057 + 7.5319085121154785, + 4.031085014343262, + 0.4597821831703186 ] }, "cluster_id": 0, @@ -675016,11 +675166,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -675430,31 +675580,31 @@ "cited_by_count": 17, "projections": { "pca": [ - 0.11664948612451553, - 0.14731477200984955 + 0.11713709682226181, + 0.14732222259044647 ], "tsne": [ - 35.04642868041992, - -5.66132116317749 + 35.624324798583984, + 7.255087375640869 ], "umap": [ - 9.286537170410156, - 7.8278632164001465 + 9.526873588562012, + 7.3362226486206055 ], "pca3d": [ - 0.11664910614490509, - 0.14731425046920776, - -0.05945681408047676 + 0.1171366423368454, + 0.14732100069522858, + -0.059626754373311996 ], "tsne3d": [ - 17.076684951782227, - -4.854025363922119, - -2.526963472366333 + 18.996021270751953, + 6.230464935302734, + -5.575073719024658 ], "umap3d": [ - 7.263863563537598, - 4.419217586517334, - 1.3827286958694458 + 6.804775714874268, + 4.1638264656066895, + 0.27822136878967285 ] }, "cluster_id": 0, @@ -675465,12 +675615,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 20, - "label": "Chromatin Regulation" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -675887,31 +676037,31 @@ "doi": "10.1101/2021.10.05.463203", "projections": { "pca": [ - 0.21913699805736542, - -0.05646192282438278 + 0.21892757713794708, + -0.05681908130645752 ], "tsne": [ - 49.97694778442383, - -9.994146347045898 + 47.0031623840332, + 12.104757308959961 ], "umap": [ - 10.192475318908691, - 7.871750831604004 + 10.418411254882812, + 7.559946060180664 ], "pca3d": [ - 0.21913722157478333, - -0.05646594613790512, - -0.019745444878935814 + 0.21892762184143066, + -0.056823618710041046, + -0.019843777641654015 ], "tsne3d": [ - 27.102563858032227, - -0.5132904648780823, - -9.875085830688477 + 27.435382843017578, + 4.9586262702941895, + 2.40681529045105 ], "umap3d": [ - 7.123932361602783, - 5.165654182434082, - 0.6946061849594116 + 6.457700729370117, + 4.776169776916504, + -0.4306941032409668 ] }, "cluster_id": 0, @@ -675922,12 +676072,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -676347,31 +676497,31 @@ "doi": "10.1101/2021.09.23.461564", "projections": { "pca": [ - 0.137391597032547, - -0.14160625636577606 + 0.13712278008460999, + -0.1420191526412964 ], "tsne": [ - 59.396705627441406, - -8.762287139892578 + 41.430908203125, + 27.556659698486328 ], "umap": [ - 9.123308181762695, - 6.623543739318848 + 9.38976001739502, + 6.219799041748047 ], "pca3d": [ - 0.13739176094532013, - -0.14161260426044464, - -0.030085477977991104 + 0.13712306320667267, + -0.1420256644487381, + -0.029888102784752846 ], "tsne3d": [ - 20.202116012573242, - 11.037875175476074, - -11.087682723999023 + 20.644908905029297, + -8.33448600769043, + -4.6570353507995605 ], "umap3d": [ - 7.097921848297119, - 5.457252025604248, - 1.7927734851837158 + 6.688202381134033, + 5.22128963470459, + 0.3504701852798462 ] }, "cluster_id": 0, @@ -676382,11 +676532,11 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 2, + "label": "DNA Regulation" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -676817,47 +676967,47 @@ "cited_by_count": 437, "projections": { "pca": [ - 0.0009711269522085786, - -0.035595037043094635 + 0.0006858256529085338, + -0.03555363416671753 ], "tsne": [ - 9.11572265625, - -29.011281967163086 + 1.4221287965774536, + 30.030710220336914 ], "umap": [ - 6.195235729217529, - 9.324517250061035 + 6.334709644317627, + 8.583305358886719 ], "pca3d": [ - 0.0009711391758173704, - -0.03557850047945976, - -0.060357850044965744 + 0.0006858279812149704, + -0.035537298768758774, + -0.06041871756315231 ], "tsne3d": [ - 6.677627086639404, - -9.627208709716797, - -21.93560791015625 + 4.615920543670654, + 15.975859642028809, + -14.30224895477295 ], "umap3d": [ - 5.164576530456543, - 2.927557945251465, - 2.4202542304992676 + 5.542733669281006, + 3.0030605792999268, + 2.665713310241699 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -677275,31 +677425,31 @@ "cited_by_count": 34, "projections": { "pca": [ - 0.04961371421813965, - -0.056295253336429596 + 0.04951116070151329, + -0.05647248029708862 ], "tsne": [ - 30.517324447631836, - -8.34363842010498 + 25.50162124633789, + 13.530978202819824 ], "umap": [ - 8.498908042907715, - 7.956284046173096 + 8.70683765411377, + 7.525364875793457 ], "pca3d": [ - 0.049613866955041885, - -0.05629605054855347, - -0.18796510994434357 + 0.04951126500964165, + -0.05647345632314682, + -0.18779556453227997 ], "tsne3d": [ - 13.941974639892578, - -7.061328411102295, - 2.5615649223327637 + 11.843546867370605, + 7.922314167022705, + -6.785741806030273 ], "umap3d": [ - 6.553490161895752, - 4.191949367523193, - 1.8384203910827637 + 6.545533657073975, + 4.066384315490723, + 1.0945037603378296 ] }, "cluster_id": 0, @@ -677310,12 +677460,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { "id": 31, - "label": "Genome Cancer" + "label": "Single Cell Seq" } ], "_embedding": [ @@ -677730,31 +677880,31 @@ "doi": "10.1101/2021.09.08.459495", "projections": { "pca": [ - 0.1228509470820427, - -0.0975755825638771 + 0.12259919941425323, + -0.09778884798288345 ], "tsne": [ - 37.98195266723633, - -13.485279083251953 + 33.51123809814453, + 19.7794189453125 ], "umap": [ - 9.082417488098145, - 8.727081298828125 + 9.350410461425781, + 8.287144660949707 ], "pca3d": [ - 0.12285099923610687, - -0.09755885601043701, - -0.12012772262096405 + 0.12259923666715622, + -0.0977712944149971, + -0.11995602399110794 ], "tsne3d": [ - 18.106950759887695, - -5.5879364013671875, - 7.88170862197876 + 16.541765213012695, + 6.0220561027526855, + -13.202532768249512 ], "umap3d": [ - 6.690619468688965, - 3.8977200984954834, - 1.1492184400558472 + 6.314442157745361, + 3.632493257522583, + 0.6767489314079285 ] }, "cluster_id": 0, @@ -677765,12 +677915,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -678193,31 +678343,31 @@ "doi": "10.1101/2021.09.09.458948", "projections": { "pca": [ - 0.27522704005241394, - 0.038201816380023956 + 0.27525433897972107, + 0.03747164458036423 ], "tsne": [ - 48.1637077331543, - 5.802008628845215 + 46.619873046875, + 3.4506144523620605 ], "umap": [ - 9.57673168182373, - 7.417232513427734 + 9.881546020507812, + 6.96751594543457 ], "pca3d": [ - 0.2752269506454468, - 0.03818812966346741, - -0.09355294704437256 + 0.2752542793750763, + 0.03745761513710022, + -0.09381730854511261 ], "tsne3d": [ - 25.07199478149414, - 3.3282506465911865, - 5.119026184082031 + 28.28984260559082, + 0.8686956763267517, + -10.32096004486084 ], "umap3d": [ - 7.891724586486816, - 4.485739707946777, - 1.3327267169952393 + 7.468091011047363, + 4.374176502227783, + 0.06513676792383194 ] }, "cluster_id": 0, @@ -678228,12 +678378,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 20, - "label": "Chromatin Regulation" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -678653,46 +678803,46 @@ "doi": "10.1101/2021.09.08.459513", "projections": { "pca": [ - 0.19602693617343903, - -0.013667167164385319 + 0.1959453970193863, + -0.013764319941401482 ], "tsne": [ - 27.735031127929688, - 6.241436004638672 + 34.23117446899414, + -10.896958351135254 ], "umap": [ - 7.157132148742676, - 7.89396858215332 + 7.4073967933654785, + 7.493336200714111 ], "pca3d": [ - 0.1960269957780838, - -0.013692336156964302, - -0.08844367414712906 + 0.19594542682170868, + -0.013789582997560501, + -0.08845775574445724 ], "tsne3d": [ - 13.79308032989502, - -13.81055736541748, - -12.853857040405273 + 13.472657203674316, + 18.81656837463379, + 1.9176925420761108 ], "umap3d": [ - 6.727341175079346, - 3.3628273010253906, - 2.8721070289611816 + 7.137674331665039, + 3.7021491527557373, + 2.191084623336792 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 26, + "id": 33, "label": "Genetic Disease" } ], @@ -679109,31 +679259,31 @@ "cited_by_count": 3, "projections": { "pca": [ - 0.19652214646339417, - -0.06844209879636765 + 0.19633595645427704, + -0.06872301548719406 ], "tsne": [ - 29.875398635864258, - -4.159205436706543 + 26.343891143798828, + 6.816817283630371 ], "umap": [ - 8.760981559753418, - 6.748624324798584 + 9.001891136169434, + 6.431227684020996 ], "pca3d": [ - 0.19652216136455536, - -0.06843072175979614, - -0.08777191489934921 + 0.19633586704730988, + -0.06871182471513748, + -0.08773152530193329 ], "tsne3d": [ - 19.896814346313477, - 3.413318157196045, - -10.20500373840332 + 21.108386993408203, + -2.321927070617676, + 3.975029468536377 ], "umap3d": [ - 6.933004856109619, - 5.165314197540283, - 1.9225528240203857 + 6.601985454559326, + 5.07899808883667, + 0.7445755004882812 ] }, "cluster_id": 0, @@ -679144,12 +679294,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 1, - "label": "Regulatory Genomics" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -679565,31 +679715,31 @@ "doi": "10.1101/2021.09.04.458817", "projections": { "pca": [ - 0.17132195830345154, - -0.03834966570138931 + 0.17129459977149963, + -0.038808535784482956 ], "tsne": [ - 45.232322692871094, - -3.759756088256836 + 31.493770599365234, + 16.039852142333984 ], "umap": [ - 9.347293853759766, - 7.893247604370117 + 9.56289291381836, + 7.514328479766846 ], "pca3d": [ - 0.17132197320461273, - -0.038352303206920624, - -0.1549309492111206 + 0.17129462957382202, + -0.03881160914897919, + -0.15486742556095123 ], "tsne3d": [ - 14.577938079833984, - -1.226871371269226, - 0.24568496644496918 + 16.218862533569336, + -0.18825693428516388, + -6.582126140594482 ], "umap3d": [ - 6.941596984863281, - 4.548294544219971, - 1.1854573488235474 + 6.622832298278809, + 4.2205986976623535, + 0.07697679847478867 ] }, "cluster_id": 0, @@ -679600,12 +679750,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -680025,31 +680175,31 @@ "doi": "10.1101/2021.09.06.459087", "projections": { "pca": [ - 0.09292709082365036, - -0.10493502765893936 + 0.09271737933158875, + -0.10484106093645096 ], "tsne": [ - 19.50999641418457, - 9.244396209716797 + 25.811792373657227, + -0.43807563185691833 ], "umap": [ - 7.854212284088135, - 7.08836555480957 + 8.10225772857666, + 6.746118068695068 ], "pca3d": [ - 0.09292727708816528, - -0.1049538254737854, - -0.11692661046981812 + 0.09271765500307083, + -0.1048598363995552, + -0.11659373342990875 ], "tsne3d": [ - 15.23105239868164, - -1.4681131839752197, - -13.668327331542969 + 15.119978904724121, + 8.240288734436035, + 6.434802532196045 ], "umap3d": [ - 6.771156311035156, - 4.425152778625488, - 2.6967387199401855 + 6.867565155029297, + 4.6156463623046875, + 1.6203656196594238 ] }, "cluster_id": 4, @@ -680060,12 +680210,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -680475,47 +680625,47 @@ "journal": "Nature Genetics", "projections": { "pca": [ - 0.16185787320137024, - 0.045783497393131256 + 0.1620585024356842, + 0.045369066298007965 ], "tsne": [ - 32.16728591918945, - 7.073066711425781 + 36.50807189941406, + -9.545343399047852 ], "umap": [ - 7.609358310699463, - 8.174580574035645 + 7.901229381561279, + 7.726604461669922 ], "pca3d": [ - 0.1618576943874359, - 0.045775651931762695, - -0.13381291925907135 + 0.16205839812755585, + 0.04536118358373642, + -0.1338215470314026 ], "tsne3d": [ - 18.389862060546875, - -12.4780855178833, - -10.923163414001465 + 17.82861328125, + 18.48566246032715, + 0.8195776343345642 ], "umap3d": [ - 7.0146965980529785, - 3.272503137588501, - 2.419339895248413 + 7.255160808563232, + 3.496074914932251, + 1.8006994724273682 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -680921,31 +681071,31 @@ ], "projections": { "pca": [ - -0.009606967680156231, - 0.05242082476615906 + -0.009306225925683975, + 0.0528300441801548 ], "tsne": [ - 11.293391227722168, - 9.663385391235352 + 13.550103187561035, + -5.743566036224365 ], "umap": [ - 7.121003150939941, - 5.858729839324951 + 7.503939628601074, + 5.708535194396973 ], "pca3d": [ - -0.009606877341866493, - 0.05239758640527725, - 0.05290413647890091 + -0.009306173771619797, + 0.052806396037340164, + 0.0529785193502903 ], "tsne3d": [ - 6.368921279907227, - 9.439310073852539, - -10.594415664672852 + 8.697497367858887, + 0.4269125759601593, + 4.448297500610352 ], "umap3d": [ - 6.080837726593018, - 5.065563678741455, - 3.481337070465088 + 6.3206868171691895, + 5.430161952972412, + 2.3804874420166016 ] }, "cluster_id": 4, @@ -680956,11 +681106,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -681377,46 +681527,46 @@ "doi": "10.1101/2021.08.25.457696", "projections": { "pca": [ - 0.07219845801591873, - -0.06042809411883354 + 0.07212615013122559, + -0.060330554842948914 ], "tsne": [ - 22.05288314819336, - -27.0533447265625 + 13.174242973327637, + 27.435962677001953 ], "umap": [ - 7.499617099761963, - 9.508474349975586 + 7.544900417327881, + 9.332051277160645 ], "pca3d": [ - 0.07219833135604858, - -0.06041490286588669, - -0.1541650891304016 + 0.07212606072425842, + -0.06031728535890579, + -0.15393155813217163 ], "tsne3d": [ - 12.545331001281738, - -14.133493423461914, - 10.069025993347168 + 6.963873863220215, + 4.084815979003906, + -19.388957977294922 ], "umap3d": [ - 5.1763739585876465, - 3.5817935466766357, - 1.4523136615753174 + 5.094832897186279, + 3.320953845977783, + 1.5704174041748047 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -681832,31 +681982,31 @@ "doi": "10.1101/2021.08.23.457433", "projections": { "pca": [ - 0.13217751681804657, - -0.15632249414920807 + 0.13195224106311798, + -0.15669386088848114 ], "tsne": [ - 14.458844184875488, - 33.72801971435547 + 18.684425354003906, + -31.164323806762695 ], "umap": [ - 7.766162872314453, - 4.520867824554443 + 8.17031478881836, + 4.045772552490234 ], "pca3d": [ - 0.13217759132385254, - -0.15630610287189484, - 0.07417784631252289 + 0.13195234537124634, + -0.15667663514614105, + 0.07442399859428406 ], "tsne3d": [ - 1.9380463361740112, - 19.470531463623047, - 4.899907112121582 + 7.674400806427002, + -15.733715057373047, + 5.607429027557373 ], "umap3d": [ - 7.140340328216553, - 6.283181190490723, - 4.091429233551025 + 7.308161735534668, + 6.839094161987305, + 2.3247592449188232 ] }, "cluster_id": 4, @@ -681867,11 +682017,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -682287,31 +682437,31 @@ "doi": "10.1101/2021.08.17.456650", "projections": { "pca": [ - 0.22192774713039398, - 0.08055690675973892 + 0.222066730260849, + 0.08060812950134277 ], "tsne": [ - 55.081878662109375, - 10.040788650512695 + 57.290985107421875, + 21.101781845092773 ], "umap": [ - 10.275774002075195, - 6.743365287780762 + 10.459152221679688, + 6.40948486328125 ], "pca3d": [ - 0.22192764282226562, - 0.08054176717996597, - -0.07089948654174805 + 0.22206652164459229, + 0.08059246093034744, + -0.07113214582204819 ], "tsne3d": [ - 33.41313171386719, - 8.557242393493652, - -0.4287242293357849 + 34.24520492553711, + -3.0207250118255615, + -5.120463848114014 ], "umap3d": [ - 8.110401153564453, - 5.3615217208862305, - 1.1314653158187866 + 7.5151777267456055, + 5.098505020141602, + -0.4632781445980072 ] }, "cluster_id": 0, @@ -682322,12 +682472,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -682749,31 +682899,31 @@ "doi": "10.1101/2021.07.27.453936", "projections": { "pca": [ - 0.19243213534355164, - 0.029119394719600677 + 0.19241544604301453, + 0.02866601198911667 ], "tsne": [ - 47.29573059082031, - -15.031026840209961 + 36.27397537231445, + 24.03966522216797 ], "umap": [ - 9.998601913452148, - 8.080487251281738 + 10.096844673156738, + 7.76863956451416 ], "pca3d": [ - 0.19243204593658447, - 0.02911808155477047, - -0.15203598141670227 + 0.19241531193256378, + 0.028664326295256615, + -0.15220710635185242 ], "tsne3d": [ - 28.475982666015625, - -11.40059757232666, - -6.367380142211914 + 29.032594680786133, + 15.886219024658203, + -1.0303254127502441 ], "umap3d": [ - 6.9905900955200195, - 4.795431613922119, - 0.7556856870651245 + 6.272624492645264, + 4.289310455322266, + -0.11034317314624786 ] }, "cluster_id": 0, @@ -682784,12 +682934,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 13, + "label": "Chromatin Regulation" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -683210,31 +683360,31 @@ "doi": "10.1101/2021.08.15.456358", "projections": { "pca": [ - 0.2639707624912262, - 0.008878398686647415 + 0.26391661167144775, + 0.008124121464788914 ], "tsne": [ - 33.134857177734375, - 27.62788200378418 + 57.222782135009766, + -1.5867938995361328 ], "umap": [ - 10.47071361541748, - 7.1477484703063965 + 10.686686515808105, + 6.694578170776367 ], "pca3d": [ - 0.263970285654068, - 0.00889937300235033, - -0.07808038592338562 + 0.2639162838459015, + 0.008145516738295555, + -0.07834458351135254 ], "tsne3d": [ - 13.623095512390137, - 18.67647933959961, - -2.840750217437744 + 23.87643814086914, + -13.01053524017334, + 3.712581157684326 ], "umap3d": [ - 7.926891803741455, - 5.0396199226379395, - 0.773928165435791 + 7.276414394378662, + 4.657879829406738, + -0.5070728063583374 ] }, "cluster_id": 0, @@ -683245,12 +683395,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -683685,31 +683835,31 @@ "openalex_url": "https://openalex.org/W3194509722", "projections": { "pca": [ - 0.08360631763935089, - 0.13846927881240845 + 0.0839724987745285, + 0.13788294792175293 ], "tsne": [ - 41.00720977783203, - -5.9272027015686035 + 40.65326690673828, + 9.299788475036621 ], "umap": [ - 9.250027656555176, - 8.331622123718262 + 9.598034858703613, + 7.830280303955078 ], "pca3d": [ - 0.08360587060451508, - 0.13847671449184418, - -0.1237228661775589 + 0.08397205919027328, + 0.13789038360118866, + -0.12399440258741379 ], "tsne3d": [ - 22.654685974121094, - -8.990474700927734, - -1.8989180326461792 + 24.596805572509766, + 12.005245208740234, + -6.211823463439941 ], "umap3d": [ - 7.174566268920898, - 3.9751415252685547, - 1.0492490530014038 + 6.690583229064941, + 3.6893997192382812, + 0.269453763961792 ] }, "cluster_id": 0, @@ -683720,12 +683870,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -684144,47 +684294,47 @@ "doi": "10.1101/2021.08.03.454669", "projections": { "pca": [ - 0.13608117401599884, - -0.0664513036608696 + 0.13576428592205048, + -0.06652464717626572 ], "tsne": [ - 28.54462432861328, - -1.5161677598953247 + 30.15506935119629, + 0.7681876420974731 ], "umap": [ - 8.185104370117188, - 7.802316665649414 + 8.403916358947754, + 7.381980895996094 ], "pca3d": [ - 0.13608133792877197, - -0.06645721942186356, - -0.07620669901371002 + 0.13576440513134003, + -0.06653022766113281, + -0.07621826976537704 ], "tsne3d": [ - 10.668682098388672, - -8.159642219543457, - -0.8895971775054932 + 10.033783912658691, + 13.810659408569336, + -10.588753700256348 ], "umap3d": [ - 7.1225504875183105, - 3.953460454940796, - 2.306123733520508 + 7.211201190948486, + 4.05994176864624, + 1.2954216003417969 ] }, - "cluster_id": 5, - "cluster_label": "Single-Cell Data", + "cluster_id": 0, + "cluster_label": "Chromatin Regulation", "cluster_levels": [ { - "id": 5, - "label": "Single-Cell Data" + "id": 0, + "label": "Chromatin Regulation" }, { "id": 13, - "label": "Genetic Variants" + "label": "Chromatin Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -684603,31 +684753,31 @@ "cited_by_count": 16, "projections": { "pca": [ - 0.11886431276798248, - -0.018273398280143738 + 0.11882239580154419, + -0.01861877180635929 ], "tsne": [ - 37.98103713989258, - -10.562933921813965 + 35.43544006347656, + 16.476024627685547 ], "umap": [ - 8.832464218139648, - 7.942683696746826 + 9.24030876159668, + 7.726621627807617 ], "pca3d": [ - 0.11886446923017502, - -0.018277430906891823, - -0.08735405653715134 + 0.11882246285676956, + -0.01862228848040104, + -0.0873761922121048 ], "tsne3d": [ - 20.057079315185547, - -2.1157236099243164, - 7.35680627822876 + 19.221723556518555, + 5.187828063964844, + -8.987186431884766 ], "umap3d": [ - 7.098803520202637, - 4.117337703704834, - 1.5613242387771606 + 6.914813041687012, + 4.016072750091553, + 0.6733608245849609 ] }, "cluster_id": 0, @@ -684638,12 +684788,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -685061,31 +685211,31 @@ "cited_by_count": 28, "projections": { "pca": [ - 0.11339890956878662, - -0.11099181324243546 + 0.11318924278020859, + -0.11144810914993286 ], "tsne": [ - 21.55697250366211, - 32.98976135253906 + 24.70817756652832, + -31.5279483795166 ], "umap": [ - 8.262487411499023, - 4.735395431518555 + 8.581096649169922, + 4.267854690551758 ], "pca3d": [ - 0.11339880526065826, - -0.11096635460853577, - 0.05036341771483421 + 0.1131892129778862, + -0.11142189800739288, + 0.05042954534292221 ], "tsne3d": [ - 5.5947723388671875, - 24.302173614501953, - 0.5967233180999756 + 15.552428245544434, + -18.5445613861084, + 9.635947227478027 ], "umap3d": [ - 7.515392303466797, - 6.076338291168213, - 3.784759283065796 + 7.58777379989624, + 6.657250881195068, + 1.921250820159912 ] }, "cluster_id": 4, @@ -685096,11 +685246,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -685519,31 +685669,31 @@ "cited_by_count": 3, "projections": { "pca": [ - 0.057748716324567795, - -0.13050229847431183 + 0.057545408606529236, + -0.1305433213710785 ], "tsne": [ - 14.823631286621094, - 8.456616401672363 + 14.547499656677246, + -0.5821518898010254 ], "umap": [ - 7.368192195892334, - 6.009904861450195 + 7.774725914001465, + 5.673234939575195 ], "pca3d": [ - 0.05774884298443794, - -0.1305030882358551, - 0.009055985137820244 + 0.05754559487104416, + -0.13054430484771729, + 0.009319901466369629 ], "tsne3d": [ - 10.09219741821289, - 8.78486442565918, - -11.270244598388672 + 11.514396667480469, + -2.8316879272460938, + 8.205284118652344 ], "umap3d": [ - 6.277245998382568, - 5.226450443267822, - 3.1490249633789062 + 6.419398784637451, + 5.526253700256348, + 1.9918149709701538 ] }, "cluster_id": 4, @@ -685554,11 +685704,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -685976,31 +686126,31 @@ "doi": "10.1101/2021.07.23.453605", "projections": { "pca": [ - 0.1986725777387619, - -0.003905718680471182 + 0.19857415556907654, + -0.004295033402740955 ], "tsne": [ - 29.85838508605957, - 24.78579330444336 + 36.3735237121582, + 0.7987978458404541 ], "umap": [ - 8.853473663330078, - 5.960938930511475 + 9.133647918701172, + 5.682774066925049 ], "pca3d": [ - 0.19867226481437683, - -0.0038786972872912884, - -0.14698994159698486 + 0.1985737830400467, + -0.004267387557774782, + -0.14709164202213287 ], "tsne3d": [ - 14.670546531677246, - 19.948448181152344, - 1.933641791343689 + 18.99556541442871, + -9.961006164550781, + 4.869074821472168 ], "umap3d": [ - 7.572482585906982, - 5.170670509338379, - 2.7976319789886475 + 7.441367149353027, + 5.403898239135742, + 1.0830413103103638 ] }, "cluster_id": 4, @@ -686011,11 +686161,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, - "label": "RNA Splicing" + "id": 2, + "label": "DNA Regulation" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -686431,31 +686581,31 @@ "doi": "10.1101/2021.07.06.451243", "projections": { "pca": [ - 0.15335628390312195, - -0.08224109560251236 + 0.15310004353523254, + -0.08222799003124237 ], "tsne": [ - 24.338523864746094, - 23.979013442993164 + 28.844167709350586, + -24.706403732299805 ], "umap": [ - 8.317726135253906, - 5.631241798400879 + 8.540521621704102, + 5.20652961730957 ], "pca3d": [ - 0.15335623919963837, - -0.08222585171461105, - 0.007459199521690607 + 0.15309995412826538, + -0.08221165835857391, + 0.007539409212768078 ], "tsne3d": [ - 10.520297050476074, - 23.523874282836914, - 6.104042053222656 + 15.743388175964355, + -9.896259307861328, + 10.458715438842773 ], "umap3d": [ - 7.451958179473877, - 5.205560684204102, - 3.320340633392334 + 7.525142669677734, + 5.651463031768799, + 1.6959465742111206 ] }, "cluster_id": 4, @@ -686466,11 +686616,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -686887,31 +687037,31 @@ "doi": "10.1101/2021.07.04.451055", "projections": { "pca": [ - 0.1352148950099945, - 0.09932337701320648 + 0.13544577360153198, + 0.0992225781083107 ], "tsne": [ - 42.648101806640625, - -5.156406879425049 + 42.1289176940918, + 8.16513442993164 ], "umap": [ - 9.557109832763672, - 8.223861694335938 + 9.893112182617188, + 7.749207973480225 ], "pca3d": [ - 0.1352146565914154, - 0.0993100181221962, - -0.11188550293445587 + 0.1354455202817917, + 0.0992082953453064, + -0.11206427961587906 ], "tsne3d": [ - 22.854869842529297, - -10.378692626953125, - -4.7102861404418945 + 25.017385482788086, + 13.356488227844238, + -3.479609727859497 ], "umap3d": [ - 7.10197639465332, - 4.154308795928955, - 0.8402567505836487 + 6.617263317108154, + 3.8724379539489746, + 0.03579597920179367 ] }, "cluster_id": 0, @@ -686922,12 +687072,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -687350,31 +687500,31 @@ "cited_by_count": 26, "projections": { "pca": [ - 0.11313533037900925, - -0.021107599139213562 + 0.11311853677034378, + -0.021053176373243332 ], "tsne": [ - 26.625898361206055, - 24.171977996826172 + 30.89017105102539, + -25.30551528930664 ], "umap": [ - 8.500720977783203, - 5.6407904624938965 + 8.753639221191406, + 5.273786544799805 ], "pca3d": [ - 0.11313518136739731, - -0.021087365224957466, - -0.05489034205675125 + 0.11311839520931244, + -0.0210323017090559, + -0.05481507256627083 ], "tsne3d": [ - 13.17938232421875, - 22.452594757080078, - 3.8776817321777344 + 18.58716583251953, + -8.689947128295898, + 8.780058860778809 ], "umap3d": [ - 7.527584552764893, - 5.17641019821167, - 3.220700740814209 + 7.612314224243164, + 5.6030497550964355, + 1.5867455005645752 ] }, "cluster_id": 4, @@ -687385,11 +687535,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -687814,31 +687964,31 @@ "doi": "10.1101/2020.04.06.028092", "projections": { "pca": [ - 0.17430350184440613, - 0.02902272716164589 + 0.174329474568367, + 0.028685884550213814 ], "tsne": [ - 31.017757415771484, - 26.721208572387695 + 56.46841812133789, + -3.63307785987854 ], "umap": [ - 8.949724197387695, - 5.689057350158691 + 9.19780445098877, + 5.253256320953369 ], "pca3d": [ - 0.1743030995130539, - 0.029047472402453423, - -0.018703041598200798 + 0.17432905733585358, + 0.028711365535855293, + -0.01892554573714733 ], "tsne3d": [ - 14.322159767150879, - 21.41257667541504, - -0.37994512915611267 + 20.857398986816406, + -12.008949279785156, + 5.273525714874268 ], "umap3d": [ - 7.703187942504883, - 5.495710849761963, - 2.9313926696777344 + 7.535979270935059, + 5.704778671264648, + 1.1375998258590698 ] }, "cluster_id": 4, @@ -687849,11 +687999,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -688275,31 +688425,31 @@ "doi": "10.1101/2021.06.28.450119", "projections": { "pca": [ - 0.28625810146331787, - 0.12437213212251663 + 0.2865373492240906, + 0.12360779196023941 ], "tsne": [ - 52.21395492553711, - 2.3566396236419678 + 54.16209411621094, + 13.224713325500488 ], "umap": [ - 10.283769607543945, - 7.223012924194336 + 10.487878799438477, + 6.856286525726318 ], "pca3d": [ - 0.2862578332424164, - 0.12436719238758087, - -0.11949292570352554 + 0.28653693199157715, + 0.12360229343175888, + -0.11990377306938171 ], "tsne3d": [ - 29.651607513427734, - 2.0841829776763916, - 1.600745439529419 + 31.23615074157715, + 1.7878326177597046, + -5.180482387542725 ], "umap3d": [ - 8.013818740844727, - 4.862936019897461, - 0.8503099083900452 + 7.321579933166504, + 4.697323322296143, + -0.4917970597743988 ] }, "cluster_id": 0, @@ -688310,12 +688460,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -688733,31 +688883,31 @@ "cited_by_count": 10, "projections": { "pca": [ - 0.053931258618831635, - -0.09056564420461655 + 0.05376249551773071, + -0.09058161079883575 ], "tsne": [ - 22.613584518432617, - 14.007168769836426 + 25.031343460083008, + -7.502111911773682 ], "umap": [ - 7.274750232696533, - 7.271020889282227 + 7.28607177734375, + 6.804844856262207 ], "pca3d": [ - 0.05393135920166969, - -0.09056814014911652, - -0.03325820341706276 + 0.053762637078762054, + -0.09058396518230438, + -0.033069074153900146 ], "tsne3d": [ - 8.884773254394531, - -5.678771495819092, - -15.785665512084961 + 7.70379638671875, + 12.100676536560059, + 8.164639472961426 ], "umap3d": [ - 6.784568786621094, - 3.95180344581604, - 3.3118088245391846 + 7.164831638336182, + 4.493093490600586, + 2.438443899154663 ] }, "cluster_id": 4, @@ -688768,12 +688918,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -689223,31 +689373,31 @@ "cited_by_count": 162, "projections": { "pca": [ - 0.19205617904663086, - 0.036424629390239716 + 0.19224634766578674, + 0.03573494032025337 ], "tsne": [ - 27.809833526611328, - 26.544330596923828 + 29.372093200683594, + -21.30786895751953 ], "umap": [ - 8.722456932067871, - 5.927807331085205 + 8.94544506072998, + 5.533392906188965 ], "pca3d": [ - 0.1920558512210846, - 0.0364445261657238, - -0.05361388251185417 + 0.19224590063095093, + 0.035755645483732224, + -0.053686290979385376 ], "tsne3d": [ - 10.265093803405762, - 18.029531478881836, - 4.145954608917236 + 16.569561004638672, + -13.418190956115723, + 5.659398078918457 ], "umap3d": [ - 7.457896709442139, - 5.245443344116211, - 3.020972728729248 + 7.423153877258301, + 5.544497013092041, + 1.4099472761154175 ] }, "cluster_id": 4, @@ -689258,11 +689408,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, - "label": "RNA Splicing" + "id": 2, + "label": "DNA Regulation" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -689702,31 +689852,31 @@ "cited_by_count": 47, "projections": { "pca": [ - 0.16542203724384308, - -0.05044177547097206 + 0.16522838175296783, + -0.05072026699781418 ], "tsne": [ - 26.309276580810547, - 22.68903350830078 + 32.06930160522461, + -25.892854690551758 ], "umap": [ - 8.500649452209473, - 5.550395488739014 + 8.725075721740723, + 5.248753070831299 ], "pca3d": [ - 0.16542181372642517, - -0.050429366528987885, - -0.04430122300982475 + 0.16522836685180664, + -0.05070758983492851, + -0.044300954788923264 ], "tsne3d": [ - 12.389512062072754, - 25.239429473876953, - 4.255654811859131 + 18.230607986450195, + -11.440439224243164, + 10.402894020080566 ], "umap3d": [ - 7.59709358215332, - 5.135156154632568, - 3.2467918395996094 + 7.694641590118408, + 5.54308557510376, + 1.5482635498046875 ] }, "cluster_id": 4, @@ -689737,11 +689887,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -690160,31 +690310,31 @@ "cited_by_count": 32, "projections": { "pca": [ - 0.21601168811321259, - -0.08806777000427246 + 0.2157251387834549, + -0.08881361782550812 ], "tsne": [ - 44.917213439941406, - 0.703047513961792 + 33.930763244628906, + 12.241700172424316 ], "umap": [ - 9.429563522338867, - 7.403875827789307 + 9.692286491394043, + 6.875109672546387 ], "pca3d": [ - 0.21601161360740662, - -0.0880608782172203, - -0.1277080923318863 + 0.21572504937648773, + -0.08880648016929626, + -0.12776018679141998 ], "tsne3d": [ - 20.467063903808594, - 1.525119423866272, - 1.5063835382461548 + 21.86513328552246, + -1.1069676876068115, + -7.2279582023620605 ], "umap3d": [ - 7.798959255218506, - 4.416420936584473, - 1.5271563529968262 + 7.475998401641846, + 4.294628143310547, + 0.20011042058467865 ] }, "cluster_id": 0, @@ -690195,11 +690345,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -690608,31 +690758,31 @@ "journal": "Nature", "projections": { "pca": [ - 0.1705423891544342, - -0.03305017203092575 + 0.1706657111644745, + -0.033501747995615005 ], "tsne": [ - 34.45519256591797, - 16.60280990600586 + 40.552921295166016, + -3.3059451580047607 ], "umap": [ - 8.410475730895996, - 6.75092887878418 + 8.590658187866211, + 6.333269119262695 ], "pca3d": [ - 0.1705424040555954, - -0.03306528180837631, - 0.024477165192365646 + 0.1706656962633133, + -0.03351593762636185, + 0.02457614429295063 ], "tsne3d": [ - 12.62305736541748, - 5.2390899658203125, - 11.39520263671875 + 17.682491302490234, + 2.348224639892578, + 14.65496826171875 ], "umap3d": [ - 7.590117931365967, - 4.469380855560303, - 2.546466588973999 + 7.550962448120117, + 4.680264949798584, + 1.244937539100647 ] }, "cluster_id": 4, @@ -690643,12 +690793,12 @@ "label": "RNA Sequence Models" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -691100,47 +691250,47 @@ "cited_by_count": 101, "projections": { "pca": [ - 0.168621227145195, - -0.04197007045149803 + 0.16853579878807068, + -0.04246900975704193 ], "tsne": [ - 29.523656845092773, - 8.4032621383667 + 33.129940032958984, + -7.434659481048584 ], "umap": [ - 7.575931072235107, - 8.066856384277344 + 7.789010047912598, + 7.612030506134033 ], "pca3d": [ - 0.16862133145332336, - -0.041982296854257584, - -0.17128540575504303 + 0.16853591799736023, + -0.042481523007154465, + -0.17120884358882904 ], "tsne3d": [ - 14.732752799987793, - -10.810498237609863, - -11.638246536254883 + 14.682958602905273, + 16.115779876708984, + 2.458326816558838 ], "umap3d": [ - 7.009720325469971, - 3.379680871963501, - 2.5889272689819336 + 7.301931381225586, + 3.6490530967712402, + 1.9250158071517944 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -691561,31 +691711,31 @@ "openalex_url": "https://openalex.org/W3172269795", "projections": { "pca": [ - 0.13143831491470337, - -0.015449234284460545 + 0.13144443929195404, + -0.015636006370186806 ], "tsne": [ - 10.52628231048584, - -8.304845809936523 + 11.748309135437012, + 10.141613960266113 ], "umap": [ - 7.891843318939209, - 7.010584831237793 + 7.903565883636475, + 6.516480445861816 ], "pca3d": [ - 0.13143795728683472, - -0.015420784242451191, - -0.0538303479552269 + 0.13144420087337494, + -0.015607034787535667, + -0.05379706248641014 ], "tsne3d": [ - 10.400940895080566, - 3.6432299613952637, - -19.326133728027344 + 11.967880249023438, + 7.8741278648376465, + 14.647733688354492 ], "umap3d": [ - 6.07335090637207, - 4.541015148162842, - 2.513340473175049 + 6.163033485412598, + 4.680708408355713, + 1.6925429105758667 ] }, "cluster_id": 4, @@ -691596,12 +691746,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -692019,31 +692169,31 @@ "doi": "10.1101/2021.06.01.446655", "projections": { "pca": [ - 0.22708915174007416, - -0.0001792198745533824 + 0.22716526687145233, + -0.0005259105237200856 ], "tsne": [ - 32.576229095458984, - 27.721031188964844 + 57.25904083251953, + -2.1738295555114746 ], "umap": [ - 8.884147644042969, - 5.571186542510986 + 9.162188529968262, + 5.142467975616455 ], "pca3d": [ - 0.2270888090133667, - -0.00016186005086638033, - 0.030729997903108597 + 0.22716499865055084, + -0.0005078779067844152, + 0.030623773112893105 ], "tsne3d": [ - 13.200726509094238, - 19.313480377197266, - -2.0213816165924072 + 23.168106079101562, + -13.228754043579102, + 4.518520832061768 ], "umap3d": [ - 7.664539337158203, - 5.61887788772583, - 2.994187831878662 + 7.4634294509887695, + 5.93458890914917, + 1.2107418775558472 ] }, "cluster_id": 4, @@ -692054,11 +692204,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -692475,31 +692625,31 @@ "doi": "10.1101/2021.05.29.446316", "projections": { "pca": [ - 0.15260149538516998, - -0.1358296126127243 + 0.1522657722234726, + -0.1363300383090973 ], "tsne": [ - 63.269351959228516, - 0.8953047394752502 + 48.82079315185547, + 25.896936416625977 ], "umap": [ - 9.739100456237793, - 6.3928422927856445 + 9.974015235900879, + 5.925027847290039 ], "pca3d": [ - 0.1526014506816864, - -0.13580718636512756, - -0.0883101373910904 + 0.1522657722234726, + -0.13630707561969757, + -0.08816006779670715 ], "tsne3d": [ - 26.557201385498047, - 10.213972091674805, - -8.559846878051758 + 24.522489547729492, + -7.300695896148682, + 0.2164989560842514 ], "umap3d": [ - 7.6726460456848145, - 5.698924541473389, - 1.6299489736557007 + 7.052629470825195, + 5.5290913581848145, + 0.14465856552124023 ] }, "cluster_id": 0, @@ -692510,12 +692660,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -692921,46 +693071,46 @@ ], "projections": { "pca": [ - 0.11315394937992096, - 0.157670795917511 + 0.11380575597286224, + 0.15732507407665253 ], "tsne": [ - 2.4258530139923096, - -7.630610942840576 + 2.390453815460205, + 6.716168403625488 ], "umap": [ - 5.261967182159424, - 7.394481658935547 + 5.468127727508545, + 6.653538227081299 ], "pca3d": [ - 0.1131533607840538, - 0.15768422186374664, - -0.01319247018545866 + 0.11380518227815628, + 0.15733873844146729, + -0.01334230788052082 ], "tsne3d": [ - 0.3855149447917938, - -3.331216335296631, - -21.20476722717285 + 3.545076608657837, + 25.40473175048828, + -4.887977123260498 ], "umap3d": [ - 4.834067344665527, - 3.3324551582336426, - 5.031148910522461 + 5.911717891693115, + 4.614044666290283, + 4.956049919128418 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, + "id": 13, "label": "Genomics Papers" } ], @@ -693384,31 +693534,31 @@ "cited_by_count": 2, "projections": { "pca": [ - 0.04648216813802719, - -0.15810009837150574 + 0.04604753479361534, + -0.15828146040439606 ], "tsne": [ - 16.39051628112793, - -6.905542373657227 + 16.928836822509766, + 6.676109790802002 ], "umap": [ - 8.659562110900879, - 6.454841613769531 + 8.839129447937012, + 6.058335304260254 ], "pca3d": [ - 0.046482399106025696, - -0.15809030830860138, - -0.043710190802812576 + 0.04604773595929146, + -0.1582716703414917, + -0.04353605955839157 ], "tsne3d": [ - 18.085481643676758, - 7.997793197631836, - -12.997879981994629 + 18.856159210205078, + -0.47973158955574036, + 6.825064182281494 ], "umap3d": [ - 6.755860328674316, - 5.472482681274414, - 2.1309731006622314 + 6.43460750579834, + 5.454092979431152, + 0.8925707340240479 ] }, "cluster_id": 4, @@ -693419,12 +693569,12 @@ "label": "RNA Sequence Models" }, { - "id": 3, - "label": "RNA Splicing" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -693845,47 +693995,47 @@ "doi": "10.1101/2021.05.26.445783", "projections": { "pca": [ - 0.0424606092274189, - -0.04423500970005989 + 0.0422946959733963, + -0.044241905212402344 ], "tsne": [ - 23.998004913330078, - 3.6801607608795166 + 31.6336727142334, + -11.801515579223633 ], "umap": [ - 7.188268661499023, - 7.680853366851807 + 7.364982604980469, + 7.402129650115967 ], "pca3d": [ - 0.04246062785387039, - -0.0442340113222599, - -0.14841999113559723 + 0.04229472577571869, + -0.04424097388982773, + -0.148326575756073 ], "tsne3d": [ - 11.6061372756958, - -16.07655906677246, - -12.044209480285645 + 8.460031509399414, + 14.555243492126465, + -0.155474454164505 ], "umap3d": [ - 6.548588752746582, - 3.6755285263061523, - 2.8977248668670654 + 6.971523761749268, + 3.9580228328704834, + 2.2153913974761963 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -694340,31 +694490,31 @@ "doi": "10.1101/2021.05.26.445798", "projections": { "pca": [ - 0.23889105021953583, - 0.15445220470428467 + 0.23946060240268707, + 0.15383999049663544 ], "tsne": [ - 37.71449661254883, - 12.940752029418945 + 44.414119720458984, + -5.257622718811035 ], "umap": [ - 8.620835304260254, - 7.5252861976623535 + 8.839262008666992, + 7.187709331512451 ], "pca3d": [ - 0.23889096081256866, - 0.15441623330116272, - -0.004291150253266096 + 0.2394605278968811, + 0.1538042426109314, + -0.004535927437245846 ], "tsne3d": [ - 17.343530654907227, - 2.811288356781006, - 11.983548164367676 + 21.189523696899414, + 7.709199905395508, + 13.52111530303955 ], "umap3d": [ - 7.617822647094727, - 4.226511001586914, - 2.1482150554656982 + 7.322178363800049, + 4.330057144165039, + 0.9232267737388611 ] }, "cluster_id": 0, @@ -694375,12 +694525,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -694794,31 +694944,31 @@ "doi": "10.1101/2021.05.19.444847", "projections": { "pca": [ - 0.20785564184188843, - -0.09971440583467484 + 0.20761315524578094, + -0.10014383494853973 ], "tsne": [ - 35.309200286865234, - 15.371463775634766 + 41.708011627197266, + -3.459249496459961 ], "umap": [ - 8.733262062072754, - 6.981360912322998 + 9.057666778564453, + 6.6597418785095215 ], "pca3d": [ - 0.20785585045814514, - -0.09973316639661789, - -0.012723344378173351 + 0.20761336386203766, + -0.10016220808029175, + -0.012674132362008095 ], "tsne3d": [ - 13.375031471252441, - 4.1066389083862305, - 9.415635108947754 + 19.744810104370117, + 2.61726713180542, + 13.593289375305176 ], "umap3d": [ - 7.858639717102051, - 4.391218662261963, - 2.0231869220733643 + 7.663464546203613, + 4.454249858856201, + 0.700563907623291 ] }, "cluster_id": 0, @@ -694829,12 +694979,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -695240,46 +695390,46 @@ ], "projections": { "pca": [ - 0.1435355544090271, - 0.03915431350469589 + 0.1438450664281845, + 0.03858126327395439 ], "tsne": [ - 1.3497241735458374, - -5.7376813888549805 + 1.6302975416183472, + 4.7895379066467285 ], "umap": [ - 5.319347858428955, - 7.318150520324707 + 5.498875617980957, + 6.576964378356934 ], "pca3d": [ - 0.1435350924730301, - 0.03917738422751427, - -0.022097861394286156 + 0.1438446342945099, + 0.03860555961728096, + -0.0221022330224514 ], "tsne3d": [ - -1.0605852603912354, - -0.8572402000427246, - -17.01747703552246 + 2.5989975929260254, + 22.798084259033203, + -1.6675269603729248 ], "umap3d": [ - 4.889975070953369, - 3.4028494358062744, - 5.039379119873047 + 5.8777174949646, + 4.7000603675842285, + 4.95070219039917 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, + "id": 13, "label": "Genomics Papers" } ], @@ -695701,31 +695851,31 @@ "openalex_url": "https://openalex.org/W3134994147", "projections": { "pca": [ - 0.12021952122449875, - 0.21509501338005066 + 0.12060543894767761, + 0.2149243801832199 ], "tsne": [ - 55.53551483154297, - -5.781912803649902 + 50.68302536010742, + 11.067217826843262 ], "umap": [ - 10.16612434387207, - 7.661095142364502 + 10.368093490600586, + 7.2966508865356445 ], "pca3d": [ - 0.12021884322166443, - 0.215114027261734, - -0.15490996837615967 + 0.12060478329658508, + 0.21494270861148834, + -0.155421644449234 ], "tsne3d": [ - 33.04145050048828, - -0.7729882597923279, - -7.578967571258545 + 31.87929344177246, + 5.185818672180176, + 2.2881243228912354 ], "umap3d": [ - 7.486826419830322, - 5.051790714263916, - 0.8131833076477051 + 6.7128095626831055, + 4.7332329750061035, + -0.37967222929000854 ] }, "cluster_id": 0, @@ -695736,12 +695886,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -696149,31 +696299,31 @@ "journal": "Nature Methods", "projections": { "pca": [ - 0.18922117352485657, - -0.05025629326701164 + 0.1891850084066391, + -0.051195647567510605 ], "tsne": [ - 24.723535537719727, - 34.0134391784668 + 26.902530670166016, + -32.909912109375 ], "umap": [ - 8.436738014221191, - 4.953450679779053 + 8.800589561462402, + 4.564674377441406 ], "pca3d": [ - 0.18922053277492523, - -0.0501997135579586, - -0.0026419342029839754 + 0.18918442726135254, + -0.05113719776272774, + -0.002686253050342202 ], "tsne3d": [ - 6.725784778594971, - 27.262067794799805, - 2.674917697906494 + 16.87673568725586, + -19.963592529296875, + 4.469743728637695 ], "umap3d": [ - 7.578426361083984, - 5.885369300842285, - 3.532360076904297 + 7.505072593688965, + 6.452075958251953, + 1.624598741531372 ] }, "cluster_id": 4, @@ -696184,12 +696334,12 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, - "label": "RNA Splicing" + "id": 8, + "label": "RNA Structure" } ], "_embedding": [ @@ -696595,46 +696745,46 @@ ], "projections": { "pca": [ - 0.13220834732055664, - 0.1419234275817871 + 0.13292023539543152, + 0.14123357832431793 ], "tsne": [ - 0.36593276262283325, - -7.0976057052612305 + 0.35444387793540955, + 5.912538528442383 ], "umap": [ - 5.180695056915283, - 7.358577728271484 + 5.404478549957275, + 6.594971656799316 ], "pca3d": [ - 0.13220779597759247, - 0.14193843305110931, - -0.01366623118519783 + 0.13291959464550018, + 0.14124897122383118, + -0.013767965137958527 ], "tsne3d": [ - -1.5960646867752075, - -2.070496082305908, - -19.590864181518555 + 1.057329535484314, + 24.0766544342041, + -3.754253387451172 ], "umap3d": [ - 4.720201015472412, - 3.4134366512298584, - 4.981333255767822 + 5.769596576690674, + 4.665003299713135, + 4.957180500030518 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, + "id": 13, "label": "Genomics Papers" } ], @@ -697051,31 +697201,31 @@ "doi": "10.1101/2021.04.29.441875", "projections": { "pca": [ - 0.25105273723602295, - 0.029241027310490608 + 0.2510709762573242, + 0.028664149343967438 ], "tsne": [ - 50.40348815917969, - 0.3621789813041687 + 55.883602142333984, + 6.9045186042785645 ], "umap": [ - 10.29543685913086, - 7.258298873901367 + 10.588153839111328, + 6.989041328430176 ], "pca3d": [ - 0.2510523796081543, - 0.02925015799701214, - -0.08963290601968765 + 0.25107061862945557, + 0.02867339737713337, + -0.08986677974462509 ], "tsne3d": [ - 32.260398864746094, - -2.839470624923706, - 1.8323582410812378 + 33.530033111572266, + 1.826674461364746, + -0.2963719666004181 ], "umap3d": [ - 7.855515956878662, - 5.0235185623168945, - 0.8310791254043579 + 7.127303123474121, + 4.67734956741333, + -0.5457131266593933 ] }, "cluster_id": 0, @@ -697086,12 +697236,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -697499,31 +697649,31 @@ "journal": "Nature", "projections": { "pca": [ - 0.2867831885814667, - 0.09608673304319382 + 0.28712353110313416, + 0.09495433419942856 ], "tsne": [ - 42.43980026245117, - -8.623462677001953 + 39.213626861572266, + 15.067399024963379 ], "umap": [ - 9.37321949005127, - 8.554121017456055 + 9.600064277648926, + 7.9826860427856445 ], "pca3d": [ - 0.2867828607559204, - 0.09607348591089249, - -0.20852823555469513 + 0.2871231436729431, + 0.09494131803512573, + -0.20873861014842987 ], "tsne3d": [ - 25.639116287231445, - -5.162357807159424, - 4.763614177703857 + 25.1792049407959, + 7.617703914642334, + -11.800740242004395 ], "umap3d": [ - 7.493899345397949, - 3.972884178161621, - 1.1109459400177002 + 6.945714473724365, + 3.6680548191070557, + 0.23296982049942017 ] }, "cluster_id": 0, @@ -697534,12 +697684,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -697960,46 +698110,46 @@ ], "projections": { "pca": [ - -0.13092753291130066, - -0.15943214297294617 + -0.1311592012643814, + -0.15865707397460938 ], "tsne": [ - -32.729251861572266, - -7.8661394119262695 + -39.145198822021484, + 7.13863468170166 ], "umap": [ - 0.9309614300727844, - 8.24979019165039 + 1.1574231386184692, + 7.448800563812256 ], "pca3d": [ - -0.13092711567878723, - -0.1594402939081192, - 0.03211419656872749 + -0.1311587542295456, + -0.1586649864912033, + 0.03262951225042343 ], "tsne3d": [ - -20.905412673950195, - -6.725584506988525, - 11.678230285644531 + -22.28157615661621, + 7.135197162628174, + 5.470943450927734 ], "umap3d": [ - 1.020767331123352, - 4.629952907562256, - 5.225165367126465 + 2.3415188789367676, + 5.115772247314453, + 5.898558139801025 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -698426,31 +698576,31 @@ "doi": "10.1101/2021.04.22.440891", "projections": { "pca": [ - 0.2995005249977112, - -0.016356900334358215 + 0.2993219792842865, + -0.017062684521079063 ], "tsne": [ - 51.47188186645508, - -0.5353039503097534 + 55.34244918823242, + 8.944446563720703 ], "umap": [ - 10.480477333068848, - 7.379578590393066 + 10.659327507019043, + 6.99670934677124 ], "pca3d": [ - 0.2995004653930664, - -0.016360556706786156, - -0.11659730970859528 + 0.29932188987731934, + -0.01706654578447342, + -0.11684905737638474 ], "tsne3d": [ - 31.63324546813965, - -1.2511775493621826, - 0.20139586925506592 + 33.44849395751953, + 3.6127021312713623, + -1.538886308670044 ], "umap3d": [ - 7.907447338104248, - 4.938354015350342, - 0.69281405210495 + 7.133176803588867, + 4.578177452087402, + -0.6596589684486389 ] }, "cluster_id": 0, @@ -698461,12 +698611,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -698876,31 +699026,31 @@ "doi": "10.1016/s0168-9525(03)00327-5", "projections": { "pca": [ - 0.03750381991267204, - 0.07203486561775208 + 0.03776776045560837, + 0.0717300996184349 ], "tsne": [ - -16.20701026916504, - 29.86509132385254 + -6.8509345054626465, + -38.002586364746094 ], "umap": [ - 10.050318717956543, - 6.5031962394714355 + 10.184499740600586, + 6.029049396514893 ], "pca3d": [ - 0.03750362619757652, - 0.07203642278909683, - 0.040202267467975616 + 0.037767596542835236, + 0.07173161953687668, + 0.03996962308883667 ], "tsne3d": [ - -3.85841703414917, - 4.080407619476318, - 14.439945220947266 + -5.974174976348877, + -18.46089744567871, + 12.763521194458008 ], "umap3d": [ - 7.94460391998291, - 5.633543968200684, - 1.4187368154525757 + 7.229265213012695, + 5.40516996383667, + -0.13716895878314972 ] }, "cluster_id": 0, @@ -698911,12 +699061,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -699342,31 +699492,31 @@ "openalex_url": "https://openalex.org/W3156793915", "projections": { "pca": [ - 0.16563105583190918, - 0.014212507754564285 + 0.1655731499195099, + 0.013891814276576042 ], "tsne": [ - 32.11662292480469, - -8.678099632263184 + 26.589406967163086, + 14.62519645690918 ], "umap": [ - 8.499075889587402, - 8.088136672973633 + 8.748316764831543, + 7.824865341186523 ], "pca3d": [ - 0.16563086211681366, - 0.014225450344383717, - -0.13046526908874512 + 0.16557297110557556, + 0.013905324973165989, + -0.1305534541606903 ], "tsne3d": [ - 16.579383850097656, - -7.026827335357666, - 2.317460775375366 + 13.492134094238281, + 7.784707546234131, + -9.061638832092285 ], "umap3d": [ - 6.528059005737305, - 4.051538944244385, - 1.6771358251571655 + 6.4746832847595215, + 3.8445522785186768, + 0.9133420586585999 ] }, "cluster_id": 0, @@ -699377,12 +699527,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { "id": 31, - "label": "Genome Cancer" + "label": "Single Cell Seq" } ], "_embedding": [ @@ -699788,46 +699938,46 @@ ], "projections": { "pca": [ - 0.11717140674591064, - 0.1405351459980011 + 0.1178186908364296, + 0.13996371626853943 ], "tsne": [ - -0.05902025103569031, - -6.555112838745117 + 0.11998813599348068, + 5.33243465423584 ], "umap": [ - 5.154979705810547, - 7.336056709289551 + 5.351407527923584, + 6.587630271911621 ], "pca3d": [ - 0.1171707808971405, - 0.14055109024047852, - -0.010569377802312374 + 0.11781809478998184, + 0.13998015224933624, + -0.010692722164094448 ], "tsne3d": [ - -1.6403762102127075, - -0.27331188321113586, - -19.151918411254883 + 1.3251891136169434, + 22.3283748626709, + -3.482018232345581 ], "umap3d": [ - 4.653367042541504, - 3.4697153568267822, - 4.902743339538574 + 5.712335109710693, + 4.659657955169678, + 4.933808326721191 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, + "id": 13, "label": "Genomics Papers" } ], @@ -700248,31 +700398,31 @@ "openalex_url": "https://openalex.org/W3156113107", "projections": { "pca": [ - 0.2209642231464386, - 0.08106829971075058 + 0.22119757533073425, + 0.08072009682655334 ], "tsne": [ - 48.855838775634766, - -7.837984085083008 + 46.16973114013672, + 9.327641487121582 ], "umap": [ - 10.071585655212402, - 8.040239334106445 + 10.234735488891602, + 7.638727188110352 ], "pca3d": [ - 0.22096389532089233, - 0.0810682475566864, - -0.07764717191457748 + 0.22119717299938202, + 0.08071908354759216, + -0.0778653621673584 ], "tsne3d": [ - 27.101167678833008, - -3.825237274169922, - -7.1718220710754395 + 26.6750431060791, + 7.808990478515625, + -0.9234365820884705 ], "umap3d": [ - 6.999718189239502, - 4.878046989440918, - 0.7250532507896423 + 6.4369049072265625, + 4.448139667510986, + -0.3735273778438568 ] }, "cluster_id": 0, @@ -700283,12 +700433,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -700710,47 +700860,47 @@ "doi": "10.1101/2021.04.14.439903", "projections": { "pca": [ - 0.13815005123615265, - -0.11823386698961258 + 0.13789992034435272, + -0.11843899637460709 ], "tsne": [ - 29.91340446472168, - -18.477434158325195 + 22.497621536254883, + 23.246929168701172 ], "umap": [ - 8.448391914367676, - 9.467264175415039 + 8.551042556762695, + 8.984556198120117 ], "pca3d": [ - 0.13815006613731384, - -0.11820903420448303, - -0.062335945665836334 + 0.13789981603622437, + -0.11841416358947754, + -0.06215156242251396 ], "tsne3d": [ - 16.526208877563477, - 11.145465850830078, - 4.454947471618652 + 10.945035934448242, + -6.134303569793701, + -13.595885276794434 ], "umap3d": [ - 5.399409294128418, - 4.527497291564941, - 1.119694709777832 + 4.967806339263916, + 4.1427483558654785, + 0.9370995163917542 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -701176,47 +701326,47 @@ "doi": "10.1101/2021.04.07.438882", "projections": { "pca": [ - 0.2715788185596466, - 0.07748986780643463 + 0.27173900604248047, + 0.07702218741178513 ], "tsne": [ - 34.433265686035156, - 4.462507724761963 + 35.6959114074707, + -2.800832986831665 ], "umap": [ - 8.228347778320312, - 8.127556800842285 + 8.419906616210938, + 7.570493221282959 ], "pca3d": [ - 0.27157866954803467, - 0.0774661973118782, - -0.18048840761184692 + 0.27173885703086853, + 0.07699830830097198, + -0.18066127598285675 ], "tsne3d": [ - 17.22880744934082, - -8.278647422790527, - -7.835413455963135 + 17.060665130615234, + 12.33356761932373, + -0.4454508125782013 ], "umap3d": [ - 7.048266410827637, - 3.804826021194458, - 2.1969335079193115 + 7.073317527770996, + 3.794255256652832, + 1.2590768337249756 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -701633,31 +701783,31 @@ "cited_by_count": 60, "projections": { "pca": [ - 0.09612657874822617, - -0.13836076855659485 + 0.09587148576974869, + -0.1383388340473175 ], "tsne": [ - 14.521804809570312, - 8.067047119140625 + 14.4061861038208, + -0.028751278296113014 ], "umap": [ - 7.459921836853027, - 5.920353412628174 + 7.849742889404297, + 5.62961483001709 ], "pca3d": [ - 0.09612676501274109, - -0.13836388289928436, - 0.04441174864768982 + 0.09587163478136063, + -0.13834227621555328, + 0.044644393026828766 ], "tsne3d": [ - 10.635697364807129, - 9.797417640686035, - -10.754324913024902 + 12.588855743408203, + -3.2081491947174072, + 7.589262962341309 ], "umap3d": [ - 6.304051876068115, - 5.264586448669434, - 3.092153310775757 + 6.37405252456665, + 5.527900695800781, + 1.922611117362976 ] }, "cluster_id": 4, @@ -701668,11 +701818,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -702079,47 +702229,47 @@ ], "projections": { "pca": [ - -0.11131580919027328, - 0.41013047099113464 + -0.11001381278038025, + 0.40992096066474915 ], "tsne": [ - -6.390183925628662, - -6.386274337768555 + -5.2778472900390625, + 4.335214614868164 ], "umap": [ - 4.6662468910217285, - 7.335103988647461 + 4.871577739715576, + 6.631793022155762 ], "pca3d": [ - -0.1113169714808464, - 0.41016414761543274, - -0.022967638447880745 + -0.11001492291688919, + 0.4099549949169159, + -0.023522743955254555 ], "tsne3d": [ - -8.807028770446777, - -4.6205620765686035, - 14.565825462341309 + -5.623387813568115, + 1.983742356300354, + -7.806210994720459 ], "umap3d": [ - 4.239393711090088, - 3.600181818008423, - 4.721745014190674 + 5.212530612945557, + 4.653921604156494, + 4.808856964111328 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, - "label": "Genomics Papers" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -702528,31 +702678,31 @@ "cited_by_count": 92, "projections": { "pca": [ - 0.14368432760238647, - -0.046199630945920944 + 0.14374352991580963, + -0.04655303433537483 ], "tsne": [ - 51.15995407104492, - -7.034115791320801 + 48.4998893737793, + 8.786561012268066 ], "umap": [ - 10.318680763244629, - 7.997277736663818 + 10.564136505126953, + 7.618652820587158 ], "pca3d": [ - 0.14368432760238647, - -0.04621627554297447, - -0.06604073941707611 + 0.1437436193227768, + -0.046570174396038055, + -0.06594791263341904 ], "tsne3d": [ - 30.30477523803711, - -4.157764911651611, - -8.797117233276367 + 30.286897659301758, + 8.777678489685059, + 3.292815923690796 ], "umap3d": [ - 7.231110095977783, - 4.967465400695801, - 0.47269779443740845 + 6.499588966369629, + 4.531015872955322, + -0.593184232711792 ] }, "cluster_id": 0, @@ -702563,12 +702713,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -702976,46 +703126,46 @@ "journal": "Nature Genetics", "projections": { "pca": [ - -0.08927947282791138, - 0.11091925948858261 + -0.08895227313041687, + 0.11123877763748169 ], "tsne": [ - 25.950626373291016, - 5.170721530914307 + 31.804052352905273, + -15.544888496398926 ], "umap": [ - 6.9611616134643555, - 8.016419410705566 + 7.121179580688477, + 7.596843719482422 ], "pca3d": [ - -0.08927947282791138, - 0.11088355630636215, - -0.22660575807094574 + -0.08895230293273926, + 0.11120252311229706, + -0.22655221819877625 ], "tsne3d": [ - 7.957221031188965, - -17.105579376220703, - -12.972166061401367 + 11.648626327514648, + 20.013904571533203, + 6.35795783996582 ], "umap3d": [ - 6.569197177886963, - 3.310835838317871, - 2.956146717071533 + 7.031766891479492, + 3.577277183532715, + 2.2914440631866455 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 26, + "id": 33, "label": "Genetic Disease" } ], @@ -703434,31 +703584,31 @@ "doi": "10.1101/2020.11.23.394478", "projections": { "pca": [ - 0.13031503558158875, - -0.11375188082456589 + 0.1298990398645401, + -0.11390840262174606 ], "tsne": [ - 37.85637283325195, - -17.76902961730957 + 30.986841201782227, + 24.59345054626465 ], "umap": [ - 8.664544105529785, - 8.128276824951172 + 8.981245994567871, + 7.767154693603516 ], "pca3d": [ - 0.13031521439552307, - -0.11374633014202118, - -0.18705838918685913 + 0.12989912927150726, + -0.11390282958745956, + -0.18693190813064575 ], "tsne3d": [ - 20.00288963317871, - 12.501382827758789, - -3.805743455886841 + 17.16005516052246, + -4.666516304016113, + -7.278947353363037 ], "umap3d": [ - 6.515346527099609, - 4.8138275146484375, - 1.44908607006073 + 6.1287922859191895, + 4.574477672576904, + 0.6713355779647827 ] }, "cluster_id": 0, @@ -703469,12 +703619,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -703899,31 +704049,31 @@ "doi": "10.1101/2021.04.07.438649", "projections": { "pca": [ - 0.18855850398540497, - -0.12797178328037262 + 0.18826201558113098, + -0.12803667783737183 ], "tsne": [ - 23.284055709838867, - -1.2268812656402588 + 25.032939910888672, + 2.9369118213653564 ], "umap": [ - 7.713144302368164, - 6.925445079803467 + 7.970831394195557, + 6.495224952697754 ], "pca3d": [ - 0.18855871260166168, - -0.12797559797763824, - -0.05304669961333275 + 0.18826209008693695, + -0.12804050743579865, + -0.052825912833213806 ], "tsne3d": [ - 15.632514953613281, - 1.4051148891448975, - -10.210695266723633 + 11.339882850646973, + 6.833438873291016, + 1.706553339958191 ], "umap3d": [ - 6.534882545471191, - 4.6432719230651855, - 2.766950845718384 + 6.652541637420654, + 4.8399224281311035, + 1.758801817893982 ] }, "cluster_id": 4, @@ -703934,12 +704084,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -704349,31 +704499,31 @@ "journal": "Nature", "projections": { "pca": [ - 0.17496651411056519, - 0.012428134679794312 + 0.17498250305652618, + 0.01165368314832449 ], "tsne": [ - 47.65098190307617, - -5.456887245178223 + 45.64849090576172, + 6.484106540679932 ], "umap": [ - 10.013777732849121, - 8.156389236450195 + 10.0867919921875, + 7.797712326049805 ], "pca3d": [ - 0.17496642470359802, - 0.012405302375555038, - -0.214167058467865 + 0.174982488155365, + 0.011630324646830559, + -0.21420815587043762 ], "tsne3d": [ - 26.726715087890625, - -7.822690963745117, - -9.1431884765625 + 27.02547264099121, + 12.487225532531738, + 1.8446627855300903 ], "umap3d": [ - 7.0898566246032715, - 4.720048427581787, - 0.7080767750740051 + 6.660160541534424, + 4.17775297164917, + -0.32176029682159424 ] }, "cluster_id": 0, @@ -704384,12 +704534,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 13, + "label": "Chromatin Regulation" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -704813,31 +704963,31 @@ "cited_by_count": 4, "projections": { "pca": [ - 0.2498994916677475, - 0.09656493365764618 + 0.25010302662849426, + 0.09573651850223541 ], "tsne": [ - 41.94524002075195, - -4.809981822967529 + 41.45685958862305, + 7.956048011779785 ], "umap": [ - 9.41356372833252, - 8.225981712341309 + 9.765742301940918, + 7.783957004547119 ], "pca3d": [ - 0.2498994916677475, - 0.09654267877340317, - -0.2024645060300827 + 0.2501028776168823, + 0.09571333974599838, + -0.20272155106067657 ], "tsne3d": [ - 21.607097625732422, - -9.756425857543945, - -3.9075329303741455 + 23.7147216796875, + 12.717218399047852, + -3.9691162109375 ], "umap3d": [ - 7.162529945373535, - 4.08646297454834, - 0.9785206913948059 + 6.690280914306641, + 3.821385622024536, + 0.1321793496608734 ] }, "cluster_id": 0, @@ -704848,12 +704998,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -705278,31 +705428,31 @@ "openalex_url": "https://openalex.org/W3144227021", "projections": { "pca": [ - 0.1789867877960205, - 0.05016116425395012 + 0.17921042442321777, + 0.04995175823569298 ], "tsne": [ - 26.69878387451172, - 21.011768341064453 + 33.35846710205078, + -24.37787628173828 ], "umap": [ - 8.48615550994873, - 5.698508262634277 + 8.777746200561523, + 5.3461995124816895 ], "pca3d": [ - 0.17898672819137573, - 0.050152141600847244, - -0.08760514855384827 + 0.17921015620231628, + 0.04994276165962219, + -0.08762674033641815 ], "tsne3d": [ - 14.551554679870605, - 23.519819259643555, - 6.405753135681152 + 20.280447006225586, + -8.556753158569336, + 10.920228004455566 ], "umap3d": [ - 7.5752105712890625, - 4.91615104675293, - 3.2124617099761963 + 7.7237548828125, + 5.364803791046143, + 1.639060616493225 ] }, "cluster_id": 4, @@ -705313,11 +705463,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -705744,31 +705894,31 @@ "doi": "10.1101/2021.04.04.438388", "projections": { "pca": [ - 0.29188206791877747, - 0.06876876205205917 + 0.29208770394325256, + 0.06760799884796143 ], "tsne": [ - 40.29384994506836, - -0.12297044694423676 + 38.7703857421875, + 6.966865539550781 ], "umap": [ - 9.30029010772705, - 8.178561210632324 + 9.54024887084961, + 7.817407608032227 ], "pca3d": [ - 0.29188182950019836, - 0.0687856450676918, - -0.21203801035881042 + 0.2920873463153839, + 0.06762497872114182, + -0.21225666999816895 ], "tsne3d": [ - 22.772409439086914, - -4.455174922943115, - -2.0385806560516357 + 22.00950813293457, + 8.455859184265137, + -3.540950059890747 ], "umap3d": [ - 6.984689235687256, - 4.300346374511719, - 1.168095588684082 + 6.503496170043945, + 4.048585891723633, + 0.29034262895584106 ] }, "cluster_id": 0, @@ -705779,12 +705929,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -706202,31 +706352,31 @@ "doi": "10.1101/2021.03.31.437978", "projections": { "pca": [ - 0.16221177577972412, - -0.11104080826044083 + 0.1618921309709549, + -0.11174257844686508 ], "tsne": [ - 38.0121955871582, - -17.74907112121582 + 31.130718231201172, + 24.606548309326172 ], "umap": [ - 8.695222854614258, - 8.097941398620605 + 8.984105110168457, + 7.633821487426758 ], "pca3d": [ - 0.1622118502855301, - -0.111032634973526, - -0.13636764883995056 + 0.16189216077327728, + -0.1117343083024025, + -0.13631369173526764 ], "tsne3d": [ - 20.380517959594727, - 12.516569137573242, - -4.368407726287842 + 17.769195556640625, + -4.739655494689941, + -7.143611431121826 ], "umap3d": [ - 6.568824768066406, - 4.829833507537842, - 1.4499516487121582 + 6.169618129730225, + 4.637385845184326, + 0.6529072523117065 ] }, "cluster_id": 0, @@ -706237,12 +706387,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -706661,31 +706811,31 @@ "doi": "10.1101/2021.03.31.437992", "projections": { "pca": [ - 0.260494589805603, - -0.016967572271823883 + 0.2603413462638855, + -0.017707817256450653 ], "tsne": [ - 38.0671272277832, - 1.4817036390304565 + 41.503997802734375, + 5.925638198852539 ], "umap": [ - 9.576767921447754, - 7.830941200256348 + 10.004969596862793, + 7.374864101409912 ], "pca3d": [ - 0.2604944705963135, - -0.01696792244911194, - -0.12432599067687988 + 0.26034125685691833, + -0.017708031460642815, + -0.1244930624961853 ], "tsne3d": [ - 18.925539016723633, - -2.4709839820861816, - -4.3182902336120605 + 19.055675506591797, + 6.7157063484191895, + -2.208648443222046 ], "umap3d": [ - 7.18009090423584, - 4.6865997314453125, - 1.264931321144104 + 6.707278728485107, + 4.499969482421875, + 0.05080031976103783 ] }, "cluster_id": 0, @@ -706696,12 +706846,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 20, - "label": "Chromatin Regulation" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -707109,31 +707259,31 @@ "journal": "Nature Genetics", "projections": { "pca": [ - 0.227760449051857, - 0.06931283324956894 + 0.22798147797584534, + 0.06813222914934158 ], "tsne": [ - 40.21260452270508, - -6.8681111335754395 + 38.43861389160156, + 12.305511474609375 ], "umap": [ - 9.21591567993164, - 8.345728874206543 + 9.539230346679688, + 7.92977237701416 ], "pca3d": [ - 0.22776030004024506, - 0.06930474191904068, - -0.2615088224411011 + 0.22798126935958862, + 0.06812389940023422, + -0.2616638243198395 ], "tsne3d": [ - 22.90764045715332, - -6.882309913635254, - 0.2272784411907196 + 24.790943145751953, + 10.07453727722168, + -7.785168170928955 ], "umap3d": [ - 7.243980407714844, - 3.9872303009033203, - 1.1849820613861084 + 6.778166770935059, + 3.7021985054016113, + 0.32251134514808655 ] }, "cluster_id": 0, @@ -707144,12 +707294,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -707569,31 +707719,31 @@ "cited_by_count": 181, "projections": { "pca": [ - 0.3151281476020813, - 0.09386444836854935 + 0.31526216864585876, + 0.09318491071462631 ], "tsne": [ - 46.92719650268555, - 7.85990571975708 + 54.504547119140625, + 4.781639575958252 ], "umap": [ - 9.691803932189941, - 7.2594218254089355 + 9.949197769165039, + 6.936065196990967 ], "pca3d": [ - 0.3151279389858246, - 0.09386160969734192, - -0.1357608288526535 + 0.31526175141334534, + 0.09318166971206665, + -0.13613945245742798 ], "tsne3d": [ - 23.2049617767334, - 1.2961928844451904, - 1.9365423917770386 + 24.51671600341797, + 1.616886854171753, + -6.706094264984131 ], "umap3d": [ - 7.8388261795043945, - 4.723880767822266, - 1.2690045833587646 + 7.305147171020508, + 4.541163921356201, + -0.15657858550548553 ] }, "cluster_id": 0, @@ -707604,12 +707754,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 20, - "label": "Chromatin Regulation" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -708031,31 +708181,31 @@ "doi": "10.1101/2021.02.17.431609", "projections": { "pca": [ - 0.317414253950119, - 0.04980996996164322 + 0.317534476518631, + 0.049006398767232895 ], "tsne": [ - 33.076499938964844, - 23.609786987304688 + 52.8380126953125, + -3.427302122116089 ], "umap": [ - 9.300206184387207, - 6.926854610443115 + 9.412779808044434, + 6.2039794921875 ], "pca3d": [ - 0.31741389632225037, - 0.04981521517038345, - -0.17129826545715332 + 0.3175341486930847, + 0.049011655151844025, + -0.17149926722049713 ], "tsne3d": [ - 18.329395294189453, - 20.822681427001953, - 1.4764999151229858 + 24.15260124206543, + -8.578110694885254, + 7.47282075881958 ], "umap3d": [ - 7.6716413497924805, - 4.721042633056641, - 2.0207366943359375 + 7.413004398345947, + 4.671727657318115, + 0.5112979412078857 ] }, "cluster_id": 0, @@ -708066,11 +708216,11 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 2, + "label": "DNA Regulation" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -708499,31 +708649,31 @@ "doi": "10.1101/2021.03.29.437476", "projections": { "pca": [ - 0.25828099250793457, - 0.10776696354150772 + 0.2584308385848999, + 0.10710559040307999 ], "tsne": [ - 52.59203338623047, - -3.015099287033081 + 53.17518997192383, + 8.608464241027832 ], "umap": [ - 10.552743911743164, - 7.476655006408691 + 10.687114715576172, + 7.179079055786133 ], "pca3d": [ - 0.25828084349632263, - 0.10774722695350647, - -0.15600785613059998 + 0.2584306001663208, + 0.10708531737327576, + -0.1563999205827713 ], "tsne3d": [ - 28.399173736572266, - -2.4865169525146484, - -0.9090810418128967 + 31.852598190307617, + 7.150790691375732, + -5.100837230682373 ], "umap3d": [ - 7.908553600311279, - 4.924378395080566, - 0.6023735404014587 + 7.048165321350098, + 4.585047245025635, + -0.7411838173866272 ] }, "cluster_id": 0, @@ -708534,12 +708684,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -708945,46 +709095,46 @@ ], "projections": { "pca": [ - 0.15268170833587646, - 0.03382246941328049 + 0.15297791361808777, + 0.03329965099692345 ], "tsne": [ - 1.4276018142700195, - -5.702284812927246 + 1.7884283065795898, + 4.839240550994873 ], "umap": [ - 5.3891754150390625, - 7.224831581115723 + 5.588813781738281, + 6.475781440734863 ], "pca3d": [ - 0.15268123149871826, - 0.03384200111031532, - -0.01893072947859764 + 0.15297751128673553, + 0.03332042321562767, + -0.01892135851085186 ], "tsne3d": [ - -1.368506669998169, - -2.0947792530059814, - -16.726112365722656 + 1.88347589969635, + 23.742977142333984, + -1.1657887697219849 ], "umap3d": [ - 4.886623859405518, - 3.423593759536743, - 5.007674694061279 + 5.884759426116943, + 4.722673416137695, + 4.929168701171875 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, + "id": 13, "label": "Genomics Papers" } ], @@ -709406,31 +709556,31 @@ "cited_by_count": 40, "projections": { "pca": [ - 0.1879836767911911, - -0.07090911269187927 + 0.18785296380519867, + -0.07102297246456146 ], "tsne": [ - 20.07143783569336, - 24.69265365600586 + 24.700939178466797, + -25.026899337768555 ], "umap": [ - 7.939535617828369, - 5.35245418548584 + 8.271133422851562, + 4.924365997314453 ], "pca3d": [ - 0.1879834532737732, - -0.07089759409427643, - 0.05057857558131218 + 0.1878528892993927, + -0.07101108133792877, + 0.050636693835258484 ], "tsne3d": [ - 7.994937419891357, - 19.08001708984375, - 1.4834561347961426 + 12.701020240783691, + -10.034263610839844, + 6.041955471038818 ], "umap3d": [ - 7.098571300506592, - 5.566991806030273, - 3.504668712615967 + 7.12337589263916, + 6.011914253234863, + 1.996507167816162 ] }, "cluster_id": 4, @@ -709441,11 +709591,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -709867,47 +710017,47 @@ "doi": "10.1101/2021.03.19.436212", "projections": { "pca": [ - 0.24569165706634521, - 0.08593538403511047 + 0.24582387506961823, + 0.08516301214694977 ], "tsne": [ - 29.778278350830078, - 4.184175491333008 + 35.7348747253418, + -6.25339937210083 ], "umap": [ - 7.496736526489258, - 8.182021141052246 + 7.737270832061768, + 7.826437950134277 ], "pca3d": [ - 0.2456914186477661, - 0.08593461662530899, - -0.26657548546791077 + 0.24582348763942719, + 0.08516214787960052, + -0.26675331592559814 ], "tsne3d": [ - 13.408391952514648, - -11.630334854125977, - -7.567865371704102 + 13.785484313964844, + 14.375472068786621, + -1.608609914779663 ], "umap3d": [ - 6.651285171508789, - 3.434847116470337, - 2.2688076496124268 + 6.820186138153076, + 3.519656181335449, + 1.690131664276123 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -710324,31 +710474,31 @@ "doi": "10.1101/2021.03.17.435909", "projections": { "pca": [ - 0.29060599207878113, - 0.061109788715839386 + 0.2907220721244812, + 0.059978023171424866 ], "tsne": [ - 43.069698333740234, - -11.64976978302002 + 37.47373962402344, + 18.038463592529297 ], "umap": [ - 9.369184494018555, - 8.436593055725098 + 9.612112998962402, + 7.915842056274414 ], "pca3d": [ - 0.29060566425323486, - 0.061124663800001144, - -0.14474090933799744 + 0.2907218039035797, + 0.05999345704913139, + -0.1450396329164505 ], "tsne3d": [ - 26.09967613220215, - -1.8435297012329102, - 7.175570011138916 + 23.66655158996582, + 4.331644535064697, + -13.14130973815918 ], "umap3d": [ - 7.416682243347168, - 4.019110679626465, - 1.129544734954834 + 6.955203533172607, + 3.7590017318725586, + 0.2227517068386078 ] }, "cluster_id": 0, @@ -710359,12 +710509,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -710785,31 +710935,31 @@ "cited_by_count": 67, "projections": { "pca": [ - 0.06211612746119499, - -0.09281846135854721 + 0.06191611662507057, + -0.09285438060760498 ], "tsne": [ - 17.70994758605957, - -4.306518077850342 + 19.077863693237305, + 1.9536638259887695 ], "umap": [ - 7.858108997344971, - 6.4330549240112305 + 8.105938911437988, + 6.158153533935547 ], "pca3d": [ - 0.06211625039577484, - -0.09280873835086823, - -0.040425777435302734 + 0.06191621348261833, + -0.09284409135580063, + -0.040248990058898926 ], "tsne3d": [ - 12.971158981323242, - 5.559533596038818, - -12.927200317382812 + 13.117973327636719, + 0.39005938172340393, + 5.795074939727783 ], "umap3d": [ - 6.2837138175964355, - 5.226901054382324, - 2.6138756275177 + 6.163908958435059, + 5.231234073638916, + 1.581781268119812 ] }, "cluster_id": 4, @@ -710820,12 +710970,12 @@ "label": "RNA Sequence Models" }, { - "id": 3, - "label": "RNA Splicing" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -711251,31 +711401,31 @@ "openalex_url": "https://openalex.org/W1971451591", "projections": { "pca": [ - 0.2550044357776642, - 0.13493183255195618 + 0.2553812861442566, + 0.13437315821647644 ], "tsne": [ - 52.640716552734375, - 6.315800666809082 + 50.29125213623047, + 17.416982650756836 ], "umap": [ - 10.180492401123047, - 6.895275115966797 + 10.323307037353516, + 6.500788688659668 ], "pca3d": [ - 0.2550041377544403, - 0.13492710888385773, - -0.08833179622888565 + 0.25538089871406555, + 0.13436798751354218, + -0.08869112282991409 ], "tsne3d": [ - 26.4652156829834, - 1.1390832662582397, - -0.6222606897354126 + 26.882211685180664, + 2.4571056365966797, + -5.063848495483398 ], "umap3d": [ - 8.197782516479492, - 5.066561222076416, - 1.1594232320785522 + 7.549312591552734, + 4.951303005218506, + -0.3555627465248108 ] }, "cluster_id": 0, @@ -711286,12 +711436,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -711697,31 +711847,31 @@ ], "projections": { "pca": [ - 0.14887826144695282, - 0.1671278178691864 + 0.14946821331977844, + 0.16662415862083435 ], "tsne": [ - 60.429222106933594, - 3.592149019241333 + 48.738487243652344, + 21.70130157470703 ], "umap": [ - 9.87671947479248, - 6.8418707847595215 + 9.999588012695312, + 6.285056114196777 ], "pca3d": [ - 0.14887776970863342, - 0.16714873909950256, - 0.009623561054468155 + 0.14946766197681427, + 0.16664528846740723, + 0.00929182767868042 ], "tsne3d": [ - 30.7412052154541, - 6.90031623840332, - -8.609411239624023 + 24.05500030517578, + -4.231544017791748, + -3.3863730430603027 ], "umap3d": [ - 7.7789764404296875, - 5.3041672706604, - 1.3989778757095337 + 7.227160453796387, + 5.086845874786377, + 0.049912527203559875 ] }, "cluster_id": 0, @@ -711732,12 +711882,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -712145,47 +712295,47 @@ "journal": "Nature Computational Science", "projections": { "pca": [ - 0.09690697491168976, - -0.0715337023139 + 0.09676563739776611, + -0.07197649031877518 ], "tsne": [ - 28.98674964904785, - -24.294240951538086 + 19.60429573059082, + 29.05968475341797 ], "umap": [ - 8.03211498260498, - 9.398395538330078 + 8.086864471435547, + 9.13947582244873 ], "pca3d": [ - 0.09690659493207932, - -0.071483314037323, - -0.19165505468845367 + 0.09676522016525269, + -0.07192504405975342, + -0.1914847493171692 ], "tsne3d": [ - 15.768392562866211, - -10.348711013793945, - 15.509293556213379 + 11.97440242767334, + 0.16377486288547516, + -20.196401596069336 ], "umap3d": [ - 5.3835601806640625, - 3.9333012104034424, - 1.191114068031311 + 5.3722076416015625, + 3.5730292797088623, + 1.1284122467041016 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -712610,31 +712760,31 @@ "doi": "10.1101/2021.03.08.434470", "projections": { "pca": [ - 0.3435139060020447, - 0.0712943896651268 + 0.34361210465431213, + 0.07032246887683868 ], "tsne": [ - 37.30631637573242, - 3.291754722595215 + 41.10231399536133, + 4.873229503631592 ], "umap": [ - 8.98104190826416, - 8.077964782714844 + 9.295180320739746, + 7.761602401733398 ], "pca3d": [ - 0.34351369738578796, - 0.07129435986280441, - -0.14607401192188263 + 0.3436117470264435, + 0.07032252103090286, + -0.14639811217784882 ], "tsne3d": [ - 22.406648635864258, - 5.08668851852417, - 9.322232246398926 + 18.340160369873047, + 9.219006538391113, + -3.1988158226013184 ], "umap3d": [ - 7.092982292175293, - 4.2059855461120605, - 1.495444416999817 + 6.721226215362549, + 3.990034818649292, + 0.531806230545044 ] }, "cluster_id": 0, @@ -712645,12 +712795,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -713075,47 +713225,47 @@ "openalex_url": "https://openalex.org/W3133607277", "projections": { "pca": [ - -0.14050108194351196, - 0.3085877001285553 + -0.13961747288703918, + 0.309490829706192 ], "tsne": [ - -11.0634183883667, - -10.640191078186035 + -15.56701946258545, + 4.829291820526123 ], "umap": [ - 3.7929272651672363, - 7.445724010467529 + 4.0416975021362305, + 6.743697166442871 ], "pca3d": [ - -0.1405019611120224, - 0.30862846970558167, - 0.12141162902116776 + -0.13961827754974365, + 0.30953261256217957, + 0.12101665139198303 ], "tsne3d": [ - -6.209736347198486, - -10.865137100219727, - 15.138994216918945 + -10.83414077758789, + 2.3729803562164307, + -12.293506622314453 ], "umap3d": [ - 3.3667924404144287, - 3.596278667449951, - 4.572751522064209 + 4.351643085479736, + 4.399162292480469, + 4.959686756134033 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -713541,31 +713691,31 @@ "openalex_url": "https://openalex.org/W3128111751", "projections": { "pca": [ - 0.18545207381248474, - 0.06704584509134293 + 0.1856222152709961, + 0.06662767380475998 ], "tsne": [ - 57.17961502075195, - 10.520988464355469 + 56.43914031982422, + 18.97968101501465 ], "umap": [ - 10.515644073486328, - 6.988031387329102 + 10.639351844787598, + 6.519383907318115 ], "pca3d": [ - 0.18545156717300415, - 0.06707344949245453, - -0.01521025225520134 + 0.1856217086315155, + 0.0666552409529686, + -0.015530494973063469 ], "tsne3d": [ - 30.736587524414062, - 7.271263599395752, - 1.9114004373550415 + 33.10181427001953, + -1.1346940994262695, + -2.4348514080047607 ], "umap3d": [ - 8.1103515625, - 5.259011268615723, - 0.8132607936859131 + 7.401508331298828, + 4.9596662521362305, + -0.6754596829414368 ] }, "cluster_id": 0, @@ -713576,12 +713726,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -714000,31 +714150,31 @@ "cited_by_count": 93, "projections": { "pca": [ - 0.10331053286790848, - -0.05606469139456749 + 0.1031513586640358, + -0.056247279047966 ], "tsne": [ - 19.753271102905273, - 9.398049354553223 + 25.72569465637207, + -0.9086596965789795 ], "umap": [ - 7.768671035766602, - 7.126880645751953 + 7.928037166595459, + 6.873812675476074 ], "pca3d": [ - 0.10331089794635773, - -0.056089337915182114, - -0.12657368183135986 + 0.10315157473087311, + -0.056271716952323914, + -0.12643183767795563 ], "tsne3d": [ - 14.80668830871582, - -2.3357045650482178, - -14.259675025939941 + 14.24073314666748, + 8.9042329788208, + 6.485508918762207 ], "umap3d": [ - 6.870476722717285, - 4.3629631996154785, - 2.775182008743286 + 6.987769603729248, + 4.581698417663574, + 1.8469723463058472 ] }, "cluster_id": 4, @@ -714035,12 +714185,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -714448,31 +714598,31 @@ "journal": "Nature Genetics", "projections": { "pca": [ - -0.03659866377711296, - -0.10093985497951508 + -0.03683527931571007, + -0.1010785773396492 ], "tsne": [ - 14.903901100158691, - -6.27040958404541 + 15.518665313720703, + 7.76503849029541 ], "umap": [ - 8.592986106872559, - 6.41674280166626 + 8.801749229431152, + 6.10502815246582 ], "pca3d": [ - -0.036598723381757736, - -0.10091616958379745, - -0.059926047921180725 + -0.036835312843322754, + -0.1010546162724495, + -0.059711962938308716 ], "tsne3d": [ - 17.697681427001953, - 5.967761039733887, - -14.835247993469238 + 16.85384750366211, + 1.6166472434997559, + 8.924381256103516 ], "umap3d": [ - 6.595649242401123, - 5.489584922790527, - 2.1831281185150146 + 6.303292751312256, + 5.369384288787842, + 0.9860092401504517 ] }, "cluster_id": 4, @@ -714483,12 +714633,12 @@ "label": "RNA Sequence Models" }, { - "id": 3, - "label": "RNA Splicing" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -714896,31 +715046,31 @@ "journal": "Nature", "projections": { "pca": [ - 0.17669296264648438, - -0.05790996178984642 + 0.17671135067939758, + -0.058218494057655334 ], "tsne": [ - 34.79831314086914, - 17.770063400268555 + 20.942380905151367, + 7.095003128051758 ], "umap": [ - 8.144096374511719, - 6.619087219238281 + 8.397493362426758, + 6.305883407592773 ], "pca3d": [ - 0.17669275403022766, - -0.057906586676836014, - 0.05789170786738396 + 0.17671126127243042, + -0.05821458250284195, + 0.0579683855175972 ], "tsne3d": [ - 13.372102737426758, - 6.762771129608154, - -7.108448028564453 + 15.99055290222168, + 1.577074646949768, + 14.07894515991211 ], "umap3d": [ - 6.498894691467285, - 5.083420276641846, - 2.5266940593719482 + 6.531870365142822, + 5.176779270172119, + 1.4659370183944702 ] }, "cluster_id": 4, @@ -714931,12 +715081,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -715344,31 +715494,31 @@ "journal": "Nature Reviews Genetics", "projections": { "pca": [ - 0.18210963904857635, - 0.10718245804309845 + 0.18248653411865234, + 0.10643590241670609 ], "tsne": [ - 49.59701156616211, - -6.190891265869141 + 47.27592468261719, + 7.6738739013671875 ], "umap": [ - 10.276782035827637, - 8.055737495422363 + 10.443860054016113, + 7.692986965179443 ], "pca3d": [ - 0.18210962414741516, - 0.10716890543699265, - -0.1195542961359024 + 0.18248629570007324, + 0.10642170161008835, + -0.11975100636482239 ], "tsne3d": [ - 29.136539459228516, - -5.736771106719971, - -9.397059440612793 + 29.163990020751953, + 10.46473503112793, + 3.045076608657837 ], "umap3d": [ - 7.152177810668945, - 4.87974214553833, - 0.5325828790664673 + 6.523812770843506, + 4.393006324768066, + -0.5796961784362793 ] }, "cluster_id": 0, @@ -715379,12 +715529,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -715804,31 +715954,31 @@ "doi": "10.1101/2021.03.08.434372", "projections": { "pca": [ - 0.1197335347533226, - 0.10356251895427704 + 0.12010881304740906, + 0.10351051390171051 ], "tsne": [ - 3.422870397567749, - 1.481929063796997 + 4.995425224304199, + -1.11308753490448 ], "umap": [ - 6.515416145324707, - 6.194687366485596 + 6.801104545593262, + 5.860978603363037 ], "pca3d": [ - 0.11973321437835693, - 0.10356930643320084, - 0.02606053836643696 + 0.12010852992534637, + 0.10351814329624176, + 0.02590833604335785 ], "tsne3d": [ - 6.156297206878662, - -0.9305773377418518, - -1.1134002208709717 + 6.722414970397949, + -5.428531169891357, + -7.36120080947876 ], "umap3d": [ - 5.978359222412109, - 4.733099460601807, - 3.909747838973999 + 6.312981128692627, + 5.387093544006348, + 2.8601856231689453 ] }, "cluster_id": 4, @@ -715839,11 +715989,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -716259,31 +716409,31 @@ "doi": "10.1101/2021.03.08.434469", "projections": { "pca": [ - 0.26806262135505676, - 0.04222797602415085 + 0.2681024670600891, + 0.04186873510479927 ], "tsne": [ - 50.435787200927734, - 0.36637648940086365 + 55.887115478515625, + 6.898521900177002 ], "umap": [ - 10.3675537109375, - 7.343963623046875 + 10.575346946716309, + 6.977553844451904 ], "pca3d": [ - 0.2680622935295105, - 0.04222365468740463, - -0.0818759873509407 + 0.26810222864151, + 0.04186422377824783, + -0.08212484419345856 ], "tsne3d": [ - 32.78264617919922, - -3.6288421154022217, - 1.6468472480773926 + 32.80662536621094, + 1.6426619291305542, + 0.2041495144367218 ], "umap3d": [ - 7.9014387130737305, - 5.090345859527588, - 0.7647948861122131 + 7.082958221435547, + 4.701103210449219, + -0.5941068530082703 ] }, "cluster_id": 0, @@ -716294,12 +716444,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 27, + "label": "Regulatory Elements" } ], "_embedding": [ @@ -716718,31 +716868,31 @@ "doi": "10.1299/transjsme.21-00156", "projections": { "pca": [ - -0.07683435082435608, - 0.17328163981437683 + -0.07626800239086151, + 0.17321263253688812 ], "tsne": [ - -24.91135597229004, - 9.14147663116455 + -27.996742248535156, + 32.27696228027344 ], "umap": [ - 2.6587634086608887, - 6.3537774085998535 + 3.1218440532684326, + 5.75566291809082 ], "pca3d": [ - -0.07683452218770981, - 0.17326892912387848, - -0.01799049600958824 + -0.07626815885305405, + 0.17319978773593903, + -0.01828388124704361 ], "tsne3d": [ - -14.756169319152832, - 19.823495864868164, - -9.00599193572998 + -5.733575820922852, + -16.585895538330078, + -13.654363632202148 ], "umap3d": [ - 2.3394460678100586, - 5.461780548095703, - 4.567528247833252 + 3.0606014728546143, + 6.033961772918701, + 4.745846748352051 ] }, "cluster_id": 2, @@ -716753,12 +716903,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 33, - "label": "Neural Networks" + "id": 19, + "label": "Graph Neural Nets" } ], "_embedding": [ @@ -717179,31 +717329,31 @@ "cited_by_count": 121, "projections": { "pca": [ - 0.1916930228471756, - -0.025286974385380745 + 0.19158372282981873, + -0.02567538619041443 ], "tsne": [ - 56.24007797241211, - 7.559605598449707 + 53.96806335449219, + 19.15922737121582 ], "umap": [ - 10.391626358032227, - 6.813472270965576 + 10.54157543182373, + 6.3799147605896 ], "pca3d": [ - 0.19169291853904724, - -0.02527150698006153, - -0.0072017633356153965 + 0.1915835738182068, + -0.025659838691353798, + -0.007389584090560675 ], "tsne3d": [ - 28.370527267456055, - 7.106648921966553, - 0.1811746507883072 + 30.373310089111328, + -2.7321724891662598, + -3.9543707370758057 ], "umap3d": [ - 8.225049018859863, - 5.331614017486572, - 1.1446421146392822 + 7.534564971923828, + 5.156941890716553, + -0.5186350345611572 ] }, "cluster_id": 0, @@ -717214,12 +717364,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -717627,31 +717777,31 @@ "journal": "Nature", "projections": { "pca": [ - 0.23994767665863037, - 0.07437532395124435 + 0.24024708569049835, + 0.07347336411476135 ], "tsne": [ - 18.134748458862305, - 29.784210205078125 + 22.097152709960938, + -28.643564224243164 ], "umap": [ - 7.8902587890625, - 4.916094779968262 + 8.282413482666016, + 4.3416829109191895 ], "pca3d": [ - 0.23994718492031097, - 0.07439319789409637, - 0.012620314955711365 + 0.24024666845798492, + 0.07349170744419098, + 0.012388039380311966 ], "tsne3d": [ - 1.827236294746399, - 23.72728157043457, - 0.4736731946468353 + 13.550394058227539, + -15.527673721313477, + 3.477630853652954 ], "umap3d": [ - 7.158994674682617, - 5.930027008056641, - 3.673414707183838 + 7.2290191650390625, + 6.483531475067139, + 2.1301465034484863 ] }, "cluster_id": 4, @@ -717662,11 +717812,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -718086,47 +718236,47 @@ "cited_by_count": 35, "projections": { "pca": [ - 0.22991670668125153, - 0.016036419197916985 + 0.22998331487178802, + 0.015500701032578945 ], "tsne": [ - 31.678010940551758, - 6.579047679901123 + 35.02510452270508, + -9.1901273727417 ], "umap": [ - 7.620438575744629, - 8.173717498779297 + 7.903982162475586, + 7.713150978088379 ], "pca3d": [ - 0.22991658747196198, - 0.01602291874587536, - -0.12785856425762177 + 0.22998324036598206, + 0.015487207099795341, + -0.1279023438692093 ], "tsne3d": [ - 16.99190330505371, - -12.462302207946777, - -12.118854522705078 + 16.61884880065918, + 18.077119827270508, + 1.9098947048187256 ], "umap3d": [ - 7.018059253692627, - 3.303912878036499, - 2.466478109359741 + 7.287475109100342, + 3.513964891433716, + 1.8203727006912231 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -718548,31 +718698,31 @@ "cited_by_count": 139, "projections": { "pca": [ - 0.20006127655506134, - -0.09678403288125992 + 0.1998232901096344, + -0.09757117927074432 ], "tsne": [ - 23.00810432434082, - 31.733989715576172 + 26.2387638092041, + -30.726028442382812 ], "umap": [ - 8.397058486938477, - 4.900867938995361 + 8.746731758117676, + 4.498493671417236 ], "pca3d": [ - 0.2000608891248703, - -0.09674473106861115, - 0.001319896662607789 + 0.19982294738292694, + -0.09753081947565079, + 0.0013051357818767428 ], "tsne3d": [ - 6.522327899932861, - 25.772850036621094, - -0.6235050559043884 + 16.14036750793457, + -17.97429656982422, + 7.446613311767578 ], "umap3d": [ - 7.539653778076172, - 5.942531585693359, - 3.6295769214630127 + 7.5649495124816895, + 6.474420547485352, + 1.7657331228256226 ] }, "cluster_id": 4, @@ -718583,11 +718733,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -718994,46 +719144,46 @@ ], "projections": { "pca": [ - 0.020665528252720833, - 0.229755237698555 + 0.02170965075492859, + 0.22901512682437897 ], "tsne": [ - -0.4117428660392761, - -8.193099021911621 + -1.1531214714050293, + 6.174599647521973 ], "umap": [ - 5.050797939300537, - 7.362601280212402 + 5.302721977233887, + 6.631722450256348 ], "pca3d": [ - 0.020664764568209648, - 0.22977817058563232, - -0.019450213760137558 + 0.02170892432332039, + 0.22903838753700256, + -0.01961948536336422 ], "tsne3d": [ - -2.0851564407348633, - -1.8599281311035156, - -22.129499435424805 + -0.6968587040901184, + 24.06191062927246, + -5.514656066894531 ], "umap3d": [ - 4.610353469848633, - 3.436535358428955, - 4.918703079223633 + 5.7352681159973145, + 4.662278652191162, + 4.967221736907959 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, + "id": 13, "label": "Genomics Papers" } ], @@ -719444,31 +719594,31 @@ "doi": "10.1016/j.cell.2016.01.004", "projections": { "pca": [ - 0.1739872395992279, - 0.1435081511735916 + 0.17423680424690247, + 0.1432894915342331 ], "tsne": [ - 54.95693588256836, - 4.509274959564209 + 53.1692008972168, + 16.048105239868164 ], "umap": [ - 10.30955982208252, - 6.905428886413574 + 10.485966682434082, + 6.426170825958252 ], "pca3d": [ - 0.17398719489574432, - 0.1434943675994873, - -0.0786566436290741 + 0.17423655092716217, + 0.14327473938465118, + -0.07907558977603912 ], "tsne3d": [ - 31.06910514831543, - 4.5611677169799805, - -5.113670349121094 + 28.436288833618164, + 1.0895850658416748, + -1.5759406089782715 ], "umap3d": [ - 8.058104515075684, - 5.329996585845947, - 1.0689995288848877 + 7.337784767150879, + 5.057870388031006, + -0.41423773765563965 ] }, "cluster_id": 0, @@ -719479,12 +719629,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -719912,31 +720062,31 @@ "openalex_url": "https://openalex.org/W3123079145", "projections": { "pca": [ - 0.25482943654060364, - 0.15127626061439514 + 0.2551017701625824, + 0.15093562006950378 ], "tsne": [ - 47.88408660888672, - -7.395666599273682 + 48.06760787963867, + 5.321828365325928 ], "umap": [ - 9.933003425598145, - 7.815084934234619 + 10.15298080444336, + 7.50079870223999 ], "pca3d": [ - 0.2548287808895111, - 0.1512821763753891, - -0.13055387139320374 + 0.25510135293006897, + 0.1509411633014679, + -0.1309320330619812 ], "tsne3d": [ - 29.84627342224121, - -8.708324432373047, - -6.1606645584106445 + 30.64727020263672, + 13.276018142700195, + 0.21491503715515137 ], "umap3d": [ - 7.1740593910217285, - 4.8401970863342285, - 0.8429385423660278 + 6.574703693389893, + 4.520597457885742, + -0.20093469321727753 ] }, "cluster_id": 0, @@ -719947,12 +720097,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 4, - "label": "Enhancer Regulation" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -720378,31 +720528,31 @@ "doi": "10.1101/2021.02.17.431699", "projections": { "pca": [ - 0.287291944026947, - 0.13465963304042816 + 0.287649929523468, + 0.13359472155570984 ], "tsne": [ - 39.22412872314453, - -6.034906387329102 + 38.73287582397461, + 10.17182731628418 ], "umap": [ - 9.085723876953125, - 8.284126281738281 + 9.407146453857422, + 7.872799873352051 ], "pca3d": [ - 0.2872914671897888, - 0.13464361429214478, - -0.218514084815979 + 0.287649542093277, + 0.13357828557491302, + -0.21881462633609772 ], "tsne3d": [ - 20.406343460083008, - -7.377141952514648, - 0.482544869184494 + 22.57674217224121, + 9.282172203063965, + -7.0002217292785645 ], "umap3d": [ - 7.207015514373779, - 3.9749951362609863, - 1.3709795475006104 + 6.805850028991699, + 3.7061409950256348, + 0.44282734394073486 ] }, "cluster_id": 0, @@ -720413,12 +720563,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -720826,31 +720976,31 @@ "journal": "Nature Genetics", "projections": { "pca": [ - 0.15527670085430145, - -0.04308638721704483 + 0.15519818663597107, + -0.04376732185482979 ], "tsne": [ - 58.12535095214844, - 1.4783086776733398 + 46.73994064331055, + 20.49622917175293 ], "umap": [ - 9.457876205444336, - 6.581540584564209 + 9.664083480834961, + 6.146508693695068 ], "pca3d": [ - 0.15527641773223877, - -0.04306413605809212, - 0.02236347645521164 + 0.15519793331623077, + -0.04374489188194275, + 0.0222656037658453 ], "tsne3d": [ - 24.33281707763672, - 5.59759521484375, - -7.094913482666016 + 24.66826820373535, + -1.7467150688171387, + 2.421492099761963 ], "umap3d": [ - 7.329830646514893, - 5.568474769592285, - 1.6515270471572876 + 6.756003379821777, + 5.392378807067871, + 0.3081497251987457 ] }, "cluster_id": 0, @@ -720861,12 +721011,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -721289,31 +721439,31 @@ "doi": "10.1101/2021.02.17.430503", "projections": { "pca": [ - 0.2217412143945694, - -0.07501139491796494 + 0.2214912325143814, + -0.07492244988679886 ], "tsne": [ - 44.142539978027344, - 10.871339797973633 + 30.320838928222656, + 3.721050500869751 ], "umap": [ - 8.774338722229004, - 6.885345935821533 + 8.899388313293457, + 6.477691173553467 ], "pca3d": [ - 0.22174134850502014, - -0.07502534240484238, - 0.08023880422115326 + 0.22149133682250977, + -0.07493648678064346, + 0.080210380256176 ], "tsne3d": [ - 16.639507293701172, - 6.439174652099609, - -2.348966121673584 + 20.36397361755371, + 4.228645324707031, + 6.481225490570068 ], "umap3d": [ - 7.126793384552002, - 5.098117351531982, - 2.163970947265625 + 6.816890716552734, + 5.028047561645508, + 0.8208601474761963 ] }, "cluster_id": 0, @@ -721324,12 +721474,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -721748,31 +721898,31 @@ "doi": "10.1101/2021.02.13.431115", "projections": { "pca": [ - 0.14020861685276031, - -0.1267748922109604 + 0.13990730047225952, + -0.12686434388160706 ], "tsne": [ - 63.88068771362305, - -3.0298492908477783 + 46.23190689086914, + 28.550071716308594 ], "umap": [ - 9.494075775146484, - 6.361989498138428 + 9.76870059967041, + 5.841870307922363 ], "pca3d": [ - 0.1402086615562439, - -0.12677380442619324, - 0.025452539324760437 + 0.13990746438503265, + -0.1268632411956787, + 0.025582045316696167 ], "tsne3d": [ - 24.448514938354492, - 13.604701042175293, - -8.03872013092041 + 26.88919448852539, + -8.118221282958984, + 3.2172646522521973 ], "umap3d": [ - 7.592254638671875, - 5.738577365875244, - 1.8901243209838867 + 7.055191993713379, + 5.592136859893799, + 0.3212350904941559 ] }, "cluster_id": 0, @@ -721783,12 +721933,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -722194,46 +722344,46 @@ ], "projections": { "pca": [ - 0.11744333803653717, - 0.14864535629749298 + 0.11810726672410965, + 0.14801786839962006 ], "tsne": [ - -0.3173159062862396, - -6.591177940368652 + -0.1292373239994049, + 5.25266170501709 ], "umap": [ - 5.103370189666748, - 7.345005512237549 + 5.2902398109436035, + 6.614059925079346 ], "pca3d": [ - 0.11744270473718643, - 0.14866144955158234, - -0.0035215215757489204 + 0.11810667812824249, + 0.1480344831943512, + -0.003677214030176401 ], "tsne3d": [ - -2.815093994140625, - -1.0784473419189453, - -18.97722816467285 + 0.07516367733478546, + 22.94594383239746, + -3.7367756366729736 ], "umap3d": [ - 4.674788475036621, - 3.4525163173675537, - 4.915938854217529 + 5.735854148864746, + 4.688867092132568, + 4.927248477935791 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, + "id": 13, "label": "Genomics Papers" } ], @@ -722655,31 +722805,31 @@ "doi": "10.1101/2021.02.02.429064", "projections": { "pca": [ - 0.15417411923408508, - -0.042885083705186844 + 0.1540108323097229, + -0.04338253661990166 ], "tsne": [ - 31.808368682861328, - -2.754605531692505 + 33.608070373535156, + 1.4701498746871948 ], "umap": [ - 8.112616539001465, - 7.527268409729004 + 8.266799926757812, + 7.168856620788574 ], "pca3d": [ - 0.15417413413524628, - -0.04289283975958824, - -0.16920284926891327 + 0.15401090681552887, + -0.043390579521656036, + -0.1691725254058838 ], "tsne3d": [ - 16.626285552978516, - -2.573941707611084, - -7.92252779006958 + 16.48320960998535, + 8.9912748336792, + 1.4971284866333008 ], "umap3d": [ - 6.832580089569092, - 4.263431072235107, - 2.2351579666137695 + 6.904252052307129, + 4.2161173820495605, + 1.2992064952850342 ] }, "cluster_id": 0, @@ -722690,12 +722840,12 @@ "label": "Chromatin Regulation" }, { - "id": 13, - "label": "Genetic Variants" + "id": 2, + "label": "DNA Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -723110,31 +723260,31 @@ "doi": "10.1101/2021.01.30.428941", "projections": { "pca": [ - 0.2065304070711136, - -0.04970413073897362 + 0.20643925666809082, + -0.050013795495033264 ], "tsne": [ - 23.77422332763672, - 30.022119522094727 + 27.1157283782959, + -28.861263275146484 ], "umap": [ - 8.56450366973877, - 5.084700107574463 + 8.84717845916748, + 4.639601707458496 ], "pca3d": [ - 0.20653054118156433, - -0.04969717189669609, - 0.13912980258464813 + 0.20643924176692963, + -0.05000666156411171, + 0.13898704946041107 ], "tsne3d": [ - 7.825819969177246, - 23.237621307373047, - 0.18972066044807434 + 18.326431274414062, + -17.82604217529297, + 9.622113227844238 ], "umap3d": [ - 7.711043357849121, - 5.892233848571777, - 3.377685785293579 + 7.616587162017822, + 6.360137462615967, + 1.4824693202972412 ] }, "cluster_id": 4, @@ -723145,11 +723295,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -723558,31 +723708,31 @@ "journal": "Nature", "projections": { "pca": [ - 0.21668162941932678, - -0.03729630261659622 + 0.2167903631925583, + -0.03828497603535652 ], "tsne": [ - 32.918617248535156, - 12.73877239227295 + 34.433834075927734, + -1.1327638626098633 ], "umap": [ - 9.832413673400879, - 6.73563289642334 + 10.142898559570312, + 6.2307515144348145 ], "pca3d": [ - 0.2166813164949417, - -0.037298545241355896, - -0.08989857137203217 + 0.21679021418094635, + -0.03828702121973038, + -0.08985107392072678 ], "tsne3d": [ - 18.90200424194336, - -4.628658294677734, - -11.235509872436523 + 18.108661651611328, + 10.55630874633789, + 4.883275032043457 ], "umap3d": [ - 7.996756553649902, - 5.0935750007629395, - 1.6615147590637207 + 7.5413384437561035, + 5.0552592277526855, + 0.2591361999511719 ] }, "cluster_id": 0, @@ -723593,12 +723743,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -724018,31 +724168,31 @@ "cited_by_count": 60, "projections": { "pca": [ - 0.14571405947208405, - -0.15735569596290588 + 0.14523079991340637, + -0.15798860788345337 ], "tsne": [ - 61.56267166137695, - -0.33559125661849976 + 46.905948638916016, + 24.94590950012207 ], "umap": [ - 9.695462226867676, - 6.374967098236084 + 9.922353744506836, + 5.929361820220947 ], "pca3d": [ - 0.1457141488790512, - -0.1573348343372345, - -0.093726247549057 + 0.14523082971572876, + -0.1579671949148178, + -0.09365430474281311 ], "tsne3d": [ - 24.001571655273438, - 8.7595796585083, - -9.000123977661133 + 23.00006866455078, + -5.174160003662109, + 1.6984703540802002 ], "umap3d": [ - 7.614871025085449, - 5.782158374786377, - 1.698142647743225 + 6.976927280426025, + 5.563626289367676, + 0.1818631887435913 ] }, "cluster_id": 0, @@ -724053,12 +724203,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -724466,31 +724616,31 @@ "journal": "Nature Genetics", "projections": { "pca": [ - 0.21561099588871002, - 0.04499255493283272 + 0.21560460329055786, + 0.044204313308000565 ], "tsne": [ - 24.324857711791992, - 32.63885498046875 + 28.35230827331543, + -32.593299865722656 ], "umap": [ - 8.432950973510742, - 5.037003993988037 + 8.850929260253906, + 4.651914596557617 ], "pca3d": [ - 0.21561060845851898, - 0.04501701146364212, - -0.0648331418633461 + 0.21560415625572205, + 0.04422951489686966, + -0.06509959697723389 ], "tsne3d": [ - 6.446701526641846, - 28.441722869873047, - 0.3059839606285095 + 16.89643096923828, + -20.416040420532227, + 7.058473587036133 ], "umap3d": [ - 7.570141792297363, - 5.932389736175537, - 3.558295488357544 + 7.56313419342041, + 6.321550369262695, + 1.6588231325149536 ] }, "cluster_id": 4, @@ -724501,11 +724651,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -724914,31 +725064,31 @@ "journal": "Nature", "projections": { "pca": [ - 0.16543421149253845, - 0.09097030013799667 + 0.16587746143341064, + 0.08965681493282318 ], "tsne": [ - 41.739776611328125, - -8.08191967010498 + 39.6909294128418, + 14.551774024963379 ], "umap": [ - 9.32355785369873, - 8.460888862609863 + 9.65397834777832, + 7.973285675048828 ], "pca3d": [ - 0.16543394327163696, - 0.09097372740507126, - -0.21822795271873474 + 0.16587713360786438, + 0.08966078609228134, + -0.21835248172283173 ], "tsne3d": [ - 24.459739685058594, - -5.462131977081299, - 3.451828718185425 + 24.196880340576172, + 8.62578296661377, + -10.851765632629395 ], "umap3d": [ - 7.512635231018066, - 4.005420684814453, - 1.1187015771865845 + 7.008931636810303, + 3.6703171730041504, + 0.21523873507976532 ] }, "cluster_id": 0, @@ -724949,12 +725099,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -725377,31 +725527,31 @@ "doi": "10.1101/2021.01.18.427139", "projections": { "pca": [ - 0.2475789487361908, - 0.015945490449666977 + 0.24758151173591614, + 0.015219527296721935 ], "tsne": [ - 32.88014602661133, - -11.499795913696289 + 28.0235595703125, + 17.307567596435547 ], "umap": [ - 8.916104316711426, - 8.300562858581543 + 9.101015090942383, + 7.931257247924805 ], "pca3d": [ - 0.24757874011993408, - 0.015961715951561928, - -0.13914011418819427 + 0.24758124351501465, + 0.015235993079841137, + -0.13928312063217163 ], "tsne3d": [ - 19.922767639160156, - 7.148664951324463, - 11.40450668334961 + 18.401927947998047, + 9.664329528808594, + -7.762744903564453 ], "umap3d": [ - 6.745273113250732, - 4.134401798248291, - 1.3176343441009521 + 6.3028764724731445, + 3.833357572555542, + 0.6321929097175598 ] }, "cluster_id": 0, @@ -725412,12 +725562,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -725825,46 +725975,46 @@ "journal": "Nature Biotechnology", "projections": { "pca": [ - 0.08565791696310043, - -0.015497553162276745 + 0.08578794449567795, + -0.01657920330762863 ], "tsne": [ - 26.80117416381836, - -24.123252868652344 + 17.465103149414062, + 28.05198860168457 ], "umap": [ - 7.735631942749023, - 9.54470157623291 + 7.881617546081543, + 9.18218994140625 ], "pca3d": [ - 0.08565749228000641, - -0.015463750809431076, - -0.20107226073741913 + 0.08578754216432571, + -0.016544664278626442, + -0.2009904682636261 ], "tsne3d": [ - 13.407234191894531, - -8.217230796813965, - 13.074905395507812 + 9.760991096496582, + 1.0428937673568726, + -17.192459106445312 ], "umap3d": [ - 5.197107791900635, - 3.7147793769836426, - 1.2357277870178223 + 5.209893703460693, + 3.413463592529297, + 1.3289353847503662 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -726285,31 +726435,31 @@ "cited_by_count": 99, "projections": { "pca": [ - 0.18048854172229767, - -0.15339449048042297 + 0.18004286289215088, + -0.15377451479434967 ], "tsne": [ - 60.39354705810547, - -0.3138372600078583 + 46.11835861206055, + 23.98644256591797 ], "umap": [ - 9.368780136108398, - 6.402192115783691 + 9.52567195892334, + 5.986316204071045 ], "pca3d": [ - 0.18048863112926483, - -0.1533779501914978, - -0.05582091584801674 + 0.18004286289215088, + -0.15375766158103943, + -0.05571720376610756 ], "tsne3d": [ - 22.907974243164062, - 7.662744998931885, - -9.443187713623047 + 21.845443725585938, + -4.081282138824463, + 1.8182380199432373 ], "umap3d": [ - 7.326596260070801, - 5.6258721351623535, - 1.8467541933059692 + 6.811320781707764, + 5.489907264709473, + 0.3867070972919464 ] }, "cluster_id": 0, @@ -726320,12 +726470,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 2, + "label": "DNA Regulation" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -726733,31 +726883,31 @@ "journal": "Nature", "projections": { "pca": [ - -0.2260374277830124, - 0.06534644961357117 + -0.2259124368429184, + 0.06526301056146622 ], "tsne": [ - -47.43883514404297, - 8.565001487731934 + -47.45939254760742, + -16.325761795043945 ], "umap": [ - 1.513666033744812, - 5.509342670440674 + 1.9277960062026978, + 4.673885822296143 ], "pca3d": [ - -0.22603751718997955, - 0.06537105143070221, - -0.06639229506254196 + -0.2259126454591751, + 0.06528854370117188, + -0.06650155037641525 ], "tsne3d": [ - -22.981121063232422, - -2.0761988162994385, - -10.962059020996094 + -20.470678329467773, + -16.261878967285156, + -7.2268595695495605 ], "umap3d": [ - 1.6099573373794556, - 6.525577545166016, - 3.964179754257202 + 1.946000576019287, + 6.681290149688721, + 4.170295238494873 ] }, "cluster_id": 2, @@ -726768,12 +726918,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 22, + "label": "Diffusion Models" } ], "_embedding": [ @@ -727196,47 +727346,47 @@ "cited_by_count": 218, "projections": { "pca": [ - 0.2191501408815384, - 0.08465638756752014 + 0.21935684978961945, + 0.08430284261703491 ], "tsne": [ - 38.92380905151367, - 16.976572036743164 + 43.48541259765625, + -9.655152320861816 ], "umap": [ - 8.174242973327637, - 7.664690017700195 + 8.224130630493164, + 7.296710968017578 ], "pca3d": [ - 0.2191501259803772, - 0.0846206396818161, - -0.05026305094361305 + 0.21935689449310303, + 0.08426666259765625, + -0.05047767236828804 ], "tsne3d": [ - 16.483898162841797, - -11.58664608001709, - -3.4675846099853516 + 19.904781341552734, + 13.768096923828125, + 11.005889892578125 ], "umap3d": [ - 7.440893173217773, - 4.01059627532959, - 2.5199217796325684 + 7.484270095825195, + 4.2299604415893555, + 1.3465532064437866 ] }, - "cluster_id": 0, - "cluster_label": "Chromatin Regulation", + "cluster_id": 6, + "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 0, - "label": "Chromatin Regulation" + "id": 6, + "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 31, - "label": "Genome Cancer" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -727654,31 +727804,31 @@ "cited_by_count": 1229, "projections": { "pca": [ - 0.1488530933856964, - -0.12763819098472595 + 0.14878076314926147, + -0.12769749760627747 ], "tsne": [ - 10.96242904663086, - 9.804920196533203 + 13.341968536376953, + -6.098292350769043 ], "umap": [ - 7.052276134490967, - 5.842027187347412 + 7.28189754486084, + 5.587215900421143 ], "pca3d": [ - 0.14885325729846954, - -0.1276543140411377, - 0.04697709158062935 + 0.14878103137016296, + -0.12771384418010712, + 0.04733387380838394 ], "tsne3d": [ - 6.121366500854492, - 8.435952186584473, - -9.498198509216309 + 8.060260772705078, + -0.5627537965774536, + 3.657892942428589 ], "umap3d": [ - 6.0252838134765625, - 5.219006538391113, - 3.5564041137695312 + 6.314260005950928, + 5.517622947692871, + 2.37971568107605 ] }, "cluster_id": 4, @@ -727689,11 +727839,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -728100,46 +728250,46 @@ ], "projections": { "pca": [ - 0.15275800228118896, - 0.032278914004564285 + 0.15304440259933472, + 0.03175567835569382 ], "tsne": [ - 1.4780950546264648, - -5.767511367797852 + 1.7674169540405273, + 4.859730243682861 ], "umap": [ - 5.322303771972656, - 7.356143951416016 + 5.5909423828125, + 6.54805850982666 ], "pca3d": [ - 0.15275758504867554, - 0.03229691833257675, - -0.021033065393567085 + 0.1530439704656601, + 0.03177494555711746, + -0.021020760759711266 ], "tsne3d": [ - -0.19351619482040405, - -1.7803043127059937, - -16.986881256103516 + 2.989978551864624, + 23.924747467041016, + -1.5844749212265015 ], "umap3d": [ - 4.858579635620117, - 3.393907070159912, - 5.019993782043457 + 5.873155117034912, + 4.6978278160095215, + 4.947658061981201 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, + "id": 13, "label": "Genomics Papers" } ], @@ -728546,46 +728696,46 @@ ], "projections": { "pca": [ - 0.10844259709119797, - 0.11680977046489716 + 0.10910794883966446, + 0.11637222766876221 ], "tsne": [ - 1.5168896913528442, - -7.710975646972656 + 1.3913623094558716, + 6.704718112945557 ], "umap": [ - 5.238700866699219, - 7.373603820800781 + 5.470046043395996, + 6.666303634643555 ], "pca3d": [ - 0.10844213515520096, - 0.11681856960058212, - 0.026368753984570503 + 0.10910753160715103, + 0.11638149619102478, + 0.026348767802119255 ], "tsne3d": [ - 0.9575759172439575, - -2.077871084213257, - -19.967727661132812 + 1.7978700399398804, + 26.03156280517578, + -4.711118221282959 ], "umap3d": [ - 4.824867248535156, - 3.3467092514038086, - 5.0280256271362305 + 5.87870979309082, + 4.635162353515625, + 4.93374490737915 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, + "id": 13, "label": "Genomics Papers" } ], @@ -729004,31 +729154,31 @@ "doi": "10.1101/2020.12.21.423873", "projections": { "pca": [ - 0.10898768901824951, - -0.162328839302063 + 0.10849946737289429, + -0.16251850128173828 ], "tsne": [ - 63.278564453125, - 0.5235257744789124 + 48.53943634033203, + 25.99939727783203 ], "umap": [ - 9.71064281463623, - 6.416983127593994 + 9.941093444824219, + 5.971933364868164 ], "pca3d": [ - 0.10898777097463608, - -0.16230329871177673, - -0.06693245470523834 + 0.1084994450211525, + -0.16249264776706696, + -0.0667816773056984 ], "tsne3d": [ - 26.142868041992188, - 10.615764617919922, - -7.631548881530762 + 25.396547317504883, + -7.085486888885498, + 0.7131693959236145 ], "umap3d": [ - 7.695728778839111, - 5.69225549697876, - 1.6369441747665405 + 7.042997360229492, + 5.542956829071045, + 0.17817099392414093 ] }, "cluster_id": 0, @@ -729039,12 +729189,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -729454,31 +729604,31 @@ "journal": "Nature", "projections": { "pca": [ - 0.1951189935207367, - 0.0252071563154459 + 0.19537609815597534, + 0.024410543963313103 ], "tsne": [ - 33.158138275146484, - 16.3297119140625 + 39.64695739746094, + -2.886618137359619 ], "umap": [ - 8.266772270202637, - 6.86381196975708 + 8.480958938598633, + 6.5679426193237305 ], "pca3d": [ - 0.19511882960796356, - 0.02520751953125, - 0.03290899470448494 + 0.19537587463855743, + 0.024411961436271667, + 0.032803695648908615 ], "tsne3d": [ - 12.755138397216797, - 4.166012287139893, - 13.670441627502441 + 17.512359619140625, + 3.9701766967773438, + 16.50741195678711 ], "umap3d": [ - 7.709399700164795, - 4.371616840362549, - 2.4632935523986816 + 7.634119510650635, + 4.5063796043396, + 1.2412350177764893 ] }, "cluster_id": 4, @@ -729489,12 +729639,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -729900,46 +730050,46 @@ ], "projections": { "pca": [ - 0.11587440967559814, - 0.12567749619483948 + 0.11649034917354584, + 0.1253056526184082 ], "tsne": [ - 0.5117180943489075, - -6.500308990478516 + 0.6508780121803284, + 5.486001491546631 ], "umap": [ - 5.322507858276367, - 7.328727722167969 + 5.452037811279297, + 6.634792327880859 ], "pca3d": [ - 0.11587382107973099, - 0.12569089233875275, - -0.004686940461397171 + 0.11648976057767868, + 0.12531962990760803, + -0.004749063402414322 ], "tsne3d": [ - -1.99908447265625, - -2.13938307762146, - -18.3458194732666 + 1.0439242124557495, + 23.490314483642578, + -2.6094212532043457 ], "umap3d": [ - 4.781993865966797, - 3.4158270359039307, - 4.885303020477295 + 5.732982158660889, + 4.702613830566406, + 4.9045586585998535 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, + "id": 13, "label": "Genomics Papers" } ], @@ -730364,31 +730514,31 @@ "openalex_url": "https://openalex.org/W3099848476", "projections": { "pca": [ - 0.052951205521821976, - 0.007898002862930298 + 0.05285825580358505, + 0.008393981494009495 ], "tsne": [ - 10.782757759094238, - -8.456650733947754 + 11.981427192687988, + 10.416419982910156 ], "umap": [ - 8.028162002563477, - 7.0195746421813965 + 8.055683135986328, + 6.499789714813232 ], "pca3d": [ - 0.05295085906982422, - 0.00793283898383379, - -0.08195248991250992 + 0.052857886999845505, + 0.008429265581071377, + -0.08194303512573242 ], "tsne3d": [ - 10.722481727600098, - 4.475045680999756, - -20.02666664123535 + 11.374663352966309, + 7.949830055236816, + 15.545182228088379 ], "umap3d": [ - 6.071839332580566, - 4.647212505340576, - 2.4107494354248047 + 6.116786956787109, + 4.644625663757324, + 1.555991291999817 ] }, "cluster_id": 4, @@ -730399,12 +730549,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -730822,31 +730972,31 @@ "doi": "10.1101/2020.11.17.384578", "projections": { "pca": [ - 0.3057473599910736, - 0.03471578657627106 + 0.3058226406574249, + 0.03381373733282089 ], "tsne": [ - 58.953487396240234, - 3.6973438262939453 + 49.51272964477539, + 20.635560989379883 ], "umap": [ - 10.079046249389648, - 6.788159370422363 + 10.28905200958252, + 6.332421779632568 ], "pca3d": [ - 0.30574727058410645, - 0.03471515327692032, - -0.07468194514513016 + 0.3058224320411682, + 0.03381340950727463, + -0.07493509352207184 ], "tsne3d": [ - 29.44840431213379, - 6.7769927978515625, - -6.437450408935547 + 25.646303176879883, + -2.9553472995758057, + -1.9806822538375854 ], "umap3d": [ - 7.975934982299805, - 5.346119403839111, - 1.3190470933914185 + 7.29464054107666, + 5.132716178894043, + -0.16141533851623535 ] }, "cluster_id": 0, @@ -730857,12 +731007,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -731281,31 +731431,31 @@ "cited_by_count": 70, "projections": { "pca": [ - 0.14916139841079712, - -0.1557314693927765 + 0.14882329106330872, + -0.15597668290138245 ], "tsne": [ - 23.30491828918457, - -3.956630229949951 + 25.423648834228516, + 5.527126789093018 ], "umap": [ - 8.573370933532715, - 6.497162818908691 + 8.747798919677734, + 6.0104146003723145 ], "pca3d": [ - 0.14916148781776428, - -0.1557241529226303, - -0.04038324952125549 + 0.14882344007492065, + -0.1559692770242691, + -0.04015672579407692 ], "tsne3d": [ - 20.3896541595459, - 6.722070693969727, - -10.552536964416504 + 19.472938537597656, + -2.7192413806915283, + 1.7238215208053589 ], "umap3d": [ - 6.880305767059326, - 5.355497360229492, - 2.171759843826294 + 6.564281940460205, + 5.3789544105529785, + 1.005646824836731 ] }, "cluster_id": 4, @@ -731316,12 +731466,12 @@ "label": "RNA Sequence Models" }, { - "id": 3, - "label": "RNA Splicing" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -731729,31 +731879,31 @@ "journal": "Nature", "projections": { "pca": [ - 0.1640903800725937, - -0.04807509481906891 + 0.16425570845603943, + -0.04852540045976639 ], "tsne": [ - 60.812564849853516, - 8.720951080322266 + 54.205108642578125, + 23.566804885864258 ], "umap": [ - 10.181146621704102, - 6.360377311706543 + 10.351861000061035, + 5.946341514587402 ], "pca3d": [ - 0.16409039497375488, - -0.04807788133621216, - 0.05322329327464104 + 0.16425561904907227, + -0.04852838069200516, + 0.0533142052590847 ], "tsne3d": [ - 27.391080856323242, - 12.519280433654785, - -1.9323036670684814 + 31.34731674194336, + -7.175802707672119, + -0.10461162775754929 ], "umap3d": [ - 8.114364624023438, - 5.743570804595947, - 1.4788129329681396 + 7.42869758605957, + 5.561392307281494, + -0.26266950368881226 ] }, "cluster_id": 0, @@ -731764,12 +731914,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -732187,31 +732337,31 @@ "doi": "10.1101/2020.08.27.270967", "projections": { "pca": [ - 0.10330206155776978, - -0.1283292919397354 + 0.10304167866706848, + -0.12829986214637756 ], "tsne": [ - 14.71912670135498, - -1.4201644659042358 + 15.543048858642578, + 4.951886177062988 ], "umap": [ - 8.373516082763672, - 6.107721328735352 + 8.626870155334473, + 5.838421821594238 ], "pca3d": [ - 0.10330220311880112, - -0.12832121551036835, - 0.007713683880865574 + 0.10304176062345505, + -0.12829165160655975, + 0.007914183661341667 ], "tsne3d": [ - 16.613256454467773, - 11.121142387390137, - -12.641240119934082 + 18.807470321655273, + -3.6782054901123047, + 6.274419784545898 ], "umap3d": [ - 6.608983516693115, - 5.557635307312012, - 2.3764278888702393 + 6.345207214355469, + 5.5732645988464355, + 1.1186295747756958 ] }, "cluster_id": 4, @@ -732222,12 +732372,12 @@ "label": "RNA Sequence Models" }, { - "id": 3, - "label": "RNA Splicing" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -732637,31 +732787,31 @@ "journal": "Nature Methods", "projections": { "pca": [ - 0.027392031624913216, - -0.1290074586868286 + 0.027297688648104668, + -0.1296204775571823 ], "tsne": [ - 18.567392349243164, - 19.77730941772461 + 18.884939193725586, + -38.5992431640625 ], "umap": [ - 8.678970336914062, - 6.6901373863220215 + 8.931066513061523, + 6.225339889526367 ], "pca3d": [ - 0.027392245829105377, - -0.12901213765144348, - 0.01747891865670681 + 0.027297915890812874, + -0.1296241730451584, + 0.017731599509716034 ], "tsne3d": [ - 10.4343900680542, - 5.171381950378418, - 9.0459623336792 + 20.264184951782227, + 0.5017200112342834, + 15.914658546447754 ], "umap3d": [ - 7.865633487701416, - 4.5497660636901855, - 2.427899122238159 + 7.788814067840576, + 4.722492218017578, + 1.0472334623336792 ] }, "cluster_id": 0, @@ -732672,12 +732822,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -733083,46 +733233,46 @@ ], "projections": { "pca": [ - 0.0950101688504219, - 0.07137925922870636 + 0.09556782245635986, + 0.07120101153850555 ], "tsne": [ - 1.6522319316864014, - -7.289198875427246 + 1.592738389968872, + 6.371640205383301 ], "umap": [ - 5.308463096618652, - 7.3879618644714355 + 5.521134853363037, + 6.649012088775635 ], "pca3d": [ - 0.09500979632139206, - 0.07138524204492569, - 0.026078684255480766 + 0.09556747227907181, + 0.07120738923549652, + 0.026184536516666412 ], "tsne3d": [ - 0.5988128185272217, - -2.713820457458496, - -18.68910789489746 + 2.2355659008026123, + 26.195375442504883, + -3.2985310554504395 ], "umap3d": [ - 4.886605739593506, - 3.330441951751709, - 5.029345989227295 + 5.91461181640625, + 4.625094890594482, + 4.911879539489746 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, + "id": 13, "label": "Genomics Papers" } ], @@ -733531,31 +733681,31 @@ "journal": "Nature Methods", "projections": { "pca": [ - -0.03096577897667885, - 0.04826643317937851 + -0.030702047049999237, + 0.04812390357255936 ], "tsne": [ - 52.54772186279297, - -16.834043502807617 + 34.498661041259766, + 29.297941207885742 ], "umap": [ - 9.536738395690918, - 7.397820472717285 + 9.759864807128906, + 7.104912281036377 ], "pca3d": [ - -0.03096623718738556, - 0.04830019176006317, - -0.04194974899291992 + -0.030702488496899605, + 0.04815845564007759, + -0.041975755244493484 ], "tsne3d": [ - 29.644184112548828, - 10.73211669921875, - 8.933232307434082 + 15.80880069732666, + -14.414152145385742, + -10.253887176513672 ], "umap3d": [ - 6.473903656005859, - 5.08455753326416, - 1.331763505935669 + 5.929912090301514, + 4.766442775726318, + 0.44593551754951477 ] }, "cluster_id": 0, @@ -733566,12 +733716,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 20, - "label": "Chromatin Regulation" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -733992,31 +734142,31 @@ "cited_by_count": 113, "projections": { "pca": [ - 0.08160407096147537, - -0.1219535768032074 + 0.08137805014848709, + -0.12171613425016403 ], "tsne": [ - 15.151366233825684, - 23.788623809814453 + 15.955657005310059, + -26.059709548950195 ], "umap": [ - 7.197731971740723, - 5.206370830535889 + 7.438449859619141, + 4.870580673217773 ], "pca3d": [ - 0.08160404860973358, - -0.12193462997674942, - 0.14121390879154205 + 0.08137808740139008, + -0.12169646471738815, + 0.14138507843017578 ], "tsne3d": [ - -3.3547136783599854, - 23.86541175842285, - 4.747505187988281 + 8.34399127960205, + -5.2954230308532715, + 10.929858207702637 ], "umap3d": [ - 6.304117679595947, - 6.010143280029297, - 3.7322628498077393 + 6.342175483703613, + 6.337947368621826, + 2.2191383838653564 ] }, "cluster_id": 4, @@ -734027,11 +734177,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, - "label": "RNA Splicing" + "id": 7, + "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -734440,31 +734590,31 @@ "journal": "Nature Methods", "projections": { "pca": [ - 0.11293338239192963, - -0.06910072267055511 + 0.11305272579193115, + -0.06967149674892426 ], "tsne": [ - 18.63564682006836, - 19.81591033935547 + 18.8836669921875, + -38.58848190307617 ], "umap": [ - 8.708431243896484, - 6.69570779800415 + 8.906685829162598, + 6.228647708892822 ], "pca3d": [ - 0.11293361335992813, - -0.06911849230527878, - 0.09243830293416977 + 0.11305298656225204, + -0.06968854367733002, + 0.09257491677999496 ], "tsne3d": [ - 10.568084716796875, - 5.483332633972168, - 9.451247215270996 + 19.912179946899414, + 0.361979216337204, + 15.61931324005127 ], "umap3d": [ - 7.856154918670654, - 4.560604572296143, - 2.4148268699645996 + 7.745082855224609, + 4.691737651824951, + 1.0301908254623413 ] }, "cluster_id": 0, @@ -734475,12 +734625,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -734896,47 +735046,47 @@ "doi": "10.1101/166868", "projections": { "pca": [ - 0.12427249550819397, - -0.02332412451505661 + 0.1241493970155716, + -0.023601671680808067 ], "tsne": [ - 26.017057418823242, - -14.443026542663574 + 19.683818817138672, + 18.43692398071289 ], "umap": [ - 8.236869812011719, - 9.032246589660645 + 8.455500602722168, + 8.477134704589844 ], "pca3d": [ - 0.12427244335412979, - -0.023307356983423233, - -0.05437452346086502 + 0.12414925545454025, + -0.023584438487887383, + -0.054459910839796066 ], "tsne3d": [ - 13.947453498840332, - 12.144604682922363, - 10.81213092803955 + 8.84231948852539, + -3.3943092823028564, + -18.854103088378906 ], "umap3d": [ - 5.818563461303711, - 4.69765043258667, - 1.392871379852295 + 5.5359954833984375, + 4.356367111206055, + 0.9348787069320679 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -735353,31 +735503,31 @@ "doi": "10.1371/journal.pcbi.1009291", "projections": { "pca": [ - 0.09153448045253754, - -0.06149940937757492 + 0.0914260745048523, + -0.06196286901831627 ], "tsne": [ - 17.3378963470459, - 34.786983489990234 + 20.639938354492188, + -33.2943115234375 ], "umap": [ - 7.9583821296691895, - 4.404826641082764 + 8.297659873962402, + 3.913926601409912 ], "pca3d": [ - 0.0915341004729271, - -0.06144661456346512, - 0.11704181879758835 + 0.09142575412988663, + -0.0619085393846035, + 0.11696737259626389 ], "tsne3d": [ - 0.11893368512392044, - 22.361738204956055, - 7.892049789428711 + 8.686695098876953, + -17.253870010375977, + 9.49930477142334 ], "umap3d": [ - 7.21434211730957, - 6.40863037109375, - 4.142352104187012 + 7.499917507171631, + 6.935596942901611, + 2.248430013656616 ] }, "cluster_id": 4, @@ -735388,11 +735538,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -735986,47 +736136,47 @@ "openalex_url": "https://openalex.org/W3133859624", "projections": { "pca": [ - 0.2149478644132614, - 0.11902102828025818 + 0.21527324616909027, + 0.11873628199100494 ], "tsne": [ - 34.421356201171875, - 7.377955913543701 + 38.754886627197266, + -11.023329734802246 ], "umap": [ - 7.965920925140381, - 8.275005340576172 + 8.34708309173584, + 7.697365760803223 ], "pca3d": [ - 0.2149476259946823, - 0.11900888383388519, - -0.15754882991313934 + 0.2152729481458664, + 0.1187237799167633, + -0.15770648419857025 ], "tsne3d": [ - 18.99817657470703, - -15.421236038208008, - -12.315678596496582 + 17.715618133544922, + 21.335838317871094, + -0.6160053014755249 ], "umap3d": [ - 7.098588466644287, - 3.500389575958252, - 2.145294427871704 + 7.129312515258789, + 3.5117788314819336, + 1.4392905235290527 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { "id": 13, - "label": "Genetic Variants" + "label": "Chromatin Regulation" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -736444,47 +736594,47 @@ "doi": "10.1017/neu.2026.10079", "projections": { "pca": [ - 0.10146375745534897, - 0.20981484651565552 + 0.10196835547685623, + 0.2091759890317917 ], "tsne": [ - 32.76661682128906, - 9.75469970703125 + 39.41332244873047, + -8.313253402709961 ], "umap": [ - 7.762219429016113, - 8.221697807312012 + 7.91360330581665, + 7.796326160430908 ], "pca3d": [ - 0.101463183760643, - 0.20983678102493286, - -0.17100153863430023 + 0.1019677147269249, + 0.20919743180274963, + -0.1714061200618744 ], "tsne3d": [ - 14.748576164245605, - -17.18475341796875, - -8.409706115722656 + 18.086681365966797, + 17.225584030151367, + -4.103097438812256 ], "umap3d": [ - 6.904178142547607, - 3.3806676864624023, - 2.267453670501709 + 7.077207565307617, + 3.4760477542877197, + 1.6975696086883545 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -736905,47 +737055,47 @@ "openalex_url": "https://openalex.org/W3083216853", "projections": { "pca": [ - 0.1719820201396942, - 0.12080241739749908 + 0.17236758768558502, + 0.11975625157356262 ], "tsne": [ - 30.668216705322266, - 7.965108394622803 + 35.70375442504883, + -8.019518852233887 ], "umap": [ - 7.5291619300842285, - 8.20663833618164 + 7.854579448699951, + 7.737926959991455 ], "pca3d": [ - 0.1719818413257599, - 0.12079016864299774, - -0.1770523339509964 + 0.17236728966236115, + 0.11974366009235382, + -0.17726673185825348 ], "tsne3d": [ - 14.141053199768066, - -13.04998779296875, - -9.941153526306152 + 14.301766395568848, + 17.05047607421875, + -0.7090644240379333 ], "umap3d": [ - 6.973393440246582, - 3.247532606124878, - 2.537391424179077 + 7.298282146453857, + 3.524165153503418, + 1.9010549783706665 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -737364,31 +737514,31 @@ "cited_by_count": 67, "projections": { "pca": [ - 0.07381734251976013, - -0.17251288890838623 + 0.0734575018286705, + -0.17242485284805298 ], "tsne": [ - 16.95406723022461, - -2.259324312210083 + 18.3826904296875, + 4.489375591278076 ], "umap": [ - 7.874282360076904, - 6.400498390197754 + 8.074344635009766, + 6.058372974395752 ], "pca3d": [ - 0.07381769269704819, - -0.17252454161643982, - -0.01876799762248993 + 0.07345785200595856, + -0.17243628203868866, + -0.01839921623468399 ], "tsne3d": [ - 14.730524063110352, - 7.7261199951171875, - -10.640192031860352 + 16.747350692749023, + -0.2699131369590759, + 4.526309013366699 ], "umap3d": [ - 6.306753158569336, - 5.180180072784424, - 2.623680353164673 + 6.227169990539551, + 5.271265029907227, + 1.6104294061660767 ] }, "cluster_id": 4, @@ -737399,12 +737549,12 @@ "label": "RNA Sequence Models" }, { - "id": 3, - "label": "RNA Splicing" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -737823,31 +737973,31 @@ "cited_by_count": 208, "projections": { "pca": [ - 0.11932282149791718, - -0.11267374455928802 + 0.11926396936178207, + -0.11294372379779816 ], "tsne": [ - 0.12552084028720856, - 36.54464340209961 + -4.832675933837891, + -16.908546447753906 ], "umap": [ - 6.357749938964844, - 3.856367826461792 + 6.991966247558594, + 3.6019976139068604 ], "pca3d": [ - 0.11932303011417389, - -0.11269161850214005, - 0.11538028717041016 + 0.11926428228616714, + -0.11296123266220093, + 0.11558404564857483 ], "tsne3d": [ - 4.458240985870361, - 14.53831672668457, - 8.533318519592285 + 6.193521499633789, + -15.139219284057617, + 0.4551294445991516 ], "umap3d": [ - 5.368364334106445, - 6.167013168334961, - 5.320268154144287 + 6.169107437133789, + 7.222169399261475, + 3.8396494388580322 ] }, "cluster_id": 3, @@ -737858,11 +738008,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -738296,31 +738446,31 @@ "cited_by_count": 444, "projections": { "pca": [ - 0.317508727312088, - 0.05269491672515869 + 0.31763559579849243, + 0.05189891532063484 ], "tsne": [ - 49.41876220703125, - 3.246913433074951 + 42.20475769042969, + 15.458992958068848 ], "umap": [ - 9.799217224121094, - 7.303096771240234 + 10.097188949584961, + 6.578073978424072 ], "pca3d": [ - 0.3175082206726074, - 0.05267878249287605, - -0.13127470016479492 + 0.31763532757759094, + 0.051882751286029816, + -0.13149991631507874 ], "tsne3d": [ - 25.568571090698242, - -1.5647087097167969, - 2.7351770401000977 + 25.09690284729004, + 4.75273323059082, + -8.219593048095703 ], "umap3d": [ - 7.813710689544678, - 4.938913822174072, - 1.3700518608093262 + 7.226630687713623, + 4.631019115447998, + 0.08660205453634262 ] }, "cluster_id": 0, @@ -738331,12 +738481,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 20, - "label": "Chromatin Regulation" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -738789,31 +738939,31 @@ "cited_by_count": 1375, "projections": { "pca": [ - 0.31843316555023193, - 0.02685638703405857 + 0.3185805678367615, + 0.025825420394539833 ], "tsne": [ - 25.03384780883789, - 30.65548324584961 + 27.745227813720703, + -29.800376892089844 ], "umap": [ - 8.58200740814209, - 5.0976057052612305 + 8.861713409423828, + 4.684118270874023 ], "pca3d": [ - 0.3184325397014618, - 0.02686961740255356, - -0.018173526972532272 + 0.3185802400112152, + 0.025839338079094887, + -0.018395282328128815 ], "tsne3d": [ - 8.946514129638672, - 24.867111206054688, - -1.8815542459487915 + 18.56011962890625, + -16.86269760131836, + 6.656270503997803 ], "umap3d": [ - 7.662725448608398, - 5.7454681396484375, - 3.418977975845337 + 7.573021411895752, + 6.272222518920898, + 1.5492538213729858 ] }, "cluster_id": 4, @@ -738824,11 +738974,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -739251,31 +739401,31 @@ "openalex_url": "https://openalex.org/W3013674494", "projections": { "pca": [ - 0.07404248416423798, - 0.0939197987318039 + 0.07434888929128647, + 0.09402531385421753 ], "tsne": [ - 29.85696792602539, - 19.390583038330078 + 30.019926071166992, + -20.047649383544922 ], "umap": [ - 8.685280799865723, - 7.733578681945801 + 8.840882301330566, + 7.141125679016113 ], "pca3d": [ - 0.07404223829507828, - 0.09392460435628891, - -0.16699761152267456 + 0.07434860616922379, + 0.09403038769960403, + -0.16699592769145966 ], "tsne3d": [ - 11.127861022949219, - 17.865392684936523, - 6.358839511871338 + 13.694010734558105, + 8.548807144165039, + -3.850006103515625 ], "umap3d": [ - 7.115662574768066, - 4.3855133056640625, - 2.1300134658813477 + 6.965811252593994, + 4.34699010848999, + 1.051950454711914 ] }, "cluster_id": 0, @@ -739286,12 +739436,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -739697,46 +739847,46 @@ ], "projections": { "pca": [ - 0.11607338488101959, - 0.19004656374454498 + 0.1169351190328598, + 0.18905583024024963 ], "tsne": [ - -0.7669474482536316, - -6.95923376083374 + -0.657985508441925, + 5.440333843231201 ], "umap": [ - 5.066906452178955, - 7.369711875915527 + 5.289285659790039, + 6.643030166625977 ], "pca3d": [ - 0.11607261002063751, - 0.1900745928287506, - 0.013452576473355293 + 0.11693430691957474, + 0.18908430635929108, + 0.01322366576641798 ], "tsne3d": [ - -2.5005993843078613, - -0.211515873670578, - -20.54975700378418 + 0.5364561676979065, + 22.36602210998535, + -5.038212299346924 ], "umap3d": [ - 4.569499969482422, - 3.4004909992218018, - 4.9440436363220215 + 5.664611339569092, + 4.639440536499023, + 4.979706764221191 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, + "id": 13, "label": "Genomics Papers" } ], @@ -740161,31 +740311,31 @@ "openalex_url": "https://openalex.org/W3035370963", "projections": { "pca": [ - 0.17998096346855164, - -0.013259819708764553 + 0.17999698221683502, + -0.013789119198918343 ], "tsne": [ - 58.51902389526367, - 8.842223167419434 + 54.66968536376953, + 21.247087478637695 ], "umap": [ - 10.28252124786377, - 6.488736152648926 + 10.449247360229492, + 6.113799095153809 ], "pca3d": [ - 0.1799807846546173, - -0.013249563984572887, - -0.02585102990269661 + 0.17999668419361115, + -0.01377883180975914, + -0.025977708399295807 ], "tsne3d": [ - 26.865909576416016, - 9.781545639038086, - 0.47557321190834045 + 30.568561553955078, + -5.90728235244751, + -4.219120025634766 ], "umap3d": [ - 8.162613868713379, - 5.637465000152588, - 1.4114172458648682 + 7.513779163360596, + 5.445413589477539, + -0.3669077157974243 ] }, "cluster_id": 0, @@ -740196,12 +740346,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -740625,47 +740775,47 @@ "doi": "10.1101/2024.12.06.627299", "projections": { "pca": [ - 0.07880761474370956, - -0.12767645716667175 + 0.0785493478178978, + -0.12809133529663086 ], "tsne": [ - 27.060888290405273, - -30.060325622558594 + 41.15105056762695, + 0.27115753293037415 ], "umap": [ - 7.872340679168701, - 8.811271667480469 + 7.993116855621338, + 8.423206329345703 ], "pca3d": [ - 0.07880756258964539, - -0.12766976654529572, - -0.02156633697450161 + 0.07854936271905899, + -0.12808428704738617, + -0.02141459845006466 ], "tsne3d": [ - 18.99554443359375, - -4.5391316413879395, - 13.785106658935547 + 20.527603149414062, + -2.3689017295837402, + -15.600634574890137 ], "umap3d": [ - 6.046534061431885, - 3.7219436168670654, - 1.5223243236541748 + 5.873859405517578, + 3.594076633453369, + 1.2126398086547852 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 12, - "label": "Genetic Variants" + "id": 28, + "label": "Single Cell Data" } ], "_embedding": [ @@ -741095,31 +741245,31 @@ "doi": "10.1038/s41587-026-03149-6", "projections": { "pca": [ - 0.12011633813381195, - -0.008950098417699337 + 0.12026049941778183, + -0.008841194212436676 ], "tsne": [ - -3.977699041366577, - 14.88009262084961 + 11.924362182617188, + -23.725767135620117 ], "umap": [ - 5.59036922454834, - 5.133784294128418 + 5.978752136230469, + 4.823397159576416 ], "pca3d": [ - 0.12011625617742538, - -0.008958449587225914, - 0.20841416716575623 + 0.12026046961545944, + -0.008849828504025936, + 0.2083819955587387 ], "tsne3d": [ - 0.8814264535903931, - -0.8853394389152527, - 0.9777083992958069 + -2.307262420654297, + -2.109626531600952, + 12.65327262878418 ], "umap3d": [ - 5.680032730102539, - 6.600893974304199, - 4.061241626739502 + 5.538229465484619, + 6.832610130310059, + 2.556994915008545 ] }, "cluster_id": 3, @@ -741130,12 +741280,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, - "label": "Protein Design" + "id": 32, + "label": "Genome Protein" } ], "_embedding": [ @@ -741543,31 +741693,31 @@ "journal": "Nature Machine Intelligence", "projections": { "pca": [ - -0.01787576824426651, - -0.014066684059798717 + -0.01781083084642887, + -0.013034622184932232 ], "tsne": [ - -1.1198846101760864, - 27.656217575073242 + -1.2786905765533447, + -21.75063705444336 ], "umap": [ - 6.140732765197754, - 4.795649528503418 + 6.544981479644775, + 4.363887310028076 ], "pca3d": [ - -0.017875811085104942, - -0.014067044481635094, - 0.21369627118110657 + -0.017810864374041557, + -0.013035071082413197, + 0.21383939683437347 ], "tsne3d": [ - -1.2968804836273193, - 11.67544937133789, - -1.105783224105835 + 0.7351660132408142, + -8.429858207702637, + 7.922680377960205 ], "umap3d": [ - 5.041285991668701, - 5.868408679962158, - 4.656764507293701 + 5.67769193649292, + 6.576144218444824, + 3.530754327774048 ] }, "cluster_id": 3, @@ -741578,11 +741728,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -741993,31 +742143,31 @@ "journal": "Nature", "projections": { "pca": [ - 0.10898616164922714, - 0.05748707056045532 + 0.10945037007331848, + 0.057418569922447205 ], "tsne": [ - -10.11977767944336, - 24.782026290893555 + -1.8838454484939575, + -28.012624740600586 ], "umap": [ - 4.992489337921143, - 4.329564571380615 + 5.681272983551025, + 3.9147260189056396 ], "pca3d": [ - 0.10898586362600327, - 0.05748483166098595, - 0.27734342217445374 + 0.10945010185241699, + 0.05741576477885246, + 0.2772046625614166 ], "tsne3d": [ - -6.036479473114014, - 7.289236545562744, - 5.284032821655273 + -6.261603355407715, + -13.317981719970703, + 8.580303192138672 ], "umap3d": [ - 4.939855098724365, - 6.813549518585205, - 4.606865406036377 + 5.221668720245361, + 7.320683002471924, + 3.3055360317230225 ] }, "cluster_id": 3, @@ -742028,7 +742178,7 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { @@ -742453,31 +742603,31 @@ "cited_by_count": 3, "projections": { "pca": [ - 0.10478007048368454, - -0.14586910605430603 + 0.10452520102262497, + -0.14558906853199005 ], "tsne": [ - 1.9942569732666016, - 24.572839736938477 + 2.138957977294922, + -20.55672264099121 ], "umap": [ - 6.320597171783447, - 4.542857646942139 + 6.803635120391846, + 4.305577278137207 ], "pca3d": [ - 0.10478033125400543, - -0.14588172733783722, - 0.1260669082403183 + 0.10452549159526825, + -0.14560188353061676, + 0.12634627521038055 ], "tsne3d": [ - 3.9578309059143066, - 7.339290142059326, - 3.117926836013794 + -2.214933395385742, + -6.633081436157227, + 0.9888430833816528 ], "umap3d": [ - 5.429206371307373, - 5.874081611633301, - 4.911221981048584 + 6.114408016204834, + 6.7531046867370605, + 3.4401071071624756 ] }, "cluster_id": 3, @@ -742488,11 +742638,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -742921,31 +743071,31 @@ ], "projections": { "pca": [ - 0.16969947516918182, - 0.0033758925274014473 + 0.1699109673500061, + 0.0035133089404553175 ], "tsne": [ - -7.758353233337402, - 26.088783264160156 + -0.6728489995002747, + -31.29939842224121 ], "umap": [ - 5.138331413269043, - 4.165673732757568 + 5.681938171386719, + 3.7116894721984863 ], "pca3d": [ - 0.16969935595989227, - 0.003357158275321126, - 0.26080524921417236 + 0.16991101205348969, + 0.003493639174848795, + 0.26068952679634094 ], "tsne3d": [ - -2.3073320388793945, - 7.785783290863037, - 9.639802932739258 + -3.122438430786133, + -12.713223457336426, + 10.134736061096191 ], "umap3d": [ - 4.982635974884033, - 7.067678451538086, - 4.792492389678955 + 5.27099609375, + 7.668952941894531, + 3.2221152782440186 ] }, "cluster_id": 3, @@ -742956,11 +743106,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -743381,31 +743531,31 @@ "doi": "10.64898/2026.02.19.706898", "projections": { "pca": [ - 0.11562614887952805, - -0.11093219369649887 + 0.11539088189601898, + -0.11036792397499084 ], "tsne": [ - -1.4289369583129883, - 18.75641632080078 + 7.3883795738220215, + -25.330352783203125 ], "umap": [ - 5.854748725891113, - 4.4472126960754395 + 6.278014659881592, + 4.1452765464782715 ], "pca3d": [ - 0.11562635004520416, - -0.11095062643289566, - 0.2530919015407562 + 0.11539109796285629, + -0.11038660258054733, + 0.2531711459159851 ], "tsne3d": [ - 2.502896785736084, - 3.1265883445739746, - 5.1954145431518555 + -3.0466222763061523, + -11.973660469055176, + -2.6531238555908203 ], "umap3d": [ - 5.740413665771484, - 6.609548568725586, - 4.742619037628174 + 6.025600433349609, + 7.389383316040039, + 2.952439308166504 ] }, "cluster_id": 3, @@ -743416,12 +743566,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, - "label": "Protein Language" + "id": 3, + "label": "Protein Design" } ], "_embedding": [ @@ -743854,31 +744004,31 @@ "openalex_url": "https://openalex.org/W7130605770", "projections": { "pca": [ - 0.12270400673151016, - -0.07102303951978683 + 0.12268036603927612, + -0.07023870944976807 ], "tsne": [ - -4.655806541442871, - 18.117870330810547 + 7.6373701095581055, + -26.889801025390625 ], "umap": [ - 5.59845495223999, - 4.593409061431885 + 6.014616012573242, + 4.332880973815918 ], "pca3d": [ - 0.12270405888557434, - -0.0710461214184761, - 0.2623266279697418 + 0.12268058210611343, + -0.07026219367980957, + 0.2624672055244446 ], "tsne3d": [ - 1.6166874170303345, - 1.1597105264663696, - 3.7929904460906982 + -5.645461559295654, + -6.276960372924805, + 4.078368663787842 ], "umap3d": [ - 5.6135406494140625, - 6.818811893463135, - 4.465704917907715 + 5.749104976654053, + 7.3017354011535645, + 2.809267044067383 ] }, "cluster_id": 3, @@ -743889,11 +744039,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -744303,31 +744453,31 @@ "title": "The Isomorphic Labs Drug Design Engine unlocks a new frontier beyond AlphaFold - Isomorphic Labs", "projections": { "pca": [ - 0.06736171990633011, - 0.15001413226127625 + 0.06801168620586395, + 0.15044336020946503 ], "tsne": [ - -11.149624824523926, - 9.210437774658203 + -2.466599941253662, + -46.6646728515625 ], "umap": [ - 5.116252422332764, - 5.5382585525512695 + 5.53081750869751, + 5.046825408935547 ], "pca3d": [ - 0.06736159324645996, - 0.14998891949653625, - 0.1633409857749939 + 0.06801158934831619, + 0.15041714906692505, + 0.16320447623729706 ], "tsne3d": [ - -1.9151349067687988, - -12.279680252075195, - -14.900574684143066 + -1.986663579940796, + 2.623126268386841, + 18.168109893798828 ], "umap3d": [ - 4.988489151000977, - 6.311143398284912, - 3.798837900161743 + 5.033025741577148, + 6.535328388214111, + 2.901874542236328 ] }, "cluster_id": 3, @@ -744338,12 +744488,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, - "label": "Protein Design" + "id": 32, + "label": "Genome Protein" } ], "_embedding": [ @@ -744763,31 +744913,31 @@ "cited_by_count": 36, "projections": { "pca": [ - 0.09548591077327728, - -0.15548278391361237 + 0.09525035321712494, + -0.1552167683839798 ], "tsne": [ - -6.239229202270508, - 31.55281639099121 + 1.3374682664871216, + -26.313440322875977 ], "umap": [ - 5.724555015563965, - 4.012597560882568 + 6.380429744720459, + 3.6782872676849365 ], "pca3d": [ - 0.09548614919185638, - -0.15549591183662415, - 0.16723501682281494 + 0.09525063633918762, + -0.15523000061511993, + 0.1674736887216568 ], "tsne3d": [ - -3.6824564933776855, - 12.311566352844238, - 3.95359468460083 + -0.8815904855728149, + -15.25522232055664, + 0.943158745765686 ], "umap3d": [ - 5.13095760345459, - 6.496797561645508, - 4.982828140258789 + 5.786741733551025, + 7.4341912269592285, + 3.4393365383148193 ] }, "cluster_id": 3, @@ -744798,11 +744948,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -745210,31 +745360,31 @@ "abstract": "We present JAM-2, a general-purpose de novo protein design system that for the first time yields VHH-Fc and full-length mAb antibodies with drug-like affinities and developability, while achieving double-digit success rates with unprecedented target and epitope breadth. Across 16 unseen targets, JAM-2 produced binders for 100% of them, with average success rates of 39% (VHH-Fcs) and 18% (mAbs). Using only 45 designs per format, JAM-2 delivered picomolar or single-digit nanomolar binders on half of these targets. Across another 10 targets, each with 20 user-specified epitopes, JAM-2 generated VHH-Fc binders to 30\u201370% of epitopes for half the targets. Remarkably, JAM-2 also produced antibody binders directly to the GPCRs CXCR4 and CXCR7 in their native cellular contexts with 11.7% and 3.8% success rates, respectively, with top designs reaching single-digit nanomolar affinities. Developability profiling of hundreds of de novo designs \u2013 the largest developability dataset for computationally designed biologics \u2013 showed more than half met core industry criteria, with many top designs exhibiting lead-quality profiles that may not require further optimization of any kind. These results position JAM-2 as the state-of-the-art de novo antibody design system, and the first ready for front-line use in drug discovery, matching or surpassing traditional discovery approaches.", "projections": { "pca": [ - 0.15628190338611603, - 0.08601351082324982 + 0.1567220687866211, + 0.08579257130622864 ], "tsne": [ - -18.119871139526367, - 26.60918426513672 + -0.18453940749168396, + -39.77724838256836 ], "umap": [ - 4.367148399353027, - 3.632702589035034 + 4.937500476837158, + 3.147540807723999 ], "pca3d": [ - 0.15628188848495483, - 0.08599220216274261, - 0.1825219690799713 + 0.1567220389842987, + 0.08577048778533936, + 0.18231162428855896 ], "tsne3d": [ - -5.650346279144287, - 13.846677780151367, - -12.933674812316895 + -2.723579168319702, + -9.138758659362793, + 18.57415008544922 ], "umap3d": [ - 4.8788042068481445, - 7.792430877685547, - 4.305951118469238 + 4.680211544036865, + 7.9974284172058105, + 2.8807530403137207 ] }, "cluster_id": 3, @@ -745245,12 +745395,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -745682,31 +745832,31 @@ "doi": "10.1101/2025.11.12.688095", "projections": { "pca": [ - 0.13464856147766113, - 0.003452052129432559 + 0.13481740653514862, + 0.00328245060518384 ], "tsne": [ - -17.117563247680664, - 26.560815811157227 + -0.5283060073852539, + -38.81996536254883 ], "umap": [ - 4.345170974731445, - 3.7854068279266357 + 4.895762920379639, + 3.3255374431610107 ], "pca3d": [ - 0.1346486210823059, - 0.0034361351281404495, - 0.1750277280807495 + 0.1348174810409546, + 0.0032657289411872625, + 0.17488126456737518 ], "tsne3d": [ - -7.307165622711182, - 13.130404472351074, - -12.698267936706543 + -1.6499438285827637, + -10.094660758972168, + 19.63577651977539 ], "umap3d": [ - 4.636886119842529, - 7.641063690185547, - 4.416060924530029 + 4.5539751052856445, + 7.946887016296387, + 3.128148078918457 ] }, "cluster_id": 3, @@ -745717,12 +745867,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -746139,31 +746289,31 @@ "cited_by_count": 6, "projections": { "pca": [ - 0.19744668900966644, - -0.09718062728643417 + 0.19738589227199554, + -0.09759902954101562 ], "tsne": [ - 61.526283264160156, - 7.036435127258301 + 52.493473052978516, + 23.986738204956055 ], "umap": [ - 10.113636016845703, - 6.399177551269531 + 10.295177459716797, + 5.9584059715271 ], "pca3d": [ - 0.19744674861431122, - -0.09717892110347748, - 0.08196984976530075 + 0.19738605618476868, + -0.09759724140167236, + 0.0820968896150589 ], "tsne3d": [ - 28.594743728637695, - 11.405506134033203, - -4.58461856842041 + 28.457719802856445, + -7.69010066986084, + -1.6314129829406738 ], "umap3d": [ - 7.9677300453186035, - 5.710999488830566, - 1.6016074419021606 + 7.460292816162109, + 5.566802978515625, + -0.2559954822063446 ] }, "cluster_id": 0, @@ -746174,12 +746324,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -746636,31 +746786,31 @@ "cited_by_count": 45, "projections": { "pca": [ - 0.1398577243089676, - -0.033925484865903854 + 0.13989883661270142, + -0.03392364829778671 ], "tsne": [ - -16.009685516357422, - 32.356529235839844 + -9.195028305053711, + -36.718780517578125 ], "umap": [ - 4.931349277496338, - 3.7455592155456543 + 5.5932841300964355, + 3.3025894165039062 ], "pca3d": [ - 0.13985781371593475, - -0.033944953233003616, - 0.21061746776103973 + 0.13989898562431335, + -0.033943597227334976, + 0.21054664254188538 ], "tsne3d": [ - -3.565448522567749, - 7.567192077636719, - 15.92104721069336 + -3.325840711593628, + -15.940760612487793, + 13.32312297821045 ], "umap3d": [ - 4.933586120605469, - 7.37539529800415, - 4.6292877197265625 + 5.080957412719727, + 7.892145156860352, + 3.1198558807373047 ] }, "cluster_id": 3, @@ -746671,11 +746821,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -747090,31 +747240,31 @@ ], "projections": { "pca": [ - 0.05857650190591812, - -0.05239684879779816 + 0.0586305633187294, + -0.051958415657281876 ], "tsne": [ - -4.680462837219238, - 25.85154151916504 + 0.566358745098114, + -24.61942481994629 ], "umap": [ - 5.760165214538574, - 4.442681312561035 + 6.155113697052002, + 4.069190979003906 ], "pca3d": [ - 0.05857660993933678, - -0.05240707844495773, - 0.2840651869773865 + 0.05863068997859955, + -0.0519690215587616, + 0.2841521203517914 ], "tsne3d": [ - -3.468003273010254, - 8.72667121887207, - 4.544634819030762 + -1.652014970779419, + -10.848612785339355, + 7.662979602813721 ], "umap3d": [ - 5.2279839515686035, - 6.3630690574646, - 4.607420444488525 + 5.500021934509277, + 7.073939323425293, + 3.2041079998016357 ] }, "cluster_id": 3, @@ -747125,12 +747275,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, - "label": "Protein Language" + "id": 3, + "label": "Protein Design" } ], "_embedding": [ @@ -747547,31 +747697,31 @@ "cited_by_count": 53, "projections": { "pca": [ - 0.04188163951039314, - -0.1231677383184433 + 0.041586533188819885, + -0.1230953112244606 ], "tsne": [ - -12.986714363098145, - 34.041202545166016 + -10.811579704284668, + -27.550907135009766 ], "umap": [ - 5.0439252853393555, - 3.818758726119995 + 5.618615627288818, + 3.4374349117279053 ], "pca3d": [ - 0.04188156500458717, - -0.1231422871351242, - 0.19832003116607666 + 0.04158654063940048, + -0.12306950986385345, + 0.19830530881881714 ], "tsne3d": [ - 0.3699183464050293, - 10.187049865722656, - 14.000627517700195 + 2.159137487411499, + -16.97608184814453, + 12.061417579650879 ], "umap3d": [ - 4.7418928146362305, - 7.106443405151367, - 4.778427600860596 + 5.1252851486206055, + 7.710273742675781, + 3.3548531532287598 ] }, "cluster_id": 3, @@ -747582,11 +747732,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -747993,31 +748143,31 @@ ], "projections": { "pca": [ - 0.15541642904281616, - 0.04380280524492264 + 0.15570878982543945, + 0.04404883086681366 ], "tsne": [ - -2.257939100265503, - 17.722431182861328 + 2.8520472049713135, + -33.62460708618164 ], "umap": [ - 5.575674057006836, - 4.111750602722168 + 6.233644485473633, + 3.8535385131835938 ], "pca3d": [ - 0.15541604161262512, - 0.04379671439528465, - 0.2613728642463684 + 0.15570855140686035, + 0.04404265806078911, + 0.26120784878730774 ], "tsne3d": [ - 2.9848036766052246, - 5.553496837615967, - 7.327678203582764 + -3.320361375808716, + -14.811047554016113, + -1.4315897226333618 ], "umap3d": [ - 5.590794563293457, - 6.770148754119873, - 4.952348709106445 + 6.040502071380615, + 7.557040691375732, + 3.133641004562378 ] }, "cluster_id": 3, @@ -748028,12 +748178,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, - "label": "Protein Language" + "id": 3, + "label": "Protein Design" } ], "_embedding": [ @@ -748449,31 +748599,31 @@ "doi": "10.1073/pnas.94.21.11324", "projections": { "pca": [ - 0.07328275591135025, - 0.10132668912410736 + 0.07359794527292252, + 0.10220067948102951 ], "tsne": [ - -2.7438600063323975, - 18.180259704589844 + 3.779813766479492, + -33.90744400024414 ], "umap": [ - 5.586912155151367, - 4.188702583312988 + 6.2013020515441895, + 3.8095669746398926 ], "pca3d": [ - 0.07328260689973831, - 0.10131096094846725, - 0.23925212025642395 + 0.0735977366566658, + 0.10218417644500732, + 0.2390129119157791 ], "tsne3d": [ - 4.430007457733154, - 4.332983016967773, - 7.347132205963135 + -3.19026255607605, + -15.133766174316406, + -3.2094738483428955 ], "umap3d": [ - 5.6350531578063965, - 6.768539905548096, - 4.943382740020752 + 6.059207439422607, + 7.589909553527832, + 3.0274300575256348 ] }, "cluster_id": 3, @@ -748484,12 +748634,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, - "label": "Protein Language" + "id": 3, + "label": "Protein Design" } ], "_embedding": [ @@ -748897,31 +749047,31 @@ "journal": "Nature", "projections": { "pca": [ - 0.0917411744594574, - 0.18694137036800385 + 0.09229880571365356, + 0.18747515976428986 ], "tsne": [ - -2.870595932006836, - 16.92892074584961 + 1.9568740129470825, + -33.73744583129883 ], "umap": [ - 5.525676250457764, - 4.190423488616943 + 6.151798725128174, + 3.874882698059082 ], "pca3d": [ - 0.0917406752705574, - 0.18694089353084564, - 0.2289961725473404 + 0.09229832887649536, + 0.18747422099113464, + 0.22856470942497253 ], "tsne3d": [ - 2.9010207653045654, - 4.730607986450195, - 9.3610258102417 + -4.5515947341918945, + -16.463172912597656, + -1.7511062622070312 ], "umap3d": [ - 5.5917510986328125, - 6.835125923156738, - 5.011918544769287 + 6.004575729370117, + 7.638680458068848, + 3.133176565170288 ] }, "cluster_id": 3, @@ -748932,8 +749082,8 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { "id": 3, @@ -749373,31 +749523,31 @@ "openalex_url": "https://openalex.org/W4414760252", "projections": { "pca": [ - 0.13476094603538513, - 0.04380815476179123 + 0.13508567214012146, + 0.04404691979289055 ], "tsne": [ - -1.5743921995162964, - 13.938532829284668 + -1.5230681896209717, + -26.615251541137695 ], "umap": [ - 5.483008861541748, - 5.232912063598633 + 5.884163856506348, + 4.901364326477051 ], "pca3d": [ - 0.13476085662841797, - 0.04379967227578163, - 0.2555234432220459 + 0.13508561253547668, + 0.04403819516301155, + 0.2554236948490143 ], "tsne3d": [ - -1.0569981336593628, - -0.3731895983219147, - -2.6836905479431152 + 0.36048999428749084, + -3.840947151184082, + 10.698020935058594 ], "umap3d": [ - 5.315086364746094, - 6.269655704498291, - 3.9965415000915527 + 5.364640712738037, + 6.570866584777832, + 2.8261241912841797 ] }, "cluster_id": 3, @@ -749408,12 +749558,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, - "label": "Protein Design" + "id": 32, + "label": "Genome Protein" } ], "_embedding": [ @@ -749821,31 +749971,31 @@ "journal": "Nature Biotechnology", "projections": { "pca": [ - 0.059491634368896484, - -0.13119833171367645 + 0.05942106246948242, + -0.13090215623378754 ], "tsne": [ - 4.663683891296387, - 29.1507625579834 + 5.512975215911865, + -18.429515838623047 ], "umap": [ - 6.261889934539795, - 4.313258647918701 + 6.7832441329956055, + 4.037729263305664 ], "pca3d": [ - 0.059491537511348724, - -0.13118751347064972, - 0.23558269441127777 + 0.05942107364535332, + -0.13089095056056976, + 0.23590759932994843 ], "tsne3d": [ - 3.648820161819458, - 15.496678352355957, - -0.3665335774421692 + 4.935400485992432, + -9.920075416564941, + -1.606810450553894 ], "umap3d": [ - 5.301655292510986, - 6.033548831939697, - 4.862553119659424 + 5.921228408813477, + 6.9685587882995605, + 3.5459156036376953 ] }, "cluster_id": 3, @@ -749856,11 +750006,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -750271,31 +750421,31 @@ "journal": "Nature Methods", "projections": { "pca": [ - -0.03870144113898277, - -0.16764016449451447 + -0.038800761103630066, + -0.16713102161884308 ], "tsne": [ - 1.3420072793960571, - 28.703521728515625 + 0.23795537650585175, + -17.911455154418945 ], "umap": [ - 6.4450602531433105, - 4.380803108215332 + 6.9284467697143555, + 4.122182369232178 ], "pca3d": [ - -0.03870155289769173, - -0.16761086881160736, - 0.15088799595832825 + -0.03880079835653305, + -0.16710145771503448, + 0.15142259001731873 ], "tsne3d": [ - 2.267061471939087, - 13.182045936584473, - -2.045072078704834 + 3.3384010791778564, + -7.100549221038818, + -1.6323782205581665 ], "umap3d": [ - 5.280065536499023, - 5.814116954803467, - 4.917330741882324 + 6.066494941711426, + 6.74327278137207, + 3.69697904586792 ] }, "cluster_id": 3, @@ -750306,11 +750456,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -750728,31 +750878,31 @@ ], "projections": { "pca": [ - 0.04316215217113495, - -0.1441405713558197 + 0.04308725893497467, + -0.14391914010047913 ], "tsne": [ - -8.779280662536621, - 31.22314453125 + -5.782792091369629, + -30.023372650146484 ], "umap": [ - 5.212151050567627, - 3.8289992809295654 + 5.87107515335083, + 3.3409645557403564 ], "pca3d": [ - 0.043162524700164795, - -0.14415745437145233, - 0.2256292849779129 + 0.04308762773871422, + -0.14393647015094757, + 0.2258778214454651 ], "tsne3d": [ - -3.165130853652954, - 13.815657615661621, - 9.377882957458496 + -0.9673337340354919, + -18.287044525146484, + 6.327625751495361 ], "umap3d": [ - 4.716948509216309, - 6.94616174697876, - 5.227941036224365 + 5.307405471801758, + 7.7334771156311035, + 3.6955225467681885 ] }, "cluster_id": 3, @@ -750763,11 +750913,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -751204,31 +751354,31 @@ "doi": "10.1101/2025.09.18.676967", "projections": { "pca": [ - 0.10554441064596176, - -0.09669171273708344 + 0.10549356788396835, + -0.09686319530010223 ], "tsne": [ - -12.556855201721191, - 31.039573669433594 + -7.9975504875183105, + -28.490726470947266 ], "umap": [ - 4.951055526733398, - 3.971147060394287 + 5.5353803634643555, + 3.465069532394409 ], "pca3d": [ - 0.10554450005292892, - -0.09668887406587601, - 0.21926501393318176 + 0.10549376159906387, + -0.09686050564050674, + 0.21929281949996948 ], "tsne3d": [ - -2.471518039703369, - 8.783608436584473, - 12.325427055358887 + -1.391546607017517, + -16.05519676208496, + 10.27209758758545 ], "umap3d": [ - 4.726775646209717, - 7.203364372253418, - 4.7599616050720215 + 5.059494972229004, + 7.767190933227539, + 3.3745017051696777 ] }, "cluster_id": 3, @@ -751239,11 +751389,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -751667,31 +751817,31 @@ "openalex_url": "https://openalex.org/W4414162128", "projections": { "pca": [ - 0.11738637089729309, - -0.13324804604053497 + 0.1172134205698967, + -0.1328687220811844 ], "tsne": [ - -1.4464092254638672, - 31.389741897583008 + -0.9794492125511169, + -17.73971939086914 ], "umap": [ - 6.3704938888549805, - 3.954989194869995 + 6.872563362121582, + 3.8812501430511475 ], "pca3d": [ - 0.11738644540309906, - -0.1332574039697647, - 0.14766651391983032 + 0.11721357703208923, + -0.1328783929347992, + 0.14795221388339996 ], "tsne3d": [ - 1.6718069314956665, - 15.35301685333252, - 2.6264712810516357 + 4.622012138366699, + -13.430827140808105, + 5.3525166511535645 ], "umap3d": [ - 5.350550174713135, - 6.066591262817383, - 5.111956596374512 + 6.224680423736572, + 7.0757036209106445, + 3.6622154712677 ] }, "cluster_id": 3, @@ -751702,11 +751852,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -752126,31 +752276,31 @@ "doi": "10.1299/mej.25-00220", "projections": { "pca": [ - -0.10957767069339752, - 0.25383439660072327 + -0.10887632519006729, + 0.2541738748550415 ], "tsne": [ - -24.90213966369629, - 9.140154838562012 + -27.996713638305664, + 32.271907806396484 ], "umap": [ - 2.6352379322052, - 6.376909255981445 + 3.1241989135742188, + 5.754266262054443 ], "pca3d": [ - -0.10957809537649155, - 0.25382810831069946, - -0.024329692125320435 + -0.10887662321329117, + 0.2541675269603729, + -0.02475384809076786 ], "tsne3d": [ - -14.583483695983887, - 19.76837730407715, - -8.775440216064453 + -5.7905964851379395, + -16.390941619873047, + -13.607635498046875 ], "umap3d": [ - 2.3542585372924805, - 5.440865993499756, - 4.551783561706543 + 3.0916523933410645, + 6.015022277832031, + 4.711261749267578 ] }, "cluster_id": 2, @@ -752161,12 +752311,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 33, - "label": "Neural Networks" + "id": 19, + "label": "Graph Neural Nets" } ], "_embedding": [ @@ -752609,31 +752759,31 @@ "cited_by_count": 146, "projections": { "pca": [ - 0.22779329121112823, - 0.03102283924818039 + 0.22805635631084442, + 0.03050975501537323 ], "tsne": [ - -17.190296173095703, - 31.478992462158203 + -8.824235916137695, + -38.20420837402344 ], "umap": [ - 4.83564567565918, - 3.7200069427490234 + 5.474281311035156, + 3.294450521469116 ], "pca3d": [ - 0.22779324650764465, - 0.03100467473268509, - 0.19393377006053925 + 0.22805631160736084, + 0.03049100935459137, + 0.19371841847896576 ], "tsne3d": [ - -5.311781406402588, - 7.560746669769287, - 14.679069519042969 + -5.253564834594727, + -14.948505401611328, + 13.25067138671875 ], "umap3d": [ - 4.914693355560303, - 7.442498207092285, - 4.600053310394287 + 5.027939796447754, + 7.893285751342773, + 3.0461230278015137 ] }, "cluster_id": 3, @@ -752644,11 +752794,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -753108,31 +753258,31 @@ "openalex_url": "https://openalex.org/W4413257724", "projections": { "pca": [ - -0.1184801235795021, - -0.0676538348197937 + -0.11838141828775406, + -0.06763849407434464 ], "tsne": [ - -16.890356063842773, - 14.277512550354004 + -10.232695579528809, + -14.973627090454102 ], "umap": [ - 5.807944297790527, - 4.819821357727051 + 5.753596782684326, + 4.41202974319458 ], "pca3d": [ - -0.1184801310300827, - -0.06763394176959991, - 0.13358572125434875 + -0.11838142573833466, + -0.0676184743642807, + 0.13380087912082672 ], "tsne3d": [ - -0.5665098428726196, - -13.109719276428223, - -22.7989559173584 + -0.29539990425109863, + 21.434852600097656, + 12.270235061645508 ], "umap3d": [ - 5.000227928161621, - 6.53804349899292, - 4.198076248168945 + 5.343469619750977, + 6.933506488800049, + 2.8979082107543945 ] }, "cluster_id": 3, @@ -753143,11 +753293,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -753573,31 +753723,31 @@ "openalex_url": "https://openalex.org/W4413312092", "projections": { "pca": [ - 0.04108278825879097, - -0.174050435423851 + 0.04074743390083313, + -0.17342381179332733 ], "tsne": [ - 1.050297498703003, - 28.377140045166016 + 0.3694935739040375, + -18.281641006469727 ], "umap": [ - 6.486144065856934, - 4.396896839141846 + 6.984786033630371, + 4.1527180671691895 ], "pca3d": [ - 0.041082963347435, - -0.17404741048812866, - 0.09511147439479828 + 0.04074765741825104, + -0.17342104017734528, + 0.09550520777702332 ], "tsne3d": [ - 2.228370428085327, - 12.258138656616211, - -1.1231184005737305 + 2.567307233810425, + -7.344936847686768, + -0.6270193457603455 ], "umap3d": [ - 5.2415595054626465, - 5.815404891967773, - 4.927006721496582 + 6.069589138031006, + 6.727914333343506, + 3.737121343612671 ] }, "cluster_id": 3, @@ -753608,11 +753758,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -754061,31 +754211,31 @@ "openalex_url": "https://openalex.org/W4412165572", "projections": { "pca": [ - 0.058259427547454834, - -0.11699613183736801 + 0.05810246616601944, + -0.11696666479110718 ], "tsne": [ - -10.751378059387207, - 30.381643295288086 + -6.93789529800415, + -29.39996337890625 ], "umap": [ - 5.157869815826416, - 4.016918182373047 + 5.780187606811523, + 3.595982074737549 ], "pca3d": [ - 0.05825955420732498, - -0.11699271947145462, - 0.20358948409557343 + 0.05810266360640526, + -0.11696324497461319, + 0.20366978645324707 ], "tsne3d": [ - -4.370955944061279, - 10.689400672912598, - 10.77365779876709 + -2.7206709384918213, + -17.956613540649414, + 8.548397064208984 ], "umap3d": [ - 4.8529744148254395, - 7.015860557556152, - 4.991634845733643 + 5.332973957061768, + 7.7704010009765625, + 3.458521604537964 ] }, "cluster_id": 3, @@ -754096,11 +754246,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -754519,31 +754669,31 @@ "doi": "10.1101/2021.09.15.460390", "projections": { "pca": [ - 0.18616962432861328, - -0.03872738033533096 + 0.1860153079032898, + -0.038969650864601135 ], "tsne": [ - 36.113311767578125, - 13.938075065612793 + 42.60874938964844, + -4.960147857666016 ], "umap": [ - 8.290395736694336, - 7.16451358795166 + 8.661205291748047, + 6.755698204040527 ], "pca3d": [ - 0.18616969883441925, - -0.03875232860445976, - 0.03703344985842705 + 0.18601544201374054, + -0.03899434208869934, + 0.03694078326225281 ], "tsne3d": [ - 15.655436515808105, - 2.7676198482513428, - 9.675508499145508 + 20.654935836791992, + 5.140340328216553, + 12.557351112365723 ], "umap3d": [ - 7.570837020874023, - 4.333877086639404, - 2.3959243297576904 + 7.473972320556641, + 4.49569845199585, + 1.1633973121643066 ] }, "cluster_id": 0, @@ -754554,12 +754704,12 @@ "label": "Chromatin Regulation" }, { - "id": 13, - "label": "Genetic Variants" + "id": 2, + "label": "DNA Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -754976,31 +755126,31 @@ "cited_by_count": 2, "projections": { "pca": [ - 0.11699715256690979, - -0.12508822977542877 + 0.11679735034704208, + -0.12498719990253448 ], "tsne": [ - 4.549742221832275, - 26.162363052368164 + 3.836256742477417, + -16.202613830566406 ], "umap": [ - 6.573551177978516, - 4.6223955154418945 + 6.946803092956543, + 4.3989057540893555 ], "pca3d": [ - 0.11699707806110382, - -0.125078946352005, - 0.110151506960392 + 0.11679735779762268, + -0.1249777153134346, + 0.11039171367883682 ], "tsne3d": [ - 7.2245869636535645, - 8.183686256408691, - 1.3793174028396606 + -0.36905935406684875, + -3.4416465759277344, + 1.6466771364212036 ], "umap3d": [ - 5.532011985778809, - 5.808709144592285, - 4.821484565734863 + 6.376155853271484, + 6.702394485473633, + 3.3729074001312256 ] }, "cluster_id": 3, @@ -755011,11 +755161,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -755432,31 +755582,31 @@ "doi": "10.1101/2025.07.29.667290", "projections": { "pca": [ - 0.09544520080089569, - -0.1000061109662056 + 0.09537338465452194, + -0.09985852986574173 ], "tsne": [ - -9.22935676574707, - 24.05706024169922 + -0.15221799910068512, + -28.982379913330078 ], "umap": [ - 5.172459602355957, - 4.173087120056152 + 5.688950538635254, + 3.7789626121520996 ], "pca3d": [ - 0.09544528275728226, - -0.10000962018966675, - 0.22232358157634735 + 0.09537350386381149, + -0.09986253082752228, + 0.2224239706993103 ], "tsne3d": [ - -5.030602931976318, - 5.634136199951172, - 6.5191497802734375 + -4.860517978668213, + -12.75796127319336, + 5.655386924743652 ], "umap3d": [ - 5.108684539794922, - 6.988837718963623, - 4.733242034912109 + 5.352936744689941, + 7.575019359588623, + 3.2734498977661133 ] }, "cluster_id": 3, @@ -755467,11 +755617,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -755901,31 +756051,31 @@ "cited_by_count": 67, "projections": { "pca": [ - 0.18825265765190125, - 0.006524238735437393 + 0.1884029060602188, + 0.006545473821461201 ], "tsne": [ - -3.8712477684020996, - 13.219503402709961 + 12.935548782348633, + -21.934993743896484 ], "umap": [ - 5.807321548461914, - 5.399634838104248 + 6.094083786010742, + 5.069360256195068 ], "pca3d": [ - 0.1882527470588684, - 0.006501967087388039, - 0.16734394431114197 + 0.18840298056602478, + 0.0065229106694459915, + 0.1673087626695633 ], "tsne3d": [ - 0.9527661204338074, - -2.7671995162963867, - 0.07799465209245682 + -1.8321295976638794, + -1.1551014184951782, + 10.996451377868652 ], "umap3d": [ - 5.76587438583374, - 6.311319828033447, - 3.7737298011779785 + 5.572273254394531, + 6.47564172744751, + 2.4057199954986572 ] }, "cluster_id": 3, @@ -755936,12 +756086,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, - "label": "Protein Design" + "id": 32, + "label": "Genome Protein" } ], "_embedding": [ @@ -756362,31 +756512,31 @@ "doi": "10.1101/2025.07.25.666746", "projections": { "pca": [ - 0.0728018656373024, - -0.14608263969421387 + 0.07258043438196182, + -0.14675390720367432 ], "tsne": [ - 17.440528869628906, - 33.37178039550781 + 20.27745246887207, + -31.549095153808594 ], "umap": [ - 7.953939914703369, - 4.485851287841797 + 8.308259010314941, + 3.978290319442749 ], "pca3d": [ - 0.07280182838439941, - -0.14604973793029785, - 0.07852284610271454 + 0.07258046418428421, + -0.146719828248024, + 0.07864899188280106 ], "tsne3d": [ - 3.4417126178741455, - 23.07897186279297, - 6.568315505981445 + 10.55603313446045, + -18.803808212280273, + 6.976657867431641 ], "umap3d": [ - 7.21504545211792, - 6.3184895515441895, - 4.042633056640625 + 7.404266357421875, + 6.8374810218811035, + 2.212770938873291 ] }, "cluster_id": 4, @@ -756397,11 +756547,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -756827,31 +756977,31 @@ "doi": "10.1101/2025.07.21.665832", "projections": { "pca": [ - 0.13024850189685822, - -0.1472165584564209 + 0.1300477534532547, + -0.1470775306224823 ], "tsne": [ - -4.300801753997803, - 25.175678253173828 + 1.5476760864257812, + -24.54609489440918 ], "umap": [ - 5.819823741912842, - 4.477883338928223 + 6.179548740386963, + 4.196166515350342 ], "pca3d": [ - 0.13024868071079254, - -0.14722271263599396, - 0.2961404025554657 + 0.1300480216741562, + -0.14708371460437775, + 0.29630088806152344 ], "tsne3d": [ - -2.432269334793091, - 7.605478286743164, - 5.377878665924072 + -1.865776538848877, + -9.670578002929688, + 6.488004207611084 ], "umap3d": [ - 5.361266613006592, - 6.421407699584961, - 4.620686054229736 + 5.6266188621521, + 7.111795902252197, + 3.173413038253784 ] }, "cluster_id": 3, @@ -756862,12 +757012,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, - "label": "Protein Language" + "id": 3, + "label": "Protein Design" } ], "_embedding": [ @@ -757320,31 +757470,31 @@ "openalex_url": "https://openalex.org/W4412491316", "projections": { "pca": [ - 0.142377570271492, - -0.055674001574516296 + 0.1424272060394287, + -0.05603858456015587 ], "tsne": [ - -15.859667778015137, - 30.301103591918945 + -7.274929046630859, + -37.53966522216797 ], "umap": [ - 4.891749858856201, - 3.731466770172119 + 5.590901851654053, + 3.3772966861724854 ], "pca3d": [ - 0.14237773418426514, - -0.055688757449388504, - 0.1820194125175476 + 0.14242734014987946, + -0.05605398118495941, + 0.18194974958896637 ], "tsne3d": [ - -4.0569539070129395, - 5.415980339050293, - 14.094310760498047 + -5.32266902923584, + -17.30155372619629, + 12.367687225341797 ], "umap3d": [ - 4.99584436416626, - 7.3468403816223145, - 4.572143077850342 + 5.132242679595947, + 7.884043216705322, + 3.0835037231445312 ] }, "cluster_id": 3, @@ -757355,11 +757505,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -757772,31 +757922,31 @@ ], "projections": { "pca": [ - 0.060381367802619934, - -0.12075822800397873 + 0.06022629141807556, + -0.12015237659215927 ], "tsne": [ - -0.8777040839195251, - 27.634185791015625 + -1.0963592529296875, + -21.55438232421875 ], "umap": [ - 6.064881801605225, - 4.6633124351501465 + 6.55165433883667, + 4.32689905166626 ], "pca3d": [ - 0.060381341725587845, - -0.12075000256299973, - 0.2724885940551758 + 0.06022631749510765, + -0.12014423310756683, + 0.2726965546607971 ], "tsne3d": [ - -0.9151572585105896, - 10.869560241699219, - -0.35339054465293884 + 0.16235032677650452, + -8.174972534179688, + 6.998610019683838 ], "umap3d": [ - 5.10715389251709, - 6.020802974700928, - 4.700723648071289 + 5.781044006347656, + 6.76776123046875, + 3.504652976989746 ] }, "cluster_id": 3, @@ -757807,11 +757957,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -758237,47 +758387,47 @@ "doi": "10.1101/2025.01.10.25320181", "projections": { "pca": [ - 0.21147876977920532, - 0.09150215983390808 + 0.2117120623588562, + 0.09111594408750534 ], "tsne": [ - 33.89089584350586, - 7.326870441436768 + 38.104583740234375, + -10.965231895446777 ], "umap": [ - 7.675243377685547, - 8.324722290039062 + 7.944525241851807, + 7.845793724060059 ], "pca3d": [ - 0.21147844195365906, - 0.09149634838104248, - -0.09661673754453659 + 0.2117118090391159, + 0.09110992401838303, + -0.09680253267288208 ], "tsne3d": [ - 18.382862091064453, - -15.311409950256348, - -11.394330024719238 + 16.971200942993164, + 20.608623504638672, + -0.9093170762062073 ], "umap3d": [ - 6.93980073928833, - 3.284605026245117, - 2.30192494392395 + 7.100403308868408, + 3.3689634799957275, + 1.704626202583313 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -758687,31 +758837,31 @@ "doi": "10.1101/2024.08.20.608844", "projections": { "pca": [ - 0.07105599343776703, - 0.069887675344944 + 0.07119304686784744, + 0.0705956369638443 ], "tsne": [ - -2.984715223312378, - 19.882333755493164 + 8.667774200439453, + -23.997940063476562 ], "umap": [ - 5.674907207489014, - 4.313657283782959 + 6.0973219871521, + 3.9906868934631348 ], "pca3d": [ - 0.07105576992034912, - 0.06987934559583664, - 0.20756499469280243 + 0.0711929127573967, + 0.07058682292699814, + 0.2073124200105667 ], "tsne3d": [ - 0.9559170007705688, - 2.955806016921997, - 8.266471862792969 + -6.097757816314697, + -13.932591438293457, + -3.303506374359131 ], "umap3d": [ - 5.662409782409668, - 6.771850109100342, - 4.870245933532715 + 6.021994113922119, + 7.556246280670166, + 2.9986214637756348 ] }, "cluster_id": 3, @@ -758722,12 +758872,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, - "label": "Protein Language" + "id": 3, + "label": "Protein Design" } ], "_embedding": [ @@ -759133,31 +759283,31 @@ ], "projections": { "pca": [ - 0.025899497792124748, - 0.1205734834074974 + 0.026365987956523895, + 0.12104450166225433 ], "tsne": [ - -19.709693908691406, - 26.708446502685547 + 0.015353950671851635, + -41.3331184387207 ], "umap": [ - 4.249513626098633, - 3.557943344116211 + 4.856685638427734, + 3.096776247024536 ], "pca3d": [ - 0.025899315252900124, - 0.12056609988212585, - 0.17265915870666504 + 0.02636580355465412, + 0.12103666365146637, + 0.172519713640213 ], "tsne3d": [ - -4.082003593444824, - 15.099315643310547, - -15.528253555297852 + -3.5484654903411865, + -7.654850482940674, + 16.568777084350586 ], "umap3d": [ - 4.88845682144165, - 7.799715042114258, - 4.2115559577941895 + 4.613114833831787, + 8.032630920410156, + 2.7948200702667236 ] }, "cluster_id": 3, @@ -759168,12 +759318,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -759593,31 +759743,31 @@ "doi": "10.1101/2025.01.17.633626", "projections": { "pca": [ - 0.07987882196903229, - -0.10269204527139664 + 0.07978741824626923, + -0.10225572437047958 ], "tsne": [ - -0.5365081429481506, - 19.02333641052246 + 6.6271514892578125, + -24.82139778137207 ], "umap": [ - 6.076825141906738, - 4.676311016082764 + 6.473790168762207, + 4.377770900726318 ], "pca3d": [ - 0.0798790454864502, - -0.10270733386278152, - 0.2614940106868744 + 0.07978765666484833, + -0.10227099061012268, + 0.2616932988166809 ], "tsne3d": [ - 2.680899143218994, - 4.2852678298950195, - 3.8205230236053467 + -2.5963315963745117, + -10.875341415405273, + -1.3551772832870483 ], "umap3d": [ - 5.8258490562438965, - 6.387555122375488, - 4.609373092651367 + 6.193874359130859, + 7.105863094329834, + 2.9332492351531982 ] }, "cluster_id": 3, @@ -759628,11 +759778,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -760058,31 +760208,31 @@ "doi": "10.1101/2025.04.15.649055", "projections": { "pca": [ - 0.0969998687505722, - -0.1526831090450287 + 0.09673865884542465, + -0.1521451324224472 ], "tsne": [ - -0.06191849336028099, - 25.136390686035156 + 3.0656533241271973, + -20.953886032104492 ], "umap": [ - 6.139814853668213, - 4.519313335418701 + 6.577409744262695, + 4.1518707275390625 ], "pca3d": [ - 0.09700008481740952, - -0.15268835425376892, - 0.22750414907932281 + 0.09673884510993958, + -0.15215010941028595, + 0.22776494920253754 ], "tsne3d": [ - 1.3581734895706177, - 8.272006034851074, - 1.963614821434021 + -0.6188828945159912, + -8.821698188781738, + 2.52803373336792 ], "umap3d": [ - 5.343447208404541, - 6.207636833190918, - 4.779878616333008 + 5.958617210388184, + 6.94814920425415, + 3.351534843444824 ] }, "cluster_id": 3, @@ -760093,11 +760243,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -760513,31 +760663,31 @@ ], "projections": { "pca": [ - 0.05721839517354965, - -0.17308400571346283 + 0.05684623867273331, + -0.172737255692482 ], "tsne": [ - -4.801095008850098, - 21.098936080932617 + 5.047881126403809, + -27.31195640563965 ], "umap": [ - 5.6222662925720215, - 4.621501922607422 + 6.076015949249268, + 4.229506492614746 ], "pca3d": [ - 0.05721861496567726, - -0.17308630049228668, - 0.1553218960762024 + 0.056846555322408676, + -0.17273938655853271, + 0.15554282069206238 ], "tsne3d": [ - -4.030370712280273, - 1.3920303583145142, - 1.0619282722473145 + -7.065149784088135, + -10.353681564331055, + -0.5777161717414856 ], "umap3d": [ - 5.641292095184326, - 6.739925861358643, - 4.646183013916016 + 5.862796306610107, + 7.4246439933776855, + 2.8114542961120605 ] }, "cluster_id": 3, @@ -760548,11 +760698,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -760970,31 +761120,31 @@ ], "projections": { "pca": [ - 0.10630211234092712, - -0.09997447580099106 + 0.10622149705886841, + -0.09981444478034973 ], "tsne": [ - -0.09852363914251328, - 30.88429832458496 + -2.4392757415771484, + -18.952219009399414 ], "umap": [ - 6.182091236114502, - 4.20505428314209 + 6.645920276641846, + 3.793839454650879 ], "pca3d": [ - 0.10630222409963608, - -0.09998099505901337, - 0.20611605048179626 + 0.10622166842222214, + -0.09982052445411682, + 0.20626506209373474 ], "tsne3d": [ - 6.46873140335083, - 10.125082015991211, - 7.14667272567749 + 4.72977876663208, + -9.666961669921875, + 7.3615922927856445 ], "umap3d": [ - 5.473952293395996, - 6.29819917678833, - 5.150972843170166 + 6.106160640716553, + 7.209107875823975, + 3.4713656902313232 ] }, "cluster_id": 3, @@ -761005,11 +761155,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -761432,31 +761582,31 @@ ], "projections": { "pca": [ - 0.14108659327030182, - 0.004743002355098724 + 0.14121133089065552, + 0.004552405793219805 ], "tsne": [ - -17.603057861328125, - 25.343578338623047 + 0.6623446941375732, + -39.06471252441406 ], "umap": [ - 4.3112897872924805, - 3.6338958740234375 + 4.863070487976074, + 3.1257505416870117 ], "pca3d": [ - 0.1410866528749466, - 0.004728666506707668, - 0.15348587930202484 + 0.14121143519878387, + 0.004537760280072689, + 0.15334711968898773 ], "tsne3d": [ - -5.983921527862549, - 11.75568962097168, - -13.286310195922852 + -1.2484047412872314, + -8.07751178741455, + 19.576505661010742 ], "umap3d": [ - 4.843856334686279, - 7.738934516906738, - 4.2934980392456055 + 4.604067325592041, + 8.014055252075195, + 2.8534674644470215 ] }, "cluster_id": 3, @@ -761467,12 +761617,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -761889,31 +762039,31 @@ ], "projections": { "pca": [ - 0.09422516077756882, - -0.11665274947881699 + 0.09415923804044724, + -0.11645656824111938 ], "tsne": [ - -8.467103958129883, - 23.72166633605957 + 0.5995407104492188, + -29.41229820251465 ], "umap": [ - 5.233310222625732, - 4.216125011444092 + 5.766796588897705, + 3.853684186935425 ], "pca3d": [ - 0.09422529488801956, - -0.11666055768728256, - 0.2760506570339203 + 0.09415946155786514, + -0.11646465212106705, + 0.27618682384490967 ], "tsne3d": [ - -3.6267220973968506, - 5.202249050140381, - 7.123440742492676 + -6.143155097961426, + -13.328474044799805, + 5.49945592880249 ], "umap3d": [ - 5.205711841583252, - 6.993640899658203, - 4.724515914916992 + 5.4581990242004395, + 7.645274639129639, + 3.0851516723632812 ] }, "cluster_id": 3, @@ -761924,7 +762074,7 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { @@ -762351,31 +762501,31 @@ "doi": "10.1101/2025.05.27.656341", "projections": { "pca": [ - 0.0886164978146553, - -0.042114272713661194 + 0.08859709650278091, + -0.04192672297358513 ], "tsne": [ - -13.517399787902832, - 36.62852096557617 + -13.563746452331543, + -28.267332077026367 ], "umap": [ - 5.166424751281738, - 3.7200403213500977 + 5.565959453582764, + 3.3606951236724854 ], "pca3d": [ - 0.08861659467220306, - -0.04210447147488594, - 0.15084896981716156 + 0.08859714865684509, + -0.04191678762435913, + 0.15081767737865448 ], "tsne3d": [ - 1.5954631567001343, - 12.122753143310547, - 15.99354362487793 + 2.1399693489074707, + -15.84289264678955, + 14.760712623596191 ], "umap3d": [ - 4.604563236236572, - 7.043700218200684, - 4.976133346557617 + 5.136231899261475, + 7.673736572265625, + 3.7250430583953857 ] }, "cluster_id": 3, @@ -762386,11 +762536,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -762797,31 +762947,31 @@ ], "projections": { "pca": [ - -0.026113372296094894, - 0.050590332597494125 + -0.025658341124653816, + 0.05061924830079079 ], "tsne": [ - 0.8592881560325623, - 23.453523635864258 + -0.03992461413145065, + -20.41855239868164 ], "umap": [ - 6.243667125701904, - 4.548061847686768 + 6.658410549163818, + 4.257606029510498 ], "pca3d": [ - -0.026113668456673622, - 0.05060632899403572, - 0.1408015638589859 + -0.02565862610936165, + 0.05063563957810402, + 0.14081962406635284 ], "tsne3d": [ - 0.9648040533065796, - 9.753546714782715, - -2.2018346786499023 + -1.2090214490890503, + -6.228837013244629, + 5.676503658294678 ], "umap3d": [ - 5.200159549713135, - 5.96040153503418, - 4.781714916229248 + 5.876809597015381, + 6.774314880371094, + 3.5023319721221924 ] }, "cluster_id": 3, @@ -762832,11 +762982,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -763254,31 +763404,31 @@ ], "projections": { "pca": [ - 0.07162605226039886, - -0.12857601046562195 + 0.07136436551809311, + -0.12812156975269318 ], "tsne": [ - -5.587853908538818, - 21.402206420898438 + 4.664669990539551, + -28.07671356201172 ], "umap": [ - 5.437666416168213, - 4.639947891235352 + 5.868946075439453, + 4.312468528747559 ], "pca3d": [ - 0.07162614911794662, - -0.12857168912887573, - 0.26202017068862915 + 0.07136449217796326, + -0.12811729311943054, + 0.26214495301246643 ], "tsne3d": [ - -3.471102237701416, - 4.82612943649292, - 1.7122505903244019 + -5.857851982116699, + -9.603179931640625, + 1.2369686365127563 ], "umap3d": [ - 5.538268089294434, - 6.765438556671143, - 4.591683864593506 + 5.718206405639648, + 7.502676486968994, + 2.9566097259521484 ] }, "cluster_id": 3, @@ -763289,11 +763439,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -763715,31 +763865,31 @@ "doi": "10.1101/2025.05.02.651993", "projections": { "pca": [ - 0.09622889757156372, - -0.1284232884645462 + 0.09607454389333725, + -0.12801378965377808 ], "tsne": [ - -3.536113977432251, - 23.990203857421875 + 2.860433340072632, + -24.414466857910156 ], "umap": [ - 5.859356880187988, - 4.516780853271484 + 6.244963645935059, + 4.2054338455200195 ], "pca3d": [ - 0.09622908383607864, - -0.12843409180641174, - 0.26466915011405945 + 0.09607482701539993, + -0.12802499532699585, + 0.2648598253726959 ], "tsne3d": [ - -1.045504093170166, - 7.211489200592041, - 3.8062329292297363 + -3.1366591453552246, + -10.030488014221191, + 4.435524940490723 ], "umap3d": [ - 5.464423179626465, - 6.456814289093018, - 4.506679058074951 + 5.762826442718506, + 7.1109843254089355, + 3.1149182319641113 ] }, "cluster_id": 3, @@ -763750,12 +763900,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, - "label": "Protein Language" + "id": 3, + "label": "Protein Design" } ], "_embedding": [ @@ -764176,31 +764326,31 @@ "doi": "10.1101/2025.05.03.652001", "projections": { "pca": [ - 0.14925047755241394, - -0.08204513043165207 + 0.14906883239746094, + -0.08214603364467621 ], "tsne": [ - -6.624259948730469, - 23.474966049194336 + 3.1653926372528076, + -31.67605209350586 ], "umap": [ - 5.333538055419922, - 4.242080211639404 + 5.962308406829834, + 3.97054386138916 ], "pca3d": [ - 0.14925049245357513, - -0.08204430341720581, - 0.16809432208538055 + 0.1490689069032669, + -0.08214550465345383, + 0.1680079847574234 ], "tsne3d": [ - -0.6936116814613342, - 3.695340633392334, - 5.593898296356201 + -5.872116565704346, + -12.433923721313477, + 0.5371286273002625 ], "umap3d": [ - 5.4089674949646, - 6.925813674926758, - 4.857112407684326 + 5.749431610107422, + 7.620953559875488, + 2.981532335281372 ] }, "cluster_id": 3, @@ -764211,8 +764361,8 @@ "label": "Protein Design" }, { - "id": 12, - "label": "Protein Design" + "id": 0, + "label": "Explainable Chemistry" }, { "id": 3, @@ -764636,31 +764786,31 @@ "doi": "10.1101/2025.04.23.646631", "projections": { "pca": [ - 0.1660441756248474, - -0.045750733464956284 + 0.16610799729824066, + -0.04578304663300514 ], "tsne": [ - -9.61009407043457, - 25.700443267822266 + -0.896838366985321, + -29.967151641845703 ], "umap": [ - 5.064079284667969, - 4.160411834716797 + 5.661584854125977, + 3.7137200832366943 ], "pca3d": [ - 0.16604435443878174, - -0.04577549919486046, - 0.2730472683906555 + 0.166108176112175, + -0.045808255672454834, + 0.27295830845832825 ], "tsne3d": [ - -6.680459976196289, - 6.410324573516846, - 8.054960250854492 + -3.477316379547119, + -14.130305290222168, + 6.029849052429199 ], "umap3d": [ - 5.027158260345459, - 7.003319263458252, - 4.730792045593262 + 5.332817554473877, + 7.641531944274902, + 3.278369426727295 ] }, "cluster_id": 3, @@ -764671,11 +764821,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -765086,31 +765236,31 @@ "abstract": "We\u2019re on a journey to advance and democratize artificial intelligence through open source and open science.", "projections": { "pca": [ - -0.12221764773130417, - 0.03621287643909454 + -0.12193644046783447, + 0.03722710534930229 ], "tsne": [ - 5.920944690704346, - 20.68585205078125 + -3.3052632808685303, + -10.877508163452148 ], "umap": [ - 6.232804298400879, - 5.0582051277160645 + 6.616790294647217, + 4.6699018478393555 ], "pca3d": [ - -0.12221724539995193, - 0.03617943823337555, - 0.14358936250209808 + -0.12193603068590164, + 0.037193212658166885, + 0.14371806383132935 ], "tsne3d": [ - -3.510227680206299, - 8.13765811920166, - -5.92525577545166 + -0.8585496544837952, + -5.672614097595215, + -5.280030250549316 ], "umap3d": [ - 5.099294185638428, - 5.5871758460998535, - 4.638899326324463 + 5.819305896759033, + 6.282247066497803, + 3.4432027339935303 ] }, "cluster_id": 3, @@ -765121,12 +765271,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, - "label": "Protein Design" + "id": 32, + "label": "Genome Protein" } ], "_embedding": [ @@ -765542,31 +765692,31 @@ "doi": "10.1101/2025.04.08.647782", "projections": { "pca": [ - 0.19023403525352478, - -0.07673462480306625 + 0.19019027054309845, + -0.07671196758747101 ], "tsne": [ - -9.621956825256348, - 33.098236083984375 + -7.796866416931152, + -31.728307723999023 ], "umap": [ - 5.383848190307617, - 3.695164680480957 + 6.02729606628418, + 3.273228645324707 ], "pca3d": [ - 0.19023416936397552, - -0.07674854248762131, - 0.22692036628723145 + 0.19019050896167755, + -0.07672606408596039, + 0.22690324485301971 ], "tsne3d": [ - -4.297931671142578, - 13.58560848236084, - 11.528258323669434 + -0.8672010898590088, + -21.17359733581543, + 6.166849136352539 ], "umap3d": [ - 5.026114463806152, - 7.045497417449951, - 5.203550338745117 + 5.4666852951049805, + 7.829352855682373, + 3.6280038356781006 ] }, "cluster_id": 3, @@ -765577,11 +765727,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -765999,31 +766149,31 @@ "doi": "10.1101/2025.04.02.646805", "projections": { "pca": [ - 0.06604284048080444, - -0.17961928248405457 + 0.06584585458040237, + -0.17921194434165955 ], "tsne": [ - 2.594949722290039, - 27.226858139038086 + 1.311169147491455, + -20.670671463012695 ], "umap": [ - 6.285365104675293, - 4.2569379806518555 + 6.762456893920898, + 3.9444429874420166 ], "pca3d": [ - 0.06604307889938354, - -0.17962907254695892, - 0.2407314032316208 + 0.06584618985652924, + -0.17922180891036987, + 0.2411280870437622 ], "tsne3d": [ - 3.878657341003418, - 9.451872825622559, - 4.240005970001221 + 1.8905707597732544, + -10.795345306396484, + 1.4346481561660767 ], "umap3d": [ - 5.410522937774658, - 6.053678512573242, - 5.017200469970703 + 6.195279121398926, + 7.037262439727783, + 3.5677764415740967 ] }, "cluster_id": 3, @@ -766034,11 +766184,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -766476,31 +766626,31 @@ "openalex_url": "https://openalex.org/W4404577932", "projections": { "pca": [ - 0.08135341107845306, - -0.08852209895849228 + 0.08122675865888596, + -0.08782299607992172 ], "tsne": [ - -5.207325458526611, - 18.046615600585938 + 7.342146873474121, + -27.352190017700195 ], "umap": [ - 5.501285552978516, - 4.652822971343994 + 5.919694900512695, + 4.390598773956299 ], "pca3d": [ - 0.08135351538658142, - -0.08852142095565796, - 0.2730179727077484 + 0.0812268778681755, + -0.08782240748405457, + 0.2731289267539978 ], "tsne3d": [ - 0.3237084150314331, - 0.7841017842292786, - 2.96148681640625 + -6.153553485870361, + -7.55628776550293, + 4.6892924308776855 ], "umap3d": [ - 5.622374057769775, - 6.823049068450928, - 4.4419145584106445 + 5.69320011138916, + 7.336519718170166, + 2.7785258293151855 ] }, "cluster_id": 3, @@ -766511,11 +766661,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -766938,31 +767088,31 @@ "doi": "10.1101/2024.09.24.614734", "projections": { "pca": [ - 0.04204472899436951, - -0.17015716433525085 + 0.04153897240757942, + -0.17030200362205505 ], "tsne": [ - -5.1804304122924805, - 30.337129592895508 + -1.3146101236343384, + -34.80380630493164 ], "umap": [ - 5.401212692260742, - 3.8777310848236084 + 6.050171852111816, + 3.4916462898254395 ], "pca3d": [ - 0.04204495623707771, - -0.1701536476612091, - 0.16864512860774994 + 0.04153924062848091, + -0.17029835283756256, + 0.16872087121009827 ], "tsne3d": [ - -3.779306650161743, - 14.30031681060791, - 5.151562213897705 + -0.3946138918399811, + -16.408002853393555, + 2.817091226577759 ], "umap3d": [ - 4.883289813995361, - 6.664593696594238, - 5.101073741912842 + 5.4208502769470215, + 7.522731304168701, + 3.544947862625122 ] }, "cluster_id": 3, @@ -766973,11 +767123,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -767393,31 +767543,31 @@ ], "projections": { "pca": [ - 0.07776542752981186, - -0.11254012584686279 + 0.07760565727949142, + -0.1123003214597702 ], "tsne": [ - 2.1626715660095215, - 20.005613327026367 + 7.819073677062988, + -31.39613914489746 ], "umap": [ - 5.750019550323486, - 4.73954963684082 + 6.301636695861816, + 4.336528778076172 ], "pca3d": [ - 0.07776546478271484, - -0.1125287115573883, - 0.21840175986289978 + 0.07760573923587799, + -0.11228881776332855, + 0.2185218632221222 ], "tsne3d": [ - -4.703425884246826, - 5.954559803009033, - -0.586341142654419 + 2.302307605743408, + -18.074932098388672, + -2.1566832065582275 ], "umap3d": [ - 5.61342191696167, - 6.557238578796387, - 4.404604434967041 + 5.804075241088867, + 7.1329264640808105, + 2.7414824962615967 ] }, "cluster_id": 3, @@ -767428,11 +767578,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -767856,31 +768006,31 @@ "doi": "10.1101/2025.04.01.646404", "projections": { "pca": [ - 0.042335499078035355, - -0.1038820743560791 + 0.042165130376815796, + -0.10388568043708801 ], "tsne": [ - -14.900460243225098, - 23.587148666381836 + 1.6040586233139038, + -36.11862564086914 ], "umap": [ - 5.413636684417725, - 4.135223865509033 + 5.805267333984375, + 3.7320261001586914 ], "pca3d": [ - 0.04233556613326073, - -0.10386881232261658, - 0.16212725639343262 + 0.04216519370675087, + -0.10387220978736877, + 0.16216826438903809 ], "tsne3d": [ - 2.835458755493164, - 10.853401184082031, - 10.861211776733398 + -1.4125157594680786, + -10.504383087158203, + 13.900979995727539 ], "umap3d": [ - 5.189457416534424, - 7.0155463218688965, - 4.764305591583252 + 5.432825088500977, + 7.578962802886963, + 3.4614615440368652 ] }, "cluster_id": 3, @@ -767891,11 +768041,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -768315,31 +768465,31 @@ "doi": "10.1101/2025.03.19.642801", "projections": { "pca": [ - 0.08226881176233292, - -0.04417223855853081 + 0.0822092816233635, + -0.044066306203603745 ], "tsne": [ - -6.14656400680542, - 35.46861267089844 + -6.7509074211120605, + -33.85125732421875 ], "umap": [ - 5.589958190917969, - 3.8290438652038574 + 6.237442493438721, + 3.393904209136963 ], "pca3d": [ - 0.08226867020130157, - -0.044153496623039246, - 0.14527375996112823 + 0.08220919221639633, + -0.04404744878411293, + 0.14519953727722168 ], "tsne3d": [ - -3.5749690532684326, - 18.105745315551758, - 8.789015769958496 + 1.6826913356781006, + -21.17086410522461, + 3.158543109893799 ], "umap3d": [ - 5.0226664543151855, - 6.636064052581787, - 5.146289825439453 + 5.774778842926025, + 7.6341938972473145, + 3.568683385848999 ] }, "cluster_id": 3, @@ -768350,11 +768500,11 @@ "label": "Protein Design" }, { - "id": 12, - "label": "Protein Design" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -768811,31 +768961,31 @@ ], "projections": { "pca": [ - 0.05097521096467972, - -0.021142706274986267 + 0.05103909969329834, + -0.02045900747179985 ], "tsne": [ - 6.304995059967041, - 16.364103317260742 + 6.4446001052856445, + -9.717872619628906 ], "umap": [ - 6.226223945617676, - 6.265646934509277 + 6.4445390701293945, + 5.7215189933776855 ], "pca3d": [ - 0.05097512900829315, - -0.021135292947292328, - 0.20828871428966522 + 0.05103910341858864, + -0.020451556891202927, + 0.20840919017791748 ], "tsne3d": [ - -0.12069579213857651, - 4.541952610015869, - -10.596870422363281 + 1.083866000175476, + 0.8302940726280212, + 3.178938388824463 ], "umap3d": [ - 5.2010297775268555, - 4.837983131408691, - 3.81101655960083 + 5.788406848907471, + 5.329341411590576, + 3.1224958896636963 ] }, "cluster_id": 4, @@ -768846,12 +768996,12 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 11, - "label": "Genomic Language" + "id": 32, + "label": "Genome Protein" } ], "_embedding": [ @@ -769272,31 +769422,31 @@ "doi": "10.1101/2024.09.23.614603", "projections": { "pca": [ - 0.05579303577542305, - -0.1013382077217102 + 0.055695537477731705, + -0.10075613856315613 ], "tsne": [ - 0.5280812382698059, - 24.57128143310547 + 3.3921446800231934, + -20.50876808166504 ], "umap": [ - 6.2460408210754395, - 4.559170246124268 + 6.6624836921691895, + 4.347587585449219 ], "pca3d": [ - 0.055793095380067825, - -0.10134031623601913, - 0.2034270018339157 + 0.055695682764053345, + -0.1007581353187561, + 0.20367546379566193 ], "tsne3d": [ - 2.0705037117004395, - 7.04724645614624, - 1.587511420249939 + -1.1421972513198853, + -8.613845825195312, + 1.0649189949035645 ], "umap3d": [ - 5.407227993011475, - 6.084158420562744, - 4.751636028289795 + 6.098989009857178, + 6.862738132476807, + 3.36480450630188 ] }, "cluster_id": 3, @@ -769307,11 +769457,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -769720,31 +769870,31 @@ "journal": "Nature", "projections": { "pca": [ - 0.062167223542928696, - 0.028360608965158463 + 0.0623880997300148, + 0.02849302813410759 ], "tsne": [ - -3.5425362586975098, - 36.03214645385742 + -7.5718607902526855, + -24.40804672241211 ], "umap": [ - 5.947822570800781, - 4.033350467681885 + 6.500669002532959, + 3.7423508167266846 ], "pca3d": [ - 0.062167175114154816, - 0.02834663912653923, - 0.08346287161111832 + 0.06238806992769241, + 0.02847878262400627, + 0.08345124125480652 ], "tsne3d": [ - 7.764484882354736, - 9.921106338500977, - 8.369826316833496 + 4.833076477050781, + -10.262421607971191, + 9.066622734069824 ], "umap3d": [ - 5.321101188659668, - 6.403715133666992, - 5.194764614105225 + 6.037899494171143, + 7.318979740142822, + 3.529844284057617 ] }, "cluster_id": 3, @@ -769755,11 +769905,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -770180,31 +770330,31 @@ "doi": "10.1101/2025.02.26.640110", "projections": { "pca": [ - 0.03145575523376465, - -0.0018504996551200747 + 0.03153223916888237, + -0.0017038077348843217 ], "tsne": [ - -17.469736099243164, - 24.01651382446289 + 1.4309910535812378, + -38.669464111328125 ], "umap": [ - 4.352125644683838, - 3.7015151977539062 + 4.974792957305908, + 3.3145384788513184 ], "pca3d": [ - 0.03145572170615196, - -0.0018378160893917084, - 0.0753592848777771 + 0.03153218328952789, + -0.0016913744620978832, + 0.07532044500112534 ], "tsne3d": [ - -6.909346580505371, - 13.600868225097656, - -15.924922943115234 + -0.801152229309082, + -9.49746322631836, + 15.865534782409668 ], "umap3d": [ - 4.879036903381348, - 7.619719505310059, - 4.242094993591309 + 4.658148765563965, + 7.813894271850586, + 2.85721492767334 ] }, "cluster_id": 3, @@ -770215,12 +770365,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -770675,31 +770825,31 @@ "doi": "10.1101/2025.02.19.639050", "projections": { "pca": [ - 0.05769970268011093, - -0.012548000551760197 + 0.057753052562475204, + -0.01232184935361147 ], "tsne": [ - -18.925556182861328, - 26.013540267944336 + 0.5598133206367493, + -40.39129638671875 ], "umap": [ - 4.30658483505249, - 3.6217145919799805 + 4.883836269378662, + 3.127606153488159 ], "pca3d": [ - 0.0576997809112072, - -0.012550502084195614, - 0.14012080430984497 + 0.057753123342990875, + -0.012324828654527664, + 0.1400904804468155 ], "tsne3d": [ - -5.248047828674316, - 13.972350120544434, - -15.367067337036133 + -2.0827925205230713, + -8.381390571594238, + 16.720766067504883 ], "umap3d": [ - 4.907378196716309, - 7.782287120819092, - 4.205220699310303 + 4.605950355529785, + 8.016029357910156, + 2.8160226345062256 ] }, "cluster_id": 3, @@ -770710,12 +770860,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -771142,31 +771292,31 @@ "openalex_url": "https://openalex.org/W4407394592", "projections": { "pca": [ - 0.07442105561494827, - -0.07543905079364777 + 0.07437576353549957, + -0.07512473315000534 ], "tsne": [ - -8.301154136657715, - 21.40333366394043 + 2.770462989807129, + -29.79241943359375 ], "umap": [ - 5.292627811431885, - 4.626162052154541 + 5.733946323394775, + 4.1476898193359375 ], "pca3d": [ - 0.07442110776901245, - -0.07543521374464035, - 0.24506844580173492 + 0.07437583804130554, + -0.07512104511260986, + 0.24510981142520905 ], "tsne3d": [ - -4.719151020050049, - 3.992661237716675, - 4.594725131988525 + -7.712974548339844, + -11.265542984008789, + 4.086647033691406 ], "umap3d": [ - 5.425436019897461, - 6.9327921867370605, - 4.526349067687988 + 5.524120330810547, + 7.5230607986450195, + 2.842163324356079 ] }, "cluster_id": 3, @@ -771177,11 +771327,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 9, + "label": "Protein Design" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -771596,31 +771746,31 @@ ], "projections": { "pca": [ - 0.05099669471383095, - -0.1953934133052826 + 0.0507364422082901, + -0.19477587938308716 ], "tsne": [ - 1.048278570175171, - 28.37613296508789 + 0.4382573664188385, + -18.301916122436523 ], "umap": [ - 6.496759414672852, - 4.410690784454346 + 6.9485602378845215, + 4.114997386932373 ], "pca3d": [ - 0.05099694803357124, - -0.19539688527584076, - 0.164552241563797 + 0.050736747682094574, + -0.194779634475708, + 0.16503307223320007 ], "tsne3d": [ - 1.9181338548660278, - 12.741085052490234, - -0.37476420402526855 + 2.7628414630889893, + -8.229141235351562, + -0.667532205581665 ], "umap3d": [ - 5.199958801269531, - 5.813242435455322, - 4.988219261169434 + 6.037022113800049, + 6.776300430297852, + 3.7522151470184326 ] }, "cluster_id": 3, @@ -771631,11 +771781,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -772059,31 +772209,31 @@ "doi": "10.1101/2024.11.29.625425", "projections": { "pca": [ - 0.05749272555112839, - -0.1207515075802803 + 0.05732656642794609, + -0.12020933628082275 ], "tsne": [ - -0.668834388256073, - 28.28286361694336 + -2.086610794067383, + -21.5527400970459 ], "umap": [ - 6.307013034820557, - 4.859063148498535 + 6.714700698852539, + 4.445143222808838 ], "pca3d": [ - 0.0574929341673851, - -0.12076231837272644, - 0.2002807855606079 + 0.05732675641775131, + -0.12022063881158829, + 0.20052379369735718 ], "tsne3d": [ - -1.1772396564483643, - 11.573265075683594, - 1.0272694826126099 + 1.7733726501464844, + -6.844905853271484, + 7.225536823272705 ], "umap3d": [ - 5.1003804206848145, - 5.775995254516602, - 4.63314962387085 + 5.7585859298706055, + 6.56841516494751, + 3.455096483230591 ] }, "cluster_id": 3, @@ -772094,11 +772244,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -772519,31 +772669,31 @@ "doi": "10.1101/2025.02.06.636901", "projections": { "pca": [ - 0.07733050733804703, - -0.18286357820034027 + 0.07707729190587997, + -0.18239209055900574 ], "tsne": [ - 0.8182386755943298, - 28.103857040405273 + 0.45785245299339294, + -18.628755569458008 ], "umap": [ - 6.451318264007568, - 4.367976188659668 + 6.896588325500488, + 4.096487522125244 ], "pca3d": [ - 0.07733068615198135, - -0.1828603744506836, - 0.1732207089662552 + 0.0770774856209755, + -0.1823890507221222, + 0.17361368238925934 ], "tsne3d": [ - 1.9881333112716675, - 11.691650390625, - -0.27339860796928406 + 1.9516780376434326, + -8.118413925170898, + -0.17483451962471008 ], "umap3d": [ - 5.272407531738281, - 5.835887432098389, - 4.944515705108643 + 6.075976371765137, + 6.803988456726074, + 3.6421494483947754 ] }, "cluster_id": 3, @@ -772554,11 +772704,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -772976,31 +773126,31 @@ ], "projections": { "pca": [ - 0.03255460038781166, - -0.17775920033454895 + 0.032324906438589096, + -0.1774909645318985 ], "tsne": [ - -3.3871994018554688, - 28.926021575927734 + -1.7640448808670044, + -24.723676681518555 ], "umap": [ - 4.646219253540039, - 4.331894397735596 + 5.199185371398926, + 3.849792242050171 ], "pca3d": [ - 0.032554831355810165, - -0.17776645720005035, - 0.1994399130344391 + 0.03232525289058685, + -0.1774987131357193, + 0.1997896134853363 ], "tsne3d": [ - -6.772109508514404, - 11.82464599609375, - 2.4835879802703857 + -6.791122913360596, + -19.199827194213867, + 3.0057930946350098 ], "umap3d": [ - 4.601134300231934, - 6.547308921813965, - 4.876410007476807 + 5.134124755859375, + 7.273890018463135, + 3.687695026397705 ] }, "cluster_id": 3, @@ -773011,12 +773161,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -773433,31 +773583,31 @@ "doi": "10.1101/2025.02.03.636309", "projections": { "pca": [ - 0.04381856694817543, - -0.06324995309114456 + 0.0436878502368927, + -0.063216932117939 ], "tsne": [ - -13.026433944702148, - 34.082618713378906 + -10.875694274902344, + -27.576343536376953 ], "umap": [ - 5.043167591094971, - 3.882194757461548 + 5.633711338043213, + 3.401780128479004 ], "pca3d": [ - 0.04381854459643364, - -0.06323235481977463, - 0.14171044528484344 + 0.043687861412763596, + -0.06319905817508698, + 0.14164698123931885 ], "tsne3d": [ - 0.1447533220052719, - 10.372599601745605, - 13.914392471313477 + 2.196002244949341, + -17.062299728393555, + 12.314355850219727 ], "umap3d": [ - 4.673422813415527, - 7.081225395202637, - 4.791271209716797 + 5.102458953857422, + 7.660307884216309, + 3.404080390930176 ] }, "cluster_id": 3, @@ -773468,11 +773618,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -773920,31 +774070,31 @@ "openalex_url": "https://openalex.org/W4406440058", "projections": { "pca": [ - 0.08850711584091187, - -0.044021740555763245 + 0.08863802254199982, + -0.04322733357548714 ], "tsne": [ - -2.239621877670288, - 24.974937438964844 + 2.313347101211548, + -22.728376388549805 ], "umap": [ - 5.968003749847412, - 4.44456148147583 + 6.495805263519287, + 4.070657253265381 ], "pca3d": [ - 0.08850721269845963, - -0.04403924196958542, - 0.27481403946876526 + 0.08863808959722519, + -0.04324528947472572, + 0.2750012278556824 ], "tsne3d": [ - 1.0719431638717651, - 8.131763458251953, - 4.525146484375 + -1.2436246871948242, + -11.085375785827637, + 3.0136377811431885 ], "umap3d": [ - 5.430338382720947, - 6.279594898223877, - 4.79905366897583 + 5.967342376708984, + 7.097469329833984, + 3.3518593311309814 ] }, "cluster_id": 3, @@ -773955,11 +774105,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -774395,31 +774545,31 @@ "openalex_url": "https://openalex.org/W4407199666", "projections": { "pca": [ - 0.16288815438747406, - -0.07034015655517578 + 0.1628073900938034, + -0.07028791308403015 ], "tsne": [ - -0.5822834372520447, - 25.96417808532715 + 4.767494201660156, + -20.09847068786621 ], "umap": [ - 6.456421375274658, - 4.492166042327881 + 6.89700174331665, + 4.070801258087158 ], "pca3d": [ - 0.16288794577121735, - -0.07033739238977432, - 0.1678977757692337 + 0.16280725598335266, + -0.07028514891862869, + 0.16793736815452576 ], "tsne3d": [ - 8.452618598937988, - 8.13486385345459, - 4.666204929351807 + 0.23311220109462738, + -9.78294849395752, + -0.5233479738235474 ], "umap3d": [ - 5.483602523803711, - 6.067037105560303, - 4.840620994567871 + 6.104140758514404, + 6.899556636810303, + 3.3116848468780518 ] }, "cluster_id": 3, @@ -774430,11 +774580,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -774856,31 +775006,31 @@ "doi": "10.1101/2023.11.27.23299062", "projections": { "pca": [ - 0.05506501346826553, - -0.05644941329956055 + 0.05507497861981392, + -0.056248340755701065 ], "tsne": [ - 11.45077133178711, - 0.5978575944900513 + 18.827543258666992, + -11.584896087646484 ], "umap": [ - 6.963860034942627, - 6.917228698730469 + 7.220713138580322, + 6.602043151855469 ], "pca3d": [ - 0.055065006017684937, - -0.05645788088440895, - -0.008755864575505257 + 0.05507498234510422, + -0.05625700205564499, + -0.008485540747642517 ], "tsne3d": [ - 6.1880974769592285, - -5.418928623199463, - -6.216015815734863 + 6.171576499938965, + 8.714858055114746, + 10.469537734985352 ], "umap3d": [ - 6.018649101257324, - 4.3270392417907715, - 3.2871079444885254 + 6.521317005157471, + 4.6819000244140625, + 2.478257179260254 ] }, "cluster_id": 4, @@ -774891,12 +775041,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -775314,31 +775464,31 @@ ], "projections": { "pca": [ - 0.0711589828133583, - -0.11111930012702942 + 0.07106224447488785, + -0.11068393290042877 ], "tsne": [ - -0.41476157307624817, - 27.226913452148438 + -0.3320620357990265, + -21.103776931762695 ], "umap": [ - 6.317378520965576, - 4.58575439453125 + 6.686662673950195, + 4.211299896240234 ], "pca3d": [ - 0.0711589977145195, - -0.1111113429069519, - 0.29352009296417236 + 0.07106234133243561, + -0.11067584902048111, + 0.2937183976173401 ], "tsne3d": [ - 0.08139494061470032, - 10.156147003173828, - -0.483369380235672 + -0.0830175057053566, + -7.693618297576904, + 5.891603946685791 ], "umap3d": [ - 5.157111167907715, - 5.876531600952148, - 4.765681266784668 + 5.879518508911133, + 6.7246198654174805, + 3.516106605529785 ] }, "cluster_id": 3, @@ -775349,11 +775499,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -775765,47 +775915,47 @@ "abstract": "OpenAI's new model, GPT-4b micro, shows promising results in redesigning proteins that could improve stem cell production efficiency by up to 50 times.", "projections": { "pca": [ - 0.011580357328057289, - 0.09123053401708603 + 0.011986065655946732, + 0.09159936010837555 ], "tsne": [ - 0.6414781212806702, - -16.428508758544922 + -2.7263143062591553, + 12.666656494140625 ], "umap": [ - 4.660436153411865, - 8.21558666229248 + 4.9089741706848145, + 7.3894853591918945 ], "pca3d": [ - 0.011580066755414009, - 0.0912364274263382, - 0.1538095325231552 + 0.011985758319497108, + 0.09160506725311279, + 0.15371647477149963 ], "tsne3d": [ - -6.730827331542969, - -6.972197532653809, - -13.351011276245117 + -1.8897600173950195, + 15.414970397949219, + 10.688852310180664 ], "umap3d": [ - 3.7559220790863037, - 4.2708845138549805, - 3.3319814205169678 + 4.458902835845947, + 4.781947135925293, + 3.421813488006592 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, - "label": "AI Agents" + "id": 4, + "label": "Genomics Tools" }, { - "id": 17, - "label": "Medical AI" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -776226,31 +776376,31 @@ "openalex_url": "https://openalex.org/W4380422659", "projections": { "pca": [ - -0.01674419641494751, - 0.05927254632115364 + -0.016595831140875816, + 0.05929285287857056 ], "tsne": [ - 21.94037628173828, - 15.43114185333252 + 24.293107986450195, + -8.203946113586426 ], "umap": [ - 7.084603786468506, - 7.111680507659912 + 7.2364115715026855, + 6.881198406219482 ], "pca3d": [ - -0.016744116321206093, - 0.05925154313445091, - -0.07465237379074097 + -0.01659577712416649, + 0.0592714324593544, + -0.07472728937864304 ], "tsne3d": [ - 9.185651779174805, - -7.8253560066223145, - -14.219442367553711 + 7.871715545654297, + 13.562409400939941, + 6.2520341873168945 ], "umap3d": [ - 6.745298385620117, - 3.899152994155884, - 3.3961150646209717 + 7.228679180145264, + 4.426619529724121, + 2.45485782623291 ] }, "cluster_id": 4, @@ -776261,12 +776411,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -776695,31 +776845,31 @@ "openalex_url": "https://openalex.org/W4225868104", "projections": { "pca": [ - 0.005365732591599226, - 0.006493560969829559 + 0.005406498443335295, + 0.007096882909536362 ], "tsne": [ - -7.506711006164551, - 7.613918304443359 + 4.724663257598877, + -41.74745559692383 ], "umap": [ - 4.458060264587402, - 3.754852533340454 + 4.937498569488525, + 3.343536615371704 ], "pca3d": [ - 0.005365736782550812, - 0.006482655182480812, - 0.10029029846191406 + 0.005406503565609455, + 0.007085585035383701, + 0.10034219920635223 ], "tsne3d": [ - -3.6485047340393066, - 7.9891157150268555, - -15.852449417114258 + 1.113526701927185, + -3.9366037845611572, + 19.784984588623047 ], "umap3d": [ - 5.1169586181640625, - 7.645268440246582, - 4.033528804779053 + 4.664323806762695, + 7.864095687866211, + 2.623429536819458 ] }, "cluster_id": 3, @@ -776730,12 +776880,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -777155,31 +777305,31 @@ ], "projections": { "pca": [ - 0.1487806737422943, - -0.10516226291656494 + 0.14869005978107452, + -0.10545116662979126 ], "tsne": [ - -8.775772094726562, - 28.716638565063477 + 1.5108673572540283, + -30.871644973754883 ], "umap": [ - 5.299969673156738, - 4.071115493774414 + 5.868221282958984, + 3.649606227874756 ], "pca3d": [ - 0.14878083765506744, - -0.10516759753227234, - 0.2132415473461151 + 0.14869023859500885, + -0.10545673221349716, + 0.21327945590019226 ], "tsne3d": [ - -1.343247890472412, - 11.088127136230469, - 9.883308410644531 + -4.5482659339904785, + -14.138113021850586, + 3.1449732780456543 ], "umap3d": [ - 5.097825050354004, - 6.931312561035156, - 4.884001731872559 + 5.492535591125488, + 7.639039039611816, + 3.3584322929382324 ] }, "cluster_id": 3, @@ -777190,11 +777340,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -777611,31 +777761,31 @@ "doi": "10.1101/2024.08.24.609531", "projections": { "pca": [ - 0.044107720255851746, - -0.2053373008966446 + 0.04375353455543518, + -0.20486252009868622 ], "tsne": [ - 1.5587488412857056, - 27.157760620117188 + 1.7652997970581055, + -18.45473861694336 ], "umap": [ - 6.483273983001709, - 4.3426594734191895 + 6.970450401306152, + 4.06791877746582 ], "pca3d": [ - 0.0441080667078495, - -0.2053404003381729, - 0.14438298344612122 + 0.04375384375452995, + -0.20486584305763245, + 0.14483429491519928 ], "tsne3d": [ - 3.461427927017212, - 10.816112518310547, - 1.8637099266052246 + 2.0406389236450195, + -8.432249069213867, + 1.7422394752502441 ], "umap3d": [ - 5.273134231567383, - 5.806844711303711, - 4.992443561553955 + 6.144739627838135, + 6.818565368652344, + 3.6484878063201904 ] }, "cluster_id": 3, @@ -777646,11 +777796,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -778066,31 +778216,31 @@ "doi": "10.1101/2024.09.22.614318", "projections": { "pca": [ - 0.06812219321727753, - 0.0510607548058033 + 0.06824089586734772, + 0.051986586302518845 ], "tsne": [ - -2.2319374084472656, - 20.219724655151367 + 8.410188674926758, + -23.256717681884766 ], "umap": [ - 5.763550758361816, - 4.321063995361328 + 6.252376079559326, + 4.014068126678467 ], "pca3d": [ - 0.06812199205160141, - 0.051057640463113785, - 0.19189199805259705 + 0.06824064254760742, + 0.05198315903544426, + 0.19174733757972717 ], "tsne3d": [ - 2.3316738605499268, - 1.4112911224365234, - 8.452442169189453 + -5.701696395874023, + -11.924630165100098, + -4.202049255371094 ], "umap3d": [ - 5.755163192749023, - 6.583096027374268, - 4.923248291015625 + 6.148431777954102, + 7.489165782928467, + 3.095088481903076 ] }, "cluster_id": 3, @@ -778101,12 +778251,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, - "label": "Protein Language" + "id": 3, + "label": "Protein Design" } ], "_embedding": [ @@ -778532,31 +778682,31 @@ "openalex_url": "https://openalex.org/W4392898481", "projections": { "pca": [ - 0.07535640895366669, - -0.0984792560338974 + 0.0753101259469986, + -0.09792713820934296 ], "tsne": [ - -3.6517128944396973, - 23.96418571472168 + 2.9971816539764404, + -24.405000686645508 ], "umap": [ - 5.831286430358887, - 4.465446949005127 + 6.195155620574951, + 4.224774360656738 ], "pca3d": [ - 0.0753563717007637, - -0.09847386181354523, - 0.27100539207458496 + 0.07531014829874039, + -0.09792201966047287, + 0.2711707651615143 ], "tsne3d": [ - -0.4649873971939087, - 6.501862049102783, - 5.260783672332764 + -3.4689908027648926, + -8.824836730957031, + 5.148059844970703 ], "umap3d": [ - 5.394747734069824, - 6.432444095611572, - 4.538763523101807 + 5.732179164886475, + 7.145762920379639, + 3.11044979095459 ] }, "cluster_id": 3, @@ -778567,12 +778717,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, - "label": "Protein Language" + "id": 3, + "label": "Protein Design" } ], "_embedding": [ @@ -778998,31 +779148,31 @@ "doi": "10.1101/2025.01.06.631536", "projections": { "pca": [ - 0.12293338775634766, - -0.10650591552257538 + 0.12284960597753525, + -0.10666847974061966 ], "tsne": [ - -5.169253349304199, - 14.144109725952148 + 11.767936706542969, + -22.303131103515625 ], "umap": [ - 5.592824459075928, - 5.101724147796631 + 6.0453619956970215, + 4.6830525398254395 ], "pca3d": [ - 0.12293361872434616, - -0.10651461035013199, - 0.18934713304042816 + 0.12284986674785614, + -0.10667751729488373, + 0.18944694101810455 ], "tsne3d": [ - 0.9179648160934448, - -3.124973773956299, - 1.9009613990783691 + -2.8013734817504883, + -2.475238084793091, + 10.0580415725708 ], "umap3d": [ - 5.636126518249512, - 6.547945976257324, - 4.064357280731201 + 5.5730438232421875, + 6.862592697143555, + 2.5954079627990723 ] }, "cluster_id": 3, @@ -779033,12 +779183,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, - "label": "Protein Design" + "id": 32, + "label": "Genome Protein" } ], "_embedding": [ @@ -779459,31 +779609,31 @@ "doi": "10.1101/2023.12.24.572591", "projections": { "pca": [ - 0.16888444125652313, - 0.03889390081167221 + 0.16915005445480347, + 0.03871188312768936 ], "tsne": [ - -0.25568637251853943, - 6.639527320861816 + 1.140966534614563, + -7.790221214294434 ], "umap": [ - 6.429097652435303, - 4.931100368499756 + 6.859946250915527, + 4.796097755432129 ], "pca3d": [ - 0.16888439655303955, - 0.03888314589858055, - 0.08167468756437302 + 0.16914992034435272, + 0.038701381534338, + 0.08163183927536011 ], "tsne3d": [ - 8.62500286102295, - 4.13002347946167, - 3.4965550899505615 + -3.6160919666290283, + -1.7075889110565186, + 4.20103645324707 ], "umap3d": [ - 5.748958587646484, - 5.601179599761963, - 4.596255302429199 + 6.313046932220459, + 6.590458393096924, + 3.209235191345215 ] }, "cluster_id": 3, @@ -779494,12 +779644,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, - "label": "Protein Language" + "id": 32, + "label": "Genome Protein" } ], "_embedding": [ @@ -779928,31 +780078,31 @@ "cited_by_count": 33, "projections": { "pca": [ - 0.18560199439525604, - -0.0025731297209858894 + 0.18574786186218262, + -0.0025612704921513796 ], "tsne": [ - -0.3062485158443451, - 6.7096028327941895 + 1.1160374879837036, + -7.847530841827393 ], "umap": [ - 6.168713569641113, - 4.762783527374268 + 6.628365516662598, + 4.691903591156006 ], "pca3d": [ - 0.1856021136045456, - -0.002595714293420315, - 0.15702994167804718 + 0.1857478767633438, + -0.0025841312017291784, + 0.15700682997703552 ], "tsne3d": [ - 8.360112190246582, - 4.043479919433594, - 4.027747631072998 + -3.983147382736206, + -2.004802942276001, + 4.480279922485352 ], "umap3d": [ - 5.698604106903076, - 6.049170970916748, - 4.629007816314697 + 6.156064987182617, + 6.964333534240723, + 2.989340305328369 ] }, "cluster_id": 3, @@ -779963,12 +780113,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, - "label": "Protein Language" + "id": 32, + "label": "Genome Protein" } ], "_embedding": [ @@ -780392,31 +780542,31 @@ "openalex_url": "https://openalex.org/W4406139332", "projections": { "pca": [ - 0.043520763516426086, - -0.11745186150074005 + 0.043065305799245834, + -0.11752700060606003 ], "tsne": [ - -8.276896476745605, - 19.629047393798828 + 4.618328094482422, + -30.585493087768555 ], "umap": [ - 5.254854202270508, - 4.6059489250183105 + 5.7604804039001465, + 4.2117838859558105 ], "pca3d": [ - 0.0435207262635231, - -0.11742455512285233, - 0.14930808544158936 + 0.043065328150987625, + -0.11749865114688873, + 0.149221271276474 ], "tsne3d": [ - -5.235691070556641, - 1.1975791454315186, - 3.995877981185913 + -9.065507888793945, + -12.209765434265137, + 1.2900199890136719 ], "umap3d": [ - 5.380151748657227, - 6.901853561401367, - 4.4761528968811035 + 5.46036434173584, + 7.483067035675049, + 2.802051305770874 ] }, "cluster_id": 3, @@ -780427,11 +780577,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -780858,31 +781008,31 @@ "doi": "10.1101/2025.01.05.630192", "projections": { "pca": [ - 0.1168200820684433, - -0.14089010655879974 + 0.11669264733791351, + -0.14055204391479492 ], "tsne": [ - -1.9129077196121216, - 26.361421585083008 + 4.7547607421875, + -17.546363830566406 ], "umap": [ - 6.255745887756348, - 4.761021137237549 + 6.6855292320251465, + 4.43117618560791 ], "pca3d": [ - 0.11682018637657166, - -0.1408926397562027, - 0.21672604978084564 + 0.11669278889894485, + -0.14055469632148743, + 0.21701455116271973 ], "tsne3d": [ - 0.709381639957428, - 10.591561317443848, - 3.5288140773773193 + 1.0326629877090454, + -4.1698384284973145, + 6.367607116699219 ], "umap3d": [ - 5.47796630859375, - 6.004647254943848, - 4.596256732940674 + 5.920213222503662, + 6.775747299194336, + 3.199265718460083 ] }, "cluster_id": 3, @@ -780893,11 +781043,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -781334,31 +781484,31 @@ "cited_by_count": 61, "projections": { "pca": [ - 0.14040175080299377, - -0.10227333009243011 + 0.1403329223394394, + -0.10223550349473953 ], "tsne": [ - -5.407174587249756, - 19.692148208618164 + 6.385133266448975, + -29.16312026977539 ], "umap": [ - 5.6142144203186035, - 4.620701789855957 + 6.122313976287842, + 4.393227577209473 ], "pca3d": [ - 0.14040198922157288, - -0.10228448361158371, - 0.1421084851026535 + 0.1403331309556961, + -0.10224705189466476, + 0.14224869012832642 ], "tsne3d": [ - -1.2118899822235107, - 0.675674557685852, - 5.836523056030273 + -8.729630470275879, + -7.748959064483643, + 6.5585126876831055 ], "umap3d": [ - 5.685052871704102, - 6.7233099937438965, - 4.54634428024292 + 5.9430832862854, + 7.338476181030273, + 2.724036693572998 ] }, "cluster_id": 3, @@ -781369,11 +781519,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -781789,31 +781939,31 @@ ], "projections": { "pca": [ - 0.045075345784425735, - -0.05776374787092209 + 0.044953055679798126, + -0.057619839906692505 ], "tsne": [ - -13.926728248596191, - 26.41097068786621 + -3.4115238189697266, + -33.90872573852539 ], "umap": [ - 4.354943752288818, - 4.318531513214111 + 4.817270755767822, + 3.9023027420043945 ], "pca3d": [ - 0.04507549852132797, - -0.05776774510741234, - 0.20260442793369293 + 0.04495324194431305, + -0.057624105364084244, + 0.2025538682937622 ], "tsne3d": [ - -9.465683937072754, - 12.833531379699707, - 4.288504600524902 + -8.276644706726074, + -20.568246841430664, + 5.20273494720459 ], "umap3d": [ - 4.09443473815918, - 6.97546911239624, - 4.793082237243652 + 4.568080902099609, + 7.549727916717529, + 3.6696343421936035 ] }, "cluster_id": 3, @@ -781824,12 +781974,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -782249,31 +782399,31 @@ "doi": "10.1101/2024.12.20.629706", "projections": { "pca": [ - 0.13603515923023224, - -0.15114939212799072 + 0.1358524113893509, + -0.15110956132411957 ], "tsne": [ - -7.967627048492432, - 29.25722312927246 + -0.08583962917327881, + -32.32072448730469 ], "umap": [ - 5.255633354187012, - 3.8855068683624268 + 5.899518966674805, + 3.500217914581299 ], "pca3d": [ - 0.13603539764881134, - -0.15116801857948303, - 0.25164300203323364 + 0.13585282862186432, + -0.1511283963918686, + 0.25178584456443787 ], "tsne3d": [ - -3.009272813796997, - 11.195549964904785, - 8.057516098022461 + -3.285186529159546, + -16.657968521118164, + 3.3405251502990723 ], "umap3d": [ - 4.979808807373047, - 6.945689678192139, - 5.080481052398682 + 5.483712673187256, + 7.7430548667907715, + 3.449990749359131 ] }, "cluster_id": 3, @@ -782284,11 +782434,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -782723,31 +782873,31 @@ "doi": "10.1101/2024.12.10.627665", "projections": { "pca": [ - 0.1495007872581482, - -0.08850491791963577 + 0.14937041699886322, + -0.0884309709072113 ], "tsne": [ - 4.657575607299805, - 26.579425811767578 + 3.4999496936798096, + -15.852010726928711 ], "umap": [ - 6.509589672088623, - 4.595247268676758 + 6.935080051422119, + 4.50460958480835 ], "pca3d": [ - 0.14950062334537506, - -0.08849968016147614, - 0.07356981933116913 + 0.14937035739421844, + -0.08842586725950241, + 0.07374604046344757 ], "tsne3d": [ - 7.464827060699463, - 9.409658432006836, - 1.7735589742660522 + -0.816430389881134, + -3.1998705863952637, + 2.8769891262054443 ], "umap3d": [ - 5.593399524688721, - 5.831780433654785, - 4.822090148925781 + 6.354978561401367, + 6.760023593902588, + 3.3649020195007324 ] }, "cluster_id": 3, @@ -782758,11 +782908,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -783173,47 +783323,47 @@ "abstract": "An AI biologist discovers previously uncharacterized systems in the Mtb genome.", "projections": { "pca": [ - 0.05223808065056801, - 0.1499730497598648 + 0.05260223150253296, + 0.15084341168403625 ], "tsne": [ - -2.9295125007629395, - -24.863595962524414 + -5.3985595703125, + 20.701499938964844 ], "umap": [ - 4.504784107208252, - 8.801921844482422 + 4.807225704193115, + 8.067537307739258 ], "pca3d": [ - 0.05223790183663368, - 0.1499617099761963, - 0.15985068678855896 + 0.05260199308395386, + 0.15083181858062744, + 0.15962794423103333 ], "tsne3d": [ - -3.273536205291748, - -11.269783973693848, - -6.608312606811523 + -2.8145859241485596, + 17.07064437866211, + 0.3396552503108978 ], "umap3d": [ - 3.6328654289245605, - 3.6246135234832764, - 2.9631497859954834 + 4.226701736450195, + 3.851802349090576, + 3.300055742263794 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, - "label": "Medical AI" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -783628,31 +783778,31 @@ ], "projections": { "pca": [ - 0.03754379227757454, - 0.000723239965736866 + 0.03760937228798866, + 0.0004245832096785307 ], "tsne": [ - -14.278573036193848, - 34.765193939208984 + -11.718742370605469, + -28.437849044799805 ], "umap": [ - 4.7598676681518555, - 4.033394813537598 + 5.437954902648926, + 3.4699645042419434 ], "pca3d": [ - 0.037543825805187225, - 0.0007313518435694277, - 0.10776113718748093 + 0.037609364837408066, + 0.00043269791058264673, + 0.10758554935455322 ], "tsne3d": [ - -1.9938105344772339, - 10.978300094604492, - 13.662549018859863 + 2.1744487285614014, + -18.46771812438965, + 13.980199813842773 ], "umap3d": [ - 4.568952560424805, - 7.098272800445557, - 4.762833118438721 + 4.979182720184326, + 7.695378303527832, + 3.418818950653076 ] }, "cluster_id": 3, @@ -783663,12 +783813,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -784077,31 +784227,31 @@ "title": "Evolutionary Scale \u00b7 ESM Cambrian: Revealing the mysteries of proteins with unsupervised learning", "projections": { "pca": [ - 0.0379599966108799, - -0.12666457891464233 + 0.0379350408911705, + -0.12601973116397858 ], "tsne": [ - -1.7095882892608643, - 25.3819522857666 + 2.0135433673858643, + -21.937393188476562 ], "umap": [ - 6.136367321014404, - 4.353993892669678 + 6.565123081207275, + 3.985137462615967 ], "pca3d": [ - 0.037960026413202286, - -0.12666144967079163, - 0.2805795967578888 + 0.03793509304523468, + -0.12601637840270996, + 0.2809290587902069 ], "tsne3d": [ - 2.6875789165496826, - 7.863359451293945, - 5.04582405090332 + -0.13931618630886078, + -11.244193077087402, + 1.732717514038086 ], "umap3d": [ - 5.404968738555908, - 6.246127605438232, - 4.90034818649292 + 5.982769966125488, + 7.097265243530273, + 3.3670928478240967 ] }, "cluster_id": 3, @@ -784112,11 +784262,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -784532,31 +784682,31 @@ ], "projections": { "pca": [ - 0.09639238566160202, - -0.10267089307308197 + 0.09635991603136063, + -0.10228732228279114 ], "tsne": [ - 4.120446681976318, - 31.54172706604004 + 3.7849385738372803, + -13.343169212341309 ], "umap": [ - 6.671319961547852, - 4.889680862426758 + 7.078766345977783, + 4.596960067749023 ], "pca3d": [ - 0.09639240056276321, - -0.10266736149787903, - 0.21343393623828888 + 0.09636000543832779, + -0.10228382050991058, + 0.2136913239955902 ], "tsne3d": [ - 5.250241756439209, - 9.697669982910156, - -2.986032247543335 + 5.087532043457031, + -5.121622562408447, + 1.3757094144821167 ], "umap3d": [ - 5.524319171905518, - 5.56317138671875, - 4.553196907043457 + 6.181003570556641, + 6.473317623138428, + 3.2962098121643066 ] }, "cluster_id": 3, @@ -784567,11 +784717,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -784998,47 +785148,47 @@ "cited_by_count": 298, "projections": { "pca": [ - 0.16056394577026367, - 0.04420790821313858 + 0.16073521971702576, + 0.04352954030036926 ], "tsne": [ - 27.927871704101562, - -30.74138641357422 + 41.88494873046875, + -0.1376762092113495 ], "umap": [ - 8.273273468017578, - 8.3040771484375 + 8.183220863342285, + 8.292308807373047 ], "pca3d": [ - 0.16056378185749054, - 0.04420488700270653, - 0.00043416096013970673 + 0.16073502600193024, + 0.04352659732103348, + 0.00019484091899357736 ], "tsne3d": [ - 20.340486526489258, - -5.328581809997559, - 14.604382514953613 + 21.67591667175293, + -1.3012638092041016, + -16.399036407470703 ], "umap3d": [ - 6.195612907409668, - 3.7856557369232178, - 1.5101968050003052 + 6.088712215423584, + 3.6622514724731445, + 1.0220659971237183 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 6, + "label": "Single Cell Data" }, { - "id": 12, - "label": "Genetic Variants" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -785459,31 +785609,31 @@ "doi": "10.1101/2024.11.11.622734", "projections": { "pca": [ - 0.0792207419872284, - -0.1200261116027832 + 0.07920405268669128, + -0.11946180462837219 ], "tsne": [ - 2.7707607746124268, - 28.573333740234375 + 1.7024364471435547, + -17.27753448486328 ], "umap": [ - 6.138114929199219, - 4.354915142059326 + 6.650498390197754, + 4.164644718170166 ], "pca3d": [ - 0.07922083884477615, - -0.12003050744533539, - 0.22172501683235168 + 0.0792042538523674, + -0.11946675181388855, + 0.2220822423696518 ], "tsne3d": [ - 3.3363609313964844, - 13.300029754638672, - 1.407976746559143 + 4.0691633224487305, + -9.538506507873535, + 0.9447944760322571 ], "umap3d": [ - 5.21299934387207, - 5.984622001647949, - 4.6999053955078125 + 5.887194633483887, + 6.874050617218018, + 3.464547634124756 ] }, "cluster_id": 3, @@ -785494,11 +785644,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -785926,31 +786076,31 @@ "cited_by_count": 406, "projections": { "pca": [ - 0.10894432663917542, - -0.10568245500326157 + 0.10879486799240112, + -0.10609257221221924 ], "tsne": [ - -14.414860725402832, - 33.205467224121094 + -9.960695266723633, + -28.74273681640625 ], "umap": [ - 5.059388637542725, - 3.7076432704925537 + 5.653512001037598, + 3.2472124099731445 ], "pca3d": [ - 0.10894453525543213, - -0.10567588359117508, - 0.13034291565418243 + 0.10879497230052948, + -0.10608574002981186, + 0.13037684559822083 ], "tsne3d": [ - -1.8831770420074463, - 8.898054122924805, - 15.43128776550293 + -0.7803436517715454, + -17.27499008178711, + 12.516185760498047 ], "umap3d": [ - 4.819169998168945, - 7.306978225708008, - 4.851354598999023 + 5.0801005363464355, + 7.847867965698242, + 3.414715051651001 ] }, "cluster_id": 3, @@ -785961,11 +786111,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -786374,47 +786524,47 @@ "abstract": "Toward High-Accuracy Open-Source Biomolecular Structure Prediction. - bytedance/Protenix", "projections": { "pca": [ - 0.07690896093845367, - 0.05522628873586655 + 0.07734626531600952, + 0.05488835275173187 ], "tsne": [ - -14.407358169555664, - 37.0432243347168 + -13.892008781433105, + -28.907529830932617 ], "umap": [ - 4.057524681091309, - 6.07653284072876 + 4.459618091583252, + 5.4301981925964355 ], "pca3d": [ - 0.07690881937742233, - 0.0552322082221508, - 0.1866205781698227 + 0.07734610140323639, + 0.054894328117370605, + 0.1864953190088272 ], "tsne3d": [ - 2.480581045150757, - 13.288573265075684, - 14.960132598876953 + -8.344472885131836, + -6.542537689208984, + 13.450169563293457 ], "umap3d": [ - 4.021116256713867, - 5.521058559417725, - 4.7082319259643555 + 4.7761335372924805, + 6.193667888641357, + 4.15028190612793 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 10, - "label": "Code Development" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -786835,31 +786985,31 @@ "cited_by_count": 152, "projections": { "pca": [ - 0.11339834332466125, - -0.08714418113231659 + 0.11337168514728546, + -0.08673551678657532 ], "tsne": [ - -11.205987930297852, - 27.187023162841797 + -2.821709394454956, + -31.25331687927246 ], "umap": [ - 4.750637531280518, - 4.17591667175293 + 5.301042079925537, + 3.6546618938446045 ], "pca3d": [ - 0.11339851468801498, - -0.08716266602277756, - 0.27039092779159546 + 0.11337192356586456, + -0.08675461262464523, + 0.27049046754837036 ], "tsne3d": [ - -6.59963321685791, - 10.279207229614258, - 5.361883163452148 + -6.5337300300598145, + -16.711750030517578, + 6.124585151672363 ], "umap3d": [ - 4.565436363220215, - 6.9261627197265625, - 4.927010536193848 + 4.994460582733154, + 7.614490985870361, + 3.557678699493408 ] }, "cluster_id": 3, @@ -786870,12 +787020,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -787294,31 +787444,31 @@ ], "projections": { "pca": [ - 0.017975183203816414, - -0.12478616088628769 + 0.01786907948553562, + -0.12427769601345062 ], "tsne": [ - 2.8048720359802246, - 22.61182975769043 + 4.266686916351318, + -21.454185485839844 ], "umap": [ - 6.290021896362305, - 4.562461853027344 + 6.574766635894775, + 4.393768787384033 ], "pca3d": [ - 0.017975326627492905, - -0.12477701902389526, - 0.218988299369812 + 0.017869265750050545, + -0.12426835298538208, + 0.21927623450756073 ], "tsne3d": [ - -0.934562087059021, - 7.528634071350098, - -1.3404505252838135 + -1.4035429954528809, + -7.177278995513916, + -1.3084862232208252 ], "umap3d": [ - 5.254802703857422, - 5.915961265563965, - 4.6772990226745605 + 5.918211460113525, + 6.768887042999268, + 3.4475104808807373 ] }, "cluster_id": 3, @@ -787329,11 +787479,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -787749,31 +787899,31 @@ "doi": "10.1101/2024.10.22.619563", "projections": { "pca": [ - 0.08508704602718353, - -0.20093971490859985 + 0.08469688147306442, + -0.2006203830242157 ], "tsne": [ - 1.7849961519241333, - 25.058134078979492 + 2.2980234622955322, + -20.578824996948242 ], "umap": [ - 6.32808256149292, - 4.437460422515869 + 6.7865142822265625, + 4.2905168533325195 ], "pca3d": [ - 0.08508726209402084, - -0.20093929767608643, - 0.1936001181602478 + 0.08469722419977188, + -0.20061980187892914, + 0.19393493235111237 ], "tsne3d": [ - 3.5064339637756348, - 8.146629333496094, - 2.732581377029419 + -1.3575689792633057, + -6.846070766448975, + 1.4963107109069824 ], "umap3d": [ - 5.407035827636719, - 5.961735725402832, - 4.855710506439209 + 6.160280704498291, + 6.861064434051514, + 3.4559731483459473 ] }, "cluster_id": 3, @@ -787784,11 +787934,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -788208,31 +788358,31 @@ "doi": "10.1101/2024.10.24.619774", "projections": { "pca": [ - 0.08375613391399384, - -0.0835118517279625 + 0.0835874006152153, + -0.08313371241092682 ], "tsne": [ - -6.028635501861572, - 18.204477310180664 + 7.4875288009643555, + -28.185993194580078 ], "umap": [ - 5.486288070678711, - 4.6611104011535645 + 5.973193168640137, + 4.3118767738342285 ], "pca3d": [ - 0.08375617116689682, - -0.0835147574543953, - 0.1830027550458908 + 0.08358750492334366, + -0.08313651382923126, + 0.18303406238555908 ], "tsne3d": [ - -1.1956984996795654, - 0.7221493721008301, - 2.8761234283447266 + -7.37868595123291, + -7.777084827423096, + 3.738335132598877 ], "umap3d": [ - 5.652468681335449, - 6.848761081695557, - 4.486783027648926 + 5.739377975463867, + 7.392562389373779, + 2.720532178878784 ] }, "cluster_id": 3, @@ -788243,11 +788393,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -788681,31 +788831,31 @@ "openalex_url": "https://openalex.org/W4403718818", "projections": { "pca": [ - 0.11992764472961426, - -0.09064467251300812 + 0.11983910948038101, + -0.09058977663516998 ], "tsne": [ - -9.589381217956543, - 26.268041610717773 + -1.5394011735916138, + -29.895627975463867 ], "umap": [ - 5.0146403312683105, - 4.172905445098877 + 5.579621315002441, + 3.730454921722412 ], "pca3d": [ - 0.1199277862906456, - -0.09065280109643936, - 0.2873355448246002 + 0.11983927339315414, + -0.09059816598892212, + 0.28732165694236755 ], "tsne3d": [ - -5.392104148864746, - 7.707337379455566, - 7.768374919891357 + -4.843400478363037, + -14.604560852050781, + 6.918769836425781 ], "umap3d": [ - 5.030695915222168, - 7.068236827850342, - 4.729091167449951 + 5.287992477416992, + 7.726951599121094, + 3.2082042694091797 ] }, "cluster_id": 3, @@ -788716,11 +788866,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -789137,31 +789287,31 @@ ], "projections": { "pca": [ - 0.08674918115139008, - -0.10888148844242096 + 0.08669380843639374, + -0.10873794555664062 ], "tsne": [ - -12.746267318725586, - 26.485795974731445 + -3.1185624599456787, + -33.05940628051758 ], "umap": [ - 4.578340530395508, - 4.376371383666992 + 5.071502208709717, + 3.965038299560547 ], "pca3d": [ - 0.08674928545951843, - -0.10888215899467468, - 0.23933999240398407 + 0.08669399470090866, + -0.10873913764953613, + 0.2394866943359375 ], "tsne3d": [ - -7.7959747314453125, - 12.290915489196777, - 3.310896635055542 + -6.944795608520508, + -19.495437622070312, + 4.276486873626709 ], "umap3d": [ - 4.489038944244385, - 6.736462116241455, - 4.785057067871094 + 4.899879455566406, + 7.299405097961426, + 3.619455099105835 ] }, "cluster_id": 3, @@ -789172,12 +789322,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -789595,31 +789745,31 @@ "doi": "10.1101/2024.10.17.618952", "projections": { "pca": [ - 0.09564629942178726, - -0.08843225240707397 + 0.09565428644418716, + -0.08858119696378708 ], "tsne": [ - 4.685378074645996, - 27.410375595092773 + 2.777482271194458, + -15.554847717285156 ], "umap": [ - 6.427248001098633, - 4.279233932495117 + 6.886521816253662, + 3.9923198223114014 ], "pca3d": [ - 0.09564601629972458, - -0.08840802311897278, - 0.13359811902046204 + 0.09565407037734985, + -0.08855678141117096, + 0.1337595134973526 ], "tsne3d": [ - 7.587117671966553, - 10.812603950500488, - 2.0775413513183594 + -0.5750604271888733, + -2.929231882095337, + 4.107029914855957 ], "umap3d": [ - 5.527876377105713, - 6.045498371124268, - 5.067780494689941 + 6.255525588989258, + 7.032809734344482, + 3.481982707977295 ] }, "cluster_id": 3, @@ -789630,11 +789780,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -790051,31 +790201,31 @@ ], "projections": { "pca": [ - -0.01014106534421444, - -0.15975834429264069 + -0.010342475958168507, + -0.1590874344110489 ], "tsne": [ - -3.192768096923828, - 28.9124813079834 + -1.7675827741622925, + -24.53929901123047 ], "umap": [ - 4.755084991455078, - 4.402329921722412 + 5.2788262367248535, + 4.038128852844238 ], "pca3d": [ - -0.010140796191990376, - -0.15976065397262573, - 0.2513160705566406 + -0.010342184454202652, + -0.15909045934677124, + 0.25167062878608704 ], "tsne3d": [ - -6.647180080413818, - 11.901123046875, - 1.6453651189804077 + -6.659753799438477, + -19.670848846435547, + 2.3775031566619873 ], "umap3d": [ - 4.782432556152344, - 6.2370781898498535, - 4.828993797302246 + 5.344032287597656, + 7.002427577972412, + 3.7239649295806885 ] }, "cluster_id": 3, @@ -790086,12 +790236,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -790506,31 +790656,31 @@ "doi": "10.1101/2024.10.07.616899", "projections": { "pca": [ - 0.09264050424098969, - -0.02018016017973423 + 0.09258777648210526, + -0.01993406191468239 ], "tsne": [ - -12.568800926208496, - 36.30028533935547 + -10.345184326171875, + -33.54528045654297 ], "umap": [ - 5.155274391174316, - 3.61445951461792 + 5.677081108093262, + 3.2427070140838623 ], "pca3d": [ - 0.092640720307827, - -0.020194200798869133, - 0.15386661887168884 + 0.09258796274662018, + -0.019948581233620644, + 0.1537836194038391 ], "tsne3d": [ - -0.9123637676239014, - 13.024806022644043, - 15.501762390136719 + 0.6262132525444031, + -20.644989013671875, + 12.480262756347656 ], "umap3d": [ - 4.861821174621582, - 7.243062973022461, - 5.062356472015381 + 5.260221481323242, + 7.779829978942871, + 3.7133846282958984 ] }, "cluster_id": 3, @@ -790541,11 +790691,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -790962,31 +791112,31 @@ "doi": "10.1101/2024.10.03.616542", "projections": { "pca": [ - 0.06575433909893036, - -0.0896807387471199 + 0.06562628597021103, + -0.08883125334978104 ], "tsne": [ - -2.2142505645751953, - 22.40572166442871 + 4.875417709350586, + -24.34559440612793 ], "umap": [ - 5.805388927459717, - 4.5940046310424805 + 6.257352828979492, + 4.259598255157471 ], "pca3d": [ - 0.06575442105531693, - -0.08968185633420944, - 0.27266451716423035 + 0.06562638282775879, + -0.08883244544267654, + 0.2728211283683777 ], "tsne3d": [ - 0.1336309164762497, - 5.031989097595215, - 2.678891658782959 + -3.8972394466400146, + -9.40946102142334, + 0.8293845057487488 ], "umap3d": [ - 5.660796642303467, - 6.59216833114624, - 4.582866191864014 + 5.952208995819092, + 7.295578956604004, + 2.8864786624908447 ] }, "cluster_id": 3, @@ -790997,11 +791147,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -791436,31 +791586,31 @@ "doi": "10.1101/2024.09.30.615802", "projections": { "pca": [ - 0.22438614070415497, - 0.050567563623189926 + 0.2246977984905243, + 0.050167083740234375 ], "tsne": [ - -17.202224731445312, - 31.471975326538086 + -8.820404052734375, + -38.217857360839844 ], "umap": [ - 4.8013834953308105, - 3.671006679534912 + 5.436540126800537, + 3.275280714035034 ], "pca3d": [ - 0.22438593208789825, - 0.05054933950304985, - 0.18323415517807007 + 0.22469767928123474, + 0.05014810338616371, + 0.18301339447498322 ], "tsne3d": [ - -5.453711032867432, - 7.35740852355957, - 14.69445514678955 + -5.369779586791992, + -14.971644401550293, + 13.322444915771484 ], "umap3d": [ - 4.9317498207092285, - 7.471890926361084, - 4.556362152099609 + 4.99100923538208, + 7.93829345703125, + 3.0124008655548096 ] }, "cluster_id": 3, @@ -791471,12 +791621,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -791911,31 +792061,31 @@ "cited_by_count": 104, "projections": { "pca": [ - 0.11933866888284683, - -0.1063157245516777 + 0.1192845031619072, + -0.10624612867832184 ], "tsne": [ - -11.072494506835938, - 27.623214721679688 + -3.143493890762329, + -30.940610885620117 ], "umap": [ - 4.768407344818115, - 4.084814548492432 + 5.2721171379089355, + 3.639737844467163 ], "pca3d": [ - 0.11933887749910355, - -0.10632558912038803, - 0.2846236526966095 + 0.11928471177816391, + -0.10625646263360977, + 0.28469163179397583 ], "tsne3d": [ - -6.255337715148926, - 10.905101776123047, - 6.531486511230469 + -5.33479118347168, + -17.07062339782715, + 5.577868461608887 ], "umap3d": [ - 4.5240631103515625, - 7.010368347167969, - 4.896151065826416 + 4.984151363372803, + 7.665615081787109, + 3.5643134117126465 ] }, "cluster_id": 3, @@ -791946,12 +792096,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -792373,31 +792523,31 @@ "doi": "10.1101/2024.09.13.612900", "projections": { "pca": [ - 0.051146119832992554, - -0.0732501819729805 + 0.051101475954055786, + -0.07275328040122986 ], "tsne": [ - 1.4114773273468018, - 20.828771591186523 + 6.919413089752197, + -32.14059066772461 ], "umap": [ - 5.360049247741699, - 4.532923698425293 + 5.885384559631348, + 4.1239166259765625 ], "pca3d": [ - 0.051146332174539566, - -0.07327226549386978, - 0.18172794580459595 + 0.05110170319676399, + -0.0727759376168251, + 0.1818893700838089 ], "tsne3d": [ - -5.177310943603516, - 7.786981582641602, - 0.6077132821083069 + 0.26850518584251404, + -17.433443069458008, + -1.8119908571243286 ], "umap3d": [ - 5.125970363616943, - 6.736133098602295, - 4.602227210998535 + 5.463810920715332, + 7.274561405181885, + 3.128145217895508 ] }, "cluster_id": 3, @@ -792408,11 +792558,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -792836,31 +792986,31 @@ ], "projections": { "pca": [ - 0.15091712772846222, - 0.017226623371243477 + 0.15125757455825806, + 0.01691965013742447 ], "tsne": [ - -11.43787670135498, - 37.45770263671875 + -11.991291999816895, + -31.411949157714844 ], "umap": [ - 5.3434882164001465, - 3.499779224395752 + 5.936008453369141, + 3.097109079360962 ], "pca3d": [ - 0.15091705322265625, - 0.01721566542983055, - 0.2025909721851349 + 0.15125761926174164, + 0.016908617690205574, + 0.2025051712989807 ], "tsne3d": [ - -1.5381218194961548, - 15.46256160736084, - 14.936559677124023 + 2.027005910873413, + -21.707056045532227, + 10.31370735168457 ], "umap3d": [ - 4.839269638061523, - 7.142871856689453, - 5.342895030975342 + 5.329707622528076, + 7.786839008331299, + 3.9255619049072266 ] }, "cluster_id": 3, @@ -792871,11 +793021,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -793286,47 +793436,47 @@ "abstract": "First published in 1869, Nature is the world\u2019s leading multidisciplinary science journal. Nature publishes the finest peer-reviewed research that drives ground-breaking discovery, and is read by thought-leaders and decision-makers around the world.", "projections": { "pca": [ - -0.022554287686944008, - 0.3986154794692993 + -0.021437842398881912, + 0.3988358676433563 ], "tsne": [ - -5.2915802001953125, - -11.672688484191895 + -5.370882987976074, + 6.879310607910156 ], "umap": [ - 4.672178268432617, - 7.573362827301025 + 4.759851932525635, + 6.911118030548096 ], "pca3d": [ - -0.022555092349648476, - 0.3986298143863678, - 0.11960432678461075 + -0.02143869921565056, + 0.3988499045372009, + 0.11895433068275452 ], "tsne3d": [ - -4.821084022521973, - -1.1987909078598022, - 14.243565559387207 + -2.6782357692718506, + 4.558436870574951, + -6.306680202484131 ], "umap3d": [ - 4.054842472076416, - 3.574589967727661, - 4.386590957641602 + 5.015739917755127, + 4.5350775718688965, + 4.646751880645752 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, - "label": "Genomics Papers" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -793748,31 +793898,31 @@ "cited_by_count": 12, "projections": { "pca": [ - 0.04330749437212944, - -0.0377725213766098 + 0.043375853449106216, + -0.03718532994389534 ], "tsne": [ - -4.158919334411621, - 27.0853328704834 + -0.1975509375333786, + -23.610740661621094 ], "umap": [ - 5.873900890350342, - 4.375158309936523 + 6.164816379547119, + 4.061351776123047 ], "pca3d": [ - 0.043307602405548096, - -0.03778262063860893, - 0.22487443685531616 + 0.04337600618600845, + -0.03719579428434372, + 0.22498643398284912 ], "tsne3d": [ - -1.3167389631271362, - 11.11573314666748, - 4.940976619720459 + -0.506495475769043, + -13.037692070007324, + 5.949012279510498 ], "umap3d": [ - 5.158222675323486, - 6.296721935272217, - 4.688918590545654 + 5.647368431091309, + 7.004291534423828, + 3.298638105392456 ] }, "cluster_id": 3, @@ -793783,12 +793933,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, - "label": "Protein Language" + "id": 3, + "label": "Protein Design" } ], "_embedding": [ @@ -794213,31 +794363,31 @@ "openalex_url": "https://openalex.org/W4401670099", "projections": { "pca": [ - 0.13759773969650269, - -0.08494605123996735 + 0.13752157986164093, + -0.08444687724113464 ], "tsne": [ - -5.581470012664795, - 24.700754165649414 + 3.2374892234802246, + -25.575363159179688 ], "umap": [ - 5.6533522605896, - 4.478363990783691 + 6.07244348526001, + 4.252312183380127 ], "pca3d": [ - 0.13759787380695343, - -0.08496610075235367, - 0.26073157787323 + 0.13752172887325287, + -0.08446752279996872, + 0.2608303427696228 ], "tsne3d": [ - -1.6136349439620972, - 9.280412673950195, - 6.89284610748291 + -4.685474872589111, + -8.397982597351074, + 7.004916191101074 ], "umap3d": [ - 5.386946678161621, - 6.570379734039307, - 4.487214088439941 + 5.643482685089111, + 7.314098358154297, + 3.0843558311462402 ] }, "cluster_id": 3, @@ -794248,11 +794398,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -794675,31 +794825,31 @@ "doi": "10.1101/2024.08.03.606485", "projections": { "pca": [ - 0.12350419163703918, - -0.11104916781187057 + 0.12344072014093399, + -0.11082982271909714 ], "tsne": [ - -4.1861772537231445, - 24.704513549804688 + 2.1860079765319824, + -24.56331443786621 ], "umap": [ - 5.77536678314209, - 4.517485618591309 + 6.157404899597168, + 4.356528282165527 ], "pca3d": [ - 0.1235044077038765, - -0.11106561124324799, - 0.26190468668937683 + 0.1234409436583519, + -0.11084684729576111, + 0.26207637786865234 ], "tsne3d": [ - -1.210538387298584, - 7.990914821624756, - 5.520449638366699 + -3.13206148147583, + -10.375153541564941, + 6.06002950668335 ], "umap3d": [ - 5.4106974601745605, - 6.506779193878174, - 4.55619478225708 + 5.650006294250488, + 7.165414333343506, + 3.205235004425049 ] }, "cluster_id": 3, @@ -794710,12 +794860,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, - "label": "Protein Language" + "id": 3, + "label": "Protein Design" } ], "_embedding": [ @@ -795123,31 +795273,31 @@ "abstract": "List of papers about Proteins Design using Deep Learning - Peldom/papers_for_protein_design_using_DL", "projections": { "pca": [ - 0.0035977058578282595, - 0.13879135251045227 + 0.004215354099869728, + 0.13850846886634827 ], "tsne": [ - -9.227182388305664, - 14.443206787109375 + -6.7682085037231445, + -19.981159210205078 ], "umap": [ - 5.308770179748535, - 5.046107292175293 + 5.756749153137207, + 4.475703239440918 ], "pca3d": [ - 0.0035971617326140404, - 0.13881456851959229, - 0.15309591591358185 + 0.004214817192405462, + 0.13853119313716888, + 0.15283969044685364 ], "tsne3d": [ - -8.873336791992188, - 3.0448365211486816, - 7.704652786254883 + -7.61341667175293, + -3.0041823387145996, + 0.8378545641899109 ], "umap3d": [ - 4.822636604309082, - 5.814260959625244, - 4.678001880645752 + 5.460615158081055, + 6.734329700469971, + 3.609332323074341 ] }, "cluster_id": 3, @@ -795158,11 +795308,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -795571,31 +795721,31 @@ "abstract": "A visual walkthrough of the AlphaFold3 architecture, with more details and diagrams than you were probably looking for.", "projections": { "pca": [ - -0.07195350527763367, - 0.17019407451152802 + -0.07127724587917328, + 0.17058725655078888 ], "tsne": [ - -11.851701736450195, - 38.188751220703125 + -12.758681297302246, + -31.40715217590332 ], "umap": [ - 5.3180155754089355, - 3.5134236812591553 + 5.914846420288086, + 3.10882306098938 ], "pca3d": [ - -0.07195335626602173, - 0.1701476275920868, - 0.13212190568447113 + -0.0712771788239479, + 0.1705397665500641, + 0.1319238692522049 ], "tsne3d": [ - -2.829038381576538, - 14.716547012329102, - 15.888111114501953 + 0.6369112730026245, + -22.676902770996094, + 10.598797798156738 ], "umap3d": [ - 4.76792573928833, - 7.21089506149292, - 5.321460723876953 + 5.291627883911133, + 7.794344902038574, + 3.8666398525238037 ] }, "cluster_id": 3, @@ -795606,11 +795756,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -796036,31 +796186,31 @@ "doi": "10.1101/2024.07.17.604015", "projections": { "pca": [ - 0.09245256334543228, - -0.08817273378372192 + 0.09235721826553345, + -0.0875234603881836 ], "tsne": [ - -5.139246463775635, - 18.020875930786133 + 7.357032775878906, + -27.29012107849121 ], "umap": [ - 5.506127834320068, - 4.712216854095459 + 5.967864990234375, + 4.384010314941406 ], "pca3d": [ - 0.09245265275239944, - -0.08817781507968903, - 0.2784770429134369 + 0.09235735237598419, + -0.08752880990505219, + 0.27858811616897583 ], "tsne3d": [ - 0.6054577827453613, - 0.5580175518989563, - 2.962287187576294 + -6.021419525146484, + -7.386198997497559, + 4.9437127113342285 ], "umap3d": [ - 5.623750686645508, - 6.810772895812988, - 4.391730308532715 + 5.700616836547852, + 7.351812362670898, + 2.757513999938965 ] }, "cluster_id": 3, @@ -796071,11 +796221,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -796494,31 +796644,31 @@ "doi": "10.1101/2024.05.14.594226", "projections": { "pca": [ - 0.0757538378238678, - -0.16944299638271332 + 0.0756419450044632, + -0.16897059977054596 ], "tsne": [ - 2.639169692993164, - 29.173828125 + 1.4337178468704224, + -16.877208709716797 ], "umap": [ - 6.415761470794678, - 4.3413591384887695 + 6.990009307861328, + 3.994363307952881 ], "pca3d": [ - 0.07575388997793198, - -0.1694401055574417, - 0.22749391198158264 + 0.07564204186201096, + -0.16896799206733704, + 0.22792832553386688 ], "tsne3d": [ - 4.221408367156982, - 12.490734100341797, - 1.7168877124786377 + 3.8719422817230225, + -9.578356742858887, + 2.125459909439087 ], "umap3d": [ - 5.356513023376465, - 5.890230178833008, - 5.0461835861206055 + 6.170881271362305, + 6.950253009796143, + 3.675565004348755 ] }, "cluster_id": 3, @@ -796529,11 +796679,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -796957,31 +797107,31 @@ "openalex_url": "https://openalex.org/W4400313991", "projections": { "pca": [ - 0.09382811933755875, - -0.05113879218697548 + 0.09372852742671967, + -0.050746701657772064 ], "tsne": [ - -19.42061424255371, - 23.60070037841797 + 3.267582416534424, + -40.88679122924805 ], "umap": [ - 4.338267803192139, - 3.644970178604126 + 4.8431196212768555, + 3.1097569465637207 ], "pca3d": [ - 0.0938282310962677, - -0.05115040764212608, - 0.22704008221626282 + 0.09372870624065399, + -0.05075874552130699, + 0.22703175246715546 ], "tsne3d": [ - -4.09649658203125, - 9.572151184082031, - -15.350996017456055 + 0.3375508785247803, + -5.271886348724365, + 19.077117919921875 ], "umap3d": [ - 5.033029556274414, - 7.760612487792969, - 4.094756603240967 + 4.603835105895996, + 7.993544578552246, + 2.720992088317871 ] }, "cluster_id": 3, @@ -796992,12 +797142,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -797405,47 +797555,47 @@ ], "projections": { "pca": [ - -0.010372207500040531, - 0.13867847621440887 + -0.009841257706284523, + 0.13950444757938385 ], "tsne": [ - -7.714608669281006, - 0.05180475115776062 + -5.5755157470703125, + -8.458127975463867 ], "umap": [ - 4.489331245422363, - 6.9768900871276855 + 4.705934524536133, + 6.372562408447266 ], "pca3d": [ - -0.010372364893555641, - 0.13866455852985382, - 0.224399134516716 + -0.009841445833444595, + 0.1394900381565094, + 0.2242572158575058 ], "tsne3d": [ - -9.188220024108887, - 3.8232078552246094, - 12.343241691589355 + -4.274836540222168, + -4.665007591247559, + -3.935746192932129 ], "umap3d": [ - 4.0274529457092285, - 4.106319427490234, - 4.620885372161865 + 5.0148844718933105, + 4.998899936676025, + 4.507669448852539 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 10, - "label": "Code Development" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -797854,31 +798004,31 @@ "title": "Evolutionary Scale \u00b7 ESM3: Simulating 500 million years of evolution with a language model", "projections": { "pca": [ - -0.007089909631758928, - 0.08581168204545975 + -0.006751207169145346, + 0.08695293962955475 ], "tsne": [ - -7.678242206573486, - 0.22123423218727112 + -5.5333638191223145, + -8.540675163269043 ], "umap": [ - 6.115907192230225, - 4.431608200073242 + 6.564273834228516, + 4.176884651184082 ], "pca3d": [ - -0.00708986958488822, - 0.0857880711555481, - 0.23246368765830994 + -0.006751202512532473, + 0.0869288221001625, + 0.2324306070804596 ], "tsne3d": [ - -9.076118469238281, - 4.277200222015381, - 12.050313949584961 + -4.2712554931640625, + -5.063509464263916, + -3.6261136531829834 ], "umap3d": [ - 5.445278644561768, - 6.13185977935791, - 4.844141960144043 + 6.102172374725342, + 7.0297980308532715, + 3.3295817375183105 ] }, "cluster_id": 3, @@ -797889,11 +798039,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -798311,31 +798461,31 @@ ], "projections": { "pca": [ - -0.000980873592197895, - -0.10291560739278793 + -0.0011946046724915504, + -0.1027476117014885 ], "tsne": [ - -13.192488670349121, - 27.931982040405273 + -4.4447550773620605, + -32.48932647705078 ], "umap": [ - 4.50636100769043, - 4.18329381942749 + 4.967479705810547, + 3.7268943786621094 ], "pca3d": [ - -0.0009806205052882433, - -0.10292673856019974, - 0.11313054710626602 + -0.0011943108402192593, + -0.10275960713624954, + 0.11318731307983398 ], "tsne3d": [ - -9.930919647216797, - 9.7795991897583, - 5.292971611022949 + -9.132746696472168, + -17.592182159423828, + 5.838364601135254 ], "umap3d": [ - 4.205585956573486, - 7.013256549835205, - 4.923759937286377 + 4.766334056854248, + 7.671739101409912, + 3.6690497398376465 ] }, "cluster_id": 3, @@ -798346,12 +798496,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -798771,31 +798921,31 @@ ], "projections": { "pca": [ - -0.07255145907402039, - -0.11760061979293823 + -0.0729394406080246, + -0.11725161224603653 ], "tsne": [ - -34.025943756103516, - 17.09583854675293 + -27.5520076751709, + -18.308109283447266 ], "umap": [ - 3.273336410522461, - 4.842775344848633 + 3.7561497688293457, + 4.271790981292725 ], "pca3d": [ - -0.07255116105079651, - -0.11760996282100677, - 0.021162312477827072 + -0.07293908298015594, + -0.1172613576054573, + 0.02127213589847088 ], "tsne3d": [ - -10.629755973815918, - 9.471919059753418, - 4.90139627456665 + -9.940835952758789, + -17.518505096435547, + 5.677640914916992 ], "umap3d": [ - 3.1587541103363037, - 6.865304470062256, - 4.785958766937256 + 3.5690488815307617, + 7.309012413024902, + 4.145541667938232 ] }, "cluster_id": 2, @@ -798806,12 +798956,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 16, - "label": "Diffusion Models" + "id": 7, + "label": "Diffusion Gen" } ], "_embedding": [ @@ -799230,31 +799380,31 @@ "doi": "10.1101/2024.06.06.597716", "projections": { "pca": [ - 0.021862678229808807, - -0.18941205739974976 + 0.021461285650730133, + -0.18898090720176697 ], "tsne": [ - 0.8972093462944031, - 25.336145401000977 + 2.977797508239746, + -19.945695877075195 ], "umap": [ - 6.416644096374512, - 4.604366302490234 + 6.833746910095215, + 4.325412273406982 ], "pca3d": [ - 0.021862907335162163, - -0.18940694630146027, - 0.13187634944915771 + 0.02146158181130886, + -0.18897554278373718, + 0.1322382241487503 ], "tsne3d": [ - 2.1507925987243652, - 8.631245613098145, - 1.1681115627288818 + -0.09111585468053818, + -7.8744707107543945, + 2.1933445930480957 ], "umap3d": [ - 5.315123081207275, - 5.797327041625977, - 4.768700122833252 + 6.03381872177124, + 6.775712966918945, + 3.4686264991760254 ] }, "cluster_id": 3, @@ -799265,11 +799415,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -799686,31 +799836,31 @@ "doi": "10.1101/2024.05.24.595730", "projections": { "pca": [ - 0.10166600346565247, - -0.13535287976264954 + 0.10157211869955063, + -0.13503596186637878 ], "tsne": [ - -0.03350592032074928, - 30.41471290588379 + -1.9742337465286255, + -18.957090377807617 ], "umap": [ - 6.2099432945251465, - 4.250142574310303 + 6.77902364730835, + 3.9210445880889893 ], "pca3d": [ - 0.10166621953248978, - -0.1353657841682434, - 0.2560887932777405 + 0.1015724316239357, + -0.13504883646965027, + 0.2563609778881073 ], "tsne3d": [ - 5.938919544219971, - 10.161419868469238, - 6.069669723510742 + 4.217432498931885, + -9.202676773071289, + 6.4054179191589355 ], "umap3d": [ - 5.384238243103027, - 6.1081366539001465, - 5.136414527893066 + 6.180729389190674, + 6.968348503112793, + 3.520627975463867 ] }, "cluster_id": 3, @@ -799721,11 +799871,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -800139,31 +800289,31 @@ "journal": "Advances in Neural Information Processing Systems", "projections": { "pca": [ - 0.06709614396095276, - -0.13604877889156342 + 0.06715895980596542, + -0.1359967589378357 ], "tsne": [ - 6.500433921813965, - 30.287689208984375 + 7.550387382507324, + -18.721477508544922 ], "umap": [ - 6.019339084625244, - 4.129745960235596 + 6.580374240875244, + 3.848426580429077 ], "pca3d": [ - 0.06709621101617813, - -0.13605880737304688, - 0.21131379902362823 + 0.06715910881757736, + -0.13600680232048035, + 0.2117055058479309 ], "tsne3d": [ - 7.686271667480469, - 13.5406494140625, - 11.57055950164795 + 4.368234157562256, + -13.349180221557617, + -2.9669125080108643 ], "umap3d": [ - 5.030047416687012, - 6.31933069229126, - 4.937292098999023 + 5.654542922973633, + 7.069591999053955, + 3.6539134979248047 ] }, "cluster_id": 3, @@ -800174,11 +800324,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -800643,31 +800793,31 @@ "cited_by_count": 13507, "projections": { "pca": [ - 0.10847093164920807, - -0.04692118614912033 + 0.10847291350364685, + -0.046925876289606094 ], "tsne": [ - -11.538310050964355, - 36.751705169677734 + -11.345088005065918, + -31.114391326904297 ], "umap": [ - 5.320206165313721, - 3.4899301528930664 + 5.895632743835449, + 3.089944839477539 ], "pca3d": [ - 0.10847103595733643, - -0.04691842198371887, - 0.2129417210817337 + 0.10847295075654984, + -0.04692299664020538, + 0.21288610994815826 ], "tsne3d": [ - -1.2040176391601562, - 14.195869445800781, - 14.229812622070312 + 1.4597748517990112, + -20.58194351196289, + 10.58071231842041 ], "umap3d": [ - 4.829636096954346, - 7.170610427856445, - 5.276275634765625 + 5.2489776611328125, + 7.773143768310547, + 3.8624372482299805 ] }, "cluster_id": 3, @@ -800678,11 +800828,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -801109,31 +801259,31 @@ "cited_by_count": 61, "projections": { "pca": [ - 0.11076181381940842, - 0.07209179550409317 + 0.11105255037546158, + 0.07232882082462311 ], "tsne": [ - -3.595888376235962, - 12.875483512878418 + 12.962725639343262, + -21.539426803588867 ], "umap": [ - 5.715313911437988, - 5.506593227386475 + 6.040236473083496, + 5.123048305511475 ], "pca3d": [ - 0.11076191812753677, - 0.07206960767507553, - 0.14669884741306305 + 0.11105252802371979, + 0.07230586558580399, + 0.14660783112049103 ], "tsne3d": [ - 0.29054099321365356, - -3.4665682315826416, - -1.1920288801193237 + -1.4993311166763306, + 0.04123608395457268, + 10.178677558898926 ], "umap3d": [ - 5.72157096862793, - 6.276620388031006, - 3.7295379638671875 + 5.493378162384033, + 6.428206443786621, + 2.3964905738830566 ] }, "cluster_id": 3, @@ -801144,12 +801294,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, - "label": "Protein Design" + "id": 32, + "label": "Genome Protein" } ], "_embedding": [ @@ -801555,31 +801705,31 @@ ], "projections": { "pca": [ - 0.043286584317684174, - -0.010826215147972107 + 0.04354424029588699, + -0.010401972569525242 ], "tsne": [ - -11.092702865600586, - 25.32323455810547 + -2.4825680255889893, + -28.286962509155273 ], "umap": [ - 4.870718002319336, - 4.344210624694824 + 5.456775188446045, + 3.9142959117889404 ], "pca3d": [ - 0.043286360800266266, - -0.010819693095982075, - 0.32087016105651855 + 0.04354405030608177, + -0.010396033525466919, + 0.3209148645401001 ], "tsne3d": [ - -6.503713130950928, - 8.286247253417969, - 4.1957926750183105 + -7.110630989074707, + -14.862054824829102, + 8.413511276245117 ], "umap3d": [ - 4.770748138427734, - 6.771787166595459, - 4.727814674377441 + 5.111562252044678, + 7.344369411468506, + 3.457663059234619 ] }, "cluster_id": 3, @@ -801590,12 +801740,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -802022,31 +802172,31 @@ "doi": "10.1101/2024.04.22.590591", "projections": { "pca": [ - 0.16293145716190338, - -0.013760160654783249 + 0.163006991147995, + -0.0135061489418149 ], "tsne": [ - -3.8545773029327393, - 13.19302749633789 + 12.890623092651367, + -21.886119842529297 ], "umap": [ - 5.765713214874268, - 5.438249111175537 + 6.05889368057251, + 5.01912260055542 ], "pca3d": [ - 0.16293156147003174, - -0.013784639537334442, - 0.18849843740463257 + 0.16300711035728455, + -0.013531025499105453, + 0.18851205706596375 ], "tsne3d": [ - 0.7902866005897522, - -2.8545825481414795, - -0.0970413014292717 + -1.7989633083343506, + -1.0071338415145874, + 10.87846565246582 ], "umap3d": [ - 5.726072788238525, - 6.3425397872924805, - 3.807020425796509 + 5.5380682945251465, + 6.526528835296631, + 2.4314751625061035 ] }, "cluster_id": 3, @@ -802057,12 +802207,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, - "label": "Protein Design" + "id": 32, + "label": "Genome Protein" } ], "_embedding": [ @@ -802488,31 +802638,31 @@ "openalex_url": "https://openalex.org/W4394011646", "projections": { "pca": [ - -0.010805459693074226, - -0.04455215856432915 + -0.010787161998450756, + -0.04410272464156151 ], "tsne": [ - -12.635483741760254, - 28.808725357055664 + -4.9058732986450195, + -32.020206451416016 ], "umap": [ - 4.554892539978027, - 4.227106094360352 + 5.052290916442871, + 3.700103759765625 ], "pca3d": [ - -0.010805344209074974, - -0.044556874781847, - 0.1983342319726944 + -0.010787013918161392, + -0.04410815238952637, + 0.19836905598640442 ], "tsne3d": [ - -9.539020538330078, - 10.7207612991333, - 7.157161235809326 + -7.097174167633057, + -19.090024948120117, + 7.285283088684082 ], "umap3d": [ - 4.284021377563477, - 7.017049312591553, - 4.883296012878418 + 4.8120198249816895, + 7.644250869750977, + 3.6298084259033203 ] }, "cluster_id": 3, @@ -802523,12 +802673,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -802959,31 +803109,31 @@ "doi": "10.1101/2024.03.21.585615", "projections": { "pca": [ - 0.15053102374076843, - 0.005690476857125759 + 0.1506803333759308, + 0.005817319732159376 ], "tsne": [ - -6.565159320831299, - 17.93218994140625 + 7.807546138763428, + -28.827552795410156 ], "umap": [ - 5.459986686706543, - 4.648299694061279 + 5.945004463195801, + 4.299683094024658 ], "pca3d": [ - 0.15053097903728485, - 0.005684470292180777, - 0.14876626431941986 + 0.1506802886724472, + 0.00581139512360096, + 0.1486869752407074 ], "tsne3d": [ - -1.9196912050247192, - -0.5105574131011963, - 3.4494216442108154 + -8.713418006896973, + -7.157602310180664, + 3.9791250228881836 ], "umap3d": [ - 5.635587215423584, - 6.894952774047852, - 4.5166192054748535 + 5.745080947875977, + 7.409896373748779, + 2.720486640930176 ] }, "cluster_id": 3, @@ -802994,11 +803144,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -803415,31 +803565,31 @@ "cited_by_count": 61, "projections": { "pca": [ - 0.07320472598075867, - -0.09570413082838058 + 0.07309260964393616, + -0.09516441822052002 ], "tsne": [ - -2.167114734649658, - 22.679868698120117 + 4.792391300201416, + -24.39450454711914 ], "umap": [ - 5.936829566955566, - 4.6083550453186035 + 6.420406341552734, + 4.306641578674316 ], "pca3d": [ - 0.07320483028888702, - -0.09570766985416412, - 0.2480592131614685 + 0.07309268414974213, + -0.09516797214746475, + 0.2482224404811859 ], "tsne3d": [ - 0.994339644908905, - 5.209784984588623, - 3.3231325149536133 + -3.1325440406799316, + -9.812101364135742, + -0.00422497047111392 ], "umap3d": [ - 5.669179439544678, - 6.359852313995361, - 4.672046661376953 + 6.089582443237305, + 7.167268753051758, + 3.0157923698425293 ] }, "cluster_id": 3, @@ -803450,12 +803600,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, - "label": "Protein Language" + "id": 3, + "label": "Protein Design" } ], "_embedding": [ @@ -803872,31 +804022,31 @@ "doi": "10.1101/2024.02.25.581968", "projections": { "pca": [ - 0.12360824644565582, - -0.08036882430315018 + 0.12369559705257416, + -0.08015794306993484 ], "tsne": [ - -15.628639221191406, - 32.31146240234375 + -9.174492835998535, + -36.12264633178711 ], "umap": [ - 5.132559776306152, - 3.950533866882324 + 5.830739498138428, + 3.6214518547058105 ], "pca3d": [ - 0.12360837310552597, - -0.08037476986646652, - 0.2260953038930893 + 0.12369566410779953, + -0.08016407489776611, + 0.2262687236070633 ], "tsne3d": [ - -2.65857195854187, - 6.741055965423584, - 16.188329696655273 + -2.6211979389190674, + -15.040889739990234, + 12.96468734741211 ], "umap3d": [ - 5.058143138885498, - 7.074569225311279, - 4.602895259857178 + 5.301506519317627, + 7.6404194831848145, + 3.206312656402588 ] }, "cluster_id": 3, @@ -803907,11 +804057,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -804337,31 +804487,31 @@ "openalex_url": "https://openalex.org/W4392345053", "projections": { "pca": [ - 0.02642519399523735, - -0.15140819549560547 + 0.026041101664304733, + -0.15167303383350372 ], "tsne": [ - -6.926726818084717, - 33.20138168334961 + -4.7835822105407715, + -35.48377990722656 ], "umap": [ - 5.633633613586426, - 3.8129920959472656 + 6.314787864685059, + 3.353937864303589 ], "pca3d": [ - 0.026425538584589958, - -0.15141843259334564, - 0.0809016078710556 + 0.026041468605399132, + -0.15168340504169464, + 0.08099405467510223 ], "tsne3d": [ - -4.819187164306641, - 16.62926483154297, - 6.2064104080200195 + -0.43406882882118225, + -19.063129425048828, + 1.6211403608322144 ], "umap3d": [ - 4.774197101593018, - 6.727542400360107, - 5.227411270141602 + 5.75300931930542, + 7.728559970855713, + 3.743485689163208 ] }, "cluster_id": 3, @@ -804372,11 +804522,11 @@ "label": "Protein Design" }, { - "id": 12, - "label": "Protein Design" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -804820,31 +804970,31 @@ "openalex_url": "https://openalex.org/W4392544734", "projections": { "pca": [ - 0.12884443998336792, - -0.07070479542016983 + 0.12886294722557068, + -0.07084672898054123 ], "tsne": [ - -12.435580253601074, - 30.950353622436523 + -7.866636276245117, + -28.513608932495117 ], "umap": [ - 4.943817615509033, - 4.015615940093994 + 5.562026023864746, + 3.4472849369049072 ], "pca3d": [ - 0.12884463369846344, - -0.07071308046579361, - 0.23798516392707825 + 0.1288631409406662, + -0.07085547596216202, + 0.23798511922359467 ], "tsne3d": [ - -2.7576041221618652, - 8.933263778686523, - 11.889748573303223 + -1.5892146825790405, + -16.067232131958008, + 9.815238952636719 ], "umap3d": [ - 4.742315769195557, - 7.168689250946045, - 4.787440776824951 + 5.129482746124268, + 7.6712965965271, + 3.3728275299072266 ] }, "cluster_id": 3, @@ -804855,11 +805005,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -805276,31 +805426,31 @@ "doi": "10.1101/2024.02.28.581983", "projections": { "pca": [ - 0.13140204548835754, - -0.15511325001716614 + 0.13121262192726135, + -0.15494734048843384 ], "tsne": [ - -0.021370314061641693, - 29.758686065673828 + -1.3154162168502808, + -19.137901306152344 ], "umap": [ - 6.338440418243408, - 4.3150410652160645 + 6.829495429992676, + 4.073986053466797 ], "pca3d": [ - 0.1314021795988083, - -0.15511521697044373, - 0.19938325881958008 + 0.13121283054351807, + -0.15494950115680695, + 0.1996438205242157 ], "tsne3d": [ - 5.813460350036621, - 10.077679634094238, - 4.915705680847168 + 3.4466187953948975, + -8.694750785827637, + 5.702610492706299 ], "umap3d": [ - 5.2898125648498535, - 5.984888076782227, - 4.979757785797119 + 6.15932559967041, + 6.87043571472168, + 3.528482675552368 ] }, "cluster_id": 3, @@ -805311,11 +805461,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -805735,31 +805885,31 @@ "doi": "10.1101/2024.02.18.580860", "projections": { "pca": [ - 0.055659547448158264, - -0.06037067621946335 + 0.055607616901397705, + -0.05975247547030449 ], "tsne": [ - -8.478979110717773, - 17.302913665771484 + -1.544445514678955, + -22.85515022277832 ], "umap": [ - 5.583421230316162, - 4.9124040603637695 + 6.191858768463135, + 4.287066459655762 ], "pca3d": [ - 0.055659353733062744, - -0.06035011261701584, - 0.25158536434173584 + 0.055607475340366364, + -0.0597321093082428, + 0.25163471698760986 ], "tsne3d": [ - -2.681061267852783, - 10.201992988586426, - -0.47482478618621826 + -1.1783456802368164, + -7.523754119873047, + 8.336333274841309 ], "umap3d": [ - 5.273513317108154, - 6.332467079162598, - 4.2839789390563965 + 5.569760799407959, + 6.918920040130615, + 3.035933494567871 ] }, "cluster_id": 3, @@ -805770,11 +805920,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -806188,31 +806338,31 @@ ], "projections": { "pca": [ - 0.06037236005067825, - -0.1462412327528 + 0.06017792969942093, + -0.14626345038414001 ], "tsne": [ - -9.459403038024902, - 30.98307228088379 + -6.211188316345215, + -30.723499298095703 ], "umap": [ - 5.182260990142822, - 3.85408878326416 + 5.859459400177002, + 3.307234287261963 ], "pca3d": [ - 0.06037274748086929, - -0.1462555080652237, - 0.19539214670658112 + 0.06017833203077316, + -0.14627769589424133, + 0.19553886353969574 ], "tsne3d": [ - -4.853104591369629, - 12.976277351379395, - 9.277698516845703 + -2.128530740737915, + -19.292905807495117, + 5.458126068115234 ], "umap3d": [ - 4.714200019836426, - 6.962625026702881, - 5.182563781738281 + 5.301905632019043, + 7.802639007568359, + 3.6517558097839355 ] }, "cluster_id": 3, @@ -806223,11 +806373,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -806650,31 +806800,31 @@ "cited_by_count": 40, "projections": { "pca": [ - 0.09126384556293488, - -0.0835391953587532 + 0.09115703403949738, + -0.08315780013799667 ], "tsne": [ - -8.07207202911377, - 23.421810150146484 + 1.0759716033935547, + -29.49718475341797 ], "umap": [ - 5.358186721801758, - 4.21496057510376 + 5.95461893081665, + 3.7742226123809814 ], "pca3d": [ - 0.09126392751932144, - -0.0835452750325203, - 0.22014732658863068 + 0.09115710854530334, + -0.08316410332918167, + 0.22018037736415863 ], "tsne3d": [ - -2.5916385650634766, - 4.43826961517334, - 7.367739200592041 + -7.120283603668213, + -13.488297462463379, + 4.915745735168457 ], "umap3d": [ - 5.332850933074951, - 6.948943138122559, - 4.761744976043701 + 5.60562801361084, + 7.6147284507751465, + 3.0706331729888916 ] }, "cluster_id": 3, @@ -806685,11 +806835,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -807098,31 +807248,31 @@ "journal": "Nature Biotechnology", "projections": { "pca": [ - 0.04422440379858017, - -0.1743105798959732 + 0.044193580746650696, + -0.17435646057128906 ], "tsne": [ - 6.5838189125061035, - 30.36318016052246 + 7.655185222625732, + -18.771682739257812 ], "umap": [ - 5.916914463043213, - 4.151029586791992 + 6.423481464385986, + 3.755857467651367 ], "pca3d": [ - 0.044224485754966736, - -0.1743098497390747, - 0.24424798786640167 + 0.04419373720884323, + -0.17435556650161743, + 0.24465017020702362 ], "tsne3d": [ - 7.41659688949585, - 13.488764762878418, - 11.801408767700195 + 4.271136283874512, + -13.744341850280762, + -3.125849485397339 ], "umap3d": [ - 5.025736331939697, - 6.453993320465088, - 5.005976676940918 + 5.7047295570373535, + 7.267640113830566, + 3.622859477996826 ] }, "cluster_id": 3, @@ -807133,11 +807283,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -807561,31 +807711,31 @@ "openalex_url": "https://openalex.org/W6884595338", "projections": { "pca": [ - 0.004929091315716505, - -0.18614205718040466 + 0.004581255838274956, + -0.1856892704963684 ], "tsne": [ - 1.4715464115142822, - 26.167949676513672 + 2.3156025409698486, + -18.881027221679688 ], "umap": [ - 6.490611553192139, - 4.404206275939941 + 6.928617000579834, + 4.141350746154785 ], "pca3d": [ - 0.004929296672344208, - -0.18613822758197784, - 0.12477172166109085 + 0.004581523593515158, + -0.18568536639213562, + 0.12514203786849976 ], "tsne3d": [ - 3.3042185306549072, - 9.651756286621094, - 0.6406506299972534 + 0.8047595620155334, + -7.519637107849121, + 1.2400628328323364 ], "umap3d": [ - 5.242976665496826, - 5.84134578704834, - 4.940530776977539 + 6.105456829071045, + 6.754030704498291, + 3.5993001461029053 ] }, "cluster_id": 3, @@ -807596,11 +807746,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -808040,31 +808190,31 @@ "openalex_url": "https://openalex.org/W4391203107", "projections": { "pca": [ - 0.07362771779298782, - 0.006820792332291603 + 0.0737476497888565, + 0.006534735206514597 ], "tsne": [ - -6.819807052612305, - 11.445127487182617 + -4.834798336029053, + -41.64475631713867 ], "umap": [ - 5.29317045211792, - 5.087646484375 + 5.693465232849121, + 4.590498447418213 ], "pca3d": [ - 0.07362772524356842, - 0.006814259570091963, - 0.1204957365989685 + 0.0737476572394371, + 0.006527907680720091, + 0.12036937475204468 ], "tsne3d": [ - -3.7501933574676514, - -1.2239058017730713, - -8.39599895477295 + 0.7076826691627502, + 0.28351926803588867, + 14.791582107543945 ], "umap3d": [ - 5.502697467803955, - 6.792430400848389, - 4.004770278930664 + 5.3660888671875, + 7.005473613739014, + 2.61377215385437 ] }, "cluster_id": 3, @@ -808075,11 +808225,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -808504,31 +808654,31 @@ "openalex_url": "https://openalex.org/W4387966539", "projections": { "pca": [ - 0.033238060772418976, - -0.1467806100845337 + 0.032644376158714294, + -0.14701376855373383 ], "tsne": [ - -12.151174545288086, - 18.961578369140625 + -15.244921684265137, + -20.8179988861084 ], "umap": [ - 5.235595703125, - 4.512022972106934 + 5.637700080871582, + 3.9940454959869385 ], "pca3d": [ - 0.03323797509074211, - -0.1467370241880417, - 0.16830630600452423 + 0.03264428302645683, + -0.1469694823026657, + 0.1681704968214035 ], "tsne3d": [ - -8.32136058807373, - 2.7137393951416016, - 1.6348299980163574 + -9.871648788452148, + -13.768020629882812, + -1.2880849838256836 ], "umap3d": [ - 5.1348137855529785, - 6.921379566192627, - 4.55491828918457 + 5.368537425994873, + 7.4869890213012695, + 3.016533851623535 ] }, "cluster_id": 3, @@ -808539,11 +808689,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -808970,31 +809120,31 @@ "doi": "10.1101/2024.01.16.575968", "projections": { "pca": [ - 0.10702218860387802, - 0.030298026278614998 + 0.10709941387176514, + 0.03063345141708851 ], "tsne": [ - 4.380014419555664, - 24.627832412719727 + 5.771512508392334, + -16.460111618041992 ], "umap": [ - 6.641765594482422, - 4.664726257324219 + 6.980066299438477, + 4.474364757537842 ], "pca3d": [ - 0.10702208429574966, - 0.030304457992315292, - 0.14409062266349792 + 0.10709933191537857, + 0.030639665201306343, + 0.14397771656513214 ], "tsne3d": [ - 5.294214725494385, - 7.250743865966797, - -0.6079208254814148 + -2.5233583450317383, + -5.190588474273682, + 3.523310661315918 ], "umap3d": [ - 5.488236904144287, - 5.7411580085754395, - 4.74287223815918 + 6.2934651374816895, + 6.682615756988525, + 3.3500490188598633 ] }, "cluster_id": 3, @@ -809005,11 +809155,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -809430,31 +809580,31 @@ "doi": "10.1101/2023.12.22.573103", "projections": { "pca": [ - 0.09179024398326874, - -0.14464019238948822 + 0.09151551872491837, + -0.14444072544574738 ], "tsne": [ - -11.240826606750488, - 23.38140296936035 + -6.625935077667236, + -26.874391555786133 ], "umap": [ - 5.159979820251465, - 4.212601661682129 + 5.694908618927002, + 3.713771104812622 ], "pca3d": [ - 0.09179038554430008, - -0.14463192224502563, - 0.20996694266796112 + 0.09151573479175568, + -0.14443239569664001, + 0.21009306609630585 ], "tsne3d": [ - -2.0471549034118652, - 5.450001239776611, - 11.303842544555664 + -0.3359386920928955, + -13.794293403625488, + 9.370906829833984 ], "umap3d": [ - 4.886171340942383, - 6.966081619262695, - 4.650228500366211 + 5.2809953689575195, + 7.59438419342041, + 3.203144073486328 ] }, "cluster_id": 3, @@ -809465,11 +809615,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -809880,31 +810030,31 @@ "doi": "10.3726/978-3-653-04149-1/7", "projections": { "pca": [ - 0.04049311578273773, - 0.038520511239767075 + 0.04069340229034424, + 0.03861383721232414 ], "tsne": [ - -10.287801742553711, - 31.98151206970215 + -6.772318363189697, + -31.594751358032227 ], "umap": [ - 5.099187850952148, - 3.821901559829712 + 5.803091526031494, + 3.2313122749328613 ], "pca3d": [ - 0.04049311950802803, - 0.03851214796304703, - 0.19932705163955688 + 0.040693387389183044, + 0.038605187088251114, + 0.19911153614521027 ], "tsne3d": [ - -6.506148815155029, - 13.003106117248535, - 10.409886360168457 + -3.1571521759033203, + -20.640913009643555, + 6.696176528930664 ], "umap3d": [ - 4.716015815734863, - 7.026666641235352, - 5.1882195472717285 + 5.29120397567749, + 7.819023609161377, + 3.6644935607910156 ] }, "cluster_id": 3, @@ -809915,11 +810065,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -810337,31 +810487,31 @@ "cited_by_count": 18, "projections": { "pca": [ - 0.026871850714087486, - -0.1645626723766327 + 0.02658453956246376, + -0.16411186754703522 ], "tsne": [ - 2.389481782913208, - 25.672929763793945 + 3.0604822635650635, + -18.50531578063965 ], "umap": [ - 6.513143539428711, - 4.488123893737793 + 6.913365364074707, + 4.278870105743408 ], "pca3d": [ - 0.026871992275118828, - -0.16454626619815826, - 0.13679368793964386 + 0.026584671810269356, + -0.16409552097320557, + 0.13715113699436188 ], "tsne3d": [ - 4.293591022491455, - 8.57568645477295, - 1.360066294670105 + -0.06863290816545486, + -6.009902477264404, + 1.2948153018951416 ], "umap3d": [ - 5.334085941314697, - 5.7700090408325195, - 4.926025390625 + 6.188855171203613, + 6.8004150390625, + 3.5424540042877197 ] }, "cluster_id": 3, @@ -810372,11 +810522,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -810805,31 +810955,31 @@ "doi": "10.1101/2023.12.07.570727", "projections": { "pca": [ - 0.15557868778705597, - -0.08950721472501755 + 0.15549850463867188, + -0.08942453563213348 ], "tsne": [ - -6.782923698425293, - 21.93055534362793 + 3.2617149353027344, + -28.96969985961914 ], "umap": [ - 5.43093204498291, - 4.450942516326904 + 6.068131446838379, + 4.124955654144287 ], "pca3d": [ - 0.15557867288589478, - -0.08951382339000702, - 0.18320326507091522 + 0.15549862384796143, + -0.08943110704421997, + 0.1832992136478424 ], "tsne3d": [ - -2.411522626876831, - 2.6008734703063965, - 4.9210920333862305 + -5.034383296966553, + -12.901378631591797, + 2.6913232803344727 ], "umap3d": [ - 5.564101696014404, - 6.835772514343262, - 4.625375270843506 + 5.8260817527771, + 7.553307056427002, + 2.79323410987854 ] }, "cluster_id": 3, @@ -810840,11 +810990,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -811269,31 +811419,31 @@ "doi": "10.1101/2023.12.01.569227", "projections": { "pca": [ - 0.04962855577468872, - 0.022497938945889473 + 0.04964940622448921, + 0.022552700713276863 ], "tsne": [ - -19.785831451416016, - 23.020532608032227 + 3.663970470428467, + -40.204444885253906 ], "umap": [ - 4.28096866607666, - 3.6033992767333984 + 4.803557395935059, + 3.1731438636779785 ], "pca3d": [ - 0.049628715962171555, - 0.02248373255133629, - 0.10135027021169662 + 0.04964955151081085, + 0.0225382000207901, + 0.10119098424911499 ], "tsne3d": [ - -5.996561050415039, - 9.630248069763184, - -15.934013366699219 + 1.6642690896987915, + -6.641598701477051, + 19.40787124633789 ], "umap3d": [ - 4.976903915405273, - 7.795374870300293, - 4.105916500091553 + 4.528542518615723, + 7.990106582641602, + 2.6828150749206543 ] }, "cluster_id": 3, @@ -811304,12 +811454,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -811728,31 +811878,31 @@ "doi": "10.1101/2023.11.26.568742", "projections": { "pca": [ - 0.12828099727630615, - -0.16118931770324707 + 0.12815985083580017, + -0.1610165238380432 ], "tsne": [ - 2.572094678878784, - 30.15972900390625 + 0.9152992963790894, + -15.98694133758545 ], "umap": [ - 6.2749857902526855, - 4.10078763961792 + 6.888669967651367, + 3.7714591026306152 ], "pca3d": [ - 0.128280907869339, - -0.16118068993091583, - 0.2306227833032608 + 0.12815989553928375, + -0.1610078662633896, + 0.23093953728675842 ], "tsne3d": [ - 5.353230953216553, - 13.177534103393555, - 2.6883740425109863 + 4.256728172302246, + -10.79379940032959, + 3.13533616065979 ], "umap3d": [ - 5.453673362731934, - 6.171621322631836, - 5.173647880554199 + 6.1949872970581055, + 7.180611610412598, + 3.6468982696533203 ] }, "cluster_id": 3, @@ -811763,11 +811913,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -812188,31 +812338,31 @@ "doi": "10.1101/2023.11.24.568570", "projections": { "pca": [ - 0.05413556098937988, - -0.07208634167909622 + 0.053952913731336594, + -0.07217046618461609 ], "tsne": [ - -18.14725685119629, - 19.122604370117188 + -14.16818904876709, + -26.55237579345703 ], "umap": [ - 4.803649425506592, - 4.5385637283325195 + 5.320091724395752, + 3.8533661365509033 ], "pca3d": [ - 0.054135508835315704, - -0.07205572724342346, - 0.08577506989240646 + 0.05395285785198212, + -0.07213959097862244, + 0.08573629707098007 ], "tsne3d": [ - 2.1814990043640137, - 11.005626678466797, - 17.222314834594727 + 2.46907901763916, + -14.793713569641113, + 15.48267650604248 ], "umap3d": [ - 4.590334892272949, - 6.920515537261963, - 4.335809230804443 + 4.818968296051025, + 7.310281753540039, + 3.10129714012146 ] }, "cluster_id": 3, @@ -812223,12 +812373,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -812656,31 +812806,31 @@ "openalex_url": "https://openalex.org/W4388937394", "projections": { "pca": [ - 0.13606694340705872, - 0.06875909119844437 + 0.13623446226119995, + 0.06926247477531433 ], "tsne": [ - -0.8138293027877808, - 16.492277145385742 + 9.61378002166748, + -27.63587188720703 ], "umap": [ - 5.813482761383057, - 4.751451015472412 + 6.227226734161377, + 4.500615119934082 ], "pca3d": [ - 0.13606679439544678, - 0.06873774528503418, - 0.16594231128692627 + 0.13623443245887756, + 0.06924112141132355, + 0.16572272777557373 ], "tsne3d": [ - -0.8690024614334106, - 1.8344610929489136, - 0.3947192430496216 + -8.665413856506348, + -7.256021022796631, + 0.549997866153717 ], "umap3d": [ - 5.8219146728515625, - 6.62595796585083, - 4.427190780639648 + 5.963402271270752, + 7.25039529800415, + 2.689835786819458 ] }, "cluster_id": 3, @@ -812691,11 +812841,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -813116,47 +813266,47 @@ "cited_by_count": 878, "projections": { "pca": [ - -0.20891141891479492, - 0.057547979056835175 + -0.20872171223163605, + 0.0577409565448761 ], "tsne": [ - -58.293975830078125, - -16.13123893737793 + -60.742347717285156, + -6.809146881103516 ], "umap": [ - 0.08052479475736618, - 6.839872360229492 + 0.318376749753952, + 5.854340076446533 ], "pca3d": [ - -0.20891135931015015, - 0.057528816163539886, - -0.12322322279214859 + -0.20872153341770172, + 0.057722024619579315, + -0.1232297271490097 ], "tsne3d": [ - -35.95642852783203, - 0.44648826122283936, - -0.5725765824317932 + -35.90473937988281, + -8.777007102966309, + 2.21468448638916 ], "umap3d": [ - 0.21642127633094788, - 6.0236921310424805, - 4.557852745056152 + 0.8751803040504456, + 6.237730979919434, + 5.1345744132995605 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 9, - "label": "Vision Transformers" + "id": 23, + "label": "Training Noise" } ], "_embedding": [ @@ -813576,31 +813726,31 @@ "cited_by_count": 78, "projections": { "pca": [ - 0.060792215168476105, - -0.1623484194278717 + 0.06048036366701126, + -0.16202762722969055 ], "tsne": [ - 3.2892472743988037, - 25.764652252197266 + 3.4490418434143066, + -17.67026710510254 ], "umap": [ - 6.579020977020264, - 4.551620960235596 + 6.92326021194458, + 4.346461772918701 ], "pca3d": [ - 0.06079224497079849, - -0.16232930123806, - 0.11920201778411865 + 0.06048043444752693, + -0.16200828552246094, + 0.11951667070388794 ], "tsne3d": [ - 5.538887023925781, - 8.467312812805176, - 1.2305477857589722 + -0.6269235014915466, + -5.103091239929199, + 2.076742172241211 ], "umap3d": [ - 5.530407905578613, - 5.783549785614014, - 4.829430103302002 + 6.292783737182617, + 6.795479774475098, + 3.424201250076294 ] }, "cluster_id": 3, @@ -813611,11 +813761,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -814056,31 +814206,31 @@ "cited_by_count": 400, "projections": { "pca": [ - 0.08880016207695007, - -0.1118835061788559 + 0.08865619450807571, + -0.11191734671592712 ], "tsne": [ - -10.119874000549316, - 28.16651153564453 + -3.335277557373047, + -29.874210357666016 ], "umap": [ - 5.051374912261963, - 4.039082050323486 + 5.563684940338135, + 3.593520164489746 ], "pca3d": [ - 0.08880028873682022, - -0.1118869036436081, - 0.22527967393398285 + 0.08865633606910706, + -0.11192101240158081, + 0.22532613575458527 ], "tsne3d": [ - -5.44541597366333, - 10.161706924438477, - 8.296154975891113 + -3.4956486225128174, + -16.625883102416992, + 6.575759410858154 ], "umap3d": [ - 4.705114841461182, - 6.936432838439941, - 4.997124671936035 + 5.207120895385742, + 7.655254364013672, + 3.519090414047241 ] }, "cluster_id": 3, @@ -814091,11 +814241,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -814514,31 +814664,31 @@ "doi": "10.1101/2023.10.27.564339", "projections": { "pca": [ - 0.10799272358417511, - -0.1258365362882614 + 0.10772509127855301, + -0.12532642483711243 ], "tsne": [ - -7.135336399078369, - 28.140520095825195 + 1.0444929599761963, + -32.55082702636719 ], "umap": [ - 5.532153129577637, - 4.17372989654541 + 6.114231109619141, + 3.689046621322632 ], "pca3d": [ - 0.10799294710159302, - -0.12584955990314484, - 0.217551589012146 + 0.10772538185119629, + -0.12533928453922272, + 0.2176617681980133 ], "tsne3d": [ - 0.5423737168312073, - 6.109373569488525, - 8.362610816955566 + -3.8483688831329346, + -16.378419876098633, + 1.065032720565796 ], "umap3d": [ - 5.289389133453369, - 6.798574447631836, - 4.9625444412231445 + 5.8113932609558105, + 7.559995174407959, + 3.213024139404297 ] }, "cluster_id": 3, @@ -814549,11 +814699,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -814967,31 +815117,31 @@ ], "projections": { "pca": [ - 0.08199158310890198, - -0.0451175794005394 + 0.08210926502943039, + -0.04456077143549919 ], "tsne": [ - -9.153882026672363, - 24.854679107666016 + -1.2928916215896606, + -27.797359466552734 ], "umap": [ - 5.14558219909668, - 4.532236099243164 + 5.790736198425293, + 4.016757965087891 ], "pca3d": [ - 0.0819915309548378, - -0.04512403532862663, - 0.34192410111427307 + 0.08210926502943039, + -0.04456806927919388, + 0.3419830799102783 ], "tsne3d": [ - -4.856574058532715, - 7.4807586669921875, - 5.530750751495361 + -5.205104827880859, + -12.424703598022461, + 7.808742523193359 ], "umap3d": [ - 5.036870956420898, - 6.703553676605225, - 4.553633213043213 + 5.363090991973877, + 7.158350467681885, + 3.253692865371704 ] }, "cluster_id": 3, @@ -815002,11 +815152,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -815553,31 +815703,31 @@ "cited_by_count": 195, "projections": { "pca": [ - 0.1536625772714615, - 0.011970520950853825 + 0.15382787585258484, + 0.011944780126214027 ], "tsne": [ - 2.414882183074951, - 10.32832145690918 + 8.221602439880371, + -15.24035930633545 ], "umap": [ - 6.324819564819336, - 5.571493148803711 + 6.610670566558838, + 5.2754316329956055 ], "pca3d": [ - 0.1536623239517212, - 0.011962998658418655, - 0.13471579551696777 + 0.15382778644561768, + 0.011937582865357399, + 0.1346535086631775 ], "tsne3d": [ - 5.0354838371276855, - 1.9327733516693115, - -1.9437274932861328 + 5.91808557510376, + -7.01458215713501, + -4.376769542694092 ], "umap3d": [ - 5.753146648406982, - 5.580606460571289, - 4.2184343338012695 + 6.058480262756348, + 6.0671796798706055, + 2.9313442707061768 ] }, "cluster_id": 4, @@ -815588,12 +815738,12 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, - "label": "Genomic Models" + "id": 32, + "label": "Genome Protein" } ], "_embedding": [ @@ -816012,31 +816162,31 @@ "doi": "10.1101/2023.09.28.560044", "projections": { "pca": [ - 0.13109353184700012, - -0.14141149818897247 + 0.13084888458251953, + -0.14109684526920319 ], "tsne": [ - -5.976935386657715, - 22.548587799072266 + 3.335038900375366, + -28.035362243652344 ], "umap": [ - 5.493593215942383, - 4.448887825012207 + 5.981654644012451, + 4.127876281738281 ], "pca3d": [ - 0.13109366595745087, - -0.14141535758972168, - 0.24410995841026306 + 0.13084912300109863, + -0.14110073447227478, + 0.24425145983695984 ], "tsne3d": [ - -1.463133454322815, - 4.257086753845215, - 4.095144748687744 + -5.47944450378418, + -11.453862190246582, + 1.8778033256530762 ], "umap3d": [ - 5.5291242599487305, - 6.815741062164307, - 4.6374616622924805 + 5.834756374359131, + 7.507884502410889, + 2.927185297012329 ] }, "cluster_id": 3, @@ -816047,11 +816197,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -816473,31 +816623,31 @@ ], "projections": { "pca": [ - 0.10764750093221664, - -0.004289621952921152 + 0.1077895388007164, + -0.004440759774297476 ], "tsne": [ - -16.199182510375977, - 26.01473045349121 + -0.923137366771698, + -37.576202392578125 ], "umap": [ - 4.4185051918029785, - 3.811206579208374 + 4.994795799255371, + 3.3593509197235107 ], "pca3d": [ - 0.10764753818511963, - -0.0043044742196798325, - 0.1446695178747177 + 0.10778965055942535, + -0.004456175956875086, + 0.14458556473255157 ], "tsne3d": [ - -8.84608268737793, - 14.4230318069458, - 7.201320648193359 + -1.8064217567443848, + -8.05232048034668, + 21.147829055786133 ], "umap3d": [ - 4.563248157501221, - 7.47843074798584, - 4.592439651489258 + 4.606076717376709, + 7.957995414733887, + 3.138082265853882 ] }, "cluster_id": 3, @@ -816508,12 +816658,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -816932,31 +817082,31 @@ "doi": "10.1101/2023.10.01.560349", "projections": { "pca": [ - 0.1054832935333252, - -0.13777557015419006 + 0.10543982684612274, + -0.13759133219718933 ], "tsne": [ - -1.3211287260055542, - 29.521526336669922 + -1.0104836225509644, + -19.963382720947266 ], "umap": [ - 6.264543056488037, - 4.170120716094971 + 6.794827461242676, + 3.9369516372680664 ], "pca3d": [ - 0.10548349469900131, - -0.13778582215309143, - 0.24864348769187927 + 0.10544001311063766, + -0.1376015543937683, + 0.24893836677074432 ], "tsne3d": [ - 2.1802031993865967, - 11.452848434448242, - 5.182033061981201 + 2.1436338424682617, + -10.17778491973877, + 4.662147521972656 ], "umap3d": [ - 5.36492919921875, - 6.129862308502197, - 5.08055305480957 + 6.049644470214844, + 7.090808868408203, + 3.6273884773254395 ] }, "cluster_id": 3, @@ -816967,11 +817117,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -817395,31 +817545,31 @@ ], "projections": { "pca": [ - 0.050832707434892654, - -0.11211058497428894 + 0.05062218755483627, + -0.11225556582212448 ], "tsne": [ - -15.547381401062012, - 26.625770568847656 + -1.7665621042251587, + -36.6734504699707 ], "umap": [ - 4.509545803070068, - 3.924687623977661 + 5.005372047424316, + 3.5154404640197754 ], "pca3d": [ - 0.050832875072956085, - -0.11210856586694717, - 0.12230321764945984 + 0.050622448325157166, + -0.11225366592407227, + 0.12234731763601303 ], "tsne3d": [ - -7.351016044616699, - 14.210400581359863, - 6.878324031829834 + -3.633068561553955, + -19.71848487854004, + 3.6209681034088135 ], "umap3d": [ - 4.273553848266602, - 7.181832790374756, - 4.864621639251709 + 4.7042036056518555, + 7.841768741607666, + 3.4560320377349854 ] }, "cluster_id": 3, @@ -817430,12 +817580,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -817841,31 +817991,31 @@ ], "projections": { "pca": [ - 0.25196707248687744, - 0.07509861141443253 + 0.2524881660938263, + 0.07462114095687866 ], "tsne": [ - -17.957454681396484, - 31.51919937133789 + -9.034285545349121, + -39.00137710571289 ], "umap": [ - 4.8921661376953125, - 3.740043878555298 + 5.494330883026123, + 3.2592012882232666 ], "pca3d": [ - 0.25196659564971924, - 0.07510404288768768, - 0.23002953827381134 + 0.2524877190589905, + 0.07462655007839203, + 0.2298257201910019 ], "tsne3d": [ - -6.604497909545898, - 8.309426307678223, - 13.862431526184082 + -6.172293186187744, + -13.715262413024902, + 12.482776641845703 ], "umap3d": [ - 4.934370517730713, - 7.466828346252441, - 4.53121280670166 + 5.034989356994629, + 7.846047878265381, + 2.9938933849334717 ] }, "cluster_id": 3, @@ -817876,11 +818026,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -818291,31 +818441,31 @@ "abstract": "A list of manuscripts/tools using diffusion on biological enttieis - biolists/biodiffusion", "projections": { "pca": [ - -0.06013382226228714, - 0.1676601767539978 + -0.05943377688527107, + 0.16787749528884888 ], "tsne": [ - -28.42180061340332, - 17.056289672851562 + -26.85915756225586, + -23.87739372253418 ], "umap": [ - 3.254490613937378, - 4.9396491050720215 + 3.6358981132507324, + 4.340897083282471 ], "pca3d": [ - -0.06013431400060654, - 0.1676768660545349, - 0.11359646916389465 + -0.05943426489830017, + 0.1678939163684845, + 0.11343367397785187 ], "tsne3d": [ - -13.148391723632812, - 15.424992561340332, - 3.732433795928955 + -11.794598579406738, + -20.534162521362305, + -1.4162955284118652 ], "umap3d": [ - 3.126457452774048, - 6.894840717315674, - 4.75989294052124 + 3.484828233718872, + 7.266121864318848, + 4.258480072021484 ] }, "cluster_id": 2, @@ -818326,12 +818476,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 16, - "label": "Diffusion Models" + "id": 7, + "label": "Diffusion Gen" } ], "_embedding": [ @@ -818766,31 +818916,31 @@ "openalex_url": "https://openalex.org/W4386861232", "projections": { "pca": [ - 0.12867292761802673, - -0.05265743285417557 + 0.12855781614780426, + -0.05242191627621651 ], "tsne": [ - 21.697052001953125, - 13.063678741455078 + 24.060903549194336, + -6.518379211425781 ], "umap": [ - 7.121519088745117, - 7.230509281158447 + 7.216420650482178, + 6.780280590057373 ], "pca3d": [ - 0.12867313623428345, - -0.052677497267723083, - 0.002222810871899128 + 0.12855800986289978, + -0.052441954612731934, + 0.0023616314865648746 ], "tsne3d": [ - 8.534154891967773, - -4.866493225097656, - -13.142191886901855 + 7.562839031219482, + 10.714388847351074, + 5.329586029052734 ], "umap3d": [ - 6.779796600341797, - 3.9067113399505615, - 3.411813497543335 + 7.213636875152588, + 4.477772235870361, + 2.4903225898742676 ] }, "cluster_id": 4, @@ -818801,12 +818951,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -819223,31 +819373,31 @@ "doi": "10.1101/2023.09.13.557264", "projections": { "pca": [ - 0.02268679253757, - -0.1635802686214447 + 0.022309299558401108, + -0.16355572640895844 ], "tsne": [ - -6.13291597366333, - 35.171600341796875 + -6.6546406745910645, + -33.83025360107422 ], "umap": [ - 5.75734806060791, - 3.733642578125 + 6.444367408752441, + 3.3152318000793457 ], "pca3d": [ - 0.0226870346814394, - -0.16357608139514923, - 0.12372801452875137 + 0.022309571504592896, + -0.16355177760124207, + 0.1238565742969513 ], "tsne3d": [ - -3.4207956790924072, - 17.51885223388672, - 8.09184455871582 + 1.4083378314971924, + -20.340627670288086, + 3.1772212982177734 ], "umap3d": [ - 4.894218921661377, - 6.579947471618652, - 5.3070478439331055 + 5.789776802062988, + 7.695374011993408, + 3.734037399291992 ] }, "cluster_id": 3, @@ -819258,12 +819408,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { "id": 15, - "label": "Protein Language" + "label": "Protein Design" } ], "_embedding": [ @@ -819689,31 +819839,31 @@ "openalex_url": "https://openalex.org/W4385637034", "projections": { "pca": [ - 0.061444688588380814, - -0.056756872683763504 + 0.061319898813962936, + -0.05657389760017395 ], "tsne": [ - 21.104957580566406, - 13.698171615600586 + 24.380834579467773, + -5.5852556228637695 ], "umap": [ - 7.170293807983398, - 7.2202959060668945 + 7.298949718475342, + 6.861397743225098 ], "pca3d": [ - 0.06144476309418678, - -0.05677223950624466, - -0.058625299483537674 + 0.06132001429796219, + -0.05658959224820137, + -0.0584443062543869 ], "tsne3d": [ - 10.806910514831543, - -4.007534027099609, - -14.622754096984863 + 9.187514305114746, + 10.26774787902832, + 7.546857833862305 ], "umap3d": [ - 6.763465881347656, - 3.924389362335205, - 3.3970911502838135 + 7.241453647613525, + 4.419092655181885, + 2.4440298080444336 ] }, "cluster_id": 4, @@ -819724,12 +819874,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -820153,31 +820303,31 @@ "cited_by_count": 150, "projections": { "pca": [ - 0.10207366198301315, - -0.16995203495025635 + 0.10188361257314682, + -0.16999563574790955 ], "tsne": [ - -0.3224644958972931, - 35.43871307373047 + -4.271479606628418, + -17.975601196289062 ], "umap": [ - 6.3456525802612305, - 3.8395674228668213 + 6.891091346740723, + 3.603036880493164 ], "pca3d": [ - 0.10207398235797882, - -0.16996397078037262, - 0.16745877265930176 + 0.10188398510217667, + -0.17000742256641388, + 0.16774803400039673 ], "tsne3d": [ - 3.8517558574676514, - 12.969661712646484, - 7.900606632232666 + 5.920310020446777, + -14.464519500732422, + 2.1102983951568604 ], "umap3d": [ - 5.342872619628906, - 6.247766494750977, - 5.34780216217041 + 6.152149677276611, + 7.281022548675537, + 3.8177976608276367 ] }, "cluster_id": 3, @@ -820188,11 +820338,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -820611,31 +820761,31 @@ "doi": "10.1101/2022.04.14.488301", "projections": { "pca": [ - 0.1734628677368164, - -0.026189226657152176 + 0.1735238879919052, + -0.025996947661042213 ], "tsne": [ - -9.87964916229248, - 34.014644622802734 + -8.499500274658203, + -31.99697494506836 ], "umap": [ - 5.432647705078125, - 3.614595413208008 + 6.051672458648682, + 3.215200901031494 ], "pca3d": [ - 0.17346294224262238, - -0.02620360068976879, - 0.1389041393995285 + 0.1735238879919052, + -0.026011701673269272, + 0.13886955380439758 ], "tsne3d": [ - -3.8983421325683594, - 13.901009559631348, - 12.830483436584473 + -0.1161670982837677, + -22.02110481262207, + 7.086673259735107 ], "umap3d": [ - 4.9734721183776855, - 7.127687931060791, - 5.278558731079102 + 5.432079315185547, + 7.775467872619629, + 3.7424752712249756 ] }, "cluster_id": 3, @@ -820646,11 +820796,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -821093,31 +821243,31 @@ "openalex_url": "https://openalex.org/W4366507128", "projections": { "pca": [ - 0.09124791622161865, - -0.0017881989479064941 + 0.09133230149745941, + -0.0015722215175628662 ], "tsne": [ - -9.169760704040527, - 26.840286254882812 + -2.04379940032959, + -29.61690902709961 ], "umap": [ - 4.9825944900512695, - 4.07177209854126 + 5.569692134857178, + 3.6175529956817627 ], "pca3d": [ - 0.09124796837568283, - -0.0018043875461444259, - 0.2530335485935211 + 0.09133237600326538, + -0.0015890308422967792, + 0.2528568208217621 ], "tsne3d": [ - -4.8791279792785645, - 7.851097106933594, - 9.337019920349121 + -4.054118633270264, + -14.81566333770752, + 8.687151908874512 ], "umap3d": [ - 4.866702556610107, - 7.105023384094238, - 4.688670635223389 + 5.266983985900879, + 7.713323593139648, + 3.2087433338165283 ] }, "cluster_id": 3, @@ -821128,11 +821278,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -821553,31 +821703,31 @@ "doi": "10.1101/2023.08.10.552783", "projections": { "pca": [ - 0.11191920936107635, - -0.11836085468530655 + 0.11183398962020874, + -0.11835484951734543 ], "tsne": [ - -11.925077438354492, - 27.639833450317383 + -3.347478151321411, + -31.62953758239746 ], "umap": [ - 4.677371501922607, - 4.179337978363037 + 5.24027156829834, + 3.6460728645324707 ], "pca3d": [ - 0.11191938817501068, - -0.1183680072426796, - 0.23459090292453766 + 0.11183421313762665, + -0.118362195789814, + 0.23470844328403473 ], "tsne3d": [ - -6.658266544342041, - 12.027092933654785, - 5.611489772796631 + -6.298287868499756, + -17.128379821777344, + 4.4593000411987305 ], "umap3d": [ - 4.459566116333008, - 7.007439613342285, - 4.912841796875 + 4.98229455947876, + 7.656820297241211, + 3.6540768146514893 ] }, "cluster_id": 3, @@ -821588,12 +821738,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -822018,31 +822168,31 @@ "doi": "10.1101/2023.07.05.547496", "projections": { "pca": [ - 0.0783272236585617, - -0.1770329475402832 + 0.0781632512807846, + -0.17668373882770538 ], "tsne": [ - -1.3143789768218994, - 28.73940658569336 + -1.3842623233795166, + -20.748857498168945 ], "umap": [ - 6.248783588409424, - 4.177618026733398 + 6.720637798309326, + 3.8763232231140137 ], "pca3d": [ - 0.07832752168178558, - -0.17704333364963531, - 0.23932190239429474 + 0.07816365361213684, + -0.17669425904750824, + 0.23970577120780945 ], "tsne3d": [ - 0.5644442439079285, - 12.29003620147705, - 2.423452854156494 + 0.8585771918296814, + -9.525969505310059, + 5.723735809326172 ], "umap3d": [ - 5.1563215255737305, - 6.067971229553223, - 5.084130764007568 + 5.849113464355469, + 6.982417583465576, + 3.714202404022217 ] }, "cluster_id": 3, @@ -822053,11 +822203,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -822466,31 +822616,31 @@ "journal": "Nature Biotechnology", "projections": { "pca": [ - 0.06617360562086105, - -0.10283488780260086 + 0.06603957712650299, + -0.10239828377962112 ], "tsne": [ - -6.5553789138793945, - 20.74072265625 + 4.758018970489502, + -28.851200103759766 ], "umap": [ - 5.504095554351807, - 4.620077133178711 + 5.955625534057617, + 4.197495460510254 ], "pca3d": [ - 0.06617346405982971, - -0.10281416028738022, - 0.25542786717414856 + 0.06603948771953583, + -0.10237697511911392, + 0.25556665658950806 ], "tsne3d": [ - -3.0810437202453613, - 2.9395484924316406, - 2.45950984954834 + -7.554691314697266, + -9.670902252197266, + 1.9122191667556763 ], "umap3d": [ - 5.637856483459473, - 6.918521881103516, - 4.579054832458496 + 5.784837245941162, + 7.488725662231445, + 2.764409303665161 ] }, "cluster_id": 3, @@ -822501,11 +822651,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -822925,31 +823075,31 @@ ], "projections": { "pca": [ - 0.0044280849397182465, - -0.13608628511428833 + 0.004128121770918369, + -0.13600249588489532 ], "tsne": [ - -13.153329849243164, - 27.1508731842041 + -3.66510272026062, + -32.576541900634766 ], "umap": [ - 4.383632183074951, - 4.1932806968688965 + 4.917491912841797, + 3.733625650405884 ], "pca3d": [ - 0.004428262356668711, - -0.1360854059457779, - 0.1810726374387741 + 0.004128401633352041, + -0.13600203394889832, + 0.18113620579242706 ], "tsne3d": [ - -8.892744064331055, - 11.148584365844727, - 4.9574432373046875 + -7.939238548278809, + -18.542129516601562, + 5.356135368347168 ], "umap3d": [ - 4.173954010009766, - 7.064182758331299, - 4.846227645874023 + 4.648758411407471, + 7.655060291290283, + 3.626157522201538 ] }, "cluster_id": 3, @@ -822960,12 +823110,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -823379,31 +823529,31 @@ ], "projections": { "pca": [ - 0.06218966469168663, - -0.135105162858963 + 0.06195736303925514, + -0.1347118318080902 ], "tsne": [ - -8.755865097045898, - 21.185110092163086 + 2.7562477588653564, + -30.050518035888672 ], "umap": [ - 5.321651935577393, - 4.65106201171875 + 5.799887180328369, + 4.114417552947998 ], "pca3d": [ - 0.06218962371349335, - -0.13508132100105286, - 0.22571229934692383 + 0.06195735186338425, + -0.13468776643276215, + 0.22587022185325623 ], "tsne3d": [ - -5.334206581115723, - 4.928559303283691, - 3.5325798988342285 + -7.764503479003906, + -10.78795337677002, + 5.430182456970215 ], "umap3d": [ - 5.355742931365967, - 6.84427547454834, - 4.486415386199951 + 5.519979953765869, + 7.417806148529053, + 2.8819615840911865 ] }, "cluster_id": 3, @@ -823414,11 +823564,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 9, + "label": "Protein Design" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -823832,31 +823982,31 @@ ], "projections": { "pca": [ - 0.040480922907590866, - -0.15203514695167542 + 0.04011137783527374, + -0.15149524807929993 ], "tsne": [ - -3.7649872303009033, - 21.252466201782227 + 5.302942276000977, + -26.33262825012207 ], "umap": [ - 5.714701175689697, - 4.649306297302246 + 6.208482265472412, + 4.185774803161621 ], "pca3d": [ - 0.0404810756444931, - -0.1520358771085739, - 0.11176170408725739 + 0.04011157155036926, + -0.15149588882923126, + 0.11198821663856506 ], "tsne3d": [ - -4.166678428649902, - 2.542490243911743, - -0.7380499839782715 + -5.916844367980957, + -9.129329681396484, + -1.926335096359253 ], "umap3d": [ - 5.665086269378662, - 6.654004096984863, - 4.6278204917907715 + 6.060983180999756, + 7.359141826629639, + 2.8348772525787354 ] }, "cluster_id": 3, @@ -823867,11 +824017,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -824284,31 +824434,31 @@ ], "projections": { "pca": [ - 0.09692584723234177, - -0.12982191145420074 + 0.0967722237110138, + -0.1297254115343094 ], "tsne": [ - -10.546154975891113, - 27.681316375732422 + -3.0806965827941895, + -30.40011215209961 ], "umap": [ - 4.865263938903809, - 4.103942394256592 + 5.474161148071289, + 3.663264036178589 ], "pca3d": [ - 0.09692597389221191, - -0.12982654571533203, - 0.24877794086933136 + 0.09677240252494812, + -0.12973013520240784, + 0.2488812357187271 ], "tsne3d": [ - -5.870937347412109, - 9.841878890991211, - 7.230599403381348 + -4.6291890144348145, + -16.46625518798828, + 6.400971412658691 ], "umap3d": [ - 4.6944756507873535, - 6.970334053039551, - 4.871143341064453 + 5.170108318328857, + 7.654541492462158, + 3.5425822734832764 ] }, "cluster_id": 3, @@ -824319,12 +824469,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -824749,31 +824899,31 @@ "cited_by_count": 444, "projections": { "pca": [ - 0.09737960249185562, - 0.04068266227841377 + 0.09749456495046616, + 0.041458457708358765 ], "tsne": [ - -19.434165954589844, - 24.33931541442871 + 2.4033238887786865, + -40.72564697265625 ], "umap": [ - 4.276660919189453, - 3.571183204650879 + 4.790306568145752, + 3.078019380569458 ], "pca3d": [ - 0.09737961739301682, - 0.040657803416252136, - 0.25654324889183044 + 0.09749463200569153, + 0.041432980448007584, + 0.256430983543396 ], "tsne3d": [ - -3.9045636653900146, - 10.911599159240723, - -15.381440162658691 + -0.42080560326576233, + -5.8465681076049805, + 18.19414710998535 ], "umap3d": [ - 5.005949974060059, - 7.836366653442383, - 4.11298942565918 + 4.575643539428711, + 8.045787811279297, + 2.765187978744507 ] }, "cluster_id": 3, @@ -824784,12 +824934,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -825212,31 +825362,31 @@ "doi": "10.1101/2023.04.26.538476", "projections": { "pca": [ - 0.06964471936225891, - -0.061810385435819626 + 0.06952358782291412, + -0.06125403940677643 ], "tsne": [ - -18.722976684570312, - 24.401323318481445 + 2.2565152645111084, + -40.02635192871094 ], "umap": [ - 4.2820563316345215, - 3.5953245162963867 + 4.81320858001709, + 3.072514772415161 ], "pca3d": [ - 0.0696449726819992, - -0.061829257756471634, - 0.13641971349716187 + 0.06952379643917084, + -0.06127335503697395, + 0.13650837540626526 ], "tsne3d": [ - -5.279647350311279, - 10.9844331741333, - -14.440651893615723 + -0.22717516124248505, + -7.2101263999938965, + 18.855955123901367 ], "umap3d": [ - 4.912904739379883, - 7.820854663848877, - 4.18892240524292 + 4.589295864105225, + 8.01015853881836, + 2.749669313430786 ] }, "cluster_id": 3, @@ -825247,12 +825397,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -825696,31 +825846,31 @@ "cited_by_count": 224, "projections": { "pca": [ - 0.09543279558420181, - -0.03998316079378128 + 0.09541717171669006, + -0.03982779383659363 ], "tsne": [ - -13.887354850769043, - 31.828632354736328 + -9.00481128692627, + -27.627452850341797 ], "umap": [ - 4.856115818023682, - 3.772791624069214 + 5.516625881195068, + 3.358315944671631 ], "pca3d": [ - 0.09543287009000778, - -0.039986804127693176, - 0.16903364658355713 + 0.09541720896959305, + -0.03983168303966522, + 0.16897298395633698 ], "tsne3d": [ - -1.4633667469024658, - 7.171891212463379, - 13.650105476379395 + -0.48125070333480835, + -14.996344566345215, + 11.923209190368652 ], "umap3d": [ - 4.783661842346191, - 7.2569050788879395, - 4.657962799072266 + 5.007763862609863, + 7.812430381774902, + 3.205789804458618 ] }, "cluster_id": 3, @@ -825731,11 +825881,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -826158,31 +826308,31 @@ "openalex_url": "https://openalex.org/W4362684783", "projections": { "pca": [ - 0.008637950755655766, - -0.0030359544325619936 + 0.008738021366298199, + -0.0032218534033745527 ], "tsne": [ - -7.547593116760254, - 33.838321685791016 + -4.440497875213623, + -36.12225341796875 ], "umap": [ - 5.413774490356445, - 3.7869393825531006 + 6.2914719581604, + 3.377840518951416 ], "pca3d": [ - 0.008638176135718822, - -0.0030533468816429377, - 0.10052041709423065 + 0.008738257922232151, + -0.003239530837163329, + 0.10039510577917099 ], "tsne3d": [ - -6.399631023406982, - 17.73292350769043, - 6.407304763793945 + -1.3301547765731812, + -20.82796859741211, + 1.3167517185211182 ], "umap3d": [ - 4.6565141677856445, - 6.741175174713135, - 5.274707317352295 + 5.6275529861450195, + 7.769924640655518, + 3.79138445854187 ] }, "cluster_id": 3, @@ -826193,11 +826343,11 @@ "label": "Protein Design" }, { - "id": 12, - "label": "Protein Design" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -826624,31 +826774,31 @@ "openalex_url": "https://openalex.org/W4362471278", "projections": { "pca": [ - 0.10653645545244217, - -0.07753448933362961 + 0.10655002295970917, + -0.07747973501682281 ], "tsne": [ - -4.529548645019531, - 39.44375228881836 + -9.982693672180176, + -19.233196258544922 ], "umap": [ - 5.723074436187744, - 4.308049201965332 + 6.23513650894165, + 3.8724045753479004 ], "pca3d": [ - 0.10653629153966904, - -0.07750990986824036, - 0.14448799192905426 + 0.10654985904693604, + -0.07745502144098282, + 0.14461785554885864 ], "tsne3d": [ - 0.868802547454834, - -3.079601287841797, - 5.750080585479736 + -5.156039714813232, + -5.298053741455078, + 9.353760719299316 ], "umap3d": [ - 5.457254409790039, - 6.543700218200684, - 4.9302568435668945 + 5.83685302734375, + 7.36787223815918, + 3.223802089691162 ] }, "cluster_id": 3, @@ -826659,12 +826809,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, - "label": "Protein Language" + "id": 3, + "label": "Protein Design" } ], "_embedding": [ @@ -827114,31 +827264,31 @@ "doi": "10.1101/2023.01.08.523187", "projections": { "pca": [ - 0.07388642430305481, - 0.014651945792138577 + 0.0740387886762619, + 0.014888184145092964 ], "tsne": [ - -18.50837516784668, - 26.068204879760742 + 0.46402105689048767, + -40.0552978515625 ], "umap": [ - 4.359584808349609, - 3.59001088142395 + 4.9198737144470215, + 3.1045498847961426 ], "pca3d": [ - 0.07388661056756973, - 0.014624305069446564, - 0.13879860937595367 + 0.07403899729251862, + 0.014859535731375217, + 0.1387375444173813 ], "tsne3d": [ - -5.434831619262695, - 13.477035522460938, - -14.250541687011719 + -1.5805455446243286, + -8.842192649841309, + 17.749691009521484 ], "umap3d": [ - 4.899198532104492, - 7.789130687713623, - 4.2263288497924805 + 4.632234573364258, + 8.020392417907715, + 2.8562817573547363 ] }, "cluster_id": 3, @@ -827149,12 +827299,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -827591,31 +827741,31 @@ "openalex_url": "https://openalex.org/W4327550249", "projections": { "pca": [ - 0.10044201463460922, - -0.14682188630104065 + 0.10031033307313919, + -0.14650540053844452 ], "tsne": [ - -2.6972463130950928, - 27.28554344177246 + 0.9616628289222717, + -22.105501174926758 ], "umap": [ - 6.067572116851807, - 4.047323226928711 + 6.533514022827148, + 3.825377941131592 ], "pca3d": [ - 0.10044221580028534, - -0.14682389795780182, - 0.2740301787853241 + 0.1003105416893959, + -0.14650699496269226, + 0.27429670095443726 ], "tsne3d": [ - 2.4161500930786133, - 9.594808578491211, - 6.269914627075195 + 1.1694127321243286, + -11.61797046661377, + 3.1614060401916504 ], "umap3d": [ - 5.424765110015869, - 6.361307144165039, - 5.0750532150268555 + 6.02598237991333, + 7.227498531341553, + 3.5294435024261475 ] }, "cluster_id": 3, @@ -827626,11 +827776,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -828051,31 +828201,31 @@ "doi": "10.1101/2023.03.04.531015", "projections": { "pca": [ - 0.08641302585601807, - -0.1335097849369049 + 0.08625855296850204, + -0.13303890824317932 ], "tsne": [ - -5.954905986785889, - 26.10662078857422 + -0.14888766407966614, + -25.77367401123047 ], "umap": [ - 5.724914073944092, - 4.157149791717529 + 6.157289028167725, + 3.907963514328003 ], "pca3d": [ - 0.08641315251588821, - -0.13350975513458252, - 0.23428398370742798 + 0.08625873178243637, + -0.13303902745246887, + 0.23451846837997437 ], "tsne3d": [ - -3.2450506687164307, - 9.720521926879883, - 2.4888012409210205 + -1.6669855117797852, + -13.854055404663086, + 3.9287941455841064 ], "umap3d": [ - 5.157214164733887, - 6.520618438720703, - 4.822498798370361 + 5.646197319030762, + 7.342069625854492, + 3.3508169651031494 ] }, "cluster_id": 3, @@ -828086,12 +828236,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, - "label": "Protein Language" + "id": 3, + "label": "Protein Design" } ], "_embedding": [ @@ -828510,46 +828660,46 @@ "doi": "10.1101/2023.02.03.526939", "projections": { "pca": [ - 0.14071355760097504, - -0.0755656510591507 + 0.14055626094341278, + -0.07547206431627274 ], "tsne": [ - 20.737642288208008, - -25.434587478637695 + 11.89892864227295, + 25.575050354003906 ], "umap": [ - 7.4127655029296875, - 9.370887756347656 + 7.4788818359375, + 9.001529693603516 ], "pca3d": [ - 0.14071358740329742, - -0.0755644366145134, - -0.026916438713669777 + 0.14055617153644562, + -0.07547065615653992, + -0.02674565650522709 ], "tsne3d": [ - 10.782049179077148, - -12.632146835327148, - 11.835546493530273 + 8.098164558410645, + 7.079745769500732, + -17.51276206970215 ], "umap3d": [ - 5.276034832000732, - 3.6263811588287354, - 1.5534096956253052 + 5.232317924499512, + 3.4380548000335693, + 1.6089541912078857 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -828967,31 +829117,31 @@ "cited_by_count": 47, "projections": { "pca": [ - 0.031374696642160416, - -0.059063445776700974 + 0.03114677406847477, + -0.059001728892326355 ], "tsne": [ - -18.491729736328125, - 24.88686180114746 + 1.7704192399978638, + -39.740318298339844 ], "umap": [ - 4.323367595672607, - 3.605304718017578 + 4.849794387817383, + 3.1006956100463867 ], "pca3d": [ - 0.03137492015957832, - -0.059071626514196396, - 0.09308366477489471 + 0.031146984547376633, + -0.05901022627949715, + 0.09302257001399994 ], "tsne3d": [ - -5.7925848960876465, - 12.014968872070312, - -15.620323181152344 + 0.09061969816684723, + -8.29157543182373, + 17.601734161376953 ], "umap3d": [ - 4.922804355621338, - 7.84990930557251, - 4.178047180175781 + 4.5757575035095215, + 8.04769229888916, + 2.7856109142303467 ] }, "cluster_id": 3, @@ -829002,12 +829152,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -829413,46 +829563,46 @@ ], "projections": { "pca": [ - 0.11876852065324783, - 0.17785868048667908 + 0.11946558952331543, + 0.1769658774137497 ], "tsne": [ - -1.6282029151916504, - -5.165156841278076 + -1.3116567134857178, + 3.6281330585479736 ], "umap": [ - 5.070590496063232, - 7.245636940002441 + 5.27475643157959, + 6.557912826538086 ], "pca3d": [ - 0.11876779049634933, - 0.1778876781463623, - 0.12176874279975891 + 0.11946478486061096, + 0.1769946664571762, + 0.12137658894062042 ], "tsne3d": [ - -4.753840446472168, - 1.2187544107437134, - -19.722759246826172 + 1.1188338994979858, + 19.743852615356445, + -5.0752668380737305 ], "umap3d": [ - 4.580197334289551, - 3.4978978633880615, - 5.001163005828857 + 5.716598033905029, + 4.752279281616211, + 5.068000793457031 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, + "id": 13, "label": "Genomics Papers" } ], @@ -829871,31 +830021,31 @@ "cited_by_count": 569, "projections": { "pca": [ - 0.17473508417606354, - -0.08706316351890564 + 0.1746741533279419, + -0.08727283030748367 ], "tsne": [ - 57.662906646728516, - -0.5137492418289185 + 47.95845413208008, + 18.46980857849121 ], "umap": [ - 10.042886734008789, - 6.892369270324707 + 10.256637573242188, + 6.353142738342285 ], "pca3d": [ - 0.17473529279232025, - -0.08707965165376663, - 0.00887964479625225 + 0.17467427253723145, + -0.08728983998298645, + 0.009009704925119877 ], "tsne3d": [ - 26.886024475097656, - 4.074914455413818, - -7.881168842315674 + 25.252235412597656, + 1.5001695156097412, + -0.34519460797309875 ], "umap3d": [ - 7.734257698059082, - 5.51492977142334, - 1.2356981039047241 + 6.9981184005737305, + 5.138640880584717, + -0.29298603534698486 ] }, "cluster_id": 0, @@ -829906,12 +830056,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -830321,31 +830471,31 @@ "journal": "Nature Biotechnology", "projections": { "pca": [ - 0.07282509654760361, - -0.13725239038467407 + 0.07280421257019043, + -0.13697919249534607 ], "tsne": [ - 5.009601593017578, - 29.44974708557129 + 6.067861557006836, + -18.68151092529297 ], "umap": [ - 6.120445728302002, - 4.281663417816162 + 6.67245626449585, + 3.9756834506988525 ], "pca3d": [ - 0.07282520085573196, - -0.13725689053535461, - 0.25572463870048523 + 0.07280436158180237, + -0.13698360323905945, + 0.25611090660095215 ], "tsne3d": [ - 4.619025707244873, - 15.072400093078613, - 0.6063374280929565 + 4.468173027038574, + -11.342586517333984, + -1.6232389211654663 ], "umap3d": [ - 5.128422260284424, - 6.174163341522217, - 4.807493209838867 + 5.844566822052002, + 6.99668025970459, + 3.509580612182617 ] }, "cluster_id": 3, @@ -830356,11 +830506,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -830795,31 +830945,31 @@ "openalex_url": "https://openalex.org/W4317891156", "projections": { "pca": [ - 0.08537360280752182, - -0.18483875691890717 + 0.08512269705533981, + -0.18458960950374603 ], "tsne": [ - -6.093137264251709, - 31.377225875854492 + 1.3198281526565552, + -26.266677856445312 ], "umap": [ - 5.7893805503845215, - 3.9988560676574707 + 6.34971284866333, + 3.6193177700042725 ], "pca3d": [ - 0.08537401258945465, - -0.1848655343055725, - 0.17760513722896576 + 0.08512312173843384, + -0.18461719155311584, + 0.1779388189315796 ], "tsne3d": [ - -3.3661110401153564, - 12.629512786865234, - 3.0870041847229004 + -0.9100624322891235, + -14.331064224243164, + 1.0660886764526367 ], "umap3d": [ - 5.1060895919799805, - 6.538687705993652, - 5.034428596496582 + 5.801388740539551, + 7.4135966300964355, + 3.5244483947753906 ] }, "cluster_id": 3, @@ -830830,12 +830980,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { "id": 15, - "label": "Protein Language" + "label": "Protein Design" } ], "_embedding": [ @@ -831252,31 +831402,31 @@ ], "projections": { "pca": [ - 0.07426631450653076, - -0.135938823223114 + 0.07411527633666992, + -0.1353728473186493 ], "tsne": [ - -0.526296079158783, - 24.572185516357422 + 3.310030221939087, + -21.75804901123047 ], "umap": [ - 6.1280975341796875, - 4.524840831756592 + 6.369443893432617, + 4.271966934204102 ], "pca3d": [ - 0.0742664709687233, - -0.1359381377696991, - 0.22734335064888 + 0.07411551475524902, + -0.13537217676639557, + 0.22763101756572723 ], "tsne3d": [ - 0.005218549631536007, - 8.431360244750977, - 1.5698530673980713 + -1.3491225242614746, + -8.573705673217773, + 3.7791731357574463 ], "umap3d": [ - 5.306304454803467, - 6.20362663269043, - 4.675233840942383 + 5.884072303771973, + 6.957499027252197, + 3.277820587158203 ] }, "cluster_id": 3, @@ -831287,12 +831437,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, - "label": "Protein Language" + "id": 3, + "label": "Protein Design" } ], "_embedding": [ @@ -831705,31 +831855,31 @@ ], "projections": { "pca": [ - 0.0599435456097126, - 0.007634137757122517 + 0.060106296092271805, + 0.0073485905304551125 ], "tsne": [ - -7.5284104347229, - 33.78504943847656 + -4.446504592895508, + -36.06136703491211 ], "umap": [ - 5.317662715911865, - 3.7991790771484375 + 6.197261810302734, + 3.3991904258728027 ], "pca3d": [ - 0.05994351953268051, - 0.007624439895153046, - 0.11627812683582306 + 0.06010628864169121, + 0.007338677067309618, + 0.11611997336149216 ], "tsne3d": [ - -6.258368015289307, - 17.065929412841797, - 6.511413097381592 + -1.433698296546936, + -20.22334861755371, + 1.4648505449295044 ], "umap3d": [ - 4.597827911376953, - 6.7994537353515625, - 5.231471538543701 + 5.577873706817627, + 7.846218109130859, + 3.766636610031128 ] }, "cluster_id": 3, @@ -831740,11 +831890,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -832160,31 +832310,31 @@ "doi": "10.1101/2022.12.15.519894", "projections": { "pca": [ - 0.033367935568094254, - -0.17280805110931396 + 0.033134788274765015, + -0.1722966730594635 ], "tsne": [ - 1.0790289640426636, - 26.697893142700195 + 3.3946216106414795, + -19.394147872924805 ], "umap": [ - 6.454089164733887, - 4.489353656768799 + 6.961705207824707, + 4.233788013458252 ], "pca3d": [ - 0.03336795046925545, - -0.17279233038425446, - 0.23598504066467285 + 0.033134859055280685, + -0.17228080332279205, + 0.23636211454868317 ], "tsne3d": [ - 1.9087128639221191, - 10.459308624267578, - 1.4925649166107178 + 2.2507336139678955, + -7.294734001159668, + 2.8601863384246826 ], "umap3d": [ - 5.429757118225098, - 5.843216896057129, - 4.8064775466918945 + 6.089902400970459, + 6.793241500854492, + 3.489877223968506 ] }, "cluster_id": 3, @@ -832195,11 +832345,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -832622,31 +832772,31 @@ "doi": "10.1101/2022.12.22.521698", "projections": { "pca": [ - 0.13281068205833435, - -0.07903553545475006 + 0.13279590010643005, + -0.07884208858013153 ], "tsne": [ - -14.616191864013672, - 29.68552017211914 + -7.069123268127441, + -36.04819107055664 ], "umap": [ - 4.86753511428833, - 3.8215579986572266 + 5.567795276641846, + 3.447441816329956 ], "pca3d": [ - 0.13281099498271942, - -0.07907278090715408, - 0.22810392081737518 + 0.13279622793197632, + -0.07888061553239822, + 0.22816656529903412 ], "tsne3d": [ - -6.674116611480713, - 8.412415504455566, - 10.639398574829102 + -4.93973445892334, + -16.416879653930664, + 10.338692665100098 ], "umap3d": [ - 4.92261266708374, - 7.257635593414307, - 4.727989673614502 + 5.172709941864014, + 7.80384635925293, + 3.272439956665039 ] }, "cluster_id": 3, @@ -832657,11 +832807,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -833085,31 +833235,31 @@ "doi": "10.1101/2022.12.21.521521", "projections": { "pca": [ - 0.10866820067167282, - -0.12141338735818863 + 0.10863438248634338, + -0.12082593888044357 ], "tsne": [ - -3.3414230346679688, - 25.546873092651367 + 1.5868680477142334, + -23.687034606933594 ], "umap": [ - 5.888006210327148, - 4.441999912261963 + 6.28964900970459, + 4.088679313659668 ], "pca3d": [ - 0.10866832733154297, - -0.12142225354909897, - 0.3305177688598633 + 0.10863455384969711, + -0.1208353042602539, + 0.3307715952396393 ], "tsne3d": [ - -0.969926655292511, - 8.982312202453613, - 4.271721363067627 + -1.7825721502304077, + -11.153213500976562, + 4.997015476226807 ], "umap3d": [ - 5.296916961669922, - 6.3810858726501465, - 4.628758907318115 + 5.716194152832031, + 7.112194538116455, + 3.327578544616699 ] }, "cluster_id": 3, @@ -833120,12 +833270,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, - "label": "Protein Language" + "id": 3, + "label": "Protein Design" } ], "_embedding": [ @@ -833546,31 +833696,31 @@ "doi": "10.1101/2022.12.21.521526", "projections": { "pca": [ - 0.12885402143001556, - -0.09364195913076401 + 0.12890318036079407, + -0.09321976453065872 ], "tsne": [ - -8.561624526977539, - 25.878210067749023 + -1.4598935842514038, + -28.8971004486084 ], "umap": [ - 5.15939998626709, - 4.209001541137695 + 5.701470375061035, + 3.7662999629974365 ], "pca3d": [ - 0.12885421514511108, - -0.09365934878587723, - 0.3455982506275177 + 0.1289033591747284, + -0.09323762357234955, + 0.3457348644733429 ], "tsne3d": [ - -3.7664148807525635, - 8.02249813079834, - 7.0298614501953125 + -3.885364055633545, + -13.045891761779785, + 7.947142124176025 ], "umap3d": [ - 5.015729904174805, - 6.988469123840332, - 4.659153938293457 + 5.347206115722656, + 7.585841655731201, + 3.189822196960449 ] }, "cluster_id": 3, @@ -833581,11 +833731,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -834000,31 +834150,31 @@ "doi": "10.1101/2022.12.20.521205", "projections": { "pca": [ - 0.05817834660410881, - -0.09192468225955963 + 0.058008767664432526, + -0.0918993428349495 ], "tsne": [ - -10.663969993591309, - 35.781761169433594 + -10.371685981750488, + -31.79072380065918 ], "umap": [ - 5.3744354248046875, - 3.5437681674957275 + 5.982229232788086, + 3.1467068195343018 ], "pca3d": [ - 0.05817846581339836, - -0.09191983193159103, - 0.12101119756698608 + 0.058008916676044464, + -0.09189420938491821, + 0.12106237560510635 ], "tsne3d": [ - -0.5965072512626648, - 16.368511199951172, - 12.384449005126953 + -0.44384634494781494, + -20.16130256652832, + 9.297472953796387 ], "umap3d": [ - 4.83984375, - 7.05311918258667, - 5.337928771972656 + 5.341441631317139, + 7.694901943206787, + 3.9168379306793213 ] }, "cluster_id": 3, @@ -834035,11 +834185,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -834461,47 +834611,47 @@ "doi": "10.1186/s13104-024-06901-w", "projections": { "pca": [ - 0.08351302891969681, - -0.007706332951784134 + 0.08351258933544159, + -0.0076861209236085415 ], "tsne": [ - 23.881155014038086, - -10.864304542541504 + 7.063754558563232, + -39.627132415771484 ], "umap": [ - 7.7732439041137695, - 7.753355026245117 + 8.096961975097656, + 7.135425090789795 ], "pca3d": [ - 0.08351287245750427, - -0.007692595943808556, - -0.051843658089637756 + 0.08351234346628189, + -0.00767284631729126, + -0.05180659517645836 ], "tsne3d": [ - 20.27316665649414, - -14.14539623260498, - 10.951105117797852 + 7.1577911376953125, + -13.126996994018555, + -14.580068588256836 ], "umap3d": [ - 5.901623725891113, - 4.352365016937256, - 2.01418399810791 + 5.9371819496154785, + 4.253148555755615, + 1.5015829801559448 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -834943,31 +835093,31 @@ "doi": "10.1101/2022.12.09.519842", "projections": { "pca": [ - 0.05396834760904312, - -0.15849646925926208 + 0.053727563470602036, + -0.15848730504512787 ], "tsne": [ - -11.336259841918945, - 27.993867874145508 + -3.6820809841156006, + -30.74966049194336 ], "umap": [ - 4.852885723114014, - 4.114352226257324 + 5.365362644195557, + 3.6177642345428467 ], "pca3d": [ - 0.05396858975291252, - -0.1585061103105545, - 0.24489238858222961 + 0.053727876394987106, + -0.15849749743938446, + 0.24502427875995636 ], "tsne3d": [ - -7.197218418121338, - 9.856609344482422, - 6.881218433380127 + -5.419965744018555, + -17.20275115966797, + 7.087880611419678 ], "umap3d": [ - 4.637720584869385, - 7.048401832580566, - 4.887962818145752 + 5.100319862365723, + 7.681385040283203, + 3.543164014816284 ] }, "cluster_id": 3, @@ -834978,12 +835128,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -835391,31 +835541,31 @@ ], "projections": { "pca": [ - 0.008882289752364159, - -0.0029588763136416674 + 0.009139038622379303, + -0.002769976854324341 ], "tsne": [ - -12.06454086303711, - 25.727697372436523 + -2.1958885192871094, + -32.46842575073242 ], "umap": [ - 4.671846389770508, - 4.3175578117370605 + 5.170439720153809, + 3.783858299255371 ], "pca3d": [ - 0.008881940506398678, - -0.0029393823351711035, - 0.18970929086208344 + 0.009138732217252254, + -0.0027504919562488794, + 0.18975545465946198 ], "tsne3d": [ - -7.637289047241211, - 9.408806800842285, - 3.5358893871307373 + -7.987764358520508, + -16.56111717224121, + 8.046158790588379 ], "umap3d": [ - 4.506570816040039, - 6.865973472595215, - 4.826160430908203 + 4.91123104095459, + 7.532504081726074, + 3.582282066345215 ] }, "cluster_id": 3, @@ -835426,12 +835576,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -835846,31 +835996,31 @@ "doi": "10.1101/2022.10.21.513099", "projections": { "pca": [ - 0.09891173988580704, - -0.14553916454315186 + 0.0987003743648529, + -0.14539137482643127 ], "tsne": [ - 0.9427803158760071, - 35.04270935058594 + -3.246140956878662, + -17.203397750854492 ], "umap": [ - 6.374205589294434, - 3.8966012001037598 + 6.966337203979492, + 3.709315538406372 ], "pca3d": [ - 0.09891185909509659, - -0.14554935693740845, - 0.1593286246061325 + 0.09870065003633499, + -0.14540140330791473, + 0.15958532691001892 ], "tsne3d": [ - 4.675299167633057, - 13.66978931427002, - 5.764093399047852 + 5.77288293838501, + -12.59274673461914, + 0.7350948452949524 ], "umap3d": [ - 5.318989276885986, - 6.145175457000732, - 5.310998916625977 + 6.202627658843994, + 7.185244083404541, + 3.8132500648498535 ] }, "cluster_id": 3, @@ -835881,11 +836031,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -836310,31 +836460,31 @@ "doi": "10.1101/2022.07.20.500902", "projections": { "pca": [ - 0.11432279646396637, - -0.15027059614658356 + 0.11414529383182526, + -0.14987701177597046 ], "tsne": [ - -2.6187493801116943, - 27.147951126098633 + 1.0379151105880737, + -22.19072723388672 ], "umap": [ - 6.132519245147705, - 4.300244331359863 + 6.581531524658203, + 3.8408620357513428 ], "pca3d": [ - 0.11432307958602905, - -0.15027473866939545, - 0.28273847699165344 + 0.11414550244808197, + -0.14988066256046295, + 0.2829899489879608 ], "tsne3d": [ - 1.6235250234603882, - 9.720708847045898, - 5.711682319641113 + 0.7524228692054749, + -11.006610870361328, + 3.7438976764678955 ], "umap3d": [ - 5.444502353668213, - 6.299046516418457, - 4.981054306030273 + 5.972270488739014, + 7.199728012084961, + 3.4500467777252197 ] }, "cluster_id": 3, @@ -836345,11 +836495,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -836758,31 +836908,31 @@ "journal": "Nature Biotechnology", "projections": { "pca": [ - 0.01686832495033741, - -0.196956604719162 + 0.01663043536245823, + -0.19672156870365143 ], "tsne": [ - -1.1692334413528442, - 33.61532974243164 + -3.9734864234924316, + -19.64064598083496 ], "umap": [ - 6.280996322631836, - 3.9697718620300293 + 6.730007648468018, + 3.7256827354431152 ], "pca3d": [ - 0.01686830073595047, - -0.19692662358283997, - 0.2002423256635666 + 0.0166305061429739, + -0.19669079780578613, + 0.20068128407001495 ], "tsne3d": [ - 1.4719868898391724, - 11.535508155822754, - 8.065927505493164 + 2.2999520301818848, + -11.910895347595215, + 6.6598405838012695 ], "umap3d": [ - 5.3253254890441895, - 6.30302619934082, - 5.207185745239258 + 6.030215740203857, + 7.249511241912842, + 3.7608067989349365 ] }, "cluster_id": 3, @@ -836793,11 +836943,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -837216,31 +837366,31 @@ ], "projections": { "pca": [ - 0.0195144135504961, - -0.11042830348014832 + 0.019411558285355568, + -0.11020813882350922 ], "tsne": [ - -11.340718269348145, - 28.566823959350586 + -4.168120384216309, + -31.101932525634766 ], "umap": [ - 4.71394681930542, - 4.09743070602417 + 5.273497581481934, + 3.6026289463043213 ], "pca3d": [ - 0.019514556974172592, - -0.11043169349431992, - 0.2447492480278015 + 0.019411742687225342, + -0.11021193116903305, + 0.24484173953533173 ], "tsne3d": [ - -7.606346607208252, - 11.087118148803711, - 7.6180243492126465 + -5.214919090270996, + -18.5400333404541, + 6.503249645233154 ], "umap3d": [ - 4.52557897567749, - 7.00738000869751, - 5.020735263824463 + 5.030411243438721, + 7.707296848297119, + 3.603405237197876 ] }, "cluster_id": 3, @@ -837251,12 +837401,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -837666,31 +837816,31 @@ "journal": "Nature Machine Intelligence", "projections": { "pca": [ - 0.005921319592744112, - -0.11823754012584686 + 0.005896465387195349, + -0.11739043146371841 ], "tsne": [ - 7.046142101287842, - 32.40268325805664 + 8.848433494567871, + -20.20803451538086 ], "umap": [ - 5.903486728668213, - 4.0772576332092285 + 6.439535140991211, + 3.7231078147888184 ], "pca3d": [ - 0.005921483505517244, - -0.11824733763933182, - 0.1998661309480667 + 0.00589666236191988, + -0.11740054190158844, + 0.20023682713508606 ], "tsne3d": [ - -3.5189647674560547, - 14.269336700439453, - 1.4349020719528198 + 0.933342695236206, + -14.014687538146973, + -2.404939889907837 ], "umap3d": [ - 5.069685459136963, - 6.388221263885498, - 4.986178874969482 + 5.8229522705078125, + 7.2111897468566895, + 3.6679368019104004 ] }, "cluster_id": 3, @@ -837701,11 +837851,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -838149,31 +838299,31 @@ "openalex_url": "https://openalex.org/W4296032638", "projections": { "pca": [ - 0.037552669644355774, - -0.15660864114761353 + 0.03727702423930168, + -0.1563979983329773 ], "tsne": [ - -11.12021541595459, - 23.374597549438477 + -6.33456563949585, + -26.82758903503418 ], "umap": [ - 5.252350807189941, - 4.195738315582275 + 5.721764087677002, + 3.802246332168579 ], "pca3d": [ - 0.03755289316177368, - -0.15660862624645233, - 0.27031540870666504 + 0.03727727010846138, + -0.1563979834318161, + 0.27045220136642456 ], "tsne3d": [ - -2.333963394165039, - 5.007972717285156, - 10.821791648864746 + -0.3658074140548706, + -13.490105628967285, + 8.792258262634277 ], "umap3d": [ - 4.980883598327637, - 6.870890140533447, - 4.755744457244873 + 5.376699447631836, + 7.488708972930908, + 3.2856130599975586 ] }, "cluster_id": 3, @@ -838184,11 +838334,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -838607,31 +838757,31 @@ "doi": "10.1101/2022.08.25.505311", "projections": { "pca": [ - 0.137285515666008, - -0.1116190105676651 + 0.13705115020275116, + -0.11146678030490875 ], "tsne": [ - 21.503040313720703, - 13.425546646118164 + 24.0531063079834, + -6.375055313110352 ], "umap": [ - 7.142873764038086, - 7.162614345550537 + 7.275266647338867, + 6.736176490783691 ], "pca3d": [ - 0.13728587329387665, - -0.11164147406816483, - 0.033534854650497437 + 0.13705143332481384, + -0.11148940771818161, + 0.03376800939440727 ], "tsne3d": [ - 9.669529914855957, - -3.79984712600708, - -13.46430778503418 + 7.939170837402344, + 10.359248161315918, + 6.681980133056641 ], "umap3d": [ - 6.786747932434082, - 3.9824297428131104, - 3.4308295249938965 + 7.203540325164795, + 4.54392671585083, + 2.500499963760376 ] }, "cluster_id": 4, @@ -838642,12 +838792,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -839095,31 +839245,31 @@ "doi": "10.1101/2022.08.16.504181", "projections": { "pca": [ - 0.0886385515332222, - -0.037081748247146606 + 0.08859413117170334, + -0.036220941692590714 ], "tsne": [ - -19.393138885498047, - 25.351472854614258 + 1.3398816585540771, + -40.737247467041016 ], "umap": [ - 4.262296199798584, - 3.549327850341797 + 4.850430488586426, + 3.0842208862304688 ], "pca3d": [ - 0.08863860368728638, - -0.037086907774209976, - 0.20883144438266754 + 0.0885942131280899, + -0.03622632473707199, + 0.20891954004764557 ], "tsne3d": [ - -3.984133720397949, - 12.998405456542969, - -15.035185813903809 + -1.6096309423446655, + -7.050563335418701, + 17.031469345092773 ], "umap3d": [ - 4.953146457672119, - 7.834815502166748, - 4.163405418395996 + 4.551629066467285, + 8.07931137084961, + 2.752190589904785 ] }, "cluster_id": 3, @@ -839130,12 +839280,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -839543,31 +839693,31 @@ "journal": "Nature", "projections": { "pca": [ - 0.01525244303047657, - -0.12316783517599106 + 0.015128419734537601, + -0.12297149002552032 ], "tsne": [ - -12.498286247253418, - 22.3153018951416 + -3.8709840774536133, + -27.247283935546875 ], "umap": [ - 5.1910176277160645, - 4.2741899490356445 + 5.775700569152832, + 3.8276891708374023 ], "pca3d": [ - 0.015252336859703064, - -0.12314864993095398, - 0.23301512002944946 + 0.01512837689369917, + -0.12295208126306534, + 0.23314937949180603 ], "tsne3d": [ - -5.059789180755615, - 3.8713326454162598, - 9.119779586791992 + -9.128825187683105, + -12.856145858764648, + 7.271592617034912 ], "umap3d": [ - 4.932677745819092, - 6.989080429077148, - 4.77755069732666 + 5.388607501983643, + 7.576833724975586, + 3.1642184257507324 ] }, "cluster_id": 3, @@ -839578,7 +839728,7 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { @@ -840012,31 +840162,31 @@ "cited_by_count": 537, "projections": { "pca": [ - 0.10364329069852829, - -0.14163519442081451 + 0.10345906764268875, + -0.1412593424320221 ], "tsne": [ - -2.8308846950531006, - 27.226591110229492 + 0.938531219959259, + -22.30959129333496 ], "umap": [ - 5.9207634925842285, - 4.031331539154053 + 6.368099212646484, + 3.720205068588257 ], "pca3d": [ - 0.10364355146884918, - -0.14163900911808014, - 0.2690920829772949 + 0.10345932841300964, + -0.14126263558864594, + 0.26930612325668335 ], "tsne3d": [ - 1.543881893157959, - 9.41812515258789, - 6.4914445877075195 + 0.686484158039093, + -11.785332679748535, + 3.8610832691192627 ], "umap3d": [ - 5.3447489738464355, - 6.561944007873535, - 5.057200908660889 + 5.887945175170898, + 7.353898525238037, + 3.4800965785980225 ] }, "cluster_id": 3, @@ -840047,11 +840197,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -840469,31 +840619,31 @@ ], "projections": { "pca": [ - 0.08416400104761124, - -0.14212293922901154 + 0.08393755555152893, + -0.14188304543495178 ], "tsne": [ - -5.034688949584961, - 22.23610496520996 + 3.7955901622772217, + -27.07154655456543 ], "umap": [ - 5.569289207458496, - 4.578873157501221 + 6.103837490081787, + 4.041969299316406 ], "pca3d": [ - 0.08416435122489929, - -0.14213775098323822, - 0.16032297909259796 + 0.083937868475914, + -0.141898050904274, + 0.1605643779039383 ], "tsne3d": [ - -1.9310097694396973, - 5.103190898895264, - 2.1166954040527344 + -3.786181688308716, + -12.2728910446167, + 1.5239747762680054 ], "umap3d": [ - 5.6239447593688965, - 6.688500881195068, - 4.656261444091797 + 5.883975028991699, + 7.41105318069458, + 2.835104465484619 ] }, "cluster_id": 3, @@ -840504,11 +840654,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -840929,31 +841079,31 @@ "cited_by_count": 15, "projections": { "pca": [ - 0.1120324432849884, - -0.11571511626243591 + 0.11179538071155548, + -0.11527261137962341 ], "tsne": [ - -6.267973899841309, - 22.64623260498047 + 3.3648416996002197, + -28.109670639038086 ], "umap": [ - 5.496987819671631, - 4.600527763366699 + 5.897881507873535, + 4.18038272857666 ], "pca3d": [ - 0.11203264445066452, - -0.1157238632440567, - 0.2664250135421753 + 0.11179559677839279, + -0.11528154462575912, + 0.2664773464202881 ], "tsne3d": [ - -2.462042808532715, - 5.031329154968262, - 4.607138156890869 + -5.514044761657715, + -10.708656311035156, + 3.732430934906006 ], "umap3d": [ - 5.514132976531982, - 6.834423542022705, - 4.568355560302734 + 5.592570781707764, + 7.434325695037842, + 2.8719167709350586 ] }, "cluster_id": 3, @@ -840964,11 +841114,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -841378,47 +841528,47 @@ ], "projections": { "pca": [ - -0.12079726904630661, - 0.29217326641082764 + -0.11984662711620331, + 0.2922700047492981 ], "tsne": [ - -13.061206817626953, - -2.6981546878814697 + -10.87552261352539, + -2.3218531608581543 ], "umap": [ - 4.296518325805664, - 7.130107879638672 + 4.589462757110596, + 6.5837321281433105 ], "pca3d": [ - -0.12079744040966034, - 0.2921619713306427, - 0.08750706166028976 + -0.11984697729349136, + 0.2922583818435669, + 0.08707187324762344 ], "tsne3d": [ - -10.317870140075684, - 3.324575901031494, - 15.423280715942383 + -3.6988325119018555, + -2.769861936569214, + -8.343038558959961 ], "umap3d": [ - 3.8493812084198, - 3.906771659851074, - 4.555750846862793 + 4.865728378295898, + 4.812190055847168, + 4.7059736251831055 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -841832,47 +841982,47 @@ "doi": "10.1038/s41592-022-01492-5", "projections": { "pca": [ - -0.0839853286743164, - 0.1512647122144699 + -0.08351731300354004, + 0.15230363607406616 ], "tsne": [ - -22.04848289489746, - -18.24671745300293 + -26.577634811401367, + 16.077836990356445 ], "umap": [ - 2.5305585861206055, - 7.96743631362915 + 2.8100180625915527, + 7.233051300048828 ], "pca3d": [ - -0.08398571610450745, - 0.15127326548099518, - 0.04314899072051048 + -0.08351771533489227, + 0.15231163799762726, + 0.043073881417512894 ], "tsne3d": [ - -11.251442909240723, - -18.595149993896484, - 8.91441822052002 + -13.692254066467285, + 7.550699234008789, + -7.912588596343994 ], "umap3d": [ - 2.16357159614563, - 4.031420707702637, - 4.072212219238281 + 3.007483720779419, + 4.380804061889648, + 4.705714225769043 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { "id": 0, - "label": "LLM Language Learning" + "label": "Math AI" } ], "_embedding": [ @@ -842293,31 +842443,31 @@ "openalex_url": "https://openalex.org/W4280570469", "projections": { "pca": [ - 0.14180392026901245, - 0.010419268161058426 + 0.1418812870979309, + 0.011104081757366657 ], "tsne": [ - -1.9596143960952759, - 19.4002742767334 + 7.928791046142578, + -24.3546199798584 ], "umap": [ - 5.752721309661865, - 4.294859409332275 + 6.160861492156982, + 4.040980339050293 ], "pca3d": [ - 0.14180389046669006, - 0.010396899655461311, - 0.18023240566253662 + 0.1418813019990921, + 0.011081013828516006, + 0.18014977872371674 ], "tsne3d": [ - 2.586681604385376, - 2.7533888816833496, - 7.035656929016113 + -4.434432029724121, + -12.970541954040527, + -3.2914905548095703 ], "umap3d": [ - 5.796235084533691, - 6.706308841705322, - 4.843935489654541 + 6.151424884796143, + 7.501195907592773, + 3.0064401626586914 ] }, "cluster_id": 3, @@ -842328,12 +842478,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, - "label": "Protein Language" + "id": 3, + "label": "Protein Design" } ], "_embedding": [ @@ -842749,31 +842899,31 @@ "doi": "10.1101/2022.05.23.493038", "projections": { "pca": [ - 0.10045471787452698, - -0.1469499170780182 + 0.1001831591129303, + -0.14673709869384766 ], "tsne": [ - 21.60122299194336, - 14.287311553955078 + 23.911651611328125, + -7.246616840362549 ], "umap": [ - 7.139934539794922, - 7.0843682289123535 + 7.261457920074463, + 6.691788673400879 ], "pca3d": [ - 0.10045492649078369, - -0.14696507155895233, - 0.06892158091068268 + 0.10018342733383179, + -0.1467522233724594, + 0.06920681893825531 ], "tsne3d": [ - 10.169896125793457, - -2.6943318843841553, - -13.118992805480957 + 6.794593811035156, + 10.493956565856934, + 7.332816123962402 ], "umap3d": [ - 6.807591438293457, - 4.088193893432617, - 3.498725175857544 + 7.2014079093933105, + 4.623502254486084, + 2.449892282485962 ] }, "cluster_id": 4, @@ -842784,12 +842934,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -843207,31 +843357,31 @@ "doi": "10.1101/2022.05.19.492714", "projections": { "pca": [ - -0.0734206810593605, - -0.23713968694210052 + -0.07388200610876083, + -0.23636358976364136 ], "tsne": [ - 1.7630020380020142, - 32.64113998413086 + -2.863436698913574, + -14.014826774597168 ], "umap": [ - 6.610409736633301, - 4.222725868225098 + 7.131411552429199, + 3.931100368499756 ], "pca3d": [ - -0.0734202042222023, - -0.23714639246463776, - 0.0977759063243866 + -0.07388157397508621, + -0.23637068271636963, + 0.09833475947380066 ], "tsne3d": [ - -0.06978446990251541, - 15.734452247619629, - -0.9865888953208923 + 1.090300440788269, + -9.700496673583984, + -4.059258460998535 ], "umap3d": [ - 4.911338806152344, - 5.781589984893799, - 5.143868446350098 + 5.897691249847412, + 6.720258712768555, + 4.01662540435791 ] }, "cluster_id": 3, @@ -843242,11 +843392,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -843664,31 +843814,31 @@ "cited_by_count": 176, "projections": { "pca": [ - 0.04270457476377487, - -0.11977978050708771 + 0.04253425821661949, + -0.11919845640659332 ], "tsne": [ - 3.677093505859375, - 33.92515182495117 + 1.1326313018798828, + -13.66374683380127 ], "umap": [ - 6.496999740600586, - 4.116307258605957 + 6.906177520751953, + 3.8202288150787354 ], "pca3d": [ - 0.04270463436841965, - -0.11977988481521606, - 0.1082160621881485 + 0.042534373700618744, + -0.11919905990362167, + 0.1084757074713707 ], "tsne3d": [ - 3.8749501705169678, - 15.476414680480957, - 3.973062038421631 + 7.249665260314941, + -9.76944637298584, + 2.7945709228515625 ], "umap3d": [ - 5.3448405265808105, - 5.899167537689209, - 5.112669944763184 + 6.211714267730713, + 7.082711219787598, + 3.7306370735168457 ] }, "cluster_id": 3, @@ -843699,11 +843849,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -844120,31 +844270,31 @@ "doi": "10.1101/2022.04.14.488405", "projections": { "pca": [ - 0.05519917234778404, - -0.19024179875850677 + 0.05507613345980644, + -0.1897684633731842 ], "tsne": [ - 0.39502090215682983, - 32.67009353637695 + -2.374176263809204, + -16.091175079345703 ], "umap": [ - 6.187828063964844, - 4.0307817459106445 + 6.781260013580322, + 3.718151569366455 ], "pca3d": [ - 0.05519958958029747, - -0.19026538729667664, - 0.2307797372341156 + 0.05507655069231987, + -0.1897927075624466, + 0.2312772125005722 ], "tsne3d": [ - -1.2567418813705444, - 14.359689712524414, - 1.1885403394699097 + 1.7546353340148926, + -12.003520011901855, + -2.120800495147705 ], "umap3d": [ - 5.124383449554443, - 6.113099098205566, - 5.140201091766357 + 5.909934043884277, + 7.072764873504639, + 3.8138186931610107 ] }, "cluster_id": 3, @@ -844155,11 +844305,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -844580,31 +844730,31 @@ "doi": "10.1101/2022.04.10.487811", "projections": { "pca": [ - 0.07895304262638092, - -0.04050750285387039 + 0.07891249656677246, + -0.039700012654066086 ], "tsne": [ - -19.30457305908203, - 24.827159881591797 + 1.862435221672058, + -40.62469482421875 ], "umap": [ - 4.312347412109375, - 3.5970869064331055 + 4.845752716064453, + 3.082970142364502 ], "pca3d": [ - 0.07895319163799286, - -0.04052804410457611, - 0.21726135909557343 + 0.07891269773244858, + -0.03972103074193001, + 0.21734802424907684 ], "tsne3d": [ - -4.022700786590576, - 11.845419883728027, - -15.098712921142578 + -0.8935489654541016, + -6.514758110046387, + 17.68535614013672 ], "umap3d": [ - 4.948666095733643, - 7.826108455657959, - 4.175422191619873 + 4.578779220581055, + 8.048885345458984, + 2.7591848373413086 ] }, "cluster_id": 3, @@ -844615,12 +844765,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -845042,31 +845192,31 @@ ], "projections": { "pca": [ - 0.05223888158798218, - -0.1420416384935379 + 0.05200903117656708, + -0.14173290133476257 ], "tsne": [ - -9.942488670349121, - 35.787723541259766 + -9.803820610046387, + -31.44814682006836 ], "umap": [ - 5.3951592445373535, - 3.5651538372039795 + 6.055559158325195, + 3.1638243198394775 ], "pca3d": [ - 0.052239179611206055, - -0.14205099642276764, - 0.14174692332744598 + 0.052009377628564835, + -0.1417415887117386, + 0.1419765055179596 ], "tsne3d": [ - -1.8053771257400513, - 15.509942054748535, - 11.491419792175293 + 0.45060446858406067, + -19.476144790649414, + 7.971244812011719 ], "umap3d": [ - 4.770531177520752, - 6.862578868865967, - 5.342936038970947 + 5.343758583068848, + 7.646503448486328, + 3.9175286293029785 ] }, "cluster_id": 3, @@ -845077,11 +845227,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -845490,31 +845640,31 @@ "journal": "Nature", "projections": { "pca": [ - -0.05983591824769974, - 0.0036732249427586794 + -0.05964963138103485, + 0.0037375788670033216 ], "tsne": [ - -9.083464622497559, - 14.239350318908691 + -6.350557804107666, + -19.91680908203125 ], "umap": [ - 5.445028781890869, - 5.7970733642578125 + 6.151890277862549, + 4.991897106170654 ], "pca3d": [ - -0.0598362497985363, - 0.0037099700421094894, - 0.09767160564661026 + -0.05964997038245201, + 0.0037747840397059917, + 0.0977059081196785 ], "tsne3d": [ - -9.034013748168945, - 1.9129222631454468, - 8.226482391357422 + -6.876623630523682, + -2.509727716445923, + -0.0012825713492929935 ], "umap3d": [ - 4.89516544342041, - 5.563920497894287, - 4.683428764343262 + 5.651965618133545, + 6.33757209777832, + 3.811336040496826 ] }, "cluster_id": 3, @@ -845525,12 +845675,12 @@ "label": "Protein Design" }, { - "id": 11, - "label": "Genomic Models" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, - "label": "Protein Design" + "id": 32, + "label": "Genome Protein" } ], "_embedding": [ @@ -845954,31 +846104,31 @@ "openalex_url": "https://openalex.org/W4205773061", "projections": { "pca": [ - 0.062135517597198486, - -0.16098473966121674 + 0.06196447089314461, + -0.16071099042892456 ], "tsne": [ - 3.16703200340271, - 29.1590633392334 + 1.9267189502716064, + -16.56136703491211 ], "umap": [ - 6.615793704986572, - 4.420566082000732 + 7.116880416870117, + 4.124216079711914 ], "pca3d": [ - 0.06213569641113281, - -0.16097499430179596, - 0.19042856991291046 + 0.06196462735533714, + -0.16070108115673065, + 0.19080594182014465 ], "tsne3d": [ - 5.062490463256836, - 12.074858665466309, - 0.5275309681892395 + 4.25040340423584, + -8.465510368347168, + 2.8916592597961426 ], "umap3d": [ - 5.493027687072754, - 5.794352054595947, - 4.893309116363525 + 6.2904863357543945, + 6.974972248077393, + 3.623034954071045 ] }, "cluster_id": 3, @@ -845989,11 +846139,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -846417,31 +846567,31 @@ "doi": "10.1007/s00284-026-04931-0", "projections": { "pca": [ - 0.020100053399801254, - -0.0034452190157026052 + 0.020314091816544533, + -0.0034738488029688597 ], "tsne": [ - 3.0736215114593506, - 15.56087875366211 + 9.195948600769043, + -16.07892608642578 ], "umap": [ - 5.971116542816162, - 4.826847553253174 + 6.4402594566345215, + 4.127044677734375 ], "pca3d": [ - 0.020099958404898643, - -0.0034506816882640123, - 0.14058266580104828 + 0.020314017310738564, + -0.0034791408106684685, + 0.1406022310256958 ], "tsne3d": [ - 2.9316892623901367, - 0.6420034170150757, - -1.4668781757354736 + 5.495675563812256, + -9.501928329467773, + -6.019994735717773 ], "umap3d": [ - 5.582870006561279, - 6.200883388519287, - 4.721789836883545 + 6.060238838195801, + 7.004356384277344, + 3.143223285675049 ] }, "cluster_id": 3, @@ -846452,12 +846602,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, - "label": "Protein Design" + "id": 20, + "label": "Protein Language" } ], "_embedding": [ @@ -846873,31 +847023,31 @@ "doi": "10.1101/2021.12.13.472419", "projections": { "pca": [ - 0.05023924633860588, - -0.05326029285788536 + 0.050466421991586685, + -0.05296054109930992 ], "tsne": [ - -19.917518615722656, - 25.781517028808594 + 1.0230907201766968, + -41.28083419799805 ], "umap": [ - 4.303093433380127, - 3.5688581466674805 + 4.911922931671143, + 3.072972536087036 ], "pca3d": [ - 0.05023937672376633, - -0.05327396094799042, - 0.23018907010555267 + 0.05046660080552101, + -0.0529748797416687, + 0.23044279217720032 ], "tsne3d": [ - -2.661691904067993, - 13.296321868896484, - -14.338348388671875 + -1.7425204515457153, + -6.076928615570068, + 15.911152839660645 ], "umap3d": [ - 4.962926387786865, - 7.82221794128418, - 4.170981407165527 + 4.619663238525391, + 8.027630805969238, + 2.7892227172851562 ] }, "cluster_id": 3, @@ -846908,12 +847058,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -847331,31 +847481,31 @@ "doi": "10.1101/2021.11.15.468653", "projections": { "pca": [ - 0.09025803208351135, - -0.171994149684906 + 0.09001845121383667, + -0.1718529760837555 ], "tsne": [ - 0.16198089718818665, - 35.04853820800781 + -3.640911340713501, + -17.799697875976562 ], "umap": [ - 6.402877330780029, - 3.8713648319244385 + 6.966137409210205, + 3.6825428009033203 ], "pca3d": [ - 0.09025832265615463, - -0.1720021516084671, - 0.1998319774866104 + 0.09001878648996353, + -0.17186081409454346, + 0.2001400887966156 ], "tsne3d": [ - 4.430000305175781, - 12.538619041442871, - 6.7948222160339355 + 5.729300022125244, + -13.25997257232666, + 2.0447962284088135 ], "umap3d": [ - 5.367472171783447, - 6.166233062744141, - 5.3307881355285645 + 6.2341227531433105, + 7.204747676849365, + 3.7783596515655518 ] }, "cluster_id": 3, @@ -847366,11 +847516,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -847790,31 +847940,31 @@ "doi": "10.1101/2021.11.14.468528", "projections": { "pca": [ - 0.10350467264652252, - -0.16521114110946655 + 0.10332658886909485, + -0.1652202308177948 ], "tsne": [ - 1.4877910614013672, - 30.45332908630371 + -0.24502575397491455, + -16.576213836669922 ], "umap": [ - 6.3193206787109375, - 4.106121063232422 + 6.817464828491211, + 3.8116259574890137 ], "pca3d": [ - 0.10350487381219864, - -0.16522251069545746, - 0.2039729803800583 + 0.10332689434289932, + -0.16523189842700958, + 0.20423021912574768 ], "tsne3d": [ - 3.9942331314086914, - 12.010062217712402, - 3.596097946166992 + 3.7415244579315186, + -11.98531436920166, + 1.5260323286056519 ], "umap3d": [ - 5.3273115158081055, - 6.062546730041504, - 5.153639793395996 + 6.145364761352539, + 7.080516338348389, + 3.685209274291992 ] }, "cluster_id": 3, @@ -847825,11 +847975,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -848238,47 +848388,47 @@ "abstract": "Trainable, memory-efficient, and GPU-friendly PyTorch reproduction of AlphaFold 2 - aqlaboratory/openfold", "projections": { "pca": [ - -0.039193831384181976, - 0.05591520294547081 + -0.03869234398007393, + 0.055678509175777435 ], "tsne": [ - -26.966543197631836, - 33.84688186645508 + -15.100019454956055, + -9.30069351196289 ], "umap": [ - 4.063660621643066, - 6.248478412628174 + 4.479925632476807, + 5.631900310516357 ], "pca3d": [ - -0.03919379413127899, - 0.05590733140707016, - 0.12198418378829956 + -0.03869231790304184, + 0.055670518428087234, + 0.12198290973901749 ], "tsne3d": [ - -8.596884727478027, - 17.553211212158203, - 12.664881706237793 + -11.90853500366211, + -7.318515777587891, + 16.16816520690918 ], "umap3d": [ - 3.831918239593506, - 4.998781204223633, - 4.627763271331787 + 4.694709777832031, + 5.759925842285156, + 4.274044513702393 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 10, - "label": "Code Development" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -848699,31 +848849,31 @@ "doi": "10.1101/2021.11.09.467890", "projections": { "pca": [ - 0.10493209958076477, - -0.1103309616446495 + 0.10467985272407532, + -0.11022299528121948 ], "tsne": [ - -7.738846302032471, - 19.855030059814453 + 4.657039165496826, + -30.22109603881836 ], "umap": [ - 5.412167072296143, - 4.603987216949463 + 5.895080089569092, + 4.242228984832764 ], "pca3d": [ - 0.10493212938308716, - -0.11032423377037048, - 0.17832748591899872 + 0.10467994958162308, + -0.1102159395813942, + 0.17835015058517456 ], "tsne3d": [ - -4.141346454620361, - 1.1842114925384521, - 4.057019233703613 + -8.075577735900879, + -12.112661361694336, + 1.1428718566894531 ], "umap3d": [ - 5.558263301849365, - 6.895792484283447, - 4.583530426025391 + 5.655694007873535, + 7.478637218475342, + 2.6918962001800537 ] }, "cluster_id": 3, @@ -848734,11 +848884,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -849163,31 +849313,31 @@ "doi": "10.1101/2021.11.02.467003", "projections": { "pca": [ - 0.08567134290933609, - 0.01582222431898117 + 0.085847407579422, + 0.015409574843943119 ], "tsne": [ - -6.797535419464111, - 11.43366813659668 + -4.839677333831787, + -41.65401077270508 ], "umap": [ - 5.361589431762695, - 5.144418716430664 + 5.774580478668213, + 4.747357368469238 ], "pca3d": [ - 0.08567144721746445, - 0.01581420935690403, - 0.09151408076286316 + 0.08584743738174438, + 0.015401246957480907, + 0.09140445291996002 ], "tsne3d": [ - -3.6837828159332275, - -1.1338047981262207, - -8.364656448364258 + 0.7623754143714905, + 0.23297807574272156, + 14.74705982208252 ], "umap3d": [ - 5.509438514709473, - 6.702813625335693, - 3.961822271347046 + 5.359622955322266, + 6.908779144287109, + 2.599484920501709 ] }, "cluster_id": 3, @@ -849198,12 +849348,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, - "label": "Protein Design" + "id": 32, + "label": "Genome Protein" } ], "_embedding": [ @@ -849625,31 +849775,31 @@ "doi": "10.1101/2021.10.23.465204", "projections": { "pca": [ - 0.10684578120708466, - -0.16670428216457367 + 0.10661520808935165, + -0.16689077019691467 ], "tsne": [ - 0.2077058106660843, - 36.698909759521484 + -4.9398674964904785, + -16.81022834777832 ], "umap": [ - 6.330713272094727, - 3.7654590606689453 + 6.99392557144165, + 3.5962297916412354 ], "pca3d": [ - 0.10684618353843689, - -0.16670958697795868, - 0.12222084403038025 + 0.10661555826663971, + -0.1668955534696579, + 0.12246619164943695 ], "tsne3d": [ - 4.531230449676514, - 15.216852188110352, - 8.408761978149414 + 6.324528694152832, + -15.507391929626465, + -0.0014665891649201512 ], "umap3d": [ - 5.310111045837402, - 6.147663116455078, - 5.348836898803711 + 6.161144256591797, + 7.228835105895996, + 3.814415216445923 ] }, "cluster_id": 3, @@ -849660,11 +849810,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -850100,31 +850250,31 @@ "doi": "10.1101/2021.10.04.463034", "projections": { "pca": [ - 0.10177182406187057, - -0.1083659753203392 + 0.10158713161945343, + -0.10859371721744537 ], "tsne": [ - -10.881587982177734, - 36.24068069458008 + -10.789381980895996, + -31.44437026977539 ], "umap": [ - 5.366869926452637, - 3.4772377014160156 + 6.059109210968018, + 3.0905988216400146 ], "pca3d": [ - 0.1017719954252243, - -0.10836603492498398, - 0.13238519430160522 + 0.10158729553222656, + -0.10859344154596329, + 0.13243922591209412 ], "tsne3d": [ - -0.2551623284816742, - 15.576116561889648, - 13.137630462646484 + 0.39027848839759827, + -19.70039939880371, + 9.91114330291748 ], "umap3d": [ - 4.896541118621826, - 7.108251571655273, - 5.362863540649414 + 5.2949676513671875, + 7.70747709274292, + 3.9837372303009033 ] }, "cluster_id": 3, @@ -850135,11 +850285,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -850548,47 +850698,47 @@ "journal": "Nature Machine Intelligence", "projections": { "pca": [ - 0.0010749646462500095, - -0.09083406627178192 + 0.0008598636486567557, + -0.09103592485189438 ], "tsne": [ - 24.38074493408203, - -10.088677406311035 + 8.111896514892578, + -39.74844741821289 ], "umap": [ - 7.723893642425537, - 7.789369106292725 + 7.962289333343506, + 7.110188007354736 ], "pca3d": [ - 0.0010749223874881864, - -0.09081652015447617, - -0.08829234540462494 + 0.0008598673157393932, + -0.09101831167936325, + -0.0881374329328537 ], "tsne3d": [ - 22.078245162963867, - -13.485441207885742, - 10.534323692321777 + 8.682456016540527, + -14.279733657836914, + -14.437231063842773 ], "umap3d": [ - 6.022543430328369, - 4.294278621673584, - 2.0957629680633545 + 6.097938537597656, + 4.226470470428467, + 1.549629807472229 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -851004,31 +851154,31 @@ "doi": "10.1299/transjsme.21-00333", "projections": { "pca": [ - -0.11619744449853897, - 0.18599724769592285 + -0.1154954582452774, + 0.18575677275657654 ], "tsne": [ - -22.443281173706055, - 9.223434448242188 + -23.047876358032227, + -10.860240936279297 ], "umap": [ - 2.778261661529541, - 6.39138126373291 + 3.2548019886016846, + 5.745964050292969 ], "pca3d": [ - -0.11619772017002106, - 0.18599537014961243, - 0.0030502041336148977 + -0.1154957041144371, + 0.18575523793697357, + 0.0027569704689085484 ], "tsne3d": [ - -12.609488487243652, - 19.69037628173828, - -6.407916069030762 + -6.423287391662598, + -13.436564445495605, + -13.1820650100708 ], "umap3d": [ - 2.4555962085723877, - 5.434606552124023, - 4.582818508148193 + 3.177008867263794, + 6.010117053985596, + 4.674534797668457 ] }, "cluster_id": 2, @@ -851039,12 +851189,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 33, - "label": "Neural Networks" + "id": 19, + "label": "Graph Neural Nets" } ], "_embedding": [ @@ -851464,31 +851614,31 @@ "doi": "10.1101/2021.08.02.454840", "projections": { "pca": [ - 0.05755246803164482, - -0.2123183310031891 + 0.057227130979299545, + -0.21207404136657715 ], "tsne": [ - -1.5735294818878174, - 33.56184005737305 + -3.9803669452667236, + -19.887624740600586 ], "umap": [ - 6.182851791381836, - 3.8266654014587402 + 6.630461692810059, + 3.602543592453003 ], "pca3d": [ - 0.05755281448364258, - -0.21232658624649048, - 0.24253368377685547 + 0.05722753703594208, + -0.21208207309246063, + 0.24288034439086914 ], "tsne3d": [ - 1.4157906770706177, - 12.431048393249512, - 7.2509050369262695 + 2.0359880924224854, + -12.492591857910156, + 5.590888500213623 ], "umap3d": [ - 5.206676959991455, - 6.413351058959961, - 5.3172807693481445 + 5.948126316070557, + 7.349254608154297, + 3.796182870864868 ] }, "cluster_id": 3, @@ -851499,11 +851649,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -851920,31 +852070,31 @@ "doi": "10.1101/2021.09.15.460468", "projections": { "pca": [ - 0.15291179716587067, - -0.06806246936321259 + 0.15291117131710052, + -0.06839302182197571 ], "tsne": [ - -11.127038955688477, - 36.497886657714844 + -11.11060619354248, + -31.16356086730957 ], "umap": [ - 5.378777503967285, - 3.477015733718872 + 6.024270057678223, + 3.091935157775879 ], "pca3d": [ - 0.1529117077589035, - -0.06805872917175293, - 0.18835848569869995 + 0.1529112607240677, + -0.06838913261890411, + 0.18833386898040771 ], "tsne3d": [ - 0.01487056352198124, - 14.806594848632812, - 13.797794342041016 + 1.2517282962799072, + -19.399967193603516, + 10.440144538879395 ], "umap3d": [ - 4.904288291931152, - 7.114967346191406, - 5.360198974609375 + 5.3365607261657715, + 7.755989074707031, + 3.9266960620880127 ] }, "cluster_id": 3, @@ -851955,11 +852105,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -852380,31 +852530,31 @@ "doi": "10.1101/2021.08.08.455394", "projections": { "pca": [ - 0.06967217475175858, - 0.08758527785539627 + 0.07002206146717072, + 0.08828597515821457 ], "tsne": [ - -20.376529693603516, - 25.154701232910156 + 1.6185333728790283, + -41.705589294433594 ], "umap": [ - 4.289671421051025, - 3.522300958633423 + 4.814546585083008, + 3.0477473735809326 ], "pca3d": [ - 0.06967224925756454, - 0.08755337446928024, - 0.10766851156949997 + 0.0700221136212349, + 0.0882532000541687, + 0.10765048116445541 ], "tsne3d": [ - -3.568305730819702, - 12.753053665161133, - -16.739215850830078 + -2.6113107204437256, + -6.051614284515381, + 18.189361572265625 ], "umap3d": [ - 4.946864604949951, - 7.834068298339844, - 4.164858818054199 + 4.569000244140625, + 8.068666458129883, + 2.7483437061309814 ] }, "cluster_id": 3, @@ -852415,12 +852565,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -852839,31 +852989,31 @@ "doi": "10.1101/2021.07.29.454330", "projections": { "pca": [ - 0.10372355580329895, - -0.12332510948181152 + 0.10351458191871643, + -0.12346125394105911 ], "tsne": [ - -5.130133152008057, - 28.948434829711914 + -3.4866292476654053, + -25.149799346923828 ], "umap": [ - 5.838212966918945, - 4.079378128051758 + 6.338613510131836, + 3.671196222305298 ], "pca3d": [ - 0.10372374206781387, - -0.12332910299301147, - 0.1294623613357544 + 0.10351476073265076, + -0.12346542626619339, + 0.1295548528432846 ], "tsne3d": [ - 3.7176873683929443, - 8.303175926208496, - 9.164216995239258 + 1.885435700416565, + -14.278239250183105, + 0.714826762676239 ], "umap3d": [ - 5.303388595581055, - 6.699361801147461, - 4.894378662109375 + 5.808160781860352, + 7.516120433807373, + 3.3188188076019287 ] }, "cluster_id": 3, @@ -852874,12 +853024,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { "id": 15, - "label": "Protein Language" + "label": "Protein Design" } ], "_embedding": [ @@ -853305,31 +853455,31 @@ "cited_by_count": 45, "projections": { "pca": [ - 0.08284653723239899, - -0.11024833470582962 + 0.08277815580368042, + -0.1096736341714859 ], "tsne": [ - -3.2265443801879883, - 25.767488479614258 + 1.3574614524841309, + -23.576780319213867 ], "umap": [ - 5.940333843231201, - 4.418588161468506 + 6.312974452972412, + 4.051016807556152 ], "pca3d": [ - 0.08284664154052734, - -0.11025584489107132, - 0.3185622990131378 + 0.08277827501296997, + -0.10968131572008133, + 0.3187796473503113 ], "tsne3d": [ - -1.1585297584533691, - 9.540363311767578, - 3.6152467727661133 + -1.5667061805725098, + -11.850884437561035, + 4.833813667297363 ], "umap3d": [ - 5.258469581604004, - 6.358400344848633, - 4.572377681732178 + 5.675556659698486, + 7.028293609619141, + 3.329033851623535 ] }, "cluster_id": 3, @@ -853340,12 +853490,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, - "label": "Protein Language" + "id": 3, + "label": "Protein Design" } ], "_embedding": [ @@ -853766,31 +853916,31 @@ "doi": "10.1101/2021.07.09.450648", "projections": { "pca": [ - 0.09378166496753693, - -0.13385295867919922 + 0.09362895786762238, + -0.1331564337015152 ], "tsne": [ - -1.4045205116271973, - 21.973129272460938 + 5.529801845550537, + -23.34629249572754 ], "umap": [ - 5.89525032043457, - 4.532965660095215 + 6.3477678298950195, + 4.32399320602417 ], "pca3d": [ - 0.09378185868263245, - -0.13385418057441711, - 0.19525811076164246 + 0.09362911432981491, + -0.1331578493118286, + 0.19558000564575195 ], "tsne3d": [ - 1.2390950918197632, - 4.340341567993164, - 1.2597999572753906 + -3.9095218181610107, + -8.711272239685059, + 2.372487783432007 ], "umap3d": [ - 5.74024772644043, - 6.519629001617432, - 4.64560079574585 + 6.073469638824463, + 7.296678066253662, + 2.8949925899505615 ] }, "cluster_id": 3, @@ -853801,12 +853951,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, - "label": "Protein Language" + "id": 3, + "label": "Protein Design" } ], "_embedding": [ @@ -854220,31 +854370,31 @@ ], "projections": { "pca": [ - -0.09865674376487732, - -0.11125247180461884 + -0.0986224114894867, + -0.11053881794214249 ], "tsne": [ - 7.352094650268555, - 32.57993698120117 + 9.135775566101074, + -20.174949645996094 ], "umap": [ - 5.874894142150879, - 4.138156414031982 + 6.349053859710693, + 3.769665002822876 ], "pca3d": [ - -0.09865663945674896, - -0.11124401539564133, - 0.10319074243307114 + -0.09862230718135834, + -0.11053045094013214, + 0.10365056246519089 ], "tsne3d": [ - -4.436300277709961, - 14.733403205871582, - 0.5926294922828674 + 0.57418292760849, + -14.398826599121094, + -3.572277069091797 ], "umap3d": [ - 5.071492671966553, - 6.346194267272949, - 4.989099502563477 + 5.778480529785156, + 7.105752468109131, + 3.692549228668213 ] }, "cluster_id": 3, @@ -854255,11 +854405,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -854675,31 +854825,31 @@ "doi": "10.1299/transjsme.21-00203", "projections": { "pca": [ - -0.11059359461069107, - 0.14643172919750214 + -0.10991081595420837, + 0.14665266871452332 ], "tsne": [ - -22.28156852722168, - 9.115429878234863 + -22.95956802368164, + -10.748534202575684 ], "umap": [ - 3.0490543842315674, - 6.5038909912109375 + 3.517972469329834, + 5.843910217285156 ], "pca3d": [ - -0.11059391498565674, - 0.14644081890583038, - 0.06753382831811905 + -0.10991118103265762, + 0.14666226506233215, + 0.06739398837089539 ], "tsne3d": [ - -12.297415733337402, - 19.679176330566406, - -5.9267258644104 + -6.5486741065979, + -12.932682991027832, + -13.041990280151367 ], "umap3d": [ - 2.6405014991760254, - 5.269838809967041, - 4.603270530700684 + 3.4603304862976074, + 5.880550861358643, + 4.666405200958252 ] }, "cluster_id": 2, @@ -854710,12 +854860,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 33, - "label": "Neural Networks" + "id": 19, + "label": "Graph Neural Nets" } ], "_embedding": [ @@ -855160,31 +855310,31 @@ "doi": "10.1101/2021.06.14.448402", "projections": { "pca": [ - 0.07398339360952377, - -0.11769849807024002 + 0.0736631229519844, + -0.11791090667247772 ], "tsne": [ - -8.712239265441895, - 36.69781494140625 + -10.094575881958008, + -30.02266502380371 ], "umap": [ - 5.668517112731934, - 3.6026883125305176 + 6.217195987701416, + 3.1590566635131836 ], "pca3d": [ - 0.07398347556591034, - -0.11768988519906998, - 0.1611189991235733 + 0.07366323471069336, + -0.11790164560079575, + 0.16109175980091095 ], "tsne3d": [ - -0.059964124113321304, - 13.497401237487793, - 11.433635711669922 + 2.95086932182312, + -18.687881469726562, + 8.319245338439941 ], "umap3d": [ - 4.980052471160889, - 6.8712968826293945, - 5.33695650100708 + 5.573230266571045, + 7.626104831695557, + 3.883885383605957 ] }, "cluster_id": 3, @@ -855195,11 +855345,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -855616,31 +855766,31 @@ "doi": "10.1101/2021.06.07.447389", "projections": { "pca": [ - 0.0753469169139862, - -0.0613112635910511 + 0.0752885490655899, + -0.0605081245303154 ], "tsne": [ - -1.56143319606781, - 18.707763671875 + 7.297761917114258, + -25.28309440612793 ], "umap": [ - 5.681738376617432, - 4.453841686248779 + 6.286376476287842, + 4.074430465698242 ], "pca3d": [ - 0.07534705102443695, - -0.061319902539253235, - 0.26892903447151184 + 0.07528866827487946, + -0.06051675230264664, + 0.2690112292766571 ], "tsne3d": [ - 2.8561818599700928, - 3.9623990058898926, - 5.507728099822998 + -3.160623550415039, + -12.54185962677002, + -1.9832427501678467 ], "umap3d": [ - 5.75817346572876, - 6.61303186416626, - 4.758317470550537 + 6.089134693145752, + 7.429904460906982, + 2.9529411792755127 ] }, "cluster_id": 3, @@ -855651,11 +855801,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -856072,31 +856222,31 @@ "doi": "10.1101/2021.05.24.445506", "projections": { "pca": [ - 0.06761089712381363, - -0.14486277103424072 + 0.06728450953960419, + -0.14441649615764618 ], "tsne": [ - -3.805422067642212, - 21.256872177124023 + 5.292464733123779, + -26.389432907104492 ], "umap": [ - 5.710031032562256, - 4.728636264801025 + 6.164975166320801, + 4.307621955871582 ], "pca3d": [ - 0.06761100888252258, - -0.14484985172748566, - 0.1360933929681778 + 0.06728458404541016, + -0.1444031298160553, + 0.13627810776233673 ], "tsne3d": [ - -3.9174351692199707, - 2.6062564849853516, - -0.3780675530433655 + -6.105044364929199, + -9.178743362426758, + -1.584901213645935 ], "umap3d": [ - 5.733959674835205, - 6.6733808517456055, - 4.61346960067749 + 5.971580505371094, + 7.369622707366943, + 2.7971556186676025 ] }, "cluster_id": 3, @@ -856107,11 +856257,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -856531,31 +856681,31 @@ ], "projections": { "pca": [ - 0.01509980671107769, - -0.1724850833415985 + 0.014795182272791862, + -0.17238014936447144 ], "tsne": [ - -4.688815116882324, - 33.56986999511719 + -5.398670196533203, + -24.100605010986328 ], "umap": [ - 6.009554386138916, - 3.6888835430145264 + 6.680281162261963, + 3.322131633758545 ], "pca3d": [ - 0.015100144781172276, - -0.17249219119548798, - 0.08885020762681961 + 0.014795515686273575, + -0.17238731682300568, + 0.0890955850481987 ], "tsne3d": [ - -0.05402086675167084, - 14.958913803100586, - 8.002532958984375 + 3.164538621902466, + -17.406166076660156, + 2.869903802871704 ], "umap3d": [ - 5.042730331420898, - 6.3847808837890625, - 5.367689609527588 + 5.949537754058838, + 7.564553737640381, + 3.9169774055480957 ] }, "cluster_id": 3, @@ -856566,11 +856716,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -856977,46 +857127,46 @@ ], "projections": { "pca": [ - 0.11999650299549103, - 0.17766474187374115 + 0.12076415866613388, + 0.17694035172462463 ], "tsne": [ - -1.6303672790527344, - -5.120471000671387 + -1.3097848892211914, + 3.583665609359741 ], "umap": [ - 5.030738830566406, - 7.302952289581299 + 5.20566987991333, + 6.525416374206543 ], "pca3d": [ - 0.11999566107988358, - 0.17769281566143036, - 0.1503424197435379 + 0.12076333910226822, + 0.17696842551231384, + 0.15000325441360474 ], "tsne3d": [ - -4.766091823577881, - 0.5374758839607239, - -18.778764724731445 + 2.039506435394287, + 20.29538917541504, + -5.26236629486084 ], "umap3d": [ - 4.562000751495361, - 3.4903202056884766, - 5.014512538909912 + 5.653111457824707, + 4.765561103820801, + 5.083536148071289 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, + "id": 13, "label": "Genomics Papers" } ], @@ -857440,31 +857590,31 @@ "cited_by_count": 350, "projections": { "pca": [ - 0.0864049419760704, - -0.12875066697597504 + 0.08626043796539307, + -0.12828029692173004 ], "tsne": [ - -4.384904861450195, - 23.42503547668457 + 2.048535108566284, + -26.090343475341797 ], "umap": [ - 5.593171119689941, - 4.2873101234436035 + 6.186293601989746, + 3.931882619857788 ], "pca3d": [ - 0.08640516549348831, - -0.12876567244529724, - 0.2030947208404541 + 0.08626071363687515, + -0.1282956302165985, + 0.2033650279045105 ], "tsne3d": [ - -2.3397810459136963, - 7.396030426025391, - 2.4543018341064453 + -1.9544893503189087, + -13.433249473571777, + 1.6207189559936523 ], "umap3d": [ - 5.381292343139648, - 6.652217388153076, - 4.604374885559082 + 5.815388202667236, + 7.307082653045654, + 3.1050491333007812 ] }, "cluster_id": 3, @@ -857475,12 +857625,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, - "label": "Protein Language" + "id": 3, + "label": "Protein Design" } ], "_embedding": [ @@ -857888,31 +858038,31 @@ "journal": "Nature Methods", "projections": { "pca": [ - -0.023603007197380066, - -0.17934520542621613 + -0.023847546428442, + -0.17928166687488556 ], "tsne": [ - -12.696603775024414, - 22.16094207763672 + -4.079514026641846, + -27.044797897338867 ], "umap": [ - 5.1886515617370605, - 4.297701358795166 + 5.83064079284668, + 3.88431715965271 ], "pca3d": [ - -0.023603077977895737, - -0.17931078374385834, - 0.15304289758205414 + -0.023847589269280434, + -0.17924648523330688, + 0.1533498764038086 ], "tsne3d": [ - -5.148898124694824, - 3.1575677394866943, - 9.910360336303711 + -9.89600944519043, + -12.423240661621094, + 7.8126325607299805 ], "umap3d": [ - 5.047243118286133, - 6.900845527648926, - 4.630087852478027 + 5.391867637634277, + 7.453577041625977, + 3.091379165649414 ] }, "cluster_id": 3, @@ -857923,7 +858073,7 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { @@ -858345,31 +858495,31 @@ "doi": "10.1101/2021.03.28.437402", "projections": { "pca": [ - 0.11158380657434464, - -0.09039923548698425 + 0.11141876131296158, + -0.09000501781702042 ], "tsne": [ - -6.787485599517822, - 20.969026565551758 + 4.302305221557617, + -28.93094825744629 ], "umap": [ - 5.3597002029418945, - 4.606821537017822 + 5.858333110809326, + 4.159653663635254 ], "pca3d": [ - 0.1115838959813118, - -0.09039510041475296, - 0.26333680748939514 + 0.11141881346702576, + -0.09000077098608017, + 0.2633652985095978 ], "tsne3d": [ - -3.179980754852295, - 3.1475682258605957, - 3.4268643856048584 + -7.176662921905518, + -10.396836280822754, + 2.488816738128662 ], "umap3d": [ - 5.563317775726318, - 6.890807151794434, - 4.539434432983398 + 5.683223247528076, + 7.529116630554199, + 2.775191068649292 ] }, "cluster_id": 3, @@ -858380,11 +858530,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -858807,31 +858957,31 @@ "openalex_url": "https://openalex.org/W3144701084", "projections": { "pca": [ - 0.032852765172719955, - -0.06599444150924683 + 0.03294386342167854, + -0.06533462554216385 ], "tsne": [ - 4.305039405822754, - 32.05299377441406 + 3.3788411617279053, + -13.084832191467285 ], "umap": [ - 6.592578411102295, - 4.700962066650391 + 7.018948554992676, + 4.3958845138549805 ], "pca3d": [ - 0.032852545380592346, - -0.06597020477056503, - 0.22848908603191376 + 0.032943665981292725, + -0.06531038880348206, + 0.2287531942129135 ], "tsne3d": [ - 5.521178245544434, - 11.019343376159668, - -2.3208208084106445 + 5.696946144104004, + -6.491891384124756, + 1.4412506818771362 ], "umap3d": [ - 5.458027362823486, - 5.610983848571777, - 4.637259006500244 + 6.090577125549316, + 6.577482223510742, + 3.437180757522583 ] }, "cluster_id": 3, @@ -858842,11 +858992,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -859255,31 +859405,31 @@ "journal": "Nature Biomedical Engineering", "projections": { "pca": [ - -0.03770989552140236, - -0.04947849363088608 + -0.03759176284074783, + -0.04950596019625664 ], "tsne": [ - -16.961240768432617, - 14.3111572265625 + -10.287827491760254, + -15.015983581542969 ], "umap": [ - 5.692500114440918, - 4.8119916915893555 + 5.7230353355407715, + 4.359338760375977 ], "pca3d": [ - -0.037709951400756836, - -0.04946194961667061, - 0.18670134246349335 + -0.03759177774190903, + -0.04948890581727028, + 0.18682800233364105 ], "tsne3d": [ - -0.7759829163551331, - -13.523150444030762, - -22.5159969329834 + -0.5690792202949524, + 21.55000877380371, + 12.665440559387207 ], "umap3d": [ - 4.946608543395996, - 6.569262981414795, - 4.281911373138428 + 5.307051658630371, + 6.928586483001709, + 2.968897819519043 ] }, "cluster_id": 3, @@ -859290,11 +859440,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -859717,31 +859867,31 @@ "cited_by_count": 146, "projections": { "pca": [ - 0.05626924708485603, - -0.03198659420013428 + 0.05618787184357643, + -0.031910207122564316 ], "tsne": [ - -12.380367279052734, - 32.897674560546875 + -9.286252975463867, + -26.17336082458496 ], "umap": [ - 5.257314205169678, - 3.89811110496521 + 5.822401523590088, + 3.394798517227173 ], "pca3d": [ - 0.056269291788339615, - -0.03197749704122543, - 0.10100649297237396 + 0.05618784576654434, + -0.031900595873594284, + 0.10094321519136429 ], "tsne3d": [ - 1.0478628873825073, - 7.911736011505127, - 12.493507385253906 + 2.158202648162842, + -15.438190460205078, + 10.069788932800293 ], "umap3d": [ - 4.787323474884033, - 6.940962314605713, - 4.846776485443115 + 5.435943603515625, + 7.480283737182617, + 3.518439531326294 ] }, "cluster_id": 3, @@ -859752,11 +859902,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -860178,31 +860328,31 @@ "doi": "10.1101/2021.02.12.430858", "projections": { "pca": [ - 0.0346381776034832, - -0.23735709488391876 + 0.03428241237998009, + -0.23703983426094055 ], "tsne": [ - 0.339832067489624, - 32.63277053833008 + -2.3848624229431152, + -16.150529861450195 ], "umap": [ - 6.396345615386963, - 3.9912362098693848 + 6.919301986694336, + 3.7728147506713867 ], "pca3d": [ - 0.03463868051767349, - -0.2373698651790619, - 0.17825260758399963 + 0.034282881766557693, + -0.23705263435840607, + 0.17876482009887695 ], "tsne3d": [ - -0.60816490650177, - 14.22561264038086, - 1.22612464427948 + 1.8927034139633179, + -11.441697120666504, + -1.9859185218811035 ], "umap3d": [ - 5.1311211585998535, - 5.997341156005859, - 5.213865280151367 + 6.017220497131348, + 7.028447151184082, + 3.9211578369140625 ] }, "cluster_id": 3, @@ -860213,11 +860363,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -860626,31 +860776,31 @@ "journal": "Nature Biotechnology", "projections": { "pca": [ - 0.053901419043540955, - -0.07285252958536148 + 0.05389337241649628, + -0.07313582301139832 ], "tsne": [ - -7.157477855682373, - 11.515857696533203 + -5.0086164474487305, + -41.92096710205078 ], "umap": [ - 5.236674785614014, - 4.944418907165527 + 5.674644947052002, + 4.463995933532715 ], "pca3d": [ - 0.05390150845050812, - -0.07285019010305405, - 0.1039872020483017 + 0.053893450647592545, + -0.07313290983438492, + 0.10406438261270523 ], "tsne3d": [ - -4.4375505447387695, - -0.6698443293571472, - -9.525558471679688 + 0.7924936413764954, + -0.286464661359787, + 16.075523376464844 ], "umap3d": [ - 5.432973861694336, - 6.843134880065918, - 4.081262588500977 + 5.340875148773193, + 7.083012580871582, + 2.6412603855133057 ] }, "cluster_id": 3, @@ -860661,11 +860811,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -861083,46 +861233,46 @@ ], "projections": { "pca": [ - -0.17905236780643463, - -0.10880514979362488 + -0.17905987799167633, + -0.10800614207983017 ], "tsne": [ - -33.55125427246094, - -7.697701930999756 + -40.157569885253906, + 7.7307233810424805 ], "umap": [ - 0.8298053741455078, - 8.156394004821777 + 1.124042272567749, + 7.416868686676025 ], "pca3d": [ - -0.17905189096927643, - -0.10882572084665298, - 0.007892653346061707 + -0.17905941605567932, + -0.10802680253982544, + 0.008403937332332134 ], "tsne3d": [ - -22.138208389282227, - -6.4954071044921875, - 10.508852005004883 + -23.281898498535156, + 5.83546257019043, + 5.8240485191345215 ], "umap3d": [ - 0.9821408987045288, - 4.611332893371582, - 5.2147135734558105 + 2.217616081237793, + 5.064161777496338, + 5.86516809463501 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -861531,31 +861681,31 @@ "journal": "Nature Genetics", "projections": { "pca": [ - -0.07511895895004272, - -0.05143164470791817 + -0.07526428252458572, + -0.05117570608854294 ], "tsne": [ - 19.428953170776367, - 14.663349151611328 + 23.120790481567383, + -3.730341672897339 ], "umap": [ - 7.087762832641602, - 7.193365097045898 + 7.2992963790893555, + 6.8606977462768555 ], "pca3d": [ - -0.07511885464191437, - -0.05143065005540848, - -0.12068033963441849 + -0.07526420801877975, + -0.051174432039260864, + -0.12043675035238266 ], "tsne3d": [ - 11.03093147277832, - -1.6382638216018677, - -16.298736572265625 + 10.520045280456543, + 8.001089096069336, + 8.602481842041016 ], "umap3d": [ - 6.7784743309021, - 3.903346538543701, - 3.2501940727233887 + 7.056752681732178, + 4.487583637237549, + 2.41011905670166 ] }, "cluster_id": 4, @@ -861566,12 +861716,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -861991,31 +862141,31 @@ "doi": "10.1101/2020.12.21.423785", "projections": { "pca": [ - 0.11268731206655502, - -0.06157396733760834 + 0.1125488430261612, + -0.06142387166619301 ], "tsne": [ - 22.243566513061523, - 12.884902954101562 + 24.694067001342773, + -6.500813961029053 ], "umap": [ - 7.189935207366943, - 7.283867359161377 + 7.370784282684326, + 6.925999164581299 ], "pca3d": [ - 0.11268748342990875, - -0.06159593164920807, - -0.004204654600471258 + 0.11254903674125671, + -0.061446353793144226, + -0.004048292059451342 ], "tsne3d": [ - 9.98159122467041, - -5.2304368019104, - -13.959826469421387 + 9.08286190032959, + 11.109652519226074, + 6.24086856842041 ], "umap3d": [ - 6.727246284484863, - 3.892622947692871, - 3.310744047164917 + 7.190542221069336, + 4.361152648925781, + 2.4079172611236572 ] }, "cluster_id": 4, @@ -862026,12 +862176,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -862449,31 +862599,31 @@ ], "projections": { "pca": [ - 0.09408509731292725, - -0.17554660141468048 + 0.09389511495828629, + -0.17570464313030243 ], "tsne": [ - -7.6275835037231445, - 30.89667320251465 + -4.95889949798584, + -29.253862380981445 ], "umap": [ - 5.589089870452881, - 3.7304441928863525 + 6.324990272521973, + 3.368187427520752 ], "pca3d": [ - 0.09408541768789291, - -0.1755603551864624, - 0.2256155163049698 + 0.09389549493789673, + -0.17571884393692017, + 0.22584380209445953 ], "tsne3d": [ - -2.3538453578948975, - 13.731740951538086, - 7.414750576019287 + -0.09000511467456818, + -17.071208953857422, + 5.060910701751709 ], "umap3d": [ - 4.858468532562256, - 6.748365879058838, - 5.27534294128418 + 5.656944274902344, + 7.662997722625732, + 3.7061092853546143 ] }, "cluster_id": 3, @@ -862484,11 +862634,11 @@ "label": "Protein Design" }, { - "id": 12, - "label": "Protein Design" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -862906,31 +863056,31 @@ ], "projections": { "pca": [ - 0.04257262870669365, - -0.20780491828918457 + 0.04221217706799507, + -0.20777234435081482 ], "tsne": [ - -4.146213531494141, - 32.75179672241211 + -5.2119317054748535, + -23.399076461791992 ], "umap": [ - 6.053500652313232, - 3.7252206802368164 + 6.736910343170166, + 3.3706672191619873 ], "pca3d": [ - 0.04257287085056305, - -0.20780329406261444, - 0.17744071781635284 + 0.04221245273947716, + -0.2077707201242447, + 0.17771103978157043 ], "tsne3d": [ - -0.21514254808425903, - 15.466568946838379, - 6.542060852050781 + 2.84717059135437, + -16.729942321777344, + 4.267693519592285 ], "umap3d": [ - 5.0697550773620605, - 6.34230375289917, - 5.394423007965088 + 5.998011589050293, + 7.524659633636475, + 3.9360687732696533 ] }, "cluster_id": 3, @@ -862941,11 +863091,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -863363,31 +863513,31 @@ ], "projections": { "pca": [ - 0.09082544595003128, - -0.17881906032562256 + 0.09042425453662872, + -0.1788720190525055 ], "tsne": [ - -4.263932704925537, - 32.822994232177734 + -5.815662384033203, + -23.17245864868164 ], "umap": [ - 6.052626609802246, - 3.750901937484741 + 6.768794536590576, + 3.405674934387207 ], "pca3d": [ - 0.09082548320293427, - -0.17880567908287048, - 0.11844543367624283 + 0.09042437374591827, + -0.17885854840278625, + 0.11861004680395126 ], "tsne3d": [ - 0.14214837551116943, - 16.680763244628906, - 6.759552955627441 + 3.6774518489837646, + -17.494956970214844, + 4.884337425231934 ], "umap3d": [ - 5.022084712982178, - 6.380050182342529, - 5.4092116355896 + 5.94734001159668, + 7.5431227684021, + 3.8352575302124023 ] }, "cluster_id": 3, @@ -863398,11 +863548,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -863819,31 +863969,31 @@ ], "projections": { "pca": [ - 0.0927102118730545, - -0.22559696435928345 + 0.09235989302396774, + -0.22543300688266754 ], "tsne": [ - -2.871788263320923, - 31.19953155517578 + -3.8634214401245117, + -21.42014503479004 ], "umap": [ - 6.292606353759766, - 3.849544048309326 + 6.864909648895264, + 3.6113686561584473 ], "pca3d": [ - 0.09271049499511719, - -0.2255958616733551, - 0.16018891334533691 + 0.09236019849777222, + -0.22543220221996307, + 0.16059668362140656 ], "tsne3d": [ - 2.3874621391296387, - 14.36029052734375, - 4.9971418380737305 + 3.256202220916748, + -14.134647369384766, + 2.9677670001983643 ], "umap3d": [ - 5.191864967346191, - 6.2309746742248535, - 5.318167686462402 + 6.1297407150268555, + 7.316678524017334, + 3.8631250858306885 ] }, "cluster_id": 3, @@ -863854,11 +864004,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -864276,31 +864426,31 @@ ], "projections": { "pca": [ - 0.09259393811225891, - -0.15549114346504211 + 0.09234660118818283, + -0.15561048686504364 ], "tsne": [ - -5.739565849304199, - 32.86650848388672 + -6.027526378631592, + -24.900634765625 ], "umap": [ - 5.8294758796691895, - 3.770625352859497 + 6.506410598754883, + 3.3041880130767822 ], "pca3d": [ - 0.092594213783741, - -0.15549789369106293, - 0.1585095375776291 + 0.09234693646430969, + -0.15561744570732117, + 0.15864761173725128 ], "tsne3d": [ - -2.0763471126556396, - 15.783878326416016, - 7.202662944793701 + 1.4482951164245605, + -18.057132720947266, + 3.96490740776062 ], "umap3d": [ - 4.862978935241699, - 6.607895851135254, - 5.330334663391113 + 5.84187650680542, + 7.634730339050293, + 3.86156964302063 ] }, "cluster_id": 3, @@ -864311,12 +864461,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { "id": 15, - "label": "Protein Language" + "label": "Protein Design" } ], "_embedding": [ @@ -864730,31 +864880,31 @@ ], "projections": { "pca": [ - 0.06417384743690491, - -0.15076962113380432 + 0.06380783766508102, + -0.15056785941123962 ], "tsne": [ - -3.582228422164917, - 33.20464324951172 + -5.1844305992126465, + -22.841787338256836 ], "umap": [ - 6.019466876983643, - 3.672661542892456 + 6.732329368591309, + 3.3709869384765625 ], "pca3d": [ - 0.06417395919561386, - -0.1507669985294342, - 0.1690177321434021 + 0.06380803138017654, + -0.150565043091774, + 0.16912460327148438 ], "tsne3d": [ - -0.8853695392608643, - 16.50208854675293, - 5.145064353942871 + 2.8855485916137695, + -16.580142974853516, + 6.208575248718262 ], "umap3d": [ - 5.039742469787598, - 6.40458869934082, - 5.394298553466797 + 5.950875282287598, + 7.544128894805908, + 3.9053561687469482 ] }, "cluster_id": 3, @@ -864765,11 +864915,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -865191,31 +865341,31 @@ ], "projections": { "pca": [ - 0.03539685532450676, - -0.21776285767555237 + 0.0349576435983181, + -0.21754884719848633 ], "tsne": [ - -3.8526010513305664, - 31.668941497802734 + -3.9666781425476074, + -23.14748191833496 ], "umap": [ - 6.11826753616333, - 3.7315752506256104 + 6.82735538482666, + 3.4715304374694824 ], "pca3d": [ - 0.03539717197418213, - -0.21776190400123596, - 0.17696699500083923 + 0.03495799005031586, + -0.2175477147102356, + 0.1772804707288742 ], "tsne3d": [ - -0.5102093815803528, - 13.895642280578613, - 5.443517684936523 + 1.4277725219726562, + -15.38263988494873, + 4.932060241699219 ], "umap3d": [ - 5.103174686431885, - 6.322651386260986, - 5.382345199584961 + 6.041911602020264, + 7.423428058624268, + 3.8844785690307617 ] }, "cluster_id": 3, @@ -865226,11 +865376,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -865649,31 +865799,31 @@ ], "projections": { "pca": [ - 0.044426415115594864, - -0.20349134504795074 + 0.04416564851999283, + -0.20306678116321564 ], "tsne": [ - -2.565671443939209, - 31.715559005737305 + -3.8715200424194336, + -22.0040283203125 ], "umap": [ - 6.246119499206543, - 3.825371265411377 + 6.844467639923096, + 3.534541606903076 ], "pca3d": [ - 0.044426605105400085, - -0.20349954068660736, - 0.1765965223312378 + 0.04416593536734581, + -0.20307542383670807, + 0.17702431976795197 ], "tsne3d": [ - 1.118080496788025, - 14.772671699523926, - 4.223635673522949 + 3.4637045860290527, + -14.608024597167969, + 4.388306617736816 ], "umap3d": [ - 5.104434013366699, - 6.228885173797607, - 5.342569351196289 + 6.0809760093688965, + 7.325933456420898, + 3.9189774990081787 ] }, "cluster_id": 3, @@ -865684,11 +865834,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -866105,31 +866255,31 @@ ], "projections": { "pca": [ - 0.0985248014330864, - -0.1907455176115036 + 0.09818605333566666, + -0.19086134433746338 ], "tsne": [ - -4.438706398010254, - 30.890186309814453 + -3.805710554122925, + -23.859817504882812 ], "umap": [ - 5.800454139709473, - 3.931976556777954 + 6.40688943862915, + 3.548658609390259 ], "pca3d": [ - 0.09852510690689087, - -0.19075065851211548, - 0.17415142059326172 + 0.09818633645772934, + -0.19086676836013794, + 0.17439356446266174 ], "tsne3d": [ - -1.846997618675232, - 14.01037311553955, - 5.194806098937988 + 0.7779332995414734, + -15.373028755187988, + 3.683683395385742 ], "umap3d": [ - 5.039095401763916, - 6.498201370239258, - 5.1480817794799805 + 5.760553359985352, + 7.413726806640625, + 3.611741304397583 ] }, "cluster_id": 3, @@ -866140,12 +866290,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { "id": 15, - "label": "Protein Language" + "label": "Protein Design" } ], "_embedding": [ @@ -866562,31 +866712,31 @@ ], "projections": { "pca": [ - 0.04305816441774368, - -0.21716344356536865 + 0.04276208579540253, + -0.21690821647644043 ], "tsne": [ - -2.5652716159820557, - 31.98470687866211 + -3.702444553375244, + -22.31230354309082 ], "umap": [ - 6.242467880249023, - 3.8271052837371826 + 6.783244609832764, + 3.450629472732544 ], "pca3d": [ - 0.04305843636393547, - -0.2171722799539566, - 0.20319660007953644 + 0.04276244342327118, + -0.2169172316789627, + 0.2036110907793045 ], "tsne3d": [ - -0.0007665687589906156, - 14.184154510498047, - 3.9716291427612305 + 2.441664218902588, + -14.433239936828613, + 5.091556549072266 ], "umap3d": [ - 5.136046409606934, - 6.229161262512207, - 5.302834510803223 + 6.029663562774658, + 7.3242292404174805, + 3.8824071884155273 ] }, "cluster_id": 3, @@ -866597,11 +866747,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -867017,31 +867167,31 @@ ], "projections": { "pca": [ - 0.06554621458053589, - -0.18469248712062836 + 0.06517615169286728, + -0.1846090853214264 ], "tsne": [ - -3.2817373275756836, - 31.423778533935547 + -4.451027870178223, + -21.85896873474121 ], "umap": [ - 6.084164619445801, - 3.733092784881592 + 6.782318115234375, + 3.4619410037994385 ], "pca3d": [ - 0.06554631888866425, - -0.18468376994132996, - 0.14140142500400543 + 0.06517632305622101, + -0.18460021913051605, + 0.1416618525981903 ], "tsne3d": [ - 1.1782594919204712, - 14.729865074157715, - 5.671947956085205 + 2.7426669597625732, + -15.271132469177246, + 3.3670592308044434 ], "umap3d": [ - 5.075291633605957, - 6.303779602050781, - 5.350601673126221 + 6.058330535888672, + 7.389748573303223, + 3.9507358074188232 ] }, "cluster_id": 3, @@ -867052,11 +867202,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -867477,31 +867627,31 @@ ], "projections": { "pca": [ - 0.08194281160831451, - -0.19072842597961426 + 0.08161848038434982, + -0.1906881183385849 ], "tsne": [ - -2.567192792892456, - 32.17116165161133 + -4.04350471496582, + -22.42512321472168 ], "umap": [ - 6.168246746063232, - 3.762284278869629 + 6.799492835998535, + 3.4120125770568848 ], "pca3d": [ - 0.08194304257631302, - -0.19073471426963806, - 0.0925096794962883 + 0.0816187709569931, + -0.19069471955299377, + 0.09283014386892319 ], "tsne3d": [ - 0.2623860836029053, - 15.388262748718262, - 3.7842495441436768 + 3.500058889389038, + -14.715083122253418, + 5.5684733390808105 ], "umap3d": [ - 5.074671268463135, - 6.332495212554932, - 5.347924709320068 + 6.016009330749512, + 7.42936897277832, + 3.8762993812561035 ] }, "cluster_id": 3, @@ -867512,11 +867662,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -867932,31 +868082,31 @@ ], "projections": { "pca": [ - 0.13538195192813873, - -0.1668606698513031 + 0.1351037323474884, + -0.16712796688079834 ], "tsne": [ - 14.785463333129883, - 33.75321578979492 + 18.57275390625, + -31.461891174316406 ], "umap": [ - 7.564812183380127, - 4.254434108734131 + 7.98325252532959, + 3.862889289855957 ], "pca3d": [ - 0.13538198173046112, - -0.16684919595718384, - 0.1915356069803238 + 0.13510380685329437, + -0.16711629927158356, + 0.1916831135749817 ], "tsne3d": [ - 0.7861495614051819, - 18.978036880493164, - 5.7373223304748535 + 6.453849792480469, + -16.58057403564453, + 5.876425743103027 ], "umap3d": [ - 6.973047733306885, - 6.300041198730469, - 4.185669422149658 + 7.25327205657959, + 6.99925422668457, + 2.439812421798706 ] }, "cluster_id": 4, @@ -867967,11 +868117,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -868392,31 +868542,31 @@ "openalex_url": "https://openalex.org/W2593933556", "projections": { "pca": [ - 0.12956556677818298, - -0.11531480401754379 + 0.12952733039855957, + -0.11544753611087799 ], "tsne": [ - 3.388350009918213, - 35.98720169067383 + 0.756301999092102, + -11.774378776550293 ], "umap": [ - 6.308309555053711, - 3.8560972213745117 + 6.892337322235107, + 3.765444040298462 ], "pca3d": [ - 0.1295657455921173, - -0.11531776934862137, - 0.14807139337062836 + 0.1295275241136551, + -0.11545020341873169, + 0.14826901257038116 ], "tsne3d": [ - 6.86490535736084, - 14.711152076721191, - 5.822338104248047 + 7.9116435050964355, + -11.017053604125977, + 0.11686481535434723 ], "umap3d": [ - 5.3152384757995605, - 6.17948055267334, - 5.266809463500977 + 6.086130142211914, + 7.246819019317627, + 3.7409346103668213 ] }, "cluster_id": 3, @@ -868427,11 +868577,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -868850,31 +869000,31 @@ "doi": "10.1101/2020.09.04.282814", "projections": { "pca": [ - 0.09647046774625778, - -0.15695710480213165 + 0.09622855484485626, + -0.15666338801383972 ], "tsne": [ - 2.6278622150421143, - 30.411279678344727 + 0.8230099081993103, + -15.67121410369873 ], "umap": [ - 6.3539719581604, - 4.211458683013916 + 6.956358909606934, + 3.8224005699157715 ], "pca3d": [ - 0.09647054970264435, - -0.15695048868656158, - 0.1452871412038803 + 0.09622867405414581, + -0.1566566675901413, + 0.14558736979961395 ], "tsne3d": [ - 6.240387439727783, - 12.643722534179688, - 2.9435911178588867 + 5.187916278839111, + -10.930357933044434, + 3.5347177982330322 ], "umap3d": [ - 5.449869632720947, - 5.9939284324646, - 5.141164779663086 + 6.197218894958496, + 7.017763137817383, + 3.7093558311462402 ] }, "cluster_id": 3, @@ -868885,11 +869035,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -869306,31 +869456,31 @@ "cited_by_count": 6, "projections": { "pca": [ - 0.004385387059301138, - -0.11884453147649765 + 0.004255950916558504, + -0.11841282248497009 ], "tsne": [ - 8.486236572265625, - 18.20784568786621 + 11.216568946838379, + -3.0934624671936035 ], "umap": [ - 6.720510482788086, - 5.62559175491333 + 7.013011932373047, + 5.012688159942627 ], "pca3d": [ - 0.004385543055832386, - -0.11884688585996628, - 0.11195652931928635 + 0.004256164189428091, + -0.11841488629579544, + 0.11229810118675232 ], "tsne3d": [ - 7.8376617431640625, - 5.331786155700684, - -2.503342866897583 + 9.250168800354004, + -8.699488639831543, + -1.809688925743103 ], "umap3d": [ - 5.847926139831543, - 5.446840763092041, - 3.986515760421753 + 6.334122657775879, + 5.940577983856201, + 2.801970958709717 ] }, "cluster_id": 4, @@ -869341,11 +869491,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -869759,46 +869909,46 @@ ], "projections": { "pca": [ - -0.2686379551887512, - -0.09912865608930588 + -0.2687634527683258, + -0.09813734889030457 ], "tsne": [ - -36.58806228637695, - -11.303746223449707 + -41.60407257080078, + 11.508892059326172 ], "umap": [ - 0.7417189478874207, - 7.934623718261719 + 1.0465052127838135, + 7.060690402984619 ], "pca3d": [ - -0.2686375081539154, - -0.09914729744195938, - -0.05295393988490105 + -0.26876306533813477, + -0.09815617650747299, + -0.052492380142211914 ], "tsne3d": [ - -25.041706085205078, - -9.388002395629883, - 11.150721549987793 + -24.842437744140625, + 7.810214042663574, + 8.827607154846191 ], "umap3d": [ - 0.8746958374977112, - 4.695711612701416, - 5.01069450378418 + 1.9386533498764038, + 4.99459171295166, + 5.713888645172119 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -870209,47 +870359,47 @@ "doi": "10.1016/s1634-6939(20)30192-7", "projections": { "pca": [ - -0.06078743189573288, - 0.33234336972236633 + -0.059592485427856445, + 0.3313397169113159 ], "tsne": [ - -13.658044815063477, - 3.116642713546753 + -6.297415256500244, + -1.0872193574905396 ], "umap": [ - 4.585780143737793, - 7.323179244995117 + 4.813787937164307, + 6.597418308258057 ], "pca3d": [ - -0.060787927359342575, - 0.3323330581188202, - -0.08792170882225037 + -0.05959290266036987, + 0.3313290476799011, + -0.08837400376796722 ], "tsne3d": [ - -6.5803680419921875, - 21.48501205444336, - -5.969271183013916 + -4.016053676605225, + -6.714084625244141, + -15.529255867004395 ], "umap3d": [ - 4.1581010818481445, - 3.6807198524475098, - 4.81097936630249 + 5.254617691040039, + 4.676524639129639, + 4.836016654968262 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -870657,31 +870807,31 @@ "journal": "Nature", "projections": { "pca": [ - 0.1854804903268814, - 0.02477392554283142 + 0.18571972846984863, + 0.024475734680891037 ], "tsne": [ - 5.844765663146973, - 26.037893295288086 + 4.709238529205322, + -15.279031753540039 ], "umap": [ - 6.58049201965332, - 4.676318168640137 + 6.911643028259277, + 4.42733097076416 ], "pca3d": [ - 0.18548007309436798, - 0.02478824555873871, - 0.11581122875213623 + 0.18571922183036804, + 0.02449086494743824, + 0.11575431376695633 ], "tsne3d": [ - 9.062623977661133, - 7.896985054016113, - 1.6287143230438232 + -1.5226353406906128, + -2.0199849605560303, + 1.3648561239242554 ], "umap3d": [ - 5.645958423614502, - 5.823385715484619, - 4.792871475219727 + 6.3787994384765625, + 6.739121913909912, + 3.3309624195098877 ] }, "cluster_id": 3, @@ -870692,11 +870842,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -871116,31 +871266,31 @@ "doi": "10.1101/2020.06.15.153643", "projections": { "pca": [ - -0.009847982786595821, - -0.2060607671737671 + -0.010194342583417892, + -0.20543940365314484 ], "tsne": [ - 1.323702096939087, - 33.272560119628906 + -2.9645073413848877, + -14.74604320526123 ], "umap": [ - 6.5540995597839355, - 4.022177696228027 + 7.1030802726745605, + 3.792165994644165 ], "pca3d": [ - -0.009847710840404034, - -0.2060672789812088, - 0.09214043617248535 + -0.010193959809839725, + -0.2054464966058731, + 0.09263947606086731 ], "tsne3d": [ - -0.16099366545677185, - 16.313678741455078, - 0.5171716809272766 + 0.8700859546661377, + -11.148531913757324, + -4.686276912689209 ], "umap3d": [ - 5.009760856628418, - 5.926562309265137, - 5.281489372253418 + 5.988544464111328, + 6.973369598388672, + 4.054100513458252 ] }, "cluster_id": 3, @@ -871151,11 +871301,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -871562,46 +871712,46 @@ ], "projections": { "pca": [ - 0.12336596101522446, - 0.17042003571987152 + 0.12410344928503036, + 0.16972152888774872 ], "tsne": [ - -1.5942684412002563, - -5.078030586242676 + -1.270532250404358, + 3.547356367111206 ], "umap": [ - 5.039900302886963, - 7.252878665924072 + 5.265815734863281, + 6.541765213012695 ], "pca3d": [ - 0.12336520850658417, - 0.17044882476329803, - 0.15602046251296997 + 0.1241026520729065, + 0.16975027322769165, + 0.1556871235370636 ], "tsne3d": [ - -3.9051923751831055, - 1.258192777633667, - -18.99236488342285 + 1.715852975845337, + 20.073577880859375, + -4.237582206726074 ], "umap3d": [ - 4.554483890533447, - 3.5068423748016357, - 5.021511554718018 + 5.682839870452881, + 4.740324974060059, + 5.060791492462158 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, + "id": 13, "label": "Genomics Papers" } ], @@ -872017,46 +872167,46 @@ ], "projections": { "pca": [ - -0.22793374955654144, - -0.10024315118789673 + -0.22777892649173737, + -0.09949667751789093 ], "tsne": [ - -33.90182876586914, - -7.36477518081665 + -40.823638916015625, + 7.772350311279297 ], "umap": [ - 0.7898522019386292, - 8.143354415893555 + 1.0928784608840942, + 7.37483549118042 ], "pca3d": [ - -0.22793328762054443, - -0.10025940835475922, - 0.00588264362886548 + -0.22777853906154633, + -0.09951315075159073, + 0.006457907613366842 ], "tsne3d": [ - -23.09051513671875, - -6.248257160186768, - 10.529934883117676 + -24.215829849243164, + 5.8077712059021, + 5.858966827392578 ], "umap3d": [ - 0.8782779574394226, - 4.654772758483887, - 5.220717430114746 + 2.074788808822632, + 5.067049503326416, + 5.90225076675415 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -872478,31 +872628,31 @@ ], "projections": { "pca": [ - -0.0390002466738224, - -0.2494226098060608 + -0.03935034200549126, + -0.24882853031158447 ], "tsne": [ - 1.749128818511963, - 32.61384201049805 + -2.8256046772003174, + -13.820625305175781 ], "umap": [ - 6.642655849456787, - 4.161834239959717 + 7.1316351890563965, + 3.913774251937866 ], "pca3d": [ - -0.03899978846311569, - -0.24943456053733826, - 0.09476697444915771 + -0.03934982791543007, + -0.24884070456027985, + 0.0954086184501648 ], "tsne3d": [ - -0.8501845598220825, - 15.418779373168945, - -1.6488404273986816 + 2.0240461826324463, + -9.751459121704102, + -3.5657336711883545 ], "umap3d": [ - 1.1226065158843994, - 4.658629894256592, - 5.293571949005127 + 5.824281215667725, + 6.6906938552856445, + 4.1450700759887695 ] }, "cluster_id": 3, @@ -872513,11 +872663,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -872942,31 +873092,31 @@ "doi": "10.1101/2020.04.01.020487", "projections": { "pca": [ - 0.14398235082626343, - -0.019876830279827118 + 0.1441199779510498, + -0.01926286332309246 ], "tsne": [ - -7.463165283203125, - 25.201772689819336 + 0.06558970361948013, + -30.738176345825195 ], "umap": [ - 5.414005279541016, - 4.399656772613525 + 5.7798051834106445, + 3.9957761764526367 ], "pca3d": [ - 0.14398238062858582, - -0.01989307813346386, - 0.3066481351852417 + 0.14412012696266174, + -0.019279465079307556, + 0.3066425621509552 ], "tsne3d": [ - -2.3560733795166016, - 6.9716033935546875, - 8.201744079589844 + -3.8674960136413574, + -11.406075477600098, + 9.526022911071777 ], "umap3d": [ - 5.322443008422852, - 6.987387657165527, - 4.550724506378174 + 5.5201287269592285, + 7.55894136428833, + 2.982325792312622 ] }, "cluster_id": 3, @@ -872977,11 +873127,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 9, + "label": "Protein Design" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -873388,46 +873538,46 @@ ], "projections": { "pca": [ - 0.17425253987312317, - 0.07418793439865112 + 0.1747099757194519, + 0.07306594401597977 ], "tsne": [ - -1.3231196403503418, - -4.561309814453125 + -0.9598846435546875, + 3.0694940090179443 ], "umap": [ - 5.146270751953125, - 7.223852634429932 + 5.349989891052246, + 6.5454421043396 ], "pca3d": [ - 0.17425180971622467, - 0.07422381639480591, - 0.12092623114585876 + 0.1747092455625534, + 0.07310257852077484, + 0.12067980319261551 ], "tsne3d": [ - -4.533346652984619, - 1.7939695119857788, - -17.94835090637207 + 2.7825796604156494, + 19.373619079589844, + -4.353536128997803 ], "umap3d": [ - 4.631951332092285, - 3.4821388721466064, - 5.046487331390381 + 5.694983959197998, + 4.775809288024902, + 5.108460903167725 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, + "id": 13, "label": "Genomics Papers" } ], @@ -873842,31 +873992,31 @@ ], "projections": { "pca": [ - -0.27545666694641113, - -0.0191823560744524 + -0.2753809094429016, + -0.01890503242611885 ], "tsne": [ - -38.454429626464844, - 15.175820350646973 + -36.57761764526367, + -17.60517120361328 ], "umap": [ - 2.0572259426116943, - 5.39546012878418 + 2.4642410278320312, + 4.733331203460693 ], "pca3d": [ - -0.2754565477371216, - -0.019195687025785446, - -0.10251762717962265 + -0.2753807008266449, + -0.018918851390480995, + -0.10233177989721298 ], "tsne3d": [ - -19.6287784576416, - 8.253362655639648, - -1.2091598510742188 + -18.995410919189453, + -16.33020782470703, + 1.1576664447784424 ], "umap3d": [ - 1.894073724746704, - 6.6641340255737305, - 4.614413738250732 + 2.4390504360198975, + 6.927241802215576, + 4.619547367095947 ] }, "cluster_id": 2, @@ -873877,11 +874027,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -874290,31 +874440,31 @@ "abstract": "Contribute to davidfstein/v2p development by creating an account on GitHub.", "projections": { "pca": [ - 0.0020462109241634607, - -0.00014054562780074775 + 0.002170510357245803, + -0.0002552158257458359 ], "tsne": [ - 23.370378494262695, - 15.198136329650879 + 25.752796173095703, + -8.599705696105957 ], "umap": [ - 7.206553936004639, - 7.293639659881592 + 7.3523054122924805, + 6.952831268310547 ], "pca3d": [ - 0.0020464183762669563, - -0.00016194010095205158, - -0.015263603068888187 + 0.0021707136183977127, + -0.00027717722696252167, + -0.01519698929041624 ], "tsne3d": [ - 9.55273151397705, - -7.617531776428223, - -17.017257690429688 + 9.038256645202637, + 14.162230491638184, + 8.684026718139648 ], "umap3d": [ - 6.7607855796813965, - 3.8808319568634033, - 3.3198559284210205 + 7.223443984985352, + 4.3487067222595215, + 2.39190673828125 ] }, "cluster_id": 4, @@ -874325,12 +874475,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -874753,46 +874903,46 @@ "doi": "10.1101/2025.11.24.685470", "projections": { "pca": [ - 0.069835364818573, - 0.012872492894530296 + 0.06980360299348831, + 0.013141736388206482 ], "tsne": [ - 16.19034194946289, - -22.307497024536133 + 10.726531028747559, + 18.648582458496094 ], "umap": [ - 7.5737152099609375, - 9.32618522644043 + 7.792956829071045, + 8.868664741516113 ], "pca3d": [ - 0.06983550637960434, - 0.012854605913162231, - -0.010365445166826248 + 0.06980372220277786, + 0.013123370707035065, + -0.010400342755019665 ], "tsne3d": [ - 11.89470386505127, - -10.578451156616211, - 5.150707244873047 + 2.4953231811523438, + 6.142767906188965, + -14.162545204162598 ], "umap3d": [ - 5.184438228607178, - 3.909775733947754, - 1.5302276611328125 + 5.16274881362915, + 3.6996171474456787, + 1.4895285367965698 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -875237,47 +875387,47 @@ "cited_by_count": 62, "projections": { "pca": [ - -0.0793500617146492, - -0.02883213572204113 + -0.07930699735879898, + -0.028648313134908676 ], "tsne": [ - 13.095597267150879, - -34.077518463134766 + 5.084437370300293, + 34.8768196105957 ], "umap": [ - 6.064430236816406, - 9.764774322509766 + 6.16038703918457, + 9.18060302734375 ], "pca3d": [ - -0.07934954017400742, - -0.028869668021798134, - -0.15498086810112 + -0.07930653542280197, + -0.02868659421801567, + -0.15470144152641296 ], "tsne3d": [ - 7.850872039794922, - -18.273216247558594, - 3.3393304347991943 + -0.48262888193130493, + 11.689606666564941, + -19.115604400634766 ], "umap3d": [ - 4.763613224029541, - 2.6503796577453613, - 2.153470993041992 + 5.150636196136475, + 2.6819376945495605, + 2.644932508468628 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -875715,31 +875865,31 @@ "cited_by_count": 10, "projections": { "pca": [ - 0.1650676280260086, - 0.09161826968193054 + 0.16549362242221832, + 0.09166796505451202 ], "tsne": [ - 15.48827075958252, - 16.07631492614746 + 36.629791259765625, + -20.197778701782227 ], "umap": [ - 6.995855331420898, - 6.816317081451416 + 7.18216609954834, + 6.405309677124023 ], "pca3d": [ - 0.1650676429271698, - 0.09158305078744888, - 0.011158721521496773 + 0.16549360752105713, + 0.09163248538970947, + 0.011110605672001839 ], "tsne3d": [ - 3.9689793586730957, - 14.48630428314209, - -13.694287300109863 + 2.9483320713043213, + 6.902761936187744, + 7.460546493530273 ], "umap3d": [ - 6.381667137145996, - 4.3566107749938965, - 3.4693074226379395 + 6.784163951873779, + 4.787729263305664, + 2.5127406120300293 ] }, "cluster_id": 4, @@ -875750,12 +875900,12 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 22, - "label": "Genetic Variants" + "id": 17, + "label": "Genomic Models" } ], "_embedding": [ @@ -876188,47 +876338,47 @@ "openalex_url": "https://openalex.org/W4392946528", "projections": { "pca": [ - 0.03331192582845688, - -0.028916271403431892 + 0.033173881471157074, + -0.028814686462283134 ], "tsne": [ - 24.7439022064209, - 1.8750296831130981 + 27.565906524658203, + -11.639463424682617 ], "umap": [ - 7.216055393218994, - 7.810906410217285 + 7.354938507080078, + 7.532059669494629 ], "pca3d": [ - 0.033312056213617325, - -0.02892391011118889, - -0.12500245869159698 + 0.033174026757478714, + -0.028822245076298714, + -0.12488381564617157 ], "tsne3d": [ - 9.953536987304688, - -13.740713119506836, - -6.5873212814331055 + 10.183398246765137, + 13.07501220703125, + -1.2598317861557007 ], "umap3d": [ - 6.3301920890808105, - 3.518120765686035, - 2.688356399536133 + 6.713525772094727, + 3.8556699752807617, + 2.200800657272339 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -876648,47 +876798,47 @@ "openalex_url": "https://openalex.org/W4282569580", "projections": { "pca": [ - -0.08830011636018753, - 0.00814933143556118 + -0.0884910449385643, + 0.008452481590211391 ], "tsne": [ - 9.036181449890137, - -29.569093704223633 + 1.2578649520874023, + 30.564716339111328 ], "umap": [ - 6.105910778045654, - 9.46217155456543 + 6.210265636444092, + 8.778780937194824 ], "pca3d": [ - -0.08829997479915619, - 0.00815280806273222, - -0.13829484581947327 + -0.08849100023508072, + 0.008456060662865639, + -0.13831466436386108 ], "tsne3d": [ - 6.8416829109191895, - -10.798727989196777, - -22.020587921142578 + 3.41251540184021, + 15.853006362915039, + -14.341464042663574 ], "umap3d": [ - 5.032670497894287, - 2.746054172515869, - 2.4083523750305176 + 5.488842010498047, + 2.8657875061035156, + 2.7231569290161133 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -877110,31 +877260,31 @@ "doi": "10.1101/2025.01.29.635579", "projections": { "pca": [ - 0.12358131259679794, - -0.05039333924651146 + 0.12355933338403702, + -0.05026615783572197 ], "tsne": [ - 19.559619903564453, - 5.969052314758301 + 24.505435943603516, + -13.825628280639648 ], "umap": [ - 6.736615180969238, - 7.436492443084717 + 6.856264114379883, + 6.913821220397949 ], "pca3d": [ - 0.1235816702246666, - -0.05042029172182083, - -0.013547778129577637 + 0.1235596165060997, + -0.05029281601309776, + -0.013346154242753983 ], "tsne3d": [ - 6.27943754196167, - -11.841815948486328, - -12.686944961547852 + 7.053799629211426, + 17.41353988647461, + 3.4686124324798584 ], "umap3d": [ - 6.143646717071533, - 3.776374578475952, - 3.3334801197052 + 6.712473392486572, + 4.205140113830566, + 2.6730899810791016 ] }, "cluster_id": 4, @@ -877145,12 +877295,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -877556,47 +877706,47 @@ ], "projections": { "pca": [ - -0.17470309138298035, - 0.15044917166233063 + -0.17404533922672272, + 0.15048374235630035 ], "tsne": [ - 6.405843257904053, - -36.749664306640625 + -3.3297970294952393, + 36.782752990722656 ], "umap": [ - 5.492773532867432, - 9.592578887939453 + 5.653648376464844, + 8.956273078918457 ], "pca3d": [ - -0.17470301687717438, - 0.1504172831773758, - -0.11896214634180069 + -0.17404519021511078, + 0.1504509001970291, + -0.11888150870800018 ], "tsne3d": [ - 2.924454927444458, - -20.130605697631836, - 1.1065733432769775 + -4.80961275100708, + 13.717414855957031, + -15.683914184570312 ], "umap3d": [ - 4.548550128936768, - 2.52822208404541, - 2.689293622970581 + 5.171159267425537, + 2.7738468647003174, + 3.201775550842285 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -878014,31 +878164,31 @@ "doi": "10.1101/2025.09.09.675184", "projections": { "pca": [ - 0.06337977200746536, - -0.08238162100315094 + 0.06329239904880524, + -0.08242838084697723 ], "tsne": [ - 20.713300704956055, - 12.969316482543945 + 23.50516700744629, + -5.843919277191162 ], "umap": [ - 7.109448432922363, - 7.209775924682617 + 7.315479755401611, + 6.811437606811523 ], "pca3d": [ - 0.06337986886501312, - -0.08238288760185242, - -0.03810654208064079 + 0.06329254060983658, + -0.08242984861135483, + -0.037832971662282944 ], "tsne3d": [ - 8.584693908691406, - -3.7592732906341553, - -14.677610397338867 + 8.676336288452148, + 9.264298439025879, + 5.973945617675781 ], "umap3d": [ - 6.769202709197998, - 3.9098713397979736, - 3.4208288192749023 + 7.220592498779297, + 4.456938743591309, + 2.478118419647217 ] }, "cluster_id": 4, @@ -878049,12 +878199,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -878479,47 +878629,47 @@ "cited_by_count": 55, "projections": { "pca": [ - -0.0901070237159729, - 0.006229998543858528 + -0.0901409164071083, + 0.007138144224882126 ], "tsne": [ - 3.9737255573272705, - -34.35697937011719 + -3.5917627811431885, + 34.072383880615234 ], "umap": [ - 5.360544204711914, - 9.472189903259277 + 5.5059895515441895, + 8.839930534362793 ], "pca3d": [ - -0.09010665863752365, - 0.006203881930559874, - -0.06794743984937668 + -0.09014055132865906, + 0.007111152168363333, + -0.06776013225317001 ], "tsne3d": [ - 2.641160249710083, - -18.31000328063965, - -3.002178430557251 + -3.888075828552246, + 15.758002281188965, + -12.742398262023926 ], "umap3d": [ - 4.540942192077637, - 2.5330188274383545, - 2.8901355266571045 + 5.206085681915283, + 2.8421106338500977, + 3.3115811347961426 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -878925,46 +879075,46 @@ ], "projections": { "pca": [ - -0.10123222321271896, - 0.3137052357196808 + -0.1003882884979248, + 0.31424862146377563 ], "tsne": [ - 1.9632868766784668, - -10.638397216796875 + 1.2777010202407837, + 9.506633758544922 ], "umap": [ - 5.279529094696045, - 7.429366111755371 + 5.625351428985596, + 6.83448600769043 ], "pca3d": [ - -0.10123275965452194, - 0.3137085437774658, - -0.025790762156248093 + -0.10038893669843674, + 0.31425145268440247, + -0.026175672188401222 ], "tsne3d": [ - -3.6565234661102295, - -6.811465263366699, - -19.509685516357422 + -1.6746736764907837, + 28.029577255249023, + -3.0204193592071533 ], "umap3d": [ - 4.8100104331970215, - 3.338650703430176, - 4.904768943786621 + 5.9322123527526855, + 4.614126205444336, + 4.828519821166992 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, + "id": 13, "label": "Genomics Papers" } ], @@ -879397,31 +879547,31 @@ "openalex_url": "https://openalex.org/W4413786315", "projections": { "pca": [ - 0.03248501569032669, - -0.015539494343101978 + 0.032407138496637344, + -0.015603147447109222 ], "tsne": [ - 24.14920425415039, - 12.536589622497559 + 26.703237533569336, + -6.117020130157471 ], "umap": [ - 7.258403301239014, - 7.372280120849609 + 7.377608776092529, + 6.977543830871582 ], "pca3d": [ - 0.03248518332839012, - -0.01555267907679081, - -0.12646864354610443 + 0.03240729868412018, + -0.015616608783602715, + -0.1263875961303711 ], "tsne3d": [ - 11.760769844055176, - -6.029821395874023, - -14.347790718078613 + 10.953649520874023, + 11.324281692504883, + 6.448429107666016 ], "umap3d": [ - 6.835590362548828, - 3.7786316871643066, - 3.258248805999756 + 7.27753210067749, + 4.297018051147461, + 2.355588674545288 ] }, "cluster_id": 4, @@ -879432,12 +879582,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -879868,47 +880018,47 @@ ], "projections": { "pca": [ - -0.1069943979382515, - 0.03675775229930878 + -0.10701494663953781, + 0.03766582906246185 ], "tsne": [ - 3.8090310096740723, - -34.323944091796875 + -3.7926506996154785, + 34.12736892700195 ], "umap": [ - 5.406276702880859, - 9.521510124206543 + 5.492129802703857, + 8.912715911865234 ], "pca3d": [ - -0.10699408501386642, - 0.0367412343621254, - -0.07911311835050583 + -0.10701469331979752, + 0.037648823112249374, + -0.07902701944112778 ], "tsne3d": [ - 2.1800923347473145, - -18.86885643005371, - -1.9458584785461426 + -3.8685078620910645, + 15.020936012268066, + -11.827994346618652 ], "umap3d": [ - 4.570255279541016, - 2.490370035171509, - 2.833702325820923 + 5.193614959716797, + 2.8041672706604004, + 3.3043224811553955 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -880332,46 +880482,46 @@ "cited_by_count": 4, "projections": { "pca": [ - 0.012500643730163574, - 0.12009585648775101 + 0.012911957688629627, + 0.12019672989845276 ], "tsne": [ - 10.672720909118652, - -13.8065824508667 + 29.224430084228516, + -14.494766235351562 ], "umap": [ - 6.489607810974121, - 8.495057106018066 + 6.653285026550293, + 7.959751129150391 ], "pca3d": [ - 0.012500597164034843, - 0.12007928639650345, - -0.09803840517997742 + 0.012911940924823284, + 0.12017959356307983, + -0.0980813130736351 ], "tsne3d": [ - 5.2017412185668945, - -18.211225509643555, - -5.770358085632324 + 9.279729843139648, + 19.640684127807617, + -2.4855525493621826 ], "umap3d": [ - 5.800858497619629, - 2.9228298664093018, - 2.875297784805298 + 6.371151447296143, + 3.290526866912842, + 2.714256763458252 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 3, + "label": "Genetic Disease" }, { - "id": 26, + "id": 33, "label": "Genetic Disease" } ], @@ -880795,47 +880945,47 @@ "doi": "10.1101/2025.08.04.25332916", "projections": { "pca": [ - -0.0793662816286087, - 0.06889626383781433 + -0.07922225445508957, + 0.06929276883602142 ], "tsne": [ - 4.4149017333984375, - -34.12075424194336 + -3.5589098930358887, + 33.78456497192383 ], "umap": [ - 5.529857635498047, - 9.487356185913086 + 5.630556106567383, + 8.893969535827637 ], "pca3d": [ - -0.07936596870422363, - 0.06887289881706238, - -0.07741077244281769 + -0.07922196388244629, + 0.069268599152565, + -0.07741951942443848 ], "tsne3d": [ - 3.843430280685425, - -18.560651779174805, - -2.919408082962036 + -2.7600107192993164, + 15.493708610534668, + -12.865707397460938 ], "umap3d": [ - 4.651697158813477, - 2.4818789958953857, - 2.828944206237793 + 5.281662940979004, + 2.8021085262298584, + 3.269059658050537 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -881262,47 +881412,47 @@ "openalex_url": "https://openalex.org/W4413786393", "projections": { "pca": [ - -0.09437083452939987, - 0.08664293587207794 + -0.09422829002141953, + 0.0873287096619606 ], "tsne": [ - 4.022185325622559, - -32.93508529663086 + -3.3830690383911133, + 32.5311279296875 ], "umap": [ - 5.471943378448486, - 9.431875228881836 + 5.604069709777832, + 8.800992012023926 ], "pca3d": [ - -0.09437089413404465, - 0.08665087819099426, - -0.05133592337369919 + -0.0942283421754837, + 0.08733609318733215, + -0.05137801542878151 ], "tsne3d": [ - 4.090194225311279, - -17.21215057373047, - -1.9472286701202393 + -2.286590099334717, + 17.005701065063477, + -11.929099082946777 ], "umap3d": [ - 4.644948482513428, - 2.5506317615509033, - 2.79451584815979 + 5.267179489135742, + 2.828176736831665, + 3.2620155811309814 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -881721,31 +881871,31 @@ ], "projections": { "pca": [ - 0.07433319836854935, - -0.07080044597387314 + 0.07426457852125168, + -0.0706210657954216 ], "tsne": [ - -15.70460319519043, - 18.896263122558594 + -12.602466583251953, + -24.59339141845703 ], "umap": [ - 4.774046897888184, - 4.620915412902832 + 5.37090539932251, + 3.986077070236206 ], "pca3d": [ - 0.07433311641216278, - -0.07078507542610168, - 0.0370047464966774 + 0.07426445186138153, + -0.07060594111680984, + 0.0371255986392498 ], "tsne3d": [ - 2.08587908744812, - 6.714045524597168, - 15.83398151397705 + 2.198613166809082, + -12.94398307800293, + 12.575932502746582 ], "umap3d": [ - 4.576723575592041, - 6.8278889656066895, - 4.235079288482666 + 4.828182697296143, + 7.183536529541016, + 3.061986207962036 ] }, "cluster_id": 3, @@ -881756,12 +881906,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -882180,31 +882330,31 @@ "doi": "10.1101/2025.04.25.650688", "projections": { "pca": [ - 0.05982949957251549, - -0.143288254737854 + 0.05950359255075455, + -0.1425067037343979 ], "tsne": [ - -1.6480522155761719, - 22.706539154052734 + 4.791774749755859, + -23.681737899780273 ], "umap": [ - 5.869986534118652, - 4.666592121124268 + 6.4641313552856445, + 4.3079986572265625 ], "pca3d": [ - 0.05982973054051399, - -0.14329057931900024, - 0.24946248531341553 + 0.05950380861759186, + -0.14250881969928741, + 0.24967430531978607 ], "tsne3d": [ - 0.480903297662735, - 5.822751045227051, - 1.7071577310562134 + -2.739078998565674, + -9.405484199523926, + 1.4563543796539307 ], "umap3d": [ - 5.677765369415283, - 6.416769981384277, - 4.697105884552002 + 6.064986705780029, + 7.24669075012207, + 2.966193675994873 ] }, "cluster_id": 3, @@ -882215,12 +882365,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, - "label": "Protein Design" + "id": 20, + "label": "Protein Language" } ], "_embedding": [ @@ -882641,47 +882791,47 @@ "cited_by_count": 46, "projections": { "pca": [ - -0.13205356895923615, - 0.0983305349946022 + -0.13186925649642944, + 0.09924228489398956 ], "tsne": [ - 3.587618827819824, - -35.00480270385742 + -2.911884069442749, + 34.813232421875 ], "umap": [ - 5.4642510414123535, - 9.531543731689453 + 5.567473411560059, + 8.945488929748535 ], "pca3d": [ - -0.13205327093601227, - 0.09830732643604279, - -0.09050681442022324 + -0.13186904788017273, + 0.0992184430360794, + -0.09051281958818436 ], "tsne3d": [ - 2.5550670623779297, - -17.188913345336914, - -4.249281883239746 + -2.916501998901367, + 16.458690643310547, + -14.52185344696045 ], "umap3d": [ - 4.591046333312988, - 2.5000224113464355, - 2.8325319290161133 + 5.264180660247803, + 2.783151865005493, + 3.2791287899017334 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -883106,46 +883256,46 @@ ], "projections": { "pca": [ - -0.15717218816280365, - 0.03677499666810036 + -0.15706710517406464, + 0.03751431778073311 ], "tsne": [ - -0.2125362902879715, - -32.37099075317383 + -7.232390403747559, + 29.764833450317383 ], "umap": [ - 4.803192615509033, - 9.500612258911133 + 4.975414276123047, + 8.81531810760498 ], "pca3d": [ - -0.15717191994190216, - 0.03676324337720871, - 3.9791801100363955e-05 + -0.15706685185432434, + 0.03750212863087654, + 0.00016796556883491576 ], "tsne3d": [ - -1.9083173274993896, - -18.162778854370117, - -1.8145098686218262 + -6.139081001281738, + 16.161108016967773, + -8.422910690307617 ], "umap3d": [ - 3.977977752685547, - 2.6073105335235596, - 3.035229206085205 + 4.6755266189575195, + 2.9927282333374023, + 3.629995822906494 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, + "id": 26, "label": "Medical AI" } ], @@ -883577,46 +883727,46 @@ "cited_by_count": 96, "projections": { "pca": [ - 0.11750871688127518, - 0.05568517744541168 + 0.11762522161006927, + 0.055654339492321014 ], "tsne": [ - 10.641855239868164, - -11.803998947143555 + 28.343481063842773, + -13.830031394958496 ], "umap": [ - 6.747822284698486, - 8.294732093811035 + 6.923810005187988, + 7.794981002807617 ], "pca3d": [ - 0.11750857532024384, - 0.055691517889499664, - -0.11603499948978424 + 0.11762506514787674, + 0.05566082522273064, + -0.11608681082725525 ], "tsne3d": [ - 6.534579753875732, - -15.889588356018066, - -7.695252895355225 + 8.09111213684082, + 17.8236083984375, + -2.561361789703369 ], "umap3d": [ - 6.134124279022217, - 3.1218173503875732, - 2.849771738052368 + 6.678682327270508, + 3.4516875743865967, + 2.4975028038024902 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 26, + "id": 33, "label": "Genetic Disease" } ], @@ -884035,47 +884185,47 @@ "doi": "10.1101/2025.01.17.25320664", "projections": { "pca": [ - 0.0007310372893698514, - -0.005748245399445295 + 0.0008500587427988648, + -0.005842198617756367 ], "tsne": [ - 5.197350978851318, - -31.19474983215332 + -2.9743900299072266, + 30.676877975463867 ], "umap": [ - 5.71601676940918, - 9.089738845825195 + 5.841709613800049, + 8.38560962677002 ], "pca3d": [ - 0.0007311683730222285, - -0.005755912978202105, - -0.12199129909276962 + 0.0008502008859068155, + -0.00585069740191102, + -0.12179283797740936 ], "tsne3d": [ - 4.415594577789307, - -22.85704803466797, - -0.3584415018558502 + -1.3050696849822998, + 12.339653015136719, + -9.237363815307617 ], "umap3d": [ - 5.054596900939941, - 2.887110948562622, - 2.977918863296509 + 5.528771877288818, + 3.1960084438323975, + 3.2057149410247803 ] }, - "cluster_id": 5, - "cluster_label": "Single-Cell Data", + "cluster_id": 1, + "cluster_label": "AI in Science", "cluster_levels": [ { - "id": 5, - "label": "Single-Cell Data" + "id": 1, + "label": "AI in Science" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -884502,46 +884652,46 @@ "doi": "10.1101/2022.05.26.22275626", "projections": { "pca": [ - 0.06198980659246445, - 0.016649948433041573 + 0.06206800416111946, + 0.016302382573485374 ], "tsne": [ - 26.790870666503906, - 3.5520880222320557 + 30.152650833129883, + -11.125337600708008 ], "umap": [ - 7.032005786895752, - 7.87036657333374 + 7.24608850479126, + 7.539488315582275 ], "pca3d": [ - 0.061989925801754, - 0.01661856658756733, - -0.23393696546554565 + 0.062068138271570206, + 0.016270531341433525, + -0.233869731426239 ], "tsne3d": [ - 8.05090045928955, - -10.6353120803833, - -8.59622573852539 + 8.569501876831055, + 15.08299446105957, + -3.715282917022705 ], "umap3d": [ - 6.4665398597717285, - 3.393632650375366, - 2.88215970993042 + 6.869266510009766, + 3.852639675140381, + 2.3418476581573486 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 26, + "id": 33, "label": "Genetic Disease" } ], @@ -884948,31 +885098,31 @@ ], "projections": { "pca": [ - 0.0012948476942256093, - -0.10591527074575424 + 0.0012461761943995953, + -0.10534065216779709 ], "tsne": [ - 10.389826774597168, - 2.9972550868988037 + 12.111233711242676, + 1.648819923400879 ], "umap": [ - 6.893640995025635, - 6.096280097961426 + 7.162752151489258, + 5.742341995239258 ], "pca3d": [ - 0.0012949336087331176, - -0.10592547804117203, - 0.026666773483157158 + 0.0012463052989915013, + -0.1053507924079895, + 0.027091100811958313 ], "tsne3d": [ - 6.223140716552734, - -0.33237582445144653, - -5.306556224822998 + 6.023705005645752, + 4.912207126617432, + -2.6627039909362793 ], "umap3d": [ - 5.669243335723877, - 5.179248332977295, - 3.380406379699707 + 5.995118141174316, + 5.349436283111572, + 2.3361334800720215 ] }, "cluster_id": 4, @@ -884983,11 +885133,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -885398,47 +885548,47 @@ "doi": "10.1016/s2589-7500(24)00220-6", "projections": { "pca": [ - -0.16366925835609436, - 0.13054855167865753 + -0.16317269206047058, + 0.13123415410518646 ], "tsne": [ - 3.864927291870117, - -35.419612884521484 + -3.503077745437622, + 35.22705078125 ], "umap": [ - 5.387395858764648, - 9.538911819458008 + 5.519192695617676, + 8.917580604553223 ], "pca3d": [ - -0.1636691838502884, - 0.13051794469356537, - -0.10249174386262894 + -0.1631726771593094, + 0.13120250403881073, + -0.10236963629722595 ], "tsne3d": [ - 1.6696513891220093, - -18.827640533447266, - -3.987543821334839 + -4.511383533477783, + 16.433204650878906, + -14.021280288696289 ], "umap3d": [ - 4.552499771118164, - 2.4860198497772217, - 2.8773412704467773 + 5.182356357574463, + 2.7650489807128906, + 3.326108694076538 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -885860,31 +886010,31 @@ "doi": "10.1101/2025.06.16.659977", "projections": { "pca": [ - 0.12643447518348694, - -0.06132364645600319 + 0.12627071142196655, + -0.06088084354996681 ], "tsne": [ - -19.171018600463867, - 24.691936492919922 + 1.9837875366210938, + -40.454410552978516 ], "umap": [ - 4.2702813148498535, - 3.5578863620758057 + 4.853729724884033, + 3.0884897708892822 ], "pca3d": [ - 0.1264345496892929, - -0.06133398786187172, - 0.14422112703323364 + 0.1262708306312561, + -0.060891538858413696, + 0.14426983892917633 ], "tsne3d": [ - -3.78468918800354, - 11.524975776672363, - -14.003241539001465 + 0.031935397535562515, + -6.6456074714660645, + 17.067304611206055 ], "umap3d": [ - 4.969439506530762, - 7.838658809661865, - 4.150362491607666 + 4.562943458557129, + 8.02359676361084, + 2.7789907455444336 ] }, "cluster_id": 3, @@ -885895,12 +886045,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -886324,46 +886474,46 @@ "openalex_url": "https://openalex.org/W4410957817", "projections": { "pca": [ - -0.1017739400267601, - 0.29790112376213074 + -0.10097309201955795, + 0.2983449697494507 ], "tsne": [ - -4.649891376495361, - -31.07878303527832 + -6.693361282348633, + 26.143918991088867 ], "umap": [ - 4.7298264503479, - 9.00870418548584 + 4.99696159362793, + 8.405938148498535 ], "pca3d": [ - -0.10177437961101532, - 0.2978877127170563, - -0.009102595038712025 + -0.10097341984510422, + 0.2983304262161255, + -0.009500563144683838 ], "tsne3d": [ - -2.718104362487793, - -13.913161277770996, - 1.444325566291809 + -6.713534832000732, + 19.66720962524414, + -5.428616046905518 ], "umap3d": [ - 3.746880054473877, - 3.126288890838623, - 3.1196179389953613 + 4.460914134979248, + 3.531757354736328, + 3.597865581512451 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, + "id": 26, "label": "Medical AI" } ], @@ -886784,47 +886934,47 @@ "cited_by_count": 8, "projections": { "pca": [ - -0.004064707085490227, - -0.009954371489584446 + -0.004233484622091055, + -0.009818029589951038 ], "tsne": [ - 11.339599609375, - -30.55607795715332 + 3.5784759521484375, + 31.454566955566406 ], "umap": [ - 5.8820695877075195, - 9.456771850585938 + 6.067972660064697, + 8.870454788208008 ], "pca3d": [ - -0.004064575303345919, - -0.009953483007848263, - -0.13741998374462128 + -0.004233368206769228, + -0.009817259386181831, + -0.1374080628156662 ], "tsne3d": [ - 9.422624588012695, - -15.696576118469238, - -0.8176490068435669 + 2.675477981567383, + 12.386796951293945, + -14.529230117797852 ], "umap3d": [ - 4.910348415374756, - 2.6397182941436768, - 2.561138868331909 + 5.4140625, + 2.8012924194335938, + 2.884941816329956 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -887230,46 +887380,46 @@ ], "projections": { "pca": [ - -0.008952864445745945, - 0.09444709122180939 + -0.008429420180618763, + 0.09479594230651855 ], "tsne": [ - 1.9659581184387207, - -8.793100357055664 + 1.6602203845977783, + 7.7383928298950195 ], "umap": [ - 5.288845062255859, - 7.421445846557617 + 5.5014214515686035, + 6.682483673095703 ], "pca3d": [ - -0.008953309617936611, - 0.09446422755718231, - 0.04692082852125168 + -0.008429884910583496, + 0.0948135182261467, + 0.046989601105451584 ], "tsne3d": [ - -1.3664551973342896, - -4.775158882141113, - -18.318876266479492 + 0.8562323451042175, + 26.626270294189453, + -2.2190871238708496 ], "umap3d": [ - 4.8541998863220215, - 3.337364435195923, - 5.005607604980469 + 5.865476608276367, + 4.652050971984863, + 4.912587642669678 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, + "id": 13, "label": "Genomics Papers" } ], @@ -887694,47 +887844,47 @@ "doi": "10.1101/2025.01.31.25321471", "projections": { "pca": [ - -0.11312263458967209, - 0.013680415228009224 + -0.11308598518371582, + 0.013607180677354336 ], "tsne": [ - 5.154614448547363, - -34.70948028564453 + -4.878515720367432, + 34.11221694946289 ], "umap": [ - 5.483827114105225, - 9.534818649291992 + 5.624288082122803, + 8.880851745605469 ], "pca3d": [ - -0.11312235891819, - 0.013673440553247929, - -0.13766945898532867 + -0.11308573186397552, + 0.013599730096757412, + -0.13759413361549377 ], "tsne3d": [ - 3.4144983291625977, - -20.409236907958984, - -2.5261218547821045 + -3.1615819931030273, + 13.339393615722656, + -12.401362419128418 ], "umap3d": [ - 4.623231887817383, - 2.454709053039551, - 2.816411256790161 + 5.2718329429626465, + 2.770859479904175, + 3.2738962173461914 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -888155,47 +888305,47 @@ "doi": "10.1101/2024.10.23.619940", "projections": { "pca": [ - 0.060184914618730545, - -0.07189580798149109 + 0.060078214854002, + -0.07162818312644958 ], "tsne": [ - 22.0301456451416, - 7.424493312835693 + 28.004993438720703, + -9.136575698852539 ], "umap": [ - 7.125570297241211, - 7.5826735496521 + 7.312338829040527, + 7.301714897155762 ], "pca3d": [ - 0.060185182839632034, - -0.07192759215831757, - -0.05041203275322914 + 0.06007848680019379, + -0.07166069746017456, + -0.050169575959444046 ], "tsne3d": [ - 9.376335144042969, - -7.543227195739746, - -7.74943733215332 + 9.6620454788208, + 8.98480224609375, + 0.11933359503746033 ], "umap3d": [ - 6.393993854522705, - 3.7102646827697754, - 2.989189386367798 + 6.860987663269043, + 4.1498589515686035, + 2.329965591430664 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -888613,46 +888763,46 @@ ], "projections": { "pca": [ - -0.11820074915885925, - -0.04299525171518326 + -0.11803077906370163, + -0.04271057993173599 ], "tsne": [ - 0.5073102712631226, - -35.565223693847656 + -8.242636680603027, + 32.86979675292969 ], "umap": [ - 4.77097225189209, - 9.601899147033691 + 4.978775501251221, + 8.87964916229248 ], "pca3d": [ - -0.11820045113563538, - -0.04300476983189583, - -0.019756464287638664 + -0.11803044378757477, + -0.04272081330418587, + -0.01933380216360092 ], "tsne3d": [ - -0.19078803062438965, - -21.09161376953125, - -0.9707258939743042 + -5.538286209106445, + 12.20083999633789, + -10.707077026367188 ], "umap3d": [ - 4.061977386474609, - 2.5125234127044678, - 3.120266914367676 + 4.802181720733643, + 2.956618070602417, + 3.6879804134368896 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, + "id": 26, "label": "Medical AI" } ], @@ -889068,31 +889218,31 @@ ], "projections": { "pca": [ - 0.09154747426509857, - -0.04526343196630478 + 0.09159231930971146, + -0.04479566961526871 ], "tsne": [ - 19.171810150146484, - 11.706608772277832 + 21.92643165588379, + -6.088523864746094 ], "umap": [ - 7.010014057159424, - 7.042529106140137 + 7.14298152923584, + 6.696817874908447 ], "pca3d": [ - 0.09154762327671051, - -0.045285530388355255, - 0.03160291910171509 + 0.0915924534201622, + -0.04481826722621918, + 0.031918954104185104 ], "tsne3d": [ - 7.990045547485352, - -2.826937198638916, - -11.781390190124512 + 7.393404960632324, + 8.080378532409668, + 3.7751381397247314 ], "umap3d": [ - 6.551505088806152, - 4.088156700134277, - 3.3463995456695557 + 6.988183498382568, + 4.628007411956787, + 2.5160255432128906 ] }, "cluster_id": 4, @@ -889103,12 +889253,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -889537,47 +889687,47 @@ "doi": "10.1101/2025.03.17.25324124", "projections": { "pca": [ - 0.24667488038539886, - 0.09587748348712921 + 0.24682778120040894, + 0.09517697989940643 ], "tsne": [ - 32.08194351196289, - 9.430474281311035 + 38.68844985961914, + -8.068387031555176 ], "umap": [ - 7.578983783721924, - 8.211590766906738 + 7.829602241516113, + 7.763322353363037 ], "pca3d": [ - 0.2466745525598526, - 0.09586254507303238, - -0.22877246141433716 + 0.24682749807834625, + 0.09516139328479767, + -0.22901871800422668 ], "tsne3d": [ - 14.220727920532227, - -15.943978309631348, - -8.732293128967285 + 17.00663185119629, + 16.727657318115234, + -3.429387092590332 ], "umap3d": [ - 6.902851581573486, - 3.4073073863983154, - 2.3714067935943604 + 7.078921794891357, + 3.4988834857940674, + 1.7288947105407715 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -889987,46 +890137,46 @@ "doi": "10.1093/bioinformatics/btae353", "projections": { "pca": [ - -0.06752612441778183, - 0.10197247564792633 + -0.06709498167037964, + 0.10241882503032684 ], "tsne": [ - -1.961029052734375, - -36.778564453125 + -11.161386489868164, + 32.716102600097656 ], "umap": [ - 4.514832496643066, - 9.356512069702148 + 4.691648006439209, + 8.583913803100586 ], "pca3d": [ - -0.06752616912126541, - 0.10196824371814728, - 0.08113212883472443 + -0.06709503382444382, + 0.10241471230983734, + 0.08111698180437088 ], "tsne3d": [ - -1.874686360359192, - -22.80501937866211, - 2.790450096130371 + -4.883820533752441, + 10.919269561767578, + -6.497643947601318 ], "umap3d": [ - 3.782579183578491, - 2.7580020427703857, - 3.2323529720306396 + 4.487907409667969, + 3.31488299369812, + 3.834306001663208 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, + "id": 26, "label": "Medical AI" } ], @@ -890444,46 +890594,46 @@ ], "projections": { "pca": [ - -0.04614083841443062, - 0.08149112015962601 + -0.04573698341846466, + 0.0817488357424736 ], "tsne": [ - -1.2774752378463745, - -36.20245361328125 + -10.281662940979004, + 32.53621292114258 ], "umap": [ - 4.589868068695068, - 9.560538291931152 + 4.8446550369262695, + 8.823083877563477 ], "pca3d": [ - -0.04614071920514107, - 0.08147772401571274, - 0.016058120876550674 + -0.0457368902862072, + 0.0817350447177887, + 0.016149500384926796 ], "tsne3d": [ - -1.645650029182434, - -22.21183967590332, - 1.6507272720336914 + -5.438920021057129, + 11.329996109008789, + -7.533390998840332 ], "umap3d": [ - 3.8496053218841553, - 2.5983664989471436, - 3.2368063926696777 + 4.61044454574585, + 3.0995802879333496, + 3.8203742504119873 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, + "id": 26, "label": "Medical AI" } ], @@ -890907,46 +891057,46 @@ "cited_by_count": 52, "projections": { "pca": [ - 0.12065501511096954, - 0.09372177720069885 + 0.12091156095266342, + 0.09389635175466537 ], "tsne": [ - 24.80025863647461, - 6.939307689666748 + 33.850318908691406, + -13.788281440734863 ], "umap": [ - 6.688624858856201, - 7.891085147857666 + 6.84031867980957, + 7.54746675491333 ], "pca3d": [ - 0.12065504491329193, - 0.09368398040533066, - -0.13422635197639465 + 0.12091153115034103, + 0.09385819733142853, + -0.13428334891796112 ], "tsne3d": [ - 9.404044151306152, - -13.211216926574707, - -14.625406265258789 + 9.823512077331543, + 20.10694122314453, + 2.529264450073242 ], "umap3d": [ - 6.465246677398682, - 3.2506465911865234, - 3.1038403511047363 + 7.001145362854004, + 3.6630518436431885, + 2.5546796321868896 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 26, + "id": 33, "label": "Genetic Disease" } ], @@ -891365,46 +891515,46 @@ "doi": "10.1101/2025.01.31.635988", "projections": { "pca": [ - 0.13223515450954437, - 0.11101492494344711 + 0.1325293630361557, + 0.11134566366672516 ], "tsne": [ - 23.880550384521484, - 7.411483287811279 + 33.629554748535156, + -14.481486320495605 ], "umap": [ - 6.761653900146484, - 7.744901180267334 + 6.883759498596191, + 7.364231586456299 ], "pca3d": [ - 0.13223528861999512, - 0.11096303164958954, - -0.05537327006459236 + 0.13252948224544525, + 0.11129330843687057, + -0.05546886846423149 ], "tsne3d": [ - 8.408927917480469, - -11.727564811706543, - -15.222967147827148 + 8.038703918457031, + 20.311931610107422, + 2.8073678016662598 ], "umap3d": [ - 6.515312671661377, - 3.3863391876220703, - 3.194512128829956 + 7.040672779083252, + 3.8649213314056396, + 2.5180559158325195 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 26, + "id": 33, "label": "Genetic Disease" } ], @@ -891811,46 +891961,46 @@ ], "projections": { "pca": [ - -0.08008242398500443, - 0.2237246185541153 + -0.07925320416688919, + 0.22369103133678436 ], "tsne": [ - 1.4696741104125977, - -10.091137886047363 + 1.0859895944595337, + 8.733118057250977 ], "umap": [ - 5.270599365234375, - 7.36472749710083 + 5.5515851974487305, + 6.67087984085083 ], "pca3d": [ - -0.08008327335119247, - 0.22375725209712982, - -0.052405212074518204 + -0.07925393432378769, + 0.22372381389141083, + -0.05255652964115143 ], "tsne3d": [ - -2.6842870712280273, - -5.619620323181152, - -19.431032180786133 + -1.163535714149475, + 26.714141845703125, + -2.2443253993988037 ], "umap3d": [ - 4.81589412689209, - 3.393842935562134, - 4.913607597351074 + 5.856900691986084, + 4.676207542419434, + 4.875168800354004 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, + "id": 13, "label": "Genomics Papers" } ], @@ -892279,46 +892429,46 @@ "doi": "10.1101/2024.12.03.24318322", "projections": { "pca": [ - -0.09264973551034927, - 0.013813275843858719 + -0.09253142774105072, + 0.014170179143548012 ], "tsne": [ - 0.844154953956604, - -35.732460021972656 + -7.774127006530762, + 33.05207824707031 ], "umap": [ - 4.894232273101807, - 9.587114334106445 + 5.043506622314453, + 8.912151336669922 ], "pca3d": [ - -0.09264961630105972, - 0.013802653178572655, - -0.005169421434402466 + -0.09253121167421341, + 0.01415889710187912, + -0.004992934409528971 ], "tsne3d": [ - 0.13805966079235077, - -22.205141067504883, - -0.03544510155916214 + -4.688368320465088, + 11.403053283691406, + -9.64649486541748 ], "umap3d": [ - 4.171842575073242, - 2.526966094970703, - 3.058478355407715 + 4.8770551681518555, + 2.9279887676239014, + 3.6482019424438477 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, + "id": 26, "label": "Medical AI" } ], @@ -892745,47 +892895,47 @@ "openalex_url": "https://openalex.org/W1839682376", "projections": { "pca": [ - 0.08333459496498108, - 0.03895259648561478 + 0.08341730386018753, + 0.03852490335702896 ], "tsne": [ - 28.18714141845703, - 2.8989951610565186 + 28.797386169433594, + -9.51169204711914 ], "umap": [ - 7.119229793548584, - 8.054464340209961 + 7.477837085723877, + 7.729537487030029 ], "pca3d": [ - 0.08333488553762436, - 0.03891650587320328, - -0.17144589126110077 + 0.08341756463050842, + 0.03848788142204285, + -0.17148709297180176 ], "tsne3d": [ - 10.1908597946167, - -9.414414405822754, - -6.916274070739746 + 11.183183670043945, + 15.325918197631836, + -3.740493059158325 ], "umap3d": [ - 6.378855228424072, - 3.382579803466797, - 2.6807384490966797 + 6.836182594299316, + 3.689051866531372, + 2.2100253105163574 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 26, - "label": "Genetic Disease" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -893201,47 +893351,47 @@ ], "projections": { "pca": [ - -0.14230598509311676, - -0.022960538044571877 + -0.14233264327049255, + -0.02246164157986641 ], "tsne": [ - 1.8816723823547363, - -36.1241569519043 + -6.990925312042236, + 33.76506042480469 ], "umap": [ - 5.174692630767822, - 9.548300743103027 + 5.31688117980957, + 8.88732624053955 ], "pca3d": [ - -0.14230579137802124, - -0.022965041920542717, - -0.06214651092886925 + -0.14233243465423584, + -0.02246682718396187, + -0.061922963708639145 ], "tsne3d": [ - 1.8146113157272339, - -22.428739547729492, - -0.39905083179473877 + -3.3106343746185303, + 11.135185241699219, + -10.345369338989258 ], "umap3d": [ - 4.483002185821533, - 2.4947664737701416, - 2.930264472961426 + 5.135838508605957, + 2.8500566482543945, + 3.471388578414917 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 26, + "label": "Medical AI" } ], "_embedding": [ @@ -893651,47 +893801,47 @@ "doi": "10.1016/j.patter.2021.100337", "projections": { "pca": [ - -0.11447522044181824, - 0.03171631693840027 + -0.11424937099218369, + 0.03199700266122818 ], "tsne": [ - 5.942235469818115, - -34.58890914916992 + -5.493029594421387, + 33.64232635498047 ], "umap": [ - 5.454014301300049, - 9.467374801635742 + 5.6090474128723145, + 8.779810905456543 ], "pca3d": [ - -0.11447510868310928, - 0.0317060612142086, - -0.13256798684597015 + -0.11424919962882996, + 0.03198608011007309, + -0.13234984874725342 ], "tsne3d": [ - 3.883246660232544, - -22.104663848876953, - -2.6610107421875 + -1.959966778755188, + 12.086434364318848, + -11.748746871948242 ], "umap3d": [ - 4.701230049133301, - 2.54221248626709, - 2.8670237064361572 + 5.31504487991333, + 2.8420798778533936, + 3.299170970916748 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -894114,47 +894264,47 @@ "cited_by_count": 196, "projections": { "pca": [ - -0.12187163531780243, - -0.002406744984909892 + -0.12186133861541748, + -0.0021382987033575773 ], "tsne": [ - 5.654147624969482, - -34.2477912902832 + -4.922805309295654, + 33.699153900146484 ], "umap": [ - 5.545124530792236, - 9.470069885253906 + 5.677295207977295, + 8.874634742736816 ], "pca3d": [ - -0.12187133729457855, - -0.0024200957268476486, - -0.18324723839759827 + -0.1218610554933548, + -0.0021524191834032536, + -0.18304738402366638 ], "tsne3d": [ - 4.2132439613342285, - -21.064611434936523, - -1.7725061178207397 + -1.9232752323150635, + 13.308804512023926, + -11.957019805908203 ], "umap3d": [ - 4.711479663848877, - 2.5091450214385986, - 2.7962188720703125 + 5.3109965324401855, + 2.811903476715088, + 3.2649874687194824 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -894576,46 +894726,46 @@ "cited_by_count": 18, "projections": { "pca": [ - 0.07831019163131714, - 0.10268817842006683 + 0.07854603230953217, + 0.10246206074953079 ], "tsne": [ - 10.714470863342285, - -13.029008865356445 + 29.009510040283203, + -13.914196968078613 ], "umap": [ - 6.568852424621582, - 8.408113479614258 + 6.8273468017578125, + 7.923498630523682 ], "pca3d": [ - 0.07831014692783356, - 0.1026788055896759, - -0.15718896687030792 + 0.07854592055082321, + 0.10245230793952942, + -0.15730226039886475 ], "tsne3d": [ - 6.670348167419434, - -17.761629104614258, - -6.414257526397705 + 10.000664710998535, + 19.140968322753906, + -3.950479745864868 ], "umap3d": [ - 5.989245891571045, - 3.0155155658721924, - 2.8340070247650146 + 6.521092414855957, + 3.3001515865325928, + 2.545992851257324 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 3, + "label": "Genetic Disease" }, { - "id": 26, + "id": 33, "label": "Genetic Disease" } ], @@ -895029,47 +895179,47 @@ "doi": "10.1093/bioinformatics/btae264", "projections": { "pca": [ - -0.16070984303951263, - -0.02781829982995987 + -0.1608288586139679, + -0.027685491368174553 ], "tsne": [ - 3.6854376792907715, - -36.492881774902344 + -5.759139537811279, + 34.884033203125 ], "umap": [ - 5.438546657562256, - 9.583738327026367 + 5.567151069641113, + 8.958890914916992 ], "pca3d": [ - -0.16070958971977234, - -0.02781587280333042, - -0.138393834233284 + -0.16082863509655, + -0.02768358588218689, + -0.1382487565279007 ], "tsne3d": [ - 1.6963919401168823, - -21.190969467163086, - -3.0033209323883057 + -4.352550983428955, + 12.188128471374512, + -13.025568008422852 ], "umap3d": [ - 4.5471978187561035, - 2.427402973175049, - 2.8815672397613525 + 5.257582187652588, + 2.7608988285064697, + 3.2744686603546143 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -895484,47 +895634,47 @@ ], "projections": { "pca": [ - -0.15969690680503845, - -0.009439128451049328 + -0.1595519483089447, + -0.009190491400659084 ], "tsne": [ - 1.8562757968902588, - -36.152748107910156 + -7.039588451385498, + 33.795284271240234 ], "umap": [ - 5.110914707183838, - 9.610754013061523 + 5.213040828704834, + 8.935580253601074 ], "pca3d": [ - -0.1596965789794922, - -0.009450867772102356, - -0.06877589225769043 + -0.15955166518688202, + -0.009203310124576092, + -0.06850415468215942 ], "tsne3d": [ - 1.4219331741333008, - -22.718996047973633, - -1.2738827466964722 + -3.84005069732666, + 10.86143970489502, + -11.127405166625977 ], "umap3d": [ - 4.452748775482178, - 2.4803645610809326, - 2.9242403507232666 + 5.094776630401611, + 2.8260302543640137, + 3.4902396202087402 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 26, + "label": "Medical AI" } ], "_embedding": [ @@ -895930,46 +896080,46 @@ ], "projections": { "pca": [ - -0.06499065458774567, - 0.2268374264240265 + -0.06414679437875748, + 0.2266654223203659 ], "tsne": [ - 1.4540925025939941, - -10.048112869262695 + 1.0598889589309692, + 8.736209869384766 ], "umap": [ - 5.2717790603637695, - 7.380046844482422 + 5.542131423950195, + 6.712439060211182 ], "pca3d": [ - -0.06499144434928894, - 0.2268708199262619, - -0.05725473910570145 + -0.06414757668972015, + 0.22669897973537445, + -0.05742856487631798 ], "tsne3d": [ - -3.264315128326416, - -5.213296413421631, - -19.56942367553711 + -1.289676308631897, + 26.50934410095215, + -2.8651585578918457 ], "umap3d": [ - 4.7738728523254395, - 3.38834285736084, - 4.946536064147949 + 5.818177700042725, + 4.666935443878174, + 4.949005603790283 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, + "id": 13, "label": "Genomics Papers" } ], @@ -896387,47 +896537,47 @@ "doi": "10.1093/jamia/ocab270", "projections": { "pca": [ - -0.061269138008356094, - 0.1929246485233307 + -0.06084032729268074, + 0.19330883026123047 ], "tsne": [ - 6.092570781707764, - -33.30882263183594 + -2.4087328910827637, + 33.376468658447266 ], "umap": [ - 5.553248405456543, - 9.503787994384766 + 5.705848693847656, + 8.895687103271484 ], "pca3d": [ - -0.06126925349235535, - 0.19291695952415466, - -0.13659651577472687 + -0.06084053963422775, + 0.1933005154132843, + -0.13679952919483185 ], "tsne3d": [ - 5.853060245513916, - -20.709375381469727, - -3.3771774768829346 + -1.5898027420043945, + 14.838547706604004, + -10.33212947845459 ], "umap3d": [ - 4.712120532989502, - 2.4553542137145996, - 2.8533191680908203 + 5.3803277015686035, + 2.7838573455810547, + 3.206087827682495 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -896843,47 +896993,47 @@ "journal": "Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing", "projections": { "pca": [ - 0.15227514505386353, - 0.1439342498779297 + 0.15260833501815796, + 0.1442444771528244 ], "tsne": [ - 29.04243278503418, - 0.016140975058078766 + 31.057985305786133, + -0.4648308753967285 ], "umap": [ - 8.070638656616211, - 7.846683025360107 + 8.298370361328125, + 7.366251468658447 ], "pca3d": [ - 0.1522749662399292, - 0.14390906691551208, - -0.13290223479270935 + 0.15260806679725647, + 0.14421865344047546, + -0.13306163251399994 ], "tsne3d": [ - 12.097935676574707, - -10.628422737121582, - -2.2285733222961426 + 9.964726448059082, + 13.032302856445312, + -7.757167339324951 ], "umap3d": [ - 7.053861618041992, - 3.93533992767334, - 2.3739919662475586 + 7.219658374786377, + 4.066683292388916, + 1.4098933935165405 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -897309,31 +897459,31 @@ "openalex_url": "https://openalex.org/W2757095267", "projections": { "pca": [ - -0.06411480158567429, - 0.06302446126937866 + -0.06390766054391861, + 0.06370867788791656 ], "tsne": [ - 10.421102523803711, - -2.2374980449676514 + 19.67966651916504, + -2.2045845985412598 ], "umap": [ - 6.503889083862305, - 7.119170188903809 + 6.693132400512695, + 6.7289719581604 ], "pca3d": [ - -0.06411480158567429, - 0.06301501393318176, - -0.06910263001918793 + -0.063907690346241, + 0.06369895488023758, + -0.06898701936006546 ], "tsne3d": [ - 14.377947807312012, - -1.0383384227752686, - -19.817432403564453 + 7.600589275360107, + 6.207730293273926, + 11.668213844299316 ], "umap3d": [ - 5.59193754196167, - 4.05897331237793, - 3.2607197761535645 + 6.056228160858154, + 4.414697647094727, + 2.793095350265503 ] }, "cluster_id": 4, @@ -897344,12 +897494,12 @@ "label": "RNA Sequence Models" }, { - "id": 11, - "label": "Genomic Models" + "id": 3, + "label": "Genetic Disease" }, { - "id": 11, - "label": "Genomic Language" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -897776,46 +897926,46 @@ ], "projections": { "pca": [ - -0.08688098192214966, - 0.08121923357248306 + -0.08654665946960449, + 0.08252961188554764 ], "tsne": [ - -13.896492004394531, - -25.084287643432617 + -16.63361358642578, + 17.87015151977539 ], "umap": [ - 3.4554431438446045, - 8.53134822845459 + 3.7503998279571533, + 7.86060094833374 ], "pca3d": [ - -0.08688072860240936, - 0.08118504285812378, - 0.22210264205932617 + -0.08654642850160599, + 0.08249508589506149, + 0.22212670743465424 ], "tsne3d": [ - -9.268784523010254, - -9.283568382263184, - -4.712959289550781 + -9.72933578491211, + 14.695197105407715, + 4.597020626068115 ], "umap3d": [ - 2.6907215118408203, - 3.812507152557373, - 3.422755479812622 + 3.469135046005249, + 4.210335731506348, + 3.9085159301757812 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, + "id": 25, "label": "AI Agents" } ], @@ -898226,46 +898376,46 @@ "abstract": "Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.", "projections": { "pca": [ - -0.1344737410545349, - 0.29097333550453186 + -0.13375921547412872, + 0.29252949357032776 ], "tsne": [ - -15.586701393127441, - -34.48492431640625 + -24.452112197875977, + -32.83013916015625 ], "umap": [ - 3.080320119857788, - 9.203441619873047 + 3.1774322986602783, + 8.425430297851562 ], "pca3d": [ - -0.13447381556034088, - 0.2909396290779114, - 0.14797791838645935 + -0.13375921547412872, + 0.29249557852745056, + 0.14762423932552338 ], "tsne3d": [ - -9.131095886230469, - -3.5730209350585938, - 2.51754093170166 + -5.588597297668457, + 9.208316802978516, + 12.404122352600098 ], "umap3d": [ - 2.975863456726074, - 3.105415105819702, - 5.1489715576171875 + 4.530333518981934, + 4.410096645355225, + 5.6822614669799805 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 32, + "id": 25, "label": "AI Agents" } ], @@ -898677,46 +898827,46 @@ "abstract": "Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.", "projections": { "pca": [ - -0.17853204905986786, - 0.2987673282623291 + -0.17782551050186157, + 0.3000643849372864 ], "tsne": [ - -15.604520797729492, - -34.41613006591797 + -24.391891479492188, + -32.69219970703125 ], "umap": [ - 3.078745126724243, - 9.207368850708008 + 3.1873979568481445, + 8.425023078918457 ], "pca3d": [ - -0.17853224277496338, - 0.29873907566070557, - 0.11202557384967804 + -0.17782562971115112, + 0.30003592371940613, + 0.1115821897983551 ], "tsne3d": [ - -8.168824195861816, - -2.760611057281494, - 2.192091464996338 + -5.003288269042969, + 9.985381126403809, + 11.619877815246582 ], "umap3d": [ - 3.0128350257873535, - 3.072409152984619, - 5.177366733551025 + 4.537063121795654, + 4.413444519042969, + 5.678882598876953 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 32, + "id": 25, "label": "AI Agents" } ], @@ -899126,46 +899276,46 @@ "abstract": "Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.", "projections": { "pca": [ - -0.1307096630334854, - 0.3179154694080353 + -0.12992438673973083, + 0.31948721408843994 ], "tsne": [ - -15.579887390136719, - -34.600467681884766 + -24.437971115112305, + -32.91263198852539 ], "umap": [ - 3.0681145191192627, - 9.212170600891113 + 3.1629037857055664, + 8.442681312561035 ], "pca3d": [ - -0.13070988655090332, - 0.31787875294685364, - 0.15441086888313293 + -0.12992455065250397, + 0.3194504380226135, + 0.1540156900882721 ], "tsne3d": [ - -9.949724197387695, - -2.927156448364258, - 2.0423264503479004 + -5.861554145812988, + 9.96010684967041, + 13.115628242492676 ], "umap3d": [ - 2.9356212615966797, - 3.0639383792877197, - 5.189330101013184 + 4.538512229919434, + 4.407038688659668, + 5.707232475280762 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 32, + "id": 25, "label": "AI Agents" } ], @@ -899576,47 +899726,47 @@ "abstract": "AI agents running research on single-GPU nanochat training automatically - karpathy/autoresearch", "projections": { "pca": [ - -0.14565059542655945, - 0.13838760554790497 + -0.14524482190608978, + 0.13944104313850403 ], "tsne": [ - -11.983642578125, - -22.277666091918945 + -8.06273365020752, + 15.506192207336426 ], "umap": [ - 3.958892822265625, - 8.569412231445312 + 4.076508522033691, + 7.887612342834473 ], "pca3d": [ - -0.1456504464149475, - 0.13836640119552612, - 0.08103824406862259 + -0.14524471759796143, + 0.13941919803619385, + 0.08094482123851776 ], "tsne3d": [ - -6.5374979972839355, - -5.138957500457764, - -6.4134721755981445 + -6.143150806427002, + 12.444942474365234, + 6.133090019226074 ], "umap3d": [ - 3.072453260421753, - 3.830888271331787, - 3.355003833770752 + 3.8874292373657227, + 4.231881141662598, + 3.7860026359558105 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -900031,47 +900181,47 @@ ], "projections": { "pca": [ - -0.11012157052755356, - 0.029848715290427208 + -0.11000538617372513, + 0.030863599851727486 ], "tsne": [ - -6.6160478591918945, - -23.118385314941406 + -10.79309368133545, + 19.285200119018555 ], "umap": [ - 4.007449150085449, - 8.669784545898438 + 4.155555725097656, + 7.983354091644287 ], "pca3d": [ - -0.11012152582406998, - 0.02984774112701416, - 0.14944718778133392 + -0.11000523716211319, + 0.030862700194120407, + 0.14950184524059296 ], "tsne3d": [ - -5.667026519775391, - -9.181868553161621, - -5.255548477172852 + -5.665461540222168, + 14.335186004638672, + 2.045015335083008 ], "umap3d": [ - 3.17417573928833, - 3.74361252784729, - 3.1930434703826904 + 3.8698008060455322, + 4.123880386352539, + 3.586451292037964 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -900501,46 +900651,46 @@ ], "projections": { "pca": [ - -0.19139185547828674, - -0.0004220319679006934 + -0.1914423555135727, + 0.0011989368358626962 ], "tsne": [ - -13.471820831298828, - -26.392133712768555 + -16.97831916809082, + 19.324525833129883 ], "umap": [ - 3.432443141937256, - 8.58991813659668 + 3.635097026824951, + 8.004603385925293 ], "pca3d": [ - -0.19139152765274048, - -0.0004489475395530462, - 0.12424586713314056 + -0.19144207239151, + 0.0011720022885128856, + 0.12440895289182663 ], "tsne3d": [ - -9.299761772155762, - -11.890543937683105, - -5.5317702293396 + -9.48262882232666, + 16.289953231811523, + 3.1216087341308594 ], "umap3d": [ - 2.68856143951416, - 3.7577147483825684, - 3.4636714458465576 + 3.400881052017212, + 4.15554141998291, + 3.992903709411621 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, + "id": 25, "label": "AI Agents" } ], @@ -901013,47 +901163,47 @@ "openalex_url": "https://openalex.org/W4416832596", "projections": { "pca": [ - -0.029320191591978073, - 0.06865344196557999 + -0.029139813035726547, + 0.06934268027544022 ], "tsne": [ - 3.3626208305358887, - -23.46906852722168 + -1.905165672302246, + 22.743202209472656 ], "umap": [ - 4.709044456481934, - 8.723786354064941 + 5.015065670013428, + 8.061349868774414 ], "pca3d": [ - -0.029320422559976578, - 0.0686694011092186, - 0.0217488594353199 + -0.029140044003725052, + 0.06935823708772659, + 0.0217403843998909 ], "tsne3d": [ - 1.088993787765503, - -10.045295715332031, - 1.4893358945846558 + -0.9249504208564758, + 14.602935791015625, + -3.4638748168945312 ], "umap3d": [ - 3.8018524646759033, - 3.4283037185668945, - 3.0620033740997314 + 4.462154865264893, + 3.8539886474609375, + 3.323756217956543 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, - "label": "Medical AI" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -901467,46 +901617,46 @@ ], "projections": { "pca": [ - -0.20410948991775513, - 0.06767425686120987 + -0.20393988490104675, + 0.06902919709682465 ], "tsne": [ - -12.913339614868164, - -25.43147087097168 + -16.162986755371094, + 18.738340377807617 ], "umap": [ - 3.4738645553588867, - 8.571617126464844 + 3.7085793018341064, + 7.956665992736816 ], "pca3d": [ - -0.20410914719104767, - 0.06766314804553986, - 0.11740776151418686 + -0.203939750790596, + 0.0690181702375412, + 0.11745253950357437 ], "tsne3d": [ - -8.175372123718262, - -10.291924476623535, - -6.2959723472595215 + -7.9900946617126465, + 15.508316993713379, + 4.163783550262451 ], "umap3d": [ - 2.6574275493621826, - 3.7632954120635986, - 3.430974245071411 + 3.434075355529785, + 4.170292854309082, + 3.934558868408203 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, + "id": 25, "label": "AI Agents" } ], @@ -901917,46 +902067,46 @@ "journal": "Nature Machine Intelligence", "projections": { "pca": [ - -0.23628206551074982, - 0.08095706254243851 + -0.235995814204216, + 0.08218741416931152 ], "tsne": [ - -26.88332748413086, - -17.817150115966797 + -31.323165893554688, + 15.226380348205566 ], "umap": [ - 2.234304904937744, - 8.390090942382812 + 2.4758684635162354, + 7.7759108543396 ], "pca3d": [ - -0.23628219962120056, - 0.08096103370189667, - 0.03962024673819542 + -0.23599591851234436, + 0.08219125121831894, + 0.03972543776035309 ], "tsne3d": [ - -15.527618408203125, - -16.235614776611328, - 3.176948070526123 + -15.894807815551758, + 10.50255298614502, + -2.9575657844543457 ], "umap3d": [ - 1.9011156558990479, - 3.8699605464935303, - 4.408949375152588 + 2.9044857025146484, + 4.277213096618652, + 5.119869709014893 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -902404,47 +902554,47 @@ ], "projections": { "pca": [ - -0.07706387341022491, - 0.08546002209186554 + -0.07691387087106705, + 0.08677645772695541 ], "tsne": [ - -7.9466142654418945, - -21.953706741333008 + -10.386225700378418, + 17.224689483642578 ], "umap": [ - 4.031435489654541, - 8.571608543395996 + 4.194973945617676, + 7.805118560791016 ], "pca3d": [ - -0.07706382870674133, - 0.08545857667922974, - 0.13768216967582703 + -0.07691392302513123, + 0.08677549660205841, + 0.137616828083992 ], "tsne3d": [ - -6.945542335510254, - -8.180145263671875, - -1.5428704023361206 + -6.117317199707031, + 11.18958568572998, + 1.045032024383545 ], "umap3d": [ - 3.2268269062042236, - 3.7853035926818848, - 3.344935655593872 + 3.8807477951049805, + 4.221330642700195, + 3.7600486278533936 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -902885,46 +903035,46 @@ ], "projections": { "pca": [ - -0.2428176999092102, - 0.08931685239076614 + -0.24248337745666504, + 0.0908246636390686 ], "tsne": [ - -12.900480270385742, - -26.728845596313477 + -16.54380989074707, + 19.793588638305664 ], "umap": [ - 3.485978841781616, - 8.618955612182617 + 3.7512731552124023, + 8.007536888122559 ], "pca3d": [ - -0.24281750619411469, - 0.08929308503866196, - 0.09718181937932968 + -0.2424832582473755, + 0.09080075472593307, + 0.09730357676744461 ], "tsne3d": [ - -8.225540161132812, - -12.472334861755371, - -5.715048313140869 + -8.650557518005371, + 16.78193473815918, + 2.547499418258667 ], "umap3d": [ - 2.737125873565674, - 3.7023375034332275, - 3.4551944732666016 + 3.5099334716796875, + 4.125467777252197, + 3.9133121967315674 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, + "id": 25, "label": "AI Agents" } ], @@ -903333,47 +903483,47 @@ "abstract": "Event will assess how reviews by models compare with those written by humans.", "projections": { "pca": [ - -0.22990913689136505, - 0.3057941198348999 + -0.22899483144283295, + 0.3068641722202301 ], "tsne": [ - -10.777400016784668, - -19.265583038330078 + -12.546713829040527, + 13.200935363769531 ], "umap": [ - 3.961308479309082, - 8.169807434082031 + 4.2014479637146, + 7.52129602432251 ], "pca3d": [ - -0.2299094796180725, - 0.30581581592559814, - 0.09486433118581772 + -0.22899547219276428, + 0.3068857192993164, + 0.09457605332136154 ], "tsne3d": [ - -4.587623596191406, - -10.176826477050781, - 2.515373945236206 + -8.459068298339844, + 9.442235946655273, + -2.4990499019622803 ], "umap3d": [ - 3.3046891689300537, - 3.4810500144958496, - 3.695467948913574 + 4.139436721801758, + 4.090503692626953, + 4.114020824432373 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -903790,46 +903940,46 @@ ], "projections": { "pca": [ - -0.15402765572071075, - 0.04807121306657791 + -0.15384158492088318, + 0.04926418513059616 ], "tsne": [ - -28.12928581237793, - -19.68536949157715 + -32.43849182128906, + 17.244787216186523 ], "umap": [ - 2.2708723545074463, - 8.415413856506348 + 2.5635268688201904, + 7.563394069671631 ], "pca3d": [ - -0.1540275365114212, - 0.0480666346848011, - 0.1034342348575592 + -0.1538415104150772, + 0.04925943538546562, + 0.10353628545999527 ], "tsne3d": [ - -15.594507217407227, - -18.882448196411133, - 8.83740234375 + -17.298595428466797, + 4.703329086303711, + -7.319849014282227 ], "umap3d": [ - 1.9804341793060303, - 3.908182144165039, - 4.24247407913208 + 2.9542033672332764, + 4.456600189208984, + 4.9529128074646 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -904244,47 +904394,47 @@ ], "projections": { "pca": [ - -0.034029096364974976, - -0.002143881982192397 + -0.03408735617995262, + -0.0011447046417742968 ], "tsne": [ - -5.95953893661499, - -23.14249610900879 + -9.401422500610352, + 19.061643600463867 ], "umap": [ - 4.216744899749756, - 8.698633193969727 + 4.5243353843688965, + 8.00700569152832 ], "pca3d": [ - -0.034029047936201096, - -0.0021398046519607306, - 0.11074303090572357 + -0.03408730775117874, + -0.0011404136894270778, + 0.11082988977432251 ], "tsne3d": [ - -5.106057643890381, - -7.566796779632568, - -4.367819309234619 + -4.808346748352051, + 12.536145210266113, + 2.144994020462036 ], "umap3d": [ - 3.4087727069854736, - 3.746004581451416, - 3.1113784313201904 + 4.041645050048828, + 4.092649459838867, + 3.46830415725708 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, - "label": "Medical AI" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -904702,46 +904852,46 @@ ], "projections": { "pca": [ - -0.18824172019958496, - -0.015145163051784039 + -0.18825002014636993, + -0.013814459554851055 ], "tsne": [ - -14.628828048706055, - -26.82796287536621 + -18.169389724731445, + 19.322751998901367 ], "umap": [ - 3.406661033630371, - 8.54646110534668 + 3.619297981262207, + 7.9396443367004395 ], "pca3d": [ - -0.1882413625717163, - -0.015159299597144127, - 0.14443117380142212 + -0.18824970722198486, + -0.013828720897436142, + 0.14460517466068268 ], "tsne3d": [ - -10.18319034576416, - -11.63902759552002, - -6.838620662689209 + -10.741596221923828, + 17.090166091918945, + 3.371103048324585 ], "umap3d": [ - 2.588101863861084, - 3.851055145263672, - 3.5194432735443115 + 3.3702635765075684, + 4.150422096252441, + 3.9984676837921143 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 32, + "id": 25, "label": "AI Agents" } ], @@ -905162,47 +905312,47 @@ ], "projections": { "pca": [ - -0.21784567832946777, - -0.0755881741642952 + -0.2178911417722702, + -0.07414960116147995 ], "tsne": [ - -30.708724975585938, - -5.257800579071045 + -37.191864013671875, + 4.327887535095215 ], "umap": [ - 1.3349268436431885, - 7.820056438446045 + 1.7973629236221313, + 7.0509490966796875 ], "pca3d": [ - -0.21784505248069763, - -0.07561472803354263, - 0.03543007746338844 + -0.21789060533046722, + -0.07417682558298111, + 0.035819102078676224 ], "tsne3d": [ - -18.596973419189453, - -6.781375885009766, - -5.149753093719482 + -19.991849899291992, + 12.154091835021973, + 3.5974557399749756 ], "umap3d": [ - 1.2886980772018433, - 4.556701183319092, - 4.3637375831604 + 2.303528308868408, + 4.8903632164001465, + 5.017062664031982 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, - "label": "Transformer Attention" + "id": 34, + "label": "Language Models" } ], "_embedding": [ @@ -905625,47 +905775,47 @@ ], "projections": { "pca": [ - -0.04422392323613167, - 0.09485083073377609 + -0.04396246746182442, + 0.09590194374322891 ], "tsne": [ - -7.555080413818359, - -21.131887435913086 + -9.577008247375488, + 16.664817810058594 ], "umap": [ - 4.0978684425354, - 8.502999305725098 + 4.371360778808594, + 7.8110737800598145 ], "pca3d": [ - -0.04422391206026077, - 0.09483662247657776, - 0.1222783774137497 + -0.043962448835372925, + 0.09588786959648132, + 0.12224855273962021 ], "tsne3d": [ - -6.1394734382629395, - -6.361328125, - -1.1695724725723267 + -5.830065727233887, + 9.60608196258545, + 1.9642215967178345 ], "umap3d": [ - 3.2733659744262695, - 3.783665180206299, - 3.4076640605926514 + 3.997239351272583, + 4.163695812225342, + 3.687326669692993 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -906072,47 +906222,47 @@ "title": "Accelerating scientific breakthroughs with an AI co-scientist", "projections": { "pca": [ - -0.14923878014087677, - 0.10925492644309998 + -0.1488279551267624, + 0.11053303629159927 ], "tsne": [ - -8.165956497192383, - -24.38413429260254 + -10.884860038757324, + 18.106239318847656 ], "umap": [ - 3.979285478591919, - 8.480877876281738 + 4.014610290527344, + 7.865381240844727 ], "pca3d": [ - -0.149239182472229, - 0.1092703565955162, - 0.1381090134382248 + -0.14882825314998627, + 0.11054913699626923, + 0.13814353942871094 ], "tsne3d": [ - -5.4669108390808105, - -9.979461669921875, - -1.8906351327896118 + -7.270487308502197, + 12.927799224853516, + -0.257956326007843 ], "umap3d": [ - 3.1309328079223633, - 3.747978687286377, - 3.3096718788146973 + 3.814866781234741, + 4.1978020668029785, + 3.6941537857055664 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -906529,47 +906679,47 @@ "doi": "10.1101/2025.09.01.673319", "projections": { "pca": [ - -0.018787624314427376, - -0.019236363470554352 + -0.01871839351952076, + -0.018662596121430397 ], "tsne": [ - -3.4974424839019775, - -23.208364486694336 + -6.455434799194336, + 19.447362899780273 ], "umap": [ - 4.6023125648498535, - 8.765647888183594 + 4.872547149658203, + 8.051502227783203 ], "pca3d": [ - -0.01878756284713745, - -0.019229358062148094, - 0.13994155824184418 + -0.01871834509074688, + -0.01865571178495884, + 0.1400979906320572 ], "tsne3d": [ - -2.8644859790802, - -8.77208423614502, - -4.966465473175049 + -3.0891480445861816, + 14.185193061828613, + 0.8304106593132019 ], "umap3d": [ - 3.673492193222046, - 3.595604658126831, - 3.0195538997650146 + 4.269878387451172, + 3.9445838928222656, + 3.2964906692504883 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, - "label": "Medical AI" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -907017,46 +907167,46 @@ ], "projections": { "pca": [ - -0.10927917808294296, - 0.2350296974182129 + -0.10860523581504822, + 0.2360071986913681 ], "tsne": [ - 1.1995009183883667, - -38.734619140625 + -8.950641632080078, + 35.9846305847168 ], "umap": [ - 4.910244464874268, - 9.552567481994629 + 5.002761363983154, + 8.866512298583984 ], "pca3d": [ - -0.10927928984165192, - 0.23500080406665802, - -0.008281386457383633 + -0.10860531777143478, + 0.235976904630661, + -0.008386393077671528 ], "tsne3d": [ - -3.8608803749084473, - -22.493587493896484, - -2.9736311435699463 + -9.53466510772705, + 14.402356147766113, + -11.50793170928955 ], "umap3d": [ - 4.088287830352783, - 2.4783806800842285, - 3.0853524208068848 + 4.812372207641602, + 2.985386610031128, + 3.6229257583618164 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, + "id": 26, "label": "Medical AI" } ], @@ -907476,47 +907626,47 @@ ], "projections": { "pca": [ - -0.15419673919677734, - 0.18018603324890137 + -0.15384075045585632, + 0.18141449987888336 ], "tsne": [ - -8.29137897491455, - -25.900285720825195 + -12.353371620178223, + 19.112939834594727 ], "umap": [ - 3.823573112487793, - 8.35237979888916 + 3.9477925300598145, + 7.750598907470703 ], "pca3d": [ - -0.154196634888649, - 0.1801646500825882, - 0.13669206202030182 + -0.15384070575237274, + 0.18139322102069855, + 0.13638512790203094 ], "tsne3d": [ - -9.380194664001465, - -6.549976348876953, - -2.263603448867798 + -11.761592864990234, + 13.508260726928711, + -0.4611411988735199 ], "umap3d": [ - 3.076737403869629, - 3.6723222732543945, - 3.363725185394287 + 3.8158397674560547, + 4.172212600708008, + 3.7902567386627197 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -907946,47 +908096,47 @@ "doi": "10.1101/2025.09.03.674085", "projections": { "pca": [ - 0.14219290018081665, - 0.03768766671419144 + 0.14235669374465942, + 0.038227930665016174 ], "tsne": [ - -2.7522428035736084, - -22.200366973876953 + -5.360282897949219, + 18.439167022705078 ], "umap": [ - 4.5939106941223145, - 8.66871166229248 + 4.960154056549072, + 7.867196083068848 ], "pca3d": [ - 0.14219290018081665, - 0.03768222779035568, - 0.17713673412799835 + 0.14235661923885345, + 0.03822210803627968, + 0.1770734041929245 ], "tsne3d": [ - -2.4369935989379883, - -5.6032304763793945, - -7.305927753448486 + -2.3557755947113037, + 13.983660697937012, + 3.8840935230255127 ], "umap3d": [ - 3.737586736679077, - 3.732506036758423, - 2.9260482788085938 + 4.322910785675049, + 4.117207050323486, + 3.135174036026001 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, - "label": "Medical AI" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -908438,47 +908588,47 @@ ], "projections": { "pca": [ - -0.07294945418834686, - 0.1417897790670395 + -0.07258555293083191, + 0.14264795184135437 ], "tsne": [ - -8.557168960571289, - -21.208330154418945 + -10.275164604187012, + 16.27986717224121 ], "umap": [ - 4.129003524780273, - 8.531434059143066 + 4.34608793258667, + 7.926307201385498 ], "pca3d": [ - -0.07294958829879761, - 0.14178317785263062, - 0.05475841835141182 + -0.07258567959070206, + 0.14264118671417236, + 0.05464715138077736 ], "tsne3d": [ - -7.663588047027588, - -7.1734771728515625, - 0.1133519634604454 + -6.163876533508301, + 9.79156494140625, + -0.2579483985900879 ], "umap3d": [ - 3.3506815433502197, - 3.7064356803894043, - 3.2722041606903076 + 3.9453377723693848, + 4.037566184997559, + 3.7403037548065186 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -908898,47 +909048,47 @@ "doi": "10.1101/2025.08.18.670981", "projections": { "pca": [ - -0.042499709874391556, - -0.08541440218687057 + -0.04286126047372818, + -0.08472899347543716 ], "tsne": [ - -17.16944694519043, - -28.2800350189209 + -20.33588218688965, + 20.745065689086914 ], "umap": [ - 4.036947727203369, - 8.761106491088867 + 4.313267230987549, + 7.944828987121582 ], "pca3d": [ - -0.04249951243400574, - -0.08540582656860352, - 0.07376988232135773 + -0.04286108911037445, + -0.08472033590078354, + 0.07385353744029999 ], "tsne3d": [ - -9.594290733337402, - -9.108181953430176, - -9.376236915588379 + -13.016571998596191, + 17.957996368408203, + 0.3409382700920105 ], "umap3d": [ - 3.1056416034698486, - 3.953726291656494, - 3.1006221771240234 + 3.831845760345459, + 4.201991081237793, + 3.322692632675171 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -909363,46 +909513,46 @@ ], "projections": { "pca": [ - -0.1785762906074524, - 0.0823204293847084 + -0.17835469543933868, + 0.0841100737452507 ], "tsne": [ - -13.570218086242676, - -25.135332107543945 + -16.348478317260742, + 18.08887481689453 ], "umap": [ - 3.5736889839172363, - 8.570610046386719 + 3.744155168533325, + 7.913285255432129 ], "pca3d": [ - -0.17857597768306732, - 0.08229675889015198, - 0.2036687135696411 + -0.17835454642772675, + 0.0840858444571495, + 0.20371797680854797 ], "tsne3d": [ - -9.321796417236328, - -10.37021255493164, - -4.871734142303467 + -9.434356689453125, + 14.649025917053223, + 3.580183982849121 ], "umap3d": [ - 2.708472728729248, - 3.7484753131866455, - 3.449726104736328 + 3.531900405883789, + 4.158214092254639, + 3.9265925884246826 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, + "id": 25, "label": "AI Agents" } ], @@ -909817,47 +909967,47 @@ ], "projections": { "pca": [ - -0.02619853802025318, - 0.05035853758454323 + -0.02599703148007393, + 0.051164545118808746 ], "tsne": [ - -4.023014068603516, - -23.515085220336914 + -7.023203372955322, + 19.41337776184082 ], "umap": [ - 4.346563339233398, - 8.789295196533203 + 4.637408256530762, + 8.087477684020996 ], "pca3d": [ - -0.02619834803044796, - 0.05033636838197708, - 0.12785400450229645 + -0.025996875017881393, + 0.051141805946826935, + 0.12788966298103333 ], "tsne3d": [ - -3.6788787841796875, - -9.368694305419922, - -4.101667404174805 + -4.307915210723877, + 14.305885314941406, + 0.33656880259513855 ], "umap3d": [ - 3.5032010078430176, - 3.623114585876465, - 3.01567006111145 + 4.131050109863281, + 3.9361791610717773, + 3.3853259086608887 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, - "label": "Medical AI" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -910282,46 +910432,46 @@ ], "projections": { "pca": [ - 0.056429311633110046, - -0.05060103163123131 + 0.056292131543159485, + -0.05023668333888054 ], "tsne": [ - 12.570252418518066, - -22.368850708007812 + 6.484449863433838, + 20.575401306152344 ], "umap": [ - 6.799079418182373, - 9.188997268676758 + 6.8809404373168945, + 8.756843566894531 ], "pca3d": [ - 0.05642933398485184, - -0.05059758946299553, - 0.03751038759946823 + 0.05629213899374008, + -0.05023335665464401, + 0.037604719400405884 ], "tsne3d": [ - 4.566831111907959, - -8.084049224853516, - 10.7842378616333 + 4.372363090515137, + 7.066497325897217, + -10.421375274658203 ], "umap3d": [ - 4.612208843231201, - 3.8581430912017822, - 2.022808074951172 + 4.7535834312438965, + 3.861595869064331, + 2.132718563079834 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -910732,47 +910882,47 @@ "journal": "Nature", "projections": { "pca": [ - -0.1252375841140747, - 0.2295800894498825 + -0.12463454157114029, + 0.2301841378211975 ], "tsne": [ - -4.064731121063232, - -18.355703353881836 + -5.929426193237305, + 13.962443351745605 ], "umap": [ - 4.201019763946533, - 8.483938217163086 + 4.405807018280029, + 7.815454006195068 ], "pca3d": [ - -0.12523792684078217, - 0.2295866757631302, - 0.15122933685779572 + -0.12463493645191193, + 0.23019050061702728, + 0.15085585415363312 ], "tsne3d": [ - -4.46958065032959, - -3.1538031101226807, - -1.760086178779602 + -4.446472644805908, + 9.22738265991211, + 7.100978851318359 ], "umap3d": [ - 3.3943214416503906, - 3.670440196990967, - 3.3007447719573975 + 3.9842307567596436, + 4.12646484375, + 3.585111379623413 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -911209,46 +911359,46 @@ ], "projections": { "pca": [ - -0.11038196831941605, - 0.037080295383930206 + -0.11017579585313797, + 0.0372389554977417 ], "tsne": [ - -0.7236600518226624, - -34.80765914916992 + -9.122564315795898, + 31.81504249572754 ], "umap": [ - 4.597452640533447, - 9.611173629760742 + 4.7989702224731445, + 8.893980026245117 ], "pca3d": [ - -0.11038186401128769, - 0.03707686439156532, - -0.014585728757083416 + -0.11017560958862305, + 0.03723505139350891, + -0.014444520696997643 ], "tsne3d": [ - -2.4691505432128906, - -20.3999080657959, - -1.0158525705337524 + -7.970282077789307, + 12.741143226623535, + -9.649179458618164 ], "umap3d": [ - 3.865321397781372, - 2.5451290607452393, - 3.2232911586761475 + 4.640344142913818, + 3.0169951915740967, + 3.803265333175659 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, + "id": 26, "label": "Medical AI" } ], @@ -911681,47 +911831,47 @@ "doi": "10.1101/2025.07.05.663295", "projections": { "pca": [ - 0.0780491977930069, - 0.017429906874895096 + 0.07827889919281006, + 0.018111595883965492 ], "tsne": [ - -2.338913917541504, - -21.3857364654541 + -4.995919227600098, + 17.6669979095459 ], "umap": [ - 4.637152194976807, - 8.730500221252441 + 4.915394306182861, + 7.989889144897461 ], "pca3d": [ - 0.07804924994707108, - 0.01741861365735531, - 0.12098225951194763 + 0.07827895879745483, + 0.018100164830684662, + 0.12115640193223953 ], "tsne3d": [ - -2.25897216796875, - -8.439714431762695, - -7.137027740478516 + -1.7766252756118774, + 15.510066986083984, + 1.9265689849853516 ], "umap3d": [ - 3.7044496536254883, - 3.7111406326293945, - 2.9136126041412354 + 4.304375171661377, + 4.022917747497559, + 3.189953327178955 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, - "label": "Medical AI" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -912127,47 +912277,47 @@ ], "projections": { "pca": [ - -0.10926030576229095, - 0.28194868564605713 + -0.10827858000993729, + 0.2823321223258972 ], "tsne": [ - -4.561427593231201, - -26.554927825927734 + -8.373157501220703, + 21.83489227294922 ], "umap": [ - 4.366415977478027, - 8.286274909973145 + 4.638652801513672, + 7.642119884490967 ], "pca3d": [ - -0.10926084965467453, - 0.28196796774864197, - 0.14856259524822235 + -0.10827919840812683, + 0.28235146403312683, + 0.14825811982154846 ], "tsne3d": [ - -0.8460351824760437, - -11.218612670898438, - -2.589722156524658 + -2.0017573833465576, + 20.00398063659668, + -3.0488109588623047 ], "umap3d": [ - 3.506561279296875, - 3.3956286907196045, - 3.4547321796417236 + 4.267345905303955, + 3.9985923767089844, + 3.9171342849731445 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, - "label": "AI Agents" + "id": 4, + "label": "Genomics Tools" }, { - "id": 32, - "label": "AI Agents" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -912577,47 +912727,47 @@ "abstract": "We\u2019re on a journey to advance and democratize artificial intelligence through open source and open science.", "projections": { "pca": [ - -0.15045584738254547, - 0.12406449764966965 + -0.15008330345153809, + 0.12504225969314575 ], "tsne": [ - -20.66031837463379, - -4.771615028381348 + -20.77947235107422, + -4.447629451751709 ], "umap": [ - 3.3448023796081543, - 7.863769054412842 + 3.6258363723754883, + 7.368587493896484 ], "pca3d": [ - -0.15045581758022308, - 0.12405484169721603, - -0.016937483102083206 + -0.15008333325386047, + 0.1250322312116623, + -0.016918854787945747 ], "tsne3d": [ - -8.67272663116455, - 2.4240520000457764, - -11.531820297241211 + -9.045494079589844, + 1.8110629320144653, + 7.8207011222839355 ], "umap3d": [ - 3.1877317428588867, - 4.402605056762695, - 3.8325917720794678 + 3.9110107421875, + 4.972393989562988, + 4.0361328125 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { "id": 0, - "label": "LLM Language Learning" + "label": "Math AI" } ], "_embedding": [ @@ -913037,47 +913187,47 @@ "doi": "10.1101/2025.06.24.661378", "projections": { "pca": [ - 0.09155664592981339, - -0.010653438977897167 + 0.09157170355319977, + -0.01000998169183731 ], "tsne": [ - -5.310100078582764, - -22.11444664001465 + -8.442977905273438, + 18.62106704711914 ], "umap": [ - 4.309053897857666, - 8.725595474243164 + 4.605588436126709, + 7.954394817352295 ], "pca3d": [ - 0.09155647456645966, - -0.010637946426868439, - 0.15961392223834991 + 0.09157159924507141, + -0.009994331747293472, + 0.15968099236488342 ], "tsne3d": [ - -3.433753490447998, - -6.800961494445801, - -3.321460008621216 + -3.318269968032837, + 10.877552032470703, + 0.13760995864868164 ], "umap3d": [ - 3.4674134254455566, - 3.7839162349700928, - 3.0492188930511475 + 4.067419528961182, + 4.136204719543457, + 3.3293042182922363 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, - "label": "Medical AI" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -913496,46 +913646,46 @@ ], "projections": { "pca": [ - 0.07243234664201736, - -0.05962290242314339 + 0.07234127074480057, + -0.05971420928835869 ], "tsne": [ - 19.833036422729492, - -22.55980682373047 + 11.899188995361328, + 22.48356056213379 ], "umap": [ - 7.4306464195251465, - 9.33032512664795 + 7.410648345947266, + 8.877504348754883 ], "pca3d": [ - 0.07243230938911438, - -0.059604838490486145, - -0.06484930962324142 + 0.07234121114015579, + -0.059695836156606674, + -0.06469274312257767 ], "tsne3d": [ - 9.050765037536621, - -8.857314109802246, - 11.055205345153809 + 8.28775691986084, + 5.755761623382568, + -13.543595314025879 ], "umap3d": [ - 5.033843517303467, - 3.8097150325775146, - 1.6318620443344116 + 5.004650592803955, + 3.6095645427703857, + 1.6822947263717651 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -913944,47 +914094,47 @@ "abstract": "On the the engineering challenges and lessons learned from building Claude's Research system", "projections": { "pca": [ - -0.13990864157676697, - 0.19487741589546204 + -0.13935136795043945, + 0.19616533815860748 ], "tsne": [ - -11.096636772155762, - -25.289127349853516 + -14.458223342895508, + 18.565921783447266 ], "umap": [ - 3.721437931060791, - 8.723382949829102 + 3.9398036003112793, + 8.002754211425781 ], "pca3d": [ - -0.13990885019302368, - 0.19486461579799652, - 0.20103567838668823 + -0.13935157656669617, + 0.19615252315998077, + 0.20081795752048492 ], "tsne3d": [ - -7.409433841705322, - -10.925390243530273, - -3.9125640392303467 + -8.586718559265137, + 14.76356029510498, + 1.5201603174209595 ], "umap3d": [ - 2.9092321395874023, - 3.5894949436187744, - 3.3287086486816406 + 3.601066827774048, + 3.981006622314453, + 3.7917633056640625 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -914391,31 +914541,31 @@ "title": "ether0: a scientific reasoning model for chemistry | FutureHouse", "projections": { "pca": [ - -0.09112802147865295, - 0.07946496456861496 + -0.09072506427764893, + 0.07997500896453857 ], "tsne": [ - -16.9238224029541, - 9.455465316772461 + -14.240873336791992, + -16.825098037719727 ], "umap": [ - 4.940881729125977, - 5.3735151290893555 + 5.285071849822998, + 5.0229668617248535 ], "pca3d": [ - -0.09112834185361862, - 0.07949205487966537, - 0.2004416286945343 + -0.09072533249855042, + 0.08000264316797256, + 0.2003972828388214 ], "tsne3d": [ - -3.786990165710449, - 12.06505298614502, - -3.5016040802001953 + 1.3381911516189575, + -8.697491645812988, + 11.201275825500488 ], "umap3d": [ - 4.455750465393066, - 5.800832271575928, - 4.184623718261719 + 4.867151260375977, + 6.236787796020508, + 3.3723301887512207 ] }, "cluster_id": 3, @@ -914426,12 +914576,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 9, + "label": "Protein Design" }, { - "id": 24, - "label": "Protein Design" + "id": 32, + "label": "Genome Protein" } ], "_embedding": [ @@ -914849,31 +914999,31 @@ "doi": "10.1101/2025.06.05.658031", "projections": { "pca": [ - 0.15021196007728577, - -0.13495048880577087 + 0.15007354319095612, + -0.13477343320846558 ], "tsne": [ - 10.218683242797852, - 6.462996006011963 + 13.2245512008667, + -9.548640251159668 ], "umap": [ - 6.840991497039795, - 5.9744696617126465 + 7.043252944946289, + 5.774777889251709 ], "pca3d": [ - 0.15021181106567383, - -0.13494619727134705, - 0.07434605807065964 + 0.15007354319095612, + -0.13476872444152832, + 0.07473959773778915 ], "tsne3d": [ - 4.436567306518555, - 4.360560894012451, - -6.967337131500244 + 6.475121021270752, + 1.366363525390625, + 0.3822138011455536 ], "umap3d": [ - 5.743578910827637, - 5.134848594665527, - 3.5201005935668945 + 6.065243721008301, + 5.463510513305664, + 2.4384820461273193 ] }, "cluster_id": 4, @@ -914884,11 +915034,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -915310,46 +915460,46 @@ "doi": "10.1101/2025.06.03.657517", "projections": { "pca": [ - 0.05017753317952156, - 0.06641680002212524 + 0.05040181800723076, + 0.06662707030773163 ], "tsne": [ - 13.247684478759766, - -22.608057022094727 + 7.075504779815674, + 21.265869140625 ], "umap": [ - 6.842259407043457, - 9.194123268127441 + 6.931732654571533, + 8.791921615600586 ], "pca3d": [ - 0.05017732456326485, - 0.06643570959568024, - 0.01885317824780941 + 0.05040157213807106, + 0.06664609163999557, + 0.018792837858200073 ], "tsne3d": [ - 5.303817272186279, - -9.389031410217285, - 11.169062614440918 + 4.633020401000977, + 7.665552139282227, + -11.85810661315918 ], "umap3d": [ - 4.56522798538208, - 3.808732271194458, - 2.042008399963379 + 4.7169108390808105, + 3.8105671405792236, + 2.1938211917877197 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -915765,46 +915915,46 @@ ], "projections": { "pca": [ - -0.1804930865764618, - 0.04738003760576248 + -0.18021464347839355, + 0.04902036115527153 ], "tsne": [ - -14.449501037597656, - -24.99867820739746 + -17.099742889404297, + 17.536312103271484 ], "umap": [ - 3.3271303176879883, - 8.458605766296387 + 3.643137216567993, + 7.698887348175049 ], "pca3d": [ - -0.18049278855323792, - 0.04734468832612038, - 0.17574498057365417 + -0.18021433055400848, + 0.04898449033498764, + 0.17591536045074463 ], "tsne3d": [ - -10.340554237365723, - -9.219584465026855, - -5.361011505126953 + -10.879901885986328, + 14.668188095092773, + 4.482186317443848 ], "umap3d": [ - 2.613701820373535, - 3.921112060546875, - 3.44116473197937 + 3.3947720527648926, + 4.307018756866455, + 3.9658637046813965 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 32, + "id": 25, "label": "AI Agents" } ], @@ -916242,47 +916392,47 @@ "cited_by_count": 90, "projections": { "pca": [ - -0.007585578598082066, - 0.05184799060225487 + -0.007419191766530275, + 0.05250425264239311 ], "tsne": [ - -3.523747444152832, - -23.611085891723633 + -6.458822727203369, + 19.76857566833496 ], "umap": [ - 4.575577259063721, - 8.903600692749023 + 4.815418243408203, + 8.159899711608887 ], "pca3d": [ - -0.007585482206195593, - 0.051836661994457245, - 0.12006395310163498 + -0.007419109344482422, + 0.052492473274469376, + 0.12006264925003052 ], "tsne3d": [ - -2.353567123413086, - -9.216946601867676, - -4.18853235244751 + -3.082520008087158, + 14.505051612854004, + -0.18148542940616608 ], "umap3d": [ - 3.664822816848755, - 3.520575761795044, - 2.9871463775634766 + 4.305011749267578, + 3.83032488822937, + 3.330850839614868 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, - "label": "Medical AI" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -916701,46 +916851,46 @@ "doi": "10.1101/2025.05.20.655112", "projections": { "pca": [ - 0.10470167547464371, - -0.0922158733010292 + 0.10473228991031647, + -0.09269647300243378 ], "tsne": [ - 19.157487869262695, - -22.788633346557617 + 11.141900062561035, + 22.670278549194336 ], "umap": [ - 7.300836086273193, - 9.238494873046875 + 7.3561553955078125, + 8.928154945373535 ], "pca3d": [ - 0.10470157861709595, - -0.09219507873058319, - -0.04361547902226448 + 0.1047322005033493, + -0.09267532825469971, + -0.04329446330666542 ], "tsne3d": [ - 8.299914360046387, - -9.655827522277832, - 11.377752304077148 + 7.947022438049316, + 6.815528392791748, + -13.635686874389648 ], "umap3d": [ - 4.991114616394043, - 3.7329330444335938, - 1.688808560371399 + 5.037077903747559, + 3.5398659706115723, + 1.734757661819458 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -917160,47 +917310,47 @@ "cited_by_count": 2, "projections": { "pca": [ - 0.15423600375652313, - -0.00998465996235609 + 0.15411308407783508, + -0.009858447127044201 ], "tsne": [ - 29.901199340820312, - -17.144001007080078 + 23.12639617919922, + 22.12354850769043 ], "umap": [ - 8.529891967773438, - 9.439553260803223 + 8.604086875915527, + 8.961127281188965 ], "pca3d": [ - 0.1542358547449112, - -0.009977494366466999, - -0.005120798945426941 + 0.15411299467086792, + -0.009851288981735706, + -0.005179372616112232 ], "tsne3d": [ - 16.890213012695312, - 12.694171905517578, - 6.603687286376953 + 12.20409107208252, + -5.577389240264893, + -15.811073303222656 ], "umap3d": [ - 5.321655750274658, - 4.558706760406494, - 1.161561369895935 + 4.990394115447998, + 4.212972640991211, + 0.9488608241081238 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -917627,46 +917777,46 @@ ], "projections": { "pca": [ - -0.10889173299074173, - 0.07102371007204056 + -0.1087605282664299, + 0.07137639820575714 ], "tsne": [ - -1.5033574104309082, - -34.474395751953125 + -9.574317932128906, + 30.989343643188477 ], "umap": [ - 4.642948627471924, - 9.58418083190918 + 4.794366359710693, + 8.897085189819336 ], "pca3d": [ - -0.10889158397912979, - 0.07102267444133759, - 0.021719619631767273 + -0.10876042395830154, + 0.0713750571012497, + 0.02168489247560501 ], "tsne3d": [ - -3.327244758605957, - -19.048555374145508, - 0.08409769833087921 + -7.863364219665527, + 13.721158981323242, + -7.525754451751709 ], "umap3d": [ - 3.8617823123931885, - 2.540712833404541, - 3.197896718978882 + 4.590029716491699, + 3.0491504669189453, + 3.8134031295776367 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, + "id": 26, "label": "Medical AI" } ], @@ -918087,47 +918237,47 @@ ], "projections": { "pca": [ - 0.07763141393661499, - 0.09652571380138397 + 0.0778990164399147, + 0.09673866629600525 ], "tsne": [ - -5.0172438621521, - -22.917827606201172 + -8.475299835205078, + 18.07796287536621 ], "umap": [ - 4.257113456726074, - 8.620652198791504 + 4.581991195678711, + 7.970256805419922 ], "pca3d": [ - 0.07763129472732544, - 0.09652138501405716, - 0.07388690859079361 + 0.07789887487888336, + 0.09673374146223068, + 0.07368882745504379 ], "tsne3d": [ - -4.075305938720703, - -7.376511096954346, - -2.165076732635498 + -4.484190464019775, + 12.019862174987793, + -0.12659704685211182 ], "umap3d": [ - 3.4918856620788574, - 3.6567585468292236, - 2.9941446781158447 + 4.142470836639404, + 3.9974634647369385, + 3.275726079940796 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, - "label": "Medical AI" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -918535,47 +918685,47 @@ "abstract": "An MCP server to run AppleScript and JXA (JavaScript for Automation) to macOS. - steipete/macos-automator-mcp", "projections": { "pca": [ - -0.052266065031290054, - 0.264427125453949 + -0.05137069150805473, + 0.26455914974212646 ], "tsne": [ - -27.02267837524414, - 23.617450714111328 + -23.203004837036133, + 3.5083820819854736 ], "umap": [ - 3.9034273624420166, - 6.523284435272217 + 3.54445481300354, + 7.448969841003418 ], "pca3d": [ - -0.05226627737283707, - 0.264396995306015, - 0.0955192968249321 + -0.05137082189321518, + 0.26452815532684326, + 0.09514520317316055 ], "tsne3d": [ - -12.764497756958008, - 11.559653282165527, - -11.4293212890625 + -6.8692803382873535, + 1.7380967140197754, + 14.555184364318848 ], "umap3d": [ - 3.121610403060913, - 4.300621032714844, - 4.171073913574219 + 4.065400123596191, + 5.00519323348999, + 4.25094747543335 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 12, + "label": "AI Agents" }, { - "id": 10, - "label": "Code Development" + "id": 0, + "label": "Math AI" } ], "_embedding": [ @@ -918983,47 +919133,47 @@ "abstract": "Large language model improves on efforts to solve combinatorics problems inspired by the card game Set.", "projections": { "pca": [ - -0.25154557824134827, - 0.13120540976524353 + -0.25115692615509033, + 0.13326099514961243 ], "tsne": [ - -18.5239200592041, - -24.620311737060547 + -19.679838180541992, + 15.782026290893555 ], "umap": [ - 3.064335823059082, - 7.904782772064209 + 3.220940113067627, + 7.211633205413818 ], "pca3d": [ - -0.2515450417995453, - 0.1311732828617096, - 0.16033196449279785 + -0.25115659832954407, + 0.13322855532169342, + 0.1604081690311432 ], "tsne3d": [ - -11.467184066772461, - -5.666091442108154, - -7.320120811462402 + -14.056957244873047, + 13.661873817443848, + 2.158561944961548 ], "umap3d": [ - 2.3901426792144775, - 4.2860822677612305, - 3.567988872528076 + 3.2034332752227783, + 4.648439884185791, + 4.0727763175964355 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { "id": 0, - "label": "LLM Language Learning" + "label": "Math AI" } ], "_embedding": [ @@ -919431,47 +919581,47 @@ "abstract": "System improves chip designs and tackles unsolved maths problems, but has not been rolled out to researchers outside the company.", "projections": { "pca": [ - -0.2292362004518509, - 0.17595818638801575 + -0.22880856692790985, + 0.1779216229915619 ], "tsne": [ - -18.408601760864258, - -24.59842300415039 + -19.571321487426758, + 15.815906524658203 ], "umap": [ - 3.2436134815216064, - 7.989838123321533 + 3.3694677352905273, + 7.212799072265625 ], "pca3d": [ - -0.22923599183559418, - 0.17593534290790558, - 0.16132469475269318 + -0.2288084477186203, + 0.17789846658706665, + 0.1612185686826706 ], "tsne3d": [ - -11.057238578796387, - -5.441452503204346, - -7.231558799743652 + -13.690988540649414, + 13.61770248413086, + 2.025543212890625 ], "umap3d": [ - 2.5202348232269287, - 4.207517147064209, - 3.5846948623657227 + 3.2805559635162354, + 4.630692958831787, + 4.044517517089844 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { "id": 0, - "label": "LLM Language Learning" + "label": "Math AI" } ], "_embedding": [ @@ -919885,46 +920035,46 @@ ], "projections": { "pca": [ - -0.23728567361831665, - -0.003685322590172291 + -0.23720058798789978, + -0.0026492616161704063 ], "tsne": [ - -29.850141525268555, - -16.150545120239258 + -34.55031967163086, + 13.51381778717041 ], "umap": [ - 2.0282156467437744, - 8.075745582580566 + 2.226364850997925, + 7.386251926422119 ], "pca3d": [ - -0.23728550970554352, - -0.003697178792208433, - 0.012467285618185997 + -0.2372003197669983, + -0.002661457285284996, + 0.012699832208454609 ], "tsne3d": [ - -18.770870208740234, - -13.352900505065918, - 4.089628219604492 + -18.776229858398438, + 9.663509368896484, + 0.33713528513908386 ], "umap3d": [ - 1.7061400413513184, - 4.2255539894104, - 4.284631252288818 + 2.694823741912842, + 4.626773357391357, + 5.022702693939209 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 1, + "label": "Attention Models" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -920345,47 +920495,47 @@ ], "projections": { "pca": [ - -0.08942899107933044, - 0.0030685290694236755 + -0.08938934653997421, + 0.004161256365478039 ], "tsne": [ - -5.907416343688965, - -21.735883712768555 + -12.22093391418457, + 21.92401695251465 ], "umap": [ - 3.9854466915130615, - 8.388803482055664 + 4.357534885406494, + 7.644596099853516 ], "pca3d": [ - -0.0894288718700409, - 0.0030650505796074867, - 0.11275186389684677 + -0.08938921988010406, + 0.004157660063356161, + 0.11292964965105057 ], "tsne3d": [ - -9.698441505432129, - -13.218886375427246, - 3.537975788116455 + -2.9096996784210205, + 9.709785461425781, + -0.8475138545036316 ], "umap3d": [ - 3.3077797889709473, - 3.968294143676758, - 3.4319145679473877 + 3.9859445095062256, + 4.347479820251465, + 3.669887065887451 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -920794,47 +920944,47 @@ "title": "FutureHouse Platform: Superintelligent AI Agents for Science | FutureHouse", "projections": { "pca": [ - -0.1658882051706314, - 0.21101942658424377 + -0.16530731320381165, + 0.21243460476398468 ], "tsne": [ - -9.028708457946777, - -26.839223861694336 + -13.073066711425781, + 19.49379539489746 ], "umap": [ - 3.8932461738586426, - 8.598739624023438 + 4.096312522888184, + 8.004121780395508 ], "pca3d": [ - -0.1658886969089508, - 0.21101726591587067, - 0.20635880529880524 + -0.16530756652355194, + 0.21243222057819366, + 0.20615652203559875 ], "tsne3d": [ - -7.240645885467529, - -12.019464492797852, - -1.1803317070007324 + -9.736751556396484, + 14.645285606384277, + -1.278577208518982 ], "umap3d": [ - 3.074481248855591, - 3.5642058849334717, - 3.3527023792266846 + 3.7863709926605225, + 3.9932358264923096, + 3.7963175773620605 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -921281,46 +921431,46 @@ "doi": "10.1101/2025.04.03.646459", "projections": { "pca": [ - 0.03335808590054512, - 0.0611538290977478 + 0.03351409733295441, + 0.06183077394962311 ], "tsne": [ - -1.7042030096054077, - -24.679296493530273 + -3.7782394886016846, + 21.09698486328125 ], "umap": [ - 4.593417644500732, - 8.792218208312988 + 4.890484809875488, + 8.209678649902344 ], "pca3d": [ - 0.03335806727409363, - 0.06114329397678375, - 0.10214273631572723 + 0.03351404517889023, + 0.06182021275162697, + 0.10208030045032501 ], "tsne3d": [ - -4.466972827911377, - -5.476032733917236, - -5.238821983337402 + -4.151857376098633, + 12.167595863342285, + 4.4687299728393555 ], "umap3d": [ - 3.754013776779175, - 3.6399261951446533, - 2.7755825519561768 + 4.255377292633057, + 3.8567473888397217, + 3.0215210914611816 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, + "id": 26, "label": "Medical AI" } ], @@ -921729,46 +921879,46 @@ ], "projections": { "pca": [ - -0.2782973051071167, - 0.12450262159109116 + -0.2777107357978821, + 0.12562063336372375 ], "tsne": [ - -11.644203186035156, - -26.636611938476562 + -15.330686569213867, + 19.97418975830078 ], "umap": [ - 3.7021775245666504, - 8.725114822387695 + 3.844069480895996, + 8.02991771697998 ], "pca3d": [ - -0.27829739451408386, - 0.12450362741947174, - 0.17639648914337158 + -0.277710884809494, + 0.12562191486358643, + 0.17648206651210785 ], "tsne3d": [ - -6.956143856048584, - -12.887472152709961, - -4.417627811431885 + -8.237197875976562, + 16.768482208251953, + 0.9546671509742737 ], "umap3d": [ - 2.846691131591797, - 3.600557565689087, - 3.351428270339966 + 3.605457067489624, + 4.021465301513672, + 3.8114590644836426 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, + "id": 25, "label": "AI Agents" } ], @@ -922186,47 +922336,47 @@ ], "projections": { "pca": [ - 0.09938323497772217, - 0.10550233721733093 + 0.09994357824325562, + 0.10565419495105743 ], "tsne": [ - -1.5960659980773926, - -20.52145004272461 + -4.134501934051514, + 16.941761016845703 ], "umap": [ - 4.778524398803711, - 8.596920013427734 + 4.947809219360352, + 7.867071151733398 ], "pca3d": [ - 0.09938310831785202, - 0.10549099743366241, - 0.11959367990493774 + 0.09994342923164368, + 0.10564273595809937, + 0.11959739029407501 ], "tsne3d": [ - -0.9317722916603088, - -9.30325984954834, - -8.219709396362305 + -0.09578174352645874, + 15.42324161529541, + 2.66550350189209 ], "umap3d": [ - 3.789395570755005, - 3.8349123001098633, - 2.9941890239715576 + 4.391796112060547, + 4.152115345001221, + 3.1481080055236816 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, - "label": "Medical AI" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -922634,47 +922784,47 @@ "abstract": "A collection of notebooks/recipes showcasing usecases of open-source models with Together AI. - together-cookbook/Agents/Together_Open_Deep_Research_CookBook.ipynb at main \u00b7 togethercomputer/together-cookbook", "projections": { "pca": [ - -0.12405705451965332, - 0.19670537114143372 + -0.12337770313024521, + 0.19662846624851227 ], "tsne": [ - -8.067424774169922, - -37.11097717285156 + -15.34997272491455, + 28.696401596069336 ], "umap": [ - 4.723779678344727, - 8.364829063415527 + 4.49680757522583, + 7.498131275177002 ], "pca3d": [ - -0.12405746430158615, - 0.19671384990215302, - 0.0518939234316349 + -0.12337806820869446, + 0.19663730263710022, + 0.05164983496069908 ], "tsne3d": [ - -5.779599189758301, - -18.01032066345215, - -9.366567611694336 + -15.714229583740234, + 12.173276901245117, + -14.169000625610352 ], "umap3d": [ - 3.767061710357666, - 4.052433967590332, - 3.3849129676818848 + 4.4798078536987305, + 4.3999409675598145, + 3.4866256713867188 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, - "label": "AI Agents" + "id": 4, + "label": "Genomics Tools" }, { - "id": 17, - "label": "Medical AI" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -923118,46 +923268,46 @@ "cited_by_count": 167, "projections": { "pca": [ - -0.10979782044887543, - 0.13002227246761322 + -0.10933171212673187, + 0.13032610714435577 ], "tsne": [ - -0.8392117023468018, - -32.2097282409668 + -7.841530799865723, + 29.40111541748047 ], "umap": [ - 4.707244396209717, - 9.574692726135254 + 4.891319751739502, + 8.904345512390137 ], "pca3d": [ - -0.10979758948087692, - 0.12998907268047333, - -0.05688364431262016 + -0.10933148115873337, + 0.13029177486896515, + -0.056855857372283936 ], "tsne3d": [ - -2.8513598442077637, - -18.82036590576172, - -2.996751308441162 + -5.990015506744385, + 15.720044136047363, + -7.068583965301514 ], "umap3d": [ - 3.9517018795013428, - 2.5513813495635986, - 3.093082904815674 + 4.672192096710205, + 2.968486785888672, + 3.727005958557129 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, + "id": 26, "label": "Medical AI" } ], @@ -923600,46 +923750,46 @@ "cited_by_count": 235, "projections": { "pca": [ - -0.16405439376831055, - 0.15562278032302856 + -0.1635541170835495, + 0.15661951899528503 ], "tsne": [ - -1.081620693206787, - -32.26729965209961 + -8.048933982849121, + 29.3117618560791 ], "umap": [ - 4.749850273132324, - 9.507527351379395 + 4.873911380767822, + 8.788004875183105 ], "pca3d": [ - -0.16405436396598816, - 0.15560095012187958, - 0.006594815291464329 + -0.16355407238006592, + 0.156596839427948, + 0.0066581303253769875 ], "tsne3d": [ - -3.419729232788086, - -18.426589965820312, - -2.4269816875457764 + -6.852978706359863, + 15.814380645751953, + -7.0395426750183105 ], "umap3d": [ - 3.9224841594696045, - 2.5832619667053223, - 3.0977416038513184 + 4.648804187774658, + 3.0422937870025635, + 3.667009115219116 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, + "id": 26, "label": "Medical AI" } ], @@ -924054,46 +924204,46 @@ "doi": "10.1101/2025.04.02.646761", "projections": { "pca": [ - 0.15613703429698944, - 0.018814275041222572 + 0.1561247706413269, + 0.018574625253677368 ], "tsne": [ - 17.45348358154297, - -20.503658294677734 + 8.559114456176758, + 23.548633575439453 ], "umap": [ - 7.259870529174805, - 9.051804542541504 + 7.359035968780518, + 8.680935859680176 ], "pca3d": [ - 0.15613682568073273, - 0.018828528001904488, - -0.0933241918683052 + 0.15612462162971497, + 0.018588874489068985, + -0.09338445216417313 ], "tsne3d": [ - 5.535770416259766, - -12.12420654296875, - 9.345617294311523 + 7.24781608581543, + 9.11711311340332, + -11.421163558959961 ], "umap3d": [ - 5.254048824310303, - 3.635467290878296, - 1.933287501335144 + 5.336862564086914, + 3.543433666229248, + 1.7513763904571533 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -924531,46 +924681,46 @@ "doi": "10.1101/2025.04.01.646731", "projections": { "pca": [ - 0.07270438224077225, - 0.014634492807090282 + 0.07264137268066406, + 0.015054373070597649 ], "tsne": [ - 15.775754928588867, - -28.51315689086914 + 7.825778961181641, + 29.457077026367188 ], "umap": [ - 6.849499702453613, - 9.651784896850586 + 6.809719085693359, + 9.237875938415527 ], "pca3d": [ - 0.0727045014500618, - 0.014616911299526691, - -0.03556916117668152 + 0.07264144718647003, + 0.015036684460937977, + -0.035577163100242615 ], "tsne3d": [ - 14.307875633239746, - -17.817523956298828, - 3.139160394668579 + 5.462547302246094, + 9.878976821899414, + -20.53688621520996 ], "umap3d": [ - 5.067958354949951, - 3.0724191665649414, - 1.6546437740325928 + 5.1516265869140625, + 2.982095956802368, + 1.982947826385498 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -924992,31 +925142,31 @@ "doi": "10.1101/2025.03.30.646211", "projections": { "pca": [ - 0.1508483737707138, - -0.05465856194496155 + 0.15099847316741943, + -0.05456065014004707 ], "tsne": [ - 10.258126258850098, - 6.284698963165283 + 13.127253532409668, + -9.531478881835938 ], "umap": [ - 6.714592456817627, - 5.887502670288086 + 6.9389119148254395, + 5.7142333984375 ], "pca3d": [ - 0.15084828436374664, - -0.05466644838452339, - 0.07712094485759735 + 0.15099839866161346, + -0.05456843972206116, + 0.07740958034992218 ], "tsne3d": [ - 5.022058963775635, - 4.682811737060547, - -6.170076847076416 + 7.228444576263428, + 0.8418537974357605, + 0.5469197034835815 ], "umap3d": [ - 5.74179744720459, - 5.057126522064209, - 3.5804975032806396 + 5.993797779083252, + 5.475218296051025, + 2.611588478088379 ] }, "cluster_id": 4, @@ -925027,11 +925177,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -925458,47 +925608,47 @@ "doi": "10.1101/2025.03.11.642548", "projections": { "pca": [ - -0.005841753911226988, - 0.050201259553432465 + -0.005535178352147341, + 0.05077023431658745 ], "tsne": [ - -1.370106816291809, - -22.19703483581543 + -4.15710973739624, + 18.490421295166016 ], "umap": [ - 4.740169048309326, - 8.621170043945312 + 4.941257953643799, + 7.906818389892578 ], "pca3d": [ - -0.0058417241089046, - 0.05019291490316391, - 0.16658872365951538 + -0.005535166244953871, + 0.05076141282916069, + 0.16665828227996826 ], "tsne3d": [ - -0.8307214975357056, - -9.338285446166992, - -6.095210075378418 + -0.9253435730934143, + 13.80082893371582, + 1.5935207605361938 ], "umap3d": [ - 3.766629457473755, - 3.8094444274902344, - 2.970184803009033 + 4.37561559677124, + 4.105391979217529, + 3.1973583698272705 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, - "label": "Medical AI" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -925923,47 +926073,47 @@ "cited_by_count": 10, "projections": { "pca": [ - 0.04473548382520676, - -0.005691630765795708 + 0.0448589101433754, + -0.005041538737714291 ], "tsne": [ - -3.4608705043792725, - -22.378828048706055 + -6.190925598144531, + 18.630640029907227 ], "umap": [ - 4.49808931350708, - 8.73439884185791 + 4.873116493225098, + 7.981788158416748 ], "pca3d": [ - 0.04473559185862541, - -0.005701901391148567, - 0.1631288081407547 + 0.04485900700092316, + -0.005051834508776665, + 0.1632651537656784 ], "tsne3d": [ - -3.54903244972229, - -8.543119430541992, - -5.991289138793945 + -3.2760186195373535, + 14.542278289794922, + 1.9991250038146973 ], "umap3d": [ - 3.6302597522735596, - 3.7463226318359375, - 2.924053430557251 + 4.209965705871582, + 4.059244632720947, + 3.2555761337280273 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, - "label": "Medical AI" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -926380,46 +926530,46 @@ ], "projections": { "pca": [ - -0.1581181436777115, - 0.02390572987496853 + -0.1579570323228836, + 0.024582382291555405 ], "tsne": [ - -25.676977157592773, - -11.190544128417969 + -29.67835807800293, + 9.1316556930542 ], "umap": [ - 1.9911106824874878, - 8.426860809326172 + 2.224454641342163, + 7.6380205154418945 ], "pca3d": [ - -0.15811799466609955, - 0.023903395980596542, - 0.09084927290678024 + -0.15795691311359406, + 0.02458057925105095, + 0.09095874428749084 ], "tsne3d": [ - -12.494719505310059, - -8.77230453491211, - 5.5579962730407715 + -14.825994491577148, + 7.20297908782959, + 2.8829314708709717 ], "umap3d": [ - 1.932736873626709, - 4.19933557510376, - 4.611260414123535 + 2.9695487022399902, + 4.686778545379639, + 5.2207183837890625 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 1, + "label": "Attention Models" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -926839,31 +926989,31 @@ ], "projections": { "pca": [ - 0.049976129084825516, - -0.14582672715187073 + 0.0498063862323761, + -0.14556585252285004 ], "tsne": [ - 10.670827865600586, - 7.501873016357422 + 12.873244285583496, + -7.991689682006836 ], "umap": [ - 6.774991989135742, - 6.134500503540039 + 7.023271083831787, + 5.827035903930664 ], "pca3d": [ - 0.04997650533914566, - -0.1458434760570526, - 0.014462878927588463 + 0.04980676621198654, + -0.14558269083499908, + 0.014928505755960941 ], "tsne3d": [ - 4.4663262367248535, - 5.099639415740967, - -8.60391616821289 + 6.0017008781433105, + 0.2607886791229248, + -1.059447169303894 ], "umap3d": [ - 5.689522743225098, - 4.964857578277588, - 3.4849283695220947 + 5.9687089920043945, + 5.326807498931885, + 2.6056106090545654 ] }, "cluster_id": 4, @@ -926874,11 +927024,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -927297,46 +927447,46 @@ ], "projections": { "pca": [ - -0.10305510461330414, - 0.12198293954133987 + -0.10271291434764862, + 0.12226400524377823 ], "tsne": [ - 0.7369617223739624, - -32.11515426635742 + -6.413296699523926, + 30.058650970458984 ], "umap": [ - 4.833242893218994, - 9.39473819732666 + 4.932703495025635, + 8.77591609954834 ], "pca3d": [ - -0.10305478423833847, - 0.1219506785273552, - 0.03218867629766464 + -0.10271264612674713, + 0.12223107367753983, + 0.03206713870167732 ], "tsne3d": [ - -1.0977389812469482, - -16.51129150390625, - -1.3029594421386719 + -7.766153812408447, + 16.399499893188477, + -9.51026439666748 ], "umap3d": [ - 3.9306480884552, - 2.701773166656494, - 2.9981420040130615 + 4.6606879234313965, + 3.0649619102478027, + 3.524730682373047 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, + "id": 26, "label": "Medical AI" } ], @@ -927747,46 +927897,46 @@ "journal": "Nature", "projections": { "pca": [ - -0.14910271763801575, - 0.16492319107055664 + -0.1486053764820099, + 0.1659054011106491 ], "tsne": [ - -5.0090813636779785, - -28.479101181030273 + -9.144760131835938, + 24.488431930541992 ], "umap": [ - 4.535855770111084, - 9.060548782348633 + 4.768206596374512, + 8.377055168151855 ], "pca3d": [ - -0.1491026133298874, - 0.16489951312541962, - 0.10358190536499023 + -0.1486053764820099, + 0.1658812314271927, + 0.10352088510990143 ], "tsne3d": [ - -3.8047609329223633, - -13.82746410369873, - -3.3020524978637695 + -7.036354064941406, + 17.158843994140625, + -2.293144702911377 ], "umap3d": [ - 3.6358466148376465, - 3.1578903198242188, - 2.9929590225219727 + 4.276618003845215, + 3.562066078186035, + 3.5415847301483154 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, + "id": 26, "label": "Medical AI" } ], @@ -928214,46 +928364,46 @@ "doi": "10.64898/2026.05.30.728937", "projections": { "pca": [ - 0.09867478907108307, - -0.017237095162272453 + 0.09868166595697403, + -0.01734725758433342 ], "tsne": [ - 16.90395736694336, - -30.23843002319336 + 8.902170181274414, + 31.156484603881836 ], "umap": [ - 6.814310550689697, - 9.718332290649414 + 6.821850299835205, + 9.295158386230469 ], "pca3d": [ - 0.09867486357688904, - -0.01724276877939701, - -0.13999062776565552 + 0.09868168085813522, + -0.017352892085909843, + -0.1398705393075943 ], "tsne3d": [ - 12.438017845153809, - -16.112329483032227, - 4.401806831359863 + 3.4924097061157227, + 8.481685638427734, + -19.45992660522461 ], "umap3d": [ - 5.179511070251465, - 3.012589693069458, - 1.634818434715271 + 5.285532474517822, + 2.821995258331299, + 1.977473258972168 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -928671,46 +928821,46 @@ ], "projections": { "pca": [ - 0.08044499903917313, - 0.010736568830907345 + 0.08052600175142288, + 0.010656395927071571 ], "tsne": [ - 17.02448081970215, - -30.184520721435547 + 8.988554954528809, + 31.07115936279297 ], "umap": [ - 6.885661602020264, - 9.709211349487305 + 6.936914443969727, + 9.308518409729004 ], "pca3d": [ - 0.08044499903917313, - 0.01072620041668415, - -0.13376067578792572 + 0.08052593469619751, + 0.010645761154592037, + -0.13370078802108765 ], "tsne3d": [ - 12.778814315795898, - -15.192981719970703, - 4.3890767097473145 + 3.585200786590576, + 7.690810680389404, + -18.895301818847656 ], "umap3d": [ - 5.19275426864624, - 3.067634105682373, - 1.5837045907974243 + 5.277698040008545, + 2.8345093727111816, + 1.9314647912979126 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -929176,46 +929326,46 @@ "openalex_url": "https://openalex.org/W7160658183", "projections": { "pca": [ - -0.004708736203610897, - -0.040424805134534836 + -0.004811648745089769, + -0.04051148518919945 ], "tsne": [ - 16.872119903564453, - -31.406774520874023 + 8.932644844055176, + 32.32133865356445 ], "umap": [ - 6.740864276885986, - 9.74905776977539 + 6.746161460876465, + 9.32261848449707 ], "pca3d": [ - -0.004708823747932911, - -0.04040658101439476, - -0.16152723133563995 + -0.0048117442056536674, + -0.04049248993396759, + -0.1613609790802002 ], "tsne3d": [ - 11.977222442626953, - -17.6865177154541, - 4.165665626525879 + 2.4425857067108154, + 9.143319129943848, + -20.688339233398438 ], "umap3d": [ - 5.087620735168457, - 2.9322636127471924, - 1.7028261423110962 + 5.262882232666016, + 2.757448673248291, + 2.0555574893951416 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -929637,47 +929787,47 @@ ], "projections": { "pca": [ - 0.038147665560245514, - -0.022510631009936333 + 0.0381348542869091, + -0.02274295501410961 ], "tsne": [ - 15.723579406738281, - -30.872636795043945 + 7.7321319580078125, + 31.818737030029297 ], "umap": [ - 6.481050968170166, - 9.74339771270752 + 6.54188871383667, + 9.239482879638672 ], "pca3d": [ - 0.03814776986837387, - -0.022516554221510887, - -0.10749884694814682 + 0.038134943693876266, + -0.02274920605123043, + -0.10740320384502411 ], "tsne3d": [ - 11.105307579040527, - -15.553206443786621, - 3.8074896335601807 + 2.591496467590332, + 9.15859603881836, + -18.455129623413086 ], "umap3d": [ - 5.007689476013184, - 2.7981903553009033, - 1.8770530223846436 + 5.242486953735352, + 2.7224910259246826, + 2.2954819202423096 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 6, + "label": "Single Cell Data" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -930096,46 +930246,46 @@ "doi": "10.64898/2026.03.02.709002", "projections": { "pca": [ - 0.051485080271959305, - -0.053140789270401 + 0.05136123672127724, + -0.05318136513233185 ], "tsne": [ - 17.120601654052734, - -30.213016510009766 + 9.087891578674316, + 31.101381301879883 ], "umap": [ - 6.918498992919922, - 9.72029972076416 + 6.976376056671143, + 9.28815746307373 ], "pca3d": [ - 0.05148515850305557, - -0.05314768850803375, - -0.19812865555286407 + 0.051361333578825, + -0.053188424557447433, + -0.19793207943439484 ], "tsne3d": [ - 12.385692596435547, - -15.052953720092773, - 5.246891021728516 + 2.9384963512420654, + 7.261208534240723, + -19.34367561340332 ], "umap3d": [ - 5.2100629806518555, - 3.0908517837524414, - 1.5711438655853271 + 5.278396129608154, + 2.860208034515381, + 1.8905962705612183 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -930559,47 +930709,47 @@ ], "projections": { "pca": [ - -0.10944052040576935, - 0.03675004467368126 + -0.10929688066244125, + 0.03741530328989029 ], "tsne": [ - 4.825468063354492, - -34.5577392578125 + -4.452792644500732, + 34.03178405761719 ], "umap": [ - 5.455164909362793, - 9.580591201782227 + 5.623781681060791, + 8.972506523132324 ], "pca3d": [ - -0.1094401627779007, - 0.03671804443001747, - -0.07894210517406464 + -0.109296515583992, + 0.037382300943136215, + -0.07876314967870712 ], "tsne3d": [ - 3.081768035888672, - -19.92891502380371, - -1.2533804178237915 + -2.7842748165130615, + 13.776905059814453, + -13.299903869628906 ], "umap3d": [ - 4.622310161590576, - 2.4623122215270996, - 2.8098039627075195 + 5.239630222320557, + 2.7802467346191406, + 3.244291067123413 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -931017,46 +931167,46 @@ "doi": "10.1101/2025.08.21.671613", "projections": { "pca": [ - 0.0020758567843586206, - -0.11596101522445679 + 0.0018225981621071696, + -0.11599218845367432 ], "tsne": [ - 22.440736770629883, - -24.180931091308594 + 13.959303855895996, + 24.980602264404297 ], "umap": [ - 7.574122428894043, - 9.371273040771484 + 7.622262477874756, + 9.058231353759766 ], "pca3d": [ - 0.0020760423503816128, - -0.11594964563846588, - -0.17804335057735443 + 0.0018227928085252643, + -0.11598102748394012, + -0.1777072250843048 ], "tsne3d": [ - 12.110788345336914, - -10.634838104248047, - 8.897321701049805 + 7.151869297027588, + 4.351593971252441, + -16.272790908813477 ], "umap3d": [ - 5.349010467529297, - 3.689105272293091, - 1.4594308137893677 + 5.201879978179932, + 3.444610118865967, + 1.4554861783981323 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -931469,31 +931619,31 @@ "doi": "10.1016/s1470-2045(25)00238-4", "projections": { "pca": [ - 0.08531535416841507, - 0.13446849584579468 + 0.08561602234840393, + 0.1343826800584793 ], "tsne": [ - 31.797121047973633, - -7.1560869216918945 + 32.379966735839844, + 2.4253430366516113 ], "umap": [ - 8.44543170928955, - 8.111589431762695 + 8.618839263916016, + 7.636923313140869 ], "pca3d": [ - 0.08531515300273895, - 0.13447503745555878, - -0.1361464560031891 + 0.08561573922634125, + 0.1343892216682434, + -0.13635343313217163 ], "tsne3d": [ - 15.043821334838867, - -8.532055854797363, - 0.1773175597190857 + 13.163519859313965, + 10.56248664855957, + -9.468405723571777 ], "umap3d": [ - 6.857746601104736, - 3.869954824447632, - 1.8108913898468018 + 6.7199859619140625, + 3.7346646785736084, + 1.1248654127120972 ] }, "cluster_id": 0, @@ -931504,12 +931654,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { "id": 31, - "label": "Genome Cancer" + "label": "Single Cell Seq" } ], "_embedding": [ @@ -931948,47 +932098,47 @@ "cited_by_count": 4, "projections": { "pca": [ - 0.14956969022750854, - 0.07756435126066208 + 0.14970313012599945, + 0.07680240273475647 ], "tsne": [ - 34.097469329833984, - -13.55941104888916 + 18.52760887145996, + 25.661602020263672 ], "umap": [ - 8.046414375305176, - 8.820713996887207 + 8.157158851623535, + 8.534194946289062 ], "pca3d": [ - 0.1495695412158966, - 0.0775628462433815, - -0.1819450557231903 + 0.1497029811143875, + 0.07680100947618484, + -0.1821623295545578 ], "tsne3d": [ - 16.430639266967773, - -11.61688232421875, - 3.928579092025757 + 13.160239219665527, + 5.899702072143555, + -16.18503189086914 ], "umap3d": [ - 6.0402655601501465, - 3.4651503562927246, - 1.4446946382522583 + 6.052669525146484, + 3.3487141132354736, + 1.1028721332550049 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 12, - "label": "Genetic Variants" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -932400,47 +932550,47 @@ ], "projections": { "pca": [ - -0.10167451202869415, - -0.01385692972689867 + -0.1016249805688858, + -0.013785513117909431 ], "tsne": [ - -39.333641052246094, - 19.67821502685547 + -34.70533752441406, + -25.494869232177734 ], "umap": [ - 2.2042558193206787, - 4.824782371520996 + 5.496283054351807, + 8.89339828491211 ], "pca3d": [ - -0.10167417675256729, - -0.013873212039470673, - -0.0869203507900238 + -0.10162463784217834, + -0.01380213350057602, + -0.08678608387708664 ], "tsne3d": [ - -21.345867156982422, - 15.279260635375977, - -2.0763285160064697 + -19.22942543029785, + -23.4174861907959, + 6.6988067626953125 ], "umap3d": [ - 2.188030481338501, - 7.075329780578613, - 4.6542534828186035 + 2.680680274963379, + 7.437514305114746, + 4.508541584014893 ] }, - "cluster_id": 2, - "cluster_label": "Generative Models", + "cluster_id": 1, + "cluster_label": "AI in Science", "cluster_levels": [ { - "id": 2, - "label": "Generative Models" + "id": 1, + "label": "AI in Science" }, { - "id": 7, - "label": "Diffusion Models" + "id": 10, + "label": "Medical AI" }, { - "id": 25, - "label": "Diffusion Models" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -932857,47 +933007,47 @@ ], "projections": { "pca": [ - -0.12034548819065094, - 0.001959020271897316 + -0.12018949538469315, + 0.0021708407439291477 ], "tsne": [ - 4.840583324432373, - -36.33405685424805 + -4.83268404006958, + 35.83152770996094 ], "umap": [ - 5.4309234619140625, - 9.511209487915039 + 5.565445423126221, + 8.91444206237793 ], "pca3d": [ - -0.12034524232149124, - 0.0019477931782603264, - -0.038581233471632004 + -0.12018921971321106, + 0.0021586325019598007, + -0.03841089829802513 ], "tsne3d": [ - 2.930712938308716, - -20.16329002380371, - -4.855719089508057 + -5.85920524597168, + 14.956917762756348, + -13.689399719238281 ], "umap3d": [ - 4.576026439666748, - 2.4749982357025146, - 2.8767096996307373 + 5.20810079574585, + 2.773773193359375, + 3.3138818740844727 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -933318,47 +933468,47 @@ "doi": "10.1145/3637528.3671836", "projections": { "pca": [ - -0.14649903774261475, - 0.13691285252571106 + -0.14614523947238922, + 0.13750040531158447 ], "tsne": [ - 4.92188024520874, - -36.71760940551758 + -4.97031831741333, + 36.21014404296875 ], "umap": [ - 5.396700859069824, - 9.517882347106934 + 5.507075309753418, + 8.927413940429688 ], "pca3d": [ - -0.14649897813796997, - 0.13690640032291412, - -0.01807914860546589 + -0.14614520967006683, + 0.13749289512634277, + -0.018195513635873795 ], "tsne3d": [ - 2.749176025390625, - -20.66455841064453, - -5.55682897567749 + -6.60948371887207, + 15.123634338378906, + -14.047540664672852 ], "umap3d": [ - 4.501935005187988, - 2.452132225036621, - 2.915165662765503 + 5.223320484161377, + 2.77835750579834, + 3.309938669204712 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -933776,46 +933926,46 @@ "doi": "10.1093/aje/kwad232", "projections": { "pca": [ - 0.042697079479694366, - 0.33225175738334656 + 0.04357217624783516, + 0.3318295478820801 ], "tsne": [ - 8.235496520996094, - -42.97930145263672 + 2.168936252593994, + 43.616844177246094 ], "umap": [ - 6.505580425262451, - 8.497374534606934 + 6.799078464508057, + 7.9429216384887695 ], "pca3d": [ - 0.04269630089402199, - 0.33226874470710754, - -0.14001815021038055 + 0.04357147216796875, + 0.3318463861942291, + -0.140557199716568 ], "tsne3d": [ - 5.887130260467529, - -19.173080444335938, - -8.780202865600586 + 7.865810394287109, + 21.00927734375, + -4.575437545776367 ], "umap3d": [ - 6.0473246574401855, - 2.955678939819336, - 2.8574435710906982 + 6.586604118347168, + 3.3174352645874023, + 2.5694048404693604 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 3, + "label": "Genetic Disease" }, { - "id": 26, + "id": 33, "label": "Genetic Disease" } ], @@ -934235,46 +934385,46 @@ "doi": "10.1007/s10654-023-01095-0", "projections": { "pca": [ - 0.008008950389921665, - 0.31376874446868896 + 0.008894708007574081, + 0.3134635388851166 ], "tsne": [ - 8.228944778442383, - -42.96773147583008 + 2.161449432373047, + 43.60796356201172 ], "umap": [ - 6.444758892059326, - 8.582054138183594 + 6.673280715942383, + 8.011686325073242 ], "pca3d": [ - 0.00800852570682764, - 0.3137693405151367, - -0.11695250868797302 + 0.008894245140254498, + 0.31346386671066284, + -0.11740109324455261 ], "tsne3d": [ - 5.527750492095947, - -19.236595153808594, - -8.514800071716309 + 7.768610000610352, + 21.057985305786133, + -4.234564304351807 ], "umap3d": [ - 5.887247085571289, - 2.864932060241699, - 2.829747200012207 + 6.463434219360352, + 3.2450053691864014, + 2.660954236984253 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 3, + "label": "Genetic Disease" }, { - "id": 26, + "id": 33, "label": "Genetic Disease" } ], @@ -934692,46 +934842,46 @@ ], "projections": { "pca": [ - -0.12692390382289886, - 0.11341322958469391 + -0.12650711834430695, + 0.11393959075212479 ], "tsne": [ - -1.215818166732788, - -33.67741012573242 + -8.981008529663086, + 30.38887596130371 ], "umap": [ - 4.632547855377197, - 9.570368766784668 + 4.78021764755249, + 8.867005348205566 ], "pca3d": [ - -0.1269235759973526, - 0.11338696628808975, - 0.09143468737602234 + -0.12650689482688904, + 0.11391261965036392, + 0.09144683182239532 ], "tsne3d": [ - -4.033731937408447, - -20.83859634399414, - -1.5917384624481201 + -8.55532169342041, + 14.413785934448242, + -9.516196250915527 ], "umap3d": [ - 3.924724578857422, - 2.5345261096954346, - 3.140907049179077 + 4.6540727615356445, + 3.02662992477417, + 3.723163366317749 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, + "id": 26, "label": "Medical AI" } ], @@ -935161,31 +935311,31 @@ "openalex_url": "https://openalex.org/W7131253969", "projections": { "pca": [ - 0.06683734059333801, - 0.040710918605327606 + 0.06711418181657791, + 0.04075584188103676 ], "tsne": [ - -9.028759002685547, - 11.429765701293945 + -4.124338626861572, + -39.55587387084961 ], "umap": [ - 5.160055637359619, - 5.091602325439453 + 5.418614387512207, + 4.727536201477051 ], "pca3d": [ - 0.06683709472417831, - 0.04072369635105133, - 0.17181983590126038 + 0.06711392849683762, + 0.04076886549592018, + 0.1717544049024582 ], "tsne3d": [ - -7.6969451904296875, - 15.17566204071045, - 2.592768907546997 + 2.0408148765563965, + 3.191460132598877, + 15.185079574584961 ], "umap3d": [ - 5.228997707366943, - 6.395089626312256, - 4.064067840576172 + 5.102511882781982, + 6.797297477722168, + 3.040492534637451 ] }, "cluster_id": 3, @@ -935196,11 +935346,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 9, + "label": "Protein Design" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -935632,46 +935782,46 @@ "cited_by_count": 17, "projections": { "pca": [ - -0.035904478281736374, - 0.10755334794521332 + -0.03562913462519646, + 0.10813138633966446 ], "tsne": [ - 0.35227447748184204, - -31.02570152282715 + -5.783563613891602, + 29.113235473632812 ], "umap": [ - 4.964072227478027, - 9.436352729797363 + 5.173309326171875, + 8.774818420410156 ], "pca3d": [ - -0.03590422123670578, - 0.10752236098051071, - 0.014250703155994415 + -0.03562895208597183, + 0.10809963941574097, + 0.014210875146090984 ], "tsne3d": [ - -1.3065475225448608, - -17.759429931640625, - -3.6662702560424805 + -4.360655307769775, + 16.58873748779297, + -8.071555137634277 ], "umap3d": [ - 4.1052751541137695, - 2.5975544452667236, - 3.0005290508270264 + 4.856152057647705, + 3.0046658515930176, + 3.5107908248901367 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, + "id": 26, "label": "Medical AI" } ], @@ -936095,46 +936245,46 @@ "cited_by_count": 51, "projections": { "pca": [ - 0.11333229392766953, - 0.0786360651254654 + 0.11350327730178833, + 0.07827907800674438 ], "tsne": [ - 21.60529899597168, - -18.970333099365234 + 15.462228775024414, + 20.107576370239258 ], "umap": [ - 7.481225490570068, - 9.405295372009277 + 7.618017673492432, + 9.008831024169922 ], "pca3d": [ - 0.11333201825618744, - 0.07865915447473526, - -0.08246250450611115 + 0.11350294947624207, + 0.07830244302749634, + -0.08263655006885529 ], "tsne3d": [ - 12.0066556930542, - -7.624324798583984, - 5.9772562980651855 + 10.153732299804688, + 5.679509162902832, + -11.037171363830566 ], "umap3d": [ - 5.280881881713867, - 3.653529405593872, - 1.5728713274002075 + 5.092376708984375, + 3.5649397373199463, + 1.514519453048706 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -936558,47 +936708,47 @@ "doi": "10.1038/s41746-025-02334-2", "projections": { "pca": [ - -0.015431346371769905, - -0.026246676221489906 + -0.015500662848353386, + -0.02641156315803528 ], "tsne": [ - 12.274614334106445, - -32.59629821777344 + 4.040311813354492, + 33.65373229980469 ], "umap": [ - 6.13137674331665, - 9.689130783081055 + 6.303088188171387, + 8.993656158447266 ], "pca3d": [ - -0.015431218780577183, - -0.026237783953547478, - -0.12524503469467163 + -0.01550054457038641, + -0.026402702555060387, + -0.125162273645401 ], "tsne3d": [ - 7.663909912109375, - -15.427717208862305, - 1.4281575679779053 + 1.5292760133743286, + 13.660172462463379, + -17.578514099121094 ], "umap3d": [ - 4.926276206970215, - 2.706289529800415, - 2.2523205280303955 + 5.2811408042907715, + 2.7161014080047607, + 2.630345106124878 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -937026,47 +937176,47 @@ "doi": "10.1101/2025.10.26.684668", "projections": { "pca": [ - 0.005816544871777296, - 0.04122573882341385 + 0.006031410302966833, + 0.04139213263988495 ], "tsne": [ - 5.057015895843506, - -35.410423278808594 + -4.458630561828613, + 35.01619338989258 ], "umap": [ - 5.507113456726074, - 9.397332191467285 + 5.617241859436035, + 8.798615455627441 ], "pca3d": [ - 0.005816821474581957, - 0.041198574006557465, - -0.08575920760631561 + 0.0060316696763038635, + 0.041364021599292755, + -0.08562465757131577 ], "tsne3d": [ - 4.493075370788574, - -18.77130889892578, - -4.797854423522949 + -4.438141822814941, + 14.321854591369629, + -13.613473892211914 ], "umap3d": [ - 4.722556114196777, - 2.4949443340301514, - 2.8673224449157715 + 5.3420023918151855, + 2.8277318477630615, + 3.246262788772583 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -937482,46 +937632,46 @@ ], "projections": { "pca": [ - -0.17002078890800476, - 0.10416921973228455 + -0.16953450441360474, + 0.1045159175992012 ], "tsne": [ - -25.02220344543457, - -19.533395767211914 + -29.44845962524414, + 17.38718032836914 ], "umap": [ - 4.299167156219482, - 9.446858406066895 + 4.462961673736572, + 8.769185066223145 ], "pca3d": [ - -0.1700209677219391, - 0.1041804775595665, - -0.06237104535102844 + -0.16953469812870026, + 0.10452684015035629, + -0.06223151832818985 ], "tsne3d": [ - -12.818824768066406, - -18.924333572387695, - 3.5755648612976074 + -14.807164192199707, + 10.732255935668945, + -5.8088507652282715 ], "umap3d": [ - 3.635044574737549, - 2.6712751388549805, - 3.460764169692993 + 4.419719219207764, + 3.2000255584716797, + 4.096773147583008 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, + "id": 26, "label": "Medical AI" } ], @@ -937932,46 +938082,46 @@ "journal": "Nature Reviews Cancer", "projections": { "pca": [ - -0.02195749804377556, - 0.18743346631526947 + -0.02143796905875206, + 0.18797656893730164 ], "tsne": [ - -3.647017002105713, - -28.906274795532227 + -7.644062519073486, + 24.32575035095215 ], "umap": [ - 4.501769542694092, - 8.985138893127441 + 4.753666400909424, + 8.287446975708008 ], "pca3d": [ - -0.021957840770483017, - 0.18744710087776184, - 0.04338641092181206 + -0.0214383602142334, + 0.18799009919166565, + 0.04319198429584503 ], "tsne3d": [ - -2.222318172454834, - -13.451176643371582, - -1.7197874784469604 + -5.433328628540039, + 16.44536781311035, + -3.3987233638763428 ], "umap3d": [ - 3.657555341720581, - 3.2155745029449463, - 3.054548978805542 + 4.277552604675293, + 3.665923833847046, + 3.5010058879852295 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, + "id": 26, "label": "Medical AI" } ], @@ -938398,47 +938548,47 @@ "cited_by_count": 9, "projections": { "pca": [ - -0.08429033309221268, - 0.061512887477874756 + -0.0842115506529808, + 0.06155192479491234 ], "tsne": [ - -0.45867908000946045, - -39.361629486083984 + -10.746780395507812, + 35.816856384277344 ], "umap": [ - 5.269019603729248, - 9.536900520324707 + 5.297787666320801, + 8.960689544677734 ], "pca3d": [ - -0.08429033309221268, - 0.06152462586760521, - -0.10913965106010437 + -0.08421160280704498, + 0.06156344711780548, + -0.1092267781496048 ], "tsne3d": [ - -3.4969568252563477, - -24.81171417236328, - -1.083857536315918 + -9.094965934753418, + 11.64473819732666, + -12.84498119354248 ], "umap3d": [ - 4.439041614532471, - 2.5202529430389404, - 2.9470746517181396 + 5.1614766120910645, + 2.8087942600250244, + 3.3591630458831787 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 26, + "label": "Medical AI" } ], "_embedding": [ @@ -938846,47 +938996,47 @@ ], "projections": { "pca": [ - -0.20793792605400085, - 0.20925717055797577 + -0.20731738209724426, + 0.20996247231960297 ], "tsne": [ - -0.7962071299552917, - -40.02819061279297 + -11.330774307250977, + 36.28295135498047 ], "umap": [ - 5.172245025634766, - 9.54053783416748 + 5.194440841674805, + 8.999903678894043 ], "pca3d": [ - -0.2079382836818695, - 0.20926564931869507, - -0.03141039237380028 + -0.20731763541698456, + 0.20997068285942078, + -0.03159431740641594 ], "tsne3d": [ - -3.77471661567688, - -26.19392204284668, - -0.8422646522521973 + -9.440595626831055, + 10.847010612487793, + -13.94861125946045 ], "umap3d": [ - 4.363255977630615, - 2.5482044219970703, - 3.0470638275146484 + 5.114686489105225, + 2.8711421489715576, + 3.4195752143859863 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 26, + "label": "Medical AI" } ], "_embedding": [ @@ -939325,46 +939475,46 @@ ], "projections": { "pca": [ - 0.06345072388648987, - 0.054846473038196564 + 0.06347327679395676, + 0.05494533106684685 ], "tsne": [ - 4.004400730133057, - -23.915367126464844 + -1.83359694480896, + 23.110200881958008 ], "umap": [ - 6.229672431945801, - 8.231167793273926 + 6.10742712020874, + 7.776022434234619 ], "pca3d": [ - 0.06345068663358688, - 0.054846152663230896, - -0.045463819056749344 + 0.06347323209047318, + 0.054944541305303574, + -0.045631855726242065 ], "tsne3d": [ - 1.1810486316680908, - -10.705312728881836, - 2.4976789951324463 + -0.7459967136383057, + 14.204519271850586, + -4.460653781890869 ], "umap3d": [ - 5.586539268493652, - 3.483625888824463, - 2.8867757320404053 + 5.707428932189941, + 3.8655812740325928, + 2.8379435539245605 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 26, + "id": 33, "label": "Genetic Disease" } ], @@ -939791,46 +939941,46 @@ ], "projections": { "pca": [ - -0.09794261306524277, - 0.051869917660951614 + -0.09772718697786331, + 0.052220601588487625 ], "tsne": [ - -1.6734869480133057, - -34.872528076171875 + -9.946563720703125, + 31.19730567932129 ], "umap": [ - 4.613842964172363, - 9.622769355773926 + 4.737624645233154, + 8.917924880981445 ], "pca3d": [ - -0.09794231504201889, - 0.05185285583138466, - 0.03330499678850174 + -0.09772689640522003, + 0.052202995866537094, + 0.0333920381963253 ], "tsne3d": [ - -3.9196770191192627, - -20.539390563964844, - 0.5727965235710144 + -8.428458213806152, + 12.345812797546387, + -8.022651672363281 ], "umap3d": [ - 3.8519482612609863, - 2.5313806533813477, - 3.220688581466675 + 4.638927459716797, + 3.0071868896484375, + 3.7897934913635254 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, + "id": 26, "label": "Medical AI" } ], @@ -940271,46 +940421,46 @@ ], "projections": { "pca": [ - -0.13053175806999207, - 0.12468302994966507 + -0.12995374202728271, + 0.12522783875465393 ], "tsne": [ - -0.26831862330436707, - -35.33723449707031 + -8.874650001525879, + 32.313167572021484 ], "umap": [ - 4.6235880851745605, - 9.587112426757812 + 4.862852096557617, + 8.870054244995117 ], "pca3d": [ - -0.13053171336650848, - 0.12467733025550842, - 0.011476203799247742 + -0.1299537718296051, + 0.1252216249704361, + 0.011637316085398197 ], "tsne3d": [ - -1.5677881240844727, - -21.500843048095703, - -0.6232343316078186 + -6.660096645355225, + 12.063192367553711, + -9.934708595275879 ], "umap3d": [ - 3.9291164875030518, - 2.5429039001464844, - 3.1626288890838623 + 4.725067138671875, + 2.946521282196045, + 3.8312978744506836 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, + "id": 26, "label": "Medical AI" } ], @@ -940732,47 +940882,47 @@ "doi": "10.2196/75340", "projections": { "pca": [ - -0.23782743513584137, - 0.0763242170214653 + -0.23768779635429382, + 0.07718317210674286 ], "tsne": [ - 4.853370666503906, - -33.08897399902344 + -4.125962734222412, + 32.797367095947266 ], "umap": [ - 5.502056121826172, - 9.544523239135742 + 5.631822109222412, + 8.917743682861328 ], "pca3d": [ - -0.2378273606300354, - 0.07632557302713394, - -0.09228473156690598 + -0.23768773674964905, + 0.07718388736248016, + -0.09223317354917526 ], "tsne3d": [ - 5.119585990905762, - -19.08972930908203, - -1.394495964050293 + -0.9952930212020874, + 15.356300354003906, + -12.549703598022461 ], "umap3d": [ - 4.63640022277832, - 2.4303348064422607, - 2.7826948165893555 + 5.304363250732422, + 2.7441768646240234, + 3.2720227241516113 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -941188,31 +941338,31 @@ ], "projections": { "pca": [ - -0.22539028525352478, - -0.11906180530786514 + -0.22556887567043304, + -0.11844589561223984 ], "tsne": [ - -41.701107025146484, - 11.423131942749023 + -38.32255554199219, + -18.795246124267578 ], "umap": [ - 1.6613010168075562, - 5.5234832763671875 + 1.9235926866531372, + 4.883660793304443 ], "pca3d": [ - -0.22538946568965912, - -0.1190987080335617, - -0.11645165830850601 + -0.22556836903095245, + -0.11848393827676773, + -0.11601467430591583 ], "tsne3d": [ - -23.79039192199707, - 8.335015296936035, - 4.398061275482178 + -23.112550735473633, + -16.928878784179688, + 5.99104118347168 ], "umap3d": [ - 1.1559213399887085, - 6.3029255867004395, - 4.930222988128662 + 1.8888013362884521, + 6.658912658691406, + 5.242766857147217 ] }, "cluster_id": 2, @@ -941223,12 +941373,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -941638,47 +941788,47 @@ "journal": "Nature Medicine", "projections": { "pca": [ - 0.018930768594145775, - 0.2917752265930176 + 0.019703155383467674, + 0.2923467457294464 ], "tsne": [ - 16.497272491455078, - -13.956289291381836 + -6.712867736816406, + -3.8551580905914307 ], "umap": [ - 6.617459774017334, - 8.608580589294434 + 6.765079021453857, + 8.283924102783203 ], "pca3d": [ - 0.018929919227957726, - 0.29178717732429504, - 0.0051798513159155846 + 0.019702404737472534, + 0.29235902428627014, + 0.004805873613804579 ], "tsne3d": [ - -0.9985119104385376, - -14.823016166687012, - -7.800093173980713 + -2.8870768547058105, + 0.331432968378067, + -15.620718002319336 ], "umap3d": [ - 5.965902328491211, - 3.01365065574646, - 2.227642059326172 + 6.3014960289001465, + 3.106123208999634, + 2.0484158992767334 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 3, + "label": "Genetic Disease" }, { - "id": 26, - "label": "Genetic Disease" + "id": 28, + "label": "Single Cell Data" } ], "_embedding": [ @@ -942125,47 +942275,47 @@ "openalex_url": "https://openalex.org/W4417164932", "projections": { "pca": [ - 0.008259380236268044, - 0.025197677314281464 + 0.008381273597478867, + 0.025470010936260223 ], "tsne": [ - 14.696086883544922, - -31.039690017700195 + 6.734138011932373, + 32.040462493896484 ], "umap": [ - 6.522207736968994, - 9.687244415283203 + 6.512029647827148, + 9.128358840942383 ], "pca3d": [ - 0.008259525522589684, - 0.025184063240885735, - -0.06746898591518402 + 0.008381403051316738, + 0.025455977767705917, + -0.06740450114011765 ], "tsne3d": [ - 10.342458724975586, - -14.551251411437988, - 2.8322906494140625 + 2.555105686187744, + 9.822149276733398, + -17.030353546142578 ], "umap3d": [ - 4.991516590118408, - 2.8115360736846924, - 1.9785575866699219 + 5.250141143798828, + 2.7828049659729004, + 2.3673319816589355 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 6, + "label": "Single Cell Data" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -942593,31 +942743,31 @@ "doi": "10.64898/2026.04.04.715328", "projections": { "pca": [ - 0.14644411206245422, - -0.004531359765678644 + 0.14647477865219116, + -0.004547470714896917 ], "tsne": [ - 23.079856872558594, - 12.676644325256348 + 25.525737762451172, + -6.320650100708008 ], "umap": [ - 7.224923610687256, - 7.247457027435303 + 7.366044998168945, + 6.9618706703186035 ], "pca3d": [ - 0.14644424617290497, - -0.004550825338810682, - -0.11659461259841919 + 0.14647483825683594, + -0.0045673358254134655, + -0.11650056391954422 ], "tsne3d": [ - 10.21271800994873, - -6.989374160766602, - -12.911558151245117 + 9.43450927734375, + 12.75050163269043, + 5.269099712371826 ], "umap3d": [ - 6.8091912269592285, - 3.865262508392334, - 3.2374677658081055 + 7.238074779510498, + 4.3695902824401855, + 2.3764584064483643 ] }, "cluster_id": 4, @@ -942628,12 +942778,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 22, - "label": "Genetic Variants" + "id": 33, + "label": "Genetic Disease" } ], "_embedding": [ @@ -943058,31 +943208,31 @@ "cited_by_count": 1, "projections": { "pca": [ - 0.19309468567371368, - 0.09165787696838379 + 0.19325920939445496, + 0.09136670082807541 ], "tsne": [ - 37.418128967285156, - -6.247148513793945 + 40.43149948120117, + 11.004719734191895 ], "umap": [ - 9.225067138671875, - 7.997433185577393 + 9.449403762817383, + 7.5787858963012695 ], "pca3d": [ - 0.19309449195861816, - 0.09164343029260635, - -0.19377128779888153 + 0.19325894117355347, + 0.09135191142559052, + -0.19393128156661987 ], "tsne3d": [ - 20.83731460571289, - -11.444231033325195, - 1.570966124534607 + 24.50994110107422, + 7.899654388427734, + -5.416352272033691 ], "umap3d": [ - 7.220742702484131, - 4.023123741149902, - 1.3162920475006104 + 6.753223419189453, + 3.991521120071411, + 0.22738967835903168 ] }, "cluster_id": 0, @@ -943093,12 +943243,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -943521,31 +943671,31 @@ "cited_by_count": 2, "projections": { "pca": [ - -0.028656719252467155, - -0.12591546773910522 + -0.02913459949195385, + -0.1257270723581314 ], "tsne": [ - 17.52614402770996, - -4.298650741577148 + 19.037778854370117, + 1.9278757572174072 ], "umap": [ - 7.806900501251221, - 6.415547847747803 + 8.029438018798828, + 6.0874834060668945 ], "pca3d": [ - -0.028656523674726486, - -0.12590137124061584, - -0.019000450149178505 + -0.029134415090084076, + -0.12571226060390472, + -0.018861213698983192 ], "tsne3d": [ - 12.806922912597656, - 6.135941982269287, - -13.691558837890625 + 13.694332122802734, + 0.6374695897102356, + 6.499991416931152 ], "umap3d": [ - 6.158275127410889, - 5.283522129058838, - 2.7281413078308105 + 6.094096660614014, + 5.274163722991943, + 1.6426209211349487 ] }, "cluster_id": 4, @@ -943556,12 +943706,12 @@ "label": "RNA Sequence Models" }, { - "id": 3, - "label": "RNA Splicing" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -943971,31 +944121,31 @@ "abstract": "The Boeke Lab at NYU Langone aims to decipher the role of substantial portions of the non-coding genome, including disease manifestation.", "projections": { "pca": [ - 0.010910066775977612, - 0.2178560346364975 + 0.01143831480294466, + 0.21791788935661316 ], "tsne": [ - 8.787625312805176, - -16.79481315612793 + 7.786738872528076, + 7.010348796844482 ], "umap": [ - 5.966983795166016, - 6.933063983917236 + 6.072895050048828, + 6.43705940246582 ], "pca3d": [ - 0.010909617878496647, - 0.21787363290786743, - 0.015724478289484978 + 0.01143784448504448, + 0.2179357260465622, + 0.01529315859079361 ], "tsne3d": [ - 7.468028545379639, - -10.940196990966797, - 4.719750881195068 + 1.7491816282272339, + 2.5290143489837646, + -14.585367202758789 ], "umap3d": [ - 4.873115062713623, - 4.290407180786133, - 3.5146663188934326 + 5.333781719207764, + 4.918944835662842, + 3.271817684173584 ] }, "cluster_id": 4, @@ -944006,12 +944156,12 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 11, - "label": "Genomic Language" + "id": 13, + "label": "Genomics Papers" } ], "_embedding": [ @@ -944542,47 +944692,47 @@ "cited_by_count": 14, "projections": { "pca": [ - 0.0582834891974926, - 0.12403832376003265 + 0.05860099196434021, + 0.1242498829960823 ], "tsne": [ - 0.737493634223938, - -30.496667861938477 + -5.113855838775635, + 28.95889663696289 ], "umap": [ - 5.440974712371826, - 9.317591667175293 + 5.580584526062012, + 8.673540115356445 ], "pca3d": [ - 0.05828361213207245, - 0.12400926649570465, - -0.053154997527599335 + 0.058601073920726776, + 0.12422022223472595, + -0.053257159888744354 ], "tsne3d": [ - -0.972503662109375, - -17.65576934814453, - -4.822504043579102 + -3.196704149246216, + 16.78054428100586, + -8.152071952819824 ], "umap3d": [ - 4.60555362701416, - 2.567815065383911, - 2.926485776901245 + 5.303950309753418, + 3.037269115447998, + 3.4068920612335205 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -944990,47 +945140,47 @@ ], "projections": { "pca": [ - -0.08402639627456665, - 0.24852152168750763 + -0.08303096145391464, + 0.24878540635108948 ], "tsne": [ - -7.0858845710754395, - -19.626798629760742 + -9.570876121520996, + 14.394436836242676 ], "umap": [ - 4.128408908843994, - 8.32945442199707 + 4.38604736328125, + 7.613622188568115 ], "pca3d": [ - -0.08402664959430695, - 0.2485170215368271, - 0.09320870786905289 + -0.08303136378526688, + 0.24878095090389252, + 0.09305284172296524 ], "tsne3d": [ - -10.013301849365234, - -8.722128868103027, - 1.3129204511642456 + -4.28473424911499, + 7.937142848968506, + 1.9568527936935425 ], "umap3d": [ - 3.3266563415527344, - 3.7864208221435547, - 3.510056495666504 + 4.139986515045166, + 4.276862621307373, + 3.8433425426483154 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -945441,31 +945591,31 @@ "journal": "Nature Methods", "projections": { "pca": [ - -0.014906282536685467, - 0.07893246412277222 + -0.014508855529129505, + 0.07941422611474991 ], "tsne": [ - 5.466197490692139, - 5.94712495803833 + 7.473333835601807, + -4.219371318817139 ], "umap": [ - 6.1599440574646, - 6.581385135650635 + 6.422091960906982, + 6.179144382476807 ], "pca3d": [ - -0.014906464144587517, - 0.07893937826156616, - 0.030639084056019783 + -0.01450909860432148, + 0.0794217512011528, + 0.03075220435857773 ], "tsne3d": [ - 1.4876474142074585, - 0.6094642877578735, - -11.823944091796875 + 0.4350128173828125, + 2.757418394088745, + 2.047808885574341 ], "umap3d": [ - 5.206231117248535, - 4.542384624481201, - 3.684638500213623 + 5.778744697570801, + 4.996163845062256, + 3.1888792514801025 ] }, "cluster_id": 4, @@ -945476,12 +945626,12 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 11, - "label": "Genomic Language" + "id": 17, + "label": "Genomic Models" } ], "_embedding": [ @@ -945902,47 +946052,47 @@ "doi": "10.1101/2023.02.13.23285820", "projections": { "pca": [ - 0.2259449064731598, - 0.08278577774763107 + 0.22610406577587128, + 0.08244257420301437 ], "tsne": [ - 29.329492568969727, - 3.1965150833129883 + 35.299598693847656, + -5.087724208831787 ], "umap": [ - 7.536017894744873, - 8.109265327453613 + 7.833804607391357, + 7.718467712402344 ], "pca3d": [ - 0.2259448915719986, - 0.08275929093360901, - -0.19533784687519073 + 0.22610393166542053, + 0.08241542428731918, + -0.1954614669084549 ], "tsne3d": [ - 12.54061508178711, - -10.21468734741211, - -6.755239009857178 + 13.339486122131348, + 14.921558380126953, + -3.4241230487823486 ], "umap3d": [ - 6.648322105407715, - 3.5194549560546875, - 2.4133899211883545 + 6.945819854736328, + 3.6567494869232178, + 1.8319240808486938 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -946350,47 +946500,47 @@ ], "projections": { "pca": [ - -0.005078056361526251, - 0.17749404907226562 + -0.00439958181232214, + 0.17770305275917053 ], "tsne": [ - -5.256552696228027, - -5.440974235534668 + -7.499902248382568, + 1.4629775285720825 ], "umap": [ - 4.536858081817627, - 7.016445636749268 + 4.80594539642334, + 6.456691741943359 ], "pca3d": [ - -0.005078118294477463, - 0.17747455835342407, - 0.02431401051580906 + -0.004399722907692194, + 0.17768345773220062, + 0.02419452555477619 ], "tsne3d": [ - -9.34853744506836, - 0.9418858885765076, - 14.024858474731445 + -3.3005495071411133, + -1.3703577518463135, + -5.989682197570801 ], "umap3d": [ - 4.040318489074707, - 4.0760111808776855, - 4.3933844566345215 + 4.8994927406311035, + 4.902945041656494, + 4.373808860778809 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 10, - "label": "Code Development" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -946817,31 +946967,31 @@ "openalex_url": "https://openalex.org/W4412395431", "projections": { "pca": [ - 0.05490315705537796, - 0.029975997284054756 + 0.0550958551466465, + 0.03024235926568508 ], "tsne": [ - 5.511303901672363, - 6.504312038421631 + 7.707203388214111, + -4.654223442077637 ], "umap": [ - 6.018502235412598, - 6.573431015014648 + 6.345828533172607, + 6.189183712005615 ], "pca3d": [ - 0.05490301921963692, - 0.029978973791003227, - 0.0310063399374485 + 0.055095743387937546, + 0.030245225876569748, + 0.031063489615917206 ], "tsne3d": [ - 1.8692868947982788, - 1.8642401695251465, - -12.07813549041748 + 0.027144035324454308, + 2.841714859008789, + 0.8077254891395569 ], "umap3d": [ - 5.169800281524658, - 4.538796901702881, - 3.734471321105957 + 5.697781085968018, + 5.031588554382324, + 3.2256839275360107 ] }, "cluster_id": 4, @@ -946852,12 +947002,12 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 11, - "label": "Genomic Language" + "id": 13, + "label": "Genomics Papers" } ], "_embedding": [ @@ -947406,47 +947556,47 @@ "cited_by_count": 20, "projections": { "pca": [ - 0.20083647966384888, - 0.0676194429397583 + 0.20095565915107727, + 0.06707663089036942 ], "tsne": [ - 31.740631103515625, - 9.391651153564453 + 38.456260681152344, + -8.032955169677734 ], "umap": [ - 7.491179466247559, - 8.08418083190918 + 7.749557971954346, + 7.63100004196167 ], "pca3d": [ - 0.20083630084991455, - 0.06760518252849579, - -0.18045681715011597 + 0.20095540583133698, + 0.06706215441226959, + -0.1806146651506424 ], "tsne3d": [ - 13.845026969909668, - -15.412766456604004, - -9.145477294921875 + 16.46046257019043, + 16.59065818786621, + -3.02529239654541 ], "umap3d": [ - 6.947108745574951, - 3.4528419971466064, - 2.594710111618042 + 7.194267749786377, + 3.599855899810791, + 1.8883397579193115 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 12, - "label": "Genetic Variants" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -947890,46 +948040,46 @@ "openalex_url": "https://openalex.org/W4411690475", "projections": { "pca": [ - 0.06912073493003845, - 0.07512609660625458 + 0.06938299536705017, + 0.0754350945353508 ], "tsne": [ - 11.195152282714844, - -21.49240493774414 + 4.689067363739014, + 20.177196502685547 ], "umap": [ - 6.732420444488525, - 9.139579772949219 + 6.907116413116455, + 8.693184852600098 ], "pca3d": [ - 0.06912035495042801, - 0.07513841241598129, - 0.012094246223568916 + 0.06938264518976212, + 0.07544737309217453, + 0.01199369691312313 ], "tsne3d": [ - 2.154390335083008, - -8.607304573059082, - 11.73795223236084 + 3.387880802154541, + 9.277950286865234, + -9.43794059753418 ], "umap3d": [ - 4.620035171508789, - 3.9613826274871826, - 2.0862319469451904 + 4.776767253875732, + 3.9588804244995117, + 2.1265532970428467 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 6, + "label": "Single Cell Data" }, { - "id": 5, + "id": 28, "label": "Single Cell Data" } ], @@ -948341,31 +948491,31 @@ "abstract": "Tool aims to solve the mystery of non-coding sequences \u2014 but is still in its infancy.", "projections": { "pca": [ - -0.008635610342025757, - 0.10186216235160828 + -0.008352028205990791, + 0.10270065069198608 ], "tsne": [ - -3.6294219493865967, - -2.2920258045196533 + 9.005091667175293, + 5.935425281524658 ], "umap": [ - 5.751439571380615, - 6.766822814941406 + 6.073254108428955, + 6.354360103607178 ], "pca3d": [ - -0.00863562896847725, - 0.10186062008142471, - 0.11607582867145538 + -0.008352109231054783, + 0.10269942134618759, + 0.1159844845533371 ], "tsne3d": [ - -7.220890522003174, - 1.3007612228393555, - 18.23098373413086 + -8.659133911132812, + 14.051301956176758, + 11.089883804321289 ], "umap3d": [ - 4.776618957519531, - 4.36924409866333, - 3.644496440887451 + 5.217494487762451, + 4.919749736785889, + 3.340096950531006 ] }, "cluster_id": 4, @@ -948376,12 +948526,12 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 11, - "label": "Genomic Language" + "id": 13, + "label": "Genomics Papers" } ], "_embedding": [ @@ -948790,31 +948940,31 @@ "title": "AlphaGenome: AI for better understanding the genome \u00e2\u0080\u0094 Google DeepMind", "projections": { "pca": [ - 0.009912869893014431, - 0.04219276085495949 + 0.010122557170689106, + 0.042696624994277954 ], "tsne": [ - -3.5476114749908447, - -2.2233707904815674 + 9.222752571105957, + 5.905340194702148 ], "umap": [ - 5.756112098693848, - 6.752007007598877 + 6.201704025268555, + 6.372114658355713 ], "pca3d": [ - 0.009912719950079918, - 0.04219499230384827, - 0.030872272327542305 + 0.010122410021722317, + 0.04269977658987045, + 0.030996102839708328 ], "tsne3d": [ - -6.945577621459961, - 1.5478395223617554, - 18.579429626464844 + -9.08969497680664, + 14.211094856262207, + 11.1921968460083 ], "umap3d": [ - 4.813205242156982, - 4.4633378982543945, - 3.519608974456787 + 5.169184684753418, + 4.891490459442139, + 3.1503403186798096 ] }, "cluster_id": 4, @@ -948825,12 +948975,12 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 11, - "label": "Genomic Language" + "id": 13, + "label": "Genomics Papers" } ], "_embedding": [ @@ -949245,47 +949395,47 @@ ], "projections": { "pca": [ - -0.14685754477977753, - 0.26200640201568604 + -0.1459185779094696, + 0.26168546080589294 ], "tsne": [ - -7.569891929626465, - -7.674018859863281 + -8.592528343200684, + 2.9236361980438232 ], "umap": [ - 4.308981895446777, - 7.230533599853516 + 4.600485801696777, + 6.643265724182129 ], "pca3d": [ - -0.1468578428030014, - 0.26201680302619934, - -0.002805865602567792 + -0.14591901004314423, + 0.26169538497924805, + -0.003171670949086547 ], "tsne3d": [ - -11.179279327392578, - -3.3187484741210938, - 13.762613296508789 + -7.444673538208008, + 0.7024804353713989, + -5.485352516174316 ], "umap3d": [ - 3.901141881942749, - 3.7651255130767822, - 4.469921588897705 + 4.7921648025512695, + 4.677406311035156, + 4.609971523284912 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -949693,47 +949843,47 @@ "abstract": "Two scientists share the Nobel Prize for Physics for their work on machine learning.", "projections": { "pca": [ - -0.17642106115818024, - 0.204361230134964 + -0.17594781517982483, + 0.20549416542053223 ], "tsne": [ - -8.807928085327148, - -24.49480628967285 + -14.096138954162598, + 15.27753734588623 ], "umap": [ - 3.77344012260437, - 8.44368839263916 + 3.949579954147339, + 7.867544651031494 ], "pca3d": [ - -0.17642152309417725, - 0.20437496900558472, - 0.09331102669239044 + -0.17594827711582184, + 0.20550833642482758, + 0.0930156335234642 ], "tsne3d": [ - -11.407374382019043, - -7.9491705894470215, - -1.409081220626831 + -11.194040298461914, + 11.119799613952637, + 0.9659376740455627 ], "umap3d": [ - 3.021827220916748, - 3.7992799282073975, - 3.324702262878418 + 3.7235915660858154, + 4.208341121673584, + 3.771723985671997 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -950153,47 +950303,47 @@ ], "projections": { "pca": [ - -0.06482767313718796, - -0.03925023227930069 + -0.06510530412197113, + -0.03889165818691254 ], "tsne": [ - 12.94710636138916, - -32.966793060302734 + 4.877919673919678, + 33.89569091796875 ], "umap": [ - 6.158411502838135, - 9.736088752746582 + 6.2367024421691895, + 9.169258117675781 ], "pca3d": [ - -0.0648273453116417, - -0.03925913944840431, - -0.15764343738555908 + -0.06510505080223083, + -0.038900770246982574, + -0.1575511395931244 ], "tsne3d": [ - 8.01186752319336, - -16.360578536987305, - 2.2339072227478027 + 1.1469554901123047, + 12.512697219848633, + -17.919788360595703 ], "umap3d": [ - 4.883357524871826, - 2.664041042327881, - 2.127992630004883 + 5.217000961303711, + 2.664661407470703, + 2.6000823974609375 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -950613,47 +950763,47 @@ "cited_by_count": 555, "projections": { "pca": [ - -0.2977985441684723, - 0.035021860152482986 + -0.29767563939094543, + 0.0359857976436615 ], "tsne": [ - -22.852996826171875, - -24.47728157043457 + -21.526464462280273, + 11.701231956481934 ], "umap": [ - 2.73050594329834, - 7.2270588874816895 + 3.0327818393707275, + 6.547443866729736 ], "pca3d": [ - -0.29779863357543945, - 0.035040877759456635, - 0.007936092093586922 + -0.2976755201816559, + 0.03600531443953514, + 0.008015998639166355 ], "tsne3d": [ - -15.73603343963623, - -3.563131093978882, - -6.847687244415283 + -15.096428871154785, + -4.401777267456055, + -5.110447883605957 ], "umap3d": [ - 2.219616174697876, - 4.703867435455322, - 3.6612071990966797 + 2.959056854248047, + 5.149991512298584, + 4.158771514892578 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { "id": 0, - "label": "LLM Language Learning" + "label": "Math AI" } ], "_embedding": [ @@ -951073,31 +951223,31 @@ ], "projections": { "pca": [ - 0.09052270650863647, - -0.12981846928596497 + 0.09036926180124283, + -0.12985435128211975 ], "tsne": [ - -8.940900802612305, - 30.56522560119629 + -5.476442337036133, + -30.497360229492188 ], "umap": [ - 5.150238037109375, - 3.808027982711792 + 5.834652423858643, + 3.344242811203003 ], "pca3d": [ - 0.09052305668592453, - -0.12983722984790802, - 0.20489278435707092 + 0.09036961942911148, + -0.12987320125102997, + 0.20500046014785767 ], "tsne3d": [ - -4.47648811340332, - 13.26202392578125, - 8.1405668258667 + -2.0723650455474854, + -18.28120994567871, + 4.870906829833984 ], "umap3d": [ - 4.599512577056885, - 6.949568271636963, - 5.158866882324219 + 5.241510391235352, + 7.791646480560303, + 3.6209566593170166 ] }, "cluster_id": 3, @@ -951108,11 +951258,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -951535,31 +951685,31 @@ ], "projections": { "pca": [ - 0.12238297611474991, - -0.14605219662189484 + 0.12219700217247009, + -0.1460648775100708 ], "tsne": [ - -8.760723114013672, - 30.454797744750977 + -5.336265563964844, + -30.394025802612305 ], "umap": [ - 5.178661823272705, - 3.8474068641662598 + 5.770423412322998, + 3.4191813468933105 ], "pca3d": [ - 0.12238321453332901, - -0.1460762917995453, - 0.2122536152601242 + 0.12219733744859695, + -0.1460893452167511, + 0.21241042017936707 ], "tsne3d": [ - -4.060031890869141, - 12.988927841186523, - 7.765318393707275 + -1.855476975440979, + -17.750289916992188, + 4.712710857391357 ], "umap3d": [ - 4.781401634216309, - 6.890741348266602, - 5.165098667144775 + 5.3120598793029785, + 7.797024250030518, + 3.581648588180542 ] }, "cluster_id": 3, @@ -951570,11 +951720,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -951983,47 +952133,47 @@ ], "projections": { "pca": [ - -0.043847713619470596, - 0.19100764393806458 + -0.04314028471708298, + 0.19082456827163696 ], "tsne": [ - -9.759416580200195, - -4.80084753036499 + -19.081859588623047, + -12.773180961608887 ], "umap": [ - 4.874655723571777, - 7.059804439544678 + 4.836954593658447, + 6.21771240234375 ], "pca3d": [ - -0.04384820535778999, - 0.19102612137794495, - 0.03149951994419098 + -0.04314080625772476, + 0.19084304571151733, + 0.031229648739099503 ], "tsne3d": [ - -12.49142837524414, - 4.888429164886475, - 9.595047950744629 + -9.598893165588379, + -5.227080345153809, + -4.52602481842041 ], "umap3d": [ - 4.375463962554932, - 3.9459304809570312, - 4.591494560241699 + 5.178709506988525, + 4.9924516677856445, + 4.504371643066406 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, - "label": "Genomics Papers" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -952434,31 +952584,31 @@ "journal": "Nature Machine Intelligence", "projections": { "pca": [ - 0.011942517012357712, - -0.0038690438959747553 + 0.011924874037504196, + -0.0038429731503129005 ], "tsne": [ - 15.520184516906738, - -5.413122653961182 + 16.205495834350586, + 8.766281127929688 ], "umap": [ - 8.473705291748047, - 6.415807247161865 + 8.679084777832031, + 6.064789295196533 ], "pca3d": [ - 0.01194233912974596, - -0.003857087343931198, - -0.00011288490350125358 + 0.01192469336092472, + -0.003830892499536276, + -0.000134372225147672 ], "tsne3d": [ - 16.494304656982422, - 7.3339524269104, - -15.508676528930664 + 16.163236618041992, + -0.07727131992578506, + 8.708259582519531 ], "umap3d": [ - 6.478354454040527, - 5.456598281860352, - 2.192213296890259 + 6.225752830505371, + 5.397468090057373, + 1.0533281564712524 ] }, "cluster_id": 4, @@ -952469,12 +952619,12 @@ "label": "RNA Sequence Models" }, { - "id": 3, - "label": "RNA Splicing" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -952882,47 +953032,47 @@ "journal": "Nature Biotechnology", "projections": { "pca": [ - -0.10773896425962448, - 0.3792768716812134 + -0.10674341768026352, + 0.3805071711540222 ], "tsne": [ - -12.325438499450684, - -11.190918922424316 + -14.256050109863281, + 5.307842254638672 ], "umap": [ - 3.8950605392456055, - 7.428738594055176 + 4.191976070404053, + 6.800646781921387 ], "pca3d": [ - -0.10773985832929611, - 0.3793112635612488, - 0.09795223921537399 + -0.10674449801445007, + 0.38054168224334717, + 0.09743661433458328 ], "tsne3d": [ - -5.896590232849121, - -8.538869857788086, - 13.869983673095703 + -8.805241584777832, + 1.206250548362732, + -11.204669952392578 ], "umap3d": [ - 3.3880393505096436, - 3.551346778869629, - 4.565585613250732 + 4.480221748352051, + 4.4535088539123535, + 4.883244037628174 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -953333,31 +953483,31 @@ "journal": "Nature Genetics", "projections": { "pca": [ - -0.019688166677951813, - 0.0380265973508358 + -0.01963958330452442, + 0.038722723722457886 ], "tsne": [ - 11.446826934814453, - -4.59051513671875 + 11.268521308898926, + 6.473955154418945 ], "umap": [ - 5.977210521697998, - 6.876851558685303 + 6.290113925933838, + 6.447799205780029 ], "pca3d": [ - -0.01968831941485405, - 0.03802984580397606, - -0.03170191869139671 + -0.019639767706394196, + 0.03872648999094963, + -0.03164258226752281 ], "tsne3d": [ - 16.70501136779785, - 2.5330827236175537, - -17.829660415649414 + 13.037079811096191, + 5.535983085632324, + 9.71383285522461 ], "umap3d": [ - 4.990301609039307, - 4.35407018661499, - 3.557875394821167 + 5.418300628662109, + 4.827186584472656, + 3.1664042472839355 ] }, "cluster_id": 4, @@ -953368,12 +953518,12 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 11, - "label": "Genomic Language" + "id": 13, + "label": "Genomics Papers" } ], "_embedding": [ @@ -953798,31 +953948,31 @@ "openalex_url": "https://openalex.org/W4388744043", "projections": { "pca": [ - 0.09985111653804779, - -0.10709311813116074 + 0.09948701411485672, + -0.10710815340280533 ], "tsne": [ - 15.624632835388184, - -5.202737808227539 + 16.279590606689453, + 8.894795417785645 ], "umap": [ - 8.26496410369873, - 6.391109466552734 + 8.53663444519043, + 6.012247085571289 ], "pca3d": [ - 0.09985120594501495, - -0.10709729045629501, - 0.0062091900035738945 + 0.09948716312646866, + -0.10711220651865005, + 0.006286496762186289 ], "tsne3d": [ - 16.037353515625, - 7.660394191741943, - -15.48136043548584 + 15.899421691894531, + -0.49679577350616455, + 8.511049270629883 ], "umap3d": [ - 6.341506004333496, - 5.395565509796143, - 2.2298851013183594 + 6.13154411315918, + 5.371737480163574, + 1.12362539768219 ] }, "cluster_id": 4, @@ -953833,12 +953983,12 @@ "label": "RNA Sequence Models" }, { - "id": 3, - "label": "RNA Splicing" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -954246,31 +954396,31 @@ "abstract": "surrogate quantitative interpretability for deepnets - evanseitz/squid-nn", "projections": { "pca": [ - -0.16201873123645782, - 0.031608764082193375 + -0.16185155510902405, + 0.031578946858644485 ], "tsne": [ - -0.09787658601999283, - 3.7812392711639404 + 16.068126678466797, + 9.439002990722656 ], "umap": [ - 8.291550636291504, - 6.4128546714782715 + 8.52724552154541, + 5.996687889099121 ], "pca3d": [ - -0.1620187610387802, - 0.03161148726940155, - 0.06396125257015228 + -0.16185151040554047, + 0.03158195689320564, + 0.0638965293765068 ], "tsne3d": [ - -6.114843845367432, - 15.671923637390137, - -7.1460347175598145 + 1.480696201324463, + -10.922789573669434, + -17.12830924987793 ], "umap3d": [ - 6.251132965087891, - 5.519245147705078, - 2.1615068912506104 + 6.053674221038818, + 5.418793201446533, + 1.1403225660324097 ] }, "cluster_id": 4, @@ -954281,12 +954431,12 @@ "label": "RNA Sequence Models" }, { - "id": 3, - "label": "RNA Splicing" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -954692,47 +954842,47 @@ ], "projections": { "pca": [ - -0.08694855123758316, - 0.32195723056793213 + -0.08604422211647034, + 0.3221593499183655 ], "tsne": [ - -10.206076622009277, - -2.6177470684051514 + -19.3123779296875, + -38.982704162597656 ], "umap": [ - 4.075353145599365, - 7.199777126312256 + 4.264029502868652, + 6.475159168243408 ], "pca3d": [ - -0.08694916218519211, - 0.3219796121120453, - 0.10823041200637817 + -0.08604496717453003, + 0.32218214869499207, + 0.10761554539203644 ], "tsne3d": [ - -5.147836685180664, - -2.972033739089966, - 19.1253719329834 + -6.909554481506348, + -5.848578453063965, + -7.852895259857178 ], "umap3d": [ - 3.8041036128997803, - 3.9240987300872803, - 4.671171188354492 + 4.685286045074463, + 4.830613613128662, + 4.767302513122559 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -955146,47 +955296,47 @@ ], "projections": { "pca": [ - -0.23469901084899902, - -0.07386014610528946 + -0.23488354682922363, + -0.07303490489721298 ], "tsne": [ - -47.63904571533203, - -18.356616973876953 + -52.5516242980957, + -3.312385082244873 ], "umap": [ - 0.8433691263198853, - 6.322362422943115 + 1.0942894220352173, + 5.441932678222656 ], "pca3d": [ - -0.2346986085176468, - -0.07388099282979965, - -0.11058153212070465 + -0.2348831743001938, + -0.07305613905191422, + -0.11030693352222443 ], "tsne3d": [ - -30.65303611755371, - -8.454180717468262, - -0.24152015149593353 + -29.039424896240234, + -2.692993402481079, + -5.393224239349365 ], "umap3d": [ - 0.6734892129898071, - 5.6291184425354, - 3.8421528339385986 + 1.299241065979004, + 5.503438472747803, + 4.437376976013184 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 23, + "label": "Training Noise" } ], "_embedding": [ @@ -955596,47 +955746,47 @@ "journal": "eLife", "projections": { "pca": [ - -0.14857542514801025, - 0.4418215751647949 + -0.1474345475435257, + 0.4426409900188446 ], "tsne": [ - -10.424232482910156, - -12.14294147491455 + -16.194683074951172, + 6.4034037590026855 ], "umap": [ - 3.775963544845581, - 7.5257744789123535 + 4.072937488555908, + 6.853865146636963 ], "pca3d": [ - -0.14857642352581024, - 0.4418596923351288, - 0.04171845689415932 + -0.14743570983409882, + 0.4426790177822113, + 0.04108944535255432 ], "tsne3d": [ - -4.047145366668701, - -11.239858627319336, - 14.36264705657959 + -9.527441024780273, + 4.247167587280273, + -11.658141136169434 ], "umap3d": [ - 3.3011269569396973, - 3.5337178707122803, - 4.578661918640137 + 4.3306145668029785, + 4.253093242645264, + 4.890915870666504 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -956056,31 +956206,31 @@ "year": 2022, "projections": { "pca": [ - -0.08853697776794434, - -0.05673474818468094 + -0.08872708678245544, + -0.05639035999774933 ], "tsne": [ - -49.95328903198242, - -23.08621597290039 + -51.86240768432617, + 13.275415420532227 ], "umap": [ - 0.34578487277030945, - 5.765531539916992 + 0.7129444479942322, + 4.863933563232422 ], "pca3d": [ - -0.08853700011968613, - -0.05671481043100357, - -0.08597131818532944 + -0.08872714638710022, + -0.05636994168162346, + -0.08581521362066269 ], "tsne3d": [ - -31.441545486450195, - -13.869210243225098, - -0.4019613564014435 + -22.1311092376709, + 4.307641506195068, + 19.19740104675293 ], "umap3d": [ - 0.4853527247905731, - 5.324682235717773, - 3.4807372093200684 + 1.1218419075012207, + 5.224150657653809, + 4.243856906890869 ] }, "cluster_id": 2, @@ -956091,12 +956241,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, - "label": "Training Noise" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -956519,31 +956669,31 @@ "cited_by_count": 4, "projections": { "pca": [ - 0.22444488108158112, - 0.0015238445485010743 + 0.22440555691719055, + 0.0007359115988947451 ], "tsne": [ - 39.12333679199219, - -12.218457221984863 + 34.88998031616211, + 18.52645492553711 ], "umap": [ - 9.204519271850586, - 8.60692024230957 + 9.42468547821045, + 8.151604652404785 ], "pca3d": [ - 0.22444449365139008, - 0.0015403787838295102, - -0.21115587651729584 + 0.2244052290916443, + 0.0007528042187914252, + -0.2112293541431427 ], "tsne3d": [ - 19.366907119750977, - -4.669903755187988, - 6.769171714782715 + 18.26507568359375, + 6.082036972045898, + -12.032418251037598 ], "umap3d": [ - 6.955920696258545, - 3.8741235733032227, - 1.116929054260254 + 6.619019985198975, + 3.6043357849121094, + 0.4676365256309509 ] }, "cluster_id": 0, @@ -956554,12 +956704,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 21, - "label": "Single Cell Genomics" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -956969,31 +957119,31 @@ "journal": "Nature Machine Intelligence", "projections": { "pca": [ - -0.05763177201151848, - -0.033643800765275955 + -0.05771508440375328, + -0.03345765918493271 ], "tsne": [ - 11.862454414367676, - -5.6305928230285645 + 12.2573881149292, + 6.970510482788086 ], "umap": [ - 7.982234954833984, - 6.960118770599365 + 8.300355911254883, + 6.707885265350342 ], "pca3d": [ - -0.057631965726614, - -0.03360738977789879, - -0.15761563181877136 + -0.05771525949239731, + -0.03342035785317421, + -0.15742294490337372 ], "tsne3d": [ - 16.97162628173828, - 1.7862282991409302, - -15.739496231079102 + 14.259337425231934, + 4.782851219177246, + 11.324872970581055 ], "umap3d": [ - 6.437014102935791, - 4.568729400634766, - 2.309645175933838 + 6.321684837341309, + 4.631547927856445, + 1.56071138381958 ] }, "cluster_id": 4, @@ -957004,12 +957154,12 @@ "label": "RNA Sequence Models" }, { - "id": 13, - "label": "Genetic Variants" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -957420,46 +957570,46 @@ ], "projections": { "pca": [ - -0.3008115291595459, - -0.15547211468219757 + -0.301239550113678, + -0.15482856333255768 ], "tsne": [ - -49.94071960449219, - 0.8634326457977295 + -50.2142333984375, + -1.9964865446090698 ], "umap": [ - 0.6030241847038269, - 6.778114318847656 + 0.7404183745384216, + 6.064043045043945 ], "pca3d": [ - -0.30081117153167725, - -0.1554803103208542, - -0.12418628484010696 + -0.3012390732765198, + -0.15483656525611877, + -0.12383906543254852 ], "tsne3d": [ - -29.0576114654541, - -0.19538764655590057, - 2.1429660320281982 + -29.232746124267578, + -6.776056289672852, + 6.655426025390625 ], "umap3d": [ - 0.6013328433036804, - 5.6572489738464355, - 4.372288227081299 + 1.372619390487671, + 5.8343377113342285, + 5.20636510848999 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -957872,47 +958022,47 @@ ], "projections": { "pca": [ - -0.2935400605201721, - -0.1460416615009308 + -0.29389017820358276, + -0.14551545679569244 ], "tsne": [ - -49.8723030090332, - 0.9449042677879333 + -50.29789733886719, + -1.9560155868530273 ], "umap": [ - 0.7488806843757629, - 6.525879383087158 + 0.8366575241088867, + 5.911770343780518 ], "pca3d": [ - -0.2935395836830139, - -0.14605142176151276, - -0.11014001816511154 + -0.2938896119594574, + -0.14552511274814606, + -0.10980897396802902 ], "tsne3d": [ - -29.362625122070312, - -0.2555358409881592, - 2.2154555320739746 + -29.41786003112793, + -6.855673313140869, + 6.866933345794678 ], "umap3d": [ - 0.6599212288856506, - 5.746158123016357, - 4.303176403045654 + 1.3957842588424683, + 5.915476322174072, + 5.031597137451172 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 19, - "label": "Neural Networks" + "id": 23, + "label": "Training Noise" } ], "_embedding": [ @@ -958329,31 +958479,31 @@ "doi": "10.1101/2022.08.05.502903", "projections": { "pca": [ - 0.1888558715581894, - -0.09870543330907822 + 0.18860936164855957, + -0.09916719049215317 ], "tsne": [ - 38.13322067260742, - -1.6860995292663574 + 32.48945617675781, + 9.434676170349121 ], "umap": [ - 9.057169914245605, - 7.09804105758667 + 9.24514102935791, + 6.743556022644043 ], "pca3d": [ - 0.1888558566570282, - -0.09869826585054398, - -0.08618847280740738 + 0.18860939145088196, + -0.09916022419929504, + -0.08612736314535141 ], "tsne3d": [ - 21.452774047851562, - 2.9098153114318848, - -2.6858913898468018 + 21.89313507080078, + -0.13308437168598175, + -2.5065104961395264 ], "umap3d": [ - 7.185978889465332, - 4.783454418182373, - 1.6696287393569946 + 6.9248833656311035, + 4.719544887542725, + 0.5486895442008972 ] }, "cluster_id": 0, @@ -958364,11 +958514,11 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], @@ -958781,47 +958931,47 @@ ], "projections": { "pca": [ - -0.25982174277305603, - 0.16576430201530457 + -0.25923535227775574, + 0.1663656085729599 ], "tsne": [ - -29.072467803955078, - -6.135740756988525 + -27.786117553710938, + 6.309976577758789 ], "umap": [ - 1.5369079113006592, - 8.03187084197998 + 1.3638191223144531, + 7.147773742675781 ], "pca3d": [ - -0.25982171297073364, - 0.16574564576148987, - 0.05206187441945076 + -0.25923529267311096, + 0.16634635627269745, + 0.05196142569184303 ], "tsne3d": [ - -15.020589828491211, - -10.751230239868164, - 13.257885932922363 + -12.454440116882324, + 2.481247663497925, + 4.063940525054932 ], "umap3d": [ - 1.1598734855651855, - 4.683012962341309, - 4.774801254272461 + 2.44042706489563, + 4.985482215881348, + 5.466858386993408 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 7, - "label": "Language Models" + "id": 14, + "label": "Transformer Attention" } ], "_embedding": [ @@ -959241,47 +959391,47 @@ "doi": "10.1016/j.cmpb.2023.107631", "projections": { "pca": [ - -0.0979522094130516, - -0.009665964171290398 + -0.09792796522378922, + -0.009491661563515663 ], "tsne": [ - 14.037775993347168, - -33.60905456542969 + 6.046870231628418, + 34.68006134033203 ], "umap": [ - 6.111987113952637, - 9.769086837768555 + 6.207977294921875, + 9.165017127990723 ], "pca3d": [ - -0.09795188158750534, - -0.009683089330792427, - -0.19727030396461487 + -0.09792767465114594, + -0.009509027004241943, + -0.19706708192825317 ], "tsne3d": [ - 7.6084513664245605, - -16.336240768432617, - 4.325039386749268 + 1.315827488899231, + 12.416889190673828, + -19.879791259765625 ], "umap3d": [ - 4.829659938812256, - 2.631514310836792, - 2.1109673976898193 + 5.196288585662842, + 2.646210193634033, + 2.6092848777770996 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -959692,47 +959842,47 @@ "abstract": "Benchmarking toolkit for patch-based histopathology image classification. - SBU-BMI/champkit", "projections": { "pca": [ - -0.09899309277534485, - 0.1620008945465088 + -0.098484106361866, + 0.16215774416923523 ], "tsne": [ - 14.45338249206543, - -33.71525573730469 + 6.453124046325684, + 34.8707160949707 ], "umap": [ - 6.147566795349121, - 9.765979766845703 + 6.1727471351623535, + 9.212532997131348 ], "pca3d": [ - -0.09899303317070007, - 0.16197721660137177, - -0.1552637666463852 + -0.09848403930664062, + 0.16213367879390717, + -0.15537071228027344 ], "tsne3d": [ - 7.317217826843262, - -15.89344596862793, - 5.0523152351379395 + 1.7063075304031372, + 12.730508804321289, + -20.603525161743164 ], "umap3d": [ - 4.817182540893555, - 2.640169620513916, - 2.0953738689422607 + 5.176140785217285, + 2.6304733753204346, + 2.551924228668213 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 10, + "label": "Medical AI" }, { - "id": 8, - "label": "Clinical Health" + "id": 5, + "label": "Clinical Cancer" } ], "_embedding": [ @@ -960140,47 +960290,47 @@ "abstract": "Minimal setup.py project that meows at you. Contribute to kaczmarj/minimal-setup.py-project development by creating an account on GitHub.", "projections": { "pca": [ - -0.1351739466190338, - 0.276917964220047 + -0.13427530229091644, + 0.2774648070335388 ], "tsne": [ - -28.943517684936523, - 31.27168846130371 + -15.647927284240723, + -5.828418731689453 ], "umap": [ - 3.9066081047058105, - 6.521692276000977 + 4.263011455535889, + 6.0161452293396 ], "pca3d": [ - -0.13517440855503082, - 0.2769300937652588, - 0.06390498578548431 + -0.13427574932575226, + 0.2774772644042969, + 0.06355903297662735 ], "tsne3d": [ - -13.035148620605469, - 15.826618194580078, - 13.658223152160645 + -11.476201057434082, + -3.853609323501587, + 13.489148139953613 ], "umap3d": [ - 3.633622646331787, - 4.687432289123535, - 4.486175060272217 + 4.522390842437744, + 5.44313907623291, + 4.320915699005127 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 10, - "label": "Code Development" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -960588,47 +960738,47 @@ "journal": "Nature", "projections": { "pca": [ - -0.14201515913009644, - 0.41810736060142517 + -0.14076153934001923, + 0.4180419147014618 ], "tsne": [ - -9.348557472229004, - -13.493083000183105 + -16.060062408447266, + 8.316840171813965 ], "umap": [ - 3.8982646465301514, - 7.630184173583984 + 4.097850799560547, + 6.9229936599731445 ], "pca3d": [ - -0.1420159935951233, - 0.4181419909000397, - 0.025820424780249596 + -0.14076264202594757, + 0.41807618737220764, + 0.025191383436322212 ], "tsne3d": [ - -3.853562831878662, - -13.180460929870605, - 12.397119522094727 + -8.591399192810059, + 5.84053897857666, + -9.446940422058105 ], "umap3d": [ - 3.4065592288970947, - 3.442063331604004, - 4.489052772521973 + 4.412783622741699, + 4.2199530601501465, + 4.826449871063232 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -961035,47 +961185,47 @@ "title": "Some Thoughts on Giving a Good Talk About Your Research", "projections": { "pca": [ - -0.13166137039661407, - 0.4387575685977936 + -0.13048960268497467, + 0.4392901062965393 ], "tsne": [ - -11.841516494750977, - -11.92740249633789 + -14.793168067932129, + 6.007498264312744 ], "umap": [ - 3.818568706512451, - 7.48941707611084 + 4.053967475891113, + 6.824830055236816 ], "pca3d": [ - -0.13166271150112152, - 0.43882638216018677, - 0.020673952996730804 + -0.13049104809761047, + 0.43935930728912354, + 0.02005045674741268 ], "tsne3d": [ - -4.551549911499023, - -8.916190147399902, - 13.819183349609375 + -8.118255615234375, + 2.246436595916748, + -11.758176803588867 ], "umap3d": [ - 3.324732780456543, - 3.5120153427124023, - 4.5760111808776855 + 4.360799789428711, + 4.3296074867248535, + 4.9373393058776855 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -961483,47 +961633,47 @@ "abstract": "Publication-ready NN-architecture schematics. . Contribute to alexlenail/NN-SVG development by creating an account on GitHub.", "projections": { "pca": [ - -0.14674024283885956, - 0.12735283374786377 + -0.14637605845928192, + 0.12723936140537262 ], "tsne": [ - -25.458541870117188, - 30.51341438293457 + -18.429641723632812, + -9.09161376953125 ], "umap": [ - 3.8099045753479004, - 6.306908130645752 + 4.107426166534424, + 5.664977550506592 ], "pca3d": [ - -0.14674025774002075, - 0.1273508369922638, - 0.027353452518582344 + -0.14637616276741028, + 0.12723764777183533, + 0.027091525495052338 ], "tsne3d": [ - -16.756999969482422, - 14.362641334533691, - 14.782157897949219 + -8.351776123046875, + -5.629316806793213, + 15.60744857788086 ], "umap3d": [ - 3.510504722595215, - 5.083131790161133, - 4.446963310241699 + 4.441407680511475, + 5.782493591308594, + 4.231832027435303 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 10, - "label": "Code Development" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -961930,47 +962080,47 @@ "title": "IEEE Xplore Full-Text PDF:", "projections": { "pca": [ - -0.3178330361843109, - 0.061456348747015 + -0.3174041211605072, + 0.061593182384967804 ], "tsne": [ - -40.59302520751953, - -10.947230339050293 + -30.39232063293457, + -0.9349092841148376 ], "umap": [ - 2.0216054916381836, - 6.845162868499756 + 1.77363920211792, + 5.932971000671387 ], "pca3d": [ - -0.3178335726261139, - 0.06148889288306236, - -0.03066376969218254 + -0.3174044191837311, + 0.06162615120410919, + -0.03053494542837143 ], "tsne3d": [ - -17.881723403930664, - -7.1450042724609375, - -11.50270938873291 + -13.27658748626709, + -2.22231125831604, + -3.133424997329712 ], "umap3d": [ - 1.5483345985412598, - 5.265750408172607, - 4.079633712768555 + 2.191190719604492, + 5.462099075317383, + 4.699315547943115 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { "id": 8, - "label": "Attention Vision" + "label": "Network Learning" }, { - "id": 33, - "label": "Neural Networks" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -962381,47 +962531,47 @@ ], "projections": { "pca": [ - -0.1399933248758316, - 0.1103246659040451 + -0.13951842486858368, + 0.11051750183105469 ], "tsne": [ - -32.102294921875, - 4.592495918273926 + -29.298585891723633, + -5.547907829284668 ], "umap": [ - 2.6741302013397217, - 6.994056224822998 + 3.0292444229125977, + 6.281732559204102 ], "pca3d": [ - -0.1399933248758316, - 0.1103203073143959, - 0.022085346281528473 + -0.13951849937438965, + 0.11051316559314728, + 0.02197200618684292 ], "tsne3d": [ - -14.295733451843262, - -0.6641315817832947, - -7.707006454467773 + -11.930344581604004, + -6.501957416534424, + -4.156579971313477 ], "umap3d": [ - 2.283526659011841, - 4.975858688354492, - 3.7173571586608887 + 2.947322368621826, + 5.355672359466553, + 4.169334888458252 ] }, - "cluster_id": 2, - "cluster_label": "Generative Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 2, - "label": "Generative Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 33, - "label": "Neural Networks" + "id": 19, + "label": "Graph Neural Nets" } ], "_embedding": [ @@ -962833,47 +962983,47 @@ "abstract": "State your main finding in your title, and don\u2019t forget to use the word \u2018but\u2019, says Bruce Kirchoff.", "projections": { "pca": [ - -0.0663885548710823, - 0.3795640170574188 + -0.06535211950540543, + 0.3806076943874359 ], "tsne": [ - -9.96555233001709, - -11.607093811035156 + -16.800172805786133, + 5.870910167694092 ], "umap": [ - 3.7608587741851807, - 7.436174392700195 + 4.051205635070801, + 6.762818336486816 ], "pca3d": [ - -0.06638925522565842, - 0.37958282232284546, - 0.06087089329957962 + -0.06535299122333527, + 0.38062649965286255, + 0.060396645218133926 ], "tsne3d": [ - -3.772881269454956, - -11.692757606506348, - 16.261821746826172 + -10.772491455078125, + 4.881794452667236, + -13.029601097106934 ], "umap3d": [ - 3.295045852661133, - 3.5065736770629883, - 4.633844375610352 + 4.342374324798584, + 4.329380989074707, + 4.933988571166992 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -963281,47 +963431,47 @@ "abstract": "\ud83d\udcfa Discover the latest machine learning / AI courses on YouTube. - dair-ai/ML-YouTube-Courses", "projections": { "pca": [ - -0.2606274485588074, - 0.15126477181911469 + -0.2601063847541809, + 0.1519792079925537 ], "tsne": [ - -19.170936584472656, - -7.95685338973999 + -18.978710174560547, + -0.6637271642684937 ], "umap": [ - 3.463663339614868, - 7.151415824890137 + 3.738966226577759, + 6.540885925292969 ], "pca3d": [ - -0.26062771677970886, - 0.15128415822982788, - 0.051884185522794724 + -0.2601066827774048, + 0.15199865400791168, + 0.051783934235572815 ], "tsne3d": [ - -9.197676658630371, - 5.525413990020752, - -8.237530708312988 + -7.860789775848389, + 1.1279079914093018, + 3.12186598777771 ], "umap3d": [ - 3.392331600189209, - 4.425320625305176, - 4.201909065246582 + 4.146857738494873, + 5.050143718719482, + 4.3495097160339355 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -963729,46 +963879,46 @@ "abstract": "This book covers the statistical models and methods that are used to understand human genetics, following the historical and recent developments of human genetics. Starting with Mendel\u2019s first experiments to genome-wide association studies, the book describes how genetic information can be incorporated into statistical models to discover disease genes.\u00a0All commonly used approaches in statistical genetics (e.g. aggregation analysis, segregation, linkage analysis, etc), are used, but the focus of the book is modern approaches to association analysis.\u00a0Numerous examples illustrate key points throughout the text, both of Mendelian and complex genetic disorders.\nThe intended audience is statisticians, biostatisticians, epidemiologists and quantitatively- oriented geneticists and health scientists wanting to learn about statistical methods for genetic analysis, whether to better analyze genetic data, or to pursue research in methodology.\u00a0A background in intermediate level statistical methods is required.\u00a0The authors include few\u00a0mathematical derivations, and the exercises provide problems for students with a broad range of skill levels.\u00a0No background in genetics is assumed.", "projections": { "pca": [ - 0.036161500960588455, - 0.17769581079483032 + 0.03663340583443642, + 0.17786309123039246 ], "tsne": [ - 22.12856674194336, - 5.588370323181152 + 34.400550842285156, + -17.19241714477539 ], "umap": [ - 6.675097465515137, - 7.79258918762207 + 6.843578338623047, + 7.444607257843018 ], "pca3d": [ - 0.03616109862923622, - 0.17769679427146912, - -0.04442698881030083 + 0.03663298860192299, + 0.1778642237186432, + -0.044665876775979996 ], "tsne3d": [ - 9.868233680725098, - -15.87605094909668, - -18.160818099975586 + 8.150108337402344, + 23.399394989013672, + 5.459255218505859 ], "umap3d": [ - 6.4422430992126465, - 3.295637845993042, - 3.144836902618408 + 7.007055759429932, + 3.6772332191467285, + 2.5744123458862305 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 26, + "id": 33, "label": "Genetic Disease" } ], @@ -964185,46 +964335,46 @@ "cited_by_count": 6273, "projections": { "pca": [ - 0.021751368418335915, - 0.21422971785068512 + 0.022416256368160248, + 0.21388021111488342 ], "tsne": [ - 22.126304626464844, - 5.579768180847168 + 34.40635299682617, + -17.212385177612305 ], "umap": [ - 6.797492980957031, - 7.876988410949707 + 6.894659996032715, + 7.533685684204102 ], "pca3d": [ - 0.021751005202531815, - 0.2142292708158493, - -0.019490698352456093 + 0.022415829822421074, + 0.21387967467308044, + -0.01983831822872162 ], "tsne3d": [ - 9.893645286560059, - -15.971766471862793, - -18.232498168945312 + 8.148048400878906, + 23.450361251831055, + 5.530241966247559 ], "umap3d": [ - 6.58480167388916, - 3.221966505050659, - 2.9923455715179443 + 7.136273384094238, + 3.5538806915283203, + 2.434337854385376 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 26, + "id": 33, "label": "Genetic Disease" } ], @@ -964635,47 +964785,47 @@ "abstract": "Bela Z. Schmidt\u2019s quest to understand an all-too-familiar career setback can be distilled into eight pieces of advice.", "projections": { "pca": [ - -0.10530753433704376, - 0.36687660217285156 + -0.10433982312679291, + 0.36746177077293396 ], "tsne": [ - -13.561969757080078, - -13.257104873657227 + -12.884292602539062, + 6.881444931030273 ], "umap": [ - 3.8753461837768555, - 7.537868499755859 + 4.230580806732178, + 6.9267659187316895 ], "pca3d": [ - -0.10530861467123032, - 0.3669041395187378, - -0.01477185171097517 + -0.10434074699878693, + 0.36748939752578735, + -0.015313362702727318 ], "tsne3d": [ - -3.4969818592071533, - -7.615434646606445, - 11.062955856323242 + -11.903294563293457, + 1.0233819484710693, + -9.397225379943848 ], "umap3d": [ - 3.4440510272979736, - 3.580061197280884, - 4.484014987945557 + 4.428144931793213, + 4.3938069343566895, + 4.74428653717041 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -965085,47 +965235,47 @@ "abstract": "Upload a portrait picture, pick one of two AnimeGAN style versions, and the app will transform it into a vibrant anime illustration. The output is a new image you can download or share.", "projections": { "pca": [ - -0.17797836661338806, - 0.22119423747062683 + -0.177300825715065, + 0.22177354991436005 ], "tsne": [ - -21.344865798950195, - -6.202563762664795 + -20.74047088623047, + -2.9073874950408936 ], "umap": [ - 3.2950377464294434, - 7.7102131843566895 + 3.5708110332489014, + 7.174219608306885 ], "pca3d": [ - -0.1779782921075821, - 0.22116021811962128, - -0.012089945375919342 + -0.17730090022087097, + 0.2217385470867157, + -0.012331396341323853 ], "tsne3d": [ - -9.60300350189209, - 5.461989402770996, - -12.027873039245605 + -9.709733963012695, + -0.1749434471130371, + 5.826852798461914 ], "umap3d": [ - 3.1336123943328857, - 4.535903453826904, - 3.941793918609619 + 3.936591386795044, + 5.104034900665283, + 4.098663806915283 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { "id": 0, - "label": "LLM Language Learning" + "label": "Math AI" } ], "_embedding": [ @@ -965532,47 +965682,47 @@ "abstract": "Researchers have proposed a wide variety of model explanation approaches, but it remains unclear how most methods are related or when one method is preferable to another. We describe a new uni\ufb01ed class of methods, removal-based explanations, that are based on the principle of simulating feature removal to quantify each feature\u2019s in\ufb02uence. These meth- ods vary in several respects, so we develop a framework that characterizes each method along three dimensions: 1) how the method removes features, 2) what model behavior the method explains, and 3) how the method summarizes each feature\u2019s in\ufb02uence. Our frame- work uni\ufb01es 26 existing methods, including several of the most widely used approaches: SHAP, LIME, Meaningful Perturbations, and permutation tests. This newly understood class of explanation methods has rich connections that we examine using tools that have been largely overlooked by the explainability literature. To anchor removal-based expla- nations in cognitive psychology, we show that feature removal is a simple application of subtractive counterfactual reasoning. Ideas from cooperative game theory shed light on the relationships and trade-o\ufb00s among di\ufb00erent methods, and we derive conditions under which all removal-based explanations have information-theoretic interpretations. Through this analysis, we develop a uni\ufb01ed framework that helps practitioners better understand model explanation tools, and that o\ufb00ers a strong theoretical foundation upon which future explainability research can build.", "projections": { "pca": [ - -0.2534483075141907, - 0.06683716922998428 + -0.25331103801727295, + 0.0685916319489479 ], "tsne": [ - -41.17291259765625, - -23.302173614501953 + -44.648624420166016, + 18.289888381958008 ], "umap": [ - 1.745036005973816, - 7.104025840759277 + 2.2304153442382812, + 6.451031684875488 ], "pca3d": [ - -0.25344812870025635, - 0.06682690232992172, - 0.0011115481611341238 + -0.2533109188079834, + 0.06858076900243759, + 0.0012439024867489934 ], "tsne3d": [ - -24.820146560668945, - -15.190398216247559, - -2.0553066730499268 + -22.890254974365234, + 2.9565603733062744, + -9.781763076782227 ], "umap3d": [ - 0.8129861354827881, - 4.706913948059082, - 3.483779191970825 + 1.5464953184127808, + 4.778403282165527, + 4.229462146759033 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -965978,47 +966128,47 @@ ], "projections": { "pca": [ - -0.0488797202706337, - 0.29066041111946106 + -0.04800385236740112, + 0.29060569405555725 ], "tsne": [ - -17.91239356994629, - -0.7502273321151733 + -23.041643142700195, + -0.30501502752304077 ], "umap": [ - 4.084608554840088, - 7.555118083953857 + 4.354901313781738, + 6.849556922912598 ], "pca3d": [ - -0.048880401998758316, - 0.29068121314048767, - 0.0005042617558501661 + -0.04800459370017052, + 0.2906268537044525, + 4.900306521449238e-05 ], "tsne3d": [ - -15.776151657104492, - 2.29486083984375, - 15.78409194946289 + -18.079856872558594, + 1.721727728843689, + 16.007259368896484 ], "umap3d": [ - 3.683628559112549, - 3.507775068283081, - 4.415097236633301 + 4.628188610076904, + 4.39421272277832, + 4.730098724365234 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -966426,47 +966576,47 @@ "abstract": "Do what you love. Create the future you want. Explore the career options that Microsoft has to offer Stage.", "projections": { "pca": [ - -0.029113728553056717, - 0.4537445902824402 + -0.027997048571705818, + 0.45413634181022644 ], "tsne": [ - -12.722175598144531, - -7.821487903594971 + -11.306811332702637, + 4.903537750244141 ], "umap": [ - 4.1062092781066895, - 7.556793212890625 + 4.2596564292907715, + 6.847803115844727 ], "pca3d": [ - -0.029114527627825737, - 0.4537384808063507, - -0.004526454955339432 + -0.027997922152280807, + 0.45413002371788025, + -0.005308140534907579 ], "tsne3d": [ - -6.001213073730469, - -5.978134632110596, - 10.458343505859375 + -9.4047212600708, + -1.463029384613037, + -7.397513389587402 ], "umap3d": [ - 3.602590799331665, - 3.756739377975464, - 4.384552001953125 + 4.584758281707764, + 4.586048603057861, + 4.626837730407715 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -966892,31 +967042,31 @@ "openalex_url": "https://openalex.org/W2342307854", "projections": { "pca": [ - 0.10052023082971573, - -0.023592902347445488 + 0.10054711252450943, + -0.02362048625946045 ], "tsne": [ - 0.3372204601764679, - 37.68167495727539 + -5.825831413269043, + -16.503564834594727 ], "umap": [ - 6.355287075042725, - 3.6789674758911133 + 7.044453144073486, + 3.51414155960083 ], "pca3d": [ - 0.10052037239074707, - -0.023596052080392838, - 0.10374277085065842 + 0.10054714232683182, + -0.023623254150152206, + 0.10373635590076447 ], "tsne3d": [ - 4.34766960144043, - 16.809946060180664, - 8.688948631286621 + 6.64139461517334, + -17.042577743530273, + -0.3628799021244049 ], "umap3d": [ - 5.319758892059326, - 6.1532697677612305, - 5.3940606117248535 + 6.200601577758789, + 7.327516555786133, + 3.8752496242523193 ] }, "cluster_id": 3, @@ -966927,11 +967077,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -967340,31 +967490,31 @@ "journal": "Nature Reviews Genetics", "projections": { "pca": [ - -0.04281095787882805, - 0.07428862899541855 + -0.04275339096784592, + 0.074783094227314 ], "tsne": [ - 9.624943733215332, - -3.972442150115967 + 21.00693702697754, + -2.217104196548462 ], "umap": [ - 6.180893421173096, - 6.940266132354736 + 6.474035263061523, + 6.544642925262451 ], "pca3d": [ - -0.04281116649508476, - 0.0743030235171318, - -0.08237728476524353 + -0.042753688991069794, + 0.07479739189147949, + -0.08245819061994553 ], "tsne3d": [ - 13.668912887573242, - 0.23444689810276031, - -17.24297332763672 + 10.17155647277832, + 5.426881790161133, + 10.777390480041504 ], "umap3d": [ - 5.247073173522949, - 4.237489223480225, - 3.4305050373077393 + 5.559381008148193, + 4.701361656188965, + 3.0863890647888184 ] }, "cluster_id": 4, @@ -967375,12 +967525,12 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 11, - "label": "Genomic Language" + "id": 13, + "label": "Genomics Papers" } ], "_embedding": [ @@ -967796,47 +967946,47 @@ ], "projections": { "pca": [ - -0.3033156096935272, - -0.1606174111366272 + -0.3037962317466736, + -0.1596554070711136 ], "tsne": [ - -43.164894104003906, - -14.998064041137695 + -43.95780944824219, + 1.790745496749878 ], "umap": [ - 1.1310760974884033, - 7.053135871887207 + 1.2404394149780273, + 6.054409503936768 ], "pca3d": [ - -0.3033151924610138, - -0.16060389578342438, - -0.09402575343847275 + -0.3037959039211273, + -0.15964145958423615, + -0.09362580627202988 ], "tsne3d": [ - -33.06428527832031, - -3.0698347091674805, - 4.110311031341553 + -30.139320373535156, + 3.383418083190918, + -1.3563264608383179 ], "umap3d": [ - 0.9658039808273315, - 5.27888822555542, - 4.0293169021606445 + 1.9149620532989502, + 5.422834396362305, + 4.624212741851807 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { "id": 8, - "label": "Attention Vision" + "label": "Network Learning" }, { - "id": 34, - "label": "Learning Methods" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -968244,47 +968394,47 @@ "abstract": "Microsoft and CMU researchers begin to unravel 3 mysteries in deep learning related to ensemble, knowledge distillation & self-distillation. Discover how their work leads to the first theoretical proof with empirical evidence for ensemble in deep learning.", "projections": { "pca": [ - -0.24168361723423004, - 0.04359623044729233 + -0.24162498116493225, + 0.04464973509311676 ], "tsne": [ - -43.154579162597656, - -15.799046516418457 + -43.201377868652344, + 1.6359506845474243 ], "umap": [ - 1.3308377265930176, - 6.942782878875732 + 1.4126983880996704, + 6.031002998352051 ], "pca3d": [ - -0.24168361723423004, - 0.04361704736948013, - -0.004349315073341131 + -0.2416251301765442, + 0.04467061907052994, + -0.004345250781625509 ], "tsne3d": [ - -34.34715270996094, - -4.61043643951416, - 4.254287242889404 + -29.751577377319336, + 4.853531360626221, + -2.6629836559295654 ], "umap3d": [ - 0.9332668781280518, - 5.2462663650512695, - 3.8444769382476807 + 1.815990924835205, + 5.340487480163574, + 4.4228315353393555 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { "id": 8, - "label": "Attention Vision" + "label": "Network Learning" }, { - "id": 34, - "label": "Learning Methods" + "id": 30, + "label": "Neural Learning" } ], "_embedding": [ @@ -968691,31 +968841,31 @@ "title": "AlphaFold 2 is here: what’s behind the structure prediction miracle | Oxford Protein Informatics Group", "projections": { "pca": [ - 0.12213429808616638, - 0.06108446046710014 + 0.1223793551325798, + 0.0610615499317646 ], "tsne": [ - -10.365668296813965, - 37.86149215698242 + -11.946245193481445, + -32.47513961791992 ], "umap": [ - 5.381040573120117, - 3.417987823486328 + 6.010843276977539, + 3.031590700149536 ], "pca3d": [ - 0.12213408201932907, - 0.061083294451236725, - 0.14685510098934174 + 0.12237915396690369, + 0.06106029823422432, + 0.14661435782909393 ], "tsne3d": [ - -3.0678350925445557, - 15.738842964172363, - 13.8392333984375 + 1.9114450216293335, + -21.974830627441406, + 8.521379470825195 ], "umap3d": [ - 4.87654972076416, - 7.141391277313232, - 5.385522365570068 + 5.290841579437256, + 7.8212199211120605, + 3.9692490100860596 ] }, "cluster_id": 3, @@ -968726,11 +968876,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -969149,46 +969299,46 @@ ], "projections": { "pca": [ - -0.31026366353034973, - -0.1546226441860199 + -0.3106304109096527, + -0.15389375388622284 ], "tsne": [ - -47.849952697753906, - -11.140438079833984 + -45.774986267089844, + -12.05938720703125 ], "umap": [ - 0.5861081480979919, - 6.203454494476318 + 1.0312565565109253, + 5.437798023223877 ], "pca3d": [ - -0.31026342511177063, - -0.15461678802967072, - -0.055579233914613724 + -0.31063002347946167, + -0.15388816595077515, + -0.055164679884910583 ], "tsne3d": [ - -25.10198402404785, - -6.616098403930664, - -3.9427287578582764 + -21.207672119140625, + -6.58273458480835, + -4.204355716705322 ], "umap3d": [ - 0.6048896312713623, - 6.08664608001709, - 4.1012468338012695 + 1.375920295715332, + 6.018431663513184, + 4.5806450843811035 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, + "id": 23, "label": "Training Noise" } ], @@ -969605,46 +969755,46 @@ ], "projections": { "pca": [ - -0.300703227519989, - -0.11072889715433121 + -0.30103132128715515, + -0.10984668135643005 ], "tsne": [ - -55.90454864501953, - -12.914958953857422 + -56.99778747558594, + -5.287021160125732 ], "umap": [ - -0.0414421372115612, - 6.471678733825684 + 0.25223276019096375, + 5.516594409942627 ], "pca3d": [ - -0.3007030785083771, - -0.11071763187646866, - -0.11935143172740936 + -0.3010312020778656, + -0.10983514040708542, + -0.11902063339948654 ], "tsne3d": [ - -31.516029357910156, - 1.3321290016174316, - -0.15035559237003326 + -31.604053497314453, + -6.2295708656311035, + 0.7861636877059937 ], "umap3d": [ - 0.12316252291202545, - 6.273407459259033, - 4.307485580444336 + 0.794942319393158, + 6.364954948425293, + 4.958990573883057 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, + "id": 23, "label": "Training Noise" } ], @@ -970095,31 +970245,31 @@ "cited_by_count": 44845, "projections": { "pca": [ - 0.1163100078701973, - -0.13856938481330872 + 0.11601372808218002, + -0.13849212229251862 ], "tsne": [ - -10.29857349395752, - 36.368839263916016 + -10.461568832397461, + -31.090110778808594 ], "umap": [ - 5.423698425292969, - 3.505401372909546 + 6.06019926071167, + 3.1269848346710205 ], "pca3d": [ - 0.11631019413471222, - -0.13856720924377441, - 0.22163407504558563 + 0.11601388454437256, + -0.13848936557769775, + 0.22168400883674622 ], "tsne3d": [ - -1.4076024293899536, - 14.474804878234863, - 12.735149383544922 + 1.5590306520462036, + -20.032379150390625, + 8.931196212768555 ], "umap3d": [ - 4.912865161895752, - 7.069081783294678, - 5.33069372177124 + 5.363039016723633, + 7.746236801147461, + 3.887436628341675 ] }, "cluster_id": 3, @@ -970130,11 +970280,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -970563,31 +970713,31 @@ "openalex_url": "https://openalex.org/W3163257742", "projections": { "pca": [ - 0.0656820684671402, - -0.15135647356510162 + 0.06521617621183395, + -0.15137414634227753 ], "tsne": [ - 14.512938499450684, - -2.8958139419555664 + 16.151769638061523, + 3.521949529647827 ], "umap": [ - 8.136382102966309, - 6.159859657287598 + 8.437189102172852, + 5.861579895019531 ], "pca3d": [ - 0.06568215042352676, - -0.1513398289680481, - -0.022003525868058205 + 0.06521628051996231, + -0.15135684609413147, + -0.021830562502145767 ], "tsne3d": [ - 14.377359390258789, - 11.059683799743652, - -11.835090637207031 + 16.80048179626465, + -4.446432590484619, + 5.483680248260498 ], "umap3d": [ - 6.516507148742676, - 5.467596530914307, - 2.5211586952209473 + 6.319206237792969, + 5.558184623718262, + 1.224265456199646 ] }, "cluster_id": 4, @@ -970598,12 +970748,12 @@ "label": "RNA Sequence Models" }, { - "id": 3, - "label": "RNA Splicing" + "id": 2, + "label": "DNA Regulation" }, { "id": 1, - "label": "Regulatory Genomics" + "label": "Genomic Regulation" } ], "_embedding": [ @@ -971010,47 +971160,47 @@ "title": "The Science of Scientific Writing", "projections": { "pca": [ - -0.10796132683753967, - 0.3480989634990692 + -0.10697995126247406, + 0.34869399666786194 ], "tsne": [ - -10.270909309387207, - -11.80971908569336 + -16.34817123413086, + 6.023678302764893 ], "umap": [ - 3.7883925437927246, - 7.460016250610352 + 4.059783935546875, + 6.771183490753174 ], "pca3d": [ - -0.107962466776371, - 0.348163366317749, - 0.09739334881305695 + -0.10698115080595016, + 0.34875908493995667, + 0.09690973907709122 ], "tsne3d": [ - -4.4170308113098145, - -11.324397087097168, - 15.115697860717773 + -10.058633804321289, + 4.092406749725342, + -12.246061325073242 ], "umap3d": [ - 3.3215720653533936, - 3.5354082584381104, - 4.6064629554748535 + 4.326683044433594, + 4.330603122711182, + 4.942203521728516 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -971458,47 +971608,47 @@ "journal": "Nature Computational Science", "projections": { "pca": [ - -0.13571709394454956, - 0.30349037051200867 + -0.13470830023288727, + 0.3034168779850006 ], "tsne": [ - -8.837063789367676, - -14.911882400512695 + -14.561851501464844, + 9.59945297241211 ], "umap": [ - 4.104255676269531, - 7.7706685066223145 + 4.400850772857666, + 7.083909511566162 ], "pca3d": [ - -0.13571804761886597, - 0.30353713035583496, - 0.055431172251701355 + -0.13470914959907532, + 0.3034639358520508, + 0.05502590164542198 ], "tsne3d": [ - -5.471264362335205, - -11.994028091430664, - 5.915154933929443 + -6.411661624908447, + 7.173083305358887, + -5.635498523712158 ], "umap3d": [ - 3.6219027042388916, - 3.4415910243988037, - 4.289629936218262 + 4.489134311676025, + 4.1841654777526855, + 4.568548202514648 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -971922,31 +972072,31 @@ "doi": "10.1101/737981", "projections": { "pca": [ - 0.17726603150367737, - -0.11454436928033829 + 0.17691117525100708, + -0.11477331072092056 ], "tsne": [ - 57.654693603515625, - 1.5624443292617798 + 46.548561096191406, + 20.20319938659668 ], "umap": [ - 9.407957077026367, - 6.548825740814209 + 9.71679401397705, + 6.102154731750488 ], "pca3d": [ - 0.17726615071296692, - -0.11454197764396667, - 0.013317285105586052 + 0.17691129446029663, + -0.11477070301771164, + 0.013339283876121044 ], "tsne3d": [ - 23.77797508239746, - 5.05949592590332, - -6.3058671951293945 + 23.991641998291016, + -1.0739648342132568, + 1.7505598068237305 ], "umap3d": [ - 7.25753927230835, - 5.644301891326904, - 1.6702667474746704 + 6.6588006019592285, + 5.409884929656982, + 0.3181198835372925 ] }, "cluster_id": 0, @@ -971957,12 +972107,12 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 11, + "label": "Regulatory Binding" }, { - "id": 29, - "label": "Transcription Binding" + "id": 12, + "label": "Transcription" } ], "_embedding": [ @@ -972373,47 +972523,47 @@ ], "projections": { "pca": [ - -0.17349500954151154, - 0.3317655324935913 + -0.1724976897239685, + 0.33292004466056824 ], "tsne": [ - -14.988279342651367, - -14.96483039855957 + -11.047699928283691, + 7.282705307006836 ], "umap": [ - 3.814643144607544, - 7.61254358291626 + 4.216159820556641, + 6.978316307067871 ], "pca3d": [ - -0.17349591851234436, - 0.3317946791648865, - -0.00746147520840168 + -0.17249855399131775, + 0.33294937014579773, + -0.007768415380269289 ], "tsne3d": [ - -6.822389125823975, - -8.927422523498535, - 9.169307708740234 + -10.799213409423828, + 1.8054630756378174, + -5.127502918243408 ], "umap3d": [ - 3.366809368133545, - 3.676114559173584, - 4.381938457489014 + 4.29212760925293, + 4.408001899719238, + 4.620453834533691 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -972819,47 +972969,47 @@ ], "projections": { "pca": [ - -0.26558417081832886, - 0.10939251631498337 + -0.26518359780311584, + 0.10996709764003754 ], "tsne": [ - -19.25242042541504, - -9.235475540161133 + -20.37921905517578, + 7.240571975708008 ], "umap": [ - 3.4782164096832275, - 7.288753032684326 + 3.8092000484466553, + 6.622923851013184 ], "pca3d": [ - -0.2655845284461975, - 0.10941579937934875, - -0.03030618652701378 + -0.2651838958263397, + 0.10999083518981934, + -0.03032938577234745 ], "tsne3d": [ - -8.759225845336914, - -9.278660774230957, - 11.964569091796875 + -13.588419914245605, + -2.72845721244812, + -6.633230686187744 ], "umap3d": [ - 3.114016056060791, - 3.9417006969451904, - 4.447422981262207 + 4.0611042976379395, + 4.753366947174072, + 4.732658863067627 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -973269,46 +973419,46 @@ "abstract": "Aviv Regev, head of Genentech Research and Early Development (gRED), discusses how advances in human biology, massively parallel high-resolution methods, and computation and mathematics could transform drug discovery.", "projections": { "pca": [ - 0.027069663628935814, - 0.24486488103866577 + 0.027733702212572098, + 0.24552412331104279 ], "tsne": [ - -0.18223552405834198, - -2.225468397140503 + 0.6135933995246887, + 12.673672676086426 ], "umap": [ - 5.257627487182617, - 7.006026744842529 + 5.474841594696045, + 6.270452976226807 ], "pca3d": [ - 0.027069244533777237, - 0.2448648363351822, - 0.06022574380040169 + 0.027733193710446358, + 0.24552372097969055, + 0.05989250913262367 ], "tsne3d": [ - -0.8524108529090881, - -3.0519490242004395, - -8.5545015335083 + -0.5872366428375244, + 3.004730701446533, + 12.357926368713379 ], "umap3d": [ - 4.708578109741211, - 3.76250958442688, - 4.636538982391357 + 5.553648471832275, + 4.889945983886719, + 4.247535228729248 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, + "id": 13, "label": "Genomics Papers" } ], @@ -973730,47 +973880,47 @@ "doi": "10.23915/distill.00030", "projections": { "pca": [ - -0.2689704895019531, - 0.06399239599704742 + -0.26895952224731445, + 0.06498900055885315 ], "tsne": [ - -34.970314025878906, - -19.692707061767578 + -38.80613708496094, + 15.966695785522461 ], "umap": [ - 1.630908489227295, - 7.433420181274414 + 1.8221074342727661, + 6.664353370666504 ], "pca3d": [ - -0.26897090673446655, - 0.06400061398744583, - -0.04632142186164856 + -0.268959641456604, + 0.06499675661325455, + -0.046366654336452484 ], "tsne3d": [ - -21.643781661987305, - -12.041763305664062, - 0.8634949922561646 + -24.369606018066406, + 7.013675212860107, + -4.504661560058594 ], "umap3d": [ - 1.2315069437026978, - 4.6757941246032715, - 3.9820268154144287 + 2.1659011840820312, + 4.834823131561279, + 4.618353843688965 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 34, - "label": "Learning Methods" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -974178,47 +974328,47 @@ "abstract": "\u26d4\ufe0f DEPRECATED \u2013 See https://github.com/ageron/handson-ml3 or handson-mlp instead. - ageron/handson-ml2", "projections": { "pca": [ - -0.18073470890522003, - 0.13216553628444672 + -0.18027769029140472, + 0.13247384130954742 ], "tsne": [ - -5.699991226196289, - 2.9934518337249756 + -19.090293884277344, + -0.028367675840854645 ], "umap": [ - 3.5307540893554688, - 6.892611980438232 + 3.8464434146881104, + 6.320805549621582 ], "pca3d": [ - -0.18073490262031555, - 0.13217967748641968, - 0.03655078634619713 + -0.18027782440185547, + 0.13248805701732635, + 0.03640194982290268 ], "tsne3d": [ - -7.838657855987549, - 5.637638568878174, - -7.47888708114624 + -6.380347728729248, + 1.5049432516098022, + 2.7523367404937744 ], "umap3d": [ - 3.535102605819702, - 4.634060859680176, - 4.124876022338867 + 4.27046012878418, + 5.244137763977051, + 4.243631839752197 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 10, - "label": "Code Development" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -974626,47 +974776,47 @@ "abstract": "Fast, general, and tested differentiable structured prediction in PyTorch - harvardnlp/pytorch-struct", "projections": { "pca": [ - -0.1011318564414978, - -0.01929743029177189 + -0.10095111280679703, + -0.019225487485527992 ], "tsne": [ - -26.87595558166504, - 33.41902542114258 + -15.55446720123291, + -9.199930191040039 ], "umap": [ - 3.93041729927063, - 6.259598731994629 + 4.324489116668701, + 5.65413236618042 ], "pca3d": [ - -0.10113180428743362, - -0.0192946195602417, - 0.07242321223020554 + -0.10095102339982986, + -0.019222095608711243, + 0.07252488285303116 ], "tsne3d": [ - -9.190603256225586, - 17.228431701660156, - 13.338716506958008 + -11.69415283203125, + -7.036495685577393, + 15.320272445678711 ], "umap3d": [ - 3.745434522628784, - 4.980984210968018, - 4.586180686950684 + 4.582993030548096, + 5.714114665985107, + 4.318192005157471 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 10, - "label": "Code Development" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -975074,47 +975224,47 @@ "abstract": "Contribute to p-koo/tfomics development by creating an account on GitHub.", "projections": { "pca": [ - -0.10560308396816254, - 0.14744645357131958 + -0.1050681620836258, + 0.14785408973693848 ], "tsne": [ - -29.391143798828125, - 29.981273651123047 + -15.472576141357422, + -4.399258613586426 ], "umap": [ - 3.891693115234375, - 6.672934532165527 + 4.316074371337891, + 6.129316329956055 ], "pca3d": [ - -0.1056034117937088, - 0.14745160937309265, - 0.016182297840714455 + -0.10506850481033325, + 0.14785900712013245, + 0.016061965376138687 ], "tsne3d": [ - -13.897515296936035, - 14.907827377319336, - 11.844139099121094 + -12.677349090576172, + -2.0218775272369385, + 13.693065643310547 ], "umap3d": [ - 3.610400915145874, - 4.569526672363281, - 4.43235445022583 + 4.50698709487915, + 5.341597557067871, + 4.303048133850098 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 10, - "label": "Code Development" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -975532,31 +975682,31 @@ "doi": "10.1016/j.patter.2021.100336", "projections": { "pca": [ - -0.21851079165935516, - 0.0522804856300354 + -0.21831268072128296, + 0.05309591069817543 ], "tsne": [ - -41.78150177001953, - -26.91862678527832 + -44.63570022583008, + 22.2884521484375 ], "umap": [ - 1.7415000200271606, - 6.60007905960083 + 2.199598550796509, + 5.869428634643555 ], "pca3d": [ - -0.21851085126399994, - 0.05229056999087334, - -0.07831010967493057 + -0.21831272542476654, + 0.05310608446598053, + -0.07818267494440079 ], "tsne3d": [ - -22.366806030273438, - -12.869138717651367, - -6.368024826049805 + -17.80733871459961, + -3.5968167781829834, + -6.660426616668701 ], "umap3d": [ - 1.2112977504730225, - 5.350193023681641, - 3.5346133708953857 + 1.8484160900115967, + 5.483457565307617, + 4.176161766052246 ] }, "cluster_id": 2, @@ -975567,12 +975717,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 33, - "label": "Neural Networks" + "id": 19, + "label": "Graph Neural Nets" } ], "_embedding": [ @@ -975981,46 +976131,46 @@ "title": "Transformers from scratch | peterbloem.nl", "projections": { "pca": [ - -0.26540055871009827, - 0.24034909904003143 + -0.2643824517726898, + 0.2413795441389084 ], "tsne": [ - -55.49271011352539, - -6.797015190124512 + -46.42548751831055, + 8.401972770690918 ], "umap": [ - 0.4581972062587738, - 7.906405448913574 + 0.753017246723175, + 7.018469333648682 ], "pca3d": [ - -0.26540037989616394, - 0.24029551446437836, - 0.07593823224306107 + -0.2643824815750122, + 0.24132460355758667, + 0.0758950263261795 ], "tsne3d": [ - -29.84667205810547, - -5.418595314025879, - 11.705649375915527 + -29.376062393188477, + 6.073942184448242, + 10.1121187210083 ], "umap3d": [ - 0.574012815952301, - 4.903774261474609, - 4.972784996032715 + 1.7643153667449951, + 5.212520122528076, + 5.816708087921143 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -976443,46 +976593,46 @@ "openalex_url": "https://openalex.org/W3006881356", "projections": { "pca": [ - -0.2938210666179657, - -0.03189705312252045 + -0.29363054037094116, + -0.030274096876382828 ], "tsne": [ - -32.534671783447266, - -13.643418312072754 + -36.48552703857422, + 11.711629867553711 ], "umap": [ - 1.4841428995132446, - 8.152970314025879 + 1.6764004230499268, + 7.331881999969482 ], "pca3d": [ - -0.29382094740867615, - -0.03190581128001213, - 0.060910314321517944 + -0.2936303913593292, + -0.03028370812535286, + 0.061403509229421616 ], "tsne3d": [ - -18.009899139404297, - -13.589162826538086, - 9.175349235534668 + -21.34719467163086, + 5.596407413482666, + -0.4115951955318451 ], "umap3d": [ - 1.4787596464157104, - 4.376850128173828, - 4.742862701416016 + 2.524812936782837, + 4.797229766845703, + 5.360085964202881 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -976890,31 +977040,31 @@ "title": "CASP14: what Google DeepMind’s AlphaFold 2 really achieved, and what it means for protein folding, biology and bioinformatics | Oxford Protein Informatics Group", "projections": { "pca": [ - 0.04135890305042267, - 0.07942017167806625 + 0.04169546440243721, + 0.07977863401174545 ], "tsne": [ - -9.770322799682617, - 38.301795959472656 + -12.284283638000488, + -33.11178207397461 ], "umap": [ - 5.404860019683838, - 3.469559907913208 + 6.036813735961914, + 3.0113255977630615 ], "pca3d": [ - 0.041358668357133865, - 0.07942275702953339, - 0.1527395099401474 + 0.0416952520608902, + 0.07978112995624542, + 0.15261641144752502 ], "tsne3d": [ - -3.3034605979919434, - 17.31572151184082, - 13.60274887084961 + 3.2993555068969727, + -21.730863571166992, + 7.927213668823242 ], "umap3d": [ - 4.863457202911377, - 7.119797706604004, - 5.3942742347717285 + 5.340697765350342, + 7.756348133087158, + 3.9758152961730957 ] }, "cluster_id": 3, @@ -976925,11 +977075,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -977337,47 +977487,47 @@ "title": "Using JAX to accelerate our research \u00e2\u0080\u0094 Google DeepMind", "projections": { "pca": [ - -0.13756245374679565, - 0.08273889124393463 + -0.1372082382440567, + 0.08323269337415695 ], "tsne": [ - -25.251544952392578, - 2.4914231300354004 + -24.08491325378418, + 24.902841567993164 ], "umap": [ - 4.697448253631592, - 6.600337028503418 + 3.4580841064453125, + 6.7707366943359375 ], "pca3d": [ - -0.13756269216537476, - 0.08275850117206573, - 0.05584404990077019 + -0.1372084766626358, + 0.08325320482254028, + 0.055877961218357086 ], "tsne3d": [ - -6.161052703857422, - -18.72506332397461, - 13.888567924499512 + -12.543553352355957, + 14.930134773254395, + 12.502087593078613 ], "umap3d": [ - 3.26761531829834, - 4.76756477355957, - 3.3935351371765137 + 3.6090188026428223, + 4.949906826019287, + 3.5353496074676514 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 12, + "label": "AI Agents" }, { - "id": 10, - "label": "Code Development" + "id": 0, + "label": "Math AI" } ], "_embedding": [ @@ -977784,47 +977934,47 @@ "title": "AlphaFold: a solution to a 50-year-old grand challenge in biology \u00e2\u0080\u0094 Google DeepMind", "projections": { "pca": [ - -0.03670860081911087, - 0.04925322160124779 + -0.0363808274269104, + 0.049888111650943756 ], "tsne": [ - -24.736942291259766, - 2.879732847213745 + -23.53455924987793, + 24.665180206298828 ], "umap": [ - 4.786368370056152, - 6.470661640167236 + 3.454038619995117, + 6.72365665435791 ], "pca3d": [ - -0.036708757281303406, - 0.04925981163978577, - 0.13518643379211426 + -0.03638095408678055, + 0.049895141273736954, + 0.13525405526161194 ], "tsne3d": [ - -4.774593830108643, - -18.352724075317383, - 15.036925315856934 + -11.408668518066406, + 14.363075256347656, + 11.273880958557129 ], "umap3d": [ - 3.332875967025757, - 4.877580642700195, - 3.433791399002075 + 3.647813081741333, + 5.034988880157471, + 3.5507090091705322 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 12, + "label": "AI Agents" }, { - "id": 10, - "label": "Code Development" + "id": 0, + "label": "Math AI" } ], "_embedding": [ @@ -978232,46 +978382,46 @@ "abstract": "Can an energy-based perspective shed light on training and improving Transformer models?", "projections": { "pca": [ - -0.24429933726787567, - 0.07564233988523483 + -0.24400021135807037, + 0.07657399028539658 ], "tsne": [ - -37.97439193725586, - -8.20765495300293 + -45.01121520996094, + 9.015154838562012 ], "umap": [ - 0.5565075874328613, - 7.816442489624023 + 0.8401504755020142, + 6.9815673828125 ], "pca3d": [ - -0.24429932236671448, - 0.07562252879142761, - -0.0401129312813282 + -0.24400022625923157, + 0.07655326277017593, + -0.040019433945417404 ], "tsne3d": [ - -27.77777862548828, - -8.671443939208984, - 8.58823013305664 + -26.62549591064453, + 5.345330715179443, + 10.48432445526123 ], "umap3d": [ - 0.6158379912376404, - 4.777882099151611, - 4.876214504241943 + 1.8160914182662964, + 5.131682395935059, + 5.724380016326904 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, + "id": 14, "label": "Transformer Attention" } ], @@ -978679,47 +978829,47 @@ "title": "GeForce RTX 3080, RTX 3090: Nvidia's 'greatest generational leap ever' | PCWorld", "projections": { "pca": [ - -0.13353095948696136, - 0.291187047958374 + -0.13263514637947083, + 0.29129064083099365 ], "tsne": [ - -19.757728576660156, - 0.9747893214225769 + -25.139253616333008, + -1.3267706632614136 ], "umap": [ - 2.4968767166137695, - 7.534574031829834 + 2.6841301918029785, + 6.743619918823242 ], "pca3d": [ - -0.1335308849811554, - 0.29114019870758057, - -0.06464724987745285 + -0.13263510167598724, + 0.2912430763244629, + -0.06502975523471832 ], "tsne3d": [ - -17.152494430541992, - 4.680641174316406, - 12.603279113769531 + -18.117155075073242, + 1.6554150581359863, + 12.277788162231445 ], "umap3d": [ - 2.1787056922912598, - 4.708163261413574, - 5.174570560455322 + 3.259629487991333, + 5.374657154083252, + 5.5446014404296875 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { "id": 0, - "label": "LLM Language Learning" + "label": "Math AI" } ], "_embedding": [ @@ -979147,47 +979297,47 @@ "openalex_url": "https://openalex.org/W4403959930", "projections": { "pca": [ - 0.07290050387382507, - 0.16839073598384857 + 0.07358831912279129, + 0.1684730350971222 ], "tsne": [ - 1.7977463006973267, - -3.61920166015625 + 3.6189236640930176, + 4.034873962402344 ], "umap": [ - 5.9273762702941895, - 5.980208873748779 + 5.803828716278076, + 6.099298000335693 ], "pca3d": [ - 0.07290016859769821, - 0.16838030517101288, - 0.08167923241853714 + 0.07358795404434204, + 0.1684625893831253, + 0.08155691623687744 ], "tsne3d": [ - -1.8641682863235474, - -2.0281360149383545, - -14.369211196899414 + 1.3547710180282593, + 23.229124069213867, + 1.0444749593734741 ], "umap3d": [ - 4.98891544342041, - 3.6122050285339355, - 4.913357734680176 + 5.843945026397705, + 6.020789623260498, + 2.8474233150482178 ] }, - "cluster_id": 4, - "cluster_label": "RNA Sequence Models", + "cluster_id": 1, + "cluster_label": "AI in Science", "cluster_levels": [ { - "id": 4, - "label": "RNA Sequence Models" + "id": 1, + "label": "AI in Science" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 11, - "label": "Genomic Language" + "id": 13, + "label": "Genomics Papers" } ], "_embedding": [ @@ -979595,47 +979745,47 @@ "abstract": "Introducing LifeSciBench, an expert-authored, expert-reviewed benchmark for evaluating how AI systems handle real-world life science research tasks and decisions.", "projections": { "pca": [ - -0.06860349327325821, - 0.27253958582878113 + -0.06785888969898224, + 0.27270716428756714 ], "tsne": [ - -4.482946395874023, - -20.563512802124023 + -7.103270530700684, + 17.02520751953125 ], "umap": [ - 4.350574493408203, - 8.632747650146484 + 4.610601425170898, + 7.949079990386963 ], "pca3d": [ - -0.06860411912202835, - 0.27255553007125854, - 0.09719495475292206 + -0.06785938888788223, + 0.2727231979370117, + 0.09674791991710663 ], "tsne3d": [ - -5.328485488891602, - -9.565679550170898, - -8.658042907714844 + -4.442014217376709, + 16.8922119140625, + 4.027663230895996 ], "umap3d": [ - 3.5503032207489014, - 3.7431702613830566, - 2.993375539779663 + 4.1294755935668945, + 4.057082176208496, + 3.3921961784362793 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, - "label": "Medical AI" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -980045,31 +980195,31 @@ "abstract": "Genome Biology is calling for submissions to our Collection on how synthetic engineered regulatory systems are reshaping our ability to decode and design gene ...", "projections": { "pca": [ - 0.13138389587402344, - 0.20872442424297333 + 0.13201075792312622, + 0.20853491127490997 ], "tsne": [ - -3.628185987472534, - 6.269318103790283 + -0.03190407529473305, + -3.8686532974243164 ], "umap": [ - 5.801737308502197, - 6.497963905334473 + 6.021836757659912, + 6.035646438598633 ], "pca3d": [ - 0.1313832551240921, - 0.2087404578924179, - 0.10269995033740997 + 0.13201014697551727, + 0.20855067670345306, + 0.10228101164102554 ], "tsne3d": [ - -2.4537672996520996, - 12.223370552062988, - -8.610517501831055 + -3.5312769412994385, + 3.4112226963043213, + 7.784003257751465 ], "umap3d": [ - 5.043591022491455, - 4.622369766235352, - 3.8006203174591064 + 5.50506591796875, + 5.087890148162842, + 3.280273199081421 ] }, "cluster_id": 4, @@ -980080,12 +980230,12 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 11, - "label": "Genomic Language" + "id": 13, + "label": "Genomics Papers" } ], "_embedding": [ @@ -980493,31 +980643,31 @@ "abstract": "A research preview of Omnii, our next-generation genome language model.", "projections": { "pca": [ - 0.04815063998103142, - 0.1273680180311203 + 0.04864102229475975, + 0.12735038995742798 ], "tsne": [ - 4.031435012817383, - 8.12134075164795 + 5.557156562805176, + -4.488018989562988 ], "umap": [ - 6.640565395355225, - 6.389379978179932 + 6.9730987548828125, + 6.001647472381592 ], "pca3d": [ - 0.04815063625574112, - 0.1273464411497116, - 0.04413516819477081 + 0.048640985041856766, + 0.12732836604118347, + 0.043988507241010666 ], "tsne3d": [ - 0.7944475412368774, - 2.6080238819122314, - -14.330364227294922 + 2.147472381591797, + 1.6865111589431763, + -2.8135459423065186 ], "umap3d": [ - 5.784848213195801, - 4.712713241577148, - 3.5103797912597656 + 6.050449371337891, + 5.1673407554626465, + 2.6544575691223145 ] }, "cluster_id": 4, @@ -980528,11 +980678,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -980943,47 +981093,47 @@ ], "projections": { "pca": [ - -0.00878907274454832, - 0.2965888977050781 + -0.007821314968168736, + 0.29621824622154236 ], "tsne": [ - -13.319867134094238, - -4.720805644989014 + -8.888253211975098, + -2.0668609142303467 ], "umap": [ - 4.292700290679932, - 7.2655348777771 + 4.414578914642334, + 6.662597179412842 ], "pca3d": [ - -0.008789651095867157, - 0.29659539461135864, - 0.000502710638102144 + -0.007821992039680481, + 0.29622480273246765, + 4.121514211874455e-05 ], "tsne3d": [ - -10.78925895690918, - 5.813652515411377, - 17.4959774017334 + -2.966418743133545, + -3.1363258361816406, + -12.091145515441895 ], "umap3d": [ - 3.814561128616333, - 3.811626672744751, - 4.664031028747559 + 4.8597893714904785, + 4.681732177734375, + 4.846409320831299 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -981391,46 +981541,46 @@ "abstract": "Anthropic has confidentially submitted a draft S-1 registration statement to the Securities and Exchange Commission", "projections": { "pca": [ - -0.052138544619083405, - 0.3919026851654053 + -0.05103594809770584, + 0.3921630382537842 ], "tsne": [ - -14.547476768493652, - -35.074459075927734 + -23.384971618652344, + -33.322391510009766 ], "umap": [ - 3.0969436168670654, - 9.193528175354004 + 3.167450189590454, + 8.464156150817871 ], "pca3d": [ - -0.05213914066553116, - 0.39188671112060547, - 0.028813999146223068 + -0.05103638395667076, + 0.3921467661857605, + 0.02821408584713936 ], "tsne3d": [ - -10.347906112670898, - -1.1012779474258423, - 2.65307879447937 + -4.384284019470215, + 10.668646812438965, + 14.269501686096191 ], "umap3d": [ - 3.0090718269348145, - 3.0871634483337402, - 5.1697821617126465 + 4.550378322601318, + 4.413713455200195, + 5.688819885253906 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 32, + "id": 25, "label": "AI Agents" } ], @@ -981839,31 +981989,31 @@ "abstract": "This web app lets you paste a DNA sequence and see the model continue it, score genetic variants, and predict the protein\u2019s 3\u2011D shape. It also visualizes gene\u2011embedding maps and shows the full trai...", "projections": { "pca": [ - 0.09901703149080276, - 0.10810772329568863 + 0.09937557578086853, + 0.10843640565872192 ], "tsne": [ - 8.248861312866211, - 15.124786376953125 + 8.917108535766602, + -11.46396541595459 ], "umap": [ - 6.621738433837891, - 5.735900402069092 + 6.870471477508545, + 5.47389030456543 ], "pca3d": [ - 0.09901691973209381, - 0.1080903559923172, - 0.08835618197917938 + 0.09937544912099838, + 0.10841838270425797, + 0.0881921723484993 ], "tsne3d": [ - 0.1516323834657669, - 7.673055171966553, - -7.785717010498047 + 4.627891540527344, + -4.634652614593506, + 6.232975482940674 ], "umap3d": [ - 5.564766883850098, - 5.33642578125, - 3.7017226219177246 + 5.9929304122924805, + 5.662761211395264, + 2.69920015335083 ] }, "cluster_id": 4, @@ -981874,11 +982024,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -982295,47 +982445,47 @@ ], "projections": { "pca": [ - -0.19122788310050964, - 0.20430848002433777 + -0.19082920253276825, + 0.20587381720542908 ], "tsne": [ - -23.49934959411621, - -18.20808982849121 + -27.93850326538086, + 16.071386337280273 ], "umap": [ - 2.571202278137207, - 8.355618476867676 + 2.75419545173645, + 7.655148506164551 ], "pca3d": [ - -0.1912280172109604, - 0.20429904758930206, - -0.05205113813281059 + -0.19082941114902496, + 0.2058635950088501, + -0.0521945022046566 ], "tsne3d": [ - -11.634471893310547, - -17.68561553955078, - 6.052955150604248 + -15.975547790527344, + 7.890876770019531, + -6.722589015960693 ], "umap3d": [ - 2.15181565284729, - 3.747389554977417, - 4.3242340087890625 + 3.1764001846313477, + 4.254656791687012, + 4.927145004272461 ] }, - "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 1, - "label": "AI Scientific Research" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 0, - "label": "LLM Language Learning" + "id": 25, + "label": "AI Agents" } ], "_embedding": [ @@ -982743,47 +982893,47 @@ "abstract": "Some units at the US funding giant are so understaffed, they are focusing on mandated grant renewals rather than new awards.", "projections": { "pca": [ - -0.11750402301549911, - 0.3286820948123932 + -0.1166367307305336, + 0.3296259343624115 ], "tsne": [ - -16.272417068481445, - -2.644718647003174 + -15.544120788574219, + 0.9590345025062561 ], "umap": [ - 4.243611812591553, - 7.708598613739014 + 4.419003009796143, + 6.9832563400268555 ], "pca3d": [ - -0.11750476062297821, - 0.3286953866481781, - -0.044632263481616974 + -0.11663743853569031, + 0.32963913679122925, + -0.04501596838235855 ], "tsne3d": [ - -14.56932544708252, - 0.11856348067522049, - 17.842992782592773 + -3.7064013481140137, + 4.587483882904053, + -10.090344429016113 ], "umap3d": [ - 3.648235559463501, - 3.3977935314178467, - 4.363788604736328 + 4.672617435455322, + 4.298088550567627, + 4.709426403045654 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -983191,47 +983341,47 @@ "abstract": "Using AI coding tools can speed up your work, but there are plenty of pitfalls.", "projections": { "pca": [ - -0.1564583033323288, - 0.22353242337703705 + -0.15593163669109344, + 0.2251487672328949 ], "tsne": [ - -10.518270492553711, - -21.239173889160156 + -12.073378562927246, + 14.968137741088867 ], "umap": [ - 3.926027774810791, - 8.37286376953125 + 4.100576400756836, + 7.748600959777832 ], "pca3d": [ - -0.15645848214626312, - 0.22353211045265198, - 0.06847783178091049 + -0.1559319794178009, + 0.2251478135585785, + 0.06825622171163559 ], "tsne3d": [ - -4.2156901359558105, - -6.184537887573242, - 1.096997857093811 + -8.476253509521484, + 8.366964340209961, + 1.9136338233947754 ], "umap3d": [ - 3.247262716293335, - 3.6056272983551025, - 3.5515294075012207 + 4.052059650421143, + 4.049637794494629, + 3.8770809173583984 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -983641,46 +983791,46 @@ "doi": "10.1016/s0140-6736(02)47634-9", "projections": { "pca": [ - -0.12355533242225647, - 0.21144837141036987 + -0.1225743219256401, + 0.21162691712379456 ], "tsne": [ - -24.901535034179688, - -20.213146209716797 + -29.20092010498047, + 18.046239852905273 ], "umap": [ - 4.17265510559082, - 9.3900785446167 + 4.264932155609131, + 8.654672622680664 ], "pca3d": [ - -0.1235557273030281, - 0.2114562839269638, - 0.017747651785612106 + -0.1225748360157013, + 0.2116348147392273, + 0.017744330689311028 ], "tsne3d": [ - -12.510787010192871, - -20.196224212646484, - 3.2635087966918945 + -14.431085586547852, + 11.850006103515625, + -6.368964672088623 ], "umap3d": [ - 3.5397422313690186, - 2.821669101715088, - 3.639965534210205 + 4.365659236907959, + 3.3509280681610107, + 4.194536209106445 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, + "id": 26, "label": "Medical AI" } ], @@ -984087,31 +984237,31 @@ ], "projections": { "pca": [ - 0.18508677184581757, - 0.034462906420230865 + 0.18536235392093658, + 0.033920519053936005 ], "tsne": [ - 14.429353713989258, - 21.923559188842773 + 15.142725944519043, + -21.256162643432617 ], "umap": [ - 5.969361305236816, - 5.500538349151611 + 6.308271884918213, + 5.135056018829346 ], "pca3d": [ - 0.1850864589214325, - 0.03447429835796356, - 0.14478722214698792 + 0.1853621006011963, + 0.03393252566456795, + 0.14462286233901978 ], "tsne3d": [ - 7.997616291046143, - 21.503890991210938, - 12.27280044555664 + 6.55190896987915, + -4.218853950500488, + 12.879890441894531 ], "umap3d": [ - 6.033578872680664, - 6.131811141967773, - 3.719391345977783 + 5.761438846588135, + 6.371650218963623, + 2.33758544921875 ] }, "cluster_id": 3, @@ -984122,12 +984272,12 @@ "label": "Protein Design" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 24, - "label": "Protein Design" + "id": 32, + "label": "Genome Protein" } ], "_embedding": [ @@ -984537,46 +984687,46 @@ "abstract": "Show usage stats for OpenAI Codex and Claude Code, without having to login. - steipete/CodexBar", "projections": { "pca": [ - -0.0870552733540535, - 0.25785771012306213 + -0.08614592999219894, + 0.25757670402526855 ], "tsne": [ - -27.06756019592285, - 25.681026458740234 + -21.676326751708984, + 4.362630844116211 ], "umap": [ - 3.1330668926239014, - 8.50548267364502 + 3.4911253452301025, + 7.7258429527282715 ], "pca3d": [ - -0.08705580979585648, - 0.25786730647087097, - 0.046020299196243286 + -0.08614639192819595, + 0.2575863003730774, + 0.04566759988665581 ], "tsne3d": [ - -11.109455108642578, - 13.100605010986328, - -8.55190372467041 + -9.70808219909668, + 1.551495909690857, + 16.546886444091797 ], "umap3d": [ - 3.0287439823150635, - 4.190622329711914, - 4.272107124328613 + 3.9196648597717285, + 4.740908622741699, + 4.424440860748291 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 32, + "id": 25, "label": "AI Agents" } ], @@ -985000,47 +985150,47 @@ "doi": "10.1038/s41586-026-10367-0", "projections": { "pca": [ - 0.2504213750362396, - -0.003785124747082591 + 0.25028297305107117, + -0.004292044788599014 ], "tsne": [ - 30.991350173950195, - -15.578354835510254 + 24.704524993896484, + 21.196813583374023 ], "umap": [ - 8.639867782592773, - 9.280272483825684 + 8.736096382141113, + 8.795369148254395 ], "pca3d": [ - 0.25042131543159485, - -0.0037811279762536287, - -0.12968142330646515 + 0.2502829432487488, + -0.004287932068109512, + -0.12985339760780334 ], "tsne3d": [ - 19.37921142578125, - 10.064433097839355, - 6.598387718200684 + 13.240056991577148, + -3.2254676818847656, + -13.423776626586914 ], "umap3d": [ - 5.6469573974609375, - 4.448825836181641, - 1.0474344491958618 + 5.238589763641357, + 4.086851119995117, + 0.7793136835098267 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 9, - "label": "Single Cell Causal" + "id": 6, + "label": "Single Cell Data" }, { - "id": 28, - "label": "Causal Cell Perturbation" + "id": 4, + "label": "Causal Cell" } ], "_embedding": [ @@ -985448,47 +985598,47 @@ "abstract": "OpenAI introduces GPT-Rosalind, a frontier reasoning model built to accelerate drug discovery, genomics analysis, protein reasoning, and scientific research workflows.", "projections": { "pca": [ - -0.053842656314373016, - 0.13469690084457397 + -0.05334510654211044, + 0.13496431708335876 ], "tsne": [ - 0.0009909106884151697, - -16.757770538330078 + -3.4311976432800293, + 12.611785888671875 ], "umap": [ - 4.621532440185547, - 8.254703521728516 + 4.864009380340576, + 7.466562271118164 ], "pca3d": [ - -0.05384271591901779, - 0.13468880951404572, - 0.13369786739349365 + -0.05334518849849701, + 0.13495607674121857, + 0.1335476189851761 ], "tsne3d": [ - -5.207333564758301, - -7.865547180175781, - -12.742984771728516 + -0.5801912546157837, + 15.447525978088379, + 9.451725959777832 ], "umap3d": [ - 3.6408095359802246, - 4.131804943084717, - 3.334474802017212 + 4.385773658752441, + 4.622652053833008, + 3.4966042041778564 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, - "label": "AI Agents" + "id": 4, + "label": "Genomics Tools" }, { - "id": 17, - "label": "Medical AI" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -985896,47 +986046,47 @@ "abstract": "Learn about the general availability of Amazon Bio Discovery, a unified application that closes the experimental loop between your dry and wet lab, enabling you to collaborate more efficiently with your computational colleagues, compress discovery cycles, and make faster, more confident decisions about which candidates to advance.", "projections": { "pca": [ - 0.04950357973575592, - 0.321859210729599 + 0.05039125680923462, + 0.32212814688682556 ], "tsne": [ - 0.136013001203537, - -20.379615783691406 + -2.3531508445739746, + 17.057872772216797 ], "umap": [ - 4.705569744110107, - 8.75694751739502 + 4.93965482711792, + 7.9331207275390625 ], "pca3d": [ - 0.049503058195114136, - 0.32186055183410645, - 0.09682301431894302 + 0.050390634685754776, + 0.3221292495727539, + 0.09627176821231842 ], "tsne3d": [ - -0.17498335242271423, - -6.264092922210693, - -8.164621353149414 + -1.4059433937072754, + 15.18614673614502, + 5.595548152923584 ], "umap3d": [ - 3.7533986568450928, - 3.7865493297576904, - 2.955049991607666 + 4.277122497558594, + 4.138803482055664, + 3.11531662940979 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, - "label": "Medical AI" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -986344,47 +986494,47 @@ "abstract": "A new initiative to secure the world\u2019s most critical software and give defenders a durable advantage in the coming AI-driven era of cybersecurity.", "projections": { "pca": [ - -0.15453845262527466, - 0.2688820958137512 + -0.15375882387161255, + 0.26989036798477173 ], "tsne": [ - -13.40416431427002, - -19.711788177490234 + -10.674477577209473, + 11.572990417480469 ], "umap": [ - 3.686889886856079, - 8.3215970993042 + 4.007810592651367, + 7.5691962242126465 ], "pca3d": [ - -0.15453845262527466, - 0.2688550055027008, - 0.104876808822155 + -0.1537589132785797, + 0.2698627710342407, + 0.10455373674631119 ], "tsne3d": [ - -4.288412570953369, - -5.321544170379639, - 4.9542951583862305 + -8.959975242614746, + 5.716922760009766, + 0.25147223472595215 ], "umap3d": [ - 2.993461847305298, - 3.7085254192352295, - 3.6557199954986572 + 3.8410277366638184, + 4.180451393127441, + 4.082406044006348 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -986793,47 +986943,47 @@ ], "projections": { "pca": [ - -0.04433544725179672, - 0.3191371262073517 + -0.04334326088428497, + 0.31989365816116333 ], "tsne": [ - -7.715418815612793, - -2.70064115524292 + -22.59762954711914, + -31.399398803710938 ], "umap": [ - 4.38947868347168, - 7.472143650054932 + 4.606886386871338, + 6.85687780380249 ], "pca3d": [ - -0.04433581978082657, - 0.31911614537239075, - 0.12953634560108185 + -0.043343666940927505, + 0.31987205147743225, + 0.12910376489162445 ], "tsne3d": [ - -9.077548027038574, - -1.6352142095565796, - 5.405383110046387 + -2.45467472076416, + 8.767448425292969, + 12.341928482055664 ], "umap3d": [ - 3.6282923221588135, - 3.478928804397583, - 4.850327491760254 + 4.76904821395874, + 4.585786819458008, + 5.2498579025268555 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -987243,46 +987393,46 @@ "abstract": "Use Codex from Claude Code to review code or delegate tasks. - openai/codex-plugin-cc", "projections": { "pca": [ - -0.09632173180580139, - 0.24542053043842316 + -0.0954427421092987, + 0.2454489916563034 ], "tsne": [ - -27.08104133605957, - 25.46525001525879 + -21.912002563476562, + 4.392027378082275 ], "umap": [ - 3.1030685901641846, - 8.51640796661377 + 3.4457998275756836, + 7.743561267852783 ], "pca3d": [ - -0.0963222086429596, - 0.24542193114757538, - 0.10967487096786499 + -0.09544315934181213, + 0.24545013904571533, + 0.10933903604745865 ], "tsne3d": [ - -11.342255592346191, - 12.593192100524902, - -8.907456398010254 + -9.469914436340332, + 1.7382187843322754, + 15.982213020324707 ], "umap3d": [ - 2.9904537200927734, - 4.108608245849609, - 4.208209037780762 + 3.8398473262786865, + 4.6841535568237305, + 4.395081520080566 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 32, + "id": 25, "label": "AI Agents" } ], @@ -987689,31 +987839,31 @@ ], "projections": { "pca": [ - -0.14622482657432556, - 0.12480159848928452 + -0.14570268988609314, + 0.12456033378839493 ], "tsne": [ - -15.051874160766602, - 4.410553932189941 + -25.282556533813477, + -9.55560302734375 ], "umap": [ - 2.801363945007324, - 6.4382219314575195 + 3.442863941192627, + 5.861130237579346 ], "pca3d": [ - -0.14622505009174347, - 0.1248193010687828, - -0.0051236823201179504 + -0.14570285379886627, + 0.1245788186788559, + -0.005315943155437708 ], "tsne3d": [ - -8.518545150756836, - 20.716787338256836, - -7.828241348266602 + -5.388855457305908, + -9.21971607208252, + -15.616601943969727 ], "umap3d": [ - 2.503683090209961, - 5.268497943878174, - 4.520715236663818 + 3.3641135692596436, + 5.878653526306152, + 4.67965841293335 ] }, "cluster_id": 2, @@ -987724,12 +987874,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 33, - "label": "Neural Networks" + "id": 19, + "label": "Graph Neural Nets" } ], "_embedding": [ @@ -988139,31 +988289,31 @@ "abstract": "The use of phage therapy to treat bacterial infections has largely existed on the fringes of medicine in the U.S. Why? And what needs to happen to make phage therapy mainstream?", "projections": { "pca": [ - -0.030777206644415855, - 0.31320300698280334 + -0.02989962138235569, + 0.31351718306541443 ], "tsne": [ - 0.3756270706653595, - 11.674641609191895 + 13.673317909240723, + -16.842309951782227 ], "umap": [ - 5.843104839324951, - 5.634707927703857 + 6.273700714111328, + 5.281817436218262 ], "pca3d": [ - -0.030777739360928535, - 0.3132012188434601, - 0.08019634336233139 + -0.02990020252764225, + 0.31351447105407715, + 0.07971180975437164 ], "tsne3d": [ - -2.2284433841705322, - 0.4387078285217285, - -6.524982452392578 + 2.920351266860962, + -0.6911203861236572, + 12.668624877929688 ], "umap3d": [ - 5.667647361755371, - 5.982249736785889, - 3.7947535514831543 + 5.620153427124023, + 6.2574687004089355, + 2.562058448791504 ] }, "cluster_id": 3, @@ -988174,12 +988324,12 @@ "label": "Protein Design" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 24, - "label": "Protein Design" + "id": 32, + "label": "Genome Protein" } ], "_embedding": [ @@ -988587,31 +988737,31 @@ "abstract": "R package for mining, calling, and importing epigenomic peaks. - neurogenomics/PeakyFinders", "projections": { "pca": [ - 0.04079825058579445, - 0.15601973235607147 + 0.041337378323078156, + 0.15573246777057648 ], "tsne": [ - 24.48434829711914, - -6.538861274719238 + 27.497886657714844, + 12.515100479125977 ], "umap": [ - 8.52490520477295, - 7.653989791870117 + 8.68451976776123, + 6.937073707580566 ], "pca3d": [ - 0.0407978780567646, - 0.15603439509868622, - -0.054626740515232086 + 0.04133698716759682, + 0.15574753284454346, + -0.054840099066495895 ], "tsne3d": [ - 11.49606990814209, - -1.2164392471313477, - 3.4696738719940186 + 12.417452812194824, + 3.0922954082489014, + -6.511698246002197 ], "umap3d": [ - 6.719756126403809, - 4.453176498413086, - 1.8109465837478638 + 6.613720417022705, + 4.519998550415039, + 0.9913914203643799 ] }, "cluster_id": 0, @@ -988622,12 +988772,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, - "label": "Chromatin Regulation" + "id": 2, + "label": "DNA Regulation" }, { - "id": 31, - "label": "Genome Cancer" + "id": 18, + "label": "Chromatin Regulation" } ], "_embedding": [ @@ -989037,47 +989187,47 @@ "abstract": "AlphaGenome PyTorch port. Contribute to genomicsxai/alphagenome-pytorch development by creating an account on GitHub.", "projections": { "pca": [ - 0.09803115576505661, - 0.1590871661901474 + 0.09870366752147675, + 0.1587972790002823 ], "tsne": [ - -27.333749771118164, - 34.29130172729492 + -14.571626663208008, + -9.166425704956055 ], "umap": [ - 4.131462097167969, - 6.372622489929199 + 4.486575603485107, + 5.744040489196777 ], "pca3d": [ - 0.09803099185228348, - 0.15907089412212372, - 0.10502928495407104 + 0.0987035408616066, + 0.15878108143806458, + 0.10480977594852448 ], "tsne3d": [ - -8.812716484069824, - 18.31888198852539, - 11.579855918884277 + -12.587759971618652, + -6.957723140716553, + 17.220460891723633 ], "umap3d": [ - 3.8512251377105713, - 4.858094692230225, - 4.5594096183776855 + 4.73090934753418, + 5.561783790588379, + 4.226348400115967 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 10, - "label": "Code Development" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -989485,47 +989635,47 @@ "abstract": "AI-Powered Bioinformatics Research Assistant. Built on OpenClaw. - Runchuan-BU/BioClaw", "projections": { "pca": [ - 0.006479692179709673, - 0.1651022881269455 + 0.007194685749709606, + 0.16526490449905396 ], "tsne": [ - -1.583914041519165, - -19.774700164794922 + -4.0413947105407715, + 16.234928131103516 ], "umap": [ - 4.806032657623291, - 8.587898254394531 + 5.0464324951171875, + 7.81854772567749 ], "pca3d": [ - 0.006479586008936167, - 0.16508296132087708, - 0.1973153054714203 + 0.0071945409290492535, + 0.16524556279182434, + 0.19714467227458954 ], "tsne3d": [ - -1.6506876945495605, - -9.358744621276855, - -9.762935638427734 + 0.009926753118634224, + 17.057064056396484, + 3.1849913597106934 ], "umap3d": [ - 3.7731878757476807, - 3.831233024597168, - 2.9900872707366943 + 4.447192668914795, + 4.192573070526123, + 3.070409059524536 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, - "label": "Medical AI" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -989935,31 +990085,31 @@ "abstract": "Benchmarking approaches to fine-tune AlphaGenome on lentiMPRA data - Al-Murphy/alphagenome_FT_MPRA", "projections": { "pca": [ - 0.037731241434812546, - 0.059502165764570236 + 0.03791225329041481, + 0.05928739532828331 ], "tsne": [ - 7.287454128265381, - -1.8006987571716309 + 9.149736404418945, + 3.283191204071045 ], "umap": [ - 6.711138725280762, - 6.736263751983643 + 7.198212146759033, + 6.241304397583008 ], "pca3d": [ - 0.037731464952230453, - 0.059482231736183167, - -0.04731527343392372 + 0.03791244328022003, + 0.059267807751894, + -0.04743524640798569 ], "tsne3d": [ - 9.557995796203613, - -3.0108866691589355, - -3.785299062728882 + 10.97540283203125, + -13.45897388458252, + -7.390627861022949 ], "umap3d": [ - 6.068628311157227, - 4.647613048553467, - 3.1447067260742188 + 6.07087516784668, + 4.913283348083496, + 2.318366050720215 ] }, "cluster_id": 4, @@ -989970,12 +990120,12 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 11, - "label": "Genomic Language" + "id": 17, + "label": "Genomic Models" } ], "_embedding": [ @@ -990385,31 +990535,31 @@ "abstract": "Research code accompanying AlphaGenome . Contribute to google-deepmind/alphagenome_research development by creating an account on GitHub.", "projections": { "pca": [ - 0.06884054839611053, - 0.09177136421203613 + 0.06901375204324722, + 0.0919112116098404 ], "tsne": [ - 7.2770819664001465, - -1.8028407096862793 + 9.148262977600098, + 3.346670150756836 ], "umap": [ - 6.629393100738525, - 6.736044406890869 + 7.140339374542236, + 6.338675022125244 ], "pca3d": [ - 0.06884055584669113, - 0.09176207333803177, - 0.012259167619049549 + 0.06901369988918304, + 0.0919022262096405, + 0.012063704431056976 ], "tsne3d": [ - 9.568460464477539, - -2.9465084075927734, - -3.4727039337158203 + 11.052473068237305, + -13.402048110961914, + -7.200263977050781 ], "umap3d": [ - 6.023839473724365, - 4.707453727722168, - 3.034895658493042 + 6.004106044769287, + 4.910083770751953, + 2.296020269393921 ] }, "cluster_id": 4, @@ -990420,12 +990570,12 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 11, - "label": "Genomic Language" + "id": 17, + "label": "Genomic Models" } ], "_embedding": [ @@ -990833,47 +990983,47 @@ "abstract": "A new principal investigator wants to help PhD students to develop resilience and creativity in the laboratory without hovering or doing the work for them.", "projections": { "pca": [ - -0.12839102745056152, - 0.3729996383190155 + -0.1274651437997818, + 0.37410402297973633 ], "tsne": [ - -13.443107604980469, - -13.643560409545898 + -12.843039512634277, + 7.227510452270508 ], "umap": [ - 3.846707820892334, - 7.6640119552612305 + 4.152142524719238, + 6.964714050292969 ], "pca3d": [ - -0.1283920258283615, - 0.37302902340888977, - 0.03322133421897888 + -0.1274661123752594, + 0.3741331696510315, + 0.03266918286681175 ], "tsne3d": [ - -3.7280828952789307, - -7.221074104309082, - 9.993141174316406 + -11.348814964294434, + 0.3138372004032135, + -8.735917091369629 ], "umap3d": [ - 3.4136557579040527, - 3.540938377380371, - 4.4380717277526855 + 4.430945873260498, + 4.3954033851623535, + 4.767160892486572 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -991284,47 +991434,47 @@ "abstract": "Turn any AI agent into an AI Scientist. The #1 Agent Skills library for science, used by 160,000+ scientists worldwide. 140 ready-to-use skills plus 100+ scientific databases covering biology, chemistry, medicine, and drug discovery. Compatible with Cursor, Claude Code, Codex, Pi, Antigravity, and the open Agent Skills standard. - K-Dense-AI/scientific-agent-skills", "projections": { "pca": [ - -0.11682303994894028, - 0.22557352483272552 + -0.1161673441529274, + 0.22653011977672577 ], "tsne": [ - -8.410043716430664, - -22.93277359008789 + -11.94339370727539, + 17.625919342041016 ], "umap": [ - 3.888188600540161, - 8.480164527893066 + 3.9527852535247803, + 7.6320013999938965 ], "pca3d": [ - -0.11682327091693878, - 0.2255789190530777, - 0.1830529421567917 + -0.11616764217615128, + 0.22653540968894958, + 0.18275055289268494 ], "tsne3d": [ - -7.1108598709106445, - -6.258031845092773, - -3.5243568420410156 + -7.030548572540283, + 10.97577953338623, + 3.741363286972046 ], "umap3d": [ - 3.2135891914367676, - 3.787889003753662, - 3.702364206314087 + 3.926378011703491, + 4.348819255828857, + 3.983757972717285 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -991734,47 +991884,47 @@ "abstract": "The US Congress rejected sweeping cuts to science agencies. But the NIH, the NSF and NASA have had their spending slowed.", "projections": { "pca": [ - -0.06860341876745224, - 0.35390764474868774 + -0.0677771344780922, + 0.3543616831302643 ], "tsne": [ - -16.080781936645508, - -2.8875155448913574 + -15.458780288696289, + 1.0698281526565552 ], "umap": [ - 4.267885684967041, - 7.692220211029053 + 4.410650730133057, + 7.033576011657715 ], "pca3d": [ - -0.06860414147377014, - 0.3539258539676666, - 0.015242259949445724 + -0.06777779757976532, + 0.35438022017478943, + 0.01458291057497263 ], "tsne3d": [ - -14.653362274169922, - -0.34468334913253784, - 18.648025512695312 + -3.456742763519287, + 5.318692684173584, + -9.734715461730957 ], "umap3d": [ - 3.6789157390594482, - 3.4627797603607178, - 4.391678333282471 + 4.771646499633789, + 4.276418209075928, + 4.62590217590332 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -992182,46 +992332,46 @@ "abstract": "Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.", "projections": { "pca": [ - -0.13032060861587524, - 0.2934539318084717 + -0.12958192825317383, + 0.29498156905174255 ], "tsne": [ - -15.528675079345703, - -34.65599060058594 + -24.428203582763672, + -32.997467041015625 ], "umap": [ - 3.0858676433563232, - 9.198027610778809 + 3.1980912685394287, + 8.414719581604004 ], "pca3d": [ - -0.13032056391239166, - 0.2934102714061737, - 0.10477359592914581 + -0.12958195805549622, + 0.2949374318122864, + 0.10445636510848999 ], "tsne3d": [ - -9.153581619262695, - -2.6928820610046387, - 1.3280409574508667 + -5.695345878601074, + 10.737039566040039, + 12.40523624420166 ], "umap3d": [ - 2.9912538528442383, - 3.078392267227173, - 5.161787509918213 + 4.553419589996338, + 4.387159824371338, + 5.710198879241943 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 32, + "id": 25, "label": "AI Agents" } ], @@ -992630,47 +992780,47 @@ "abstract": "Mainstream chatbots presented varying levels of resistance to deliberate requests for fabrication, study finds.", "projections": { "pca": [ - -0.22050170600414276, - 0.33395421504974365 + -0.21944618225097656, + 0.33497053384780884 ], "tsne": [ - -19.873058319091797, - -17.344877243041992 + -25.008195877075195, + 14.78406047821045 ], "umap": [ - 2.798243999481201, - 8.374825477600098 + 3.2128279209136963, + 7.672696590423584 ], "pca3d": [ - -0.22050243616104126, - 0.3339657187461853, - 0.07406485825777054 + -0.21944677829742432, + 0.33498162031173706, + 0.07377815991640091 ], "tsne3d": [ - -8.076349258422852, - -16.324304580688477, - 6.7859787940979 + -11.113065719604492, + 8.006863594055176, + -4.6303629875183105 ], "umap3d": [ - 2.542721748352051, - 3.5792205333709717, - 4.25329065322876 + 3.6826815605163574, + 4.116086959838867, + 4.742327690124512 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 0, - "label": "LLM Language Learning" + "id": 25, + "label": "AI Agents" } ], "_embedding": [ @@ -993078,47 +993228,47 @@ "abstract": "Unlinkable inference is a technique that provably sandboxes your AI requests from each other and from your identity. We discuss its building blocks, applications, and how it fits into the broader landscape of private personal intelligence.", "projections": { "pca": [ - -0.1819770634174347, - 0.11558204889297485 + -0.18177343904972076, + 0.11624211072921753 ], "tsne": [ - -27.276569366455078, - -23.250131607055664 + -30.220474243164062, + 20.844497680664062 ], "umap": [ - 2.4982101917266846, - 8.386298179626465 + 2.77974796295166, + 7.513723373413086 ], "pca3d": [ - -0.1819770187139511, - 0.11557544022798538, - 0.01703198440372944 + -0.18177345395088196, + 0.11623556911945343, + 0.016848960891366005 ], "tsne3d": [ - -20.287405014038086, - -19.152326583862305, - 0.9237670302391052 + -19.23776626586914, + 8.325397491455078, + -10.143287658691406 ], "umap3d": [ - 1.7880198955535889, - 3.979349136352539, - 3.795320510864258 + 2.7631776332855225, + 4.256750583648682, + 4.466222286224365 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 7, - "label": "Language Models" + "id": 25, + "label": "AI Agents" } ], "_embedding": [ @@ -993535,46 +993685,46 @@ ], "projections": { "pca": [ - -0.15642650425434113, - 0.12747938930988312 + -0.1559036821126938, + 0.12815040349960327 ], "tsne": [ - -13.269776344299316, - -27.451629638671875 + -17.16785430908203, + 20.31625747680664 ], "umap": [ - 3.2918407917022705, - 8.54142951965332 + 3.5731849670410156, + 7.792899131774902 ], "pca3d": [ - -0.1564262956380844, - 0.12746159732341766, - 0.11083566397428513 + -0.155903622508049, + 0.12813213467597961, + 0.11080659925937653 ], "tsne3d": [ - -7.839406967163086, - -12.948174476623535, - -7.236120223999023 + -10.175612449645996, + 1.3892534971237183, + 13.263635635375977 ], "umap3d": [ - 2.9470956325531006, - 3.983267307281494, - 3.931584358215332 + 3.7619807720184326, + 4.500603199005127, + 4.197628498077393 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 32, + "id": 25, "label": "AI Agents" } ], @@ -993983,46 +994133,46 @@ "abstract": "Persistent Context Across Sessions for Every Agent \u2013 Captures everything your agent does during sessions, compresses it with AI, and injects relevant context back into future sessions. Works with Claude Code, OpenClaw, Codex, Gemini, Hermes, Copilot, OpenCode + More - thedotmack/claude-mem", "projections": { "pca": [ - -0.14456583559513092, - 0.17523865401744843 + -0.1439731866121292, + 0.17586199939250946 ], "tsne": [ - -27.041950225830078, - 24.737197875976562 + -22.7760066986084, + 4.727423191070557 ], "umap": [ - 3.0774343013763428, - 8.589573860168457 + 3.329094409942627, + 7.83965539932251 ], "pca3d": [ - -0.14456585049629211, - 0.17522436380386353, - 0.07057278603315353 + -0.1439732164144516, + 0.17584683001041412, + 0.07038545608520508 ], "tsne3d": [ - -11.635693550109863, - 11.195871353149414, - -9.283188819885254 + -9.20851993560791, + 2.6387338638305664, + 14.744365692138672 ], "umap3d": [ - 2.865583896636963, - 4.072427272796631, - 4.201755523681641 + 3.7961478233337402, + 4.612936496734619, + 4.442697048187256 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 32, + "id": 25, "label": "AI Agents" } ], @@ -994433,47 +994583,47 @@ "abstract": "Preprint repositories and conference organizers are having to counter a tide of \u2018AI slop\u2019 submissions.", "projections": { "pca": [ - -0.2201933115720749, - 0.2626303434371948 + -0.21946868300437927, + 0.2637355327606201 ], "tsne": [ - -12.62757396697998, - -18.19539451599121 + -11.446796417236328, + 10.5956449508667 ], "umap": [ - 3.9608240127563477, - 8.079225540161133 + 4.193798065185547, + 7.398649215698242 ], "pca3d": [ - -0.22019365429878235, - 0.2626289427280426, - 0.06056325510144234 + -0.21946901082992554, + 0.26373425126075745, + 0.060266606509685516 ], "tsne3d": [ - -4.87322998046875, - -6.931856155395508, - 6.55465030670166 + -8.871902465820312, + 6.236752510070801, + -2.04144549369812 ], "umap3d": [ - 3.304485559463501, - 3.533712148666382, - 3.9085264205932617 + 4.239328861236572, + 4.230945587158203, + 4.3498735427856445 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -994900,31 +995050,31 @@ "openalex_url": "https://openalex.org/W7128692586", "projections": { "pca": [ - 0.19980794191360474, - 0.11196097731590271 + 0.20015741884708405, + 0.1115410253405571 ], "tsne": [ - 14.874836921691895, - 31.156320571899414 + 16.301631927490234, + -30.603965759277344 ], "umap": [ - 7.742330074310303, - 4.657670021057129 + 8.028905868530273, + 4.258059978485107 ], "pca3d": [ - 0.1998075544834137, - 0.11197315901517868, - 0.18294201791286469 + 0.200157031416893, + 0.11155356466770172, + 0.18250393867492676 ], "tsne3d": [ - -0.023105284199118614, - 25.875743865966797, - 3.781623363494873 + 8.810659408569336, + -20.01494789123535, + 3.0690784454345703 ], "umap3d": [ - 6.996848106384277, - 6.193073272705078, - 3.8703393936157227 + 7.049676418304443, + 6.69317626953125, + 2.1937997341156006 ] }, "cluster_id": 4, @@ -994935,11 +995085,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 6, + "id": 8, "label": "RNA Structure" } ], @@ -995348,47 +995498,47 @@ "abstract": "A new preprint shows GPT-5.2 proposing a new formula for a gluon amplitude, later formally proved and verified by OpenAI and academic collaborators.", "projections": { "pca": [ - -0.13795673847198486, - 0.1344258040189743 + -0.13753670454025269, + 0.13516777753829956 ], "tsne": [ - -19.399805068969727, - -22.504688262939453 + -19.042638778686523, + 13.11856746673584 ], "umap": [ - 3.0748324394226074, - 7.265398025512695 + 3.2971487045288086, + 6.428832530975342 ], "pca3d": [ - -0.1379568725824356, - 0.13442519307136536, - 0.08459941297769547 + -0.137536883354187, + 0.13516709208488464, + 0.08440856635570526 ], "tsne3d": [ - -12.603461265563965, - -3.4553635120391846, - -3.313030958175659 + -13.62552261352539, + 9.892678260803223, + 5.028883457183838 ], "umap3d": [ - 2.6087188720703125, - 4.683525085449219, - 3.743610382080078 + 3.215672016143799, + 5.245173931121826, + 4.157863616943359 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { "id": 0, - "label": "LLM Language Learning" + "label": "Math AI" } ], "_embedding": [ @@ -995800,47 +995950,47 @@ ], "projections": { "pca": [ - -0.13197505474090576, - 0.21176479756832123 + -0.13133695721626282, + 0.21220272779464722 ], "tsne": [ - -22.493154525756836, - -25.65983009338379 + -22.44793701171875, + 12.254652976989746 ], "umap": [ - 2.7814629077911377, - 7.584141254425049 + 3.022709369659424, + 6.8508076667785645 ], "pca3d": [ - -0.13197508454322815, - 0.21175652742385864, - 0.021426981315016747 + -0.1313370317220688, + 0.21219421923160553, + 0.02111256867647171 ], "tsne3d": [ - -16.45325469970703, - -5.981492042541504, - -7.281659126281738 + -18.0216064453125, + 13.421917915344238, + 5.838399887084961 ], "umap3d": [ - 2.1914725303649902, - 4.487260341644287, - 3.6234323978424072 + 2.9748501777648926, + 4.920083522796631, + 4.1510443687438965 ] }, - "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 1, - "label": "AI Scientific Research" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { "id": 0, - "label": "LLM Language Learning" + "label": "Math AI" } ], "_embedding": [ @@ -996263,31 +996413,31 @@ "openalex_url": "https://openalex.org/W7124264436", "projections": { "pca": [ - 0.2073724865913391, - 0.09085221588611603 + 0.207549586892128, + 0.09078582376241684 ], "tsne": [ - 29.704566955566406, - 31.48460578918457 + 26.643211364746094, + -20.438241958618164 ], "umap": [ - 8.81753158569336, - 5.6692214012146 + 9.06266975402832, + 5.096795558929443 ], "pca3d": [ - 0.2073720246553421, - 0.0908685103058815, - 0.018000178039073944 + 0.20754915475845337, + 0.09080252796411514, + 0.01773509941995144 ], "tsne3d": [ - 12.67322826385498, - 22.086036682128906, - -5.339994430541992 + 17.974868774414062, + -15.98873519897461, + 1.0042439699172974 ], "umap3d": [ - 7.438711166381836, - 5.579246997833252, - 2.9263927936553955 + 7.269301891326904, + 5.821159839630127, + 1.2200589179992676 ] }, "cluster_id": 4, @@ -996298,11 +996448,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -996709,46 +996859,46 @@ ], "projections": { "pca": [ - -0.03808843344449997, - 0.26421743631362915 + -0.037182945758104324, + 0.2638739347457886 ], "tsne": [ - -4.69460916519165, - -0.09484314918518066 + -4.141397476196289, + -0.21771468222141266 ], "umap": [ - 5.116249084472656, - 6.893192291259766 + 5.1111650466918945, + 6.3976569175720215 ], "pca3d": [ - -0.03808921203017235, - 0.26424217224121094, - 0.03290846198797226 + -0.03718356788158417, + 0.26389873027801514, + 0.03251957520842552 ], "tsne3d": [ - -11.80229377746582, - 0.9627246856689453, - 10.179887771606445 + -1.1083269119262695, + 2.0906972885131836, + -0.32588139176368713 ], "umap3d": [ - 4.4958086013793945, - 4.150571823120117, - 4.142181396484375 + 5.182642936706543, + 4.867274761199951, + 4.037074089050293 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, + "id": 13, "label": "Genomics Papers" } ], @@ -997156,46 +997306,46 @@ "title": "Life on Claude Nine", "projections": { "pca": [ - -0.13798131048679352, - 0.3473345637321472 + -0.13693608343601227, + 0.3481713533401489 ], "tsne": [ - -20.104089736938477, - -12.758743286132812 + -24.01837921142578, + 6.388132572174072 ], "umap": [ - 2.892839193344116, - 8.700112342834473 + 3.1370980739593506, + 7.885695934295654 ], "pca3d": [ - -0.13798171281814575, - 0.34732785820961, - 0.07245898246765137 + -0.13693657517433167, + 0.34816449880599976, + 0.07205896079540253 ], "tsne3d": [ - -11.543083190917969, - -17.230735778808594, - -0.7026864290237427 + -8.909346580505371, + 5.453170299530029, + 15.443325996398926 ], "umap3d": [ - 2.7737231254577637, - 3.838843822479248, - 4.422764778137207 + 3.8449301719665527, + 4.554025173187256, + 4.811648368835449 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 32, + "id": 25, "label": "AI Agents" } ], @@ -997606,31 +997756,31 @@ "journal": "Nature", "projections": { "pca": [ - 0.2902834415435791, - 0.029911691322922707 + 0.2904457747936249, + 0.02872103825211525 ], "tsne": [ - 33.127830505371094, - 28.749326705932617 + 58.29011535644531, + -1.9715709686279297 ], "umap": [ - 8.820515632629395, - 5.521897792816162 + 9.094162940979004, + 5.086956024169922 ], "pca3d": [ - 0.29028284549713135, - 0.029940130189061165, - 0.028601620346307755 + 0.2904452979564667, + 0.028750326484441757, + 0.028291480615735054 ], "tsne3d": [ - 11.63593578338623, - 18.345117568969727, - -1.7078807353973389 + 23.94740104675293, + -14.76409912109375, + 4.950580596923828 ], "umap3d": [ - 7.592223167419434, - 5.690680980682373, - 3.0709681510925293 + 7.479373455047607, + 5.990570068359375, + 1.3281891345977783 ] }, "cluster_id": 4, @@ -997641,11 +997791,11 @@ "label": "RNA Sequence Models" }, { - "id": 3, + "id": 14, "label": "RNA Splicing" }, { - "id": 13, + "id": 21, "label": "RNA Splicing" } ], @@ -998054,47 +998204,47 @@ "abstract": "Write, edit, and collaborate on scientific documents in LaTeX with Prism\u2014the free LaTeX editor and scientific workspace integrating ChatGPT and Codex into research and writing.", "projections": { "pca": [ - -0.08732019364833832, - 0.2953145205974579 + -0.0863194465637207, + 0.2954026758670807 ], "tsne": [ - -8.312954902648926, - -18.506803512573242 + -7.060030460357666, + 11.350364685058594 ], "umap": [ - 4.140711784362793, - 8.083620071411133 + 4.263462066650391, + 7.420967102050781 ], "pca3d": [ - -0.08732043206691742, - 0.295298308134079, - 0.05538909509778023 + -0.08631971478462219, + 0.2953858971595764, + 0.05502026528120041 ], "tsne3d": [ - -8.457498550415039, - -8.930583953857422, - 3.506207227706909 + -5.000417709350586, + 6.393787384033203, + -0.4762914478778839 ], "umap3d": [ - 3.388935089111328, - 3.9033122062683105, - 3.7364509105682373 + 4.224049091339111, + 4.410886764526367, + 4.052009105682373 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 23, - "label": "Scientific Research" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -998510,46 +998660,46 @@ ], "projections": { "pca": [ - -0.2396070808172226, - -0.020614946261048317 + -0.23952625691890717, + -0.019452853128314018 ], "tsne": [ - -25.449440002441406, - -13.409627914428711 + -29.677433013916016, + 11.153061866760254 ], "umap": [ - 2.2554433345794678, - 8.415491104125977 + 2.5172014236450195, + 7.679102897644043 ], "pca3d": [ - -0.23960693180561066, - -0.020610786974430084, - 0.025569012388586998 + -0.23952604830265045, + -0.019448474049568176, + 0.02593155950307846 ], "tsne3d": [ - -13.433452606201172, - -11.954282760620117, - 5.708197593688965 + -14.762101173400879, + 8.002869606018066, + -0.7190357446670532 ], "umap3d": [ - 2.072610855102539, - 3.9364237785339355, - 4.4441304206848145 + 3.042102336883545, + 4.454843997955322, + 5.088621139526367 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -998961,47 +999111,47 @@ "abstract": "After turning off ChatGPT\u2019s \u2018data consent\u2019 option, Marcel Bucher lost the work behind grant applications, teaching materials and publication drafts. Here\u2019s what happened next.", "projections": { "pca": [ - -0.13030847907066345, - 0.3303564190864563 + -0.12926112115383148, + 0.33068764209747314 ], "tsne": [ - -9.691253662109375, - -7.1473822593688965 + -8.764636993408203, + 5.399345397949219 ], "umap": [ - 4.142744541168213, - 7.647181987762451 + 4.265158653259277, + 6.97292423248291 ], "pca3d": [ - -0.13030914962291718, - 0.33036699891090393, - -0.022976605221629143 + -0.1292618066072464, + 0.3306979835033417, + -0.023376544937491417 ], "tsne3d": [ - -7.095661640167236, - -2.121171474456787, - 10.016021728515625 + -5.632138252258301, + 3.346108913421631, + -3.4254491329193115 ], "umap3d": [ - 3.6311419010162354, - 3.7403886318206787, - 4.288305759429932 + 4.511107921600342, + 4.510267734527588, + 4.454432010650635 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -999410,47 +999560,47 @@ ], "projections": { "pca": [ - -0.16915538907051086, - 0.14897359907627106 + -0.1685004085302353, + 0.14896106719970703 ], "tsne": [ - -18.7655029296875, - -10.207785606384277 + -19.409400939941406, + 7.311631679534912 ], "umap": [ - 3.7521278858184814, - 7.381837368011475 + 3.809556722640991, + 6.75673246383667 ], "pca3d": [ - -0.1691557914018631, - 0.1490040272474289, - 0.033304572105407715 + -0.1685008406639099, + 0.14899232983589172, + 0.033229801803827286 ], "tsne3d": [ - -9.183791160583496, - -11.302018165588379, - 10.859599113464355 + -14.039806365966797, + 0.08976900577545166, + -6.237795352935791 ], "umap3d": [ - 3.230203866958618, - 3.8301801681518555, - 4.387328147888184 + 4.07159948348999, + 4.665725231170654, + 4.691004276275635 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 12, + "label": "AI Agents" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -999860,47 +1000010,47 @@ "abstract": "Introducing Claude for Healthcare with HIPAA-ready infrastructure, plus expanded Life Sciences tools for clinical trials and regulatory submissions. New connectors to CMS, Medidata, and ClinicalTrials.gov.", "projections": { "pca": [ - -0.021440811455249786, - 0.3517489731311798 + -0.02049841918051243, + 0.35205966234207153 ], "tsne": [ - -2.009999990463257, - -13.937124252319336 + -3.820556163787842, + -3.889296531677246 ], "umap": [ - 4.475561141967773, - 7.982486724853516 + 4.820469856262207, + 7.282605171203613 ], "pca3d": [ - -0.021441327407956123, - 0.3517405688762665, - 0.08687470108270645 + -0.020499013364315033, + 0.3520503640174866, + 0.0863121747970581 ], "tsne3d": [ - -6.399312496185303, - -13.399072647094727, - -13.353514671325684 + -5.922155380249023, + 6.230565071105957, + 18.322134017944336 ], "umap3d": [ - 3.9850122928619385, - 3.7665274143218994, - 4.185824871063232 + 4.772223472595215, + 4.547804355621338, + 4.236387729644775 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -1000315,47 +1000465,47 @@ ], "projections": { "pca": [ - -0.2306843400001526, - 0.12891487777233124 + -0.23029616475105286, + 0.13030658662319183 ], "tsne": [ - -22.412561416625977, - -17.51990509033203 + -27.030622482299805, + 15.272208213806152 ], "umap": [ - 2.4920828342437744, - 8.348222732543945 + 2.740788221359253, + 7.629802703857422 ], "pca3d": [ - -0.23068445920944214, - 0.12891936302185059, - -0.024151332676410675 + -0.2302963137626648, + 0.1303105652332306, + -0.02405610866844654 ], "tsne3d": [ - -11.329065322875977, - -17.430007934570312, - 8.141983985900879 + -13.878840446472168, + 7.317141056060791, + -6.526475429534912 ], "umap3d": [ - 2.1729736328125, - 3.761715888977051, - 4.352913856506348 + 3.2444400787353516, + 4.250067710876465, + 4.973300457000732 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 7, - "label": "Language Models" + "id": 25, + "label": "AI Agents" } ], "_embedding": [ @@ -1000766,46 +1000916,46 @@ "abstract": "ChatGPT Health is a dedicated experience that securely connects your health data and apps, with privacy protections and a physician-informed design.", "projections": { "pca": [ - -0.03106524795293808, - 0.3577530086040497 + -0.03008069470524788, + 0.3579123616218567 ], "tsne": [ - 1.7564102411270142, - -39.038490295410156 + -8.595370292663574, + 36.49091720581055 ], "umap": [ - 4.9230637550354, - 9.51506233215332 + 5.095379829406738, + 8.809952735900879 ], "pca3d": [ - -0.031065698713064194, - 0.3577481508255005, - -0.06510592997074127 + -0.03008129633963108, + 0.3579064905643463, + -0.06556947529315948 ], "tsne3d": [ - -3.694573163986206, - -22.68452262878418, - -4.403421878814697 + -10.203262329101562, + 15.434593200683594, + -12.226120948791504 ], "umap3d": [ - 4.152292728424072, - 2.5318617820739746, - 3.061893939971924 + 4.925436496734619, + 3.106696128845215, + 3.5347282886505127 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, + "id": 26, "label": "Medical AI" } ], @@ -1001235,47 +1001385,47 @@ "openalex_url": "https://openalex.org/W4417474099", "projections": { "pca": [ - -0.11278903484344482, - 0.27026861906051636 + -0.11213523894548416, + 0.27153465151786804 ], "tsne": [ - -17.386600494384766, - -18.179290771484375 + -26.255760192871094, + 19.602493286132812 ], "umap": [ - 3.263812303543091, - 7.984668254852295 + 3.685238838195801, + 7.230151653289795 ], "pca3d": [ - -0.11278941482305527, - 0.2702592611312866, - -0.0566934198141098 + -0.1121356338262558, + 0.27152490615844727, + -0.0569620281457901 ], "tsne3d": [ - -13.73530101776123, - -11.56190299987793, - -2.0527639389038086 + -14.943090438842773, + 6.679778575897217, + -11.543596267700195 ], "umap3d": [ - 2.590608596801758, - 3.9267656803131104, - 3.82548451423645 + 3.513252019882202, + 4.302822113037109, + 4.401270389556885 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { "id": 0, - "label": "LLM Language Learning" + "label": "Math AI" } ], "_embedding": [ @@ -1001682,47 +1001832,47 @@ "title": "Rewarding Our Team as BioRender Hits $900M Valuation", "projections": { "pca": [ - -0.07715336978435516, - 0.24584060907363892 + -0.07631508260965347, + 0.24636489152908325 ], "tsne": [ - -7.569062232971191, - -2.478048086166382 + -22.186195373535156, + -31.0871639251709 ], "umap": [ - 4.553191184997559, - 7.389995098114014 + 4.8162522315979, + 6.68388032913208 ], "pca3d": [ - -0.07715364545583725, - 0.2458362728357315, - 0.10856934636831284 + -0.07631557434797287, + 0.24636006355285645, + 0.10827085375785828 ], "tsne3d": [ - -9.308460235595703, - -1.081756830215454, - 6.336878776550293 + -1.4213522672653198, + 8.472159385681152, + 12.206183433532715 ], "umap3d": [ - 4.031980037689209, - 3.59411358833313, - 4.811938285827637 + 5.074969291687012, + 4.687698841094971, + 5.07395076751709 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -1002133,31 +1002283,31 @@ "abstract": "Contribute to MasayukiNagai/esm_analysis development by creating an account on GitHub.", "projections": { "pca": [ - 0.009622951038181782, - 0.05426579341292381 + 0.009947105310857296, + 0.05430860072374344 ], "tsne": [ - 2.7765183448791504, - 24.17973518371582 + 0.8989879488945007, + -20.103958129882812 ], "umap": [ - 6.298336982727051, - 4.371216297149658 + 6.738938808441162, + 4.096530437469482 ], "pca3d": [ - 0.009622916579246521, - 0.054256830364465714, - 0.13754814863204956 + 0.009947078302502632, + 0.05429944396018982, + 0.13745611906051636 ], "tsne3d": [ - 5.2875590324401855, - 6.845485687255859, - 4.193263530731201 + -3.463745594024658, + -5.54838752746582, + 0.20140208303928375 ], "umap3d": [ - 5.259582996368408, - 5.872223854064941, - 5.105047702789307 + 6.002723217010498, + 6.862298488616943, + 3.5994973182678223 ] }, "cluster_id": 3, @@ -1002168,11 +1002318,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -1002583,47 +1002733,47 @@ "abstract": "NVIDIA will continue to distribute SchedMD\u2019s open-source, vendor-neutral Slurm software, ensuring wide availability for high-performance computing and AI.", "projections": { "pca": [ - -0.12328694015741348, - 0.2692722678184509 + -0.1223890408873558, + 0.26935726404190063 ], "tsne": [ - -18.97994041442871, - 0.018898023292422295 + -24.197242736816406, + -0.6451355814933777 ], "umap": [ - 2.5177078247070312, - 7.499932289123535 + 2.763733386993408, + 6.780839920043945 ], "pca3d": [ - -0.12328726798295975, - 0.2692558765411377, - 0.03748268261551857 + -0.12238923460245132, + 0.2693406343460083, + 0.03710802271962166 ], "tsne3d": [ - -16.831260681152344, - 3.2907557487487793, - 14.127701759338379 + -18.739912033081055, + 1.3353365659713745, + 14.093574523925781 ], "umap3d": [ - 2.238874912261963, - 4.656369209289551, - 5.1496901512146 + 3.370793581008911, + 5.317141532897949, + 5.487256050109863 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { "id": 0, - "label": "LLM Language Learning" + "label": "Math AI" } ], "_embedding": [ @@ -1003031,47 +1003181,47 @@ "journal": "Nature Methods", "projections": { "pca": [ - -0.03509906306862831, - 0.11731503903865814 + -0.03461258113384247, + 0.1182217225432396 ], "tsne": [ - -5.141197681427002, - -26.903928756713867 + -9.430524826049805, + 22.310762405395508 ], "umap": [ - 4.34145975112915, - 8.869884490966797 + 4.683865547180176, + 8.135047912597656 ], "pca3d": [ - -0.03509938716888428, - 0.11732464283704758, - 0.2163873314857483 + -0.034612949937582016, + 0.1182316243648529, + 0.21636413037776947 ], "tsne3d": [ - -3.2733683586120605, - -12.283805847167969, - -3.7208828926086426 + -6.0937395095825195, + 16.513729095458984, + -1.066506028175354 ], "umap3d": [ - 3.562213897705078, - 3.386348247528076, - 3.0490756034851074 + 4.1851959228515625, + 3.739043712615967, + 3.5363450050354004 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, - "label": "Medical AI" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -1003479,47 +1003629,47 @@ "abstract": "Contribute to togethercomputer/reviewing-agents development by creating an account on GitHub.", "projections": { "pca": [ - -0.09928309917449951, - 0.29380109906196594 + -0.09830280393362045, + 0.29398277401924133 ], "tsne": [ - -28.71958351135254, - 28.7614803314209 + -16.667003631591797, + -3.8147614002227783 ], "umap": [ - 3.753875494003296, - 6.929800033569336 + 4.012515068054199, + 7.2433977127075195 ], "pca3d": [ - -0.09928365051746368, - 0.2938152253627777, - 0.08107905089855194 + -0.09830338507890701, + 0.2939968705177307, + 0.08069738000631332 ], "tsne3d": [ - -14.88546371459961, - 16.425716400146484, - 11.354107856750488 + -11.20913028717041, + -0.9565148949623108, + 14.376049041748047 ], "umap3d": [ - 3.4383597373962402, - 4.308753967285156, - 4.2427287101745605 + 4.268652439117432, + 5.032842636108398, + 4.23684549331665 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 12, + "label": "AI Agents" }, { - "id": 10, - "label": "Code Development" + "id": 0, + "label": "Math AI" } ], "_embedding": [ @@ -1003925,47 +1004075,47 @@ ], "projections": { "pca": [ - -0.12422892451286316, - 0.23191748559474945 + -0.12324389070272446, + 0.23182782530784607 ], "tsne": [ - -13.165166854858398, - -1.8997166156768799 + -10.990976333618164, + -3.022994041442871 ], "umap": [ - 4.32533073425293, - 7.012117862701416 + 4.587622165679932, + 6.293658256530762 ], "pca3d": [ - -0.1242290735244751, - 0.23189078271389008, - 0.10824141651391983 + -0.12324395775794983, + 0.23180092871189117, + 0.1079990491271019 ], "tsne3d": [ - -10.021114349365234, - 2.2045905590057373, - 14.616470336914062 + -3.1202757358551025, + -2.5692973136901855, + -6.986950874328613 ], "umap3d": [ - 3.893206834793091, - 4.150703430175781, - 4.535487174987793 + 4.7792205810546875, + 5.061176776885986, + 4.521223545074463 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 10, - "label": "Code Development" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -1004375,47 +1004525,47 @@ "title": "Sign in to your account", "projections": { "pca": [ - -0.20764146745204926, - 0.3990499675273895 + -0.20632295310497284, + 0.3993534743785858 ], "tsne": [ - -11.797974586486816, - -7.413701057434082 + -10.44803524017334, + 4.194396018981934 ], "umap": [ - 4.2082839012146, - 7.427819728851318 + 4.45073938369751, + 6.684660911560059 ], "pca3d": [ - -0.20764197409152985, - 0.39904364943504333, - 0.06677787005901337 + -0.20632368326187134, + 0.39934661984443665, + 0.06626847386360168 ], "tsne3d": [ - -7.620107173919678, - -5.074834823608398, - 10.656413078308105 + -7.773657321929932, + -1.743192195892334, + -6.992862224578857 ], "umap3d": [ - 3.797809362411499, - 3.821057081222534, - 4.484581470489502 + 4.7614593505859375, + 4.671892166137695, + 4.6160125732421875 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -1004823,31 +1004973,31 @@ "abstract": "This tool helps design new proteins that can bind to specified targets like other proteins or small molecules. Users provide target structures in PDB format and specify conditions, and the tool gen...", "projections": { "pca": [ - 0.12636323273181915, - 0.1536482572555542 + 0.12685014307498932, + 0.15393678843975067 ], "tsne": [ - -16.48383140563965, - 32.89064407348633 + -9.715093612670898, + -36.888206481933594 ], "umap": [ - 4.8684892654418945, - 3.720581531524658 + 5.51913595199585, + 3.279998779296875 ], "pca3d": [ - 0.12636305391788483, - 0.15362943708896637, - 0.1321437805891037 + 0.12684988975524902, + 0.15391719341278076, + 0.13183346390724182 ], "tsne3d": [ - -4.20334005355835, - 7.939410209655762, - 17.07752799987793 + -3.3281099796295166, + -16.477128982543945, + 14.48585319519043 ], "umap3d": [ - 4.93855094909668, - 7.414062976837158, - 4.587313175201416 + 5.109847068786621, + 7.862354278564453, + 3.1287453174591064 ] }, "cluster_id": 3, @@ -1004858,11 +1005008,11 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -1005286,47 +1005436,47 @@ "openalex_url": "https://openalex.org/W4416633569", "projections": { "pca": [ - -0.09799396991729736, - 0.28393733501434326 + -0.09728468209505081, + 0.2853931188583374 ], "tsne": [ - -9.405503273010254, - -21.498376846313477 + -10.926470756530762, + 15.872365951538086 ], "umap": [ - 3.995638370513916, - 8.461843490600586 + 4.187787055969238, + 7.874281883239746 ], "pca3d": [ - -0.09799453616142273, - 0.28393879532814026, - 0.1280430406332016 + -0.09728515893220901, + 0.2853948473930359, + 0.12769271433353424 ], "tsne3d": [ - -6.341452121734619, - -7.029767036437988, - 1.6036707162857056 + -8.401747703552246, + 10.691949844360352, + 1.5915888547897339 ], "umap3d": [ - 3.2141690254211426, - 3.63020920753479, - 3.3414828777313232 + 3.882211923599243, + 4.028324604034424, + 3.7966673374176025 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -1005735,47 +1005885,47 @@ "title": "AlphaFold: Five Years of Impact \u00e2\u0080\u0094 Google DeepMind", "projections": { "pca": [ - -0.21926644444465637, - -0.008465813472867012 + -0.2191462218761444, + -0.007584959268569946 ], "tsne": [ - -25.09465789794922, - 2.564804792404175 + -23.98321533203125, + 24.7635555267334 ], "umap": [ - 4.745625972747803, - 6.533725261688232 + 3.376922607421875, + 6.7130656242370605 ], "pca3d": [ - -0.2192663997411728, - -0.008445916697382927, - 0.006989654619246721 + -0.21914634108543396, + -0.007564065046608448, + 0.007260395213961601 ], "tsne3d": [ - -5.438216686248779, - -19.0365047454834, - 14.617181777954102 + -12.373011589050293, + 14.632739067077637, + 11.543590545654297 ], "umap3d": [ - 3.2682712078094482, - 4.828280448913574, - 3.3945605754852295 + 3.5789780616760254, + 5.007216930389404, + 3.537105083465576 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 12, + "label": "AI Agents" }, { - "id": 10, - "label": "Code Development" + "id": 0, + "label": "Math AI" } ], "_embedding": [ @@ -1006185,47 +1006335,47 @@ "abstract": "A report describing an a highly sophisticated AI-led cyberattack", "projections": { "pca": [ - -0.2117893099784851, - 0.27069801092147827 + -0.2109922617673874, + 0.27211084961891174 ], "tsne": [ - -13.36594295501709, - -19.17551612854004 + -10.644892692565918, + 11.113468170166016 ], "umap": [ - 3.8339836597442627, - 8.231245040893555 + 4.109350204467773, + 7.481321334838867 ], "pca3d": [ - -0.21178945899009705, - 0.27069270610809326, - 0.13554905354976654 + -0.21099260449409485, + 0.27210533618927, + 0.13529638946056366 ], "tsne3d": [ - -5.0326080322265625, - -5.202315330505371, - 5.87190055847168 + -8.46235179901123, + 5.297834873199463, + -0.7378659844398499 ], "umap3d": [ - 3.1515378952026367, - 3.5766308307647705, - 3.8038175106048584 + 4.024673938751221, + 4.204838752746582, + 4.176629066467285 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -1006633,47 +1006783,47 @@ "abstract": "The repository is taking steps to tackle a surge in low quality, AI-generated content.", "projections": { "pca": [ - -0.19287601113319397, - 0.2709146738052368 + -0.1919812709093094, + 0.2715839743614197 ], "tsne": [ - -10.857110977172852, - -17.676265716552734 + -12.884695053100586, + 11.285346984863281 ], "umap": [ - 4.087578296661377, - 7.895192623138428 + 4.2404351234436035, + 7.252378940582275 ], "pca3d": [ - -0.19287627935409546, - 0.27093175053596497, - 0.10029285401105881 + -0.19198180735111237, + 0.2716009020805359, + 0.10001035779714584 ], "tsne3d": [ - -4.7959771156311035, - -10.310585975646973, - 5.268690586090088 + -7.803084373474121, + 7.5305986404418945, + -4.254369258880615 ], "umap3d": [ - 3.517061471939087, - 3.458834171295166, - 4.079422950744629 + 4.417410850524902, + 4.182600498199463, + 4.449875354766846 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -1007081,47 +1007231,47 @@ "abstract": "Biohub is launching a large-scale scientific initiative that combines frontier AI with frontier biology to solve diseases and accelerate discovery.", "projections": { "pca": [ - -0.009005887433886528, - 0.26787737011909485 + -0.008284184150397778, + 0.26880672574043274 ], "tsne": [ - -0.4402395486831665, - -20.143224716186523 + -2.9095702171325684, + 16.719270706176758 ], "umap": [ - 4.787485122680664, - 8.699332237243652 + 4.934999942779541, + 7.9105658531188965 ], "pca3d": [ - -0.009006218984723091, - 0.26787158846855164, - 0.14245003461837769 + -0.008284603245556355, + 0.26880109310150146, + 0.1420973539352417 ], "tsne3d": [ - -0.27704334259033203, - -7.558480262756348, - -9.172890663146973 + -0.020429231226444244, + 15.283696174621582, + 4.780304431915283 ], "umap3d": [ - 3.6885550022125244, - 3.7841503620147705, - 2.9457945823669434 + 4.338894844055176, + 4.128056049346924, + 3.1417665481567383 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, - "label": "Medical AI" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -1007531,31 +1007681,31 @@ "abstract": "A company\u2019s plan to edit the genomes of human embryos worries some researchers \u2014 but it might reflect the changing attitudes towards the controversial approach.", "projections": { "pca": [ - 0.11385966837406158, - 0.3553120791912079 + 0.11471284925937653, + 0.35563531517982483 ], "tsne": [ - -3.403550148010254, - 12.063274383544922 + 13.02982234954834, + -20.747024536132812 ], "umap": [ - 5.744296073913574, - 5.50771951675415 + 6.0461835861206055, + 5.102522373199463 ], "pca3d": [ - 0.11385910212993622, - 0.35530078411102295, - 0.09644636511802673 + 0.1147121787071228, + 0.35562342405319214, + 0.09577937424182892 ], "tsne3d": [ - 2.2899134159088135, - -3.849307060241699, - -1.0026339292526245 + -3.2145168781280518, + 0.4394986629486084, + 11.065125465393066 ], "umap3d": [ - 5.729416847229004, - 6.2875542640686035, - 3.734250545501709 + 5.501377105712891, + 6.467817306518555, + 2.3660879135131836 ] }, "cluster_id": 3, @@ -1007566,12 +1007716,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, - "label": "Protein Design" + "id": 32, + "label": "Genome Protein" } ], "_embedding": [ @@ -1007981,47 +1008131,47 @@ "abstract": "A visual, intuitive deep dive into transformer architecture: history, design choices, and the why behind attention, masking, and positional encoding.", "projections": { "pca": [ - -0.2434796392917633, - 0.17071747779846191 + -0.24294357001781464, + 0.17207814753055573 ], "tsne": [ - -56.5480842590332, - -5.764628887176514 + -47.143104553222656, + 9.506707191467285 ], "umap": [ - 0.43587711453437805, - 7.845712184906006 + 0.6564940810203552, + 6.9447197914123535 ], "pca3d": [ - -0.24347952008247375, - 0.17066799104213715, - -0.01532854326069355 + -0.24294354021549225, + 0.17202703654766083, + -0.01537826843559742 ], "tsne3d": [ - -30.161014556884766, - -5.860823631286621, - 9.261943817138672 + -30.183181762695312, + 5.740619659423828, + 6.921663761138916 ], "umap3d": [ - 0.45851898193359375, - 4.972681045532227, - 4.899479389190674 + 1.5510494709014893, + 5.317899227142334, + 5.866513729095459 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 27, - "label": "Transformer Attention" + "id": 16, + "label": "Vision Transformers" } ], "_embedding": [ @@ -1008431,47 +1008581,47 @@ "abstract": "Discover how Claude accelerates life sciences research with new scientific connectors, skills, and improved performance for drug discovery and clinical work.", "projections": { "pca": [ - 0.012755345553159714, - 0.31185510754585266 + 0.013551929034292698, + 0.31264781951904297 ], "tsne": [ - -1.9939502477645874, - -13.974180221557617 + -3.8034632205963135, + -3.910588502883911 ], "umap": [ - 4.364861011505127, - 8.461666107177734 + 4.73569917678833, + 7.753315448760986 ], "pca3d": [ - 0.01275476161390543, - 0.31186816096305847, - 0.07954277843236923 + 0.013551204465329647, + 0.31266042590141296, + 0.07905630022287369 ], "tsne3d": [ - -6.461771488189697, - -13.363645553588867, - -13.32130241394043 + -5.902006149291992, + 6.275665760040283, + 18.370784759521484 ], "umap3d": [ - 3.5870261192321777, - 3.7471771240234375, - 3.2863903045654297 + 4.261404514312744, + 4.269020080566406, + 3.657150983810425 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, - "label": "Medical AI" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -1008881,47 +1009031,47 @@ "abstract": "Researchers are increasingly turning to artificial-intelligence tools that can handle complex, multi-step processes.", "projections": { "pca": [ - -0.14001421630382538, - 0.3121403157711029 + -0.1391746550798416, + 0.31370124220848083 ], "tsne": [ - -9.848553657531738, - -22.516389846801758 + -12.005806922912598, + 16.053661346435547 ], "umap": [ - 3.9275383949279785, - 8.330428123474121 + 4.168823719024658, + 7.736103057861328 ], "pca3d": [ - -0.14001479744911194, - 0.3121447265148163, - 0.14765414595603943 + -0.1391751915216446, + 0.31370556354522705, + 0.14731130003929138 ], "tsne3d": [ - -4.685145854949951, - -8.466434478759766, - 0.8344587683677673 + -8.780110359191895, + 9.982490539550781, + -0.26534152030944824 ], "umap3d": [ - 3.2641241550445557, - 3.538564920425415, - 3.581968069076538 + 4.008387565612793, + 4.085928916931152, + 3.947922706604004 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -1009332,47 +1009482,47 @@ "abstract": "From GPU architecture and PTX/SASS to warp-tiling and deep asynchronous tensor core pipelines.", "projections": { "pca": [ - -0.15799099206924438, - 0.10072999447584152 + -0.15764164924621582, + 0.10158316045999527 ], "tsne": [ - -21.421716690063477, - 0.36684808135032654 + -26.705209732055664, + -0.6849567294120789 ], "umap": [ - 2.2578353881835938, - 7.604269504547119 + 2.4399917125701904, + 6.822677135467529 ], "pca3d": [ - -0.15799088776111603, - 0.10071054846048355, - 0.014758958481252193 + -0.15764160454273224, + 0.10156381875276566, + 0.01469370536506176 ], "tsne3d": [ - -19.563129425048828, - 2.9080727100372314, - 11.857069969177246 + -20.675567626953125, + 1.684572458267212, + 11.478378295898438 ], "umap3d": [ - 1.9416630268096924, - 4.7579731941223145, - 5.146270751953125 + 3.0864226818084717, + 5.393517971038818, + 5.556805610656738 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 1, + "label": "Attention Models" }, { - "id": 0, - "label": "LLM Language Learning" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -1009779,47 +1009929,47 @@ "title": "Failing to Understand the Exponential, Again", "projections": { "pca": [ - -0.2618858516216278, - 0.24505989253520966 + -0.26120349764823914, + 0.24660730361938477 ], "tsne": [ - -13.680597305297852, - -16.412899017333984 + -10.218184471130371, + 8.458344459533691 ], "umap": [ - 3.921311616897583, - 7.880092144012451 + 4.359286785125732, + 7.080225467681885 ], "pca3d": [ - -0.261886328458786, - 0.24508674442768097, - 0.11862269043922424 + -0.2612040638923645, + 0.24663442373275757, + 0.11840647459030151 ], "tsne3d": [ - -7.099586486816406, - -7.941650390625, - 7.615762233734131 + -9.984716415405273, + 2.1274921894073486, + -3.5154197216033936 ], "umap3d": [ - 3.3765134811401367, - 3.6866507530212402, - 4.202141284942627 + 4.359220504760742, + 4.376806259155273, + 4.492926120758057 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -1010229,46 +1010379,46 @@ "title": "Anthropic's AI for Science Program | Claude Help Center", "projections": { "pca": [ - -0.20446452498435974, - 0.3102782666683197 + -0.20358671247959137, + 0.3117442727088928 ], "tsne": [ - -14.903800010681152, - -34.043548583984375 + -23.67320442199707, + -32.341678619384766 ], "umap": [ - 3.109804153442383, - 9.147326469421387 + 3.224247932434082, + 8.37747573852539 ], "pca3d": [ - -0.20446453988552094, - 0.3102565109729767, - 0.116604745388031 + -0.20358683168888092, + 0.31172212958335876, + 0.1162753701210022 ], "tsne3d": [ - -8.994527816772461, - -2.3811275959014893, - 3.8158040046691895 + -4.0026726722717285, + 9.116429328918457, + 12.92442798614502 ], "umap3d": [ - 3.0599076747894287, - 3.14593768119812, - 5.097987651824951 + 4.571372032165527, + 4.428457736968994, + 5.598989486694336 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 32, + "id": 25, "label": "AI Agents" } ], @@ -1010680,46 +1010830,46 @@ "abstract": "Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.", "projections": { "pca": [ - -0.13941478729248047, - 0.34125998616218567 + -0.13857218623161316, + 0.3428482115268707 ], "tsne": [ - -15.238856315612793, - -34.455448150634766 + -24.095746994018555, + -32.77083206176758 ], "umap": [ - 3.1036648750305176, - 9.244190216064453 + 3.1804380416870117, + 8.434856414794922 ], "pca3d": [ - -0.13941499590873718, - 0.34122228622436523, - 0.14839643239974976 + -0.1385723352432251, + 0.3428100049495697, + 0.1479514241218567 ], "tsne3d": [ - -9.237406730651855, - -2.204995632171631, - 2.6502554416656494 + -4.688741683959961, + 10.061241149902344, + 12.883970260620117 ], "umap3d": [ - 2.995196580886841, - 3.0961854457855225, - 5.1575822830200195 + 4.544395446777344, + 4.412503719329834, + 5.694672107696533 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 32, + "id": 25, "label": "AI Agents" } ], @@ -1011129,47 +1011279,47 @@ ], "projections": { "pca": [ - -0.1728591024875641, - 0.29152029752731323 + -0.17220669984817505, + 0.2926645576953888 ], "tsne": [ - -12.650167465209961, - -10.565116882324219 + -13.981240272521973, + 4.661707401275635 ], "umap": [ - 3.8793928623199463, - 7.339190483093262 + 4.243563175201416, + 6.654880046844482 ], "pca3d": [ - -0.1728595793247223, - 0.2915292978286743, - 0.03580782562494278 + -0.17220713198184967, + 0.2926735579967499, + 0.03534211218357086 ], "tsne3d": [ - -6.733945846557617, - -7.9292378425598145, - 14.410621643066406 + -9.241659164428711, + 0.1997300386428833, + -10.95728588104248 ], "umap3d": [ - 3.4455597400665283, - 3.687223434448242, - 4.6263275146484375 + 4.457055568695068, + 4.6015191078186035, + 4.864753723144531 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -1011579,46 +1011729,46 @@ "abstract": "Research shows that beer drinkers are split depending on which types of flavour chemicals they prefer.", "projections": { "pca": [ - -0.06327544152736664, - 0.26846274733543396 + -0.06268893927335739, + 0.26943013072013855 ], "tsne": [ - 22.85819435119629, - -36.16144943237305 + 19.636045455932617, + 35.174564361572266 ], "umap": [ - 6.675851821899414, - 8.029483795166016 + 7.013125896453857, + 7.760176658630371 ], "pca3d": [ - -0.06327614933252335, - 0.2684851884841919, - -0.008922471664845943 + -0.06268970668315887, + 0.2694525122642517, + -0.00937456451356411 ], "tsne3d": [ - 12.242773056030273, - -7.5582756996154785, - -23.621557235717773 + 14.805660247802734, + 24.164310455322266, + 3.3478338718414307 ], "umap3d": [ - 6.462976455688477, - 3.0882747173309326, - 2.686062812805176 + 7.026101112365723, + 3.4243292808532715, + 2.368971109390259 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 26, + "id": 33, "label": "Genetic Disease" } ], @@ -1012029,47 +1012179,47 @@ "abstract": "Discover how a specialized AI model, GPT-4b micro, helped OpenAI and Retro Bio engineer more effective proteins for stem cell therapy and longevity research.", "projections": { "pca": [ - -0.023758362978696823, - 0.16294345259666443 + -0.02321576327085495, + 0.16360409557819366 ], "tsne": [ - 0.5631327629089355, - -16.492143630981445 + -2.8021199703216553, + 12.699542045593262 ], "umap": [ - 4.695306301116943, - 8.346091270446777 + 4.902324199676514, + 7.579673767089844 ], "pca3d": [ - -0.023758539929986, - 0.16293737292289734, - 0.09918549656867981 + -0.02321605198085308, + 0.16359758377075195, + 0.09903717041015625 ], "tsne3d": [ - -6.474328994750977, - -7.123722076416016, - -13.21366024017334 + -1.7105062007904053, + 15.412786483764648, + 10.469565391540527 ], "umap3d": [ - 3.6682803630828857, - 4.074342250823975, - 3.2314846515655518 + 4.369985103607178, + 4.528963565826416, + 3.463226318359375 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, - "label": "AI Agents" + "id": 4, + "label": "Genomics Tools" }, { - "id": 17, - "label": "Medical AI" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -1012479,46 +1012629,46 @@ "abstract": "NanoGPT (124M) in 90 seconds. Contribute to KellerJordan/modded-nanogpt development by creating an account on GitHub.", "projections": { "pca": [ - -0.13942347466945648, - 0.11724831908941269 + -0.13901832699775696, + 0.1178281232714653 ], "tsne": [ - -23.727357864379883, - -4.1510748863220215 + -23.896329879760742, + -5.342016696929932 ], "umap": [ - 2.070568323135376, - 7.821300983428955 + 2.1364662647247314, + 7.053027629852295 ], "pca3d": [ - -0.13942359387874603, - 0.11725001037120819, - 0.022877976298332214 + -0.1390184909105301, + 0.11782996356487274, + 0.022779053077101707 ], "tsne3d": [ - -15.11404037475586, - -0.8709619045257568, - 7.114641189575195 + -12.336566925048828, + 3.8164353370666504, + 8.545787811279297 ], "umap3d": [ - 1.9580622911453247, - 4.865922451019287, - 4.617675304412842 + 3.033141851425171, + 5.352409362792969, + 4.917266845703125 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 1, + "label": "Attention Models" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -1012928,46 +1013078,46 @@ "title": "Building and evaluating alignment auditing agents", "projections": { "pca": [ - -0.15373586118221283, - 0.135489359498024 + -0.15302830934524536, + 0.1358659863471985 ], "tsne": [ - -12.1178560256958, - -27.612476348876953 + -16.14198875427246, + 20.867597579956055 ], "umap": [ - 3.560685634613037, - 8.738049507141113 + 3.7634637355804443, + 8.030440330505371 ], "pca3d": [ - -0.15373587608337402, - 0.13546422123908997, - 0.06355001032352448 + -0.15302833914756775, + 0.1358412206172943, + 0.063633032143116 ], "tsne3d": [ - -8.191169738769531, - -14.258959770202637, - -5.2033820152282715 + -8.133963584899902, + 18.401498794555664, + 2.187174081802368 ], "umap3d": [ - 2.721902370452881, - 3.6476082801818848, - 3.4140827655792236 + 3.5513641834259033, + 4.039430141448975, + 3.8870880603790283 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, + "id": 25, "label": "AI Agents" } ], @@ -1013376,46 +1013526,46 @@ "journal": "Nature", "projections": { "pca": [ - -0.24545873701572418, - 0.034018587321043015 + -0.24513176083564758, + 0.0345907025039196 ], "tsne": [ - -27.508426666259766, - -14.593857765197754 + -31.130680084228516, + 12.64371395111084 ], "umap": [ - 1.9035965204238892, - 8.385374069213867 + 2.0690510272979736, + 7.4941911697387695 ], "pca3d": [ - -0.24545888602733612, - 0.03401312977075577, - -0.008960667997598648 + -0.2451317310333252, + 0.034585315734148026, + -0.008687164634466171 ], "tsne3d": [ - -13.417713165283203, - -13.104881286621094, - 10.352567672729492 + -14.806385040283203, + 2.376143455505371, + -1.7312101125717163 ], "umap3d": [ - 1.6559933423995972, - 4.218531131744385, - 4.713757514953613 + 2.9409735202789307, + 4.61605978012085, + 5.313205718994141 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 1, + "label": "Attention Models" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -1013826,47 +1013976,47 @@ "abstract": "Google Colab offers free Colab Pro for students, interactive slideshows and AI controls in notebooks.", "projections": { "pca": [ - -0.167944997549057, - 0.3155312240123749 + -0.16690261662006378, + 0.3159664273262024 ], "tsne": [ - -17.921688079833984, - -7.412716388702393 + -17.668886184692383, + -1.0789768695831299 ], "umap": [ - 3.6333940029144287, - 7.081512928009033 + 3.9583826065063477, + 6.501511096954346 ], "pca3d": [ - -0.16794553399085999, - 0.3155447840690613, - 0.030092347413301468 + -0.1669032871723175, + 0.3159792721271515, + 0.02972688153386116 ], "tsne3d": [ - -10.915131568908691, - 5.900771617889404, - -7.183426856994629 + -8.617846488952637, + -0.08790791034698486, + 1.6867598295211792 ], "umap3d": [ - 3.5189900398254395, - 4.387065410614014, - 4.238926410675049 + 4.297369003295898, + 5.002495288848877, + 4.326745986938477 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -1014274,47 +1014424,47 @@ "abstract": "We\u2019re on a journey to advance and democratize artificial intelligence through open source and open science.", "projections": { "pca": [ - -0.1999828815460205, - 0.239303320646286 + -0.19924648106098175, + 0.24058958888053894 ], "tsne": [ - -15.522972106933594, - -21.11919593811035 + -16.08531379699707, + 13.687763214111328 ], "umap": [ - 3.596205472946167, - 8.249521255493164 + 3.720106363296509, + 7.537032127380371 ], "pca3d": [ - -0.19998300075531006, - 0.2392854541540146, - 0.1669541895389557 + -0.19924674928188324, + 0.24057143926620483, + 0.16671669483184814 ], "tsne3d": [ - -8.706231117248535, - -2.1272635459899902, - -3.68522572517395 + -7.893962383270264, + 5.845390796661377, + 3.8584492206573486 ], "umap3d": [ - 2.7955830097198486, - 3.930850028991699, - 3.58282732963562 + 3.708096981048584, + 4.388019561767578, + 4.0344133377075195 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 0, + "label": "Math AI" } ], "_embedding": [ @@ -1014725,47 +1014875,47 @@ "journal": "Nature", "projections": { "pca": [ - -0.18838241696357727, - 0.3771529793739319 + -0.18722930550575256, + 0.37800219655036926 ], "tsne": [ - -10.759672164916992, - -18.373994827270508 + -12.772173881530762, + 12.319626808166504 ], "umap": [ - 3.9749302864074707, - 8.230805397033691 + 4.229588985443115, + 7.575199604034424 ], "pca3d": [ - -0.18838323652744293, - 0.37718114256858826, - 0.12042658030986786 + -0.18723011016845703, + 0.3780306279659271, + 0.11998641490936279 ], "tsne3d": [ - -4.1687235832214355, - -9.9461669921875, - 4.08435583114624 + -8.381009101867676, + 8.717422485351562, + -3.850236415863037 ], "umap3d": [ - 3.3108408451080322, - 3.445255756378174, - 3.709256172180176 + 4.135836124420166, + 4.033412933349609, + 4.141603469848633 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -1015174,31 +1015324,31 @@ ], "projections": { "pca": [ - -0.2495650202035904, - 0.32474061846733093 + -0.24838058650493622, + 0.32411202788352966 ], "tsne": [ - -51.11734390258789, - -14.034004211425781 + -52.43281936645508, + -11.467586517333984 ], "umap": [ - 0.7116276025772095, - 5.88676118850708 + 1.0374013185501099, + 5.22230863571167 ], "pca3d": [ - -0.24956537783145905, - 0.3247486352920532, - -0.10948058217763901 + -0.2483811229467392, + 0.32411977648735046, + -0.10985638946294785 ], "tsne3d": [ - -29.566255569458008, - -8.003767013549805, - -3.9748406410217285 + -23.905933380126953, + -8.332687377929688, + -7.275383472442627 ], "umap3d": [ - 0.5676562190055847, - 5.9386820793151855, - 3.614067316055298 + 1.1580626964569092, + 5.926800727844238, + 4.380541801452637 ] }, "cluster_id": 2, @@ -1015209,12 +1015359,12 @@ "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, - "label": "Training Noise" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -1015622,47 +1015772,47 @@ "abstract": "Emerging \u2018co-scientist\u2019 systems use chatbots to mimic the deliberations of a research group. Nature asked researchers to test them out.", "projections": { "pca": [ - -0.1535913199186325, - 0.30104386806488037 + -0.15289177000522614, + 0.3029422163963318 ], "tsne": [ - -9.909819602966309, - -23.191255569458008 + -12.419122695922852, + 16.365291595458984 ], "umap": [ - 3.8933825492858887, - 8.451851844787598 + 4.12208366394043, + 7.876351356506348 ], "pca3d": [ - -0.15359190106391907, - 0.30105504393577576, - 0.15296269953250885 + -0.15289244055747986, + 0.30295300483703613, + 0.1526353359222412 ], "tsne3d": [ - -5.13429069519043, - -9.839802742004395, - 0.36575987935066223 + -8.359241485595703, + 11.134407997131348, + -1.1828993558883667 ], "umap3d": [ - 3.1469805240631104, - 3.549017906188965, - 3.4976871013641357 + 3.9436864852905273, + 4.024982929229736, + 3.884490966796875 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -1016072,47 +1016222,47 @@ "abstract": "The action comes as high-ranking US officials criticize leading journals as \u2018woke\u2019 and \u2018corrupt\u2019.", "projections": { "pca": [ - -0.06114925444126129, - 0.3851042687892914 + -0.060013748705387115, + 0.3851751685142517 ], "tsne": [ - -6.890031814575195, - -12.809637069702148 + -13.072308540344238, + 10.070566177368164 ], "umap": [ - 4.185691833496094, - 7.815976142883301 + 4.29127311706543, + 7.10177755355835 ], "pca3d": [ - -0.061150189489126205, - 0.3851337134838104, - 0.08651379495859146 + -0.06001470237970352, + 0.3852050006389618, + 0.08592230826616287 ], "tsne3d": [ - -13.722829818725586, - -1.8728042840957642, - 18.074138641357422 + -4.433104515075684, + 5.4578537940979, + -8.03474235534668 ], "umap3d": [ - 3.620905876159668, - 3.364316940307617, - 4.235564708709717 + 4.636207580566406, + 4.167591094970703, + 4.579127311706543 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -1016532,47 +1016682,47 @@ ], "projections": { "pca": [ - -0.18976077437400818, - 0.20531539618968964 + -0.1891467571258545, + 0.20605552196502686 ], "tsne": [ - -21.658157348632812, - -16.455657958984375 + -26.498516082763672, + 13.975481033325195 ], "umap": [ - 2.62492036819458, - 8.341034889221191 + 2.8419241905212402, + 7.671362400054932 ], "pca3d": [ - -0.18976108729839325, - 0.205325186252594, - -0.09338267892599106 + -0.18914717435836792, + 0.20606450736522675, + -0.09347695857286453 ], "tsne3d": [ - -10.142430305480957, - -14.885642051696777, - 7.730201721191406 + -12.524476051330566, + 5.729972839355469, + -4.707239627838135 ], "umap3d": [ - 2.3142290115356445, - 3.6641855239868164, - 4.381140232086182 + 3.3886260986328125, + 4.2311692237854, + 4.934939861297607 ] }, - "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 1, - "label": "AI Scientific Research" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 0, - "label": "LLM Language Learning" + "id": 25, + "label": "AI Agents" } ], "_embedding": [ @@ -1016980,46 +1017130,46 @@ "abstract": "We study how training on incorrect responses can cause broader misalignment in language models and identify an internal feature driving this behavior\u2014one that can be reversed with minimal fine-tuning.", "projections": { "pca": [ - -0.22846506536006927, - 0.003427373943850398 + -0.22835177183151245, + 0.004830026533454657 ], "tsne": [ - -24.351486206054688, - -15.982845306396484 + -28.729936599731445, + 13.951743125915527 ], "umap": [ - 2.2104785442352295, - 8.350510597229004 + 2.422431707382202, + 7.480199813842773 ], "pca3d": [ - -0.22846491634845734, - 0.0034137361217290163, - -0.04742177948355675 + -0.22835147380828857, + 0.004816059023141861, + -0.0470888577401638 ], "tsne3d": [ - -15.05529499053955, - -15.45017147064209, - 8.011638641357422 + -16.98031997680664, + 4.889966011047363, + -3.9500303268432617 ], "umap3d": [ - 1.7935917377471924, - 4.064073085784912, - 4.369471549987793 + 2.886796236038208, + 4.436618328094482, + 5.014037132263184 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -1017429,31 +1017579,31 @@ "title": "Isomorphic Labs appoints Dr. Ben Wolf as Chief Medical Officer and establishes US Presence - Isomorphic Labs", "projections": { "pca": [ - 0.07786725461483002, - 0.29770728945732117 + 0.07865608483552933, + 0.2978019416332245 ], "tsne": [ - -11.19576358795166, - 8.958547592163086 + -2.441819190979004, + -46.852413177490234 ], "umap": [ - 5.054193496704102, - 5.677353382110596 + 5.494670867919922, + 5.224300861358643 ], "pca3d": [ - 0.07786701619625092, - 0.2976841926574707, - 0.05122976005077362 + 0.07865579426288605, + 0.2977781593799591, + 0.050717759877443314 ], "tsne3d": [ - -2.589921712875366, - -13.534168243408203, - -15.263978004455566 + -1.990012764930725, + 3.5169687271118164, + 19.311960220336914 ], "umap3d": [ - 4.943893909454346, - 6.252139091491699, - 3.732090950012207 + 4.999755382537842, + 6.429680347442627, + 2.9042630195617676 ] }, "cluster_id": 3, @@ -1017464,12 +1017614,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, - "label": "Protein Design" + "id": 32, + "label": "Genome Protein" } ], "_embedding": [ @@ -1017885,31 +1018035,31 @@ ], "projections": { "pca": [ - 0.13337942957878113, - -0.09068146347999573 + 0.1333717405796051, + -0.09053847193717957 ], "tsne": [ - 8.711641311645508, - 10.937053680419922 + 10.759522438049316, + -8.372920989990234 ], "umap": [ - 6.879490375518799, - 5.754279613494873 + 7.096111297607422, + 5.516713619232178 ], "pca3d": [ - 0.13337963819503784, - -0.09069477766752243, - 0.08572765439748764 + 0.1333720088005066, + -0.09055145829916, + 0.08602029830217361 ], "tsne3d": [ - 5.456268787384033, - 7.550469875335693, - -6.07000207901001 + 6.448282241821289, + -1.2599397897720337, + 2.720792531967163 ], "umap3d": [ - 5.8259782791137695, - 5.080114364624023, - 3.7354445457458496 + 6.1616363525390625, + 5.553012847900391, + 2.6662206649780273 ] }, "cluster_id": 4, @@ -1017920,11 +1018070,11 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 14, + "id": 17, "label": "Genomic Models" } ], @@ -1018331,47 +1018481,47 @@ ], "projections": { "pca": [ - -0.14054149389266968, - 0.366782546043396 + -0.13958458602428436, + 0.36767449975013733 ], "tsne": [ - -20.35548973083496, - -17.196367263793945 + -25.43832778930664, + 14.750256538391113 ], "umap": [ - 2.7134969234466553, - 8.292168617248535 + 3.003108263015747, + 7.64725923538208 ], "pca3d": [ - -0.1405421793460846, - 0.3668002784252167, - 0.004000233020633459 + -0.13958539068698883, + 0.3676919937133789, + 0.003557350020855665 ], "tsne3d": [ - -9.239665985107422, - -16.926389694213867, - 7.176056861877441 + -11.744431495666504, + 7.576796531677246, + -5.752443790435791 ], "umap3d": [ - 2.4658966064453125, - 3.632405996322632, - 4.343369007110596 + 3.613142967224121, + 4.192696571350098, + 4.9200239181518555 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 0, - "label": "LLM Language Learning" + "id": 25, + "label": "AI Agents" } ], "_embedding": [ @@ -1018780,46 +1018930,46 @@ "title": "Dario Amodei \u2014 The Urgency of Interpretability", "projections": { "pca": [ - -0.16454680263996124, - 0.29057952761650085 + -0.16365781426429749, + 0.29194435477256775 ], "tsne": [ - -17.111841201782227, - -34.667449951171875 + -25.922658920288086, + -33.0200080871582 ], "umap": [ - 3.0122230052948, - 9.171977043151855 + 3.104848861694336, + 8.392997741699219 ], "pca3d": [ - -0.16454733908176422, - 0.29056623578071594, - 0.016251107677817345 + -0.16365821659564972, + 0.29193055629730225, + 0.016070270910859108 ], "tsne3d": [ - -10.738934516906738, - -4.663114547729492, - 2.4079368114471436 + -7.4540791511535645, + 8.817039489746094, + 12.429420471191406 ], "umap3d": [ - 2.868151903152466, - 3.1493027210235596, - 5.119083404541016 + 4.442246913909912, + 4.406973361968994, + 5.669126987457275 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 32, + "id": 25, "label": "AI Agents" } ], @@ -1019239,47 +1019389,47 @@ "doi": "10.1101/2025.04.16.649078", "projections": { "pca": [ - -0.061363887041807175, - 0.1712801456451416 + -0.061076123267412186, + 0.17226798832416534 ], "tsne": [ - 15.939327239990234, - -13.11792278289795 + 40.721099853515625, + -15.225488662719727 ], "umap": [ - 6.705300807952881, - 8.582056999206543 + 6.852051734924316, + 8.215263366699219 ], "pca3d": [ - -0.061363935470581055, - 0.17125700414180756, - -0.1250099241733551 + -0.061076194047927856, + 0.17224402725696564, + -0.12519071996212006 ], "tsne3d": [ - 16.347270965576172, - -17.319339752197266, - -1.8794163465499878 + 13.841848373413086, + 19.982250213623047, + -7.554068565368652 ], "umap3d": [ - 6.015225410461426, - 2.9471781253814697, - 2.324232578277588 + 6.371444225311279, + 3.0671370029449463, + 2.122487783432007 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 3, + "label": "Genetic Disease" }, { - "id": 26, - "label": "Genetic Disease" + "id": 28, + "label": "Single Cell Data" } ], "_embedding": [ @@ -1019689,31 +1019839,31 @@ "abstract": "Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations - understandable-machine-intelligence-lab/Quantus", "projections": { "pca": [ - -0.1274207979440689, - 0.15102368593215942 + -0.12703828513622284, + 0.15170888602733612 ], "tsne": [ - -16.6297664642334, - 7.463194847106934 + -14.734220504760742, + -14.81021499633789 ], "umap": [ - 5.113699913024902, - 5.758448600769043 + 5.426016330718994, + 5.519137859344482 ], "pca3d": [ - -0.12742090225219727, - 0.151028573513031, - 0.020505467429757118 + -0.12703841924667358, + 0.15171395242214203, + 0.020342746749520302 ], "tsne3d": [ - -11.594306945800781, - -4.949032783508301, - 7.143327236175537 + -1.3470174074172974, + -7.25823450088501, + 11.35065746307373 ], "umap3d": [ - 4.4637908935546875, - 5.40464973449707, - 4.083856582641602 + 4.931001663208008, + 5.853805065155029, + 3.401923418045044 ] }, "cluster_id": 3, @@ -1019724,12 +1019874,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 4, + "label": "Genomics Tools" }, { - "id": 24, - "label": "Protein Design" + "id": 32, + "label": "Genome Protein" } ], "_embedding": [ @@ -1020137,47 +1020287,47 @@ ], "projections": { "pca": [ - -0.10999081283807755, - 0.2445559799671173 + -0.1092580035328865, + 0.2449849545955658 ], "tsne": [ - -13.104384422302246, - -11.036937713623047 + -13.48031997680664, + 5.216291904449463 ], "umap": [ - 3.80134916305542, - 7.341063499450684 + 4.131231784820557, + 6.684172630310059 ], "pca3d": [ - -0.10999147593975067, - 0.24459081888198853, - 0.02368190884590149 + -0.10925862193107605, + 0.24502022564411163, + 0.023364782333374023 ], "tsne3d": [ - -7.45285701751709, - -8.465117454528809, - 12.608407020568848 + -8.690451622009277, + 0.7184176445007324, + -9.42790412902832 ], "umap3d": [ - 3.3359251022338867, - 3.644254207611084, - 4.52174711227417 + 4.407243251800537, + 4.49723482131958, + 4.870662212371826 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -1020587,46 +1020737,46 @@ "abstract": "Code-MCP: Connect Claude AI to your development environment through the Model Context Protocol (MCP), enabling terminal commands and file operations through the AI interface. - 54yyyu/code-mcp", "projections": { "pca": [ - -0.11198676377534866, - 0.15198040008544922 + -0.11152781546115875, + 0.15212951600551605 ], "tsne": [ - -27.023622512817383, - 24.203947067260742 + -22.95817756652832, + 4.077585220336914 ], "umap": [ - 3.09212327003479, - 8.56064510345459 + 3.3862152099609375, + 7.76561975479126 ], "pca3d": [ - -0.11198697239160538, - 0.15197867155075073, - 0.1411391794681549 + -0.11152786761522293, + 0.15212740004062653, + 0.14081647992134094 ], "tsne3d": [ - -12.199673652648926, - 11.565147399902344, - -10.363166809082031 + -8.022171020507812, + 2.0978946685791016, + 14.748136520385742 ], "umap3d": [ - 2.9312291145324707, - 4.143166542053223, - 4.221805095672607 + 3.818514585494995, + 4.702962398529053, + 4.374971389770508 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 32, + "id": 25, "label": "AI Agents" } ], @@ -1021035,46 +1021185,46 @@ "abstract": "The drop in body temperature that occurs during a torpid state is linked to molecular markers of longer life in mice.", "projections": { "pca": [ - 0.02875647321343422, - 0.29445552825927734 + 0.029386421665549278, + 0.29488784074783325 ], "tsne": [ - 16.48663902282715, - -12.220458984375 + 40.090606689453125, + -15.662126541137695 ], "umap": [ - 6.911843299865723, - 8.522297859191895 + 7.07753324508667, + 8.132533073425293 ], "pca3d": [ - 0.028755655512213707, - 0.294471800327301, - -0.059713464230298996 + 0.029385648667812347, + 0.2949037253856659, + -0.06024443358182907 ], "tsne3d": [ - 18.37795639038086, - -17.637102127075195, - -1.9974946975708008 + 15.774078369140625, + 20.27166748046875, + -6.48976469039917 ], "umap3d": [ - 6.243623733520508, - 3.0233771800994873, - 2.283522844314575 + 6.533315181732178, + 3.150726079940796, + 2.0269761085510254 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 3, + "label": "Genetic Disease" }, { - "id": 26, + "id": 33, "label": "Genetic Disease" } ], @@ -1021487,31 +1021637,31 @@ "abstract": "De-extinction company Colossal mixed mammoth and mouse mutations in a single strain to create a shaggy-haired rodent.", "projections": { "pca": [ - 0.09206721186637878, - 0.26917386054992676 + 0.09257828444242477, + 0.2700282037258148 ], "tsne": [ - 41.09442901611328, - 12.846786499023438 + 48.72906494140625, + -6.799253940582275 ], "umap": [ - 9.292236328125, - 7.697570323944092 + 9.546521186828613, + 7.204321384429932 ], "pca3d": [ - 0.09206681698560715, - 0.26915597915649414, - 0.06282414495944977 + 0.09257777035236359, + 0.27001023292541504, + 0.06228441745042801 ], "tsne3d": [ - 22.638713836669922, - -14.381479263305664, - 0.07250159233808517 + 23.820283889770508, + 11.006372451782227, + 7.468929767608643 ], "umap3d": [ - 7.425673484802246, - 4.376672744750977, - 1.5184333324432373 + 6.95007848739624, + 4.2714314460754395, + 0.19648811221122742 ] }, "cluster_id": 0, @@ -1021522,12 +1021672,12 @@ "label": "Chromatin Regulation" }, { - "id": 5, + "id": 13, "label": "Chromatin Regulation" }, { - "id": 20, - "label": "Chromatin Regulation" + "id": 6, + "label": "Cell Regulation" } ], "_embedding": [ @@ -1021935,47 +1022085,47 @@ ], "projections": { "pca": [ - -0.16616898775100708, - 0.27525055408477783 + -0.165131077170372, + 0.274942547082901 ], "tsne": [ - -13.506016731262207, - 3.348691940307617 + -6.4086384773254395, + -0.8780725598335266 ], "umap": [ - 4.465095043182373, - 7.290358066558838 + 4.661734104156494, + 6.594855308532715 ], "pca3d": [ - -0.16616946458816528, - 0.2752578556537628, - 0.04634444788098335 + -0.16513147950172424, + 0.2749501168727875, + 0.046030014753341675 ], "tsne3d": [ - -6.749508380889893, - 21.422508239746094, - -7.1776909828186035 + -4.524792194366455, + -7.524272441864014, + -16.21229362487793 ], "umap3d": [ - 4.01334285736084, - 3.7400870323181152, - 4.703423976898193 + 5.11005163192749, + 4.694626331329346, + 4.7778191566467285 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -1022383,47 +1022533,47 @@ "abstract": "Today, we\u2019re announcing Claude 3.7 Sonnet, our most intelligent model to date and the first hybrid reasoning model generally available on the market.", "projections": { "pca": [ - -0.17370827496051788, - 0.18099389970302582 + -0.1731632947921753, + 0.18189050257205963 ], "tsne": [ - -20.978416442871094, - -13.012090682983398 + -24.601327896118164, + 6.875051021575928 ], "umap": [ - 2.757917881011963, - 8.652074813842773 + 3.1019651889801025, + 7.951818943023682 ], "pca3d": [ - -0.1737082451581955, - 0.1809750348329544, - 0.1551569551229477 + -0.1731632798910141, + 0.181871235370636, + 0.15497490763664246 ], "tsne3d": [ - -12.285520553588867, - -16.168792724609375, - 0.05905204638838768 + -9.568891525268555, + 5.156037330627441, + 14.127615928649902 ], "umap3d": [ - 2.5194907188415527, - 3.89096999168396, - 4.3864545822143555 + 3.6000101566314697, + 4.587973117828369, + 4.819123268127441 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 0, - "label": "LLM Language Learning" + "id": 25, + "label": "AI Agents" } ], "_embedding": [ @@ -1022831,47 +1022981,47 @@ "abstract": "This repository contains the Hugging Face Agents Course. - GitHub - huggingface/agents-course: This repository contains the Hugging Face Agents Course.", "projections": { "pca": [ - -0.18649913370609283, - 0.20144647359848022 + -0.18590900301933289, + 0.20247003436088562 ], "tsne": [ - -20.35862159729004, - -6.586396217346191 + -19.5216121673584, + -2.6343910694122314 ], "umap": [ - 3.3914408683776855, - 7.684525966644287 + 3.718005657196045, + 7.141853332519531 ], "pca3d": [ - -0.18649917840957642, - 0.20143452286720276, - 0.07150164246559143 + -0.18590913712978363, + 0.20245744287967682, + 0.07131017744541168 ], "tsne3d": [ - -9.07613468170166, - 4.924938201904297, - -10.216059684753418 + -8.74374771118164, + 1.2657928466796875, + 5.090943813323975 ], "umap3d": [ - 3.204061508178711, - 4.413761615753174, - 4.04020881652832 + 4.03669548034668, + 5.001456260681152, + 4.199838638305664 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { "id": 0, - "label": "LLM Language Learning" + "label": "Math AI" } ], "_embedding": [ @@ -1023277,47 +1023427,47 @@ ], "projections": { "pca": [ - -0.1260819137096405, - 0.3450256884098053 + -0.12514901161193848, + 0.34545230865478516 ], "tsne": [ - -16.344661712646484, - -2.5370476245880127 + -15.544124603271484, + 0.8277537226676941 ], "umap": [ - 4.3078999519348145, - 7.607057571411133 + 4.396966934204102, + 6.907145023345947 ], "pca3d": [ - -0.126082643866539, - 0.34504008293151855, - -0.09623292088508606 + -0.125149667263031, + 0.3454670011997223, + -0.09668426960706711 ], "tsne3d": [ - -14.461459159851074, - 0.8193668723106384, - 17.401731491088867 + -3.6646742820739746, + 3.970036745071411, + -10.65489387512207 ], "umap3d": [ - 3.6806130409240723, - 3.415588140487671, - 4.4008026123046875 + 4.723336696624756, + 4.337527275085449, + 4.7145891189575195 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -1023727,47 +1023877,47 @@ "abstract": "Ask questions of your documents with DeepSeek-R1 + RAG. - local_rag_deepseek-r1.py", "projections": { "pca": [ - -0.1306053251028061, - 0.2633666396141052 + -0.12962688505649567, + 0.26332753896713257 ], "tsne": [ - -32.09613037109375, - 33.03824234008789 + -18.635425567626953, + -5.9745306968688965 ], "umap": [ - 3.960629940032959, - 6.981657981872559 + 3.8464882373809814, + 7.332179069519043 ], "pca3d": [ - -0.1306057721376419, - 0.2633719742298126, - 0.033003151416778564 + -0.12962736189365387, + 0.2633330821990967, + 0.03272872045636177 ], "tsne3d": [ - -12.844600677490234, - 13.860100746154785, - 17.18011474609375 + -9.963878631591797, + -0.7396591901779175, + 10.434866905212402 ], "umap3d": [ - 3.531583547592163, - 4.340944766998291, - 4.07938289642334 + 4.280632019042969, + 5.015845775604248, + 4.13905668258667 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 12, + "label": "AI Agents" }, { - "id": 10, - "label": "Code Development" + "id": 0, + "label": "Math AI" } ], "_embedding": [ @@ -1024378,47 +1024528,47 @@ "doi": "10.1038/s41586-025-09422-z", "projections": { "pca": [ - -0.22886216640472412, - -0.03258088603615761 + -0.22894692420959473, + -0.03133215755224228 ], "tsne": [ - -18.099260330200195, - -27.72103500366211 + -21.1325626373291, + 19.873817443847656 ], "umap": [ - 2.91772198677063, - 8.184195518493652 + 3.026533365249634, + 7.394625663757324 ], "pca3d": [ - -0.2288617491722107, - -0.03259780630469322, - 0.05315080285072327 + -0.2289465069770813, + -0.03134942799806595, + 0.05338401347398758 ], "tsne3d": [ - -11.252102851867676, - -9.446244239807129, - -9.463878631591797 + -13.903738021850586, + 17.630338668823242, + 1.6454031467437744 ], "umap3d": [ - 2.2740838527679443, - 4.119091987609863, - 3.7678732872009277 + 3.124830961227417, + 4.46993350982666, + 4.3222784996032715 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { "id": 0, - "label": "LLM Language Learning" + "label": "Math AI" } ], "_embedding": [ @@ -1024826,47 +1024976,47 @@ "abstract": "DeepSeek-R1 performs reasoning tasks at the same level as OpenAI\u2019s o1 \u2014 and is open for researchers to examine.", "projections": { "pca": [ - -0.194631889462471, - 0.11706162244081497 + -0.19427737593650818, + 0.11803578585386276 ], "tsne": [ - -17.891447067260742, - -26.92024040222168 + -21.644418716430664, + 20.649389266967773 ], "umap": [ - 3.098921060562134, - 8.1033296585083 + 3.3776416778564453, + 7.34078311920166 ], "pca3d": [ - -0.19463174045085907, - 0.1170497015118599, - 0.07975787669420242 + -0.19427737593650818, + 0.11802367866039276, + 0.07969308644533157 ], "tsne3d": [ - -11.472972869873047, - -8.110164642333984, - -10.416177749633789 + -15.039321899414062, + 16.879051208496094, + 0.8561840653419495 ], "umap3d": [ - 2.611495018005371, - 4.066558361053467, - 3.7025468349456787 + 3.4193880558013916, + 4.54033088684082, + 4.170633792877197 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { "id": 0, - "label": "LLM Language Learning" + "label": "Math AI" } ], "_embedding": [ @@ -1025289,31 +1025439,31 @@ ], "projections": { "pca": [ - 0.017270047217607498, - -0.13140137493610382 + 0.017080852761864662, + -0.13080981373786926 ], "tsne": [ - 0.5286810994148254, - 25.809284210205078 + 2.212895154953003, + -19.578638076782227 ], "umap": [ - 6.3455023765563965, - 4.652517795562744 + 6.710338592529297, + 4.315188884735107 ], "pca3d": [ - 0.017270149663090706, - -0.13139857351779938, - 0.16940826177597046 + 0.017081057652831078, + -0.13080744445323944, + 0.16969835758209229 ], "tsne3d": [ - 1.8390061855316162, - 8.577044486999512, - -0.36013782024383545 + 0.30699411034584045, + -7.378288269042969, + 3.589961290359497 ], "umap3d": [ - 5.227758407592773, - 5.817359447479248, - 4.793781280517578 + 6.006739139556885, + 6.70273494720459, + 3.523021936416626 ] }, "cluster_id": 3, @@ -1025324,11 +1025474,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, + "id": 20, "label": "Protein Language" } ], @@ -1025737,47 +1025887,47 @@ ], "projections": { "pca": [ - -0.1524985134601593, - 0.38124263286590576 + -0.15152223408222198, + 0.3823835551738739 ], "tsne": [ - -13.868429183959961, - -5.6344990730285645 + -9.502752304077148, + -1.2382524013519287 ], "umap": [ - 4.139344692230225, - 7.392714977264404 + 4.411034107208252, + 6.760494232177734 ], "pca3d": [ - -0.15249912440776825, - 0.3812464773654938, - 0.09191139787435532 + -0.15152288973331451, + 0.3823876976966858, + 0.0913671925663948 ], "tsne3d": [ - -11.527434349060059, - 3.9524900913238525, - 18.357955932617188 + -4.261687755584717, + -1.4346204996109009, + -11.79786491394043 ], "umap3d": [ - 3.5978341102600098, - 3.637446880340576, - 4.573545932769775 + 4.692153453826904, + 4.5748395919799805, + 4.8821210861206055 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -1026185,47 +1026335,47 @@ "abstract": "UMAP is a powerful tool for exploratory data analysis, but without a clear understanding of how it works, it can easily lead to confusion and misinterpretation.", "projections": { "pca": [ - 0.0010322935413569212, - 0.24620288610458374 + 0.0018995612626895308, + 0.24622373282909393 ], "tsne": [ - -0.7157027125358582, - 0.006506170146167278 + -1.5243746042251587, + -0.6173715591430664 ], "umap": [ - 5.378857135772705, - 8.184004783630371 + 5.481157302856445, + 7.602147579193115 ], "pca3d": [ - 0.0010317701380699873, - 0.24620573222637177, - 0.020377399399876595 + 0.001899147406220436, + 0.24622584879398346, + 0.020099075511097908 ], "tsne3d": [ - 5.603085041046143, - -13.12065315246582, - -17.626161575317383 + 4.599536418914795, + 21.540449142456055, + 3.2020068168640137 ], "umap3d": [ - 4.367064952850342, - 3.87448787689209, - 3.3279647827148438 + 4.860837459564209, + 4.2932844161987305, + 3.3225784301757812 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 18, - "label": "Genomics Papers" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -1026683,47 +1026833,47 @@ "openalex_url": "https://openalex.org/W4405306490", "projections": { "pca": [ - -0.15240979194641113, - 0.41109344363212585 + -0.15145175158977509, + 0.41218802332878113 ], "tsne": [ - -14.283965110778809, - -9.750574111938477 + -12.576976776123047, + 2.079866886138916 ], "umap": [ - 4.056017875671387, - 7.3674516677856445 + 4.36214017868042, + 6.755556106567383 ], "pca3d": [ - -0.15241031348705292, - 0.41109004616737366, - -0.005338931456208229 + -0.1514524519443512, + 0.4121847450733185, + -0.0059342156164348125 ], "tsne3d": [ - -8.131209373474121, - -7.431752681732178, - 15.749603271484375 + -8.20740032196045, + -1.2096973657608032, + -11.557290077209473 ], "umap3d": [ - 3.594930648803711, - 3.6124236583709717, - 4.678666591644287 + 4.674276351928711, + 4.510515213012695, + 4.89753532409668 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -1027133,47 +1027283,47 @@ "abstract": "Try Deep Research, a new research assistant in Gemini Advanced, and a chat optimized version of Gemini 2.0 Flash Experimental.", "projections": { "pca": [ - -0.20567336678504944, - 0.24616722762584686 + -0.205150306224823, + 0.24740439653396606 ], "tsne": [ - -4.768009185791016, - -15.669479370117188 + -8.319432258605957, + 12.62231159210205 ], "umap": [ - 3.837249755859375, - 8.396422386169434 + 4.056145668029785, + 7.742663860321045 ], "pca3d": [ - -0.20567366480827332, - 0.2461729496717453, - 0.026871060952544212 + -0.20515072345733643, + 0.24741007387638092, + 0.026552896946668625 ], "tsne3d": [ - -9.322517395019531, - -3.6575839519500732, - -10.967459678649902 + -9.217361450195312, + 8.424887657165527, + 6.2555108070373535 ], "umap3d": [ - 3.1462628841400146, - 3.714484453201294, - 3.501016855239868 + 3.894730567932129, + 4.259035587310791, + 3.925091505050659 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -1027581,47 +1027731,47 @@ ], "projections": { "pca": [ - -0.18475911021232605, - 0.4005996584892273 + -0.18362179398536682, + 0.401824951171875 ], "tsne": [ - -15.5185546875, - -9.314069747924805 + -11.887566566467285, + 0.9264967441558838 ], "umap": [ - 4.034393310546875, - 7.394011497497559 + 4.400139331817627, + 6.771829605102539 ], "pca3d": [ - -0.18475984036922455, - 0.4006136655807495, - -0.006989979185163975 + -0.18362267315387726, + 0.4018391966819763, + -0.007392884697765112 ], "tsne3d": [ - -9.60131549835205, - -7.776374340057373, - 17.440065383911133 + -8.066156387329102, + -2.7759287357330322, + -13.165105819702148 ], "umap3d": [ - 3.648515462875366, - 3.65860652923584, - 4.683938503265381 + 4.622098922729492, + 4.347998142242432, + 4.92563009262085 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -1028050,31 +1028200,31 @@ "openalex_url": "https://openalex.org/W1983153900", "projections": { "pca": [ - -0.06967921555042267, - 0.2097277045249939 + -0.06939974427223206, + 0.21053622663021088 ], "tsne": [ - -35.54140090942383, - 5.147017955780029 + -21.982070922851562, + 29.95838165283203 ], "umap": [ - 2.887589454650879, - 6.582619667053223 + 3.1453535556793213, + 5.697056293487549 ], "pca3d": [ - -0.06967928260564804, - 0.2097073644399643, - -0.001909271813929081 + -0.06939990818500519, + 0.2105160504579544, + -0.0023850358556956053 ], "tsne3d": [ - -11.968688011169434, - 1.9729069471359253, - -5.007036209106445 + -11.764540672302246, + -7.484795093536377, + 0.06942141056060791 ], "umap3d": [ - 2.7160227298736572, - 5.1805009841918945, - 3.6504478454589844 + 3.366802453994751, + 5.492072105407715, + 3.7933309078216553 ] }, "cluster_id": 2, @@ -1028085,12 +1028235,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 33, - "label": "Neural Networks" + "id": 19, + "label": "Graph Neural Nets" } ], "_embedding": [ @@ -1028500,46 +1028650,46 @@ "journal": "Nature", "projections": { "pca": [ - -0.1022694855928421, - 0.17531292140483856 + -0.10173734277486801, + 0.17588423192501068 ], "tsne": [ - 12.660618782043457, - -16.637266159057617 + 12.612041473388672, + 13.785540580749512 ], "umap": [ - 6.639157295227051, - 8.278414726257324 + 6.961374282836914, + 7.841634273529053 ], "pca3d": [ - -0.10227007418870926, - 0.1753266602754593, - -0.022720137611031532 + -0.10173788666725159, + 0.17589759826660156, + -0.022887015715241432 ], "tsne3d": [ - 10.685065269470215, - -5.799190998077393, - -23.969135284423828 + 14.028841972351074, + 25.67265510559082, + 1.8162000179290771 ], "umap3d": [ - 6.278912544250488, - 3.043156623840332, - 2.688554286956787 + 6.828011989593506, + 3.349121570587158, + 2.451463222503662 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 26, + "id": 33, "label": "Genetic Disease" } ], @@ -1028962,46 +1029112,46 @@ "openalex_url": "https://openalex.org/W2964274690", "projections": { "pca": [ - -0.21322543919086456, - -0.16237981617450714 + -0.21373200416564941, + -0.16212177276611328 ], "tsne": [ - -55.59408950805664, - -14.495233535766602 + -57.6313591003418, + -6.724032402038574 ], "umap": [ - -0.17634721100330353, - 6.318097114562988 + 0.1478855311870575, + 5.39443302154541 ], "pca3d": [ - -0.21322517096996307, - -0.1623542606830597, - -0.11364033073186874 + -0.2137317657470703, + -0.16209563612937927, + -0.11335589736700058 ], "tsne3d": [ - -32.079498291015625, - 2.034886598587036, - -2.5268192291259766 + -31.89360809326172, + -8.512863159179688, + 0.045629341155290604 ], "umap3d": [ - 0.019072994589805603, - 6.402027130126953, - 4.124679088592529 + 0.6822046041488647, + 6.427210330963135, + 4.868690013885498 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, + "id": 23, "label": "Training Noise" } ], @@ -1029425,46 +1029575,46 @@ "openalex_url": "https://openalex.org/W2951459130", "projections": { "pca": [ - -0.2261994630098343, - -0.10775861144065857 + -0.22653424739837646, + -0.10728374868631363 ], "tsne": [ - -55.89822769165039, - -14.748525619506836 + -58.02223587036133, + -6.752624988555908 ], "umap": [ - -0.22260665893554688, - 6.34427547454834 + 0.12677685916423798, + 5.3742547035217285 ], "pca3d": [ - -0.2261991798877716, - -0.10773606598377228, - -0.11889825761318207 + -0.22653400897979736, + -0.10726102441549301, + -0.11863111704587936 ], "tsne3d": [ - -33.12999725341797, - 1.7327210903167725, - -1.7279680967330933 + -32.834678649902344, + -8.057806968688965, + 0.8246078491210938 ], "umap3d": [ - -0.03326502442359924, - 6.446469783782959, - 4.127146244049072 + 0.646984875202179, + 6.448241710662842, + 4.868418216705322 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, + "id": 23, "label": "Training Noise" } ], @@ -1029886,46 +1030036,46 @@ "openalex_url": "https://openalex.org/W2964273174", "projections": { "pca": [ - -0.27470389008522034, - -0.1540706902742386 + -0.2752181887626648, + -0.15354023873806 ], "tsne": [ - -55.196346282958984, - -14.318822860717773 + -57.204002380371094, + -6.796777725219727 ], "umap": [ - -0.0965220183134079, - 6.331451892852783 + 0.1926143616437912, + 5.402670860290527 ], "pca3d": [ - -0.27470362186431885, - -0.15402752161026, - -0.1471560299396515 + -0.27521800994873047, + -0.15349633991718292, + -0.14684228599071503 ], "tsne3d": [ - -30.916027069091797, - 1.2022784948349, - -2.9390463829040527 + -30.589252471923828, + -8.878847122192383, + 0.7254531979560852 ], "umap3d": [ - 0.06130128353834152, - 6.380350112915039, - 4.091563701629639 + 0.721455454826355, + 6.409378528594971, + 4.819593906402588 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, + "id": 23, "label": "Training Noise" } ], @@ -1030347,46 +1030497,46 @@ "openalex_url": "https://openalex.org/W1514928307", "projections": { "pca": [ - -0.21324291825294495, - -0.07287895679473877 + -0.21344450116157532, + -0.0726190060377121 ], "tsne": [ - -55.70329666137695, - -15.375666618347168 + -58.1803092956543, + -7.391752243041992 ], "umap": [ - -0.20387625694274902, - 6.337323188781738 + 0.1122143343091011, + 5.392364501953125 ], "pca3d": [ - -0.21324291825294495, - -0.07284750044345856, - -0.09543673694133759 + -0.21344439685344696, + -0.0725872814655304, + -0.09527179598808289 ], "tsne3d": [ - -33.572383880615234, - 0.9992680549621582, - -3.0882084369659424 + -32.8367805480957, + -9.438806533813477, + -0.7991756796836853 ], "umap3d": [ - -0.04267219826579094, - 6.446487903594971, - 4.133367538452148 + 0.6161255240440369, + 6.46990966796875, + 4.880478858947754 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, + "id": 23, "label": "Training Noise" } ], @@ -1030809,46 +1030959,46 @@ "openalex_url": "https://openalex.org/W2963697299", "projections": { "pca": [ - -0.21311374008655548, - -0.16329678893089294 + -0.21365675330162048, + -0.1632375717163086 ], "tsne": [ - -54.997894287109375, - -14.75563907623291 + -57.27641296386719, + -7.316948413848877 ], "umap": [ - -0.18981719017028809, - 6.322502136230469 + 0.17708434164524078, + 5.3875346183776855 ], "pca3d": [ - -0.21311339735984802, - -0.16326221823692322, - -0.11251915246248245 + -0.213656485080719, + -0.16320256888866425, + -0.1123136430978775 ], "tsne3d": [ - -32.11635971069336, - 1.2806156873703003, - -3.9136738777160645 + -31.31598663330078, + -9.828932762145996, + -0.14751026034355164 ], "umap3d": [ - -0.010647757910192013, - 6.427056312561035, - 4.1239190101623535 + 0.6378211379051208, + 6.440953254699707, + 4.87390661239624 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, + "id": 23, "label": "Training Noise" } ], @@ -1031268,46 +1031418,46 @@ "openalex_url": "https://openalex.org/W2963999980", "projections": { "pca": [ - -0.2022184282541275, - -0.0601077564060688 + -0.2023884654045105, + -0.05964114889502525 ], "tsne": [ - -56.23534393310547, - -15.166399955749512 + -58.530128479003906, + -6.914636611938477 ], "umap": [ - -0.20936855673789978, - 6.332741737365723 + 0.08403167128562927, + 5.387140274047852 ], "pca3d": [ - -0.20221854746341705, - -0.06007489189505577, - -0.12462601810693741 + -0.2023884505033493, + -0.05960822105407715, + -0.1244116723537445 ], "tsne3d": [ - -33.22637939453125, - 2.981705904006958, - -2.972750663757324 + -33.12921142578125, + -9.359183311462402, + 0.9419857859611511 ], "umap3d": [ - -0.06589333713054657, - 6.468365669250488, - 4.121369361877441 + 0.5847229957580566, + 6.494783401489258, + 4.852182388305664 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, + "id": 23, "label": "Training Noise" } ], @@ -1031737,46 +1031887,46 @@ "openalex_url": "https://openalex.org/W2589453516", "projections": { "pca": [ - -0.198177307844162, - 0.008837095461785793 + -0.19809821248054504, + 0.009121968410909176 ], "tsne": [ - -57.86713790893555, - -15.991544723510742 + -60.30802536010742, + -6.8881144523620605 ], "umap": [ - -0.23958221077919006, - 6.4170098304748535 + 0.0679892897605896, + 5.475663661956787 ], "pca3d": [ - -0.19817732274532318, - 0.008844729512929916, - -0.14784444868564606 + -0.19809812307357788, + 0.009129959158599377, + -0.14772623777389526 ], "tsne3d": [ - -35.31184768676758, - 0.7915323972702026, - -1.1330379247665405 + -35.203407287597656, + -8.889320373535156, + 1.7014151811599731 ], "umap3d": [ - -0.038041915744543076, - 6.413924217224121, - 4.182397842407227 + 0.671070396900177, + 6.404900550842285, + 4.955362319946289 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, + "id": 23, "label": "Training Noise" } ], @@ -1032204,46 +1032354,46 @@ "openalex_url": "https://openalex.org/W4297004340", "projections": { "pca": [ - -0.12800632417201996, - 0.009508032351732254 + -0.12800255417823792, + 0.009529182687401772 ], "tsne": [ - -56.38711929321289, - -16.061309814453125 + -59.118263244628906, + -7.577507495880127 ], "umap": [ - -0.23716093599796295, - 6.337958812713623 + 0.07560306787490845, + 5.38981294631958 ], "pca3d": [ - -0.1280064880847931, - 0.009535983204841614, - -0.19538922607898712 + -0.12800268828868866, + 0.009557094424962997, + -0.19534064829349518 ], "tsne3d": [ - -34.889923095703125, - 2.180460214614868, - -2.7670459747314453 + -34.34994888305664, + -10.053411483764648, + -0.02530243620276451 ], "umap3d": [ - -0.06783681362867355, - 6.467464447021484, - 4.118323802947998 + 0.5822579264640808, + 6.4892578125, + 4.852546215057373 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, + "id": 23, "label": "Training Noise" } ], @@ -1032667,46 +1032817,46 @@ "openalex_url": "https://openalex.org/W1866072925", "projections": { "pca": [ - -0.2596019506454468, - -0.15947094559669495 + -0.2601051330566406, + -0.1591532826423645 ], "tsne": [ - -55.65205764770508, - -14.148316383361816 + -57.476558685302734, + -6.430318355560303 ], "umap": [ - -0.1968384087085724, - 6.310332298278809 + 0.1469264030456543, + 5.401062965393066 ], "pca3d": [ - -0.2596016228199005, - -0.15944015979766846, - -0.1704540252685547 + -0.26010483503341675, + -0.1591220200061798, + -0.1701321005821228 ], "tsne3d": [ - -31.834022521972656, - 1.0615286827087402, - -1.7050361633300781 + -31.615692138671875, + -7.897896766662598, + 1.1419694423675537 ], "umap3d": [ - 0.009511814452707767, - 6.412900447845459, - 4.121932029724121 + 0.6751627326011658, + 6.429663181304932, + 4.841168403625488 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, + "id": 23, "label": "Training Noise" } ], @@ -1033124,46 +1033274,46 @@ "openalex_url": "https://openalex.org/W4297813944", "projections": { "pca": [ - -0.2394532412290573, - -0.15020519495010376 + -0.23977559804916382, + -0.14953655004501343 ], "tsne": [ - -55.017189025878906, - -15.257993698120117 + -57.56168746948242, + -7.611316680908203 ], "umap": [ - -0.1005811020731926, - 6.317441463470459 + 0.2057388871908188, + 5.386563777923584 ], "pca3d": [ - -0.23945291340351105, - -0.15018245577812195, - -0.10239113867282867 + -0.23977522552013397, + -0.14951324462890625, + -0.10197056084871292 ], "tsne3d": [ - -32.66279602050781, - -0.031225847080349922, - -2.4908549785614014 + -31.88486671447754, + -8.179471969604492, + -1.363243818283081 ], "umap3d": [ - 0.054739102721214294, - 6.370908737182617, - 4.103147983551025 + 0.673926591873169, + 6.434237003326416, + 4.786398410797119 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, + "id": 23, "label": "Training Noise" } ], @@ -1033586,46 +1033736,46 @@ "openalex_url": "https://openalex.org/W2803237185", "projections": { "pca": [ - -0.24688297510147095, - -0.14393045008182526 + -0.247343510389328, + -0.14337195456027985 ], "tsne": [ - -55.16844940185547, - -13.811445236206055 + -56.90914535522461, + -6.387110710144043 ], "umap": [ - -0.029321402311325073, - 6.32792329788208 + 0.209452286362648, + 5.3852972984313965 ], "pca3d": [ - -0.2468828707933426, - -0.1438976675271988, - -0.10135573148727417 + -0.24734337627887726, + -0.14333848655223846, + -0.10106654465198517 ], "tsne3d": [ - -30.801103591918945, - 2.1302194595336914, - -1.7710782289505005 + -30.71623420715332, + -7.592000484466553, + 0.07499323785305023 ], "umap3d": [ - 0.06588621437549591, - 6.3823628425598145, - 4.138616561889648 + 0.70947265625, + 6.409879207611084, + 4.83065938949585 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, + "id": 23, "label": "Training Noise" } ], @@ -1034048,46 +1034198,46 @@ "openalex_url": "https://openalex.org/W2946376679", "projections": { "pca": [ - -0.20865823328495026, - -0.0934416726231575 + -0.20899136364459991, + -0.09305118769407272 ], "tsne": [ - -56.10895919799805, - -14.369547843933105 + -57.98949432373047, + -6.338650703430176 ], "umap": [ - -0.20051664113998413, - 6.304015636444092 + 0.12400969862937927, + 5.377987861633301 ], "pca3d": [ - -0.20865806937217712, - -0.09341453015804291, - -0.1562730371952057 + -0.20899128913879395, + -0.09302385151386261, + -0.15608513355255127 ], "tsne3d": [ - -31.562973022460938, - 2.874460458755493, - -3.6154439449310303 + -31.71292495727539, + -9.504178047180176, + 1.6206121444702148 ], "umap3d": [ - -0.0018849059706553817, - 6.425618648529053, - 4.115821838378906 + 0.6545664668083191, + 6.437729358673096, + 4.835477828979492 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, + "id": 23, "label": "Training Noise" } ], @@ -1034510,46 +1034660,46 @@ "openalex_url": "https://openalex.org/W3139017810", "projections": { "pca": [ - -0.14800380170345306, - -0.0207635797560215 + -0.14803913235664368, + -0.020610259845852852 ], "tsne": [ - -55.67786407470703, - -15.966276168823242 + -58.489837646484375, + -7.871500492095947 ], "umap": [ - -0.23947106301784515, - 6.328000545501709 + 0.07776869088411331, + 5.387123107910156 ], "pca3d": [ - -0.14800380170345306, - -0.020746376365423203, - -0.12857083976268768 + -0.14803898334503174, + -0.02059308998286724, + -0.12843334674835205 ], "tsne3d": [ - -33.831180572509766, - 2.2386062145233154, - -4.3245463371276855 + -32.78004837036133, + -10.868937492370605, + 0.45237135887145996 ], "umap3d": [ - -0.08537609875202179, - 6.484551429748535, - 4.100238800048828 + 0.6141982078552246, + 6.462856292724609, + 4.86169958114624 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, + "id": 23, "label": "Training Noise" } ], @@ -1034966,46 +1035116,46 @@ "openalex_url": "https://openalex.org/W4306802233", "projections": { "pca": [ - -0.17797428369522095, - -0.09059356153011322 + -0.1781741976737976, + -0.0901106670498848 ], "tsne": [ - -56.571083068847656, - -14.771814346313477 + -58.58626174926758, + -6.3684234619140625 ], "umap": [ - -0.1876252442598343, - 6.3318071365356445 + 0.10300686210393906, + 5.4224395751953125 ], "pca3d": [ - -0.1779741644859314, - -0.09057463705539703, - -0.1343841254711151 + -0.17817403376102448, + -0.09009169787168503, + -0.1340869516134262 ], "tsne3d": [ - -32.674781799316406, - 3.2932403087615967, - -1.6162840127944946 + -33.59309005737305, + -7.975240230560303, + -0.35758572816848755 ], "umap3d": [ - -0.059935346245765686, - 6.455697059631348, - 4.153117656707764 + 0.6254166960716248, + 6.470442295074463, + 4.885718822479248 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 2, + "cluster_label": "Generative Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 2, + "label": "Generative Models" }, { - "id": 1, + "id": 8, "label": "Network Learning" }, { - "id": 30, + "id": 23, "label": "Training Noise" } ], @@ -1035416,31 +1035566,31 @@ "abstract": "Educational implementation of the Discrete Flow Matching paper - lebellig/discrete-fm", "projections": { "pca": [ - -0.2251349240541458, - 0.1608453243970871 + -0.2245197743177414, + 0.16091516613960266 ], "tsne": [ - -38.91455078125, - 23.536970138549805 + -34.0772819519043, + -28.977766036987305 ], "umap": [ - 2.467573881149292, - 4.760138034820557 + 2.8954520225524902, + 4.149989604949951 ], "pca3d": [ - -0.22513516247272491, - 0.16084544360637665, - 0.044247400015592575 + -0.22451989352703094, + 0.16091495752334595, + 0.044047556817531586 ], "tsne3d": [ - -18.045377731323242, - 17.818286895751953, - 0.33154207468032837 + -15.510124206542969, + -25.234289169311523, + 7.586187362670898 ], "umap3d": [ - 2.302957534790039, - 7.094326496124268, - 4.786660671234131 + 2.7783896923065186, + 7.471229076385498, + 4.658154010772705 ] }, "cluster_id": 2, @@ -1035451,11 +1035601,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -1035866,31 +1036016,31 @@ "abstract": "Annotated Flow Matching paper. Contribute to lebellig/flow-matching development by creating an account on GitHub.", "projections": { "pca": [ - -0.15129567682743073, - 0.1413164734840393 + -0.1506856381893158, + 0.14113813638687134 ], "tsne": [ - -38.89577865600586, - 23.55852508544922 + -34.06587219238281, + -28.985097885131836 ], "umap": [ - 2.5081467628479004, - 4.726181507110596 + 2.916060209274292, + 4.1386919021606445 ], "pca3d": [ - -0.15129582583904266, - 0.14131993055343628, - 0.05831358581781387 + -0.15068580210208893, + 0.14114156365394592, + 0.05812511220574379 ], "tsne3d": [ - -17.464597702026367, - 17.83615493774414, - -0.35112398862838745 + -15.382970809936523, + -25.89514923095703, + 7.059028148651123 ], "umap3d": [ - 2.302889347076416, - 7.109794616699219, - 4.779744625091553 + 2.784024715423584, + 7.493879795074463, + 4.628592491149902 ] }, "cluster_id": 2, @@ -1035901,11 +1036051,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -1036316,31 +1036466,31 @@ "abstract": "Minimal implementation of flow matching with JAX. Contribute to lebellig/jax-fm development by creating an account on GitHub.", "projections": { "pca": [ - -0.12266828119754791, - 0.1331474781036377 + -0.12215869873762131, + 0.13314402103424072 ], "tsne": [ - -38.88302993774414, - 23.596105575561523 + -34.04978942871094, + -29.025564193725586 ], "umap": [ - 2.520965099334717, - 4.741096019744873 + 2.9546070098876953, + 4.1574015617370605 ], "pca3d": [ - -0.12266840785741806, - 0.13314324617385864, - 0.0922408401966095 + -0.12215868383646011, + 0.13313961029052734, + 0.0920085534453392 ], "tsne3d": [ - -17.301467895507812, - 18.249887466430664, - 0.33341798186302185 + -14.882987022399902, + -25.73992919921875, + 7.621525764465332 ], "umap3d": [ - 2.322587728500366, - 7.116076946258545, - 4.779858112335205 + 2.8193023204803467, + 7.491031169891357, + 4.623500823974609 ] }, "cluster_id": 2, @@ -1036351,11 +1036501,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -1036764,46 +1036914,46 @@ ], "projections": { "pca": [ - -0.2040320187807083, - 0.3511033356189728 + -0.2030150443315506, + 0.35189375281333923 ], "tsne": [ - -17.639732360839844, - -34.68040084838867 + -26.43773651123047, + -33.0479621887207 ], "umap": [ - 2.9641432762145996, - 9.107158660888672 + 3.073794364929199, + 8.350310325622559 ], "pca3d": [ - -0.2040327936410904, - 0.35112062096595764, - 0.026205355301499367 + -0.20301571488380432, + 0.35191112756729126, + 0.025816043838858604 ], "tsne3d": [ - -11.57807731628418, - -5.4286298751831055, - 2.865424633026123 + -8.487373352050781, + 8.240944862365723, + 12.222864151000977 ], "umap3d": [ - 2.846097946166992, - 3.1976218223571777, - 5.074029445648193 + 4.365522861480713, + 4.445242404937744, + 5.5951104164123535 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 32, + "id": 25, "label": "AI Agents" } ], @@ -1037210,47 +1037360,47 @@ ], "projections": { "pca": [ - -0.16069592535495758, - 0.2748933732509613 + -0.1598111242055893, + 0.2753181755542755 ], "tsne": [ - -6.550847053527832, - -4.455903053283691 + -5.479817867279053, + 2.6045024394989014 ], "umap": [ - 4.486192226409912, - 7.262311935424805 + 4.6825995445251465, + 6.591458797454834 ], "pca3d": [ - -0.16069628298282623, - 0.27490127086639404, - 0.01958569325506687 + -0.15981167554855347, + 0.27532580494880676, + 0.01926971599459648 ], "tsne3d": [ - -9.746844291687012, - -1.9029452800750732, - 16.060651779174805 + -4.387683391571045, + -0.3482736647129059, + -3.3013577461242676 ], "umap3d": [ - 4.049075603485107, - 3.794344663619995, - 4.456970691680908 + 4.857297420501709, + 4.68012809753418, + 4.58099365234375 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -1037665,46 +1037815,46 @@ ], "projections": { "pca": [ - -0.2042609304189682, - -0.019741415977478027 + -0.2041347771883011, + -0.01881343498826027 ], "tsne": [ - -28.11526107788086, - -19.648061752319336 + -32.416534423828125, + 17.21772575378418 ], "umap": [ - 2.28326678276062, - 8.421438217163086 + 2.5046818256378174, + 7.571236610412598 ], "pca3d": [ - -0.20426075160503387, - -0.01973908394575119, - 0.0602693147957325 + -0.20413458347320557, + -0.01881111040711403, + 0.06056636944413185 ], "tsne3d": [ - -15.390777587890625, - -18.625530242919922, - 9.042545318603516 + -17.049301147460938, + 4.6369805335998535, + -7.118760108947754 ], "umap3d": [ - 2.003725528717041, - 3.9378700256347656, - 4.368185997009277 + 2.9925644397735596, + 4.431551456451416, + 5.043449878692627 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -1038123,47 +1038273,47 @@ "doi": "10.1038/s41598-026-56320-z", "projections": { "pca": [ - -0.158576101064682, - 0.28675827383995056 + -0.15765054523944855, + 0.28669247031211853 ], "tsne": [ - -8.653268814086914, - -10.429511070251465 + -17.238603591918945, + 3.958406686782837 ], "umap": [ - 3.855358362197876, - 7.278855323791504 + 4.175137996673584, + 6.567142486572266 ], "pca3d": [ - -0.15857693552970886, - 0.2867947518825531, - 0.050909753888845444 + -0.15765127539634705, + 0.2867289185523987, + 0.05045582354068756 ], "tsne3d": [ - -4.81599235534668, - -8.997329711914062, - 17.33679962158203 + -8.947020530700684, + 3.0881896018981934, + -14.967838287353516 ], "umap3d": [ - 3.5427663326263428, - 3.699328899383545, - 4.673235893249512 + 4.425719738006592, + 4.676530361175537, + 4.967606544494629 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -1038574,31 +1038724,31 @@ "year": 2024, "projections": { "pca": [ - -0.2809673249721527, - -0.0004689970810431987 + -0.28085649013519287, + -3.79238736059051e-05 ], "tsne": [ - -43.98723602294922, - 14.952460289001465 + -40.60397720336914, + -21.832061767578125 ], "umap": [ - 1.8020354509353638, - 5.782286643981934 + 2.130647897720337, + 5.094522476196289 ], "pca3d": [ - -0.2809671461582184, - -0.0004735963302664459, - 0.047692589461803436 + -0.28085631132125854, + -4.273403828847222e-05, + 0.047818623483181 ], "tsne3d": [ - -25.40220069885254, - 13.003735542297363, - 4.857386589050293 + -25.558902740478516, + -19.376983642578125, + 2.7863175868988037 ], "umap3d": [ - 1.6717255115509033, - 6.313822269439697, - 4.64145565032959 + 2.2635960578918457, + 6.675881385803223, + 4.799179553985596 ] }, "cluster_id": 2, @@ -1038609,12 +1038759,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 22, + "label": "Diffusion Models" } ], "_embedding": [ @@ -1039020,47 +1039170,47 @@ ], "projections": { "pca": [ - -0.056451547890901566, - 0.2963145077228546 + -0.055607862770557404, + 0.29672113060951233 ], "tsne": [ - -16.344919204711914, - -10.623207092285156 + -10.995176315307617, + 1.4161536693572998 ], "umap": [ - 4.00779914855957, - 7.3937296867370605 + 4.368356227874756, + 6.908271312713623 ], "pca3d": [ - -0.056452177464962006, - 0.2963264286518097, - 0.04120509698987007 + -0.05560862272977829, + 0.29673293232917786, + 0.04081018269062042 ], "tsne3d": [ - -2.2948997020721436, - -10.749040603637695, - 8.93598461151123 + -8.419004440307617, + -2.9217777252197266, + -14.91901969909668 ], "umap3d": [ - 3.6327106952667236, - 3.6449828147888184, - 4.640661716461182 + 4.474959850311279, + 4.3091020584106445, + 4.889039516448975 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -1039468,47 +1039618,47 @@ ], "projections": { "pca": [ - -0.13246381282806396, - 0.28218206763267517 + -0.13171136379241943, + 0.28351494669914246 ], "tsne": [ - -6.675160884857178, - -25.52465057373047 + -9.80656909942627, + 20.40380859375 ], "umap": [ - 4.110396385192871, - 8.72767162322998 + 4.359132289886475, + 8.016063690185547 ], "pca3d": [ - -0.13246437907218933, - 0.28220492601394653, - 0.15621653199195862 + -0.13171200454235077, + 0.2835373878479004, + 0.15585967898368835 ], "tsne3d": [ - -3.452277660369873, - -11.15255355834961, - -0.7291542291641235 + -6.598712921142578, + 13.438124656677246, + -2.624192476272583 ], "umap3d": [ - 3.3782896995544434, - 3.4495441913604736, - 3.1607186794281006 + 3.9802002906799316, + 3.8692939281463623, + 3.624685287475586 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -1039916,47 +1040066,47 @@ "abstract": "Styling matplotlib made easy. Contribute to lgienapp/aquarel development by creating an account on GitHub.", "projections": { "pca": [ - -0.12065618485212326, - 0.1638302356004715 + -0.11999165266752243, + 0.16384823620319366 ], "tsne": [ - -25.665048599243164, - 33.16823196411133 + -16.42247200012207, + -10.189960479736328 ], "umap": [ - 3.9004838466644287, - 6.1977009773254395 + 4.251855373382568, + 5.613061428070068 ], "pca3d": [ - -0.12065637111663818, - 0.16381992399692535, - 0.06334327161312103 + -0.1199917271733284, + 0.16383787989616394, + 0.06313060969114304 ], "tsne3d": [ - -8.686553955078125, - 16.49751853942871, - 15.057855606079102 + -10.514904022216797, + -7.891071796417236, + 14.059786796569824 ], "umap3d": [ - 3.711573362350464, - 5.083977699279785, - 4.60727071762085 + 4.532779216766357, + 5.752961158752441, + 4.306698322296143 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 10, - "label": "Code Development" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -1040364,47 +1040514,47 @@ "abstract": "A player wins by racking up more citations than the competition, even if that means engaging in a little light plagiarism.", "projections": { "pca": [ - -0.1219937726855278, - 0.3976079225540161 + -0.12074891477823257, + 0.39842531085014343 ], "tsne": [ - -16.11931037902832, - -13.063135147094727 + -17.58523941040039, + 8.883779525756836 ], "umap": [ - 3.847270965576172, - 7.602224349975586 + 3.968153476715088, + 6.91201114654541 ], "pca3d": [ - -0.12199444323778152, - 0.397603839635849, - 0.10839006304740906 + -0.12074948102235794, + 0.3984207212924957, + 0.10792568325996399 ], "tsne3d": [ - -4.891615867614746, - -11.446619987487793, - 10.552607536315918 + -10.141194343566895, + 3.66416335105896, + -8.116466522216797 ], "umap3d": [ - 3.341688871383667, - 3.5384597778320312, - 4.492178916931152 + 4.324413776397705, + 4.336506366729736, + 4.833588600158691 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -1040821,31 +1040971,31 @@ ], "projections": { "pca": [ - 0.09512804448604584, - -0.030463309958577156 + 0.09521409124135971, + -0.030429143458604813 ], "tsne": [ - -4.825254440307617, - 39.46859359741211 + -10.256623268127441, + -19.23546028137207 ], "umap": [ - 5.622900485992432, - 4.183064937591553 + 6.180923938751221, + 3.881425619125366 ], "pca3d": [ - 0.09512797743082047, - -0.030448192730545998, - 0.12193161249160767 + 0.09521402418613434, + -0.030414093285799026, + 0.12196280807256699 ], "tsne3d": [ - 1.9534064531326294, - -2.7313764095306396, - 6.074826717376709 + -5.924010753631592, + -4.6211256980896, + 8.807663917541504 ], "umap3d": [ - 5.339141368865967, - 6.66165018081665, - 4.928564548492432 + 5.85295295715332, + 7.380299091339111, + 3.268371343612671 ] }, "cluster_id": 3, @@ -1040856,12 +1041006,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, - "label": "Protein Language" + "id": 3, + "label": "Protein Design" } ], "_embedding": [ @@ -1041269,31 +1041419,31 @@ "abstract": "CARE: a Benchmark Suite for the Classification and Retrieval of Enzymes - GitHub - jsunn-y/CARE: CARE: a Benchmark Suite for the Classification and Retrieval of Enzymes", "projections": { "pca": [ - 0.038852956146001816, - 0.03037073463201523 + 0.03907232731580734, + 0.030375216156244278 ], "tsne": [ - -4.906431674957275, - 39.51554870605469 + -10.373262405395508, + -19.234844207763672 ], "umap": [ - 5.52736234664917, - 4.269657135009766 + 6.111265182495117, + 3.912393569946289 ], "pca3d": [ - 0.03885280340909958, - 0.030384160578250885, - 0.09999223053455353 + 0.039072174578905106, + 0.03038862533867359, + 0.09993565827608109 ], "tsne3d": [ - 2.358351230621338, - -2.6061065196990967, - 6.234591484069824 + -6.232122421264648, + -4.398087978363037, + 8.61473560333252 ], "umap3d": [ - 5.319981575012207, - 6.696887493133545, - 4.834524154663086 + 5.736612796783447, + 7.325521469116211, + 3.1623361110687256 ] }, "cluster_id": 3, @@ -1041304,12 +1041454,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 15, - "label": "Protein Language" + "id": 3, + "label": "Protein Design" } ], "_embedding": [ @@ -1041717,47 +1041867,47 @@ "journal": "Nature", "projections": { "pca": [ - -0.16952967643737793, - 0.4085462987422943 + -0.16839875280857086, + 0.4086911082267761 ], "tsne": [ - -16.4903507232666, - -13.348945617675781 + -17.495332717895508, + 8.94092845916748 ], "umap": [ - 3.7308731079101562, - 7.62206506729126 + 3.889209747314453, + 7.023214817047119 ], "pca3d": [ - -0.16953030228614807, - 0.4085440933704376, - 0.03863060101866722 + -0.16839921474456787, + 0.40868857502937317, + 0.0379573218524456 ], "tsne3d": [ - -4.768556594848633, - -12.710484504699707, - 10.91301155090332 + -9.823995590209961, + 4.85211706161499, + -8.415523529052734 ], "umap3d": [ - 3.295626163482666, - 3.4811646938323975, - 4.475671291351318 + 4.306716442108154, + 4.255722522735596, + 4.888215065002441 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 12, + "label": "AI Agents" }, { - "id": 23, - "label": "Scientific Research" + "id": 0, + "label": "Math AI" } ], "_embedding": [ @@ -1042165,31 +1042315,31 @@ "abstract": "Gen-3 Alpha is the first of the next generation of foundation models trained by Runway on a new infrastructure built for large-scale multimodal training. It is a major improvement in fidelity, consistency, and motion over Gen-2, and a step towards building General World Models.", "projections": { "pca": [ - -0.08934008330106735, - 0.12154100835323334 + -0.08901866525411606, + 0.12268663942813873 ], "tsne": [ - -2.0353920459747314, - 2.3204185962677 + 3.0161352157592773, + -4.090301513671875 ], "umap": [ - 6.8033528327941895, - 6.58130407333374 + 7.192365646362305, + 6.124062538146973 ], "pca3d": [ - -0.08933959901332855, - 0.12148881703615189, - -0.009914375841617584 + -0.08901835232973099, + 0.12263381481170654, + -0.009940928779542446 ], "tsne3d": [ - -7.793529510498047, - 2.6899220943450928, - -4.421335220336914 + -1.8169090747833252, + 3.45989727973938, + 4.0569233894348145 ], "umap3d": [ - 5.489798545837402, - 4.886795997619629, - 3.359822988510132 + 5.766899108886719, + 5.131707191467285, + 2.73077392578125 ] }, "cluster_id": 4, @@ -1042200,12 +1042350,12 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 11, - "label": "Genomic Language" + "id": 17, + "label": "Genomic Models" } ], "_embedding": [ @@ -1042613,47 +1042763,47 @@ "journal": "Nature Methods", "projections": { "pca": [ - -0.14438961446285248, - 0.3053969442844391 + -0.14336639642715454, + 0.30596742033958435 ], "tsne": [ - -10.950886726379395, - -9.843839645385742 + -15.584545135498047, + 4.296937942504883 ], "umap": [ - 3.768864870071411, - 7.430582523345947 + 4.036899089813232, + 6.712106704711914 ], "pca3d": [ - -0.1443903148174286, - 0.3054170608520508, - 0.11180378496646881 + -0.14336714148521423, + 0.30598756670951843, + 0.11148564517498016 ], "tsne3d": [ - -7.517134666442871, - -11.186715126037598, - 14.714082717895508 + -11.565402030944824, + 1.6159945726394653, + -13.352742195129395 ], "umap3d": [ - 3.425420045852661, - 3.630368947982788, - 4.55732536315918 + 4.335278034210205, + 4.4111151695251465, + 4.937384605407715 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -1043068,31 +1043218,31 @@ "cited_by_count": 725, "projections": { "pca": [ - -0.22128702700138092, - -0.02098628133535385 + -0.22125199437141418, + -0.020817261189222336 ], "tsne": [ - -46.62258529663086, - 15.806512832641602 + -43.648494720458984, + -21.52052879333496 ], "umap": [ - 1.6868427991867065, - 5.347740650177002 + 1.888898491859436, + 4.697849273681641 ], "pca3d": [ - -0.2212868481874466, - -0.020976165309548378, - -0.07807793468236923 + -0.22125178575515747, + -0.02080693282186985, + -0.07796987891197205 ], "tsne3d": [ - -23.301677703857422, - 15.493013381958008, - 1.6372495889663696 + -21.954320907592773, + -19.278881072998047, + -1.2127645015716553 ], "umap3d": [ - 1.6097960472106934, - 6.592578411102295, - 4.415226936340332 + 2.0763421058654785, + 6.828410625457764, + 4.567353248596191 ] }, "cluster_id": 2, @@ -1043103,12 +1043253,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -1043518,31 +1043668,31 @@ "abstract": "An overview for our UAI 2019 paper on Sliced Score Matching. We show how to use random projections to scale up score matching\u2014a classic method to learn unnormalized probabilisic models\u2014to high-dimensional data. Theoretically, sliced score matching produces a consistent and asymptotic normal estimator under some regularity conditions. We apply sliced score matching to training deep energy-based models, learning VAEs with implicit encoders and training Wasserstein Auto-Encoders (WAEs).", "projections": { "pca": [ - -0.2302643060684204, - -0.03775820881128311 + -0.23025088012218475, + -0.037114422768354416 ], "tsne": [ - -46.64902114868164, - 15.79968547821045 + -43.6485710144043, + -21.510637283325195 ], "umap": [ - 1.702433705329895, - 5.387369155883789 + 1.8405290842056274, + 4.716386795043945 ], "pca3d": [ - -0.23026391863822937, - -0.03776255249977112, - -0.05890047922730446 + -0.2302505075931549, + -0.037118490785360336, + -0.05867503583431244 ], "tsne3d": [ - -23.827537536621094, - 15.7208890914917, - 2.109665632247925 + -22.526918411254883, + -19.7069034576416, + -1.1726211309432983 ], "umap3d": [ - 1.5248851776123047, - 6.556755542755127, - 4.427019119262695 + 2.028298854827881, + 6.763127326965332, + 4.6333818435668945 ] }, "cluster_id": 2, @@ -1043553,12 +1043703,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 2, - "label": "Uncertainty Learning" + "id": 9, + "label": "Model Uncertainty" } ], "_embedding": [ @@ -1043977,47 +1044127,47 @@ "openalex_url": "https://openalex.org/W4394716017", "projections": { "pca": [ - -0.17409072816371918, - 0.31296563148498535 + -0.17324769496917725, + 0.313649982213974 ], "tsne": [ - -11.312390327453613, - -10.809272766113281 + -15.302535057067871, + 4.9631876945495605 ], "umap": [ - 3.8177144527435303, - 7.43076753616333 + 4.054098129272461, + 6.792275428771973 ], "pca3d": [ - -0.17409147322177887, - 0.31300899386405945, - 0.0771111473441124 + -0.1732485443353653, + 0.3136943280696869, + 0.07668139040470123 ], "tsne3d": [ - -6.165966987609863, - -10.038349151611328, - 14.57262134552002 + -10.058216094970703, + 1.8216941356658936, + -12.1889009475708 ], "umap3d": [ - 3.346055746078491, - 3.543046236038208, - 4.619994640350342 + 4.410619258880615, + 4.4151530265808105, + 4.90502405166626 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -1044425,47 +1044575,47 @@ "abstract": "The confidential 124-page report from the University of Rochester, disclosed in a lawsuit, details the extent of Ranga Dias\u2019s scientific misconduct.", "projections": { "pca": [ - -0.031003214418888092, - 0.33466437458992004 + -0.030101679265499115, + 0.3347422778606415 ], "tsne": [ - -10.24094295501709, - -2.4673008918762207 + -19.14056968688965, + -38.88588333129883 ], "umap": [ - 4.073551177978516, - 7.20744514465332 + 4.281596660614014, + 6.577784538269043 ], "pca3d": [ - -0.031003987416625023, - 0.3346872329711914, - 0.013110587373375893 + -0.030102459713816643, + 0.33476558327674866, + 0.012475235387682915 ], "tsne3d": [ - -4.929595470428467, - -3.77238130569458, - 18.47024917602539 + -6.229184627532959, + -5.882131576538086, + -8.632662773132324 ], "umap3d": [ - 3.777298927307129, - 3.847837209701538, - 4.606363296508789 + 4.664604187011719, + 4.732306957244873, + 4.753892421722412 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -1044873,47 +1045023,47 @@ ], "projections": { "pca": [ - -0.16004110872745514, - 0.19704751670360565 + -0.15929381549358368, + 0.19732694327831268 ], "tsne": [ - -31.95217514038086, - 30.753849029541016 + -12.90926456451416, + -5.055814266204834 ], "umap": [ - 4.122673034667969, - 6.783009052276611 + 4.516653537750244, + 6.2020111083984375 ], "pca3d": [ - -0.1600411832332611, - 0.19703514873981476, - 0.05462384968996048 + -0.1592939794063568, + 0.1973145455121994, + 0.054388172924518585 ], "tsne3d": [ - -13.215455055236816, - 11.652398109436035, - 14.350091934204102 + -12.666315078735352, + -3.4185898303985596, + 9.888608932495117 ], "umap3d": [ - 3.7594006061553955, - 4.36013126373291, - 4.502737998962402 + 4.7047929763793945, + 5.162858486175537, + 4.402841567993164 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 10, - "label": "Code Development" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -1045321,31 +1045471,31 @@ "abstract": "Toolkit for attaching, training, saving and loading of new heads for transformer models - center-for-humans-and-machines/transformer-heads", "projections": { "pca": [ - -0.10372641682624817, - 0.14634765684604645 + -0.10312134027481079, + 0.1468014419078827 ], "tsne": [ - -4.244119644165039, - 2.8555190563201904 + -18.525930404663086, + 1.1285682916641235 ], "umap": [ - 5.662292003631592, - 6.630863189697266 + 6.043086528778076, + 6.201419353485107 ], "pca3d": [ - -0.1037263423204422, - 0.1463150829076767, - -0.024499785155057907 + -0.10312124341726303, + 0.14676830172538757, + -0.02447289042174816 ], "tsne3d": [ - -6.004104137420654, - 4.409306049346924, - -7.476646423339844 + -4.096726894378662, + 2.283675193786621, + 1.8787461519241333 ], "umap3d": [ - 4.496352195739746, - 4.519476890563965, - 3.878349781036377 + 5.206904888153076, + 5.008969306945801, + 3.5215542316436768 ] }, "cluster_id": 4, @@ -1045356,12 +1045506,12 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 11, - "label": "Genomic Language" + "id": 13, + "label": "Genomics Papers" } ], "_embedding": [ @@ -1045775,46 +1045925,46 @@ ], "projections": { "pca": [ - -0.2142665833234787, - -0.11877816915512085 + -0.21443024277687073, + -0.11804595589637756 ], "tsne": [ - -30.673738479614258, - -7.788926124572754 + -36.574462890625, + 7.025808334350586 ], "umap": [ - 1.546277642250061, - 8.186426162719727 + 1.7344307899475098, + 7.432036876678467 ], "pca3d": [ - -0.21426613628864288, - -0.11878453195095062, - -0.014298112131655216 + -0.2144297957420349, + -0.11805236339569092, + -0.01385845709592104 ], "tsne3d": [ - -18.95401382446289, - -5.74431848526001, - 8.572868347167969 + -20.96819305419922, + 4.7226881980896, + 2.967707872390747 ], "umap3d": [ - 1.5748958587646484, - 4.469970703125, - 4.806359767913818 + 2.6398682594299316, + 5.01289701461792, + 5.553532123565674 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -1046223,47 +1046373,47 @@ "journal": "Nature", "projections": { "pca": [ - -0.06893912702798843, - 0.3859732747077942 + -0.06798908114433289, + 0.3866056799888611 ], "tsne": [ - -10.552011489868164, - -0.6001048684120178 + -17.253393173217773, + -37.72383117675781 ], "umap": [ - 4.034703731536865, - 7.090237140655518 + 4.210006237030029, + 6.431380271911621 ], "pca3d": [ - -0.06893980503082275, - 0.38598179817199707, - 0.05187522619962692 + -0.06798988580703735, + 0.3866138756275177, + 0.05116504430770874 ], "tsne3d": [ - -3.966417074203491, - -5.10368013381958, - 16.518436431884766 + -4.740965366363525, + -7.496599197387695, + -9.48904800415039 ], "umap3d": [ - 3.6161370277404785, - 3.8870913982391357, - 4.687579154968262 + 4.593238830566406, + 4.859719276428223, + 4.840953350067139 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -1046673,47 +1046823,47 @@ "abstract": "Elon Musk is suing OpenAI and its CEO Sam Altman over what he says is a betrayal of the ChatGPT maker\u2019s founding aims of benefiting humanity.", "projections": { "pca": [ - -0.12380344420671463, - 0.3177240490913391 + -0.12288019806146622, + 0.31860998272895813 ], "tsne": [ - -13.795333862304688, - -18.80382537841797 + -10.172636032104492, + 10.54631519317627 ], "umap": [ - 4.132680892944336, - 8.060945510864258 + 4.312446594238281, + 7.222883701324463 ], "pca3d": [ - -0.12380398064851761, - 0.31772252917289734, - 0.1023784726858139 + -0.12288068234920502, + 0.3186083734035492, + 0.10199088603258133 ], "tsne3d": [ - -5.676737308502197, - -4.143467903137207, - 6.606646537780762 + -7.608418941497803, + 4.305056095123291, + -0.9308221936225891 ], "umap3d": [ - 3.3708932399749756, - 3.610534906387329, - 4.013089656829834 + 4.292351245880127, + 4.351927280426025, + 4.296091556549072 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, - "label": "AI Agents" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -1047123,46 +1047273,46 @@ "abstract": "Debate over figure connecting genes, race and ethnicity reignites concerns among geneticists about how to represent human diversity.", "projections": { "pca": [ - -0.02351013757288456, - 0.3145584762096405 + -0.022647494450211525, + 0.3152013123035431 ], "tsne": [ - -1.0082579851150513, - 0.09784844517707825 + -1.6566054821014404, + -0.6382039189338684 ], "umap": [ - 5.860312461853027, - 7.859612464904785 + 5.920105934143066, + 7.28026008605957 ], "pca3d": [ - -0.023510435596108437, - 0.31452783942222595, - -0.09698140621185303 + -0.02264789491891861, + 0.3151698112487793, + -0.09730692207813263 ], "tsne3d": [ - 6.700684547424316, - -13.087004661560059, - -16.998991012573242 + 5.811733722686768, + 21.464258193969727, + 3.029559850692749 ], "umap3d": [ - 4.618789196014404, - 3.8469016551971436, - 3.6752078533172607 + 5.216666221618652, + 4.446194171905518, + 3.5961556434631348 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, + "id": 13, "label": "Genomics Papers" } ], @@ -1047573,47 +1047723,47 @@ "abstract": "Blazingly \ud83d\udd25 fast \ud83d\ude80 memory vulnerabilities, written in 100% safe Rust. \ud83e\udd80 - cve-rs/LICENSE at main \u00b7 Speykious/cve-rs", "projections": { "pca": [ - -0.0590897873044014, - 0.2688707709312439 + -0.05806802213191986, + 0.26874569058418274 ], "tsne": [ - -33.43941879272461, - 31.610614776611328 + -11.442214965820312, + -7.740421772003174 ], "umap": [ - 4.226247310638428, - 6.6918253898620605 + 4.611580848693848, + 6.03346061706543 ], "pca3d": [ - -0.059089869260787964, - 0.2688392996788025, - 0.07816322147846222 + -0.05806810036301613, + 0.2687138020992279, + 0.07782392203807831 ], "tsne3d": [ - -13.571495056152344, - 10.67760181427002, - 16.77185821533203 + -11.116615295410156, + -4.103663921356201, + 8.06158447265625 ], "umap3d": [ - 4.0201544761657715, - 4.561373233795166, - 4.421500205993652 + 4.7541069984436035, + 5.307584762573242, + 4.244934558868408 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 10, - "label": "Code Development" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -1048021,47 +1048171,47 @@ "abstract": "The official PyTorch implementation of Google's Gemma models - google/gemma_pytorch", "projections": { "pca": [ - -0.15976181626319885, - 0.158172145485878 + -0.15920020639896393, + 0.1586979478597641 ], "tsne": [ - -27.854333877563477, - 32.942752838134766 + -15.658228874206543, + -8.064016342163086 ], "umap": [ - 3.948378801345825, - 6.36458683013916 + 4.325109958648682, + 5.802306175231934 ], "pca3d": [ - -0.1597616970539093, - 0.15815310180187225, - 0.06208223104476929 + -0.1592002809047699, + 0.15867869555950165, + 0.06196513772010803 ], "tsne3d": [ - -10.768864631652832, - 17.466543197631836, - 12.317243576049805 + -12.670520782470703, + -5.4240946769714355, + 15.667753219604492 ], "umap3d": [ - 3.6915717124938965, - 4.83266019821167, - 4.56191873550415 + 4.540546417236328, + 5.565307140350342, + 4.337527751922607 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 10, - "label": "Code Development" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -1048468,46 +1048618,46 @@ "title": "The Shift from Models to Compound AI Systems", "projections": { "pca": [ - -0.12874624133110046, - 0.09993153065443039 + -0.12849664688110352, + 0.10151056200265884 ], "tsne": [ - -2.964954376220703, - 1.2697877883911133 + -15.76351547241211, + 25.61871337890625 ], "umap": [ - 5.484523773193359, - 6.836034774780273 + 5.042531967163086, + 6.949767112731934 ], "pca3d": [ - -0.12874601781368256, - 0.09990719705820084, - 0.10184568166732788 + -0.12849657237529755, + 0.10148587077856064, + 0.10187216103076935 ], "tsne3d": [ - -6.792287349700928, - 0.9506452083587646, - -5.832709312438965 + -2.1563942432403564, + 4.527894496917725, + 1.6723304986953735 ], "umap3d": [ - 4.405028820037842, - 4.346405029296875, - 3.761808395385742 + 4.996872425079346, + 4.789292335510254, + 3.607083320617676 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, + "id": 13, "label": "Genomics Papers" } ], @@ -1048919,47 +1049069,47 @@ "abstract": "An extremely fast Python linter and code formatter, written in Rust. - astral-sh/ruff", "projections": { "pca": [ - -0.05642368644475937, - 0.1725711226463318 + -0.055622052401304245, + 0.1725611537694931 ], "tsne": [ - -33.33575439453125, - 31.587635040283203 + -11.495086669921875, + -7.711756229400635 ], "umap": [ - 4.177720069885254, - 6.61406135559082 + 4.612298965454102, + 5.9500346183776855 ], "pca3d": [ - -0.05642366781830788, - 0.17255103588104248, - 0.10285168886184692 + -0.05562205985188484, + 0.17254069447517395, + 0.10267812758684158 ], "tsne3d": [ - -13.401798248291016, - 11.001214981079102, - 16.273515701293945 + -11.445663452148438, + -3.931981086730957, + 8.486590385437012 ], "umap3d": [ - 3.9989521503448486, - 4.650778770446777, - 4.446451187133789 + 4.741077423095703, + 5.413724422454834, + 4.260176181793213 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 10, - "label": "Code Development" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -1049367,31 +1049517,31 @@ "abstract": "Artistic impression of the three data types key to machine learning for functional protein design: structure, sequence and labels. The structure of carbonic...", "projections": { "pca": [ - 0.054169781506061554, - 0.1421167254447937 + 0.054620493203401566, + 0.14278411865234375 ], "tsne": [ - -10.559470176696777, - 20.859128952026367 + -0.17837271094322205, + -27.3182373046875 ], "umap": [ - 5.383300304412842, - 4.836906433105469 + 5.685068607330322, + 4.225434303283691 ], "pca3d": [ - 0.05416927486658096, - 0.14212533831596375, - 0.17228253185749054 + 0.054620031267404556, + 0.14279253780841827, + 0.17205487191677094 ], "tsne3d": [ - -8.374222755432129, - 4.842041015625, - 4.803689479827881 + -6.913956642150879, + -10.028809547424316, + 8.647259712219238 ], "umap3d": [ - 5.110901832580566, - 6.485326766967773, - 4.541012287139893 + 5.439539909362793, + 7.04666805267334, + 3.2178306579589844 ] }, "cluster_id": 3, @@ -1049402,11 +1049552,11 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 9, + "label": "Protein Design" }, { - "id": 24, + "id": 3, "label": "Protein Design" } ], @@ -1049815,47 +1049965,47 @@ "abstract": "Universities must declare all their retractions and launch investigations into misconduct cases; a Nature analysis reveals that since 2021 there have been more than 17,000 retractions with Chinese co-authors.", "projections": { "pca": [ - -0.09455457329750061, - 0.3422909080982208 + -0.09355488419532776, + 0.34233346581459045 ], "tsne": [ - -10.585711479187012, - -15.49596881866455 + -14.692370414733887, + 11.138961791992188 ], "umap": [ - 4.024442672729492, - 7.757181644439697 + 4.217885971069336, + 7.137878894805908 ], "pca3d": [ - -0.09455521404743195, - 0.3423072397708893, - -0.03671666979789734 + -0.09355557709932327, + 0.3423495590686798, + -0.03719927370548248 ], "tsne3d": [ - -6.937243461608887, - -12.811904907226562, - 7.895523548126221 + -7.67421817779541, + 5.4007649421691895, + -5.60048770904541 ], "umap3d": [ - 3.603818416595459, - 3.4581480026245117, - 4.2635722160339355 + 4.45477819442749, + 4.159570693969727, + 4.559147834777832 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -1050263,47 +1050413,47 @@ "abstract": "AutoRaise (and focus) a window when hovering over it with the mouse - sbmpost/AutoRaise", "projections": { "pca": [ - -0.14803265035152435, - 0.29000556468963623 + -0.14722515642642975, + 0.2905612587928772 ], "tsne": [ - -12.4309720993042, - -22.10619354248047 + -7.775337219238281, + 15.092120170593262 ], "umap": [ - 0.9198196530342102, - 7.783041954040527 + 3.0878851413726807, + 7.295722007751465 ], "pca3d": [ - -0.1480327546596527, - 0.28996938467025757, - -0.03887084499001503 + -0.14722539484500885, + 0.29052379727363586, + -0.03927185758948326 ], "tsne3d": [ - -6.852237701416016, - -4.398266792297363, - -7.433844566345215 + -6.338171005249023, + 12.678787231445312, + 7.368083953857422 ], "umap3d": [ - 2.6880130767822266, - 4.3334455490112305, - 3.878726005554199 + 3.8609731197357178, + 4.685394763946533, + 4.251316547393799 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 1, + "cluster_label": "AI in Science", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 1, + "label": "AI in Science" }, { - "id": 8, - "label": "Attention Vision" + "id": 12, + "label": "AI Agents" }, { - "id": 27, - "label": "Transformer Attention" + "id": 0, + "label": "Math AI" } ], "_embedding": [ @@ -1050713,47 +1050863,47 @@ "journal": "Nature Biotechnology", "projections": { "pca": [ - -0.22600798308849335, - 0.2204430103302002 + -0.22543540596961975, + 0.2216990888118744 ], "tsne": [ - -9.574420928955078, - -26.224828720092773 + -13.657331466674805, + 16.8677978515625 ], "umap": [ - 3.9544835090637207, - 8.585660934448242 + 4.111511707305908, + 7.905793190002441 ], "pca3d": [ - -0.22600838541984558, - 0.22045791149139404, - 0.08787920325994492 + -0.22543580830097198, + 0.22171464562416077, + 0.08767672628164291 ], "tsne3d": [ - -8.431432723999023, - -10.312761306762695, - -0.5702347755432129 + -9.737009048461914, + 12.708516120910645, + -2.228351354598999 ], "umap3d": [ - 3.102147340774536, - 3.5648727416992188, - 3.395150661468506 + 3.8542637825012207, + 4.102372169494629, + 3.862859010696411 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -1051161,46 +1051311,46 @@ "abstract": "Nature talks to Sholto David about his process for flagging image manipulation and his tips for scientists under scrutiny.", "projections": { "pca": [ - -0.07191167026758194, - 0.27433550357818604 + -0.07111825048923492, + 0.2747979760169983 ], "tsne": [ - 1.0337306261062622, - -12.033387184143066 + 0.9399141669273376, + 0.5699266195297241 ], "umap": [ - 5.448683738708496, - 6.978463649749756 + 5.8551812171936035, + 6.480780124664307 ], "pca3d": [ - -0.07191246002912521, - 0.27435991168022156, - -0.010490304790437222 + -0.07111893594264984, + 0.2748226225376129, + -0.010820983909070492 ], "tsne3d": [ - 3.793024778366089, - -7.208549499511719, - 3.8335065841674805 + -2.9824907779693604, + 26.454769134521484, + -0.614857017993927 ], "umap3d": [ - 4.88883113861084, - 3.802109479904175, - 4.584881782531738 + 5.818944931030273, + 4.861508369445801, + 4.430091857910156 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, + "id": 13, "label": "Genomics Papers" } ], @@ -1051611,47 +1051761,47 @@ "abstract": "Friends don't let friends make certain types of data visualization - What are they and why are they bad. - GitHub - cxli233/FriendsDontLetFriends: Friends don't let friends make certain types of data visualization - What are they and why are they bad.", "projections": { "pca": [ - -0.13821959495544434, - 0.28097525238990784 + -0.1373177319765091, + 0.2813725173473358 ], "tsne": [ - -6.665315628051758, - -7.9798431396484375 + -9.038084983825684, + 1.9700429439544678 ], "umap": [ - 4.254785060882568, - 7.078104019165039 + 4.546499252319336, + 6.341400146484375 ], "pca3d": [ - -0.13822011649608612, - 0.28097885847091675, - -0.022497763857245445 + -0.1373182237148285, + 0.28137585520744324, + -0.022819042205810547 ], "tsne3d": [ - -11.13661003112793, - -2.6627516746520996, - 11.660146713256836 + -6.59639310836792, + -1.2272824048995972, + -4.292348384857178 ], "umap3d": [ - 3.8647446632385254, - 4.103525638580322, - 4.337162017822266 + 4.739882946014404, + 4.883240699768066, + 4.382962226867676 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -1052059,47 +1052209,47 @@ "journal": "Nature Genetics", "projections": { "pca": [ - 0.07688034325838089, - 0.2795194089412689 + 0.077643021941185, + 0.27950146794319153 ], "tsne": [ - 6.008880138397217, - -5.8576202392578125 + 5.6180806159973145, + 8.964341163635254 ], "umap": [ - 6.1690354347229, - 6.946474552154541 + 9.089434623718262, + 8.135702133178711 ], "pca3d": [ - 0.07687977701425552, - 0.27952319383621216, - -0.025377485901117325 + 0.07764235883951187, + 0.27950549125671387, + -0.025835689157247543 ], "tsne3d": [ - 17.65521240234375, - -17.138723373413086, - 6.977994918823242 + 0.7688301801681519, + 1.5560411214828491, + -6.013604640960693 ], "umap3d": [ - 6.762345790863037, - 3.6249587535858154, - 1.3413945436477661 + 6.715926647186279, + 3.406262159347534, + 0.7083557844161987 ] }, - "cluster_id": 4, - "cluster_label": "RNA Sequence Models", + "cluster_id": 0, + "cluster_label": "Chromatin Regulation", "cluster_levels": [ { - "id": 4, - "label": "RNA Sequence Models" + "id": 0, + "label": "Chromatin Regulation" }, { - "id": 11, - "label": "Genomic Models" + "id": 13, + "label": "Chromatin Regulation" }, { - "id": 11, - "label": "Genomic Language" + "id": 31, + "label": "Single Cell Seq" } ], "_embedding": [ @@ -1052506,31 +1052656,31 @@ "title": "Isomorphic Labs kicks off 2024 with two pharmaceutical collaborations - Isomorphic Labs", "projections": { "pca": [ - 0.07747367769479752, - 0.2917758822441101 + 0.07838740199804306, + 0.2917615473270416 ], "tsne": [ - -11.170248031616211, - 9.139046669006348 + -2.4654324054718018, + -46.708839416503906 ], "umap": [ - 5.04595422744751, - 5.688401699066162 + 5.454049587249756, + 5.219379425048828 ], "pca3d": [ - 0.07747320830821991, - 0.29177364706993103, - 0.09953584522008896 + 0.0783868283033371, + 0.2917584478855133, + 0.09907028079032898 ], "tsne3d": [ - -2.1306798458099365, - -12.703554153442383, - -14.578546524047852 + -2.3312666416168213, + 2.8123350143432617, + 18.505218505859375 ], "umap3d": [ - 4.921199321746826, - 6.21045446395874, - 3.6907241344451904 + 4.974968910217285, + 6.427216053009033, + 2.8999757766723633 ] }, "cluster_id": 3, @@ -1052541,12 +1052691,12 @@ "label": "Protein Design" }, { - "id": 2, - "label": "Protein Language" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 24, - "label": "Protein Design" + "id": 32, + "label": "Genome Protein" } ], "_embedding": [ @@ -1052954,47 +1053104,47 @@ "abstract": "The rollout will unfold in phases, with less sophisticated versions of Gemini called \u201cNano\u201d and \u201cPro\u201d being immediately incorporated into Google\u2019s AI-powered chatbot Bard and its Pixel 8 Pro smartphone.", "projections": { "pca": [ - -0.1358155608177185, - 0.3071572780609131 + -0.13498026132583618, + 0.3082817792892456 ], "tsne": [ - -4.745455265045166, - -15.653253555297852 + -8.310195922851562, + 12.60753345489502 ], "umap": [ - 3.7773513793945312, - 8.41038703918457 + 4.016683101654053, + 7.683611869812012 ], "pca3d": [ - -0.13581590354442596, - 0.30714496970176697, - 0.08347270637750626 + -0.13498063385486603, + 0.3082694113254547, + 0.08310430496931076 ], "tsne3d": [ - -9.233940124511719, - -3.550546884536743, - -11.178327560424805 + -9.205223083496094, + 8.274517059326172, + 6.368890762329102 ], "umap3d": [ - 3.0642731189727783, - 3.7900338172912598, - 3.542311906814575 + 3.858593463897705, + 4.351162910461426, + 4.016873836517334 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -1053401,47 +1053551,47 @@ "title": "NVIDIA Announces cuDF Pandas Accelerator Mode: Supercharge Your Data Manipulation in Python | DataCamp", "projections": { "pca": [ - -0.08097431808710098, - 0.20272324979305267 + -0.08028116077184677, + 0.20280660688877106 ], "tsne": [ - -20.271400451660156, - 0.4538300037384033 + -25.574748992919922, + -0.8378386497497559 ], "umap": [ - 2.4279892444610596, - 7.550296783447266 + 2.6044938564300537, + 6.752251148223877 ], "pca3d": [ - -0.08097445964813232, - 0.20271410048007965, - -0.025221383199095726 + -0.08028128743171692, + 0.202797532081604, + -0.025516603142023087 ], "tsne3d": [ - -18.267724990844727, - 3.3878889083862305, - 12.95918083190918 + -19.675048828125, + 0.8818122148513794, + 12.666878700256348 ], "umap3d": [ - 2.138303518295288, - 4.7068095207214355, - 5.153356075286865 + 3.2333145141601562, + 5.365954875946045, + 5.538809299468994 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 0, - "label": "LLM Language Learning" + "id": 2, + "label": "Model Learning" } ], "_embedding": [ @@ -1053851,46 +1054001,46 @@ "doi": "10.1164/ajrccm/141.2.352", "projections": { "pca": [ - 0.028134705498814583, - 0.2766735553741455 + 0.028792966157197952, + 0.2767646312713623 ], "tsne": [ - 14.640093803405762, - -10.696639060974121 + 38.33191680908203, + -14.481916427612305 ], "umap": [ - 6.903958797454834, - 8.28787612915039 + 7.099608421325684, + 7.888354778289795 ], "pca3d": [ - 0.028134040534496307, - 0.2766905426979065, - -0.0281424168497324 + 0.028792236000299454, + 0.27678149938583374, + -0.028651591390371323 ], "tsne3d": [ - 17.949161529541016, - -18.104307174682617, - -5.8505964279174805 + 12.62881088256836, + 22.822423934936523, + -1.5190602540969849 ], "umap3d": [ - 6.471505641937256, - 3.051969528198242, - 2.569655656814575 + 6.83870792388916, + 3.2917816638946533, + 2.189988374710083 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 26, + "id": 33, "label": "Genetic Disease" } ], @@ -1054314,31 +1054464,31 @@ "cited_by_count": 330, "projections": { "pca": [ - 0.1929483413696289, - -0.01833871565759182 + 0.1930285096168518, + -0.01851375214755535 ], "tsne": [ - -7.155159950256348, - 28.190284729003906 + 1.371978759765625, + -32.90965270996094 ], "umap": [ - 5.4922380447387695, - 3.9627037048339844 + 6.185455799102783, + 3.6263744831085205 ], "pca3d": [ - 0.1929481476545334, - -0.018343191593885422, - 0.20849046111106873 + 0.19302843511104584, + -0.01851818338036537, + 0.20834144949913025 ], "tsne3d": [ - 1.6022049188613892, - 5.694896697998047, - 8.695455551147461 + -3.9877686500549316, + -16.230451583862305, + -0.11990167200565338 ], "umap3d": [ - 5.375822067260742, - 6.877659797668457, - 5.08026123046875 + 5.9360198974609375, + 7.671627521514893, + 3.250899076461792 ] }, "cluster_id": 3, @@ -1054349,11 +1054499,11 @@ "label": "Protein Design" }, { - "id": 12, - "label": "Protein Design" + "id": 0, + "label": "Explainable Chemistry" }, { - "id": 3, + "id": 15, "label": "Protein Design" } ], @@ -1054761,46 +1054911,46 @@ "title": "DALL\u00b7E 3", "projections": { "pca": [ - -0.06642018258571625, - 0.16523981094360352 + -0.06553196161985397, + 0.16497722268104553 ], "tsne": [ - -15.753341674804688, - 1.9861618280410767 + -8.949614524841309, + -5.247334957122803 ], "umap": [ - 1.5945875644683838, - 8.347518920898438 + 2.1648521423339844, + 7.662744522094727 ], "pca3d": [ - -0.06642036139965057, - 0.16522309184074402, - 0.04068584740161896 + -0.06553202867507935, + 0.16495992243289948, + 0.0406392440199852 ], "tsne3d": [ - -7.584935665130615, - 20.720685958862305, - -3.191431760787964 + -2.3771865367889404, + -5.397853851318359, + -13.778646469116211 ], "umap3d": [ - 2.3236582279205322, - 4.354790687561035, - 4.846069812774658 + 3.071763753890991, + 4.91469669342041, + 5.452986717224121 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 1, + "label": "Attention Models" }, { - "id": 7, + "id": 34, "label": "Language Models" } ], @@ -1055209,47 +1055359,47 @@ "abstract": "Efforts to replicate the material have pieced together the puzzle of why it displayed superconducting-like behaviours.", "projections": { "pca": [ - -0.09291405230760574, - 0.30303287506103516 + -0.0921144187450409, + 0.3038223385810852 ], "tsne": [ - -10.235543251037598, - -2.5783767700195312 + -19.347705841064453, + -39.01336669921875 ], "umap": [ - 4.051002502441406, - 7.130911827087402 + 4.196617126464844, + 6.429937839508057 ], "pca3d": [ - -0.09291426837444305, - 0.3030189871788025, - 0.13759084045886993 + -0.09211475402116776, + 0.30380815267562866, + 0.13701951503753662 ], "tsne3d": [ - -5.029097557067871, - -2.677626609802246, - 19.620590209960938 + -7.200294494628906, + -6.166884422302246, + -7.510138988494873 ], "umap3d": [ - 3.717498302459717, - 3.94438099861145, - 4.6726813316345215 + 4.610926628112793, + 4.8435211181640625, + 4.832857608795166 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -1055655,47 +1055805,47 @@ ], "projections": { "pca": [ - -0.04395033046603203, - 0.37043020129203796 + -0.0427517406642437, + 0.3704793155193329 ], "tsne": [ - -7.851693630218506, - -5.472885608673096 + -6.767757415771484, + 3.9706642627716064 ], "umap": [ - 4.351152420043945, - 7.338589668273926 + 4.597259044647217, + 6.68341064453125 ], "pca3d": [ - -0.0439511239528656, - 0.370445191860199, - 0.1335117518901825 + -0.04275250434875488, + 0.3704943060874939, + 0.1329430341720581 ], "tsne3d": [ - -7.382981300354004, - -2.986776113510132, - 14.449376106262207 + -5.606771945953369, + -0.2156737595796585, + -7.666661739349365 ], "umap3d": [ - 3.9811434745788574, - 3.6544229984283447, - 4.659982204437256 + 4.9470133781433105, + 4.6208648681640625, + 4.772594451904297 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -1056116,47 +1056266,47 @@ "openalex_url": "https://openalex.org/W4384154861", "projections": { "pca": [ - -0.2502906322479248, - 0.2436579465866089 + -0.24976125359535217, + 0.2456800788640976 ], "tsne": [ - -16.057498931884766, - -22.71600341796875 + -16.507585525512695, + 15.081230163574219 ], "umap": [ - 3.4110536575317383, - 8.160711288452148 + 3.6029348373413086, + 7.43737268447876 ], "pca3d": [ - -0.25029081106185913, - 0.24364601075649261, - 0.09766194969415665 + -0.24976147711277008, + 0.24566790461540222, + 0.09745032340288162 ], "tsne3d": [ - -9.916346549987793, - -4.0567779541015625, - -4.417821407318115 + -11.177794456481934, + 10.889342308044434, + 3.914314031600952 ], "umap3d": [ - 2.7544002532958984, - 4.00095272064209, - 3.5659754276275635 + 3.555617332458496, + 4.44205904006958, + 4.041019439697266 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 0, + "label": "Math AI" } ], "_embedding": [ @@ -1056564,47 +1056714,47 @@ "abstract": "A team of Italian researchers has reached the edge of space, flying aboard Virgin Galactic\u2019s rocket-powered plane.", "projections": { "pca": [ - -0.04579466953873634, - 0.333190381526947 + -0.0450526662170887, + 0.33371207118034363 ], "tsne": [ - -6.381975173950195, - -17.244596481323242 + -14.54190444946289, + 23.169628143310547 ], "umap": [ - 3.506701946258545, - 8.002412796020508 + 3.927781343460083, + 7.368624687194824 ], "pca3d": [ - -0.045795001089572906, - 0.3331730365753174, - -0.007167634554207325 + -0.04505297914147377, + 0.33369511365890503, + -0.007800563704222441 ], "tsne3d": [ - -10.474786758422852, - 0.9656944274902344, - -8.674225807189941 + -2.2796378135681152, + 6.953760623931885, + -3.0918984413146973 ], "umap3d": [ - 3.139275550842285, - 4.151374816894531, - 3.5287365913391113 + 3.9085187911987305, + 4.663802623748779, + 3.895883798599243 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 0, + "label": "Math AI" } ], "_embedding": [ @@ -1057013,47 +1057163,47 @@ "title": "Show outputs below cells during interactive sessions \u00b7 Issue #183350 \u00b7 microsoft/vscode \u00b7 GitHub", "projections": { "pca": [ - -0.08157763630151749, - 0.21510331332683563 + -0.08063039183616638, + 0.21434934437274933 ], "tsne": [ - -13.435481071472168, - 0.32651451230049133 + 3.5580036640167236, + 20.000713348388672 ], "umap": [ - 4.6324639320373535, - 7.209412574768066 + 4.981631755828857, + 6.386680603027344 ], "pca3d": [ - -0.08157824724912643, - 0.21513551473617554, - -0.04307322949171066 + -0.08063113689422607, + 0.2143813520669937, + -0.04331013560295105 ], "tsne3d": [ - -8.615742683410645, - 24.79081153869629, - -0.8004494905471802 + 0.7408756017684937, + 1.5438611507415771, + -10.989363670349121 ], "umap3d": [ - 4.0273847579956055, - 4.071259498596191, - 4.391122341156006 + 4.812960624694824, + 5.031546592712402, + 4.3355841636657715 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, - "label": "Genomics Papers" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -1057461,47 +1057611,47 @@ "abstract": "Talks that conference attendees could see in person are more likely to be cited than talks they most likely missed.", "projections": { "pca": [ - -0.05178139731287956, - 0.45302626490592957 + -0.05073250085115433, + 0.45372268557548523 ], "tsne": [ - -11.470772743225098, - -12.541789054870605 + -15.054277420043945, + 6.5894670486450195 ], "umap": [ - 3.7631609439849854, - 7.561922550201416 + 4.066836833953857, + 6.863298416137695 ], "pca3d": [ - -0.05178273469209671, - 0.4530768394470215, - -0.0032779788598418236 + -0.05073375999927521, + 0.4537733197212219, + -0.0040434119291603565 ], "tsne3d": [ - -3.2447166442871094, - -9.485771179199219, - 13.809642791748047 + -7.698554992675781, + 3.5203521251678467, + -11.678473472595215 ], "umap3d": [ - 3.315721035003662, - 3.5002710819244385, - 4.557912349700928 + 4.305905342102051, + 4.228567123413086, + 4.927978038787842 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -1057909,47 +1058059,47 @@ "abstract": "Chat with your database or your datalake (SQL, CSV, parquet). PandasAI makes data analysis conversational using LLMs and RAG. - sinaptik-ai/pandas-ai", "projections": { "pca": [ - -0.13405582308769226, - 0.17335593700408936 + -0.133506640791893, + 0.17386455833911896 ], "tsne": [ - -19.992603302001953, - -3.867387056350708 + -19.844860076904297, + -5.446048736572266 ], "umap": [ - 3.5322060585021973, - 8.045823097229004 + 3.8302807807922363, + 7.519791603088379 ], "pca3d": [ - -0.1340559720993042, - 0.17336219549179077, - 0.048321403563022614 + -0.13350681960582733, + 0.17387086153030396, + 0.04816775023937225 ], "tsne3d": [ - -7.579503536224365, - 1.1202967166900635, - -12.072054862976074 + -9.133674621582031, + 0.8116387724876404, + 9.449708938598633 ], "umap3d": [ - 3.33565354347229, - 4.167505741119385, - 3.788386821746826 + 4.06638240814209, + 4.787405490875244, + 4.005596160888672 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 0, + "label": "Math AI" } ], "_embedding": [ @@ -1058365,47 +1058515,47 @@ ], "projections": { "pca": [ - -0.22069880366325378, - 0.09973933547735214 + -0.2203110009431839, + 0.10105060040950775 ], "tsne": [ - -23.194448471069336, - -15.676254272460938 + -27.800575256347656, + 13.340126037597656 ], "umap": [ - 2.4798049926757812, - 8.36827564239502 + 2.6878814697265625, + 7.624648094177246 ], "pca3d": [ - -0.22069860994815826, - 0.0997081845998764, - -0.022630564868450165 + -0.22031079232692719, + 0.10101842880249023, + -0.022431427612900734 ], "tsne3d": [ - -12.577591896057129, - -15.07245922088623, - 8.22756576538086 + -14.493796348571777, + 4.713255882263184, + -4.084177494049072 ], "umap3d": [ - 2.1276237964630127, - 3.8370132446289062, - 4.386600494384766 + 3.236179828643799, + 4.381782531738281, + 5.006054878234863 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 7, - "label": "Language Models" + "id": 25, + "label": "AI Agents" } ], "_embedding": [ @@ -1058813,47 +1058963,47 @@ "abstract": "GitHub Copilot works alongside you directly in your editor, suggesting whole lines or entire functions for you.", "projections": { "pca": [ - -0.20839890837669373, - 0.19010281562805176 + -0.2077585905790329, + 0.1911982148885727 ], "tsne": [ - -28.60593605041504, - 28.82746124267578 + -16.80785369873047, + -4.048708438873291 ], "umap": [ - 3.806612491607666, - 6.737716197967529 + 4.205827713012695, + 6.111466884613037 ], "pca3d": [ - -0.20839913189411163, - 0.19010010361671448, - 0.12737633287906647 + -0.20775866508483887, + 0.1911955177783966, + 0.1272159367799759 ], "tsne3d": [ - -14.388300895690918, - 17.24947166442871, - 11.820585250854492 + -10.833518028259277, + -1.8272531032562256, + 14.86284351348877 ], "umap3d": [ - 3.4996376037597656, - 4.534892559051514, - 4.3860626220703125 + 4.395328044891357, + 5.2981038093566895, + 4.311158180236816 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 10, - "label": "Code Development" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -1059263,47 +1059413,47 @@ "abstract": "Magnificent app which corrects your previous console command. - nvbn/thefuck", "projections": { "pca": [ - -0.07373952865600586, - 0.2674887180328369 + -0.07284088432788849, + 0.2675860822200775 ], "tsne": [ - -27.169273376464844, - 30.47726058959961 + -13.867755889892578, + -6.675759792327881 ], "umap": [ - 3.922701120376587, - 6.379921913146973 + 4.274507522583008, + 5.933109760284424 ], "pca3d": [ - -0.07373985648155212, - 0.2674958109855652, - 0.062320318073034286 + -0.07284124940633774, + 0.26759329438209534, + 0.06192228943109512 ], "tsne3d": [ - -14.340109825134277, - 16.776493072509766, - 15.176804542541504 + -9.317285537719727, + -4.599778175354004, + 13.149857521057129 ], "umap3d": [ - 3.642496109008789, - 4.8129377365112305, - 4.5508952140808105 + 4.555296421051025, + 5.6573028564453125, + 4.326717853546143 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 10, - "label": "Code Development" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -1059710,47 +1059860,47 @@ "title": "Research – Elya Lab", "projections": { "pca": [ - -0.0031827855855226517, - 0.28606414794921875 + -0.0023888659197837114, + 0.2867850065231323 ], "tsne": [ - 3.933074712753296, - -20.49410629272461 + 1.0864160060882568, + 16.799165725708008 ], "umap": [ - 5.231405258178711, - 8.299457550048828 + 5.437465190887451, + 7.750299453735352 ], "pca3d": [ - -0.0031835653353482485, - 0.2860734760761261, - 0.04712029919028282 + -0.0023895851336419582, + 0.28679412603378296, + 0.04672598838806152 ], "tsne3d": [ - -1.7727718353271484, - -5.30283784866333, - -12.224035263061523 + 0.8684819340705872, + 11.836973190307617, + 7.455094337463379 ], "umap3d": [ - 4.383995532989502, - 4.119205951690674, - 2.903087615966797 + 4.8240132331848145, + 4.365391731262207, + 2.770827531814575 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, - "label": "Medical AI" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -1060165,47 +1060315,47 @@ "doi": "10.1101/2023.01.21.525030", "projections": { "pca": [ - -0.18863266706466675, - 0.1193220391869545 + -0.18806427717208862, + 0.12014799565076828 ], "tsne": [ - -8.603775024414062, - -19.21821403503418 + -10.710460662841797, + 14.478440284729004 ], "umap": [ - 4.05884313583374, - 8.316279411315918 + 4.1886515617370605, + 7.606834888458252 ], "pca3d": [ - -0.18863284587860107, - 0.11932384967803955, - 0.1567656695842743 + -0.188064306974411, + 0.12014985829591751, + 0.15684524178504944 ], "tsne3d": [ - -7.722257614135742, - -9.409915924072266, - 2.1410796642303467 + -5.9026360511779785, + 7.695170879364014, + -0.7075292468070984 ], "umap3d": [ - 3.277765989303589, - 3.6258676052093506, - 3.6375062465667725 + 4.12615442276001, + 4.0851545333862305, + 4.094605445861816 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -1060613,31 +1060763,31 @@ "abstract": "A collaboration between InstaDeep, the Technical University of Munich (TUM), and NVIDIA has led to the development of multiple super-computing scale foundation…", "projections": { "pca": [ - 0.048834942281246185, - 0.17065846920013428 + 0.04939116910099983, + 0.1708722710609436 ], "tsne": [ - 2.3409299850463867, - -0.8783866167068481 + 20.497812271118164, + -13.183441162109375 ], "umap": [ - 6.306237697601318, - 7.031795978546143 + 6.480517387390137, + 6.607444763183594 ], "pca3d": [ - 0.048834946006536484, - 0.17062588036060333, - -0.005788584239780903 + 0.049391139298677444, + 0.17083942890167236, + -0.005950139369815588 ], "tsne3d": [ - 2.9305551052093506, - -4.148458957672119, - -14.498454093933105 + 3.62507963180542, + 10.990285873413086, + 3.59592342376709 ], "umap3d": [ - 5.7563018798828125, - 4.043097496032715, - 3.8340671062469482 + 6.375026226043701, + 4.686762809753418, + 3.110607147216797 ] }, "cluster_id": 4, @@ -1060648,12 +1060798,12 @@ "label": "RNA Sequence Models" }, { - "id": 11, + "id": 7, "label": "Genomic Models" }, { - "id": 11, - "label": "Genomic Language" + "id": 13, + "label": "Genomics Papers" } ], "_embedding": [ @@ -1061074,46 +1061224,46 @@ "openalex_url": "https://openalex.org/W4312083290", "projections": { "pca": [ - -0.211293563246727, - 0.28230705857276917 + -0.21031178534030914, + 0.2832357883453369 ], "tsne": [ - -6.130984783172607, - -31.03632926940918 + -8.573199272155762, + 26.518510818481445 ], "umap": [ - 4.374222755432129, - 8.908044815063477 + 4.581532955169678, + 8.353559494018555 ], "pca3d": [ - -0.21129414439201355, - 0.28231462836265564, - 0.0791742205619812 + -0.21031230688095093, + 0.2832426130771637, + 0.07903709262609482 ], "tsne3d": [ - -5.171677589416504, - -13.69835376739502, - 0.8824844360351562 + -9.172396659851074, + 18.445405960083008, + -5.479309558868408 ], "umap3d": [ - 3.5184690952301025, - 3.1449036598205566, - 3.2512714862823486 + 4.322054386138916, + 3.5508337020874023, + 3.785698175430298 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, + "id": 26, "label": "Medical AI" } ], @@ -1061524,46 +1061674,46 @@ "abstract": "Health-care systems are rolling out artificial-intelligence tools for diagnosis and monitoring. But how reliable are the models?", "projections": { "pca": [ - -0.2299780547618866, - 0.21895839273929596 + -0.2293671816587448, + 0.219905287027359 ], "tsne": [ - -4.3346381187438965, - -31.98151969909668 + -6.994065761566162, + 27.162555694580078 ], "umap": [ - 4.810878276824951, - 9.234800338745117 + 5.021504878997803, + 8.580050468444824 ], "pca3d": [ - -0.22997811436653137, - 0.2189527153968811, - -0.0094993207603693 + -0.22936733067035675, + 0.21989931166172028, + -0.00961354561150074 ], "tsne3d": [ - -3.3859479427337646, - -15.453460693359375, - 0.8917799592018127 + -7.270915985107422, + 19.188783645629883, + -7.032770156860352 ], "umap3d": [ - 3.8305587768554688, - 2.8969953060150146, - 3.0539402961730957 + 4.526241779327393, + 3.3173720836639404, + 3.6077880859375 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, + "id": 26, "label": "Medical AI" } ], @@ -1061972,47 +1062122,47 @@ "journal": "Nature", "projections": { "pca": [ - -0.08617918193340302, - 0.3731311559677124 + -0.08513087779283524, + 0.37369295954704285 ], "tsne": [ - -8.576478958129883, - -8.673611640930176 + -7.581811904907227, + 5.838429927825928 ], "umap": [ - 4.040761470794678, - 7.573861598968506 + 4.434067726135254, + 6.895959854125977 ], "pca3d": [ - -0.0861799567937851, - 0.3731510639190674, - 0.04865918681025505 + -0.08513172715902328, + 0.37371233105659485, + 0.048085350543260574 ], "tsne3d": [ - -5.953172206878662, - -4.271572113037109, - 13.128841400146484 + -7.334651470184326, + 3.2197885513305664, + -6.091444492340088 ], "umap3d": [ - 3.676335334777832, - 3.514312505722046, - 4.4794697761535645 + 4.6421403884887695, + 4.433647632598877, + 4.700266361236572 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -1062420,31 +1062570,31 @@ "journal": "Nature", "projections": { "pca": [ - 0.01778600551187992, - 0.31366389989852905 + 0.018427714705467224, + 0.3135339021682739 ], "tsne": [ - -7.52394437789917, - 7.037547588348389 + 5.271076679229736, + -42.18700408935547 ], "umap": [ - 4.437017440795898, - 3.8330798149108887 + 4.77968692779541, + 3.3282291889190674 ], "pca3d": [ - 0.017785586416721344, - 0.313662052154541, - -0.04727994278073311 + 0.018427105620503426, + 0.31353116035461426, + -0.04793775454163551 ], "tsne3d": [ - -3.859956979751587, - 6.784038066864014, - -16.79547691345215 + 2.179438829421997, + -3.2052574157714844, + 20.510709762573242 ], "umap3d": [ - 5.083180904388428, - 7.659478664398193, - 4.009916305541992 + 4.622962951660156, + 7.858519077301025, + 2.606311798095703 ] }, "cluster_id": 3, @@ -1062455,12 +1062605,12 @@ "label": "Protein Design" }, { - "id": 12, + "id": 9, "label": "Protein Design" }, { - "id": 3, - "label": "Protein Design" + "id": 24, + "label": "Protein Diffusion" } ], "_embedding": [ @@ -1062866,47 +1063016,47 @@ ], "projections": { "pca": [ - -0.18185533583164215, - 0.1769227832555771 + -0.18129785358905792, + 0.17777478694915771 ], "tsne": [ - -7.441526889801025, - -27.654016494750977 + -7.634428977966309, + 20.43226432800293 ], "umap": [ - 4.312701225280762, - 8.627989768981934 + 4.681902885437012, + 7.937868118286133 ], "pca3d": [ - -0.18185575306415558, - 0.17695534229278564, - 0.12376459687948227 + -0.18129830062389374, + 0.17780737578868866, + 0.1236390545964241 ], "tsne3d": [ - -0.3739118278026581, - -7.774468898773193, - -2.7365949153900146 + -1.3412851095199585, + 12.95186996459961, + -1.0304903984069824 ], "umap3d": [ - 3.4518988132476807, - 3.5880537033081055, - 3.233443021774292 + 4.132847785949707, + 4.021359443664551, + 3.672605037689209 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, - "label": "Medical AI" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -1063314,46 +1063464,46 @@ "journal": "eLife", "projections": { "pca": [ - -0.03989429771900177, - 0.2508392035961151 + -0.03910472244024277, + 0.25107821822166443 ], "tsne": [ - 5.832334518432617, - -13.0728120803833 + 4.47238302230835, + 25.705434799194336 ], "umap": [ - 6.195736885070801, - 8.306346893310547 + 6.571204662322998, + 7.768222808837891 ], "pca3d": [ - -0.03989478573203087, - 0.25084346532821655, - -0.09314551949501038 + -0.03910524398088455, + 0.2510823905467987, + -0.09340561181306839 ], "tsne3d": [ - -12.029995918273926, - -3.529040575027466, - -18.76609230041504 + -11.995444297790527, + -8.46527099609375, + -15.7543306350708 ], "umap3d": [ - 5.163751602172852, - 3.4454946517944336, - 2.744551181793213 + 5.898414611816406, + 3.632129192352295, + 2.75827956199646 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 3, + "label": "Genetic Disease" }, { - "id": 26, + "id": 33, "label": "Genetic Disease" } ], @@ -1063773,47 +1063923,47 @@ ], "projections": { "pca": [ - -0.13638624548912048, - 0.11014912277460098 + -0.1360144168138504, + 0.11111701279878616 ], "tsne": [ - -7.142413139343262, - -17.562580108642578 + -13.350191116333008, + 22.547271728515625 ], "umap": [ - 3.885995864868164, - 8.234383583068848 + 4.185413837432861, + 7.531876087188721 ], "pca3d": [ - -0.1363864243030548, - 0.11015626788139343, - 0.06646979600191116 + -0.13601458072662354, + 0.11112446337938309, + 0.06651371717453003 ], "tsne3d": [ - -8.810565948486328, - -11.999948501586914, - 3.175185441970825 + -3.3815760612487793, + 8.224774360656738, + -2.4979910850524902 ], "umap3d": [ - 3.2585248947143555, - 3.902008295059204, - 3.5132668018341064 + 4.071216583251953, + 4.409024715423584, + 3.9024133682250977 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 10, + "id": 12, "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 11, + "label": "AI Science" } ], "_embedding": [ @@ -1064221,31 +1064371,31 @@ "abstract": "\ud83e\uddec Generative modeling of regulatory DNA sequences with diffusion probabilistic models \ud83d\udca8 - GitHub - pinellolab/DNA-Diffusion: \ud83e\uddec Generative modeling of regulatory DNA sequences with diffusion probabilistic models \ud83d\udca8", "projections": { "pca": [ - 0.047249678522348404, - 0.031527768820524216 + 0.04747026041150093, + 0.03138361871242523 ], "tsne": [ - -28.693777084350586, - 17.430889129638672 + -26.460533142089844, + -23.508420944213867 ], "umap": [ - 3.3472440242767334, - 4.877442836761475 + 3.725438356399536, + 4.322399139404297 ], "pca3d": [ - 0.04724946245551109, - 0.031535930931568146, - 0.05680026859045029 + 0.04747011512517929, + 0.031391434371471405, + 0.05674295127391815 ], "tsne3d": [ - -12.416584014892578, - 14.83222484588623, - 2.7549846172332764 + -11.23022174835205, + -20.908935546875, + -0.28696420788764954 ], "umap3d": [ - 3.1753385066986084, - 6.903000831604004, - 4.741361618041992 + 3.586028575897217, + 7.271835803985596, + 4.180450916290283 ] }, "cluster_id": 2, @@ -1064256,12 +1064406,12 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 16, - "label": "Diffusion Models" + "id": 7, + "label": "Diffusion Gen" } ], "_embedding": [ @@ -1064669,31 +1064819,31 @@ "abstract": "Raising the Cost of Malicious AI-Powered Image Editing - MadryLab/photoguard", "projections": { "pca": [ - -0.19161012768745422, - 0.15368013083934784 + -0.19097331166267395, + 0.15442810952663422 ], "tsne": [ - -32.62863540649414, - 7.190652370452881 + -27.108741760253906, + -12.834012031555176 ], "umap": [ - 2.009493827819824, - 5.531186580657959 + 2.2580535411834717, + 4.955630779266357 ], "pca3d": [ - -0.19160999357700348, - 0.15365995466709137, - 0.05930698290467262 + -0.19097325205802917, + 0.1544073522090912, + 0.05925072357058525 ], "tsne3d": [ - -20.42262077331543, - 10.429370880126953, - 9.213676452636719 + -21.32235336303711, + -14.113540649414062, + 8.476874351501465 ], "umap3d": [ - 1.7056599855422974, - 6.226067543029785, - 4.831365585327148 + 2.4971272945404053, + 6.651741027832031, + 5.058284759521484 ] }, "cluster_id": 2, @@ -1064704,11 +1064854,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -1065138,46 +1065288,46 @@ "openalex_url": "https://openalex.org/W4306748633", "projections": { "pca": [ - 0.0725538581609726, - 0.18700848519802094 + 0.0731181725859642, + 0.1872541904449463 ], "tsne": [ - 12.737777709960938, - -16.59278106689453 + 12.686599731445312, + 13.778390884399414 ], "umap": [ - 6.7889404296875, - 8.17628288269043 + 7.092045783996582, + 7.770668983459473 ], "pca3d": [ - 0.07255354523658752, - 0.1869889497756958, - 0.0031922394409775734 + 0.07311783730983734, + 0.18723361194133759, + 0.002912788884714246 ], "tsne3d": [ - 11.328143119812012, - -6.5354695320129395, - -23.746320724487305 + 14.247539520263672, + 24.86881446838379, + 2.3354685306549072 ], "umap3d": [ - 6.501610279083252, - 3.101163625717163, - 2.661362648010254 + 7.006709575653076, + 3.389251232147217, + 2.3037045001983643 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 26, + "id": 33, "label": "Genetic Disease" } ], @@ -1065586,46 +1065736,46 @@ "abstract": "AI models for health care that predict disease are not as accurate as reports might suggest. Here\u2019s why", "projections": { "pca": [ - -0.2403617650270462, - 0.2812685966491699 + -0.2397170513868332, + 0.2827928364276886 ], "tsne": [ - -4.276669502258301, - -32.10599136352539 + -6.923513412475586, + 27.255598068237305 ], "umap": [ - 4.804076194763184, - 9.173534393310547 + 5.043540000915527, + 8.622450828552246 ], "pca3d": [ - -0.24036194384098053, - 0.2812662720680237, - 0.0065615493804216385 + -0.23971736431121826, + 0.2827897071838379, + 0.006319138687103987 ], "tsne3d": [ - -3.281186819076538, - -15.994831085205078, - 1.154316782951355 + -7.140719890594482, + 19.26765251159668, + -7.627305030822754 ], "umap3d": [ - 3.862037420272827, - 2.8786942958831787, - 3.1297767162323 + 4.618252754211426, + 3.277437686920166, + 3.636821985244751 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { "id": 10, - "label": "AI Agents" + "label": "Medical AI" }, { - "id": 17, + "id": 26, "label": "Medical AI" } ], @@ -1066033,31 +1066183,31 @@ "title": "Stable Diffusion Inpainting - a Hugging Face Space by multimodalart", "projections": { "pca": [ - -0.3165280520915985, - 0.04405827075242996 + -0.31625935435295105, + 0.04485902190208435 ], "tsne": [ - -38.80720138549805, - 10.897934913635254 + -34.28520202636719, + -20.78288459777832 ], "umap": [ - 2.1799983978271484, - 5.196174621582031 + 2.5634257793426514, + 4.659824848175049 ], "pca3d": [ - -0.3165275752544403, - 0.04402349889278412, - -0.1137859895825386 + -0.3162590563297272, + 0.04482344165444374, + -0.11358685046434402 ], "tsne3d": [ - -19.8459529876709, - 9.788594245910645, - 5.74401330947876 + -19.755645751953125, + -18.59854507446289, + 7.590111255645752 ], "umap3d": [ - 2.003998279571533, - 6.672459602355957, - 4.871103286743164 + 2.5399372577667236, + 7.105734348297119, + 4.837840557098389 ] }, "cluster_id": 2, @@ -1066068,11 +1066218,11 @@ "label": "Generative Models" }, { - "id": 7, + "id": 5, "label": "Diffusion Models" }, { - "id": 25, + "id": 22, "label": "Diffusion Models" } ], @@ -1066481,47 +1066631,47 @@ "abstract": "Today we are more than thrilled to welcome PyTorch to the Linux Foundation. Honestly, it\u2019s hard to capture how big a deal this is for us in a single post but I\u2019ll try.  TL;DR \u2014 PyTorch is one of the most important and successful machine learning software projects in the world today. We are excited [\u2026]", "projections": { "pca": [ - -0.14928586781024933, - 0.15788286924362183 + -0.14869825541973114, + 0.15860074758529663 ], "tsne": [ - -28.02992820739746, - 33.048912048339844 + -15.74585247039795, + -8.08896541595459 ], "umap": [ - 3.9355385303497314, - 6.350529193878174 + 4.3230061531066895, + 5.760595321655273 ], "pca3d": [ - -0.14928601682186127, - 0.15788549184799194, - 0.10474701970815659 + -0.14869846403598785, + 0.15860331058502197, + 0.10460951924324036 ], "tsne3d": [ - -10.912033081054688, - 18.463376998901367, - 12.968232154846191 + -13.504172325134277, + -6.018076419830322, + 15.064258575439453 ], "umap3d": [ - 3.6971914768218994, - 4.865666389465332, - 4.571722030639648 + 4.573951244354248, + 5.600239276885986, + 4.34373664855957 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 10, - "label": "Code Development" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -1066929,46 +1067079,46 @@ ], "projections": { "pca": [ - 0.030333949252963066, - 0.2607620060443878 + 0.03115556575357914, + 0.26099154353141785 ], "tsne": [ - -3.967878580093384, - -7.220395565032959 + -4.114262104034424, + 5.044268608093262 ], "umap": [ - 4.97885799407959, - 7.273488998413086 + 5.1382527351379395, + 6.549668788909912 ], "pca3d": [ - 0.030333261936903, - 0.2607792317867279, - 0.08600620180368423 + 0.031154848635196686, + 0.2610091269016266, + 0.08566734194755554 ], "tsne3d": [ - -6.698782920837402, - -0.2562853991985321, - 13.462937355041504 + -2.996523141860962, + 2.2681076526641846, + -6.438969135284424 ], "umap3d": [ - 4.471492767333984, - 3.603391170501709, - 4.700604438781738 + 5.342903137207031, + 4.701512813568115, + 4.743150234222412 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, + "id": 13, "label": "Genomics Papers" } ], @@ -1067378,47 +1067528,47 @@ "title": "Wordalle - a Hugging Face Space by huggingface-projects", "projections": { "pca": [ - -0.18315966427326202, - 0.15896697342395782 + -0.1824682205915451, + 0.1598394215106964 ], "tsne": [ - -21.450056076049805, - -5.551483631134033 + -21.184417724609375, + -3.483865261077881 ], "umap": [ - 3.2994654178619385, - 7.713107109069824 + 3.5084571838378906, + 7.22476863861084 ], "pca3d": [ - -0.18315955996513367, - 0.15893521904945374, - -0.023807847872376442 + -0.18246819078922272, + 0.15980710089206696, + -0.023716654628515244 ], "tsne3d": [ - -8.879477500915527, - 4.260339260101318, - -12.70297622680664 + -10.498673439025879, + 0.9156441688537598, + 6.581197738647461 ], "umap3d": [ - 3.1157267093658447, - 4.570708274841309, - 3.9289391040802 + 3.884113311767578, + 5.115175724029541, + 4.124843597412109 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { "id": 0, - "label": "LLM Language Learning" + "label": "Math AI" } ], "_embedding": [ @@ -1067826,47 +1067976,47 @@ "abstract": "With origins that stretch back to the late 1950s, functional programming might seem dated\u2014but it can be a boon for growing teams working with large codebases. Read more from @klintron:", "projections": { "pca": [ - -0.13451655209064484, - 0.3001956343650818 + -0.1337912231683731, + 0.30175843834877014 ], "tsne": [ - -17.190799713134766, - -21.646148681640625 + -17.588132858276367, + 14.472084045410156 ], "umap": [ - 3.496330738067627, - 8.148873329162598 + 3.764848470687866, + 7.475089073181152 ], "pca3d": [ - -0.13451677560806274, - 0.30017247796058655, - 0.0914047583937645 + -0.1337914764881134, + 0.3017351031303406, + 0.09102831780910492 ], "tsne3d": [ - -9.813557624816895, - -2.0521039962768555, - -6.407121181488037 + -5.381227970123291, + 4.875934600830078, + 6.222037315368652 ], "umap3d": [ - 2.946937322616577, - 4.01030969619751, - 3.6418938636779785 + 3.755380630493164, + 4.561027526855469, + 4.080770015716553 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { - "id": 32, - "label": "AI Agents" + "id": 0, + "label": "Math AI" } ], "_embedding": [ @@ -1068282,47 +1068432,47 @@ "doi": "10.1007/s11938-015-0046-y", "projections": { "pca": [ - 0.05623212084174156, - 0.2224821299314499 + 0.05686722695827484, + 0.22192171216011047 ], "tsne": [ - 13.232108116149902, - -13.042411804199219 + 41.479759216308594, + -13.236956596374512 ], "umap": [ - 7.148494243621826, - 8.283743858337402 + 7.441024303436279, + 7.874285697937012 ], "pca3d": [ - 0.056232012808322906, - 0.22245390713214874, - -0.09403003752231598 + 0.05686710774898529, + 0.22189316153526306, + -0.09445462375879288 ], "tsne3d": [ - 13.696505546569824, - -15.559486389160156, - -3.3517463207244873 + 11.749812126159668, + 21.61841583251953, + -5.358522891998291 ], "umap3d": [ - 6.479720115661621, - 3.337005853652954, - 2.370044469833374 + 6.611159801483154, + 3.3318870067596436, + 1.9311431646347046 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 26, - "label": "Genetic Disease" + "id": 10, + "label": "Genetic Cell" } ], "_embedding": [ @@ -1068731,47 +1068881,47 @@ ], "projections": { "pca": [ - -0.1729673594236374, - 0.4186203181743622 + -0.17179261147975922, + 0.41912657022476196 ], "tsne": [ - -15.114710807800293, - -10.086670875549316 + -12.918472290039062, + 1.2346992492675781 ], "umap": [ - 4.090321063995361, - 7.326107501983643 + 4.3574066162109375, + 6.752724647521973 ], "pca3d": [ - -0.17296813428401947, - 0.4186389744281769, - -0.025102969259023666 + -0.1717933863401413, + 0.41914498805999756, + -0.025701189413666725 ], "tsne3d": [ - -10.494866371154785, - -7.845425128936768, - 15.449655532836914 + -9.3181791305542, + -3.1258866786956787, + -11.358696937561035 ], "umap3d": [ - 3.587911367416382, - 3.647385835647583, - 4.6105241775512695 + 4.6235761642456055, + 4.451794624328613, + 4.867672920227051 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -1069177,47 +1069327,47 @@ ], "projections": { "pca": [ - -0.1354074478149414, - 0.3946075141429901 + -0.13435380160808563, + 0.394822895526886 ], "tsne": [ - -13.839466094970703, - -5.394506931304932 + -9.372796058654785, + -1.4714674949645996 ], "umap": [ - 4.275936126708984, - 7.302537441253662 + 4.457247257232666, + 6.749988079071045 ], "pca3d": [ - -0.13540852069854736, - 0.3946356475353241, - 0.060906268656253815 + -0.134354829788208, + 0.39485156536102295, + 0.060261331498622894 ], "tsne3d": [ - -11.265101432800293, - 4.456109523773193, - 18.01641082763672 + -3.8970046043395996, + -1.8866889476776123, + -12.00679874420166 ], "umap3d": [ - 3.8254268169403076, - 3.7146689891815186, - 4.625116348266602 + 4.83629035949707, + 4.619372367858887, + 4.8718390464782715 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -1069628,47 +1069778,47 @@ "abstract": "The Senate has unanimously approved a measure that would make daylight saving time permanent next year.", "projections": { "pca": [ - -0.09512536972761154, - 0.40676409006118774 + -0.09408167004585266, + 0.40714913606643677 ], "tsne": [ - -15.413573265075684, - -4.9500322341918945 + -7.084762096405029, + -3.6469178199768066 ], "umap": [ - 6.252211570739746, - 8.475723266601562 + 4.723466396331787, + 6.862316608428955 ], "pca3d": [ - -0.09512584656476974, - 0.4067592918872833, - -0.028717748820781708 + -0.0940822884440422, + 0.40714380145072937, + -0.029421864077448845 ], "tsne3d": [ - -14.548859596252441, - 25.282472610473633, - -1.9934146404266357 + -3.1558892726898193, + -0.4575846493244171, + -14.570352554321289 ], "umap3d": [ - 4.015091896057129, - 3.699779748916626, - 4.519124984741211 + 6.231721878051758, + 3.1394944190979004, + 2.193843126296997 ] }, - "cluster_id": 5, - "cluster_label": "Single-Cell Data", + "cluster_id": 1, + "cluster_label": "AI in Science", "cluster_levels": [ { - "id": 5, - "label": "Single-Cell Data" + "id": 1, + "label": "AI in Science" }, { - "id": 0, - "label": "Alzheimer Biomarkers" + "id": 4, + "label": "Genomics Tools" }, { - "id": 26, - "label": "Genetic Disease" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -1070076,47 +1070226,47 @@ "abstract": "Change Python code while it's running without losing state - julvo/reloading", "projections": { "pca": [ - -0.14253172278404236, - 0.12819397449493408 + -0.14201226830482483, + 0.12840253114700317 ], "tsne": [ - -29.599332809448242, - 31.981456756591797 + -15.292770385742188, + -6.339661598205566 ], "umap": [ - 3.9200551509857178, - 6.476124286651611 + 4.267318248748779, + 5.961147785186768 ], "pca3d": [ - -0.1425318568944931, - 0.12819471955299377, - 0.08477627485990524 + -0.14201246201992035, + 0.12840336561203003, + 0.08459728956222534 ], "tsne3d": [ - -11.615638732910156, - 15.070725440979004, - 14.20931625366211 + -12.361824989318848, + -5.070882797241211, + 12.632065773010254 ], "umap3d": [ - 3.684049367904663, - 4.752863883972168, - 4.548656463623047 + 4.544238090515137, + 5.50908088684082, + 4.335188388824463 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 10, - "label": "Code Development" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -1070532,46 +1070682,46 @@ ], "projections": { "pca": [ - -0.29720696806907654, - -0.16231770813465118 + -0.29762032628059387, + -0.16175007820129395 ], "tsne": [ - -47.634918212890625, - -1.428073525428772 + -44.66008758544922, + -1.1867986917495728 ], "umap": [ - 0.7945020198822021, - 7.406296253204346 + 1.0319443941116333, + 6.513258934020996 ], "pca3d": [ - -0.2972063422203064, - -0.1623411327600479, - -0.03278687223792076 + -0.29761970043182373, + -0.16177351772785187, + -0.03241976723074913 ], "tsne3d": [ - -21.603328704833984, - -2.620396137237549, - 5.37153959274292 + -22.761781692504883, + -2.166818618774414, + 7.297266483306885 ], "umap3d": [ - 0.8927926421165466, - 5.302979469299316, - 4.807641506195068 + 1.915081262588501, + 5.641790390014648, + 5.504156112670898 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 8, - "label": "Attention Vision" + "id": 1, + "label": "Attention Models" }, { - "id": 9, + "id": 16, "label": "Vision Transformers" } ], @@ -1070981,47 +1071131,47 @@ "title": "Competitive programming with AlphaCode \u00e2\u0080\u0094 Google DeepMind", "projections": { "pca": [ - -0.2030564844608307, - 0.0901561826467514 + -0.20270663499832153, + 0.09102586656808853 ], "tsne": [ - -24.734172821044922, - 1.844719648361206 + -24.298187255859375, + 24.066118240356445 ], "umap": [ - 4.693986415863037, - 6.570866584777832 + 3.3604445457458496, + 6.8187384605407715 ], "pca3d": [ - -0.2030564695596695, - 0.09016173332929611, - 0.0558689720928669 + -0.2027066946029663, + 0.09103196859359741, + 0.055919669568538666 ], "tsne3d": [ - -6.493281364440918, - -19.426002502441406, - 15.776406288146973 + -12.628798484802246, + 15.98852825164795, + 10.7683744430542 ], "umap3d": [ - 3.2126474380493164, - 4.787031650543213, - 3.412337064743042 + 3.499898672103882, + 4.931794166564941, + 3.628857135772705 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 12, + "label": "AI Agents" }, { - "id": 10, - "label": "Code Development" + "id": 0, + "label": "Math AI" } ], "_embedding": [ @@ -1071464,46 +1071614,46 @@ "openalex_url": "https://openalex.org/W2484915396", "projections": { "pca": [ - 0.15932942926883698, - 0.24536357820034027 + 0.15997964143753052, + 0.24527499079704285 ], "tsne": [ - 14.76364803314209, - -10.78561019897461 + 38.24668502807617, + -14.309159278869629 ], "umap": [ - 6.91395902633667, - 8.192407608032227 + 7.130212306976318, + 7.839478969573975 ], "pca3d": [ - 0.1593291014432907, - 0.24533702433109283, - -0.07641502469778061 + 0.15997926890850067, + 0.24524760246276855, + -0.07680322974920273 ], "tsne3d": [ - 17.300308227539062, - -17.21742057800293, - -5.559093475341797 + 12.423236846923828, + 21.95397186279297, + -0.8293322324752808 ], "umap3d": [ - 6.544205665588379, - 3.097508430480957, - 2.607212781906128 + 6.892996788024902, + 3.3262441158294678, + 2.155730724334717 ] }, - "cluster_id": 5, + "cluster_id": 6, "cluster_label": "Single-Cell Data", "cluster_levels": [ { - "id": 5, + "id": 6, "label": "Single-Cell Data" }, { - "id": 13, - "label": "Genetic Variants" + "id": 3, + "label": "Genetic Disease" }, { - "id": 26, + "id": 33, "label": "Genetic Disease" } ], @@ -1071910,47 +1072060,47 @@ ], "projections": { "pca": [ - -0.23184962570667267, - 0.3962865471839905 + -0.23077133297920227, + 0.39676398038864136 ], "tsne": [ - -15.117036819458008, - -8.527584075927734 + -11.742239952087402, + 0.217848539352417 ], "umap": [ - 4.081043720245361, - 7.357269287109375 + 4.420621871948242, + 6.75634765625 ], "pca3d": [ - -0.23185016214847565, - 0.3962998390197754, - 0.03719335421919823 + -0.23077213764190674, + 0.396777480840683, + 0.036624349653720856 ], "tsne3d": [ - -8.708544731140137, - -6.645345211029053, - 17.72956657409668 + -6.7236504554748535, + -2.278343677520752, + -12.427484512329102 ], "umap3d": [ - 3.685154438018799, - 3.591043472290039, - 4.708115577697754 + 4.728848934173584, + 4.488300800323486, + 4.917197227478027 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -1072358,47 +1072508,47 @@ "journal": "Nature", "projections": { "pca": [ - -0.12398844957351685, - 0.2869742810726166 + -0.12302495539188385, + 0.2871897518634796 ], "tsne": [ - -21.73725700378418, - 8.256671905517578 + -22.314876556396484, + -10.010360717773438 ], "umap": [ - 3.2419073581695557, - 6.91673469543457 + 3.637085199356079, + 6.146266937255859 ], "pca3d": [ - -0.12398887425661087, - 0.2869749367237091, - 0.051727212965488434 + -0.12302538752555847, + 0.2871905267238617, + 0.051340773701667786 ], "tsne3d": [ - -11.61700439453125, - 19.615318298339844, - -4.336792945861816 + -7.04166316986084, + -11.436408996582031, + -12.436444282531738 ], "umap3d": [ - 2.7249083518981934, - 4.709043979644775, - 4.711347579956055 + 3.6457712650299072, + 5.388363838195801, + 4.937260627746582 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 10, - "label": "Code Development" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -1072806,47 +1072956,47 @@ ], "projections": { "pca": [ - -0.14504919946193695, - 0.33679887652397156 + -0.14412885904312134, + 0.3382387161254883 ], "tsne": [ - -15.362083435058594, - -12.373510360717773 + -11.612241744995117, + 6.0256500244140625 ], "umap": [ - 3.9186081886291504, - 7.464582443237305 + 4.282622814178467, + 6.813453197479248 ], "pca3d": [ - -0.14504949748516083, - 0.3367812931537628, - 0.08214225620031357 + -0.14412923157215118, + 0.33822062611579895, + 0.08176438510417938 ], "tsne3d": [ - -4.82296895980835, - -9.857073783874512, - 9.959979057312012 + -9.950060844421387, + 2.1494317054748535, + -7.34970235824585 ], "umap3d": [ - 3.5622458457946777, - 3.636465072631836, - 4.5287675857543945 + 4.533431053161621, + 4.51570987701416, + 4.830156326293945 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 23, - "label": "Scientific Research" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -1073260,47 +1073410,47 @@ "cited_by_count": 32, "projections": { "pca": [ - -0.11491052806377411, - 0.28916671872138977 + -0.11422869563102722, + 0.2897195518016815 ], "tsne": [ - -10.60546875, - -0.3104070723056793 + -17.115705490112305, + -37.648067474365234 ], "umap": [ - 3.8935019969940186, - 6.9705328941345215 + 4.006588935852051, + 6.241017818450928 ], "pca3d": [ - -0.11491100490093231, - 0.28916338086128235, - 0.10604099184274673 + -0.11422907561063766, + 0.28971609473228455, + 0.10541435331106186 ], "tsne3d": [ - -2.919032096862793, - -5.121081352233887, - 16.50830078125 + -4.294177055358887, + -8.375783920288086, + -9.47181224822998 ], "umap3d": [ - 3.5072028636932373, - 4.058973789215088, - 4.719474792480469 + 4.445632457733154, + 5.088359355926514, + 4.863214015960693 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 10, - "label": "Code Development" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -1073708,47 +1073858,47 @@ "abstract": "18.337 - Parallel Computing and Scientific Machine Learning - mitmath/18337", "projections": { "pca": [ - -0.18365220725536346, - 0.07609594613313675 + -0.18340180814266205, + 0.07680444419384003 ], "tsne": [ - -23.5718936920166, - -24.463951110839844 + -21.409286499023438, + 10.980600357055664 ], "umap": [ - 2.8975985050201416, - 7.123774528503418 + 3.169679641723633, + 6.436890602111816 ], "pca3d": [ - -0.18365231156349182, - 0.07612360268831253, - 0.060585811734199524 + -0.18340207636356354, + 0.07683305442333221, + 0.06054007261991501 ], "tsne3d": [ - -15.903813362121582, - -3.1134085655212402, - -8.14385986328125 + -14.786162376403809, + -5.322214603424072, + -5.775477409362793 ], "umap3d": [ - 2.3662686347961426, - 4.796835422515869, - 3.7960047721862793 + 3.1692256927490234, + 5.194570064544678, + 4.159679412841797 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { "id": 0, - "label": "LLM Language Learning" + "label": "Math AI" } ], "_embedding": [ @@ -1074154,47 +1074304,47 @@ ], "projections": { "pca": [ - 0.07472006976604462, - 0.19525417685508728 + 0.07534758001565933, + 0.1950303018093109 ], "tsne": [ - -4.377818584442139, - -5.04459285736084 + -6.676249027252197, + 1.3640297651290894 ], "umap": [ - 4.751080513000488, - 6.993347644805908 + 4.868605613708496, + 6.372932434082031 ], "pca3d": [ - 0.07471974939107895, - 0.19524961709976196, - -0.017537569627165794 + 0.0753471851348877, + 0.19502535462379456, + -0.017836276441812515 ], "tsne3d": [ - -10.715405464172363, - -0.24759475886821747, - 12.809831619262695 + -1.6121468544006348, + -0.09640850871801376, + -5.6044158935546875 ], "umap3d": [ - 4.138660907745361, - 4.036611080169678, - 4.402564525604248 + 5.000641822814941, + 4.903139591217041, + 4.352548599243164 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 6, - "label": "Genomics GitHub" + "id": 4, + "label": "Genomics Tools" }, { - "id": 18, - "label": "Genomics Papers" + "id": 29, + "label": "Science GitHub" } ], "_embedding": [ @@ -1074602,47 +1074752,47 @@ ], "projections": { "pca": [ - -0.13222746551036835, - 0.35291123390197754 + -0.13126060366630554, + 0.35334187746047974 ], "tsne": [ - -20.765546798706055, - -15.873775482177734 + -25.657466888427734, + 13.252541542053223 ], "umap": [ - 2.782097101211548, - 8.184930801391602 + 3.0921247005462646, + 7.47770881652832 ], "pca3d": [ - -0.1322280466556549, - 0.3529331386089325, - -0.050465457141399384 + -0.13126139342784882, + 0.3533637225627899, + -0.05091647431254387 ], "tsne3d": [ - -9.731228828430176, - -15.648728370666504, - 9.75308895111084 + -12.654047966003418, + 5.073950290679932, + -6.608730316162109 ], "umap3d": [ - 2.52209734916687, - 3.6751575469970703, - 4.457359790802002 + 3.5852184295654297, + 4.322290420532227, + 4.995314121246338 ] }, "cluster_id": 1, - "cluster_label": "AI Scientific Research", + "cluster_label": "AI in Science", "cluster_levels": [ { "id": 1, - "label": "AI Scientific Research" + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { "id": 0, - "label": "LLM Language Learning" + "label": "Math AI" } ], "_embedding": [ @@ -1075050,47 +1075200,47 @@ "abstract": "Introducing nanochat: The best ChatGPT that $100 can buy.", "projections": { "pca": [ - -0.1658061444759369, - 0.15771809220314026 + -0.16534599661827087, + 0.15860125422477722 ], "tsne": [ - -23.599119186401367, - -4.212264537811279 + -23.788379669189453, + -5.282116889953613 ], "umap": [ - 2.4528563022613525, - 8.023202896118164 + 3.279470682144165, + 7.454825401306152 ], "pca3d": [ - -0.16580630838871002, - 0.15773345530033112, - 0.08883567154407501 + -0.16534629464149475, + 0.1586167961359024, + 0.08868392556905746 ], "tsne3d": [ - -14.5694580078125, - -0.9354200959205627, - 7.107754707336426 + -11.870593070983887, + 3.602905511856079, + 8.292407989501953 ], "umap3d": [ - 2.592735767364502, - 4.409276962280273, - 4.116363525390625 + 3.583211898803711, + 4.945245742797852, + 4.45231294631958 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 1, + "cluster_label": "AI in Science", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 1, + "label": "AI in Science" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { "id": 0, - "label": "LLM Language Learning" + "label": "Math AI" } ], "_embedding": [ @@ -1075501,47 +1075651,47 @@ "abstract": "The NVIDIA Graduate Fellowship Program provides grants, mentors and technical support to doctoral students doing outstanding research relevant to NVIDIA technologies. The application deadline for the 2026-2027 academic year is Monday, Sept. 15, 2025.", "projections": { "pca": [ - -0.10395757853984833, - 0.31419047713279724 + -0.10304572433233261, + 0.31409141421318054 ], "tsne": [ - -18.391780853271484, - -0.3631190061569214 + -23.550018310546875, + -0.45208612084388733 ], "umap": [ - 2.674889087677002, - 7.5162577629089355 + 2.881653308868408, + 6.7543816566467285 ], "pca3d": [ - -0.1039578840136528, - 0.3141912817955017, - -0.032314326614141464 + -0.10304615646600723, + 0.31409239768981934, + -0.03282679617404938 ], "tsne3d": [ - -16.270780563354492, - 2.812314748764038, - 15.026848793029785 + -18.374736785888672, + 1.5578964948654175, + 15.130642890930176 ], "umap3d": [ - 2.305687189102173, - 4.594385147094727, - 5.117440223693848 + 3.4476242065429688, + 5.280307769775391, + 5.473136901855469 ] }, - "cluster_id": 6, - "cluster_label": "Attention Models", + "cluster_id": 5, + "cluster_label": "Transformer Models", "cluster_levels": [ { - "id": 6, - "label": "Attention Models" + "id": 5, + "label": "Transformer Models" }, { - "id": 4, - "label": "Language Models" + "id": 12, + "label": "AI Agents" }, { "id": 0, - "label": "LLM Language Learning" + "label": "Math AI" } ], "_embedding": [ @@ -1075951,31 +1076101,31 @@ "journal": "Nature", "projections": { "pca": [ - 0.20954859256744385, - 0.10615848749876022 + 0.210007905960083, + 0.10561852157115936 ], "tsne": [ - 40.116859436035156, - 9.719829559326172 + 46.227088928222656, + -1.8111082315444946 ], "umap": [ - 9.41273021697998, - 7.238935470581055 + 9.21863079071045, + 7.212949752807617 ], "pca3d": [ - 0.20954836905002594, - 0.10614246129989624, - 0.07356835156679153 + 0.21000774204730988, + 0.10560216009616852, + 0.0733085349202156 ], "tsne3d": [ - 25.40738296508789, - 10.144868850708008, - 5.943088054656982 + 24.517818450927734, + 7.810380458831787, + 10.186129570007324 ], "umap3d": [ - 7.09306526184082, - 5.231409072875977, - 1.4609159231185913 + 6.782260894775391, + 4.852344989776611, + 0.24143342673778534 ] }, "cluster_id": 0, @@ -1075986,11 +1076136,11 @@ "label": "Chromatin Regulation" }, { - "id": 14, - "label": "Transcription Binding" + "id": 13, + "label": "Chromatin Regulation" }, { - "id": 20, + "id": 18, "label": "Chromatin Regulation" } ], diff --git a/data/standardmodelbio/papers_final.json b/data/standardmodelbio/papers_final.json index 09de29e..bad8669 100644 --- a/data/standardmodelbio/papers_final.json +++ b/data/standardmodelbio/papers_final.json @@ -17,47 +17,47 @@ "abstract": "The open agent skills tool - npx skills. Contribute to vercel-labs/skills development by creating an account on GitHub.", "projections": { "pca": [ - 0.2517773509025574, - 0.07835391163825989 + 0.25305497646331787, + 0.07991495728492737 ], "tsne": [ - 20.35982894897461, - 19.705595016479492 + 26.60643196105957, + 2.1967668533325195 ], "umap": [ - 3.6892168521881104, - 5.750836372375488 + 2.8905415534973145, + 4.575941562652588 ], "pca3d": [ - 0.25177741050720215, - 0.07834973931312561, - -0.0481095127761364 + 0.2530553936958313, + 0.07991097867488861, + -0.05164622142910957 ], "tsne3d": [ - 37.72216033935547, - 18.519474029541016, - 6.850533485412598 + 22.492155075073242, + 25.016576766967773, + 5.677004337310791 ], "umap3d": [ - 4.392548561096191, - 0.4230278432369232, - 2.095907211303711 + 4.0287957191467285, + 1.978783369064331, + 5.899611473083496 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 20, - "label": "AI Agents" + "id": 4, + "label": "Open Agent Tools" } ], "_embedding": [ @@ -465,47 +465,47 @@ "abstract": "GPU-accelerated sparse non-negative matrix factorization with PyTorch - bschilder/sparseNMF", "projections": { "pca": [ - -0.005555862095206976, - 0.06213134527206421 + -0.006924910005182028, + 0.06241350248456001 ], "tsne": [ - 6.4324445724487305, - 5.613139629364014 + 12.305413246154785, + -9.867151260375977 ], "umap": [ - 2.5039942264556885, - 4.5517778396606445 + 1.64057195186615, + 3.8130099773406982 ], "pca3d": [ - -0.005556213669478893, - 0.06213006749749184, - -0.15386921167373657 + -0.0069252545945346355, + 0.06241317465901375, + -0.15229322016239166 ], "tsne3d": [ - -4.376130104064941, - 30.664052963256836, - 25.58281135559082 + 33.63157272338867, + -6.773342132568359, + -14.04669189453125 ], "umap3d": [ - 4.537041187286377, - -0.7115499973297119, - 1.5828135013580322 + 3.94649600982666, + 0.9596836566925049, + 6.741777420043945 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 5, - "label": "GPU Inference" + "id": 15, + "label": "PyTorch Models" } ], "_embedding": [ @@ -914,47 +914,47 @@ "abstract": "Promoting openness in scientific communication and the peer-review process", "projections": { "pca": [ - 0.11392970383167267, - 0.005486438982188702 + 0.11492516100406647, + 0.006780137773603201 ], "tsne": [ - 16.218400955200195, - -17.607507705688477 + 3.906635046005249, + 2.5339412689208984 ], "umap": [ - 1.0748015642166138, - 4.798961162567139 + 0.22613488137722015, + 5.012571334838867 ], "pca3d": [ - 0.11393043398857117, - 0.005491259042173624, - 0.05965215712785721 + 0.11492598801851273, + 0.006781818810850382, + 0.056100234389305115 ], "tsne3d": [ - -21.582252502441406, - 21.82480239868164, - 24.740232467651367 + 15.686192512512207, + -33.42936325073242, + 1.6311397552490234 ], "umap3d": [ - 3.049940824508667, - 0.960425078868866, - 0.22427237033843994 + 1.8357391357421875, + 1.6789600849151611, + 5.165239334106445 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 4, - "label": "Com Area Amp" + "id": 9, + "label": "Drug Discovery" }, { "id": 23, - "label": "Research Authors" + "label": "Peer Review" } ], "_embedding": [ @@ -1392,47 +1392,47 @@ "doi": "10.1038/s41597-026-06861-9", "projections": { "pca": [ - 0.04709158092737198, - -0.29135745763778687 + 0.04784778505563736, + -0.29040151834487915 ], "tsne": [ - -26.231040954589844, - -10.98887825012207 + -24.792675018310547, + 2.3818092346191406 ], "umap": [ - -0.9668102264404297, - 7.493259429931641 + -2.2996652126312256, + 2.876424551010132 ], "pca3d": [ - 0.04709132760763168, - -0.2913772761821747, - 0.11720852553844452 + 0.04784759506583214, + -0.2904195189476013, + 0.12016090005636215 ], "tsne3d": [ - -19.569164276123047, - 7.410336971282959, - 38.290069580078125 + -36.159400939941406, + 6.733560562133789, + 25.79888153076172 ], "umap3d": [ - 3.644791603088379, - -1.748164415359497, - -1.5645262002944946 + 1.1266506910324097, + -0.23455384373664856, + 7.836993217468262 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 12, - "label": "ECG Models" + "id": 13, + "label": "ECG AI Models" } ], "_embedding": [ @@ -1883,46 +1883,46 @@ "openalex_url": "https://openalex.org/W2795989238", "projections": { "pca": [ - 0.07993758469820023, - -0.30966708064079285 + 0.08073289692401886, + -0.3088129460811615 ], "tsne": [ - 4.981412410736084, - -13.09167194366455 + -16.218931198120117, + -6.937586784362793 ], "umap": [ - -2.0087034702301025, - 6.401176452636719 + -2.36232590675354, + 4.958981513977051 ], "pca3d": [ - 0.07993841916322708, - -0.3096725642681122, - 0.07296176999807358 + 0.08073373883962631, + -0.3088188171386719, + 0.07482866197824478 ], "tsne3d": [ - -19.971481323242188, - -28.659578323364258, - 35.98136520385742 + -19.64256477355957, + -16.801958084106445, + 28.517667770385742 ], "umap3d": [ - 2.743411064147949, - -0.9456071257591248, - -0.41180792450904846 + 0.2874961495399475, + 0.4189669191837311, + 5.971354961395264 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 24, + "id": 1, "label": "Cancer Data" } ], @@ -2334,47 +2334,47 @@ "abstract": "The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond. - affaan-m/ECC", "projections": { "pca": [ - 0.11368623375892639, - 0.0763372853398323 + 0.11462602764368057, + 0.07827623188495636 ], "tsne": [ - 23.142135620117188, - 23.842100143432617 + 32.793880462646484, + 6.05803918838501 ], "umap": [ - 4.3401079177856445, - 5.935004234313965 + 3.6153512001037598, + 4.500417232513428 ], "pca3d": [ - 0.11368544399738312, - 0.07632436603307724, - -0.03242330253124237 + 0.11462531238794327, + 0.07826514542102814, + -0.03310868516564369 ], "tsne3d": [ - 48.201988220214844, - 15.815335273742676, - 10.649957656860352 + 26.15306282043457, + 34.589569091796875, + 6.314672470092773 ], "umap3d": [ - 4.966277122497559, - 0.3694606423377991, - 2.904008388519287 + 4.867806434631348, + 2.011685609817505, + 6.27360200881958 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { - "id": 16, - "label": "Claude Code" + "id": 11, + "label": "Code Tools" } ], "_embedding": [ @@ -2794,46 +2794,46 @@ ], "projections": { "pca": [ - -0.24659405648708344, - -0.04710930958390236 + -0.24437673389911652, + -0.044041622430086136 ], "tsne": [ - -9.8404541015625, - -3.7553060054779053 + -14.282126426696777, + 5.6608099937438965 ], "umap": [ - -0.11997910588979721, - 6.698028087615967 + -1.8841465711593628, + 3.445189952850342 ], "pca3d": [ - -0.24659353494644165, - -0.04710513353347778, - 0.14773280918598175 + -0.2443763017654419, + -0.04403897002339363, + 0.15515866875648499 ], "tsne3d": [ - 3.128807783126831, - -21.99264144897461, - 3.2136924266815186 + -4.143139362335205, + -4.004339218139648, + 13.188654899597168 ], "umap3d": [ - 4.346164226531982, - -1.095197319984436, - -1.6905590295791626 + 1.1604207754135132, + 0.700359582901001, + 7.166053295135498 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, + "id": 14, "label": "Clinical Health" } ], @@ -3222,7 +3222,11 @@ -0.0154, 0.0479, -0.0473 - ] + ], + "year": 2026, + "journal": "Open MIND", + "cited_by_count": 0, + "openalex_url": "https://openalex.org/W7126160866" }, { "channel": "standardmodelbio", @@ -3240,47 +3244,47 @@ ], "projections": { "pca": [ - 0.3044539988040924, - 0.09607059508562088 + 0.30298489332199097, + 0.09537190943956375 ], "tsne": [ - 16.787145614624023, - 1.8342961072921753 + 13.530077934265137, + -1.0963577032089233 ], "umap": [ - 2.165043354034424, - 5.688867568969727 + 1.790054202079773, + 4.8389105796813965 ], "pca3d": [ - 0.3044538199901581, - 0.09607023000717163, - -0.04021308198571205 + 0.30298465490341187, + 0.09536951035261154, + -0.045063264667987823 ], "tsne3d": [ - 12.342619895935059, - 12.524868965148926, - 12.71098804473877 + 21.215665817260742, + 0.20789584517478943, + 8.054203033447266 ], "umap3d": [ - 3.560206413269043, - 0.47634199261665344, - 1.4676917791366577 + 3.057412624359131, + 1.9702714681625366, + 5.796756744384766 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 4, - "label": "Com Area Amp" + "label": "AI Agents" }, { - "id": 26, - "label": "AI Information" + "id": 24, + "label": "SMB Models" } ], "_embedding": [ @@ -3687,47 +3691,47 @@ "title": "Gemini-backed Paper Assistant Tool provides automated feedback for theoretical computer scientists at STOC 2026", "projections": { "pca": [ - 0.14667843282222748, - 0.08526850491762161 + 0.14830802381038666, + 0.08656587451696396 ], "tsne": [ - 17.26218605041504, - 13.63369083404541 + 19.41573143005371, + 2.237062692642212 ], "umap": [ - 3.467921018600464, - 5.059860706329346 + 2.5183873176574707, + 4.176006317138672 ], "pca3d": [ - 0.14667825400829315, - 0.08526461571455002, - 0.05992377549409866 + 0.14830784499645233, + 0.0865643322467804, + 0.05747346207499504 ], "tsne3d": [ - 24.490262985229492, - 16.6286678314209, - -5.609622955322266 + 25.845422744750977, + 12.412858963012695, + -2.138002395629883 ], "umap3d": [ - 4.721590518951416, - 0.4332055151462555, - 1.320924162864685 + 3.7176547050476074, + 1.2718242406845093, + 5.714022159576416 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 20, - "label": "AI Agents" + "id": 4, + "label": "Open Agent Tools" } ], "_embedding": [ @@ -4136,47 +4140,47 @@ ], "projections": { "pca": [ - 0.32668086886405945, - -0.07792304456233978 + 0.32692375779151917, + -0.07905478775501251 ], "tsne": [ - 14.61420726776123, - -0.935783863067627 + 10.744327545166016, + -2.113405704498291 ], "umap": [ - 1.8452924489974976, - 5.419112682342529 + 1.1184829473495483, + 4.8798298835754395 ], "pca3d": [ - 0.3266806900501251, - -0.07792060822248459, - -0.014995885081589222 + 0.3269239366054535, + -0.07905393093824387, + -0.022517643868923187 ], "tsne3d": [ - -0.23364108800888062, - 27.376232147216797, - -0.47190117835998535 + 28.13014030456543, + -14.440027236938477, + -1.9947917461395264 ], "umap3d": [ - 3.285834312438965, - 0.8050076365470886, - 1.1569875478744507 + 2.735508441925049, + 1.896955132484436, + 5.435743808746338 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 4, - "label": "Com Area Amp" + "id": 6, + "label": "Data Needs" }, { - "id": 26, - "label": "AI Information" + "id": 10, + "label": "Research Data" } ], "_embedding": [ @@ -4585,47 +4589,47 @@ "abstract": "Ressources of histopathology datasets. Contribute to maduc7/Histopathology-Datasets development by creating an account on GitHub.", "projections": { "pca": [ - 0.17155936360359192, - -0.21846294403076172 + 0.17122043669223785, + -0.21819987893104553 ], "tsne": [ - 28.277605056762695, - 9.644022941589355 + 29.713571548461914, + -9.916705131530762 ], "umap": [ - 3.0242719650268555, - 7.16128396987915 + 2.759796142578125, + 5.894354820251465 ], "pca3d": [ - 0.17156071960926056, - -0.21845479309558868, - -0.08445580303668976 + 0.17122207581996918, + -0.21819347143173218, + -0.08304180204868317 ], "tsne3d": [ - 25.388158798217773, - 52.31592559814453, - 14.606425285339355 + 46.96791076660156, + 11.629365921020508, + 19.2093448638916 ], "umap3d": [ - 3.200148582458496, - -0.3149763345718384, - 2.5488696098327637 + 3.37058687210083, + 2.7803595066070557, + 6.825055122375488 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 18, - "label": "Github Tools" + "id": 17, + "label": "GitHub Creation" } ], "_embedding": [ @@ -5033,47 +5037,47 @@ "abstract": "We\u2019re on a journey to advance and democratize artificial intelligence through open source and open science.", "projections": { "pca": [ - 0.10048981010913849, - 0.20223847031593323 + 0.10399047285318375, + 0.20448575913906097 ], "tsne": [ - 7.606062889099121, - 20.104463577270508 + 21.920320510864258, + 12.793599128723145 ], "umap": [ - 5.038498401641846, - 4.770840644836426 + 4.075727939605713, + 2.9493696689605713 ], "pca3d": [ - 0.10048944503068924, - 0.2022545486688614, - 0.21021588146686554 + 0.10399004817008972, + 0.20449946820735931, + 0.20767657458782196 ], "tsne3d": [ - 42.490360260009766, - -4.049165725708008, - 8.942212104797363 + 2.711869955062866, + 38.8988151550293, + 5.925535202026367 ], "umap3d": [ - 5.400947570800781, - 1.785988688468933, - 2.118246078491211 + 4.908210277557373, + 2.11271595954895, + 4.678064346313477 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { "id": 7, - "label": "Open AI" + "label": "Hugging Face Models" } ], "_embedding": [ @@ -5485,47 +5489,47 @@ ], "projections": { "pca": [ - 0.11238761246204376, - 0.08364327251911163 + 0.11317212879657745, + 0.08457744121551514 ], "tsne": [ - 11.349786758422852, - 20.42840576171875 + 19.8907527923584, + 9.091029167175293 ], "umap": [ - 4.284832954406738, - 4.774107933044434 + 3.2946269512176514, + 3.2781615257263184 ], "pca3d": [ - 0.11238802969455719, - 0.08365331590175629, - 0.17359855771064758 + 0.11317266523838043, + 0.08458693325519562, + 0.17061734199523926 ], "tsne3d": [ - 39.01755142211914, - 5.632665157318115, - -1.9529603719711304 + 7.544991970062256, + 27.24433135986328, + -1.6753417253494263 ], "umap3d": [ - 5.114238262176514, - 1.1181795597076416, - 1.775225043296814 + 4.5570759773254395, + 1.7665131092071533, + 5.075582504272461 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { - "id": 7, - "label": "Open AI" + "id": 22, + "label": "AI Gemini" } ], "_embedding": [ @@ -5913,7 +5917,15 @@ -0.0436, 0.0315, -0.0371 - ] + ], + "year": 2024, + "journal": "Proceedings of the IEEE", + "affiliations": [ + "New York University", + "University of Malta" + ], + "cited_by_count": 11, + "openalex_url": "https://openalex.org/W4392405529" }, { "channel": "standardmodelbio", @@ -5942,47 +5954,47 @@ ], "projections": { "pca": [ - -0.1156601831316948, - 0.18429310619831085 + -0.1142376959323883, + 0.1856115162372589 ], "tsne": [ - -20.7297420501709, - 11.876749992370605 + -5.815478801727295, + 23.00027847290039 ], "umap": [ - 1.5220950841903687, - 2.961383819580078 + 0.48814472556114197, + 1.9430066347122192 ], "pca3d": [ - -0.11566083133220673, - 0.1842823177576065, - -0.05053270235657692 + -0.11423834413290024, + 0.18560060858726501, + -0.04589153081178665 ], "tsne3d": [ - -7.704741477966309, - 6.089726448059082, - -26.018375396728516 + -1.6346254348754883, + -3.4823150634765625, + -16.572587966918945 ], "umap3d": [ - 5.9555840492248535, - -1.1026806831359863, - 0.04479503259062767 + 3.405686855316162, + -1.043272614479065, + 6.399136066436768 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 15, - "label": "Latent Models" + "id": 8, + "label": "JEPA Video Models" } ], "_embedding": [ @@ -6370,7 +6382,8 @@ -0.028, -0.0034, -0.0382 - ] + ], + "year": 2023 }, { "channel": "standardmodelbio", @@ -6391,47 +6404,47 @@ "abstract": "An open-source library for Latent Space Exploration (LSE) - broadinstitute/ml4h-latentverse", "projections": { "pca": [ - 0.15732689201831818, - 0.04938563331961632 + 0.15785670280456543, + 0.05076361820101738 ], "tsne": [ - 8.940278053283691, - 12.457789421081543 + 17.29579734802246, + 2.873000144958496 ], "umap": [ - 2.802159547805786, - 5.313192367553711 + 1.9525266885757446, + 4.292773246765137 ], "pca3d": [ - 0.1573275476694107, - 0.049397099763154984, - -0.020318709313869476 + 0.15785737335681915, + 0.050773199647665024, + -0.02184937335550785 ], "tsne3d": [ - -6.273847579956055, - 23.052993774414062, - 35.62385940551758 + 20.93380355834961, + -10.234379768371582, + -15.152971267700195 ], "umap3d": [ - 4.394914627075195, - -0.061757005751132965, - 1.3414227962493896 + 3.6049253940582275, + 1.4574450254440308, + 6.117830276489258 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 5, - "label": "GPU Inference" + "id": 15, + "label": "PyTorch Models" } ], "_embedding": [ @@ -6851,46 +6864,46 @@ "cited_by_count": 80, "projections": { "pca": [ - -0.11679500341415405, - -0.14048855006694794 + -0.11421123892068863, + -0.13955365121364594 ], "tsne": [ - -23.07486343383789, - -5.301902770996094 + -5.238426208496094, + 7.892443656921387 ], "umap": [ - -0.03265368938446045, - 7.660370826721191 + -1.3204638957977295, + 2.5134236812591553 ], "pca3d": [ - -0.1167948991060257, - -0.1405036598443985, - 0.1756402999162674 + -0.11421121656894684, + -0.1395675390958786, + 0.182062029838562 ], "tsne3d": [ - -13.989360809326172, - -2.875061273574829, - -0.4397405683994293 + -14.302042007446289, + 5.506213188171387, + 34.628665924072266 ], "umap3d": [ - 4.334598541259766, - -1.720990538597107, - -0.62747722864151 + 1.7567721605300903, + -0.299795001745224, + 6.720529079437256 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 11, + "id": 19, "label": "Medical Imaging" } ], @@ -7317,47 +7330,47 @@ "openalex_url": "https://openalex.org/W4402621536", "projections": { "pca": [ - -0.009458604268729687, - -0.1678653359413147 + -0.008073141798377037, + -0.16726355254650116 ], "tsne": [ - -23.203487396240234, - -5.57634162902832 + -5.190547466278076, + 7.593526840209961 ], "umap": [ - -0.11905664205551147, - 7.298903942108154 + -1.5947328805923462, + 2.8436009883880615 ], "pca3d": [ - -0.009457551874220371, - -0.16786420345306396, - 0.08690635114908218 + -0.008072045631706715, + -0.1672637015581131, + 0.0913337990641594 ], "tsne3d": [ - -16.262847900390625, - -4.441373825073242, - 3.1693174839019775 + -15.80045223236084, + 3.780433416366577, + 37.688743591308594 ], "umap3d": [ - 3.9835689067840576, - -1.4489870071411133, - -0.5942351818084717 + 1.4472113847732544, + 0.43462711572647095, + 6.539981842041016 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 11, - "label": "Medical Imaging" + "id": 13, + "label": "ECG AI Models" } ], "_embedding": [ @@ -7766,47 +7779,47 @@ ], "projections": { "pca": [ - 0.08714043349027634, - -0.01775669865310192 + 0.08562075346708298, + -0.019052788615226746 ], "tsne": [ - 12.484240531921387, - -12.344910621643066 + -31.280902862548828, + -15.881857872009277 ], "umap": [ - 1.0048165321350098, - 5.012768745422363 + 0.5093759894371033, + 5.2258782386779785 ], "pca3d": [ - 0.08713925629854202, - -0.01778327114880085, - -0.10779513418674469 + 0.08561958372592926, + -0.019079450517892838, + -0.1101372092962265 ], "tsne3d": [ - -19.55324363708496, - 19.880271911621094, - 8.85093879699707 + 16.115062713623047, + -31.933576583862305, + -16.440406799316406 ], "umap3d": [ - 2.822568893432617, - 0.7436999082565308, - 0.4678328335285187 + 1.9839705228805542, + 1.526127815246582, + 5.076524257659912 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 3, + "label": "Open Source Tools" }, { - "id": 4, - "label": "Com Area Amp" + "id": 6, + "label": "Data Needs" }, { - "id": 23, - "label": "Research Authors" + "id": 16, + "label": "Lab Research" } ], "_embedding": [ @@ -8212,47 +8225,47 @@ ], "projections": { "pca": [ - 0.2037491798400879, - 0.03289905935525894 + 0.19978077709674835, + 0.02944384329020977 ], "tsne": [ - 13.196240425109863, - -6.518142223358154 + 8.684206008911133, + -0.43072396516799927 ], "umap": [ - 1.3717483282089233, - 5.131289005279541 + 0.8921602964401245, + 5.029386043548584 ], "pca3d": [ - 0.20374883711338043, - 0.0328882671892643, - -0.047142740339040756 + 0.1997806429862976, + 0.029430387541651726, + -0.052569981664419174 ], "tsne3d": [ - -9.618718147277832, - 25.969717025756836, - -1.1303819417953491 + 29.00276756286621, + -17.73697853088379, + 9.646817207336426 ], "umap3d": [ - 2.826240301132202, - 0.9464356303215027, - 0.8137814998626709 + 2.424743890762329, + 1.7299294471740723, + 5.2165141105651855 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 3, + "label": "Open Source Tools" }, { - "id": 4, - "label": "Com Area Amp" + "id": 6, + "label": "Data Needs" }, { - "id": 23, - "label": "Research Authors" + "id": 10, + "label": "Research Data" } ], "_embedding": [ @@ -8662,47 +8675,47 @@ "abstract": "Standard Model Biomedicine, Inc. has 7 repositories available. Follow their code on GitHub.", "projections": { "pca": [ - 0.23016901314258575, - -0.18078583478927612 + 0.2295040637254715, + -0.1794305443763733 ], "tsne": [ - 21.10022735595703, - 8.373679161071777 + 24.99627113342285, + -6.904185771942139 ], "umap": [ - 3.1233866214752197, - 6.563330173492432 + 2.7195777893066406, + 5.534756183624268 ], "pca3d": [ - 0.2301694005727768, - -0.18078038096427917, - -0.02402781881392002 + 0.2295045703649521, + -0.17942583560943604, + -0.023064592853188515 ], "tsne3d": [ - 16.028398513793945, - 38.135440826416016, - 3.6586246490478516 + 35.67757797241211, + -0.6343209147453308, + 18.303590774536133 ], "umap3d": [ - 3.375976324081421, - 0.06648125499486923, - 2.220621109008789 + 3.42691707611084, + 2.6318960189819336, + 6.320326328277588 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 18, - "label": "Github Tools" + "id": 17, + "label": "GitHub Creation" } ], "_embedding": [ @@ -9112,47 +9125,47 @@ "abstract": "We\u2019ve been pretty quiet on here so far, but today we\u2019re excited to introduce ourselves and share three new papers on foundation models in bio. \ud83d\udc4b Come take a look at our Substack post to learn more, and follow us here to receive future updates, too. \ud83d\udce5", "projections": { "pca": [ - 0.18462549149990082, - -0.05406137928366661 + 0.18108367919921875, + -0.056413646787405014 ], "tsne": [ - 20.33885383605957, - 8.122779846191406 + 24.50826072692871, + -6.295063495635986 ], "umap": [ - 3.076720952987671, - 6.278512477874756 + 2.529162883758545, + 5.413381099700928 ], "pca3d": [ - 0.18462567031383514, - -0.0540509857237339, - -0.09363938122987747 + 0.1810840368270874, + -0.05640598386526108, + -0.09343357384204865 ], "tsne3d": [ - 19.17066764831543, - 33.744102478027344, - 5.0735979080200195 + 33.838680267333984, + -1.7091984748840332, + 14.255030632019043 ], "umap3d": [ - 3.372295618057251, - 0.2866984009742737, - 2.120720386505127 + 3.3616409301757812, + 2.5996994972229004, + 6.066622734069824 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 3, - "label": "SMB Data" + "id": 2, + "label": "Bioinformatics Tools" } ], "_embedding": [ @@ -9559,47 +9572,47 @@ "title": "Achieving 10,000x training data reduction with high-fidelity labels", "projections": { "pca": [ - -0.1767960637807846, - 0.1631520837545395 + -0.1728561669588089, + 0.1666341871023178 ], "tsne": [ - -11.713996887207031, - 16.377939224243164 + -9.6542387008667, + 29.521272659301758 ], "umap": [ - 2.5631048679351807, - 3.010631561279297 + 1.828021764755249, + 2.3555262088775635 ], "pca3d": [ - -0.17679664492607117, - 0.1631423830986023, - 0.031031912192702293 + -0.17285668849945068, + 0.16662615537643433, + 0.03210827708244324 ], "tsne3d": [ - -29.446712493896484, - 6.909008026123047, - -38.46643829345703 + 6.28642463684082, + -5.294957160949707, + -45.17389678955078 ], "umap3d": [ - 6.3348002433776855, - -0.28133872151374817, - 1.135841965675354 + 4.670079708099365, + -0.39868494868278503, + 6.207363605499268 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 18, + "label": "Retrieval Learning" } ], "_embedding": [ @@ -10007,47 +10020,47 @@ "abstract": "Independent technology for modern publishing, memberships, subscriptions and newsletters. - TryGhost/Ghost", "projections": { "pca": [ - 0.3417251408100128, - 0.027181779965758324 + 0.34283551573753357, + 0.026571184396743774 ], "tsne": [ - 22.814998626708984, - 15.303502082824707 + 27.60210609436035, + -0.8982514142990112 ], "umap": [ - 3.636368751525879, - 6.259603500366211 + 3.025050401687622, + 5.10115909576416 ], "pca3d": [ - 0.34172576665878296, - 0.027198325842618942, - -0.058134108781814575 + 0.34283608198165894, + 0.0265851691365242, + -0.06272580474615097 ], "tsne3d": [ - 29.015989303588867, - 24.37012481689453, - 24.549715042114258 + 33.93529510498047, + 23.770355224609375, + 6.187930107116699 ], "umap3d": [ - 3.8552844524383545, - 0.2770102620124817, - 2.5589919090270996 + 3.987086772918701, + 2.438704490661621, + 6.160572528839111 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 18, - "label": "Github Tools" + "id": 17, + "label": "GitHub Creation" } ], "_embedding": [ @@ -10455,47 +10468,47 @@ "abstract": "The Quarto blog has a new home# The Quarto blog has moved to the Posit Open Source blog. All past and future Quarto posts now live here alongside posts from our other open-source projects.", "projections": { "pca": [ - 0.4125514626502991, - 0.03689239174127579 + 0.4116077721118927, + 0.03585021570324898 ], "tsne": [ - 22.40619468688965, - 12.72939682006836 + 27.363672256469727, + -2.9056313037872314 ], "umap": [ - 3.5671610832214355, - 6.473690509796143 + 2.972229480743408, + 5.314191818237305 ], "pca3d": [ - 0.4125516712665558, - 0.03689868748188019, - -0.060290757566690445 + 0.4116082191467285, + 0.035855066031217575, + -0.06424117088317871 ], "tsne3d": [ - 27.727773666381836, - 39.47404479980469, - 2.6242361068725586 + 34.21202087402344, + 5.516321659088135, + 7.226020336151123 ], "umap3d": [ - 3.62750506401062, - 0.0257098488509655, - 2.5413107872009277 + 3.754611015319824, + 2.6307601928710938, + 6.476731300354004 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 18, - "label": "Github Tools" + "id": 17, + "label": "GitHub Creation" } ], "_embedding": [ @@ -10922,46 +10935,46 @@ "cited_by_count": 8, "projections": { "pca": [ - -0.1879841536283493, - -0.1701143980026245 + -0.18541987240314484, + -0.16739216446876526 ], "tsne": [ - -10.458603858947754, - -15.199665069580078 + -20.02309226989746, + -5.861867427825928 ], "umap": [ - -1.7158007621765137, - 6.0823235511779785 + -2.1276121139526367, + 4.8618950843811035 ], "pca3d": [ - -0.1879839301109314, - -0.1701141893863678, - 0.11142227053642273 + -0.18541958928108215, + -0.16739121079444885, + 0.11748814582824707 ], "tsne3d": [ - -15.053279876708984, - -33.27656173706055, - -6.370378494262695 + -19.944869995117188, + -13.425629615783691, + 14.242897033691406 ], "umap3d": [ - 2.9482526779174805, - -0.8671246767044067, - -0.9463483095169067 + 0.5505566000938416, + 0.1603199690580368, + 5.942564964294434 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 24, + "id": 1, "label": "Cancer Data" } ], @@ -11350,7 +11363,13 @@ -0.0602, 0.0457, -0.0154 - ] + ], + "affiliations": [ + "Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg", + "Universit\u00e4tsklinikum Erlangen", + "Comprehensive Cancer Center Erlangen" + ], + "openalex_url": "https://openalex.org/W4412931509" }, { "channel": "standardmodelbio", @@ -11376,46 +11395,46 @@ "doi": "10.1148/ryai.230466", "projections": { "pca": [ - 0.14116787910461426, - -0.14683780074119568 + 0.14266261458396912, + -0.14702892303466797 ], "tsne": [ - -21.49609375, - -6.38839054107666 + -3.9984378814697266, + 8.895625114440918 ], "umap": [ - -0.13919803500175476, - 7.574898719787598 + -1.2536590099334717, + 2.404353618621826 ], "pca3d": [ - 0.14116819202899933, - -0.14683684706687927, - 0.08793910592794418 + 0.14266295731067657, + -0.1470295786857605, + 0.09048737585544586 ], "tsne3d": [ - -20.95686912536621, - 2.7936487197875977, - 2.2758963108062744 + -17.98897361755371, + -0.23454062640666962, + 31.832246780395508 ], "umap3d": [ - 4.045794486999512, - -1.5363104343414307, - -0.404690682888031 + 1.795840859413147, + -0.20845933258533478, + 6.5188469886779785 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 11, + "id": 19, "label": "Medical Imaging" } ], @@ -11804,7 +11823,12 @@ -0.0145, 0.0635, -0.0036 - ] + ], + "affiliations": [ + "The University of Texas MD Anderson Cancer Center" + ], + "cited_by_count": 0, + "openalex_url": "https://openalex.org/W4390037374" }, { "channel": "standardmodelbio", @@ -11837,46 +11861,46 @@ "doi": "10.1002/jmri.29101", "projections": { "pca": [ - 0.03034265898168087, - -0.046518124639987946 + 0.03468486666679382, + -0.04397871717810631 ], "tsne": [ - -15.965840339660645, - 2.732475519180298 + -1.6478419303894043, + 7.730847358703613 ], "umap": [ - 0.11481848359107971, - 7.3885016441345215 + -1.2380287647247314, + 2.858314037322998 ], "pca3d": [ - 0.030342338606715202, - -0.04652928560972214, - 0.2746182382106781 + 0.03468460589647293, + -0.04398736357688904, + 0.27928560972213745 ], "tsne3d": [ - 4.588983058929443, - -17.77457618713379, - 40.90979766845703 + -1.9979560375213623, + -2.205726385116577, + 37.56174087524414 ], "umap3d": [ - 4.128587245941162, - -1.7121565341949463, - -0.881115198135376 + 1.365173578262329, + -0.25941547751426697, + 7.170642852783203 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 11, + "id": 19, "label": "Medical Imaging" } ], @@ -12265,7 +12289,15 @@ -0.0056, 0.0674, -0.0012 - ] + ], + "affiliations": [ + "Memorial Sloan Kettering Cancer Center", + "SUNY Downstate Health Sciences University", + "Cornell University", + "Weill Cornell Medicine" + ], + "cited_by_count": 18, + "openalex_url": "https://openalex.org/W4387964872" }, { "channel": "standardmodelbio", @@ -12285,47 +12317,47 @@ "abstract": "Collection of scripts and notebooks for OpenAI's latest GPT OSS models - huggingface/gpt-oss-recipes", "projections": { "pca": [ - 0.27097204327583313, - 0.06968814134597778 + 0.27040407061576843, + 0.07026626914739609 ], "tsne": [ - 11.532716751098633, - 17.627079010009766 + 23.352066040039062, + 9.084548950195312 ], "umap": [ - 4.511807441711426, - 5.2685017585754395 + 3.571993827819824, + 3.9781746864318848 ], "pca3d": [ - 0.2709720730781555, - 0.06969431042671204, - -0.0844247117638588 + 0.27040401101112366, + 0.07027175277471542, + -0.08629689365625381 ], "tsne3d": [ - 30.239482879638672, - -1.3954849243164062, - 9.142988204956055 + 19.207679748535156, + 25.209918975830078, + 13.82827377319336 ], "umap3d": [ - 4.946173667907715, - 1.0145511627197266, - 2.3080551624298096 + 4.655704975128174, + 2.1860299110412598, + 5.421382904052734 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { - "id": 7, - "label": "Open AI" + "id": 11, + "label": "Code Tools" } ], "_embedding": [ @@ -12733,47 +12765,47 @@ "abstract": "We\u2019re on a journey to advance and democratize artificial intelligence through open source and open science.", "projections": { "pca": [ - 0.16400820016860962, - 0.15742884576320648 + 0.16803313791751862, + 0.160707488656044 ], "tsne": [ - 6.676684379577637, - 18.8815975189209 + 21.910207748413086, + 11.321959495544434 ], "umap": [ - 4.947219371795654, - 4.714252471923828 + 3.979384660720825, + 3.0197062492370605 ], "pca3d": [ - 0.16400781273841858, - 0.15743699669837952, - 0.18060968816280365 + 0.16803275048732758, + 0.16071400046348572, + 0.1774478405714035 ], "tsne3d": [ - 36.45539855957031, - -1.1860569715499878, - 6.860159397125244 + 5.145322322845459, + 33.4188232421875, + 2.0263173580169678 ], "umap3d": [ - 5.382080078125, - 1.6093941926956177, - 1.9982659816741943 + 4.769376277923584, + 2.064288377761841, + 4.8410773277282715 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { "id": 7, - "label": "Open AI" + "label": "Hugging Face Models" } ], "_embedding": [ @@ -13181,47 +13213,47 @@ "abstract": "We\u2019re on a journey to advance and democratize artificial intelligence through open source and open science.", "projections": { "pca": [ - 0.1619095802307129, - 0.15971091389656067 + 0.16587482392787933, + 0.163045734167099 ], "tsne": [ - 6.929973602294922, - 19.08380699157715 + 22.102924346923828, + 11.604904174804688 ], "umap": [ - 4.850207328796387, - 4.802377700805664 + 3.898409128189087, + 3.0561866760253906 ], "pca3d": [ - 0.16190941631793976, - 0.15971970558166504, - 0.17881369590759277 + 0.1658744215965271, + 0.16305285692214966, + 0.1758621335029602 ], "tsne3d": [ - 36.47292709350586, - -6.593172073364258, - 6.68770694732666 + 1.3269062042236328, + 35.9496955871582, + 1.3969806432724 ], "umap3d": [ - 5.397040367126465, - 1.621230125427246, - 1.9635053873062134 + 4.859809398651123, + 2.139841079711914, + 4.694091796875 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { "id": 7, - "label": "Open AI" + "label": "Hugging Face Models" } ], "_embedding": [ @@ -13629,47 +13661,47 @@ "abstract": "We\u2019re on a journey to advance and democratize artificial intelligence through open source and open science.", "projections": { "pca": [ - 0.1720028817653656, - 0.13171029090881348 + 0.17450541257858276, + 0.1345992535352707 ], "tsne": [ - 5.480593204498291, - 17.511354446411133 + 22.687841415405273, + 10.127936363220215 ], "umap": [ - 4.734116554260254, - 4.824536323547363 + 3.6861345767974854, + 3.0096933841705322 ], "pca3d": [ - 0.1720026135444641, - 0.13171346485614777, - 0.11150911450386047 + 0.1745051145553589, + 0.13460257649421692, + 0.10884582251310349 ], "tsne3d": [ - 31.305418014526367, - -3.9204089641571045, - 3.449064016342163 + 2.6770761013031006, + 34.184452056884766, + -3.475264072418213 ], "umap3d": [ - 5.428829669952393, - 1.4908928871154785, - 1.7696055173873901 + 4.792277812957764, + 1.9313337802886963, + 4.926319599151611 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { "id": 7, - "label": "Open AI" + "label": "Hugging Face Models" } ], "_embedding": [ @@ -14077,47 +14109,47 @@ "abstract": "We\u2019re on a journey to advance and democratize artificial intelligence through open source and open science.", "projections": { "pca": [ - 0.17268183827400208, - 0.15768329799175262 + 0.1767316460609436, + 0.15992073714733124 ], "tsne": [ - 7.213107585906982, - 19.82607650756836 + 22.077999114990234, + 13.189026832580566 ], "umap": [ - 5.137181758880615, - 4.744722366333008 + 4.159877777099609, + 2.929739475250244 ], "pca3d": [ - 0.17268137633800507, - 0.15769124031066895, - 0.24952581524848938 + 0.1767309606075287, + 0.15992718935012817, + 0.24492870271205902 ], "tsne3d": [ - 48.305381774902344, - -3.285912036895752, - 10.431169509887695 + 2.8291375637054443, + 34.24542999267578, + 6.656050205230713 ], "umap3d": [ - 5.495276927947998, - 1.7256810665130615, - 2.0430150032043457 + 4.921189308166504, + 2.168778657913208, + 4.644350528717041 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { "id": 7, - "label": "Open AI" + "label": "Hugging Face Models" } ], "_embedding": [ @@ -14527,47 +14559,47 @@ "abstract": "We\u2019re on a journey to advance and democratize artificial intelligence through open source and open science.", "projections": { "pca": [ - 0.20944418013095856, - 0.2245393991470337 + 0.21232400834560394, + 0.2264673113822937 ], "tsne": [ - 5.891754627227783, - 21.77656364440918 + 23.882160186767578, + 11.791061401367188 ], "umap": [ - 4.95169734954834, - 4.863137722015381 + 3.989565134048462, + 2.990659475326538 ], "pca3d": [ - 0.20944324135780334, - 0.22454164922237396, - 0.13218849897384644 + 0.2123231142759323, + 0.22647006809711456, + 0.12786102294921875 ], "tsne3d": [ - 42.18777847290039, - -4.154725074768066, - -3.176417589187622 + 4.466007232666016, + 42.55770492553711, + 1.6659821271896362 ], "umap3d": [ - 5.5440993309021, - 1.5080304145812988, - 1.8968929052352905 + 4.936733722686768, + 1.9539905786514282, + 4.841429710388184 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { "id": 7, - "label": "Open AI" + "label": "Hugging Face Models" } ], "_embedding": [ @@ -14975,47 +15007,47 @@ "abstract": "A collection of awesome bio-foundation models, including protein, RNA, DNA, gene, single-cell, and so on. - GitHub - apeterswu/Awesome-Bio-Foundation-Models: A collection of awesome bio-foundation models, including protein, RNA, DNA, gene, single-cell, and so on.", "projections": { "pca": [ - 0.07128754258155823, - -0.1512494534254074 + 0.0672186091542244, + -0.15241102874279022 ], "tsne": [ - 5.55133056640625, - -17.312606811523438 + -21.377845764160156, + -26.66412353515625 ], "umap": [ - -0.853832483291626, - 3.6856653690338135 + -0.4234740436077118, + 6.905561447143555 ], "pca3d": [ - 0.07128652185201645, - -0.15125486254692078, - -0.23484565317630768 + 0.06721775978803635, + -0.1524166762828827, + -0.23194807767868042 ], "tsne3d": [ - -33.3277473449707, - 1.563651442527771, - -24.77446174621582 + -19.576086044311523, + -39.43186950683594, + 2.024094820022583 ], "umap3d": [ - 1.853773832321167, - 1.1587927341461182, - -0.9020528197288513 + 0.9423235058784485, + 0.5057410001754761, + 4.0655646324157715 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 19, - "label": "Cell Models" + "id": 20, + "label": "Cell Foundation" } ], "_embedding": [ @@ -15424,47 +15456,47 @@ "journal": "METR Blog", "projections": { "pca": [ - 0.022633202373981476, - 0.2511785328388214 + 0.026414714753627777, + 0.25385674834251404 ], "tsne": [ - -1.6262898445129395, - 17.95347785949707 + 10.526642799377441, + 14.05587387084961 ], "umap": [ - 3.5989878177642822, - 3.791140556335449 + 2.7124929428100586, + 2.7265615463256836 ], "pca3d": [ - 0.022632669657468796, - 0.2511771023273468, - 0.1882702261209488 + 0.02641422487795353, + 0.2538570463657379, + 0.18577507138252258 ], "tsne3d": [ - 39.19234085083008, - -8.225751876831055, - -15.614931106567383 + 0.6854350566864014, + 21.871566772460938, + -8.310608863830566 ], "umap3d": [ - 5.694810390472412, - 0.7116877436637878, - 1.1044220924377441 + 4.336547374725342, + 0.42404595017433167, + 5.289444923400879 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 0, - "label": "AI Agents" + "id": 8, + "label": "Open AI" }, { - "id": 1, - "label": "AI Agents" + "id": 22, + "label": "AI Gemini" } ], "_embedding": [ @@ -16033,46 +16065,46 @@ "openalex_url": "https://openalex.org/W4308100452", "projections": { "pca": [ - -0.10523495078086853, - -0.28030112385749817 + -0.10527929663658142, + -0.27939295768737793 ], "tsne": [ - -7.542751789093018, - -15.920425415039062 + -20.25140380859375, + -8.736859321594238 ], "umap": [ - -2.150853395462036, - 6.098790168762207 + -2.325674057006836, + 5.247276782989502 ], "pca3d": [ - -0.10523460060358047, - -0.280300498008728, - 0.024739746004343033 + -0.10527897626161575, + -0.27939245104789734, + 0.030155235901474953 ], "tsne3d": [ - -24.30276107788086, - -24.563344955444336, - 0.45537397265434265 + -31.244993209838867, + -16.63936424255371, + 25.282129287719727 ], "umap3d": [ - 2.7292816638946533, - -0.9571436047554016, - -0.6378360986709595 + 0.2451309710741043, + 0.3479837477207184, + 5.790786266326904 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 24, + "id": 1, "label": "Cancer Data" } ], @@ -16481,47 +16513,47 @@ "abstract": "We\u2019re on a journey to advance and democratize artificial intelligence through open source and open science.", "projections": { "pca": [ - 0.10206221789121628, - 0.0016533833695575595 + 0.10299693048000336, + 0.002402506535872817 ], "tsne": [ - 9.457526206970215, - 17.026676177978516 + 18.643539428710938, + 14.416955947875977 ], "umap": [ - 4.872312545776367, - 4.7496657371521 + 3.9962596893310547, + 3.427015781402588 ], "pca3d": [ - 0.10206194967031479, - 0.0016428373055532575, - 0.11049959063529968 + 0.10299661010503769, + 0.0023922272957861423, + 0.11087333410978317 ], "tsne3d": [ - 41.009666442871094, - -8.377593040466309, - 24.29134750366211 + -1.5511857271194458, + 27.72504425048828, + 16.81686019897461 ], "umap3d": [ - 5.0459303855896, - 1.525124430656433, - 2.0481626987457275 + 4.502816677093506, + 2.225170135498047, + 4.726437091827393 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { "id": 7, - "label": "Open AI" + "label": "Hugging Face Models" } ], "_embedding": [ @@ -16927,47 +16959,47 @@ ], "projections": { "pca": [ - 0.08068116754293442, - 0.020052405074238777 + 0.08029965311288834, + 0.020254552364349365 ], "tsne": [ - 15.490190505981445, - 5.250133037567139 + 17.76592254638672, + -0.6546115875244141 ], "umap": [ - 2.6201364994049072, - 5.9788689613342285 + 1.908215880393982, + 4.726231575012207 ], "pca3d": [ - 0.08068007230758667, - 0.0200368482619524, - 0.029975272715091705 + 0.0802985429763794, + 0.020240722224116325, + 0.0325721874833107 ], "tsne3d": [ - 13.521206855773926, - 22.568035125732422, - 2.5092451572418213 + 10.837162971496582, + 11.761004447937012, + 12.921555519104004 ], "umap3d": [ - 3.7157044410705566, - 0.03514277935028076, - 1.449051856994629 + 3.1172759532928467, + 1.5096982717514038, + 5.985664367675781 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 4, + "label": "AI Agents" }, { - "id": 3, - "label": "SMB Data" + "id": 24, + "label": "SMB Models" } ], "_embedding": [ @@ -17375,47 +17407,47 @@ "abstract": "We\u2019re on a journey to advance and democratize artificial intelligence through open source and open science.", "projections": { "pca": [ - 0.17100703716278076, - 0.1769474446773529 + 0.17456354200839996, + 0.1788676679134369 ], "tsne": [ - 7.081078052520752, - 21.237775802612305 + 22.975250244140625, + 12.633987426757812 ], "umap": [ - 5.144256114959717, - 4.7476301193237305 + 4.077041149139404, + 2.896793842315674 ], "pca3d": [ - 0.17100654542446136, - 0.1769549399614334, - 0.18588002026081085 + 0.1745632141828537, + 0.17887379229068756, + 0.1814357191324234 ], "tsne3d": [ - 45.44002151489258, - 2.0654335021972656, - 7.727842807769775 + 7.726037502288818, + 37.68478012084961, + 4.840109348297119 ], "umap3d": [ - 5.5391845703125, - 1.721204400062561, - 2.0532548427581787 + 4.971595287322998, + 2.132477283477783, + 4.620146751403809 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { "id": 7, - "label": "Open AI" + "label": "Hugging Face Models" } ], "_embedding": [ @@ -17850,47 +17882,47 @@ "doi": "10.1101/2025.05.30.656746", "projections": { "pca": [ - -0.18689163029193878, - -0.027760585770010948 + -0.1858452558517456, + -0.024392960593104362 ], "tsne": [ - 1.0273109674453735, - -4.7003254890441895 + -5.235047340393066, + -3.261491060256958 ], "umap": [ - -0.3122616708278656, - 5.005359172821045 + -0.7570378184318542, + 4.826629161834717 ], "pca3d": [ - -0.1868913620710373, - -0.02777571976184845, - 0.08347786962985992 + -0.18584518134593964, + -0.02440756931900978, + 0.08686613291501999 ], "tsne3d": [ - -12.275425910949707, - -7.870328903198242, - 26.747106552124023 + -3.1579627990722656, + -20.21945571899414, + -9.56134033203125 ], "umap3d": [ - 3.7963297367095947, - 0.6205918192863464, - -0.9978799819946289 + 2.3913955688476562, + 0.16068704426288605, + 5.001572608947754 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ - { - "id": 0, - "label": "Medical Data AI" - }, { "id": 2, - "label": "AI Drug Discovery" + "label": "Clinical AI Data" }, { - "id": 13, + "id": 9, "label": "Drug Discovery" + }, + { + "id": 9, + "label": "Biomedical AI" } ], "_embedding": [ @@ -18298,47 +18330,47 @@ "abstract": "Bin packing algorithms in numba. GitHub Gist: instantly share code, notes, and snippets.", "projections": { "pca": [ - 0.06370022147893906, - 0.03862559050321579 + 0.06366147100925446, + 0.0379214845597744 ], "tsne": [ - 7.566385746002197, - -7.83098030090332 + 2.7797772884368896, + -5.58406400680542 ], "umap": [ - 1.2145800590515137, - 3.940478801727295 + 0.3809688091278076, + 5.5019989013671875 ], "pca3d": [ - 0.06369978189468384, - 0.03862815722823143, - -0.12121356278657913 + 0.06366093456745148, + 0.037925515323877335, + -0.11984265595674515 ], "tsne3d": [ - -15.505763053894043, - 12.083089828491211, - 18.403728485107422 + -16.398082733154297, + 12.516785621643066, + -19.002201080322266 ], "umap3d": [ - 2.72859787940979, - 0.032588910311460495, - 0.28859829902648926 + 1.8460239171981812, + 0.8804966807365417, + 5.378541946411133 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 4, - "label": "Com Area Amp" + "id": 6, + "label": "Data Needs" }, { - "id": 27, - "label": "Embeddings" + "id": 16, + "label": "Lab Research" } ], "_embedding": [ @@ -18748,47 +18780,47 @@ "abstract": "Anthropic's agentic coding tool for developers. Claude Code understands your codebase, edits files, runs commands, and helps you ship faster.", "projections": { "pca": [ - 0.2196153700351715, - 0.10897794365882874 + 0.2216455340385437, + 0.11032391339540482 ], "tsne": [ - 23.590978622436523, - 24.43050765991211 + 33.42127990722656, + 6.294161796569824 ], "umap": [ - 4.628593444824219, - 6.025888442993164 + 4.016382217407227, + 4.620968341827393 ], "pca3d": [ - 0.21961408853530884, - 0.1089579239487648, - -0.005875604692846537 + 0.22164428234100342, + 0.1103062555193901, + -0.008298016153275967 ], "tsne3d": [ - 53.303009033203125, - 17.71860122680664, - 9.45715618133545 + 24.877559661865234, + 37.97522735595703, + 9.391459465026855 ], "umap3d": [ - 5.048315048217773, - 0.4982055425643921, - 3.069460868835449 + 5.09716796875, + 2.1905956268310547, + 6.2824177742004395 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { - "id": 16, - "label": "Claude Code" + "id": 11, + "label": "Code Tools" } ], "_embedding": [ @@ -19196,46 +19228,46 @@ "abstract": "VIBESegmentator: Full Torso Segmentation for the NAKO and UK Biobank in Volumetric Interpolated Breath-hold Examination Body Images - robert-graf/VIBESegmentator", "projections": { "pca": [ - 0.116971954703331, - -0.048040661960840225 + 0.1183602437376976, + -0.04719424992799759 ], "tsne": [ - -24.012327194213867, - 0.5768091678619385 + -4.379880905151367, + 13.657902717590332 ], "umap": [ - -0.2448839545249939, - 7.859038829803467 + -1.127394676208496, + 2.162034273147583 ], "pca3d": [ - 0.11697213351726532, - -0.04804231598973274, - -0.016526823863387108 + 0.11836059391498566, + -0.047194745391607285, + -0.0144101707264781 ], "tsne3d": [ - -16.32548713684082, - 13.440881729125977, - -9.623043060302734 + -3.5028231143951416, + 18.039012908935547, + 26.156814575195312 ], "umap3d": [ - 4.477346420288086, - -1.6848223209381104, - -0.2985099256038666 + 2.016099691390991, + -0.5049506425857544, + 6.755147457122803 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 11, + "id": 19, "label": "Medical Imaging" } ], @@ -19645,47 +19677,47 @@ "title": "AlphaGenome: AI for better understanding the genome \u00e2\u0080\u0094 Google DeepMind", "projections": { "pca": [ - -0.20742443203926086, - 0.07440590858459473 + -0.20595328509807587, + 0.07751866430044174 ], "tsne": [ - 3.350369691848755, - -30.15578842163086 + -10.493461608886719, + -22.972408294677734 ], "umap": [ - -2.0849714279174805, - 3.936652183532715 + -1.8344533443450928, + 6.943675994873047 ], "pca3d": [ - -0.20742450654506683, - 0.07440263032913208, - -0.009463987313210964 + -0.2059534192085266, + 0.07751667499542236, + -0.010004531592130661 ], "tsne3d": [ - -34.446773529052734, - 3.2410435676574707, - 3.4212069511413574 + -22.598764419555664, + -8.83039379119873, + -26.357572555541992 ], "umap3d": [ - 1.445477843284607, - 0.19449104368686676, - -1.0547354221343994 + 0.08713047951459885, + 1.0380744934082031, + 4.248769760131836 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 22, - "label": "Genetic Variants" + "id": 3, + "label": "Genomic Sequences" } ], "_embedding": [ @@ -20093,47 +20125,47 @@ "abstract": "Playwright MCP server. Contribute to microsoft/playwright-mcp development by creating an account on GitHub.", "projections": { "pca": [ - 0.3477115333080292, - 0.01641976088285446 + 0.347501277923584, + 0.015371723100543022 ], "tsne": [ - 26.560060501098633, - 12.318352699279785 + 30.104572296142578, + -6.5218119621276855 ], "umap": [ - 3.5737438201904297, - 6.847714900970459 + 3.2036561965942383, + 5.734705924987793 ], "pca3d": [ - 0.34771275520324707, - 0.016442516818642616, - -0.1226436048746109 + 0.34750303626060486, + 0.01539122685790062, + -0.128062903881073 ], "tsne3d": [ - 33.18072509765625, - 45.39362716674805, - 12.20487117767334 + 43.4619140625, + 20.826807022094727, + 6.172348976135254 ], "umap3d": [ - 3.4749913215637207, - -0.11922568827867508, - 2.9045090675354004 + 3.918044090270996, + 2.8153140544891357, + 6.726466178894043 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 18, - "label": "Github Tools" + "id": 17, + "label": "GitHub Creation" } ], "_embedding": [ @@ -20541,47 +20573,47 @@ "abstract": "Contribute to standardmodelbio/smb-vision development by creating an account on GitHub.", "projections": { "pca": [ - 0.28091296553611755, - 0.004667801316827536 + 0.27964547276496887, + 0.004802229814231396 ], "tsne": [ - 24.310625076293945, - 7.071946144104004 + 24.35609245300293, + -9.08938217163086 ], "umap": [ - 3.1696279048919678, - 6.656514644622803 + 2.6720924377441406, + 5.3833394050598145 ], "pca3d": [ - 0.28091341257095337, - 0.004682413302361965, - -0.19134412705898285 + 0.27964603900909424, + 0.004814852029085159, + -0.19318625330924988 ], "tsne3d": [ - 11.345633506774902, - 38.28127670288086, - 15.53562068939209 + 34.67097854614258, + 11.665064811706543, + 22.017955780029297 ], "umap3d": [ - 3.60206937789917, - -0.2551874816417694, - 2.346992254257202 + 3.5475800037384033, + 2.4535303115844727, + 6.624943256378174 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 18, - "label": "Github Tools" + "id": 17, + "label": "GitHub Creation" } ], "_embedding": [ @@ -20989,47 +21021,47 @@ "abstract": "Contribute to standardmodelbio/smb-vision development by creating an account on GitHub.", "projections": { "pca": [ - 0.3142681419849396, - -0.02566356398165226 + 0.31298983097076416, + -0.02554849162697792 ], "tsne": [ - 24.44211196899414, - 6.720189571380615 + 24.16558074951172, + -9.418395042419434 ], "umap": [ - 3.160336494445801, - 6.680746078491211 + 2.7930736541748047, + 5.53108549118042 ], "pca3d": [ - 0.3142684996128082, - -0.025649650022387505, - -0.17176835238933563 + 0.3129903972148895, + -0.025535481050610542, + -0.17216543853282928 ], "tsne3d": [ - 6.984488010406494, - 39.58399963378906, - 14.70379638671875 + 32.11703109741211, + 12.791853904724121, + 19.429885864257812 ], "umap3d": [ - 3.586911916732788, - -0.1519923061132431, - 2.2191836833953857 + 3.481276273727417, + 2.4594390392303467, + 6.633439540863037 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 18, - "label": "Github Tools" + "id": 17, + "label": "GitHub Creation" } ], "_embedding": [ @@ -21438,47 +21470,47 @@ "title": "Automatically disable copilot in dotenv files \u00b7 Issue #135 \u00b7 github/copilot.vim \u00b7 GitHub", "projections": { "pca": [ - 0.23670285940170288, - 0.11604822427034378 + 0.23846356570720673, + 0.11779090762138367 ], "tsne": [ - 13.717500686645508, - 10.72603702545166 + 19.107072830200195, + -9.014504432678223 ], "umap": [ - 3.1361258029937744, - 5.544270992279053 + 2.399488925933838, + 4.477879047393799 ], "pca3d": [ - 0.2367025464773178, - 0.11605250090360641, - -0.1408570110797882 + 0.23846299946308136, + 0.11779499053955078, + -0.14498795568943024 ], "tsne3d": [ - 0.0717131569981575, - 46.11818313598633, - 7.473289966583252 + 36.1700439453125, + -0.5212053060531616, + -1.1272591352462769 ], "umap3d": [ - 4.175832748413086, - -0.37942448258399963, - 2.170954704284668 + 3.8838284015655518, + 1.7240244150161743, + 6.702950954437256 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 20, - "label": "AI Agents" + "id": 24, + "label": "SMB Models" } ], "_embedding": [ @@ -21889,47 +21921,47 @@ "abstract": "\ud83e\udd89 OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation - camel-ai/owl", "projections": { "pca": [ - 0.10277091711759567, - 0.19114959239959717 + 0.10475421696901321, + 0.19364680349826813 ], "tsne": [ - 17.021333694458008, - 21.91620635986328 + 27.165611267089844, + 7.255834579467773 ], "umap": [ - 3.6377084255218506, - 5.386996269226074 + 3.088181257247925, + 4.23323917388916 ], "pca3d": [ - 0.10277104377746582, - 0.19115717709064484, - 0.012430964969098568 + 0.10475438088178635, + 0.1936538964509964, + 0.008627651259303093 ], "tsne3d": [ - 39.40884017944336, - 17.42996597290039, - -7.0466203689575195 + 22.175466537475586, + 28.894729614257812, + -5.31784725189209 ], "umap3d": [ - 4.6514458656311035, - 0.543694257736206, - 2.1828179359436035 + 4.2853593826293945, + 1.8781273365020752, + 5.751250743865967 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 20, - "label": "AI Agents" + "id": 4, + "label": "Open Agent Tools" } ], "_embedding": [ @@ -22337,47 +22369,47 @@ "abstract": "From today, all new submissions to Nature that are published will be accompanied by referees\u2019 reports and author responses \u2014 to illuminate the process of producing rigorous science.", "projections": { "pca": [ - 0.15197311341762543, - -0.08350464701652527 + 0.1529282033443451, + -0.08359977602958679 ], "tsne": [ - 16.24789047241211, - -17.692115783691406 + 3.8256373405456543, + 2.595919370651245 ], "umap": [ - 1.0746983289718628, - 4.804935932159424 + 0.188161239027977, + 5.051266670227051 ], "pca3d": [ - 0.15197299420833588, - -0.08350163698196411, - 0.08336048573255539 + 0.1529282182455063, + -0.0835966169834137, + 0.07936335355043411 ], "tsne3d": [ - -21.4130916595459, - 18.003934860229492, - 26.998577117919922 + 13.351967811584473, + -36.08573532104492, + 2.5961055755615234 ], "umap3d": [ - 2.9286155700683594, - 0.8829202651977539, - 0.19971534609794617 + 1.7613226175308228, + 1.5724503993988037, + 5.2016706466674805 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 4, - "label": "Com Area Amp" + "id": 9, + "label": "Drug Discovery" }, { "id": 23, - "label": "Research Authors" + "label": "Peer Review" } ], "_embedding": [ @@ -22785,47 +22817,47 @@ "abstract": "A tool for creating and running Linux containers using lightweight virtual machines on a Mac. It is written in Swift, and optimized for Apple silicon. - GitHub - apple/container: A tool for creating and running Linux containers using lightweight virtual machines on a Mac. It is written in Swift, and optimized for Apple silicon.", "projections": { "pca": [ - 0.26548027992248535, - -0.01442998182028532 + 0.26581814885139465, + -0.013524041511118412 ], "tsne": [ - 26.613765716552734, - 6.3054704666137695 + 24.483489990234375, + -11.713093757629395 ], "umap": [ - 3.2297310829162598, - 6.749831676483154 + 2.7844138145446777, + 5.5309977531433105 ], "pca3d": [ - 0.2654801309108734, - -0.01441468857228756, - -0.14718937873840332 + 0.26581794023513794, + -0.0135093554854393, + -0.14885647594928741 ], "tsne3d": [ - -1.275073528289795, - 38.2056770324707, - 17.599708557128906 + 36.81477355957031, + 17.757476806640625, + 23.59527015686035 ], "umap3d": [ - 3.652139663696289, - -0.37279123067855835, - 2.4402496814727783 + 3.658876895904541, + 2.4820659160614014, + 6.781321048736572 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 18, - "label": "Github Tools" + "id": 17, + "label": "GitHub Creation" } ], "_embedding": [ @@ -23235,47 +23267,47 @@ "abstract": "Contribute to shaunporwal/make-planning-notes-for-nate development by creating an account on GitHub.", "projections": { "pca": [ - 0.36109936237335205, - 0.05776847153902054 + 0.3595358729362488, + 0.0561758391559124 ], "tsne": [ - 25.548633575439453, - 13.194456100463867 + 29.622976303100586, + -4.7981061935424805 ], "umap": [ - 3.695378541946411, - 6.711609840393066 + 3.2552311420440674, + 5.476481914520264 ], "pca3d": [ - 0.36110031604766846, - 0.05777609720826149, - -0.1481056958436966 + 0.3595370948314667, + 0.056181538850069046, + -0.15353049337863922 ], "tsne3d": [ - 31.539752960205078, - 35.880409240722656, - 17.545503616333008 + 36.575897216796875, + 17.321521759033203, + 9.560949325561523 ], "umap3d": [ - 3.7084672451019287, - -0.14588794112205505, - 2.8678371906280518 + 4.0249152183532715, + 2.7287955284118652, + 6.715633392333984 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 18, - "label": "Github Tools" + "id": 17, + "label": "GitHub Creation" } ], "_embedding": [ @@ -23683,47 +23715,47 @@ "abstract": "davidlaprade has 54 repositories available. Follow their code on GitHub.", "projections": { "pca": [ - 0.3432082533836365, - -0.022045698016881943 + 0.34091877937316895, + -0.023651162162423134 ], "tsne": [ - 18.74238395690918, - 11.594222068786621 + 23.052928924560547, + -1.3342480659484863 ], "umap": [ - 3.4781875610351562, - 6.470809459686279 + 2.862797975540161, + 5.201817035675049 ], "pca3d": [ - 0.34320852160453796, - -0.02204527147114277, - -0.1694505363702774 + 0.34091949462890625, + -0.02365177683532238, + -0.1751578003168106 ], "tsne3d": [ - 29.780149459838867, - 35.83359909057617, - -7.293223857879639 + 43.32017517089844, + 18.201358795166016, + -4.3985915184021 ], "umap3d": [ - 3.8715450763702393, - -0.10844402015209198, - 2.317828416824341 + 3.8143720626831055, + 2.4817280769348145, + 6.471514701843262 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 18, - "label": "Github Tools" + "id": 17, + "label": "GitHub Creation" } ], "_embedding": [ @@ -24131,47 +24163,47 @@ "abstract": "Memory layer for AI Agents. Replace complex RAG pipelines with a serverless, single-file memory layer. Give your agents instant retrieval and long-term memory. - memvid/memvid", "projections": { "pca": [ - 0.0855436623096466, - 0.1724860668182373 + 0.08662424236536026, + 0.17310994863510132 ], "tsne": [ - -7.014229774475098, - 12.013935089111328 + 14.111533164978027, + 25.618274688720703 ], "umap": [ - 2.5205960273742676, - 3.6517529487609863 + 1.4504873752593994, + 2.720132827758789 ], "pca3d": [ - 0.08554282039403915, - 0.1724749058485031, - -0.0411890484392643 + 0.08662354201078415, + 0.17310084402561188, + -0.040885597467422485 ], "tsne3d": [ - 17.689186096191406, - 1.5859681367874146, - -4.805796146392822 + 24.52134132385254, + -8.419699668884277, + -23.408802032470703 ], "umap3d": [ - 5.571160316467285, - -0.18876706063747406, - 0.687535285949707 + 3.9547181129455566, + -0.022391624748706818, + 6.0280890464782715 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 27, - "label": "Embeddings" + "id": 18, + "label": "Retrieval Learning" } ], "_embedding": [ @@ -24581,47 +24613,47 @@ "abstract": "Get started with building Fullstack Agents using Gemini 2.5 and LangGraph - google-gemini/gemini-fullstack-langgraph-quickstart", "projections": { "pca": [ - 0.29928112030029297, - 0.0406637080013752 + 0.2989857792854309, + 0.04052794724702835 ], "tsne": [ - 19.723491668701172, - 14.519153594970703 + 24.729042053222656, + 0.4876540005207062 ], "umap": [ - 3.5399091243743896, - 6.052345275878906 + 2.980506420135498, + 4.8188886642456055 ], "pca3d": [ - 0.29928097128868103, - 0.04065914824604988, - -0.09070603549480438 + 0.29898563027381897, + 0.040524061769247055, + -0.09374144673347473 ], "tsne3d": [ - 28.837265014648438, - 28.588329315185547, - 5.671135425567627 + 26.323429107666016, + 17.295223236083984, + 3.7668964862823486 ], "umap3d": [ - 4.0802507400512695, - 0.2994411587715149, - 2.3731913566589355 + 3.988407611846924, + 2.2281312942504883, + 6.032588958740234 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 20, - "label": "AI Agents" + "id": 4, + "label": "Open Agent Tools" } ], "_embedding": [ @@ -25029,47 +25061,47 @@ "abstract": "[NeurIPS 2025] Open-source Multi-agent Poster Generation from Papers - Paper2Poster/Paper2Poster", "projections": { "pca": [ - 0.20497366786003113, - 0.12536805868148804 + 0.20673784613609314, + 0.12620943784713745 ], "tsne": [ - 21.70600700378418, - 15.73773193359375 + 26.90401268005371, + 0.17647601664066315 ], "umap": [ - 3.573305606842041, - 5.719647407531738 + 2.921088933944702, + 4.651294231414795 ], "pca3d": [ - 0.2049742490053177, - 0.12538141012191772, - -0.028296545147895813 + 0.20673877000808716, + 0.1262200027704239, + -0.03155055642127991 ], "tsne3d": [ - 29.666425704956055, - 18.936147689819336, - 21.227615356445312 + 30.03782844543457, + 25.10835075378418, + 3.2551960945129395 ], "umap3d": [ - 4.152176856994629, - 0.5396114587783813, - 2.1877939701080322 + 3.8730342388153076, + 2.089468240737915, + 5.867773532867432 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 20, - "label": "AI Agents" + "id": 4, + "label": "Open Agent Tools" } ], "_embedding": [ @@ -25487,47 +25519,47 @@ ], "projections": { "pca": [ - -0.06242179498076439, - 0.14069804549217224 + -0.05974854901432991, + 0.14302043616771698 ], "tsne": [ - -8.070740699768066, - 14.018423080444336 + 3.8203365802764893, + 13.109172821044922 ], "umap": [ - 1.8815088272094727, - 3.983508586883545 + 0.5677996277809143, + 2.7511467933654785 ], "pca3d": [ - -0.06242189556360245, - 0.14070095121860504, - 0.05365709960460663 + -0.05974858999252319, + 0.14302143454551697, + 0.05572576820850372 ], "tsne3d": [ - 4.356252193450928, - -0.6911265850067139, - -1.2754225730895996 + 14.539239883422852, + -11.806227684020996, + -8.807478904724121 ], "umap3d": [ - 5.195952415466309, - -0.6834849715232849, - 0.13018517196178436 + 3.088822603225708, + 0.27750909328460693, + 6.2592997550964355 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, - "label": "Model Training" + "id": 2, + "label": "Medical Models" }, { - "id": 27, - "label": "Embeddings" + "id": 0, + "label": "Multimodal Models" } ], "_embedding": [ @@ -25915,7 +25947,11 @@ 0.0384, 0.083, 0.0037 - ] + ], + "year": 2025, + "journal": "ArXiv.org", + "cited_by_count": 0, + "openalex_url": "https://openalex.org/W4415037686" }, { "channel": "standardmodelbio", @@ -25937,47 +25973,47 @@ "abstract": "Next Generation Experimental Tracking for Machine Learning Operations - mlop-ai/mlop", "projections": { "pca": [ - 0.18000416457653046, - 0.18333183228969574 + 0.18181855976581573, + 0.18488809466362 ], "tsne": [ - 17.47195816040039, - 11.84584903717041 + 22.093355178833008, + -0.5066439509391785 ], "umap": [ - 3.3926539421081543, - 5.134526252746582 + 2.575434684753418, + 3.692131996154785 ], "pca3d": [ - 0.18000435829162598, - 0.18333497643470764, - 0.028626590967178345 + 0.18181878328323364, + 0.1848921775817871, + 0.025219744071364403 ], "tsne3d": [ - 27.40836524963379, - 26.450159072875977, - -8.981152534484863 + 38.215911865234375, + 24.044767379760742, + -7.205906391143799 ], "umap3d": [ - 4.757756233215332, - 0.40715715289115906, - 1.5017050504684448 + 3.9836626052856445, + 1.466128945350647, + 5.700850009918213 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 20, - "label": "AI Agents" + "id": 15, + "label": "PyTorch Models" } ], "_embedding": [ @@ -26384,47 +26420,47 @@ "title": "Better Error Message for SymIntArrayRef expected to contain only concrete integers \u00b7 Issue #140960 \u00b7 pytorch/pytorch \u00b7 GitHub", "projections": { "pca": [ - 0.22566743195056915, - -0.034878868609666824 + 0.22419923543930054, + -0.03591856732964516 ], "tsne": [ - 31.461254119873047, - 5.238511562347412 + 21.200834274291992, + -12.674922943115234 ], "umap": [ - 2.7839648723602295, - 6.517889499664307 + 2.2766523361206055, + 5.434258460998535 ], "pca3d": [ - 0.22566691040992737, - -0.03487633913755417, - -0.20382463932037354 + 0.2241986244916916, + -0.035915084183216095, + -0.20474009215831757 ], "tsne3d": [ - 3.9715373516082764, - 32.69184494018555, - 27.808698654174805 + 30.23529624938965, + 14.595134735107422, + 29.05986785888672 ], "umap3d": [ - 3.84016489982605, - -0.7297233939170837, - 1.9672702550888062 + 3.54109525680542, + 1.8151545524597168, + 6.946429252624512 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 3, - "label": "SMB Data" + "id": 2, + "label": "Bioinformatics Tools" } ], "_embedding": [ @@ -26830,47 +26866,47 @@ ], "projections": { "pca": [ - -0.0854736715555191, - 0.17754939198493958 + -0.08380201458930969, + 0.17913959920406342 ], "tsne": [ - -22.078447341918945, - 14.8306884765625 + -1.3691290616989136, + 18.045900344848633 ], "umap": [ - 1.9907864332199097, - 2.902939796447754 + 1.1661001443862915, + 1.928520679473877 ], "pca3d": [ - -0.08547373116016388, - 0.17755357921123505, - 0.01000300794839859 + -0.08380208164453506, + 0.17914333939552307, + 0.011411660350859165 ], "tsne3d": [ - -19.179609298706055, - 17.003883361816406, - -38.36735534667969 + 2.962054491043091, + -6.149164199829102, + -34.97907638549805 ], "umap3d": [ - 6.08082389831543, - -0.7971415519714355, - 0.3278929591178894 + 3.8764419555664062, + -0.8086342811584473, + 6.316863536834717 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 15, - "label": "Latent Models" + "id": 18, + "label": "Retrieval Learning" } ], "_embedding": [ @@ -27278,435 +27314,458 @@ "journal": "Nature Communications", "projections": { "pca": [ - -0.25009873509407043, - -0.01216265931725502 + -0.1957097351551056, + -0.06134355440735817 ], "tsne": [ - -6.386794090270996, - -31.72372055053711 + -20.701705932617188, + -21.087251663208008 ], "umap": [ - -1.334102988243103, - 3.0617215633392334 + -0.7737578749656677, + 6.8681488037109375 ], "pca3d": [ - -0.2500990927219391, - -0.01216257456690073, - -0.0929776281118393 + -0.1957099586725235, + -0.06134069710969925, + -0.18106359243392944 ], "tsne3d": [ - -47.45967102050781, - -16.447925567626953, - -18.38374900817871 + -29.87615966796875, + -26.419410705566406, + -2.103844404220581 ], "umap3d": [ - 1.2925690412521362, - 1.2021147012710571, - -1.3060804605484009 + 0.5987065434455872, + 0.20413519442081451, + 3.855508804321289 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 19, - "label": "Cell Models" + "id": 20, + "label": "Cell Foundation" } ], "_embedding": [ - -0.0406, - 0.0126, - -0.0033, - -0.0364, - 0.0407, - 0.0547, - -0.0529, - -0.0205, - 0.0416, - 0.0054, - 0.0142, - -0.027, - -0.0102, - 0.0104, - 0.0225, - 0.0206, - -0.0422, - 0.0362, - -0.0177, - 0.0073, - 0.0502, - -0.0533, - -0.0045, - -0.044, - -0.0201, + -0.0259, + -0.0004, + -0.0167, + -0.0182, + 0.0448, + 0.0364, + -0.0384, + -0.035, + 0.0249, + 0.0027, + 0.0456, + -0.0348, + -0.0152, + 0.0125, + 0.0089, + 0.0059, + -0.0616, + 0.0028, + 0.0022, -0.0057, - -0.0422, - -0.0242, - -0.0591, - -0.1721, - 0.05, - 0.0386, - 0.0606, - 0.0167, - 0.0256, - -0.0491, - -0.0218, - 0.0066, - -0.0218, - 0.0313, - -0.0025, - 0.0034, - 0.0176, - 0.0049, - 0.0124, - -0.0225, - -0.0338, - -0.0331, - -0.0457, - -0.0157, - -0.0452, - -0.1034, - -0.0148, - 0.0074, - -0.0038, - 0.0031, - 0.0526, - 0.0415, - 0.0209, - 0.0495, - -0.0216, - 0.0066, - -0.1224, - 0.0488, - 0.0342, - -0.0233, - -0.0111, - -0.039, - 0.0178, - 0.0408, - -0.0507, - 0.0444, - 0.0837, - 0.04, - 0.0083, - 0.0632, - -0.0281, - -0.0072, - 0.0625, - 0.0036, - -0.0052, - -0.0143, - 0.0, - -0.0267, - -0.0243, + 0.0546, + -0.0716, + -0.0003, + -0.0499, + 0.0035, + 0.0052, + -0.0203, + -0.0511, + -0.0414, + -0.2486, + 0.0365, + 0.0426, + 0.0519, + 0.014, + 0.0142, + -0.0251, + -0.0094, -0.0141, - -0.0228, - -0.025, - 0.0074, - 0.0031, - -0.0276, - 0.0191, - 0.0096, - 0.0292, - -0.0274, - -0.0161, - 0.0424, - -0.0432, - -0.0564, - 0.3438, - -0.0207, - 0.0155, - -0.0436, - -0.0074, - 0.0101, - -0.0213, - -0.0112, - 0.022, - -0.0052, - -0.0005, - -0.0546, - -0.0436, - -0.0154, - 0.0034, - 0.0328, - 0.0256, - 0.0599, - -0.0365, - -0.0079, - 0.0024, - 0.0308, - -0.0379, - 0.0242, - -0.0116, + -0.0089, + 0.0464, + -0.0138, + 0.0445, + 0.0123, + 0.0209, + 0.0112, + -0.0217, + -0.0321, + -0.0183, + -0.0442, + -0.0152, + -0.0317, + -0.0642, + -0.0241, + 0.0377, + -0.0218, + 0.0263, + 0.0417, + 0.0627, + 0.0192, + 0.049, + -0.0061, + -0.0002, + -0.1438, 0.0387, - -0.0261, - -0.0347, - 0.1485, - 0.0225, - 0.0157, - 0.0776, - 0.1169, - -0.0724, - 0.0449, - 0.0233, - 0.0144, - -0.0239, - -0.0462, - -0.0397, - 0.0399, - 0.0153, - -0.0344, - -0.0246, - -0.0477, - -0.0761, - 0.1005, - -0.0181, - 0.0053, - -0.0689, - -0.0693, - 0.0195, - 0.0699, - 0.0686, - -0.0158, - 0.0218, - 0.0236, - 0.0021, - -0.0259, - -0.0129, - 0.0042, - -0.0275, - -0.0135, - -0.0173, + 0.0382, + -0.0411, + 0.0089, + -0.051, + 0.0282, + 0.0451, + -0.037, + 0.0452, 0.0669, - -0.0134, - -0.0903, - -0.0153, - -0.0385, - 0.0438, - 0.0142, - 0.0219, - -0.0606, - 0.0016, - 0.0443, - -0.0332, - 0.0039, - -0.0765, - -0.0278, - 0.0656, - 0.0543, - 0.0739, - -0.0259, - -0.0146, - 0.0803, - -0.0367, - -0.043, - 0.02, - 0.003, - -0.0453, - -0.0447, - -0.0176, - 0.0696, - -0.0261, - 0.0299, - -0.0465, - -0.0904, - -0.0097, - 0.02, + 0.0191, + 0.0185, + 0.038, + -0.0305, + -0.0007, + 0.0688, + 0.0036, + -0.0376, + -0.0142, + -0.0098, + -0.0422, + -0.0339, + -0.0045, + -0.0445, -0.0374, - -0.0434, - 0.0079, - 0.0096, - -0.0215, - 0.047, - -0.0378, - -0.0021, - -0.0545, - 0.0284, - 0.0522, - 0.0399, + -0.0054, + -0.0091, + -0.0013, + 0.0324, + 0.0166, + 0.0471, + -0.0673, + -0.0049, + 0.044, + -0.0174, + -0.0277, + 0.4172, + -0.018, + 0.0085, + -0.0388, + 0.0379, + 0.0045, + -0.0462, + -0.0103, + 0.0038, + 0.0208, + 0.0133, + -0.0509, + -0.0504, + -0.01, + -0.0192, + 0.0075, + -0.0126, + 0.0565, + -0.0375, + -0.0025, + 0.0161, + 0.002, + -0.0353, + 0.0259, + -0.0129, + 0.0638, + -0.0035, + -0.0352, + 0.1131, + 0.05, -0.0245, - 0.0112, - 0.0304, - 0.0185, - -0.0212, - -0.0119, - 0.0193, - 0.0233, - 0.0462, - 0.0499, - 0.0521, - 0.0257, - -0.086, - -0.2959, - 0.0265, - 0.0666, - -0.0545, + 0.0806, + 0.0634, + -0.0716, + 0.0349, + -0.0013, + 0.0056, + 0.0054, + -0.041, + -0.0378, + 0.0103, + 0.0105, + -0.0448, + 0.0101, -0.0529, - 0.0197, - 0.0322, - 0.0152, - 0.0413, - 0.0877, - 0.0086, - 0.0443, - -0.0302, - 0.0083, - -0.0239, - -0.0045, - 0.0454, - 0.0374, - -0.0308, - 0.0121, - 0.0604, - -0.0021, - 0.0368, - -0.0518, - 0.0624, + -0.1006, + 0.1029, + -0.0033, + -0.0076, + -0.0509, + -0.0494, + 0.0175, + 0.0692, + 0.0552, -0.0127, - 0.1364, - -0.0059, - -0.0523, - -0.055, - -0.002, - 0.0245, - 0.0148, - -0.08, - 0.0762, - -0.0015, - 0.0155, - -0.0288, - 0.0005, + 0.0131, + 0.0393, + 0.0059, + -0.0258, + -0.0077, + -0.0094, + -0.0455, + 0.0145, + -0.0326, + 0.0526, + 0.0216, + -0.0806, + -0.0293, + 0.0015, + -0.0091, + -0.0291, + 0.0018, + -0.0576, + -0.023, + 0.0431, + -0.0628, + 0.0057, + -0.0696, + -0.0412, + 0.064, + 0.0569, + 0.0143, + -0.0216, + -0.0069, + 0.0475, + -0.033, + -0.0387, + -0.0138, + 0.0176, + -0.024, + -0.051, + -0.0326, + 0.0756, + -0.0141, + 0.0462, + -0.0581, + -0.0796, + 0.0111, + 0.0236, + -0.0017, + -0.0329, + 0.0114, + 0.0355, 0.0035, - 0.0021, - -0.0088, - -0.0446, - -0.1133, - -0.0559, - 0.0009, + 0.0481, + -0.0251, + 0.032, + -0.0577, + 0.008, + 0.022, + 0.0176, -0.0524, - -0.0374, - -0.0976, - -0.0128, - 0.0679, - -0.0645, - 0.0211, - -0.0488, - -0.0028, - -0.0091, - -0.0473, - 0.0618, - -0.0946, - -0.0021, - 0.0304, - -0.0379, - -0.0721, + -0.0034, + -0.0041, + 0.0003, + 0.007, + 0.0021, + 0.029, + 0.0092, + 0.0742, + 0.0323, + 0.0249, + -0.0109, + -0.0791, + -0.2949, + -0.0188, + 0.0493, + -0.0745, -0.0201, - 0.0044, - -0.0067, - -0.0479, - -0.0244, - 0.0228, - -0.092, - 0.0111, + 0.0142, + 0.0194, + 0.0003, + 0.0252, 0.062, - -0.0029, - 0.0922, - 0.0529, - -0.021, - 0.014, - -0.0716, - -0.0993, - -0.0223, - 0.1004, - 0.0223, - 0.0664, - -0.0049, - 0.0579, - 0.0554, - 0.0872, - 0.0005, - 0.0236, - 0.0286, - -0.0252, - -0.0195, - -0.0753, + 0.0088, + 0.0722, + -0.0617, + -0.0154, + -0.0119, + -0.005, + 0.044, + 0.0312, + -0.0509, + 0.0059, + 0.0594, + -0.0106, + 0.0284, + -0.031, + 0.0511, + -0.0115, + 0.1119, + -0.0011, + -0.0031, + -0.0521, + 0.0007, + 0.0316, + 0.0228, + -0.0502, + 0.0401, + -0.0084, + 0.0176, + -0.0418, + -0.0011, + -0.0074, -0.0203, - 0.0263, - -0.0169, - -0.2081, - 0.0185, - -0.0181, - 0.0638, - -0.0278, - 0.003, - 0.052, - -0.0363, - 0.0236, - 0.0131, - 0.0016, - 0.0326, - 0.1114, - 0.0241, + -0.0102, + -0.0207, + -0.1055, + -0.0655, + 0.0248, + -0.0261, + -0.0282, + -0.0558, + 0.0102, + 0.064, + -0.0636, + 0.0249, + -0.0329, + 0.0064, + -0.0044, + -0.0383, + 0.0628, + -0.0808, + 0.0051, + 0.0046, + -0.0303, + -0.0226, + -0.0311, + 0.0238, + -0.0134, + -0.0574, + -0.0058, + 0.0568, + -0.0825, + 0.0162, + 0.0737, + 0.0079, + 0.0737, + 0.0592, + -0.0269, + 0.0062, + -0.0664, + -0.0853, + -0.038, + 0.0939, 0.0185, - 0.005, - 0.0975, - -0.0082, - 0.0075, - -0.0934, - 0.039, - 0.0082, - 0.1099, - -0.045, - 0.0462, - 0.052, - -0.0144, - -0.032, - 0.0863, + 0.0656, + -0.0003, + 0.0301, + 0.0531, + 0.1009, + 0.009, + 0.023, + 0.0126, + -0.0195, -0.0379, - 0.0051, - 0.0067, - 0.0702, - -0.0095, - -0.0392, - 0.0735, - 0.0109, - 0.0192, + -0.0259, + -0.022, + 0.0297, + 0.0038, + -0.2445, + 0.04, + -0.0048, + 0.0693, + -0.0212, -0.0031, - -0.0341, - 0.0719, - -0.0289, - 0.0444, - -0.0278, + 0.078, + -0.0321, + 0.0124, + -0.0022, + 0.0091, + 0.042, + 0.1052, + 0.0236, + 0.0019, + 0.0017, + 0.0563, + 0.0033, + 0.0049, + -0.0674, + 0.0417, + 0.0063, + 0.1151, + -0.0273, + 0.0583, + 0.0453, + -0.0056, + -0.0221, + 0.0795, + -0.036, + -0.0049, + 0.0264, + 0.046, + -0.0053, + -0.006, + 0.0313, + 0.0213, + 0.0322, + -0.0041, + -0.0108, + 0.0706, + -0.0149, + 0.0292, + 0.005, + 0.0136, + -0.0296, + 0.0506, + -0.0528, + -0.0495, + -0.0012, + -0.0513, + -0.0053, + 0.0256, + 0.0119, + -0.0046, + 0.0677, 0.0182, - -0.0083, - 0.0514, - -0.0548, - -0.0408, - 0.0052, - -0.0256, - 0.011, - 0.033, - -0.0017, - -0.0055, - 0.0522, - 0.0407, - -0.0576, - -0.0029, - -0.0237, - -0.015, - -0.0203, - -0.0062, - 0.1111, - -0.066 - ] + -0.0485, + 0.0131, + 0.0012, + 0.009, + 0.0072, + -0.0202, + 0.103, + -0.0495 + ], + "year": 2025, + "authors": [ + "Yuansong Zeng", + "Jiancong Xie", + "Ningyuan Shangguan", + "Zhuoyi Wei", + "Wenbing Li", + "Yun Su", + "Shuangyu Yang", + "Chengyang Zhang", + "Jinbo Zhang", + "Nan Fang" + ], + "affiliations": [ + "Sun Yat-sen University", + "Chongqing University", + "Huawei Technologies (China)", + "Sun Yat-sen Memorial Hospital", + "Singleron Biotechnologies (china)" + ], + "abstract": "Single-cell sequencing provides transcriptomic profiling at single-cell resolution, uncovering cellular heterogeneity with unprecedented precision. Yet, current single cell data analysis suffers from the inherent data noises, batch effects, and sparsity, highlighting the requirement of a unified model to represent cellular states. To circumvent this problem, many recent efforts focus on training single-cell foundation models based on large datasets. However, current human foundation models are still limited by the sizes of training data and model parameters. Here, we have collected a diverse dataset of 100 million human cells, on which we train a single-cell foundation model (CellFM) containing 800 million parameters. To balance efficiency and performance, the model is trained through a modified RetNet framework on the MindSpore. Extensive experiments have shown that CellFM outperforms existing models in cell annotation, perturbation prediction, gene function prediction, and gene-gene relationship capturing. Single-cell sequencing reveals cellular heterogeneity but is challenged by technical noise and batch effects. Here, authors present CellFM, an 800-million-parameter foundation model trained on 100 million human cells through the MindSpore framework, which outperforms existing models in downstream tasks.", + "cited_by_count": 54, + "openalex_url": "https://openalex.org/W4410521040" }, { "channel": "standardmodelbio", @@ -27732,47 +27791,47 @@ ], "projections": { "pca": [ - -0.17130324244499207, - 0.20819047093391418 + -0.17103737592697144, + 0.20872341096401215 ], "tsne": [ - -12.792583465576172, - 11.073494911193848 + -6.080803394317627, + 31.38216209411621 ], "umap": [ - 1.9921226501464844, - 3.314180612564087 + 0.9766025543212891, + 2.3771729469299316 ], "pca3d": [ - -0.1713031828403473, - 0.2081790417432785, - -0.05187414214015007 + -0.1710374504327774, + 0.20870928466320038, + -0.048684410750865936 ], "tsne3d": [ - 3.8600006103515625, - -0.39861389994621277, - -15.58880615234375 + 9.471752166748047, + -13.94006633758545, + -26.715049743652344 ], "umap3d": [ - 5.9290313720703125, - -0.48673123121261597, - 0.3197939097881317 + 3.954801321029663, + -0.7196281552314758, + 6.516315460205078 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 27, - "label": "Embeddings" + "id": 18, + "label": "Retrieval Learning" } ], "_embedding": [ @@ -28160,7 +28219,11 @@ -0.0279, 0.0475, -0.048 - ] + ], + "year": 2025, + "journal": "ArXiv.org", + "cited_by_count": 2, + "openalex_url": "https://openalex.org/W4417301036" }, { "channel": "standardmodelbio", @@ -28182,435 +28245,454 @@ "journal": "Nature", "projections": { "pca": [ - -0.1918489784002304, - -0.12358126044273376 + -0.11234644055366516, + -0.2108617126941681 ], "tsne": [ - -13.92740535736084, - -15.051937103271484 + -18.838958740234375, + -10.610260963439941 ], "umap": [ - -1.5006351470947266, - 5.218012809753418 + -2.105668544769287, + 5.465033531188965 ], "pca3d": [ - -0.19184915721416473, - -0.12358572334051132, - -0.02754606492817402 + -0.11234696954488754, + -0.2108701765537262, + -0.09995466470718384 ], "tsne3d": [ - -9.574559211730957, - -38.704410552978516, - -16.58164405822754 + -25.418275833129883, + -15.132232666015625, + 7.718672275543213 ], "umap3d": [ - 2.7162423133850098, - -0.0489703007042408, - -1.1244240999221802 + 0.28850677609443665, + 0.2877867817878723, + 5.405318737030029 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 1, "label": "Cancer Data" }, { - "id": 9, - "label": "Cancer Biobank" + "id": 1, + "label": "Cancer Data" } ], "_embedding": [ - -0.0996, - 0.0004, - -0.0092, - 0.0035, - 0.0084, - -0.0176, - 0.0055, - 0.0492, - 0.0346, - -0.0213, - 0.0281, - -0.0148, - -0.0005, - -0.0001, - -0.0116, - 0.0075, - 0.0044, - 0.0104, - -0.0504, - 0.0105, - 0.0087, - -0.0721, - -0.0668, - -0.0293, - -0.0181, - -0.0031, - -0.0278, - -0.0284, - -0.0523, - -0.176, - 0.0465, - 0.0053, - 0.0382, - -0.0074, - 0.0191, - -0.0104, - -0.0382, - 0.0119, - -0.0278, - 0.0165, - 0.0017, - 0.0211, - -0.0005, - 0.0336, - 0.0606, - -0.076, + -0.0674, + -0.0303, + -0.0461, + -0.0028, + 0.0402, + -0.0247, + 0.0339, + 0.0199, + 0.0269, + -0.0255, + 0.0392, -0.0311, - -0.0197, - -0.039, - 0.0258, - -0.0333, - -0.0769, - 0.0103, - 0.0183, - 0.0277, - 0.0296, - 0.0146, - 0.0329, - 0.0206, - 0.0545, - 0.0103, - 0.0691, - -0.1036, - 0.0459, - 0.0502, - -0.0024, - -0.0244, - -0.0709, - 0.066, - 0.0373, - -0.0062, - 0.0744, - 0.0484, - 0.0661, - -0.0121, - 0.08, - -0.0049, - -0.0361, - 0.0548, - 0.0016, - -0.0285, - 0.0242, - -0.0224, - 0.0073, - -0.0488, - 0.0021, - -0.0396, - -0.0577, - -0.0027, - 0.019, - 0.0069, - 0.0692, - 0.0281, - 0.0253, - -0.0522, - 0.0455, - 0.0341, + -0.0253, -0.0016, - 0.0094, - 0.3312, - 0.0077, - -0.0452, - -0.0064, - -0.0099, - 0.0124, - -0.0773, - -0.0662, - -0.0678, - -0.0222, - -0.0465, - -0.0011, - -0.017, - 0.0787, - 0.0111, - -0.0269, - 0.0262, - 0.0503, - -0.0173, - 0.0019, - -0.0105, - -0.0061, - -0.0604, - -0.0073, - 0.0333, - 0.0277, + -0.0256, + 0.0068, + 0.0263, + 0.0096, + -0.0768, + -0.0037, + 0.0162, + -0.0655, + -0.0388, + -0.0255, + 0.0017, + -0.0006, + -0.0271, + -0.045, + -0.0715, + -0.1986, + 0.064, + -0.0034, 0.0205, - -0.0225, - 0.1139, - -0.0149, - -0.0112, - 0.0617, - 0.1032, - -0.0878, - -0.0011, -0.0108, - 0.0019, - -0.0534, - -0.0339, - -0.0335, - 0.0393, - -0.0244, - -0.0523, - 0.0011, - -0.0419, - -0.0634, - 0.0807, - -0.0203, - 0.0375, - -0.0196, - -0.0498, - 0.0039, - 0.0845, - -0.0047, - -0.0065, - -0.0244, - 0.0342, - -0.0702, - -0.0131, - -0.0417, - 0.015, - 0.0058, - 0.0211, - -0.0105, - 0.1399, - -0.035, - -0.039, - -0.0128, - -0.0165, - 0.0171, - 0.0611, - -0.0048, - 0.004, - -0.0407, - 0.0126, - -0.0368, + 0.0216, + -0.0285, + -0.02, + 0.0027, + -0.0321, + 0.0156, + -0.0187, + 0.0259, + -0.0155, + 0.0314, + 0.0463, + -0.0377, + -0.0031, + 0.0038, + -0.0216, + 0.0358, + -0.0359, + -0.0531, + 0.0132, + 0.0318, + 0.009, + 0.0248, + -0.0068, + 0.0407, + 0.0295, + 0.0401, -0.0048, + 0.0577, + -0.1135, + 0.0692, + 0.0463, + 0.0197, + 0.0071, + -0.0886, + 0.088, + 0.02, + -0.034, + 0.0394, + 0.0288, + 0.0734, + -0.0181, + 0.0272, + -0.005, + -0.0531, + 0.0601, + 0.0098, + -0.019, + 0.0233, + 0.01, + 0.0015, + -0.0465, + -0.0008, + -0.0024, -0.0772, - -0.0243, - 0.0481, - 0.0352, - -0.0278, - -0.0241, - -0.0164, - 0.0474, - 0.0371, - -0.0451, - -0.0272, + 0.0045, + 0.0095, + -0.0151, + 0.0044, + 0.0066, + 0.0415, + -0.0754, + 0.0041, + 0.0285, + 0.0301, + 0.0428, + 0.3713, + 0.0073, + -0.036, + -0.0463, + 0.0184, + 0.0313, + -0.1037, + -0.0585, + -0.0084, + 0.012, + -0.0506, + 0.0144, + 0.0084, + 0.0717, + -0.0383, + -0.0347, + -0.009, + 0.0506, + -0.0073, + -0.0146, + 0.023, + 0.0065, + -0.0662, + -0.022, + -0.0119, + 0.0302, + 0.0527, + -0.0092, + 0.1018, + 0.0268, + -0.036, + 0.0734, + 0.06, + -0.0771, + -0.0105, + 0.0024, + 0.0075, + -0.0167, + -0.0001, + 0.0163, + 0.0449, + -0.0166, + -0.062, + 0.0041, + -0.032, + -0.0978, + 0.0686, + -0.0158, + 0.037, + -0.0298, + -0.0443, + 0.0323, + 0.105, + -0.0375, + 0.0131, + -0.002, + 0.0539, + -0.0341, + 0.002, + -0.0285, + -0.0118, + -0.0156, + 0.0097, + -0.004, + 0.1264, + -0.0026, + -0.0334, + -0.0196, + -0.0051, + -0.0084, + 0.0288, + -0.0076, -0.0034, - 0.0476, - 0.0249, - -0.0435, + -0.0042, + -0.0093, + -0.0286, + -0.0401, + -0.0769, + -0.0214, + 0.0223, + 0.017, + -0.0109, + -0.003, + -0.0146, 0.042, - 0.0236, - 0.0189, - -0.0212, - -0.0738, - -0.0511, - -0.0404, - -0.0043, - -0.0588, - 0.0406, - 0.0516, - 0.0185, - -0.0116, - -0.0302, - 0.0013, - 0.0331, - -0.0334, - 0.073, - 0.0335, - -0.029, - -0.0276, - -0.0265, - 0.0227, - -0.0357, - 0.0588, - -0.0262, - -0.0272, - 0.0579, - 0.0396, - 0.0396, - 0.0145, + 0.0313, + -0.0317, + -0.0484, + 0.0059, + 0.0434, + 0.04, + -0.0279, + 0.0546, + 0.0012, + 0.0173, + -0.0098, + -0.0649, + -0.0316, + -0.0349, + -0.0261, + -0.0331, + 0.0014, + 0.0597, + 0.0329, + 0.0193, + -0.0031, + 0.0156, + 0.0147, + -0.059, + 0.0663, + -0.0006, -0.0195, - -0.2526, - 0.024, - -0.0193, - 0.0336, - -0.0745, - -0.0027, - 0.0097, - 0.0058, - 0.0592, - 0.031, - 0.0543, - 0.0713, - -0.0151, - -0.0277, - -0.0141, - -0.0169, - 0.0379, - -0.0043, - -0.0313, - 0.0445, - 0.059, - 0.0026, - 0.0216, - -0.0605, - 0.0067, - 0.0333, - 0.1522, - -0.0469, + -0.0195, + -0.0603, + 0.0255, + -0.0523, + 0.0332, + -0.0286, + -0.0153, + 0.0334, + 0.0164, + 0.0025, + -0.0332, + -0.0356, + -0.3089, + -0.0015, + -0.0019, + 0.0323, + -0.0563, + -0.0059, + 0.0077, + -0.0181, + 0.0254, + 0.008, + 0.0298, + 0.083, + -0.0367, + -0.0613, + 0.0487, + 0.0264, + 0.0422, + -0.0096, + -0.0107, + 0.0252, + 0.0772, + 0.0099, + 0.0288, + -0.072, + 0.0538, + 0.0204, + 0.117, + -0.0233, + 0.0019, + 0.0124, + 0.0114, + -0.0391, + 0.0161, + -0.0471, + 0.0339, + -0.013, + -0.0492, + -0.0534, + -0.0041, + 0.04, + 0.0022, + -0.0808, + -0.021, + -0.1145, + -0.007, + -0.0131, 0.0084, - 0.0168, - -0.0091, - -0.0025, - -0.0128, - -0.0499, - 0.0455, - 0.0016, - -0.0212, - -0.0272, - 0.0105, - 0.0417, - -0.0089, - -0.0755, - -0.0023, - -0.1152, - -0.0226, - -0.0074, - -0.015, - 0.0611, - -0.0709, - -0.0053, - 0.0401, - -0.0642, - 0.0521, + 0.0388, + -0.0209, 0.0048, + 0.0434, + -0.049, + 0.0513, + 0.02, + 0.0101, + 0.0207, + -0.0468, + 0.0168, + -0.0345, + -0.0488, + 0.0721, + -0.0116, + 0.0231, + -0.0016, 0.0268, - 0.0094, - -0.0518, - 0.0847, - -0.0138, - -0.051, - 0.0359, - -0.0671, - -0.0366, - 0.0123, - 0.013, - -0.0653, - 0.029, - 0.0157, - 0.0317, - -0.0886, - -0.0134, - 0.1077, - 0.0262, - 0.0395, - -0.0018, - -0.0028, - -0.0223, - -0.0643, - -0.06, - 0.018, - 0.1109, - 0.0255, - 0.044, - -0.001, - 0.0247, - 0.0554, - 0.0733, - -0.0156, - 0.0243, - 0.0638, - 0.0166, - -0.0294, - -0.0184, - -0.0356, - -0.0169, - 0.0086, - -0.2818, - 0.0215, - 0.0112, - 0.0713, - -0.0141, - -0.0013, - 0.0146, - -0.0743, - 0.0182, - -0.0133, - -0.0069, + -0.0225, + 0.014, -0.0044, - 0.0586, - -0.0183, - 0.0227, - -0.0455, - 0.0946, - -0.0913, - 0.0195, + 0.0598, + -0.0738, + -0.0327, + 0.1336, + 0.0138, + 0.0602, + 0.0153, + 0.0113, + -0.0294, + -0.042, -0.0461, - 0.0558, + 0.0252, + 0.1197, + 0.014, + 0.0553, + 0.0146, + 0.0275, + 0.0651, + 0.0993, + 0.0038, + -0.0053, + 0.037, + -0.0109, + -0.006, + -0.0075, + -0.0864, + -0.0429, + 0.0069, + -0.2718, + 0.0652, + 0.0209, + 0.0456, + 0.0261, + -0.028, + 0.037, + -0.05, + -0.0186, + -0.0135, + 0.0204, + 0.0064, + 0.0191, + -0.0176, + 0.0325, + -0.0239, + 0.0883, + -0.0472, + -0.0058, + -0.036, + 0.0285, + -0.0281, + 0.1742, + 0.0212, + 0.0196, + -0.01, + -0.007, + 0.0072, -0.0136, - 0.1635, - -0.0179, - 0.0376, - 0.0253, - -0.0104, - 0.0167, + -0.0159, -0.007, - -0.0086, - -0.0184, - 0.048, - 0.0599, - 0.0114, - -0.0275, - 0.1196, - -0.0093, - -0.0045, - -0.0437, - 0.0049, - -0.0155, - 0.039, - -0.0375, - 0.003, - 0.1478, - -0.0117, - -0.037, - -0.0095, - -0.0006, - -0.0172, - -0.061, - 0.0407, - -0.014, - -0.0641, - 0.0086, - 0.0063, - 0.0356, - -0.0625, - 0.0077, - -0.0844, - 0.0751, - -0.0256, - 0.0059, - 0.0604, - -0.0717 - ] + 0.0299, + 0.0382, + 0.023, + -0.0274, + 0.0982, + -0.0065, + 0.0075, + -0.0318, + -0.0119, + -0.0031, + 0.0366, + -0.0457, + -0.0339, + 0.1144, + -0.021, + -0.0047, + -0.0308, + 0.0141, + -0.0164, + -0.0624, + 0.0288, + -0.0481, + -0.054, + -0.0146, + 0.0117, + 0.0472, + -0.0405, + -0.0101, + -0.0694, + 0.1004, + 0.0019, + -0.0103, + 0.0779, + -0.0263 + ], + "year": 2025, + "authors": [ + "Gustavo Arango-Argoty", + "Marzieh Haghighi", + "Gerald J. Sun", + "Elizabeth Choe", + "Aleksandra Markovets", + "J. Carl Barrett", + "Zhongwu Lai", + "Etai Jacob" + ], + "affiliations": [ + "AstraZeneca (South Korea)", + "AstraZeneca (Brazil)", + "AstraZeneca (United States)" + ], + "abstract": "Circulating tumor DNA is a critical biomarker in cancer diagnostics, but its accurate interpretation requires careful consideration of clonal hematopoiesis (CH), which can contribute to variants in cell-free DNA and potentially obscure true tumor-derived signals. Accurate detection of somatic variants of CH origin in plasma samples remains challenging in the absence of matched white blood cells sequencing. Here we present an open-source machine learning framework (MetaCH) which classifies variants in cfDNA from plasma-only samples as CH or tumor origin, surpassing state-of-the-art classification rates.", + "cited_by_count": 6, + "openalex_url": "https://openalex.org/W4410523444" }, { "channel": "standardmodelbio", @@ -28630,47 +28712,47 @@ "abstract": "Very fast, high quality, platform-independent hashing algorithm. - Nicoshev/rapidhash", "projections": { "pca": [ - 0.13568198680877686, - 0.039578475058078766 + 0.13562726974487305, + 0.03886987641453743 ], "tsne": [ - 8.006772994995117, - 9.49604606628418 + 15.80920124053955, + -10.29836654663086 ], "umap": [ - 2.836244821548462, - 4.85106086730957 + 2.056551218032837, + 3.9938602447509766 ], "pca3d": [ - 0.13568085432052612, - 0.039576709270477295, - -0.14949411153793335 + 0.13562603294849396, + 0.038870345801115036, + -0.1514545977115631 ], "tsne3d": [ - -6.262844085693359, - 40.31216812133789, - -9.310591697692871 + 33.018638610839844, + 11.170768737792969, + -10.407966613769531 ], "umap3d": [ - 4.701195240020752, - -0.5495440363883972, - 1.933764934539795 + 4.084426403045654, + 1.248986005783081, + 6.729625701904297 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 5, - "label": "GPU Inference" + "id": 15, + "label": "PyTorch Models" } ], "_embedding": [ @@ -29095,46 +29177,46 @@ ], "projections": { "pca": [ - -0.1560814082622528, - 0.16362042725086212 + -0.15602847933769226, + 0.16432735323905945 ], "tsne": [ - -23.423778533935547, - 17.810678482055664 + 1.3261003494262695, + 27.919612884521484 ], "umap": [ - 2.4518942832946777, - 2.553309679031372 + 1.6154451370239258, + 1.47217857837677 ], "pca3d": [ - -0.1560821533203125, - 0.16361691057682037, - -0.11132413893938065 + -0.1560293734073639, + 0.16432444751262665, + -0.1074225902557373 ], "tsne3d": [ - 4.785468578338623, - 22.804302215576172, - -31.826005935668945 + -7.476326942443848, + 11.335918426513672, + -23.889799118041992 ], "umap3d": [ - 6.552367210388184, - -1.0867615938186646, - 0.7514088153839111 + 3.83363938331604, + -0.9616196155548096, + 5.620027542114258 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 4, + "id": 26, "label": "World Models" } ], @@ -29523,7 +29605,8 @@ -0.0181, -0.002, -0.0104 - ] + ], + "year": 2025 }, { "channel": "standardmodelbio", @@ -29543,47 +29626,47 @@ "abstract": "\ud83e\udd17 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training. - transformers/src/transformers/models/qwen2_5_vl/modeling_qwen2_5_vl.py at b311a3f50697c9602cc5d13a5faf7f6059c392ca \u00b7 huggingface/transformers", "projections": { "pca": [ - -0.0711204931139946, - 0.13098682463169098 + -0.06843089312314987, + 0.1332738995552063 ], "tsne": [ - -28.30215072631836, - 15.123191833496094 + -9.553590774536133, + 20.333900451660156 ], "umap": [ - 2.2245306968688965, - 2.4298713207244873 + 0.8554468154907227, + 1.590050220489502 ], "pca3d": [ - -0.07112070918083191, - 0.13100197911262512, - 0.05399813875555992 + -0.06843110918998718, + 0.13328826427459717, + 0.05889704078435898 ], "tsne3d": [ - -20.847984313964844, - -0.3346889913082123, - -30.810823440551758 + 6.704535484313965, + 9.78096866607666, + 0.9309291839599609 ], "umap3d": [ - 6.385691165924072, - -1.4258594512939453, - 0.4738196134567261 + 3.3683063983917236, + -1.0072741508483887, + 5.737880229949951 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 4, - "label": "World Models" + "id": 8, + "label": "JEPA Video Models" } ], "_embedding": [ @@ -29991,47 +30074,47 @@ "abstract": "Implementation of Enformer, Deepmind's attention network for predicting gene expression, in Pytorch - lucidrains/enformer-pytorch", "projections": { "pca": [ - 0.02083578333258629, - 0.11014384776353836 + 0.020138105377554893, + 0.11029071360826492 ], "tsne": [ - 13.934685707092285, - 25.872779846191406 + 18.45237922668457, + -12.003702163696289 ], "umap": [ - 2.8814585208892822, - 5.274946212768555 + 1.826795220375061, + 3.585378408432007 ], "pca3d": [ - 0.020835211500525475, - 0.11013123393058777, - -0.11865857243537903 + 0.020137563347816467, + 0.11027940362691879, + -0.11899733543395996 ], "tsne3d": [ - -16.34514617919922, - 41.127357482910156, - 3.2450408935546875 + 32.79335021972656, + 27.009288787841797, + -15.810493469238281 ], "umap3d": [ - 4.617058277130127, - -0.6035948991775513, - 1.2078807353973389 + 3.641641855239868, + 0.9006929993629456, + 6.349671840667725 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 5, - "label": "GPU Inference" + "id": 15, + "label": "PyTorch Models" } ], "_embedding": [ @@ -30439,47 +30522,47 @@ "abstract": "Working with Attention. It's all we need. lucidrains has 400 repositories available. Follow their code on GitHub.", "projections": { "pca": [ - 0.2198570817708969, - 0.13454942405223846 + 0.21997253596782684, + 0.13518010079860687 ], "tsne": [ - 18.175756454467773, - 11.725473403930664 + 22.632036209106445, + -0.939842939376831 ], "umap": [ - 3.1956303119659424, - 5.3903632164001465 + 2.5169198513031006, + 4.136957168579102 ], "pca3d": [ - 0.2198571413755417, - 0.13454298675060272, - -0.014917544089257717 + 0.21997275948524475, + 0.13517343997955322, + -0.019078953191637993 ], "tsne3d": [ - 28.03144645690918, - 31.399658203125, - -7.694925308227539 + 40.71129608154297, + 20.894804000854492, + -5.714405059814453 ], "umap3d": [ - 4.392881393432617, - 0.20823930203914642, - 1.7267637252807617 + 3.8752520084381104, + 1.761769413948059, + 5.943489074707031 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 20, - "label": "AI Agents" + "id": 4, + "label": "Open Agent Tools" } ], "_embedding": [ @@ -30887,47 +30970,47 @@ "abstract": "This repository contains the official implementation of "FastVLM: Efficient Vision Encoding for Vision Language Models" - CVPR 2025 - apple/ml-fastvlm", "projections": { "pca": [ - 0.10760805755853653, - 0.06199220195412636 + 0.10765580087900162, + 0.06225733831524849 ], "tsne": [ - 6.740433692932129, - 10.660125732421875 + 15.647254943847656, + -11.734716415405273 ], "umap": [ - 2.6281521320343018, - 4.91917085647583 + 1.7883416414260864, + 3.7058796882629395 ], "pca3d": [ - 0.1076076328754425, - 0.061994075775146484, - -0.11761673539876938 + 0.10765545070171356, + 0.06226000189781189, + -0.11581018567085266 ], "tsne3d": [ - -5.823924541473389, - 23.96057891845703, - -17.463899612426758 + 33.34109115600586, + 16.6813907623291, + -12.362353324890137 ], "umap3d": [ - 4.7572550773620605, - -0.7306069731712341, - 1.2414652109146118 + 3.6782801151275635, + 0.8257645964622498, + 6.724133491516113 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 5, - "label": "GPU Inference" + "id": 15, + "label": "PyTorch Models" } ], "_embedding": [ @@ -31335,47 +31418,47 @@ "abstract": "Pytorch implementation of Evolutionary Policy Optimization, from Wang et al. of the Robotics Institute at Carnegie Mellon University - lucidrains/evolutionary-policy-optimization", "projections": { "pca": [ - 0.12239240109920502, - 0.18760089576244354 + 0.1228439211845398, + 0.1884484440088272 ], "tsne": [ - 14.00351333618164, - 25.720869064331055 + 18.560468673706055, + -11.8873929977417 ], "umap": [ - 3.1638870239257812, - 5.158462047576904 + 1.9204081296920776, + 3.4870433807373047 ], "pca3d": [ - 0.12239301949739456, - 0.1876148283481598, - -0.06815756857395172 + 0.12284442782402039, + 0.18846139311790466, + -0.07068604230880737 ], "tsne3d": [ - -12.059799194335938, - 43.405303955078125, - 2.6758933067321777 + 28.8947811126709, + 26.510723114013672, + -16.808889389038086 ], "umap3d": [ - 4.758430004119873, - -0.5576761364936829, - 1.4380807876586914 + 3.8236632347106934, + 0.9372101426124573, + 6.2935686111450195 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 5, - "label": "GPU Inference" + "id": 15, + "label": "PyTorch Models" } ], "_embedding": [ @@ -31783,47 +31866,47 @@ "abstract": "We\u2019re on a journey to advance and democratize artificial intelligence through open source and open science.", "projections": { "pca": [ - 0.08563417196273804, - 0.2045891135931015 + 0.08949046581983566, + 0.2068304717540741 ], "tsne": [ - 6.951988220214844, - 20.513731002807617 + 21.156757354736328, + 12.237101554870605 ], "umap": [ - 5.070242404937744, - 4.650181770324707 + 4.025043964385986, + 2.9695212841033936 ], "pca3d": [ - 0.08563371002674103, - 0.20459313690662384, - 0.22678197920322418 + 0.08948995918035507, + 0.20683254301548004, + 0.22381135821342468 ], "tsne3d": [ - 42.51227569580078, - -8.213041305541992, - 3.8694567680358887 + -0.2716880440711975, + 31.191049575805664, + 3.7502052783966064 ], "umap3d": [ - 5.495882034301758, - 1.6218104362487793, - 1.9684028625488281 + 4.90348482131958, + 2.1034460067749023, + 4.668264389038086 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { "id": 7, - "label": "Open AI" + "label": "Hugging Face Models" } ], "_embedding": [ @@ -32231,435 +32314,456 @@ "journal": "Nature", "projections": { "pca": [ - -0.2373175024986267, - -0.13072848320007324 + -0.1971290409564972, + -0.2730083167552948 ], "tsne": [ - -10.696877479553223, - -13.514808654785156 + -18.41256332397461, + -6.040890693664551 ], "umap": [ - -1.6176393032073975, - 6.0449957847595215 + -2.2501115798950195, + 5.063115119934082 ], "pca3d": [ - -0.23731666803359985, - -0.1307240128517151, - 0.16036073863506317 + -0.19712822139263153, + -0.2730061113834381, + 0.0024911595974117517 ], "tsne3d": [ - -10.679413795471191, - -38.85402297973633, - -3.046093463897705 + -20.65643310546875, + -15.40279483795166, + 21.326128005981445 ], "umap3d": [ - 3.086448907852173, - -0.7618131637573242, - -1.1691410541534424 + 0.4029756486415863, + 0.2878761887550354, + 5.887693881988525 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 24, + "id": 1, "label": "Cancer Data" } ], "_embedding": [ - -0.0401, - 0.0073, - 0.0091, - -0.0022, - 0.0258, - 0.0598, - 0.0373, - 0.022, - 0.0207, - -0.0344, - 0.0544, - -0.0278, - -0.0072, - 0.0007, - 0.015, - 0.0514, - 0.0109, - 0.0319, - -0.0427, - 0.0108, - -0.0152, - 0.0082, - -0.024, - -0.0087, - 0.0329, - -0.0025, - -0.0027, - -0.0325, - -0.0492, - -0.157, - 0.044, - -0.0086, + -0.0575, + 0.0057, + -0.025, + 0.0099, + 0.0255, + 0.0349, 0.0094, - -0.0547, - -0.0419, - 0.0115, - 0.0108, - 0.0868, - -0.0217, - -0.0062, - -0.0859, - -0.0034, - -0.0359, - 0.03, - 0.0243, - -0.0755, - -0.0177, - -0.0447, - -0.009, - -0.0127, - -0.0422, - -0.0673, - 0.0226, - 0.0618, - 0.0662, - -0.0293, - -0.003, - 0.0762, - 0.0312, - 0.0152, - 0.0157, - 0.0611, - -0.1001, - 0.0152, - 0.0271, - -0.0164, - -0.038, - -0.0762, - 0.0359, - 0.0458, - 0.0179, - 0.0296, - 0.0554, - 0.0887, + 0.017, + -0.0003, -0.0487, - 0.0653, - 0.0667, - -0.0002, - 0.0136, - 0.0738, - 0.0335, + 0.0632, + -0.0435, + -0.022, + 0.004, + 0.0043, + 0.0397, + 0.0044, 0.009, - -0.0526, - -0.0461, - -0.0431, - -0.0022, - -0.0422, - -0.1025, - -0.0059, - -0.0194, - -0.0426, - 0.0526, + -0.0121, + -0.0133, + -0.0122, + 0.0231, + 0.0139, + -0.0254, + 0.0509, + -0.0164, + -0.0233, + -0.0615, + -0.0468, + -0.2097, + 0.0497, + -0.0007, + -0.0058, + -0.0514, + -0.0408, + -0.0203, 0.0041, - 0.0422, - -0.0274, - 0.0193, - 0.0256, - 0.0323, - 0.0175, - 0.2796, - 0.0172, - -0.0214, - -0.0379, - -0.004, - -0.0085, - -0.1094, - 0.005, - -0.06, - 0.0006, - -0.0244, - 0.0023, - -0.0503, - 0.0266, - -0.0119, + 0.0627, + -0.0277, + 0.017, + -0.0305, + 0.0361, + -0.0265, + 0.0529, + 0.0267, + -0.0497, + -0.0544, + -0.0208, + 0.0099, + -0.0023, + -0.0141, + -0.0856, + -0.0016, + 0.1051, + 0.0042, + -0.0047, + 0.0394, + 0.0778, + 0.0136, + -0.0036, + 0.0099, + 0.0522, + -0.1118, + 0.0671, + 0.0424, + 0.0065, + -0.0166, + -0.0659, + 0.0671, + 0.0286, + -0.0298, + 0.0431, + 0.0359, + 0.064, + -0.011, + 0.0708, + 0.0754, + 0.0041, + 0.0682, + 0.0482, + 0.0406, + 0.0447, + -0.0186, + -0.0163, + -0.0455, + -0.0035, + -0.0565, + -0.0827, + -0.0227, + -0.0205, + 0.0179, + 0.019, + 0.0093, + 0.0565, + -0.0525, + -0.0011, + 0.0478, + 0.0001, + 0.0182, + 0.3213, + -0.0013, + -0.0169, + -0.0148, + 0.0244, + -0.0128, + -0.1, + -0.0257, + -0.0304, + 0.0132, + -0.0105, + 0.004, + -0.0045, + 0.0281, + -0.0151, + -0.0141, + 0.0173, + 0.0123, + 0.033, + 0.0249, -0.0066, - 0.0452, - 0.0047, - 0.0276, - 0.0556, - -0.0351, - -0.0193, - -0.0355, - 0.0349, - 0.0114, - 0.0387, - -0.0032, + -0.0521, + -0.016, + 0.0197, 0.0159, - 0.071, - -0.0357, - -0.0219, + 0.0189, + 0.0434, + -0.0121, + 0.0731, + 0.0272, + -0.0297, + 0.0787, 0.0678, - 0.0957, - -0.0978, - -0.0012, - -0.0305, - 0.0119, - 0.0172, - -0.0059, + -0.119, + 0.0085, + -0.0475, + -0.0152, + 0.0068, + 0.0339, -0.0035, - 0.0314, - 0.0119, - -0.0575, - 0.0549, - -0.0282, - -0.057, - 0.0817, - -0.0647, - -0.0139, - -0.0451, - -0.0332, - 0.0036, - 0.061, - -0.0382, - -0.0141, - -0.0139, - -0.0041, - 0.0138, - -0.0017, - -0.0734, 0.0309, - -0.0063, - -0.028, - 0.0048, - 0.1576, - -0.0398, - -0.0732, - 0.0101, - 0.0413, - 0.0534, - 0.0089, - 0.0841, - -0.0301, - 0.0397, + 0.0028, + -0.0414, + 0.0526, + -0.0396, + -0.0758, + 0.1383, + -0.0635, + -0.0041, + -0.0492, + -0.034, + 0.0086, + 0.0754, + -0.0287, + 0.0235, + -0.034, + 0.0041, + 0.0093, + 0.0169, + -0.0622, + 0.0191, + -0.0125, + -0.004, + -0.0073, + 0.1048, -0.0074, - -0.0304, - -0.0012, - -0.0415, - -0.0764, - -0.0069, - -0.0042, - -0.0018, - 0.0292, - -0.019, - 0.0499, - -0.012, - -0.0164, - 0.0242, - -0.0119, - 0.0559, - 0.0113, - 0.0413, - 0.0236, - 0.0157, - 0.0158, - -0.0043, - -0.0783, - -0.0345, - 0.0395, - -0.0166, - -0.0386, - -0.0009, - 0.0104, - -0.0116, + -0.0779, + -0.0117, + 0.0554, + 0.0416, + -0.004, + 0.0707, + -0.0226, + 0.0098, + -0.0142, + -0.0492, + -0.0279, + -0.0453, + -0.0553, + -0.0184, + 0.0298, + 0.0324, + 0.0182, + -0.0095, + 0.026, + -0.0008, + 0.0241, + 0.0086, + 0.0087, + 0.04, + 0.0282, + 0.025, + 0.0365, + 0.0033, + 0.023, + -0.0054, + -0.0963, + -0.0139, + 0.0451, + -0.0095, + -0.0546, + 0.0046, + 0.032, + 0.0171, + 0.0059, + -0.0732, + 0.001, + 0.0037, + 0.0095, + 0.0738, + -0.0289, + 0.0112, -0.0367, - -0.0203, - 0.0074, - 0.0355, - 0.0818, - 0.0815, - 0.0156, - 0.0029, - -0.057, - -0.0031, - -0.0003, - -0.0408, - 0.057, - 0.0325, - 0.0457, - 0.0, - 0.0957, + -0.0399, + 0.0424, + -0.0242, + 0.0588, + 0.0467, + 0.0369, + -0.0255, + 0.091, + 0.0017, + -0.012, + -0.0537, + -0.3265, + -0.0327, + -0.0347, + 0.044, + -0.0183, + -0.0081, + -0.0262, + 0.0228, + -0.0127, + -0.001, + 0.0328, + 0.1098, + -0.0251, + -0.0225, + -0.0215, + 0.0014, + 0.0289, 0.0151, - 0.0384, - -0.0553, - -0.2956, - -0.0252, - -0.0079, - 0.0212, - -0.0117, - -0.0015, - 0.0113, + -0.0879, + -0.0374, + -0.0199, + -0.0166, + 0.0078, + -0.0599, + 0.0484, + 0.0346, + 0.1166, + -0.0393, + -0.0221, + -0.0095, + 0.0391, + 0.0406, + -0.0409, + -0.0596, + 0.0923, + -0.0579, + 0.0066, + 0.0533, + -0.0659, + 0.0091, + -0.0569, + -0.0051, + 0.0128, + -0.139, + -0.0568, + 0.0167, + -0.0012, + 0.0617, + -0.0023, + 0.0053, + 0.0152, + 0.0023, + 0.0429, + -0.0346, + -0.0026, + 0.0247, + -0.077, + 0.0024, + -0.0521, + -0.0429, + 0.0306, + -0.0209, 0.0189, - 0.0169, - -0.0111, - 0.0647, - 0.047, + -0.0071, -0.0205, - -0.0143, - 0.0054, - -0.0414, + -0.0844, + -0.0106, + -0.0139, + 0.0157, + -0.0799, + -0.0518, + 0.1175, + -0.0506, + 0.0096, 0.0364, - 0.0184, - -0.0767, - -0.0238, - -0.0167, - -0.0074, - 0.0473, - -0.0739, - 0.0381, - 0.0477, - 0.1408, - -0.0597, - -0.0515, - -0.0072, - 0.0142, - 0.053, - -0.0838, - -0.0747, - 0.0842, - -0.0814, - 0.0065, - 0.0427, - -0.0734, - 0.0394, - -0.025, - 0.016, - 0.0225, - -0.1266, - -0.0465, - -0.0209, - -0.0241, - 0.0973, - -0.0322, - 0.017, - 0.0258, - -0.0285, - 0.0676, - -0.0569, - -0.0036, - -0.0048, - -0.0505, - 0.0109, - -0.0517, - -0.0228, - 0.0219, + 0.0146, + -0.0316, + -0.0489, + -0.06, + 0.0286, + 0.0725, + -0.008, + 0.0625, + 0.0312, + 0.0012, + 0.0337, + 0.0599, + 0.0032, + -0.0054, + 0.0353, + 0.0054, + -0.0521, + 0.0175, + -0.0449, + -0.0194, + 0.0622, + -0.2525, + 0.0556, + 0.0084, + 0.008, + -0.0082, + -0.0293, + -0.0004, + -0.0512, + -0.0102, + 0.0303, + 0.0042, + 0.0554, + 0.0697, + -0.0082, + -0.0012, + 0.0037, + 0.1061, + -0.0319, + -0.0155, + -0.0229, + 0.0736, + -0.0351, + 0.1537, + -0.0371, + 0.0331, + 0.0481, + -0.0559, + -0.0159, + 0.0113, + 0.0365, + -0.0259, + 0.0318, + 0.0302, + 0.0032, + 0.0127, + 0.0292, + 0.0056, + -0.0052, + -0.0222, + 0.0156, + 0.0625, + 0.0218, + -0.0275, + -0.0388, + 0.0936, + -0.0679, + 0.0165, + -0.0398, -0.0535, - -0.0109, - -0.065, - 0.0123, - -0.0676, - 0.0182, - -0.0076, - 0.0095, - -0.1042, - -0.0707, - 0.0771, - -0.0281, - 0.0092, - 0.0741, - 0.0086, - -0.0027, - -0.0307, - -0.0544, - 0.0404, - 0.0921, - -0.0014, - 0.0513, - 0.0231, - 0.0167, - -0.0126, - 0.0604, - 0.0249, - -0.005, - 0.0412, - 0.0003, - -0.0141, - -0.0022, - -0.0244, - 0.0049, - 0.0642, - -0.2807, - 0.0215, - -0.0067, - 0.0011, - -0.0219, - -0.0091, - -0.0272, - -0.0447, - -0.0095, - 0.0373, - -0.0326, - 0.0173, - 0.0412, - -0.0254, - 0.0085, - 0.005, - 0.0652, - -0.053, - -0.0178, - -0.0692, - 0.0913, - 0.0106, - 0.1446, - -0.0772, - 0.0317, - 0.0787, - -0.0736, + -0.0093, -0.0064, - 0.0172, - 0.0151, - -0.0118, - 0.0253, - 0.0413, - -0.0014, - 0.012, - 0.0633, - -0.0367, - 0.0088, - 0.0118, - 0.0109, - 0.1063, - 0.0179, - 0.0093, - -0.0405, - 0.1157, - -0.0226, - 0.0237, - -0.0226, - -0.0202, - 0.0081, - 0.0058, - 0.0322, - -0.0172, - -0.0572, - 0.0478, - -0.0308, - 0.0677, - -0.032, - 0.0069, - -0.07, - 0.0404, - -0.0099, - -0.0168, - -0.0007, - -0.0625 - ] + 0.0276, + -0.0466, + -0.0499, + 0.0287, + 0.0006, + 0.0545, + -0.0112, + 0.0061, + -0.0426, + 0.0092, + -0.0147, + -0.0433, + 0.0386, + -0.0362 + ], + "year": 2025, + "authors": [ + "\u039d\u03b9\u03ba\u03cc\u03bb\u03b1\u03bf\u03c2 \u039d\u03b9\u03ba\u03bf\u03bb\u03ac\u03bf\u03c5", + "Domingo M. Salazar", + "Harish RaviPrakash", + "Miguel Chichorro Gon\u00e7alves", + "Rob Mulla", + "Nikolay Burlutskiy", + "Natasha Markuzon", + "Etai Jacob" + ], + "affiliations": [ + "AstraZeneca (United Kingdom)", + "University College London", + "AstraZeneca (United States)", + "AstraZeneca (Brazil)", + "AstraZeneca (South Korea)" + ], + "abstract": "Technological advancements of the past decade have transformed cancer research, improving patient survival predictions through genotyping and multimodal data analysis. However, there is no comprehensive machine-learning pipeline for comparing methods to enhance these predictions. To address this, a versatile pipeline using The Cancer Genome Atlas (TCGA) data was developed, incorporating various data modalities such as transcripts, proteins, metabolites, and clinical factors. This approach manages challenges like high dimensionality, small sample sizes, and data heterogeneity. By applying different feature extraction and fusion strategies, notably late fusion models, the effectiveness of integrating diverse data types was demonstrated. Late fusion models consistently outperformed single-modality approaches in TCGA lung, breast, and pan-cancer datasets, offering higher accuracy and robustness. This research highlights the potential of comprehensive multimodal data integration in precision oncology to improve survival predictions for cancer patients. The study provides a reusable pipeline for the research community, suggesting future work on larger cohorts.", + "cited_by_count": 23, + "openalex_url": "https://openalex.org/W4410082395" }, { "channel": "standardmodelbio", @@ -32681,47 +32785,47 @@ "abstract": "Contribute to juntotechnologies/biocompart development by creating an account on GitHub.", "projections": { "pca": [ - 0.162580206990242, - -0.19941051304340363 + 0.16165918111801147, + -0.198405459523201 ], "tsne": [ - 28.163654327392578, - 3.3965704441070557 + 29.732059478759766, + -14.031303405761719 ], "umap": [ - 2.7913880348205566, - 6.921991348266602 + 2.1958353519439697, + 5.826862812042236 ], "pca3d": [ - 0.16258153319358826, - -0.1994054615497589, - -0.09774547070264816 + 0.1616605818271637, + -0.19840221107006073, + -0.09634879231452942 ], "tsne3d": [ - 18.354808807373047, - 48.13478469848633, - 28.154640197753906 + 45.73381805419922, + -0.7116708755493164, + 17.597824096679688 ], "umap3d": [ - 3.298855781555176, - -0.24618330597877502, - 2.0254454612731934 + 3.1618990898132324, + 2.467334032058716, + 6.628154754638672 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 25, - "label": "Python Data" + "id": 2, + "label": "Bioinformatics Tools" } ], "_embedding": [ @@ -33131,47 +33235,47 @@ "abstract": "A Python package for performing bioequivalence calculations and analysis. - juntotechnologies/bioeq", "projections": { "pca": [ - 0.1396508365869522, - -0.23797549307346344 + 0.13893751800060272, + -0.23749326169490814 ], "tsne": [ - 28.20465660095215, - 3.2509429454803467 + 29.752819061279297, + -14.180377006530762 ], "umap": [ - 2.3139355182647705, - 7.052663803100586 + 1.7315181493759155, + 6.154400825500488 ], "pca3d": [ - 0.13965120911598206, - -0.23797565698623657, - -0.07060696184635162 + 0.13893793523311615, + -0.237493634223938, + -0.0684005543589592 ], "tsne3d": [ - 17.56501579284668, - 50.18376541137695, - 31.557823181152344 + 47.031620025634766, + -3.668564796447754, + 18.381956100463867 ], "umap3d": [ - 3.1399800777435303, - -0.7552510499954224, - 1.3442702293395996 + 2.6644184589385986, + 2.1675167083740234, + 6.81060266494751 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 6, + "label": "Data Needs" }, { "id": 25, - "label": "Python Data" + "label": "Data Standards" } ], "_embedding": [ @@ -33579,47 +33683,47 @@ "abstract": "Contribute to standardmodelbio/smb-vision development by creating an account on GitHub.", "projections": { "pca": [ - 0.1713268756866455, - -0.0037630361039191484 + 0.17029164731502533, + -0.003910587634891272 ], "tsne": [ - 23.83733558654785, - 7.748718738555908 + 24.985034942626953, + -8.648372650146484 ], "umap": [ - 2.9513704776763916, - 6.749210357666016 + 2.669304609298706, + 5.41676139831543 ], "pca3d": [ - 0.17132672667503357, - -0.003763121087104082, - -0.17052733898162842 + 0.17029167711734772, + -0.003910465631633997, + -0.169033482670784 ], "tsne3d": [ - 11.609147071838379, - 38.85382080078125, - 10.08745288848877 + 35.616355895996094, + 7.884641647338867, + 20.009220123291016 ], "umap3d": [ - 3.606501340866089, - -0.2501033842563629, - 2.01485276222229 + 3.3248825073242188, + 2.4013726711273193, + 6.569968223571777 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 18, - "label": "Github Tools" + "id": 17, + "label": "GitHub Creation" } ], "_embedding": [ @@ -34027,47 +34131,47 @@ "abstract": "We\u2019re on a journey to advance and democratize artificial intelligence through open source and open science.", "projections": { "pca": [ - 0.0691334679722786, - 0.15934860706329346 + 0.07263863831758499, + 0.16117864847183228 ], "tsne": [ - 8.76063346862793, - 20.020639419555664 + 20.41399574279785, + 12.572654724121094 ], "umap": [ - 5.083225727081299, - 4.668645858764648 + 4.116561412811279, + 3.003119468688965 ], "pca3d": [ - 0.06913241744041443, - 0.15935206413269043, - 0.1932617425918579 + 0.07263745367527008, + 0.16118139028549194, + 0.19220323860645294 ], "tsne3d": [ - 38.436222076416016, - -3.4088289737701416, - 13.404738426208496 + 2.6622393131256104, + 29.303098678588867, + 8.132996559143066 ], "umap3d": [ - 5.283669471740723, - 1.6773701906204224, - 2.0392205715179443 + 4.781996250152588, + 2.132481575012207, + 4.66657018661499 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { "id": 7, - "label": "Open AI" + "label": "Hugging Face Models" } ], "_embedding": [ @@ -34475,47 +34579,47 @@ "abstract": "Meta FAIR is releasing several new research artifacts that advance our understanding of perception and support our goal of achieving advanced machine...", "projections": { "pca": [ - 0.03630135953426361, - 0.21260203421115875 + 0.03982966020703316, + 0.21419765055179596 ], "tsne": [ - 4.119562149047852, - 21.477079391479492 + 18.949573516845703, + 11.627707481384277 ], "umap": [ - 4.786194324493408, - 4.4573564529418945 + 3.845210552215576, + 2.7891809940338135 ], "pca3d": [ - 0.03630102798342705, - 0.21260741353034973, - 0.16877974569797516 + 0.03982928767800331, + 0.21420325338840485, + 0.1660231053829193 ], "tsne3d": [ - 44.6367301940918, - -12.129108428955078, - -1.2106294631958008 + -1.2365288734436035, + 26.035959243774414, + 2.0932464599609375 ], "umap3d": [ - 5.57862663269043, - 1.3583492040634155, - 1.6806840896606445 + 4.850093364715576, + 1.7759528160095215, + 4.751455307006836 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { "id": 7, - "label": "Open AI" + "label": "Hugging Face Models" } ], "_embedding": [ @@ -34925,47 +35029,47 @@ "doi": "10.1016/s1535-5535(07)00130-x", "projections": { "pca": [ - 0.21423061192035675, - -0.03769892454147339 + 0.21642382442951202, + -0.03751368075609207 ], "tsne": [ - 20.37836456298828, - -19.439027786254883 + 2.254735231399536, + -19.43076515197754 ], "umap": [ - 0.5651774406433105, - 4.374829292297363 + -0.053110890090465546, + 4.919344425201416 ], "pca3d": [ - 0.21423062682151794, - -0.03768657147884369, - 0.030967313796281815 + 0.21642395853996277, + -0.03750215098261833, + 0.026807941496372223 ], "tsne3d": [ - -17.975343704223633, - -5.860838890075684, - -41.64995193481445 + 9.904260635375977, + -40.53559112548828, + -9.689940452575684 ], "umap3d": [ - 2.8465735912323, - 1.4920939207077026, - 0.079996757209301 + 1.7808253765106201, + 1.9026097059249878, + 4.8755083084106445 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 4, - "label": "Com Area Amp" + "id": 9, + "label": "Drug Discovery" }, { - "id": 17, - "label": "Literature AI" + "id": 23, + "label": "Peer Review" } ], "_embedding": [ @@ -35353,7 +35457,9 @@ -0.0349, 0.0123, -0.0185 - ] + ], + "cited_by_count": 1, + "openalex_url": "https://openalex.org/W4253418042" }, { "channel": "standardmodelbio", @@ -35381,47 +35487,47 @@ ], "projections": { "pca": [ - -0.16982069611549377, - 0.2300223708152771 + -0.16828900575637817, + 0.2310871183872223 ], "tsne": [ - -21.165706634521484, - 10.471811294555664 + -5.004495620727539, + 21.747350692749023 ], "umap": [ - 1.5732382535934448, - 3.1152234077453613 + 0.4383557438850403, + 2.0215723514556885 ], "pca3d": [ - -0.16982141137123108, - 0.23002444207668304, - 0.01791275665163994 + -0.1682896465063095, + 0.23108959197998047, + 0.02271849475800991 ], "tsne3d": [ - -0.9481443762779236, - 14.748042106628418, - -23.04586410522461 + -5.039589881896973, + 3.3075098991394043, + -16.097042083740234 ], "umap3d": [ - 5.9178009033203125, - -1.1114966869354248, - 0.10339149832725525 + 3.400566577911377, + -0.9300163388252258, + 6.383555889129639 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 15, - "label": "Latent Models" + "id": 8, + "label": "JEPA Video Models" } ], "_embedding": [ @@ -35809,7 +35915,14 @@ -0.0074, 0.0068, -0.0382 - ] + ], + "year": 2026, + "journal": "Zenodo (CERN European Organization for Nuclear Research)", + "affiliations": [ + "Autonomous Healthcare" + ], + "cited_by_count": 0, + "openalex_url": "https://openalex.org/W7162498648" }, { "channel": "standardmodelbio", @@ -35829,47 +35942,47 @@ "abstract": "BioMCP: Biomedical Model Context Protocol. Contribute to genomoncology/biomcp development by creating an account on GitHub.", "projections": { "pca": [ - 0.07826162129640579, - -0.1593683511018753 + 0.07891581952571869, + -0.15745019912719727 ], "tsne": [ - 28.503677368164062, - 5.189513206481934 + 30.52098846435547, + -12.256175994873047 ], "umap": [ - 2.9820401668548584, - 7.111911773681641 + 2.495758056640625, + 5.977813243865967 ], "pca3d": [ - 0.0782625824213028, - -0.15934942662715912, - -0.06585580855607986 + 0.07891705632209778, + -0.1574341207742691, + -0.06313226372003555 ], "tsne3d": [ - 21.236146926879883, - 50.05704879760742, - 22.24359703063965 + 44.19414138793945, + 4.968869686126709, + 16.921314239501953 ], "umap3d": [ - 3.193150520324707, - -0.20932438969612122, - 2.329580783843994 + 3.282258987426758, + 2.6528992652893066, + 6.641705513000488 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 18, - "label": "Github Tools" + "id": 2, + "label": "Bioinformatics Tools" } ], "_embedding": [ @@ -36277,435 +36390,450 @@ "journal": "Nature", "projections": { "pca": [ - -0.10163666307926178, - -0.005016271956264973 + -0.09119176864624023, + -0.08785837888717651 ], "tsne": [ - -26.01327133178711, - 6.3773980140686035 + -11.153885841369629, + -0.10468432307243347 ], "umap": [ - 1.720475673675537, - 3.858593225479126 + -2.146571397781372, + 3.707225799560547 ], "pca3d": [ - -0.10163601487874985, - -0.005002870224416256, - 0.06087692081928253 + -0.09119188040494919, + -0.08785773813724518, + 0.13264136016368866 ], "tsne3d": [ - -30.414127349853516, - 17.574953079223633, - -29.963001251220703 + -6.421648979187012, + -12.088849067687988, + 29.833349227905273 ], "umap3d": [ - 4.879927635192871, - -1.0475753545761108, - -0.07887525111436844 + 1.3926682472229004, + 0.9893659353256226, + 6.722402095794678 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 2, + "label": "Clinical AI Data" }, { "id": 5, - "label": "Model Training" + "label": "Clinical Data" }, { - "id": 27, - "label": "Embeddings" + "id": 14, + "label": "Clinical Health" } ], "_embedding": [ + 0.0028, + 0.0151, + -0.0082, + -0.0352, + 0.0679, + 0.0223, + -0.0303, + -0.0167, + -0.0147, -0.0023, - 0.0284, - 0.0227, - -0.0621, - 0.0257, - 0.0606, - -0.0581, - 0.0162, - 0.0253, - -0.0111, - -0.0345, - -0.0226, - 0.0612, - 0.0241, - -0.0031, - -0.0169, - -0.0545, - 0.0134, - 0.0436, - 0.0044, - 0.0811, - -0.0134, - -0.044, - -0.0573, - -0.0149, - 0.0848, - -0.0374, - -0.0391, - -0.0699, - -0.2302, - -0.0029, - -0.0024, - 0.0448, - -0.0256, - 0.0261, - -0.0632, - -0.0363, - 0.0324, - 0.0027, - 0.0054, - -0.0207, - -0.0297, - 0.0008, - 0.0264, - 0.0178, - -0.0221, - -0.0495, - 0.0245, - 0.0072, - 0.0251, - -0.081, - -0.0137, - 0.0378, - 0.0313, - 0.0158, - 0.0308, - 0.0219, - 0.041, - 0.0355, - -0.0308, - 0.1072, - 0.0523, - -0.1031, - 0.0706, + -0.0375, + -0.0262, + 0.0003, + 0.016, + -0.0187, + -0.0063, + -0.0246, -0.006, - 0.0094, - -0.054, - -0.0335, - 0.0057, - -0.0062, - -0.0157, - -0.0125, - 0.0292, - 0.0677, - 0.0683, - 0.0422, - -0.0426, - -0.0265, - -0.0188, - -0.0358, - -0.0029, - 0.0042, - 0.0147, - 0.0118, - -0.0207, - 0.0105, - -0.0199, - -0.0304, - 0.029, - -0.0136, - 0.0211, - 0.0354, - -0.0111, - 0.0058, - -0.056, - -0.0216, - -0.03, - -0.0126, - 0.0413, - 0.3193, - -0.055, - 0.0114, - 0.0327, - -0.0136, - -0.0327, - -0.0386, - -0.0023, - -0.1027, - -0.0188, - -0.0407, + 0.0309, + 0.022, + 0.0452, + 0.0567, + -0.033, + -0.0833, + 0.0127, + 0.0328, + 0.0139, + -0.0705, + -0.0323, + -0.218, + 0.0302, + -0.0036, + -0.0113, + -0.012, + 0.0055, + -0.0009, + -0.0016, + 0.083, + -0.011, + 0.0335, -0.0299, - -0.0456, - 0.0054, - -0.013, - -0.0409, - -0.0038, - 0.0902, - 0.0091, + -0.0109, + 0.0003, + 0.0101, + 0.04, 0.0106, - -0.0338, - -0.0208, - 0.0457, - -0.0054, - 0.0128, - 0.0921, - -0.0893, - 0.0137, - 0.1151, - 0.0, - 0.0564, - 0.0377, - 0.1092, - -0.0608, - 0.0319, - 0.0234, -0.049, - -0.035, - -0.0415, - -0.0072, - 0.0282, - -0.0446, - 0.0075, - 0.0022, - 0.0167, - -0.0777, - 0.1306, - -0.0015, - 0.0624, - -0.0423, - -0.0622, - 0.0463, - 0.0209, - -0.0037, - -0.0159, - 0.0115, - 0.0719, - -0.0157, - 0.0038, - -0.0562, - -0.0299, - -0.0104, + 0.0185, + 0.0004, + 0.0071, + -0.0056, + 0.0165, + 0.0135, + 0.069, + 0.0034, -0.0082, - -0.0324, - 0.1672, - -0.034, - -0.1158, - 0.012, - 0.0068, - 0.0448, - 0.0345, - 0.0266, - -0.0152, - -0.0263, - 0.0724, - -0.0173, - 0.0014, - -0.0457, - -0.0332, - 0.018, - 0.0227, - 0.0534, - -0.0353, - -0.0412, - 0.0628, - -0.0061, - -0.0304, - -0.02, - -0.0218, - 0.0175, - 0.0257, - -0.0121, - -0.0107, - 0.0024, + 0.0056, + 0.0508, + 0.0298, + -0.0345, + 0.0551, + 0.0801, + -0.1196, + 0.053, + -0.0405, + 0.0192, + -0.0095, + -0.0451, 0.0208, - 0.0338, - -0.089, - 0.0074, - -0.0124, - 0.0151, + 0.0222, + 0.0164, + 0.0065, + 0.026, + 0.0353, + 0.0316, + 0.0619, + 0.02, + -0.0333, + 0.0604, + 0.0142, + 0.0351, + 0.0318, + -0.0261, + -0.0011, + -0.0331, + 0.0205, + -0.0224, + -0.086, + -0.0154, + -0.0058, + -0.0082, + 0.0403, + -0.0427, + 0.0289, -0.0698, - 0.0759, - 0.0471, - -0.0064, + 0.015, + -0.0259, + 0.0345, + -0.0289, + 0.3668, + -0.0343, + -0.0046, + 0.0106, + -0.0131, + -0.0013, + -0.0356, + -0.0265, + -0.0815, + 0.0077, + -0.046, + -0.0445, + -0.0366, + 0.0191, + -0.0168, + -0.0355, + -0.0093, + 0.0038, + 0.0279, + 0.0502, + -0.0262, + -0.0295, + 0.0145, + 0.0085, + 0.0127, + 0.0678, + -0.0182, + -0.0001, + 0.1351, + 0.0312, + 0.0258, + 0.053, + 0.0316, + -0.0742, 0.0019, - 0.019, - -0.0332, - 0.0046, - 0.0111, - 0.0866, - 0.0308, - -0.0415, + 0.0134, + -0.0526, + -0.0627, + -0.0237, + 0.0097, + 0.037, + -0.0283, + 0.0339, + 0.0066, + 0.0103, + -0.0981, + 0.1213, + -0.0188, + 0.0407, + -0.0724, + -0.062, + 0.0453, + 0.044, + -0.0103, + -0.038, + -0.008, + 0.0716, + 0.01, + 0.0049, + -0.0576, + -0.021, + -0.0188, + -0.0319, + -0.026, + 0.0999, + 0.0062, + -0.0714, + -0.0313, + 0.0174, + 0.0256, + 0.013, + 0.0514, + 0.0173, -0.0262, - 0.0224, - 0.0005, - -0.0311, - -0.0014, - 0.0197, - 0.0175, - -0.003, - 0.0616, - -0.0263, - -0.0085, - -0.0559, - -0.2821, - -0.0136, - 0.0076, - 0.0159, - -0.0079, - -0.0018, - -0.0118, - 0.0057, - -0.0027, - 0.0461, - 0.0114, - -0.0041, - -0.05, - 0.0119, - -0.053, - -0.0227, - -0.0169, - 0.0155, - -0.0689, - 0.0094, - -0.0004, - 0.0198, - 0.0028, - -0.0751, - 0.0492, - -0.0072, - 0.1846, - -0.0366, - -0.025, + 0.0206, + -0.0411, 0.024, - 0.012, - 0.0057, - 0.0023, - -0.0435, - 0.0444, - 0.0514, - -0.0183, - 0.0634, - -0.0279, - -0.008, + -0.0711, + -0.0603, + 0.0034, + 0.0329, + 0.0193, + 0.0236, + -0.0233, + 0.0176, + -0.0306, + -0.0085, + -0.0126, + -0.0016, + 0.0407, + 0.0129, + 0.0338, + 0.0282, + -0.037, + -0.0124, + 0.03, + -0.068, + -0.0151, + 0.0209, + 0.0284, -0.017, - -0.0024, - 0.033, - -0.0964, - -0.0076, - -0.0584, - -0.0357, - -0.0259, - -0.0533, - 0.0183, + -0.0018, + 0.0574, + 0.0063, + 0.0134, + 0.0325, + -0.0406, + 0.0104, + 0.0085, + 0.0819, + 0.0754, + -0.0307, + -0.001, + 0.0268, + -0.0347, + -0.0336, + 0.0258, + -0.015, + -0.0125, + -0.0565, + 0.0796, + -0.0158, + 0.0079, + -0.0685, + -0.339, -0.0275, - -0.0293, - 0.0714, - -0.0023, + 0.01, + -0.0032, + -0.0043, + -0.0172, + 0.0044, + -0.0201, + -0.0255, + 0.0009, + 0.0486, + 0.0595, + -0.0574, -0.0053, - -0.0445, - -0.0424, - 0.0525, - -0.0165, - -0.0481, + -0.0084, + 0.0079, + 0.0375, + -0.0269, + -0.052, + 0.0008, + 0.0274, + 0.0265, + 0.0061, + -0.0523, + 0.0649, + -0.0214, + 0.1381, + -0.0782, + 0.0252, + 0.024, + -0.0106, 0.0286, - -0.0336, - -0.0315, - -0.0041, - -0.0119, - -0.0353, - -0.0112, - -0.0195, - 0.0287, - -0.033, - -0.0139, - 0.0814, - 0.0181, - 0.0748, - 0.0377, - 0.028, - -0.0122, - -0.0375, - -0.0293, - -0.0323, - 0.0088, - -0.0036, - 0.079, - 0.023, - 0.019, - 0.0287, - 0.0167, + 0.0046, + -0.0234, + 0.0668, + -0.0271, + 0.0248, + 0.0995, + -0.0289, + 0.0059, + -0.0262, + 0.0336, + 0.0206, + -0.093, + -0.0182, + -0.0511, + 0.0054, + -0.0354, + -0.041, + 0.0213, + -0.0291, + -0.0095, + 0.0324, + 0.0348, + -0.0196, + -0.0352, + -0.1077, + 0.0553, + -0.0537, + -0.0475, + 0.0352, + -0.0541, + 0.0369, + -0.0305, + -0.0215, + -0.0513, + -0.0032, + -0.0128, + 0.0322, + 0.0045, + -0.0108, + 0.1137, + 0.0439, + 0.0477, + 0.053, + 0.0074, + -0.0233, + -0.0656, + -0.0592, + 0.0201, + -0.0092, + 0.0202, + 0.0622, + 0.066, 0.0264, - 0.0703, - -0.023, - 0.0435, - 0.0304, - -0.0449, - -0.0107, - 0.0547, - 0.0409, - -0.2461, - -0.0043, - -0.0706, - 0.0277, - -0.0138, - 0.0176, - -0.0264, - 0.0049, - 0.0166, - 0.0013, - -0.0301, - 0.014, - 0.0312, - -0.0036, - 0.0167, - -0.0135, - 0.0533, - -0.0615, - 0.0224, - -0.0547, - 0.0461, - -0.0111, - 0.2016, - -0.0344, + 0.0285, + 0.0202, + 0.0251, + 0.0116, + 0.011, + 0.0165, + -0.0167, + -0.0367, + -0.0266, + 0.0287, + 0.0154, + -0.2661, 0.0227, - 0.0441, - 0.0441, - -0.0543, - -0.0006, - 0.0025, - -0.0379, - -0.0181, - 0.0779, - 0.0522, - -0.047, - 0.1353, - -0.0538, - 0.0237, + -0.0396, + 0.0486, + -0.0429, + -0.0428, + -0.0384, + -0.0394, -0.0302, - -0.0725, - 0.0165, - 0.0454, - -0.0142, - 0.0284, - 0.0915, - 0.0301, - -0.0061, - -0.024, - 0.039, - -0.0015, - -0.0091, - 0.0121, - -0.0375, - 0.0072, - 0.0218, - -0.0031, - 0.073, - 0.0061, + -0.0069, + -0.0022, + 0.0178, + 0.0816, + -0.0239, + -0.0127, + 0.0058, + 0.0857, + -0.0493, + 0.0503, + -0.0734, + 0.0324, + -0.0425, + 0.1808, + -0.0255, + 0.0374, + -0.015, + 0.0271, + -0.0727, + 0.0321, + -0.0159, + -0.0053, -0.0042, - -0.0575, - -0.0579, - -0.0404, - 0.0296, - 0.0244, - -0.0367 - ] + 0.0785, + 0.0388, + 0.0313, + 0.036, + -0.0615, + -0.0196, + -0.023, + -0.0387, + 0.0437, + 0.0571, + -0.049, + -0.0025, + 0.0626, + -0.0249, + 0.0016, + -0.0235, + 0.0048, + 0.0103, + 0.0183, + 0.0233, + -0.064, + 0.0104, + 0.0465, + 0.0188, + 0.0607, + -0.0142, + 0.0164, + -0.0482, + 0.0092, + -0.0368, + 0.0159, + 0.0448, + -0.0397 + ], + "year": 2024, + "authors": [ + "Hooman H. Rashidi", + "Samer Albahra", + "Brian P. Rubin", + "Bo Hu" + ], + "affiliations": [ + "Cleveland Clinic", + "University of Pittsburgh", + "Computational Diagnostics (United States)" + ], + "abstract": "Healthcare data accessibility for machine learning (ML) is encumbered by a range of stringent regulations and limitations. Using synthetic data that mirrors the underlying properties in the real data is emerging as a promising solution to overcome these barriers. We propose a fully automated synthetic tabular neural generator (STNG), which comprises multiple synthetic data generators and integrates an Auto-ML module to validate and comprehensively compare the synthetic datasets generated from different approaches. An empirical study was conducted to demonstrate the performance of STNG using twelve different datasets. The results highlight STNG's robustness and its pivotal role in enhancing the accessibility of validated synthetic healthcare data, thereby offering a promising solution to a critical barrier in ML applications in healthcare.", + "cited_by_count": 10, + "openalex_url": "https://openalex.org/W4403191965" }, { "channel": "standardmodelbio", @@ -36725,435 +36853,452 @@ "journal": "Nature", "projections": { "pca": [ - -0.15742500126361847, - -0.09339389204978943 + -0.08418955653905869, + -0.1925545483827591 ], "tsne": [ - -14.1803560256958, - 6.611511707305908 + -11.6450777053833, + -0.896954357624054 ], "umap": [ - -0.7519751787185669, - 6.321335315704346 + -2.1855788230895996, + 4.126053810119629 ], "pca3d": [ - -0.15742366015911102, - -0.093378446996212, - 0.1622728407382965 + -0.08418847620487213, + -0.1925436109304428, + 0.08001250773668289 ], "tsne3d": [ - 21.315271377563477, - -31.763547897338867, - 28.046236038208008 + -6.6799492835998535, + -15.271428108215332, + 25.70283317565918 ], "umap3d": [ - 4.11153507232666, - -0.9655564427375793, - -1.202531337738037 + 1.1163883209228516, + 1.1028234958648682, + 6.481531143188477 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 28, - "label": "Clinical AI" + "id": 21, + "label": "Clinical Trials" } ], "_embedding": [ - -0.0031, - 0.0268, - -0.0229, - -0.0416, - 0.0106, - 0.0447, - 0.0644, - 0.0365, - 0.0588, - -0.0067, - -0.0255, - 0.023, - 0.0465, - 0.0148, - -0.0184, - 0.0217, - -0.0169, - 0.0448, - 0.0037, - 0.0726, - 0.0274, + 0.0042, + 0.0309, + -0.0094, + -0.0363, + 0.0406, + 0.0235, + 0.0194, + -0.0081, + 0.0313, + -0.0172, + -0.0256, + -0.0352, + -0.0026, + 0.0188, + -0.0358, + 0.0255, + -0.041, + -0.0267, + 0.0113, + 0.0353, + 0.0198, + 0.0069, + -0.0015, + -0.0448, + -0.0167, + 0.0271, + -0.0152, + -0.0251, + -0.0478, + -0.1813, + 0.0473, + -0.0294, + 0.0027, + 0.0208, + 0.0275, + -0.0087, + 0.007, + 0.0599, + -0.0361, + 0.0906, + -0.055, + -0.0068, + -0.0158, + 0.0497, + 0.058, + 0.0244, + -0.0317, + 0.0309, + -0.0415, + 0.0009, + -0.0075, + 0.0312, + -0.0235, + 0.0991, + -0.0129, -0.0178, - 0.0075, - -0.0625, - -0.0458, - 0.0183, - -0.0052, - -0.0078, - -0.0767, - -0.1386, - 0.0431, - -0.0377, - 0.0416, + 0.0422, + 0.0043, + -0.0161, + -0.0145, 0.0129, - 0.052, - -0.0364, - -0.0187, - 0.07, - -0.0141, 0.0657, - -0.0747, - -0.0244, - -0.0185, + -0.1108, + 0.0487, + -0.0034, + 0.0624, + -0.0446, + -0.0751, + 0.0672, + -0.0073, + -0.0053, + 0.0296, + 0.0029, + 0.0615, + 0.0173, + 0.0434, + 0.0219, + -0.0078, + 0.0227, + 0.041, + 0.0379, + 0.0292, 0.0299, - 0.0417, - -0.0283, - -0.0082, - 0.0223, - -0.0264, - 0.016, - -0.0333, - 0.0166, - 0.014, - 0.0783, - 0.0236, - -0.0438, - 0.0583, - -0.0162, - 0.003, - 0.0192, - -0.0068, - 0.0746, - -0.0721, - 0.0553, - -0.0003, - 0.0651, - -0.0647, - -0.0684, - 0.0412, - 0.0022, - -0.0003, - 0.0251, - -0.0002, - 0.0334, - -0.0088, - 0.0742, - 0.0177, - -0.0132, - -0.01, - 0.0298, - 0.0074, - 0.0195, - 0.0223, - 0.0132, - -0.047, - -0.031, - 0.0213, - -0.0576, - 0.0016, - -0.0101, - -0.0713, - -0.0238, - -0.0084, - 0.0215, - -0.0138, - 0.0231, - -0.0163, - 0.0504, - -0.0037, - 0.3048, - -0.0787, + -0.0303, + -0.0495, + -0.0199, + -0.0108, + -0.0796, + -0.0157, + 0.0211, + -0.0447, + -0.0029, + 0.0033, + 0.0309, + -0.0416, + 0.01, + 0.0075, + 0.0518, + 0.0156, + 0.3776, + -0.0551, -0.0106, - -0.0356, - -0.0178, - 0.034, - -0.0561, - 0.0094, - -0.0825, - -0.002, - -0.0231, - 0.0186, - -0.0146, - 0.0462, - -0.035, - -0.014, - 0.0581, - 0.0562, - 0.0609, - 0.0084, - -0.0213, - -0.0219, - 0.0094, - 0.0355, - 0.0459, - 0.0531, - -0.0498, - 0.0024, - 0.1499, - -0.0113, - 0.0565, - -0.0076, - 0.0737, - -0.0381, - 0.0495, - 0.0153, - -0.0532, - -0.0894, - 0.0179, - 0.0336, - 0.0021, - 0.0073, - 0.0553, + -0.0365, + 0.0087, 0.0049, - -0.0093, - -0.086, - 0.1183, - -0.01, - 0.0495, - -0.0262, - -0.0919, - 0.0472, - 0.0677, - 0.0121, - -0.0344, - 0.0219, - -0.0254, - -0.0281, - -0.0091, - -0.0798, - 0.004, - 0.0181, - -0.0174, + -0.0729, + -0.0249, + -0.0406, + 0.0243, + -0.02, + -0.0004, -0.0012, - 0.1712, - -0.0271, - -0.0774, - 0.0423, - 0.0568, - 0.0621, - 0.0073, - 0.0296, - -0.0115, - 0.0534, - 0.0237, - -0.0537, + 0.0522, + -0.0401, + -0.0021, + -0.0098, + 0.0415, + 0.0567, + -0.0014, + -0.0263, + -0.0052, + 0.0419, 0.036, - -0.0256, - -0.0179, - -0.0131, - -0.0069, - -0.0716, - -0.0423, - -0.0301, - 0.0487, - -0.0192, - -0.0128, + 0.0373, + 0.0411, + 0.0047, + -0.013, + 0.1221, + -0.0025, + 0.0211, + 0.0347, + 0.0661, + -0.0648, + 0.0239, -0.0217, - 0.0112, - 0.0545, - 0.0773, - -0.0246, - -0.0265, - -0.0704, - -0.0035, + -0.0664, + -0.0467, + 0.0128, + 0.025, + -0.0005, + 0.0055, + 0.0519, + -0.0028, + -0.0141, + -0.1212, + 0.1118, + 0.024, + 0.0652, + -0.0598, + -0.052, + 0.0249, + 0.0732, + -0.0142, + -0.043, + 0.008, + -0.0065, + -0.0046, + -0.0258, + -0.0742, + -0.0295, + -0.0143, + -0.0194, + 0.0093, + 0.1318, + -0.0174, + -0.0668, + 0.0097, + 0.0469, + 0.0718, + 0.0169, + 0.0323, + -0.0317, + 0.0382, + 0.009, + -0.0562, + 0.0267, + -0.0169, -0.0118, - -0.0546, - -0.0218, + 0.0089, + -0.0125, + -0.0343, + -0.0409, + -0.0168, + 0.0129, + -0.0219, + -0.0276, + -0.0586, + 0.0283, + 0.0612, + 0.0578, + -0.0022, + -0.0024, + -0.0749, + 0.0069, + -0.0069, + -0.052, + -0.0261, + -0.0096, + -0.0058, + -0.0219, + 0.0002, + 0.0685, + 0.032, + -0.0219, -0.0199, - -0.0367, - -0.0182, - 0.0028, - 0.0374, + 0.0027, 0.0379, - -0.0315, - -0.006, - -0.0159, - 0.0109, - 0.0087, - 0.0663, - 0.0188, - -0.0122, - 0.0192, - 0.0038, - -0.0259, - -0.0363, - 0.033, - 0.026, - 0.0461, - 0.0308, - -0.0151, - -0.0069, - 0.0636, - -0.0518, - -0.2934, - -0.0247, - -0.0102, - 0.0067, - -0.0374, + -0.0171, + 0.0595, + 0.0215, + -0.0507, + -0.0106, + -0.0184, + -0.0148, -0.048, - 0.0113, - -0.0247, - 0.0293, - 0.0308, - 0.0557, - 0.0044, - -0.0961, - 0.028, - -0.0443, - -0.0088, - 0.0252, - -0.04, - -0.0676, - -0.0214, - -0.0297, - 0.0002, - 0.0593, - -0.0877, - 0.0403, - 0.0192, - 0.1812, - -0.0491, - -0.0384, - 0.0273, - 0.003, - -0.0336, - -0.0166, - -0.0852, - 0.064, - -0.0065, - -0.0122, - 0.0567, - -0.0648, - -0.0151, - -0.0175, - -0.0167, - 0.0241, - -0.0753, - -0.0332, - -0.0474, - -0.0415, - 0.0451, - -0.09, - 0.0183, - 0.0017, - 0.011, - 0.0937, - -0.0372, - -0.0049, - -0.0457, - -0.0516, - -0.0095, - -0.0447, - 0.0204, - 0.0082, + 0.0195, + 0.016, + -0.0004, + 0.0056, + 0.0175, + -0.0276, + 0.038, + -0.0319, + -0.3366, + -0.0434, -0.0077, - -0.0157, - -0.0648, - 0.0191, - -0.0908, + 0.0179, + 0.0168, + -0.0257, + -0.0156, + 0.0053, + 0.0026, + 0.0114, + 0.0473, + 0.0397, + -0.0757, + 0.0016, + -0.0507, + 0.0066, + 0.0307, + -0.0569, + -0.0963, + -0.0514, + -0.0318, + -0.0171, + 0.0129, + -0.0753, + 0.0675, + 0.01, + 0.1385, + -0.0702, -0.0023, - -0.0442, - 0.0151, - -0.0342, - -0.0686, - 0.1059, - -0.0085, - 0.0355, - 0.0351, - 0.0435, - -0.0279, - -0.0521, - -0.0984, - 0.022, - 0.0638, - 0.0357, - 0.0445, - 0.0245, - 0.0213, - 0.0124, - 0.081, - 0.0511, - 0.0375, - -0.0299, - 0.0118, - -0.0304, - -0.0443, - -0.0494, - 0.0434, - 0.019, - -0.2676, - -0.0242, - -0.0841, - 0.0466, + 0.0642, -0.0041, - -0.0364, - 0.0265, - 0.0017, - -0.022, - 0.0093, - -0.0162, - 0.0217, - 0.093, - -0.0057, - -0.0001, - -0.0471, - 0.0598, - -0.0669, - 0.0039, - -0.0243, + -0.0113, + -0.0289, + -0.1198, + 0.0512, + -0.0518, + -0.0165, + 0.0564, + -0.0476, + -0.0015, + -0.0111, + -0.0179, + 0.0049, + -0.0837, + -0.0419, + -0.0046, + -0.0197, + 0.0624, + -0.0713, + 0.0206, + 0.0, + 0.0411, 0.0558, - -0.0068, - 0.1816, - -0.0241, - -0.0067, - 0.0365, - 0.0378, - 0.0068, - -0.0198, - -0.0118, - -0.0101, - -0.0131, - 0.0283, - -0.0096, - 0.0253, - 0.0644, - -0.0062, - -0.0254, - -0.0137, - -0.0093, - 0.0175, - -0.0114, - 0.0094, - 0.0247, - 0.0821, - 0.0024, - -0.024, - -0.0211, - -0.0102, - 0.0449, - -0.0175, + -0.0036, + -0.0038, + 0.0023, + -0.0619, + 0.0053, + -0.0575, + 0.0284, + 0.0415, + 0.0004, + 0.0059, + -0.0444, + 0.0135, + -0.0975, + 0.0036, + -0.0326, + 0.0279, + -0.028, + -0.0302, + 0.1251, + -0.0267, 0.0228, - -0.0214, - -0.0147, - 0.0645, - 0.0106, - 0.0429, - -0.0313, - 0.0067, - -0.0683, - -0.0283, + 0.0625, + 0.029, + -0.0393, + -0.0677, + -0.0652, + 0.0221, + 0.0585, + 0.03, + 0.078, + 0.0502, + 0.0213, + 0.0465, + 0.0908, + 0.0393, + 0.0152, + -0.0324, + 0.0123, + -0.0239, + -0.0162, + -0.0634, + 0.0431, + 0.0133, + -0.2527, + -0.0138, + -0.0473, + 0.0423, + 0.0047, + -0.027, + 0.0144, + 0.0126, + -0.0305, 0.0255, - -0.0294, - 0.0301, - -0.0909 - ] + 0.003, + 0.0152, + 0.1069, + 0.0185, + -0.022, + -0.0319, + 0.0778, + -0.069, + -0.0112, + -0.0361, + 0.0439, + -0.05, + 0.1582, + -0.0111, + -0.0045, + 0.0039, + 0.012, + 0.0058, + -0.0037, + 0.0112, + 0.0116, + 0.0188, + 0.0186, + -0.0191, + 0.04, + 0.0385, + 0.0029, + 0.0098, + -0.0268, + -0.0077, + 0.0173, + 0.0098, + 0.0053, + 0.0074, + 0.063, + -0.0051, + -0.0396, + -0.0058, + -0.0451, + 0.0403, + -0.0219, + 0.0161, + -0.0215, + -0.0241, + 0.0601, + 0.0081, + 0.0542, + -0.0123, + 0.0273, + -0.0115, + -0.0033, + 0.0325, + -0.0272, + 0.0308, + -0.0779 + ], + "year": 2024, + "authors": [ + "Zhaozhi Qian", + "Thomas Callender", + "Bogdan Cebere", + "Sam M. Janes", + "Neal Navani", + "Mihaela van der Schaar" + ], + "affiliations": [ + "University of Cambridge", + "University College London", + "Turing Institute" + ], + "abstract": "Synthetic data promise privacy-preserving data sharing for healthcare research and development. Compared with other privacy-enhancing approaches-such as federated learning-analyses performed on synthetic data can be applied downstream without modification, such that synthetic data can act in place of real data for a wide range of use cases. However, the role that synthetic data might play in all aspects of clinical model development remains unknown. In this work, we used state-of-the-art generators explicitly designed for privacy preservation to create a synthetic version of ever-smokers in the UK Biobank before building prognostic models for lung cancer under several data release assumptions. We demonstrate that synthetic data can be effectively used throughout the medical prognostic modeling pipeline even without eventual access to the real data. Furthermore, we show the implications of different data release approaches on how synthetic biobank data could be deployed within the healthcare system.", + "cited_by_count": 40, + "openalex_url": "https://openalex.org/W4403844673" }, { "channel": "standardmodelbio", @@ -37174,47 +37319,47 @@ "journal": "Nature", "projections": { "pca": [ - -0.2296728640794754, - -0.06135480850934982 + -0.2253665328025818, + -0.057475555688142776 ], "tsne": [ - -12.009902954101562, - -11.46034049987793 + -19.93978500366211, + 8.351227760314941 ], "umap": [ - -0.9011304974555969, - 5.868030548095703 + -1.6574000120162964, + 4.241914749145508 ], "pca3d": [ - -0.229673832654953, - -0.061361394822597504, - 0.12247180938720703 + -0.22536757588386536, + -0.05748048797249794, + 0.1253855973482132 ], "tsne3d": [ - 5.082833290100098, - -25.933433532714844, - -8.287936210632324 + -16.06835174560547, + -5.228611946105957, + 4.121476650238037 ], "umap3d": [ - 3.8298332691192627, - -0.5081343054771423, - -1.3093639612197876 + 0.8369444608688354, + 0.16135480999946594, + 6.724139213562012 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 1, - "label": "Cancer Data" + "id": 5, + "label": "Clinical Data" }, { - "id": 28, - "label": "Clinical AI" + "id": 14, + "label": "Clinical Health" } ], "_embedding": [ @@ -37602,7 +37747,27 @@ -0.0195, 0.0793, -0.0759 - ] + ], + "year": 2025, + "authors": [ + "Jinxi Xiang", + "Xiyue Wang", + "Xiaoming Zhang", + "Yinghua Xi", + "Feyisope Eweje", + "Yijiang Chen", + "Yuchen Li", + "Colin P. Bergstrom", + "Matthew Gopaulchan", + "Ted Kim" + ], + "affiliations": [ + "Stanford University", + "Harvard University", + "Stanford Medicine" + ], + "cited_by_count": 169, + "openalex_url": "https://openalex.org/W4406168947" }, { "channel": "standardmodelbio", @@ -37626,47 +37791,47 @@ ], "projections": { "pca": [ - 0.08322365581989288, - -0.009877351112663746 + 0.08639032393693924, + -0.0065246461890637875 ], "tsne": [ - 0.7653191685676575, - 19.181900024414062 + 9.703385353088379, + 11.709695816040039 ], "umap": [ - 3.7196526527404785, - 4.060731410980225 + 2.833181381225586, + 3.044708013534546 ], "pca3d": [ - 0.08322364836931229, - -0.00987032987177372, - 0.25263118743896484 + 0.08639030903577805, + -0.006516134832054377, + 0.2504850924015045 ], "tsne3d": [ - 46.69922637939453, - -16.7207088470459, - -9.274237632751465 + -7.285884857177734, + 26.54190444946289, + -3.4392688274383545 ], "umap3d": [ - 5.431094169616699, - 0.973023533821106, - 1.085816740989685 + 4.349937915802002, + 0.7514886856079102, + 5.222379207611084 ] }, "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_label": "Open Source Tools", "cluster_levels": [ { "id": 3, - "label": "AI Training Models" + "label": "Open Source Tools" }, { - "id": 0, - "label": "AI Agents" + "id": 8, + "label": "Open AI" }, { - "id": 1, - "label": "AI Agents" + "id": 22, + "label": "AI Gemini" } ], "_embedding": [ @@ -38086,46 +38251,46 @@ ], "projections": { "pca": [ - -0.1979110836982727, - -0.06689915806055069 + -0.19612804055213928, + -0.06430213153362274 ], "tsne": [ - -9.677227020263672, - -2.016679525375366 + -13.885976791381836, + 7.271519660949707 ], "umap": [ - 0.15141689777374268, - 6.612582683563232 + -1.669776439666748, + 3.526200532913208 ], "pca3d": [ - -0.19791150093078613, - -0.06691297143697739, - 0.09736711531877518 + -0.19612851738929749, + -0.06431658565998077, + 0.10475071519613266 ], "tsne3d": [ - 5.602491855621338, - -15.415204048156738, - 8.550151824951172 + 1.0711381435394287, + -5.815074920654297, + 9.36773681640625 ], "umap3d": [ - 4.61815071105957, - -0.8115565180778503, - -1.699946641921997 + 1.3729498386383057, + 0.8655414581298828, + 7.287844657897949 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, + "id": 14, "label": "Clinical Health" } ], @@ -38514,7 +38679,11 @@ -0.0542, 0.0437, -0.0356 - ] + ], + "year": 2025, + "journal": "ArXiv.org", + "cited_by_count": 5, + "openalex_url": "https://openalex.org/W4415319839" }, { "channel": "standardmodelbio", @@ -38581,46 +38750,46 @@ "openalex_url": "https://openalex.org/W2107429737", "projections": { "pca": [ - -0.08925388753414154, - -0.2865558862686157 + -0.09034143388271332, + -0.2859303951263428 ], "tsne": [ - -7.203832149505615, - -18.942750930786133 + -23.07364845275879, + -6.966319561004639 ], "umap": [ - -2.3074512481689453, - 5.971418380737305 + -2.436662435531616, + 5.2699198722839355 ], "pca3d": [ - -0.08925370126962662, - -0.2865583300590515, - -0.08255089819431305 + -0.09034126251935959, + -0.28593218326568604, + -0.07848973572254181 ], "tsne3d": [ - -29.199722290039062, - -36.28893280029297, - -2.8850109577178955 + -30.164146423339844, + -20.739511489868164, + 14.104958534240723 ], "umap3d": [ - 2.5109972953796387, - -1.0631771087646484, - -0.8612768650054932 + 0.040641505271196365, + -0.02843398042023182, + 5.690135955810547 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 24, + "id": 1, "label": "Cancer Data" } ], @@ -39028,47 +39197,47 @@ "title": "Preparing to download ...", "projections": { "pca": [ - 0.3274495005607605, - 0.01860622689127922 + 0.32909509539604187, + 0.019647222012281418 ], "tsne": [ - 13.6385498046875, - 9.09503173828125 + 17.959909439086914, + -7.267586708068848 ], "umap": [ - 3.2004687786102295, - 5.796535968780518 + 2.401287794113159, + 4.675756931304932 ], "pca3d": [ - 0.32744941115379333, - 0.018619509413838387, - -0.06595610082149506 + 0.3290952146053314, + 0.01966121792793274, + -0.07211059331893921 ], "tsne3d": [ - 2.782855987548828, - 52.89731216430664, - 0.7958536148071289 + 28.8543701171875, + 4.069253444671631, + 0.7752891778945923 ], "umap3d": [ - 4.18809175491333, - -0.27134835720062256, - 2.3233630657196045 + 3.995009422302246, + 1.9027668237686157, + 6.547943592071533 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 20, - "label": "AI Agents" + "id": 24, + "label": "SMB Models" } ], "_embedding": [ @@ -39476,47 +39645,47 @@ "abstract": "Gemini 2.5 is our most intelligent AI model, now with thinking.", "projections": { "pca": [ - 0.1085328683257103, - 0.17474184930324554 + 0.11237961798906326, + 0.17679202556610107 ], "tsne": [ - 3.5679032802581787, - 30.59384536743164 + 27.382444381713867, + 18.70203399658203 ], "umap": [ - 4.331092357635498, - 4.591220855712891 + 3.39143443107605, + 2.7920055389404297 ], "pca3d": [ - 0.10853125154972076, - 0.17473654448986053, - 0.184911847114563 + 0.11237793415784836, + 0.17679104208946228, + 0.18117664754390717 ], "tsne3d": [ - 52.83788299560547, - -5.246243000030518, - -11.970709800720215 + -12.032013893127441, + 34.593441009521484, + -7.117872714996338 ], "umap3d": [ - 5.442966461181641, - 1.1354347467422485, - 1.5284759998321533 + 4.740800380706787, + 1.4302440881729126, + 4.843337059020996 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { - "id": 7, - "label": "Open AI" + "id": 22, + "label": "AI Gemini" } ], "_embedding": [ @@ -39925,47 +40094,47 @@ "abstract": "ChatGPT Images 2.0 introduces a state-of-the-art image generation model with improved text rendering, multilingual support, and advanced visual reasoning.", "projections": { "pca": [ - 0.030730225145816803, - 0.10996575653553009 + 0.03193894773721695, + 0.11127308756113052 ], "tsne": [ - -0.9087496399879456, - 5.828876972198486 + 4.655993938446045, + 10.514754295349121 ], "umap": [ - 1.4453535079956055, - 4.175853252410889 + 0.1733400523662567, + 2.7266595363616943 ], "pca3d": [ - 0.030729759484529495, - 0.10996603965759277, - -0.01348209660500288 + 0.03193855658173561, + 0.1112731322646141, + -0.011859550140798092 ], "tsne3d": [ - 4.510522365570068, - -4.621007442474365, - 28.09121322631836 + 13.633561134338379, + -22.33024024963379, + 23.09880256652832 ], "umap3d": [ - 5.2755022048950195, - -0.8150486350059509, - -0.21130329370498657 + 2.6938469409942627, + 0.15498720109462738, + 6.552972316741943 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 4, - "label": "Com Area Amp" + "id": 2, + "label": "Medical Models" }, { - "id": 27, - "label": "Embeddings" + "id": 0, + "label": "Multimodal Models" } ], "_embedding": [ @@ -40371,47 +40540,47 @@ ], "projections": { "pca": [ - 0.14093849062919617, - 0.031582120805978775 + 0.14088067412376404, + 0.031106479465961456 ], "tsne": [ - 6.8700385093688965, - -0.6100314259529114 + 6.926673889160156, + 3.988438606262207 ], "umap": [ - 1.5111706256866455, - 5.2125349044799805 + 0.8650535941123962, + 4.759190559387207 ], "pca3d": [ - 0.14093875885009766, - 0.03159305825829506, - 0.06772425770759583 + 0.1408809870481491, + 0.03111315332353115, + 0.06256703287363052 ], "tsne3d": [ - -0.2847196161746979, - 13.288995742797852, - -5.475704193115234 + 24.454797744750977, + -31.83780860900879, + 9.555785179138184 ], "umap3d": [ - 3.2673871517181396, - 0.8413878679275513, - 0.7085138559341431 + 2.481320381164551, + 1.6439944505691528, + 5.227438449859619 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 4, - "label": "Com Area Amp" + "id": 6, + "label": "Data Needs" }, { - "id": 23, - "label": "Research Authors" + "id": 10, + "label": "Research Data" } ], "_embedding": [ @@ -40822,46 +40991,46 @@ ], "projections": { "pca": [ - 0.022831236943602562, - 0.1572989970445633 + 0.022972993552684784, + 0.1572950780391693 ], "tsne": [ - -27.682992935180664, - 17.249025344848633 + 2.1482460498809814, + 26.36455535888672 ], "umap": [ - 2.4430341720581055, - 2.2898528575897217 + 1.3587095737457275, + 1.4240154027938843 ], "pca3d": [ - 0.022829677909612656, - 0.15728844702243805, - -0.12373562902212143 + 0.022971155121922493, + 0.15728558599948883, + -0.12338143587112427 ], "tsne3d": [ - 0.18313954770565033, - 29.975065231323242, - -41.769195556640625 + -12.508456230163574, + 7.710137367248535, + -37.189273834228516 ], "umap3d": [ - 6.765525817871094, - -1.2801331281661987, - 0.5629291534423828 + 3.862060308456421, + -1.076622486114502, + 5.469136714935303 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 4, + "id": 26, "label": "World Models" } ], @@ -41250,7 +41419,11 @@ 0.0259, 0.0738, 0.029 - ] + ], + "year": 2026, + "journal": "arXiv (Cornell University)", + "cited_by_count": 0, + "openalex_url": "https://openalex.org/W7140166797" }, { "channel": "social-fun", @@ -41268,47 +41441,47 @@ ], "projections": { "pca": [ - 0.23127645254135132, - 0.05990052968263626 + 0.2315974086523056, + 0.05960822477936745 ], "tsne": [ - 6.998755931854248, - -0.39200639724731445 + 7.172277450561523, + 4.029154300689697 ], "umap": [ - 1.8668274879455566, - 5.252713203430176 + 1.075460433959961, + 4.497688293457031 ], "pca3d": [ - 0.2312769591808319, - 0.059908200055360794, - 0.00686244759708643 + 0.23159797489643097, + 0.05961225554347038, + 0.00042624861816875637 ], "tsne3d": [ - 4.081042289733887, - 11.809819221496582, - -6.02441930770874 + 27.203536987304688, + -33.8608512878418, + 7.678465366363525 ], "umap3d": [ - 3.520617961883545, - 0.9551499485969543, - 1.0307751893997192 + 2.869191884994507, + 1.834047794342041, + 5.362894535064697 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 4, - "label": "Com Area Amp" + "id": 6, + "label": "Data Needs" }, { - "id": 26, - "label": "AI Information" + "id": 10, + "label": "Research Data" } ], "_embedding": [ @@ -41714,47 +41887,47 @@ ], "projections": { "pca": [ - 0.2569572329521179, - 0.16149479150772095 + 0.2572447657585144, + 0.16102655231952667 ], "tsne": [ - 26.123327255249023, - 20.86529541015625 + 32.54313659667969, + 3.146599769592285 ], "umap": [ - 4.017160892486572, - 5.9425272941589355 + 3.2614214420318604, + 4.3930792808532715 ], "pca3d": [ - 0.25695693492889404, - 0.16148802638053894, - -0.0454646460711956 + 0.2572445273399353, + 0.16101813316345215, + -0.050126418471336365 ], "tsne3d": [ - 48.36339569091797, - 26.468019485473633, - 20.89691925048828 + 29.90996742248535, + 31.58231544494629, + 18.863975524902344 ], "umap3d": [ - 4.746947288513184, - 0.3253944516181946, - 2.5581517219543457 + 4.514525890350342, + 1.904160499572754, + 6.165735721588135 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 8, - "label": "Open Models" + "id": 4, + "label": "AI Agents" }, { - "id": 16, - "label": "Claude Code" + "id": 4, + "label": "Open Agent Tools" } ], "_embedding": [ @@ -42162,46 +42335,46 @@ "abstract": "We are excited to introduce the Waymo World Model, a frontier generative model that sets a new bar for large-scale, hyper-realistic autonomous driving simulation.", "projections": { "pca": [ - 0.047905128449201584, - 0.16631391644477844 + 0.049292635172605515, + 0.1666085422039032 ], "tsne": [ - -9.364577293395996, - 27.39080047607422 + 3.635784864425659, + 20.293235778808594 ], "umap": [ - 2.017500162124634, - 1.9450989961624146 + 1.3374825716018677, + 0.9820699691772461 ], "pca3d": [ - 0.04790513962507248, - 0.16634225845336914, - 0.049981292337179184 + 0.04929272457957268, + 0.16663548350334167, + 0.050224870443344116 ], "tsne3d": [ - 4.067495822906494, - -5.181890964508057, - -54.549007415771484 + 4.757530212402344, + 11.166888236999512, + -8.333178520202637 ], "umap3d": [ - 6.936456203460693, - -0.5323788523674011, - 0.1435321420431137 + 4.4565110206604, + -1.5813356637954712, + 5.792412757873535 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 4, + "id": 26, "label": "World Models" } ], @@ -42610,47 +42783,47 @@ "abstract": "A consolidated list of the Jeff Dean Facts! Contribute to LRitzdorf/TheJeffDeanFacts development by creating an account on GitHub.", "projections": { "pca": [ - 0.3328280746936798, - -0.04650500416755676 + 0.3297930955886841, + -0.048756085336208344 ], "tsne": [ - 29.204927444458008, - 14.773759841918945 + 33.13836669921875, + -5.714784145355225 ], "umap": [ - 3.6224524974823, - 6.892123222351074 + 3.173658847808838, + 5.721186637878418 ], "pca3d": [ - 0.33282962441444397, - -0.04649016633629799, - -0.20207558572292328 + 0.32979461550712585, + -0.04874598607420921, + -0.20717860758304596 ], "tsne3d": [ - 41.89013671875, - 39.82297134399414, - 19.288219451904297 + 46.617881774902344, + 12.25922679901123, + 2.940530776977539 ], "umap3d": [ - 3.4760990142822266, - -0.12745288014411926, - 2.9683938026428223 + 3.9135446548461914, + 2.8950767517089844, + 6.707164287567139 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 18, - "label": "Github Tools" + "id": 17, + "label": "GitHub Creation" } ], "_embedding": [ @@ -43056,47 +43229,47 @@ ], "projections": { "pca": [ - 0.26212218403816223, - 0.08476278930902481 + 0.2613825500011444, + 0.08148603141307831 ], "tsne": [ - 8.899068832397461, - -0.2895190417766571 + 8.813126564025879, + 3.3877665996551514 ], "umap": [ - 1.6726230382919312, - 5.298126697540283 + 0.9850166440010071, + 4.742237091064453 ], "pca3d": [ - 0.2621207535266876, - 0.08474761992692947, - -0.014895725063979626 + 0.2613810896873474, + 0.08146964013576508, + -0.02083047479391098 ], "tsne3d": [ - 2.9127354621887207, - 18.76177406311035, - -9.662918090820312 + 26.607070922851562, + -27.341676712036133, + 4.830170631408691 ], "umap3d": [ - 3.2645103931427, - 1.0016008615493774, - 0.9160998463630676 + 2.6743860244750977, + 1.8464192152023315, + 5.229273796081543 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 4, - "label": "Com Area Amp" + "id": 6, + "label": "Data Needs" }, { - "id": 26, - "label": "AI Information" + "id": 10, + "label": "Research Data" } ], "_embedding": [ @@ -43504,47 +43677,47 @@ "abstract": "FizzBuzz Enterprise Edition is a no-nonsense implementation of FizzBuzz made by serious businessmen for serious business purposes. - EnterpriseQualityCoding/FizzBuzzEnterpriseEdition", "projections": { "pca": [ - 0.2949785888195038, - -0.04022159054875374 + 0.29598918557167053, + -0.03956630080938339 ], "tsne": [ - 29.19695281982422, - 21.519468307495117 + 35.52655792236328, + 0.581823468208313 ], "umap": [ - 4.226597785949707, - 6.224574565887451 + 3.7119150161743164, + 4.83660364151001 ], "pca3d": [ - 0.2949780821800232, - -0.040214721113443375, - -0.05628671869635582 + 0.29598894715309143, + -0.039559707045555115, + -0.05923869088292122 ], "tsne3d": [ - 49.86817169189453, - 12.903055191040039, - 22.065608978271484 + 21.560855865478516, + 34.07179260253906, + 20.11679458618164 ], "umap3d": [ - 4.697183132171631, - 0.4067903161048889, - 2.994438409805298 + 4.787573337554932, + 2.334789991378784, + 6.364739894866943 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 8, - "label": "Open Models" + "id": 0, + "label": "GitHub Tools" }, { - "id": 16, - "label": "Claude Code" + "id": 11, + "label": "Code Tools" } ], "_embedding": [ @@ -43956,47 +44129,47 @@ "doi": "10.1016/s0960-9822(00)80094-9", "projections": { "pca": [ - -0.025461245328187943, - -0.1106676384806633 + -0.023990526795387268, + -0.10925997048616409 ], "tsne": [ - -4.342865943908691, - -39.69363784790039 + -27.423397064208984, + -25.029726028442383 ], "umap": [ - -1.2088338136672974, - 3.109668016433716 + -1.4588444232940674, + 6.225706100463867 ], "pca3d": [ - -0.025462094694375992, - -0.11068136990070343, - -0.06086084619164467 + -0.023991398513317108, + -0.10927111655473709, + -0.06172614544630051 ], "tsne3d": [ - -11.115199089050293, - -45.369224548339844, - -21.167879104614258 + -40.215538024902344, + -38.030189514160156, + -5.197571754455566 ], "umap3d": [ - 1.9716159105300903, - 1.040902018547058, - -0.5999985337257385 + 0.8868287801742554, + 0.9605647921562195, + 4.353419303894043 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 19, - "label": "Cell Models" + "id": 28, + "label": "Spatial Drug" } ], "_embedding": [ @@ -44402,47 +44575,47 @@ ], "projections": { "pca": [ - 0.1893620491027832, - 0.02803848870098591 + 0.18947863578796387, + 0.027233067899942398 ], "tsne": [ - -4.415665149688721, - 3.5782973766326904 + 8.356261253356934, + 7.009393692016602 ], "umap": [ - 0.9406464099884033, - 5.884278297424316 + 0.13553939759731293, + 3.8987362384796143 ], "pca3d": [ - 0.18936051428318024, - 0.028027523308992386, - -0.01214043702930212 + 0.18947716057300568, + 0.02722141705453396, + -0.015030056238174438 ], "tsne3d": [ - 14.195438385009766, - 21.63214111328125, - -14.39966869354248 + 11.96017074584961, + -10.809772491455078, + 5.640471458435059 ], "umap3d": [ - 4.928915023803711, - -0.37503448128700256, - -0.8670425415039062 + 2.2244467735290527, + 1.0854594707489014, + 6.620704174041748 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 4, - "label": "Com Area Amp" + "id": 9, + "label": "Drug Discovery" }, { "id": 0, - "label": "Clinical LLMs" + "label": "Multimodal Models" } ], "_embedding": [ @@ -44855,435 +45028,441 @@ "doi": "10.1136/bmj.315.7123.1685", "projections": { "pca": [ - -0.021809561178088188, - 0.033067572861909866 + 0.05563416704535484, + -0.17168155312538147 ], "tsne": [ - -24.02725601196289, - 27.061843872070312 + -4.457174777984619, + 5.241796016693115 ], "umap": [ - 0.2446892112493515, - 5.553189277648926 + -1.7560232877731323, + 2.6455070972442627 ], "pca3d": [ - -0.021810099482536316, - 0.03306194022297859, - 0.1461334228515625 + 0.055633217096328735, + -0.17169667780399323, + 0.13256928324699402 ], "tsne3d": [ - 6.941705703735352, - -14.250285148620605, - -15.343664169311523 + -22.78289031982422, + -1.624677300453186, + 39.157169342041016 ], "umap3d": [ - 4.120263576507568, - -0.5013672709465027, - -1.037056565284729 + 1.4218802452087402, + 0.2439812272787094, + 6.485830783843994 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 4, - "label": "Com Area Amp" + "id": 5, + "label": "Clinical Data" }, { - "id": 0, - "label": "Clinical LLMs" + "id": 13, + "label": "ECG AI Models" } ], "_embedding": [ - -0.0094, - 0.0057, - 0.0458, - 0.0369, - -0.0316, - 0.0621, - 0.1029, - 0.009, - 0.0324, - -0.0655, - 0.0216, - -0.0145, - 0.0039, - -0.0169, - 0.05, + -0.0065, + -0.0195, + 0.0017, 0.0471, - 0.0176, - 0.0182, - 0.0076, - 0.0188, - 0.03, - 0.0287, - 0.0058, - 0.0093, - -0.0499, - -0.0099, - 0.0006, - -0.0494, - -0.0756, - -0.1353, - 0.0115, - -0.0151, - 0.0559, - -0.0356, - 0.0344, - 0.0255, - -0.0068, - 0.0714, - -0.0081, - 0.0309, - 0.0261, - 0.0059, - 0.0026, - -0.108, - -0.0073, - -0.0723, - -0.0287, - -0.0265, - 0.0345, - -0.0167, - -0.0891, - -0.0507, - 0.0481, - 0.0443, - -0.0305, - 0.0043, - 0.1009, - 0.0235, - 0.0383, - -0.0035, - -0.0057, - 0.0306, - -0.0731, - 0.0499, - 0.0347, - 0.0083, - -0.0508, - -0.0701, - 0.0112, - -0.0227, - -0.0165, - -0.0437, - 0.0419, - 0.0159, - 0.0305, - -0.0018, - 0.0052, - -0.0413, - -0.0076, - -0.0124, - 0.0216, - 0.0801, - -0.0037, - -0.0613, - 0.059, - -0.0285, - 0.013, - -0.0118, - -0.0579, - -0.0044, - -0.0572, - -0.0041, - -0.0191, - 0.0347, - -0.0111, - -0.0549, - -0.0117, - -0.0101, - 0.0046, - 0.3815, - 0.0196, - -0.0288, - 0.0291, - -0.0386, - -0.029, - -0.0254, - 0.0026, + -0.0234, + 0.0691, + 0.0939, + 0.0211, + -0.0264, + -0.0532, + 0.0149, + -0.0047, + -0.0249, + -0.0249, + 0.0022, + 0.0659, + 0.0254, + -0.0334, + 0.009, + 0.0519, + -0.0457, + 0.0592, + -0.023, + 0.0313, + 0.0223, + -0.01, + 0.014, + -0.0064, -0.0517, - -0.0142, - 0.0505, + -0.1499, + 0.0044, 0.0214, - -0.0025, - 0.0786, - 0.001, + 0.0401, + -0.0522, + 0.048, + 0.0403, + 0.0189, + 0.0869, + -0.0441, + 0.0608, + -0.0189, + 0.0379, + 0.0271, + -0.0786, + 0.0014, + -0.0202, + 0.0019, + -0.0135, + 0.076, + -0.0136, + -0.0366, + -0.0176, + 0.0033, + 0.0498, + -0.0177, + -0.0366, + 0.0754, + 0.01, + 0.0225, + 0.0025, + -0.0373, 0.0358, - 0.0646, - 0.0586, - -0.0398, - -0.0119, - 0.0153, - -0.0212, - -0.0335, + -0.0799, + 0.017, + -0.0006, + 0.0181, + -0.0252, + -0.0928, 0.03, - -0.0566, - 0.0373, - -0.0428, - 0.0465, - 0.1138, - 0.0183, - 0.0131, - 0.0326, - 0.0251, - -0.0126, - 0.0087, - 0.0237, + 0.0204, + 0.0113, + -0.0043, + 0.0177, + 0.0387, + 0.0125, + -0.0129, + 0.0559, + -0.0293, + 0.032, 0.0026, - 0.0099, - -0.0096, - 0.0367, - 0.0594, - 0.0063, - -0.0388, - -0.0553, - 0.0135, - -0.0514, - 0.047, - -0.1004, - 0.0324, - -0.0451, - 0.0465, - 0.0128, - 0.0268, + 0.0226, + 0.0604, + -0.0045, + -0.0455, + 0.0229, + -0.0292, + -0.0482, + -0.0657, + -0.0459, + -0.0037, + -0.0436, + 0.0012, + -0.046, + 0.0421, + -0.0353, + -0.0804, + -0.043, + 0.007, + 0.0241, + 0.4379, + 0.0078, + 0.0017, -0.0194, - -0.0328, - 0.0359, - -0.0358, + -0.0144, + -0.031, + -0.029, + 0.0208, + -0.0355, + 0.0211, + 0.0375, + -0.0012, + -0.0278, + 0.0849, + -0.0178, + -0.0056, + 0.0617, + 0.0622, + -0.0452, + -0.0236, + 0.0064, + -0.0224, + -0.0425, + 0.0008, + -0.0483, + 0.028, + 0.0259, + 0.0688, + 0.0637, + 0.027, + -0.0224, + 0.0472, + -0.0463, + -0.0138, + 0.0246, + 0.0315, + 0.0037, + 0.0089, + -0.0298, + -0.0031, + 0.0247, + 0.0159, + -0.0946, + -0.0384, + 0.0404, + -0.0678, + 0.0215, + -0.0836, + 0.0167, + -0.0258, + 0.0622, + 0.0236, + 0.0354, + -0.0121, + -0.0265, + 0.0629, + -0.012, + 0.0338, + 0.0361, + -0.0216, + 0.0254, + 0.0542, + -0.0717, + -0.0115, + 0.0996, + 0.0533, + -0.0166, + -0.0861, + -0.0597, + 0.0311, + -0.0312, + 0.0392, + -0.0087, + -0.0129, + 0.0029, + -0.0785, + -0.082, + -0.0193, + 0.04, + -0.0339, + -0.0199, + -0.038, + -0.0012, + -0.0032, + 0.0293, + -0.036, + -0.0908, + 0.017, 0.0011, - 0.0192, + 0.032, + 0.0253, + 0.0369, + 0.0135, + -0.0675, + 0.0089, + -0.0206, + -0.0409, 0.0116, - 0.0598, - 0.039, - -0.0562, - -0.0194, - 0.1487, - 0.0333, - 0.0387, - -0.0876, - -0.0776, - 0.0121, - -0.039, - -0.012, - -0.0142, - -0.0042, - 0.0039, - -0.0738, - -0.0172, - -0.0286, - 0.0454, - -0.0671, + 0.0027, + 0.0237, + 0.0278, + -0.0311, + 0.0051, + 0.0235, + 0.0501, + 0.0281, + -0.0118, + 0.0331, + -0.0179, + 0.0473, + 0.0111, + 0.0312, 0.0101, - -0.0068, - -0.0539, - -0.0337, - 0.0337, - -0.003, - -0.1182, - 0.0231, - -0.0582, - 0.0395, - 0.0588, - 0.0125, - 0.0179, + 0.0519, + 0.0252, + -0.033, + 0.0271, + -0.027, + -0.0136, + 0.0065, + 0.034, + 0.0449, + -0.0657, + -0.0976, + -0.3134, + -0.0473, + -0.0714, + -0.026, + 0.0149, + -0.0499, + -0.0207, + -0.0357, + -0.0248, + 0.0065, + 0.0216, + 0.0716, + -0.0356, + -0.0095, + -0.0345, + 0.0308, + 0.021, + -0.0509, + -0.019, + 0.02, + -0.0046, + -0.0438, + 0.0308, + -0.036, + 0.0264, + -0.0046, + 0.1567, + 0.0378, + 0.0253, + 0.0313, + -0.0312, + -0.0375, + 0.035, + -0.067, + 0.0178, + -0.0131, + 0.0048, + -0.0269, + -0.0372, + -0.0171, + -0.0975, + -0.0834, + -0.0584, + 0.0285, + -0.0932, + -0.0045, + -0.0072, + 0.0896, + 0.0235, + 0.0043, + 0.0156, + -0.0052, + -0.0017, + -0.0004, + -0.0236, + -0.0458, + -0.0999, -0.0523, - -0.0163, - -0.013, - -0.0333, - 0.0113, - -0.009, - -0.0391, - -0.0044, - -0.0022, + -0.0511, + 0.05, + 0.0001, + 0.0494, + 0.0144, + -0.0572, + -0.0472, + 0.0377, + 0.0012, + 0.03, + 0.0292, + 0.0103, -0.0081, + 0.1311, + -0.0217, + -0.004, + 0.0882, + 0.0295, + 0.04, + -0.0704, + -0.0072, + -0.0287, 0.0025, - 0.0215, - 0.0367, - 0.0031, - -0.0245, + -0.039, + 0.0232, + 0.0606, + -0.0083, + 0.0208, + -0.0115, + -0.0484, + -0.0156, + -0.0437, + -0.0368, + -0.0061, + -0.032, + -0.0404, + 0.001, + -0.0164, + -0.2288, + 0.0509, + 0.0064, + 0.0151, + -0.0231, + 0.0368, + -0.0748, + 0.1017, + 0.0173, + -0.029, + 0.0297, + -0.0611, + 0.0693, + -0.024, + -0.0024, + 0.0109, + 0.0673, + 0.0059, + 0.0407, + 0.0224, + -0.0188, + 0.0572, + 0.1014, + 0.0652, + -0.0164, + -0.0467, + 0.0046, + 0.0364, + 0.0046, + 0.0146, + -0.0161, + 0.014, + 0.0296, 0.0298, - 0.064, - 0.0156, - 0.0113, - -0.017, - 0.0626, - 0.0333, - -0.0335, - -0.01, - -0.0472, - 0.0203, - 0.004, - 0.0198, - 0.0397, + 0.0131, + -0.0144, + 0.015, + 0.0247, + 0.0033, + -0.0159, + 0.0385, + -0.0173, + 0.0483, -0.0201, - -0.1257, - -0.2918, - -0.0225, - -0.0149, - -0.0618, - 0.0128, - -0.0579, - -0.0032, - -0.0557, - 0.0412, - 0.0217, - 0.0089, - 0.0315, - -0.023, - 0.0182, - -0.0195, - 0.0414, - 0.0014, - -0.0128, - -0.0211, - 0.0152, - -0.0065, - -0.0107, - 0.0581, - -0.0373, + 0.0503, + 0.0016, -0.0049, + -0.052, -0.0055, - 0.1933, - 0.0558, - 0.0101, - 0.0154, - -0.042, - -0.0266, - -0.0018, - -0.093, - 0.0325, - 0.0172, - 0.007, - -0.0348, - -0.013, - -0.0475, - -0.076, - -0.0482, - -0.0012, - 0.0255, - -0.0707, - -0.0165, - -0.0186, - 0.0594, - 0.019, - 0.0162, - 0.0361, - -0.0433, - 0.0493, - -0.0064, - -0.0236, - -0.0757, - -0.083, - -0.0315, - -0.0478, - 0.0502, - 0.0017, - 0.0371, - 0.0192, - -0.0834, - 0.0114, - 0.0475, - -0.0314, - 0.023, - 0.0424, - 0.022, - -0.013, - 0.079, - -0.0134, - 0.0231, - 0.0588, - 0.0406, - 0.0649, - -0.0948, - 0.0039, - -0.024, - 0.0203, - -0.003, - 0.0207, - 0.039, - 0.0215, - 0.0046, - -0.0106, - -0.0522, - 0.0161, - -0.0675, - 0.0085, - 0.0253, - -0.0401, - -0.0402, - -0.0109, - 0.0451, - -0.234, - 0.0367, - 0.0278, - 0.0276, - -0.0103, - 0.0386, - -0.0473, - 0.0597, - -0.0009, - -0.0484, - -0.0044, - -0.031, - 0.0498, - -0.033, - -0.03, + -0.0094, + -0.0094, + 0.0052, + 0.0559, + 0.011, + 0.0565, + 0.0511, + 0.0142, + 0.0041, + 0.0349, + -0.0621, + 0.0107, + 0.0229, + 0.0567, 0.0477, - 0.0884, - -0.062, - 0.0569, - -0.0092, - -0.081, - 0.0846, - 0.1741, - -0.0105, - -0.0301, - -0.03, - 0.0764, - 0.0074, - -0.0043, - -0.0273, - -0.014, - 0.0083, - 0.0834, - -0.0087, - -0.0099, - 0.0145, - -0.0389, - -0.0515, - 0.0302, - -0.0132, - 0.0264, - -0.0093, - 0.0234, - -0.0078, - 0.0927, - 0.0413, - -0.0012, - 0.0102, - 0.0085, - -0.011, - -0.0118, - -0.0091, - 0.0424, - 0.0274, - 0.0407, - 0.0461, - 0.0302, - -0.0089, - 0.0123, - -0.0959, - -0.0289, - 0.0249, - 0.0329, - -0.0042, - -0.0577 - ] + -0.0567 + ], + "affiliations": [ + "Lambeth Hospital" + ], + "abstract": "A previously healthy woman began to hear hallucinatory voices telling her to have a brain scan for a tumour. The prediction was true; she was operated on and had an uneventful recovery.\n\nBorn in continental Europe in the mid-1940s the patient settled in Britain in the late 1960s. After a series of jobs, she got married, started a family, and settled down to a full time commitment as a housewife and mother. She rarely went to her general practitioner as she enjoyed good health and had never had any hospital treatment. Her children had also been in good health.\n\nIn the winter of 1984, as she was at home reading, she heard a distinct voice inside her head. The voice told her, \u201cPlease don't be afraid. I know it must be shocking for you to hear me speaking to you like this, but this is the easiest way I could think of. My friend and I used to work at the Children's Hospital, Great Ormond Street, and we would like to help you.\u201d\n\nAB had heard of the Children's Hospital, but did not know where it was and had never visited it. Her children were well, so she had no reason to worry about them. This made it all the more frightening for her, and the voice intervened again: \u201cTo help you see that we are sincere, we would like you to check out the following\u201d\u2014and the voice gave her three separate pieces of information, which she did not possess at the time. She checked them out, and they were true, but this did not help because she had already come to the conclusion \u2026", + "cited_by_count": 15, + "openalex_url": "https://openalex.org/W2341374595" }, { "channel": "social-fun", @@ -45327,47 +45506,47 @@ "doi": "10.1101/2022.09.26.507364", "projections": { "pca": [ - 0.07714326679706573, - -0.19683504104614258 + 0.0766671746969223, + -0.19584690034389496 ], "tsne": [ - 6.032242774963379, - -35.72285842895508 + -11.082300186157227, + -33.00891876220703 ], "umap": [ - -0.9557467699050903, - 3.9647583961486816 + -0.5011796951293945, + 7.231935977935791 ], "pca3d": [ - 0.0771428570151329, - -0.1968524008989334, - -0.09414532780647278 + 0.07666667550802231, + -0.19586367905139923, + -0.09165084362030029 ], "tsne3d": [ - -41.5911979675293, - -34.88557434082031, - 11.751096725463867 + -24.993221282958984, + -42.427364349365234, + -15.219124794006348 ], "umap3d": [ - 2.422314405441284, - 1.0274512767791748, - -0.9938392043113708 + 1.3521348237991333, + 0.4996633231639862, + 3.9569919109344482 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 19, - "label": "Cell Models" + "id": 12, + "label": "Protein Language" } ], "_embedding": [ @@ -45774,47 +45953,47 @@ "title": "Claude can now create and edit files | Claude", "projections": { "pca": [ - 0.315642774105072, - 0.05708756670355797 + 0.31744933128356934, + 0.057544875890016556 ], "tsne": [ - 26.061542510986328, - 24.63336181640625 + 35.30320739746094, + 5.220818042755127 ], "umap": [ - 4.479373455047607, - 6.059055328369141 + 3.9115264415740967, + 4.638469219207764 ], "pca3d": [ - 0.31564146280288696, - 0.057070083916187286, - -0.021801849827170372 + 0.31744831800460815, + 0.0575316920876503, + -0.02606046386063099 ], "tsne3d": [ - 54.263309478759766, - 28.207563400268555, - 8.823777198791504 + 32.67322540283203, + 40.76704406738281, + 9.005749702453613 ], "umap3d": [ - 4.971612930297852, - 0.24883446097373962, - 2.9964873790740967 + 4.989881992340088, + 2.1413660049438477, + 6.407315731048584 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { - "id": 16, - "label": "Claude Code" + "id": 11, + "label": "Code Tools" } ], "_embedding": [ @@ -46244,46 +46423,46 @@ ], "projections": { "pca": [ - -0.21322891116142273, - -0.07599756866693497 + -0.21140679717063904, + -0.07314206659793854 ], "tsne": [ - -9.281733512878418, - -5.384296417236328 + -14.923314094543457, + 3.584174871444702 ], "umap": [ - -0.3912023901939392, - 6.520172595977783 + -1.991246223449707, + 3.741828680038452 ], "pca3d": [ - -0.21322815120220184, - -0.07599009573459625, - 0.15967413783073425 + -0.2114061415195465, + -0.07313510030508041, + 0.16523240506649017 ], "tsne3d": [ - -2.448906898498535, - -24.781970977783203, - 7.654324531555176 + -6.468990802764893, + -11.796957969665527, + 12.335491180419922 ], "umap3d": [ - 4.088858604431152, - -0.8996215462684631, - -1.96192467212677 + 0.8241873979568481, + 0.8648301959037781, + 7.10025691986084 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, + "id": 14, "label": "Clinical Health" } ], @@ -46672,7 +46851,11 @@ -0.0155, 0.0052, -0.0447 - ] + ], + "year": 2025, + "journal": "ArXiv.org", + "cited_by_count": 2, + "openalex_url": "https://openalex.org/W4414460302" }, { "channel": "social-fun", @@ -46692,495 +46875,495 @@ "abstract": "The conjecture that every convex polyhedron is Rupert is settled in the negative! The convex body in the image cannot pass straight through a hole inside itself.\narxiv.org/abs/2508.18475\n#Mathematics #Geometry #MathSky", "projections": { "pca": [ - 0.2007008045911789, - 0.07793626934289932 + 0.20021075010299683, + 0.07629378139972687 + ], + "tsne": [ + 11.811633110046387, + 6.7067389488220215 + ], + "umap": [ + 3.1176652908325195, + 3.580259323120117 + ], + "pca3d": [ + 0.20021022856235504, + 0.07629208266735077, + -0.025781795382499695 + ], + "tsne3d": [ + -1.2944526672363281, + 31.8737735748291, + 30.522573471069336 + ], + "umap3d": [ + 4.3521409034729, + 1.0727487802505493, + 5.64845085144043 + ] + }, + "cluster_id": 3, + "cluster_label": "Open Source Tools", + "cluster_levels": [ + { + "id": 3, + "label": "Open Source Tools" + }, + { + "id": 8, + "label": "Open AI" + }, + { + "id": 22, + "label": "AI Gemini" + } + ], + "_embedding": [ + -0.016, + -0.0352, + -0.0326, + 0.0198, + -0.0462, + 0.0138, + 0.0479, + 0.0358, + -0.0079, + 0.0155, + 0.0319, + -0.0973, + 0.0401, + 0.0556, + 0.0206, + 0.0041, + -0.0411, + 0.0259, + 0.003, + 0.0455, + 0.0865, + -0.0736, + -0.012, + -0.0549, + 0.0479, + 0.0512, + 0.0544, + -0.0104, + 0.0188, + -0.2807, + -0.0412, + -0.0122, + 0.004, + 0.0053, + 0.036, + -0.0105, + -0.031, + 0.0019, + -0.0649, + 0.0511, + 0.0156, + 0.047, + -0.0122, + 0.0329, + 0.0648, + 0.0259, + -0.0462, + 0.0301, + 0.0048, + 0.0041, + 0.0292, + -0.034, + 0.0124, + 0.034, + -0.0435, + 0.0114, + 0.0264, + 0.0, + 0.0425, + 0.0559, + 0.0333, + 0.0301, + -0.0708, + 0.0348, + 0.0862, + -0.0052, + -0.0095, + -0.0719, + -0.0979, + 0.0763, + 0.0838, + -0.0005, + -0.0169, + -0.0304, + 0.0122, + -0.0314, + 0.0199, + -0.0015, + -0.0103, + 0.0175, + 0.0212, + 0.0191, + 0.0001, + -0.041, + -0.0014, + -0.0117, + -0.0382, + -0.0226, + 0.0193, + 0.0314, + -0.026, + -0.0118, + 0.0293, + -0.0193, + -0.0338, + -0.0313, + 0.0031, + 0.0289, + 0.0196, + 0.4835, + -0.0765, + 0.0737, + 0.0742, + 0.0062, + -0.0055, + 0.0125, + -0.0149, + -0.0375, + 0.0259, + -0.0341, + -0.0318, + 0.0153, + 0.0611, + 0.0024, + -0.0032, + -0.0941, + 0.0388, + 0.0333, + -0.004, + -0.0126, + -0.0479, + 0.0704, + 0.0516, + -0.0098, + -0.0299, + -0.0288, + -0.0319, + 0.0222, + 0.0069, + -0.0126, + 0.0061, + -0.0237, + 0.0046, + -0.0155, + -0.0049, + 0.0373, + 0.0399, + 0.0081, + 0.0076, + -0.0356, + -0.0248, + -0.0385, + -0.0018, + -0.0021, + -0.0037, + 0.0272, + -0.0278, + -0.0022, + -0.0089, + -0.0345, + 0.0304, + 0.0039, + -0.0278, + -0.0141, + -0.0258, + -0.0493, + -0.0114, + 0.0255, + -0.093, + 0.0169, + -0.0059, + 0.0332, + -0.0234, + 0.0793, + 0.0084, + -0.0515, + 0.0168, + -0.0084, + 0.0585, + -0.0788, + -0.0026, + 0.0571, + -0.027, + -0.0053, + 0.0732, + 0.009, + -0.0621, + -0.0506, + 0.026, + -0.0148, + 0.0058, + -0.0652, + -0.0498, + 0.0032, + -0.003, + -0.0066, + -0.0072, + 0.0087, + 0.0253, + -0.0058, + -0.0224, + -0.0125, + -0.1101, + 0.017, + -0.0026, + -0.0083, + 0.0149, + -0.0035, + -0.04, + 0.0473, + 0.0157, + -0.0231, + 0.0015, + 0.044, + 0.0378, + 0.012, + 0.0378, + -0.0189, + 0.02, + 0.0386, + -0.0424, + -0.0764, + -0.0877, + 0.0205, + 0.0088, + -0.0036, + 0.0639, + -0.0502, + -0.0242, + -0.0125, + 0.0006, + -0.0244, + -0.0976, + -0.284, + -0.0372, + -0.0296, + -0.0573, + 0.0226, + -0.0322, + -0.0222, + 0.0418, + -0.0267, + 0.034, + 0.0309, + 0.0245, + 0.0041, + -0.0441, + 0.0231, + -0.0077, + -0.0064, + -0.0107, + -0.0489, + 0.0191, + -0.0333, + 0.0482, + -0.0556, + 0.0016, + -0.0525, + -0.0204, + 0.1494, + 0.0732, + 0.0473, + 0.0297, + 0.0005, + -0.0007, + -0.0383, + -0.0208, + -0.0161, + -0.0243, + 0.0093, + 0.0141, + -0.0407, + 0.0076, + -0.0548, + 0.0257, + -0.0005, + 0.0068, + 0.0297, + 0.034, + -0.0344, + -0.001, + 0.0231, + 0.0988, + 0.0366, + -0.0022, + 0.015, + 0.0426, + 0.0014, + 0.001, + 0.001, + -0.002, + -0.0408, + 0.0712, + -0.0285, + -0.0053, + 0.0276, + 0.0026, + 0.0046, + 0.0493, + -0.0257, + -0.0224, + 0.0618, + -0.0441, + -0.0008, + 0.1299, + 0.0098, + 0.0024, + 0.0175, + -0.0015, + 0.0568, + 0.1059, + 0.0541, + -0.0255, + 0.0046, + -0.0547, + 0.0032, + 0.0145, + -0.0081, + -0.0424, + -0.0283, + 0.0214, + 0.0207, + -0.066, + -0.0258, + 0.0485, + 0.0059, + -0.0173, + 0.0069, + 0.0116, + -0.2218, + -0.0364, + -0.0127, + 0.0076, + -0.0501, + 0.0242, + -0.0208, + 0.0176, + -0.0171, + -0.0513, + 0.0241, + 0.0669, + -0.0326, + 0.0156, + -0.0248, + -0.0532, + 0.016, + 0.0074, + 0.0098, + -0.012, + -0.0037, + 0.0601, + 0.1566, + -0.0351, + -0.036, + -0.0311, + 0.0203, + 0.0118, + -0.0461, + 0.0437, + -0.0432, + -0.0147, + 0.0038, + -0.0121, + -0.0091, + 0.0125, + 0.0151, + 0.024, + 0.0537, + -0.0304, + -0.0138, + -0.0292, + 0.0193, + 0.0066, + 0.0204, + 0.0508, + 0.0002, + 0.0226, + 0.0612, + -0.0164, + 0.005, + 0.0097, + -0.0027, + 0.0112, + -0.0283, + 0.0458, + 0.0028, + -0.0643, + 0.0083, + -0.0068, + -0.0381, + -0.0275, + 0.0041, + 0.0107, + 0.0298 + ] + }, + { + "channel": "social-fun", + "shared_by": "Shaun Porwal", + "timestamp": "2025-07-23T04:08:35.660409+00:00", + "first_shared": "2025-07-23T04:08:35.660409+00:00", + "slack_url": "https://standardmodelbio.slack.com/archives/C08KCG81XM1/p1753243715660409", + "url": "https://github.com/shaunporwal/get-barber-violin-part/blob/main/download_barber_violin.sh", + "text": "In case someone wants the barber violin concerto:", + "reactions_count": 0, + "reply_count": 0, + "reaction_details": {}, + "channels": [ + "social-fun" + ], + "title": "get-barber-violin-part/download_barber_violin.sh at main \u00b7 shaunporwal/get-barber-violin-part", + "abstract": "Contribute to shaunporwal/get-barber-violin-part development by creating an account on GitHub.", + "projections": { + "pca": [ + 0.3779734969139099, + -0.04079986736178398 ], "tsne": [ - -1.790686011314392, - 13.179239273071289 + 33.560489654541016, + -1.6758368015289307 ], "umap": [ - 3.685474395751953, - 4.017703056335449 + 3.0448853969573975, + 5.110970497131348 ], "pca3d": [ - 0.2007003128528595, - 0.07793772965669632, - -0.02541467547416687 + 0.37797409296035767, + -0.04079745337367058, + -0.15798023343086243 ], "tsne3d": [ - 27.671659469604492, - -1.0963025093078613, - -11.8947114944458 + 29.895816802978516, + 27.68358612060547, + 22.94486427307129 ], "umap3d": [ - 5.325503349304199, - 0.516441822052002, - 1.7502696514129639 + 4.193620204925537, + 2.30572772026062, + 6.629859924316406 ] }, "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_label": "Open Source Tools", "cluster_levels": [ { "id": 3, - "label": "AI Training Models" + "label": "Open Source Tools" }, { "id": 0, - "label": "AI Agents" - }, - { - "id": 1, - "label": "AI Agents" - } - ], - "_embedding": [ - -0.016, - -0.0352, - -0.0326, - 0.0198, - -0.0462, - 0.0138, - 0.0479, - 0.0358, - -0.0079, - 0.0155, - 0.0319, - -0.0973, - 0.0401, - 0.0556, - 0.0206, - 0.0041, - -0.0411, - 0.0259, - 0.003, - 0.0455, - 0.0865, - -0.0736, - -0.012, - -0.0549, - 0.0479, - 0.0512, - 0.0544, - -0.0104, - 0.0188, - -0.2807, - -0.0412, - -0.0122, - 0.004, - 0.0053, - 0.036, - -0.0105, - -0.031, - 0.0019, - -0.0649, - 0.0511, - 0.0156, - 0.047, - -0.0122, - 0.0329, - 0.0648, - 0.0259, - -0.0462, - 0.0301, - 0.0048, - 0.0041, - 0.0292, - -0.034, - 0.0124, - 0.034, - -0.0435, - 0.0114, - 0.0264, - 0.0, - 0.0425, - 0.0559, - 0.0333, - 0.0301, - -0.0708, - 0.0348, - 0.0862, - -0.0052, - -0.0095, - -0.0719, - -0.0979, - 0.0763, - 0.0838, - -0.0005, - -0.0169, - -0.0304, - 0.0122, - -0.0314, - 0.0199, - -0.0015, - -0.0103, - 0.0175, - 0.0212, - 0.0191, - 0.0001, - -0.041, - -0.0014, - -0.0117, - -0.0382, - -0.0226, - 0.0193, - 0.0314, - -0.026, - -0.0118, - 0.0293, - -0.0193, - -0.0338, - -0.0313, - 0.0031, - 0.0289, - 0.0196, - 0.4835, - -0.0765, - 0.0737, - 0.0742, - 0.0062, - -0.0055, - 0.0125, - -0.0149, - -0.0375, - 0.0259, - -0.0341, - -0.0318, - 0.0153, - 0.0611, - 0.0024, - -0.0032, - -0.0941, - 0.0388, - 0.0333, - -0.004, - -0.0126, - -0.0479, - 0.0704, - 0.0516, - -0.0098, - -0.0299, - -0.0288, - -0.0319, - 0.0222, - 0.0069, - -0.0126, - 0.0061, - -0.0237, - 0.0046, - -0.0155, - -0.0049, - 0.0373, - 0.0399, - 0.0081, - 0.0076, - -0.0356, - -0.0248, - -0.0385, - -0.0018, - -0.0021, - -0.0037, - 0.0272, - -0.0278, - -0.0022, - -0.0089, - -0.0345, - 0.0304, - 0.0039, - -0.0278, - -0.0141, - -0.0258, - -0.0493, - -0.0114, - 0.0255, - -0.093, - 0.0169, - -0.0059, - 0.0332, - -0.0234, - 0.0793, - 0.0084, - -0.0515, - 0.0168, - -0.0084, - 0.0585, - -0.0788, - -0.0026, - 0.0571, - -0.027, - -0.0053, - 0.0732, - 0.009, - -0.0621, - -0.0506, - 0.026, - -0.0148, - 0.0058, - -0.0652, - -0.0498, - 0.0032, - -0.003, - -0.0066, - -0.0072, - 0.0087, - 0.0253, - -0.0058, - -0.0224, - -0.0125, - -0.1101, - 0.017, - -0.0026, - -0.0083, - 0.0149, - -0.0035, - -0.04, - 0.0473, - 0.0157, - -0.0231, - 0.0015, - 0.044, - 0.0378, - 0.012, - 0.0378, - -0.0189, - 0.02, - 0.0386, - -0.0424, - -0.0764, - -0.0877, - 0.0205, - 0.0088, - -0.0036, - 0.0639, - -0.0502, - -0.0242, - -0.0125, - 0.0006, - -0.0244, - -0.0976, - -0.284, - -0.0372, - -0.0296, - -0.0573, - 0.0226, - -0.0322, - -0.0222, - 0.0418, - -0.0267, - 0.034, - 0.0309, - 0.0245, - 0.0041, - -0.0441, - 0.0231, - -0.0077, - -0.0064, - -0.0107, - -0.0489, - 0.0191, - -0.0333, - 0.0482, - -0.0556, - 0.0016, - -0.0525, - -0.0204, - 0.1494, - 0.0732, - 0.0473, - 0.0297, - 0.0005, - -0.0007, - -0.0383, - -0.0208, - -0.0161, - -0.0243, - 0.0093, - 0.0141, - -0.0407, - 0.0076, - -0.0548, - 0.0257, - -0.0005, - 0.0068, - 0.0297, - 0.034, - -0.0344, - -0.001, - 0.0231, - 0.0988, - 0.0366, - -0.0022, - 0.015, - 0.0426, - 0.0014, - 0.001, - 0.001, - -0.002, - -0.0408, - 0.0712, - -0.0285, - -0.0053, - 0.0276, - 0.0026, - 0.0046, - 0.0493, - -0.0257, - -0.0224, - 0.0618, - -0.0441, - -0.0008, - 0.1299, - 0.0098, - 0.0024, - 0.0175, - -0.0015, - 0.0568, - 0.1059, - 0.0541, - -0.0255, - 0.0046, - -0.0547, - 0.0032, - 0.0145, - -0.0081, - -0.0424, - -0.0283, - 0.0214, - 0.0207, - -0.066, - -0.0258, - 0.0485, - 0.0059, - -0.0172, - 0.0069, - 0.0116, - -0.2218, - -0.0364, - -0.0127, - 0.0076, - -0.0501, - 0.0242, - -0.0208, - 0.0176, - -0.0171, - -0.0513, - 0.0241, - 0.0669, - -0.0326, - 0.0156, - -0.0248, - -0.0532, - 0.016, - 0.0074, - 0.0098, - -0.012, - -0.0037, - 0.0601, - 0.1566, - -0.0351, - -0.036, - -0.0311, - 0.0203, - 0.0118, - -0.0461, - 0.0437, - -0.0432, - -0.0147, - 0.0038, - -0.0121, - -0.0091, - 0.0125, - 0.0151, - 0.024, - 0.0537, - -0.0304, - -0.0138, - -0.0292, - 0.0193, - 0.0066, - 0.0204, - 0.0508, - 0.0002, - 0.0226, - 0.0612, - -0.0164, - 0.005, - 0.0097, - -0.0027, - 0.0112, - -0.0283, - 0.0458, - 0.0028, - -0.0643, - 0.0083, - -0.0068, - -0.0381, - -0.0275, - 0.0041, - 0.0107, - 0.0298 - ] - }, - { - "channel": "social-fun", - "shared_by": "Shaun Porwal", - "timestamp": "2025-07-23T04:08:35.660409+00:00", - "first_shared": "2025-07-23T04:08:35.660409+00:00", - "slack_url": "https://standardmodelbio.slack.com/archives/C08KCG81XM1/p1753243715660409", - "url": "https://github.com/shaunporwal/get-barber-violin-part/blob/main/download_barber_violin.sh", - "text": "In case someone wants the barber violin concerto:", - "reactions_count": 0, - "reply_count": 0, - "reaction_details": {}, - "channels": [ - "social-fun" - ], - "title": "get-barber-violin-part/download_barber_violin.sh at main \u00b7 shaunporwal/get-barber-violin-part", - "abstract": "Contribute to shaunporwal/get-barber-violin-part development by creating an account on GitHub.", - "projections": { - "pca": [ - 0.3786892592906952, - -0.03988486900925636 - ], - "tsne": [ - 27.6119441986084, - 19.081586837768555 - ], - "umap": [ - 3.789764642715454, - 6.378541469573975 - ], - "pca3d": [ - 0.3786894381046295, - -0.03988175094127655, - -0.15342968702316284 - ], - "tsne3d": [ - 45.70043182373047, - 27.072399139404297, - 26.816448211669922 - ], - "umap3d": [ - 4.1603474617004395, - -0.0054078614339232445, - 2.6617701053619385 - ] - }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", - "cluster_levels": [ - { - "id": 1, - "label": "Open Agent Tools" - }, - { - "id": 3, - "label": "Github Data" + "label": "GitHub Tools" }, { - "id": 18, - "label": "Github Tools" + "id": 17, + "label": "GitHub Creation" } ], "_embedding": [ @@ -47588,47 +47771,47 @@ "abstract": "We let Claude run a small shop in the Anthropic office. Here's what happened.", "projections": { "pca": [ - 0.37348732352256775, - 0.0736866444349289 + 0.37179502844810486, + 0.07207084447145462 ], "tsne": [ - 24.387954711914062, - 27.861045837402344 + 31.842519760131836, + 8.858514785766602 ], "umap": [ - 4.6954145431518555, - 5.820527076721191 + 4.036099910736084, + 4.519776821136475 ], "pca3d": [ - 0.37348708510398865, - 0.0736805647611618, - -0.01722395233809948 + 0.37179455161094666, + 0.07206317782402039, + -0.023599181324243546 ], "tsne3d": [ - 59.48659896850586, - 24.626548767089844, - 0.30015626549720764 + 27.4744873046875, + 40.95579528808594, + -1.6397706270217896 ], "umap3d": [ - 4.908561706542969, - 0.6886435747146606, - 2.6855902671813965 + 5.114697456359863, + 2.1721954345703125, + 6.052381992340088 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { - "id": 16, - "label": "Claude Code" + "id": 11, + "label": "Code Tools" } ], "_embedding": [ @@ -48036,47 +48219,47 @@ "abstract": "At Cisco Research, we are at the forefront of designing and developing practical and efficient quantum networks, a foundational technology for the future Quantum Internet.", "projections": { "pca": [ - 0.15339955687522888, - 0.000588722643442452 + 0.1541646271944046, + 0.0007040437776595354 ], "tsne": [ - 3.159682035446167, - 11.594733238220215 + 1.375074028968811, + -0.8284674882888794 ], "umap": [ - 1.9453150033950806, - 4.786801815032959 + 1.2187960147857666, + 4.756275653839111 ], "pca3d": [ - 0.1533990055322647, - 0.0005870028981007636, - 0.016212446615099907 + 0.15416403114795685, + 0.0007029487169347703, + 0.014880037866532803 ], "tsne3d": [ - 20.097259521484375, - -9.547536849975586, - -4.747300624847412 + -2.829338550567627, + 32.481834411621094, + -15.473437309265137 ], "umap3d": [ - 4.732894420623779, - 1.0228841304779053, - 0.7471345663070679 + 3.281355857849121, + 0.7295825481414795, + 4.884210109710693 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 4, - "label": "Com Area Amp" + "id": 6, + "label": "Data Needs" }, { - "id": 23, - "label": "Research Authors" + "id": 10, + "label": "Research Data" } ], "_embedding": [ @@ -48484,47 +48667,47 @@ "abstract": "Someone asked me an interesting question the other day: "How did you justify taking such a hu...", "projections": { "pca": [ - 0.1963619887828827, - 0.03924267366528511 + 0.19682344794273376, + 0.03848126530647278 ], "tsne": [ - 12.027853012084961, - 1.8454827070236206 + 11.828889846801758, + 2.141345262527466 ], "umap": [ - 1.924341082572937, - 5.330055236816406 + 1.2783169746398926, + 4.723121643066406 ], "pca3d": [ - 0.1963621824979782, - 0.03924592211842537, - 0.06523717939853668 + 0.19682367146015167, + 0.038481809198856354, + 0.062407754361629486 ], "tsne3d": [ - 13.232885360717773, - 13.824681282043457, - -1.325552225112915 + 20.415258407592773, + -9.126245498657227, + 1.5214579105377197 ], "umap3d": [ - 3.606736183166504, - 0.675915002822876, - 1.1238484382629395 + 2.8531746864318848, + 1.7015211582183838, + 5.480589866638184 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 4, - "label": "Com Area Amp" + "id": 6, + "label": "Data Needs" }, { - "id": 26, - "label": "AI Information" + "id": 10, + "label": "Research Data" } ], "_embedding": [ @@ -48943,46 +49126,46 @@ "journal": "Nature", "projections": { "pca": [ - -0.11450769007205963, - -0.17411722242832184 + -0.11864418536424637, + -0.17481684684753418 ], "tsne": [ - 5.032566547393799, - -31.09010124206543 + -11.27048397064209, + -21.46053695678711 ], "umap": [ - -2.2134957313537598, - 3.8649613857269287 + -2.091567039489746, + 6.826011657714844 ], "pca3d": [ - -0.11450756341218948, - -0.17412163317203522, - -0.2393563687801361 + -0.1186440959572792, + -0.174821674823761, + -0.23573991656303406 ], "tsne3d": [ - -50.10024642944336, - -10.04419994354248, - 15.184277534484863 + -36.10905838012695, + -2.826439380645752, + -18.458908081054688 ], "umap3d": [ - 1.378939151763916, - 0.12226976454257965, - -1.180253028869629 + -0.0034857571590691805, + 1.2583022117614746, + 4.301894187927246 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 22, + "id": 27, "label": "Genetic Variants" } ], @@ -49413,47 +49596,47 @@ "doi": "10.1101/2024.12.15.628568", "projections": { "pca": [ - -0.20966294407844543, - -0.1570551097393036 + -0.21259254217147827, + -0.15696169435977936 ], "tsne": [ - -4.190009117126465, - -28.753082275390625 + -18.87749481201172, + -21.976146697998047 ], "umap": [ - -1.6898488998413086, - 3.4799365997314453 + -1.3523684740066528, + 7.039524555206299 ], "pca3d": [ - -0.2096622884273529, - -0.15704463422298431, - -0.22594307363033295 + -0.212592214345932, + -0.15695136785507202, + -0.22292181849479675 ], "tsne3d": [ - -56.39674758911133, - -21.80599021911621, - 0.02926046960055828 + -46.86136245727539, + -15.159972190856934, + -13.418699264526367 ], "umap3d": [ - 1.3642066717147827, - 0.7307127118110657, - -1.398047685623169 + 0.049650538712739944, + 0.5668219923973083, + 3.7575912475585938 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 8, - "label": "Gene Regulation" + "id": 3, + "label": "Genomic Sequences" } ], "_embedding": [ @@ -49872,46 +50055,46 @@ "doi": "10.64898/2026.06.12.731496", "projections": { "pca": [ - -0.2517125606536865, - -0.09641719609498978 + -0.2510847747325897, + -0.09367164969444275 ], "tsne": [ - -11.983755111694336, - -18.421232223510742 + -23.631790161132812, + -10.23912525177002 ], "umap": [ - -1.7015470266342163, - 6.127790451049805 + -1.6315619945526123, + 5.260472774505615 ], "pca3d": [ - -0.25171199440956116, - -0.09641419351100922, - -0.04007244110107422 + -0.25108423829078674, + -0.09366951882839203, + -0.03282950818538666 ], "tsne3d": [ - -25.052942276000977, - -19.429426193237305, - -15.3463773727417 + -25.397066116333008, + -6.057075023651123, + 17.57852554321289 ], "umap3d": [ - 3.1163017749786377, - -0.8174933195114136, - -0.5986048579216003 + 0.77585369348526, + -0.18210040032863617, + 5.523092269897461 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 24, + "id": 1, "label": "Cancer Data" } ], @@ -50353,46 +50536,46 @@ "cited_by_count": 10, "projections": { "pca": [ - -0.16282066702842712, - -0.1631668508052826 + -0.1602764129638672, + -0.16145038604736328 ], "tsne": [ - -12.468864440917969, - -17.5338077545166 + -23.864315032958984, + -8.858467102050781 ], "umap": [ - -1.785353660583496, - 6.369033336639404 + -1.965438961982727, + 5.193474292755127 ], "pca3d": [ - -0.16282068192958832, - -0.16316717863082886, - 0.015944059938192368 + -0.1602765917778015, + -0.16144998371601105, + 0.022065112367272377 ], "tsne3d": [ - -17.60917091369629, - -19.68813705444336, - -10.055747985839844 + -30.291057586669922, + -9.217517852783203, + 20.843618392944336 ], "umap3d": [ - 3.021721124649048, - -1.0350629091262817, - -0.7535058856010437 + 0.610200822353363, + -0.15355685353279114, + 5.761967658996582 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 24, + "id": 1, "label": "Cancer Data" } ], @@ -50801,47 +50984,47 @@ "abstract": "Contribute to hms-dbmi/fairpath development by creating an account on GitHub.", "projections": { "pca": [ - 0.360530823469162, - -0.08643897622823715 + 0.3606582581996918, + -0.08697499334812164 ], "tsne": [ - 28.01534652709961, - 13.222634315490723 + 31.466466903686523, + -6.571212291717529 ], "umap": [ - 3.5612120628356934, - 6.895138740539551 + 3.1353342533111572, + 5.744302749633789 ], "pca3d": [ - 0.3605325222015381, - -0.08641210943460464, - -0.1145796850323677 + 0.36065998673439026, + -0.08695047348737717, + -0.11951348930597305 ], "tsne3d": [ - 35.81514358520508, - 45.52680587768555, - 18.958858489990234 + 46.007537841796875, + 17.699840545654297, + 11.42568302154541 ], "umap3d": [ - 3.4798288345336914, - -0.18670207262039185, - 2.900076150894165 + 3.8929343223571777, + 2.8620047569274902, + 6.716985702514648 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 18, - "label": "Github Tools" + "id": 17, + "label": "GitHub Creation" } ], "_embedding": [ @@ -51284,46 +51467,46 @@ "cited_by_count": 428, "projections": { "pca": [ - -0.19319362938404083, - -0.127712219953537 + -0.19101543724536896, + -0.12499316036701202 ], "tsne": [ - -12.972418785095215, - -18.032333374023438 + -24.291553497314453, + -9.56904411315918 ], "umap": [ - -1.6102298498153687, - 6.641836166381836 + -1.7539942264556885, + 4.992181777954102 ], "pca3d": [ - -0.19319353997707367, - -0.12771150469779968, - 0.03243190050125122 + -0.19101525843143463, + -0.124991275370121, + 0.03876783326268196 ], "tsne3d": [ - -19.595964431762695, - -18.210039138793945, - -15.205442428588867 + -28.762306213378906, + -4.613208293914795, + 20.40802001953125 ], "umap3d": [ - 3.3630974292755127, - -1.0699001550674438, - -0.5027770400047302 + 0.9303218126296997, + -0.24620452523231506, + 5.821742057800293 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 24, + "id": 1, "label": "Cancer Data" } ], @@ -51744,47 +51927,47 @@ ], "projections": { "pca": [ - -0.17364665865898132, - 0.0003301573160570115 + -0.17696909606456757, + -0.00022937777976039797 ], "tsne": [ - -0.054243799299001694, - -33.027809143066406 + -14.45071029663086, + -25.771812438964844 ], "umap": [ - -2.0113697052001953, - 3.034449815750122 + -1.1543025970458984, + 7.533414840698242 ], "pca3d": [ - -0.17364735901355743, - 0.00031805309117771685, - -0.27240240573883057 + -0.17697016894817352, + -0.0002407646825304255, + -0.26888009905815125 ], "tsne3d": [ - -50.0133171081543, - 1.4425556659698486, - -8.74732494354248 + -33.687843322753906, + -23.608381271362305, + -24.42734718322754 ], "umap3d": [ - 1.0291345119476318, - 0.8158283829689026, - -0.8336102366447449 + 0.3415062725543976, + 1.04595947265625, + 3.5537400245666504 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 8, - "label": "Gene Regulation" + "id": 12, + "label": "Protein Language" } ], "_embedding": [ @@ -52201,46 +52384,46 @@ ], "projections": { "pca": [ - -0.1676972359418869, - -0.07502414286136627 + -0.1640579253435135, + -0.07148140668869019 ], "tsne": [ - -8.469985008239746, - -4.684998035430908 + -14.136651992797852, + 3.846187114715576 ], "umap": [ - -0.39500105381011963, - 6.584205627441406 + -1.7098021507263184, + 3.773338556289673 ], "pca3d": [ - -0.1676967889070511, - -0.07501032948493958, - 0.25497785210609436 + -0.16405750811100006, + -0.0714673176407814, + 0.2607060968875885 ], "tsne3d": [ - 4.723121643066406, - -30.390703201293945, - 6.180126667022705 + -9.348243713378906, + -3.8558189868927, + 10.00052261352539 ], "umap3d": [ - 4.0360517501831055, - -0.6039102673530579, - -2.112698793411255 + 0.7459239363670349, + 0.650534451007843, + 7.280181407928467 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, + "id": 14, "label": "Clinical Health" } ], @@ -52648,47 +52831,47 @@ "title": "SFT, RL, and On-Policy Distillation Through a Distributional Lens | wh", "projections": { "pca": [ - -0.013568613678216934, - 0.21530719101428986 + -0.009645979851484299, + 0.2183608114719391 ], "tsne": [ - -13.521032333374023, - 26.19627571105957 + 9.358631134033203, + 32.514278411865234 ], "umap": [ - 3.3642051219940186, - 2.4994850158691406 + 2.4312281608581543, + 1.5111401081085205 ], "pca3d": [ - -0.013567609712481499, - 0.21533800661563873, - 0.07241155952215195 + -0.009645082987844944, + 0.21838891506195068, + 0.07002078741788864 ], "tsne3d": [ - 24.34256935119629, - -4.364248275756836, - -47.58687973022461 + 20.580320358276367, + 19.833059310913086, + -29.84947395324707 ], "umap3d": [ - 6.971950531005859, - -0.35769131779670715, - 1.1844441890716553 + 4.971467018127441, + -0.7680324912071228, + 5.500704288482666 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 6, + "label": "Learning Techniques" } ], "_embedding": [ @@ -53109,47 +53292,47 @@ ], "projections": { "pca": [ - -0.2161663919687271, - 0.05470151826739311 + -0.2140229493379593, + 0.057202212512493134 ], "tsne": [ - -18.881528854370117, - 2.069817304611206 + -10.183806419372559, + 13.274057388305664 ], "umap": [ - -0.5652972459793091, - 7.371394634246826 + -1.3392813205718994, + 2.6169536113739014 ], "pca3d": [ - -0.216166153550148, - 0.0547056645154953, - 0.06263173371553421 + -0.21402250230312347, + 0.05720554292201996, + 0.06989988684654236 ], "tsne3d": [ - -9.840459823608398, - -9.34630012512207, - -25.987661361694336 + -3.1305251121520996, + 2.0138027667999268, + 23.79244613647461 ], "umap3d": [ - 4.568091869354248, - -1.6113373041152954, - -0.9090908765792847 + 1.768746018409729, + -0.45804280042648315, + 7.095463275909424 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 12, - "label": "ECG Models" + "id": 19, + "label": "Medical Imaging" } ], "_embedding": [ @@ -53564,47 +53747,47 @@ ], "projections": { "pca": [ - -0.16454675793647766, - 0.22165384888648987 + -0.1640848070383072, + 0.22228485345840454 ], "tsne": [ - -20.239126205444336, - 15.54742431640625 + -3.47063946723938, + 25.703956604003906 ], "umap": [ - 2.030864715576172, - 2.974181652069092 + 1.0452390909194946, + 2.0726561546325684 ], "pca3d": [ - -0.1645464152097702, - 0.22166147828102112, - -0.0020285702776163816 + -0.16408437490463257, + 0.22228969633579254, + 0.0013338943244889379 ], "tsne3d": [ - 2.879307746887207, - -0.2519665062427521, - -23.741981506347656 + 7.925209045410156, + -6.614892482757568, + -28.97664451599121 ], "umap3d": [ - 6.215888977050781, - -0.5489793419837952, - 0.2785729169845581 + 4.1051249504089355, + -1.0160396099090576, + 6.39050817489624 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 15, - "label": "Latent Models" + "id": 18, + "label": "Retrieval Learning" } ], "_embedding": [ @@ -54017,47 +54200,47 @@ ], "projections": { "pca": [ - -0.15721717476844788, - 0.28581321239471436 + -0.15491744875907898, + 0.28730520606040955 ], "tsne": [ - -15.73336124420166, - 26.05526351928711 + 7.525486469268799, + 32.923160552978516 ], "umap": [ - 3.429929494857788, - 2.6144275665283203 + 2.5134217739105225, + 1.6122465133666992 ], "pca3d": [ - -0.1572166532278061, - 0.28582197427749634, - 0.013909616507589817 + -0.15491686761379242, + 0.28731295466423035, + 0.014842412434518337 ], "tsne3d": [ - 14.419343948364258, - -9.149015426635742, - -44.8633918762207 + 14.14501953125, + 22.997011184692383, + -32.80356979370117 ], "umap3d": [ - 6.929475784301758, - -0.41566285490989685, - 1.2232506275177002 + 4.957857131958008, + -0.7620931267738342, + 5.526264190673828 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 6, + "label": "Learning Techniques" } ], "_embedding": [ @@ -54473,47 +54656,47 @@ ], "projections": { "pca": [ - -0.11082588881254196, - 0.20390920341014862 + -0.11055496335029602, + 0.2050599753856659 ], "tsne": [ - -15.417254447937012, - 20.675004959106445 + 4.443731784820557, + 31.151737213134766 ], "umap": [ - 3.062298059463501, - 2.7136154174804688 + 2.1302099227905273, + 1.9704294204711914 ], "pca3d": [ - -0.11082590371370316, - 0.20391333103179932, - -0.048832979053258896 + -0.11055516451597214, + 0.2050645649433136, + -0.04714405909180641 ], "tsne3d": [ - 25.693716049194336, - -1.1569499969482422, - -33.29235076904297 + 8.961957931518555, + 10.689910888671875, + -38.11548614501953 ], "umap3d": [ - 6.460237979888916, - -0.6664183139801025, - 1.2664201259613037 + 4.550146102905273, + -0.5659311413764954, + 5.895922660827637 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 6, + "label": "Learning Techniques" } ], "_embedding": [ @@ -54926,47 +55109,47 @@ ], "projections": { "pca": [ - -0.1603095680475235, - 0.165736585855484 + -0.1613653600215912, + 0.16673684120178223 ], "tsne": [ - -17.272695541381836, - 22.54070472717285 + 5.430085182189941, + 28.527423858642578 ], "umap": [ - 2.973235607147217, - 2.7122113704681396 + 1.997285008430481, + 1.8472155332565308 ], "pca3d": [ - -0.1603098064661026, - 0.1657404601573944, - -0.054360270500183105 + -0.1613655686378479, + 0.16674116253852844, + -0.05302486941218376 ], "tsne3d": [ - 18.422563552856445, - -4.1977763175964355, - -32.8917236328125 + 15.467251777648926, + 9.43139362335205, + -34.5499153137207 ], "umap3d": [ - 6.585608959197998, - -0.4743407368659973, - 1.1396187543869019 + 4.8116326332092285, + -0.6769019365310669, + 5.955322265625 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 6, + "label": "Learning Techniques" } ], "_embedding": [ @@ -55392,47 +55575,47 @@ ], "projections": { "pca": [ - -0.1842702031135559, - -0.10097581148147583 + -0.1840270459651947, + -0.09872996062040329 ], "tsne": [ - -0.15942709147930145, - -7.126397132873535 + -7.816829204559326, + -4.540180683135986 ], "umap": [ - -0.6125983595848083, - 4.968486785888672 + -0.9678809642791748, + 5.025580883026123 ], "pca3d": [ - -0.1842697113752365, - -0.10098617523908615, - 0.042129598557949066 + -0.1840265840291977, + -0.0987413078546524, + 0.046071503311395645 ], "tsne3d": [ - -10.375499725341797, - -10.584431648254395, - 20.162708282470703 + -2.7435495853424072, + -17.49191665649414, + -3.6563642024993896 ], "umap3d": [ - 3.3884854316711426, - 0.4688459038734436, - -1.0397175550460815 + 1.9672032594680786, + 0.25377097725868225, + 5.0358195304870605 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ - { - "id": 0, - "label": "Medical Data AI" - }, { "id": 2, - "label": "AI Drug Discovery" + "label": "Clinical AI Data" }, { - "id": 13, + "id": 9, "label": "Drug Discovery" + }, + { + "id": 9, + "label": "Biomedical AI" } ], "_embedding": [ @@ -55840,46 +56023,46 @@ "journal": "Nature", "projections": { "pca": [ - 0.04031332954764366, - -0.0032794964499771595 + 0.043194420635700226, + -0.0015407833270728588 ], "tsne": [ - -4.502067565917969, - -3.882488489151001 + -9.469497680664062, + 1.6338164806365967 ], "umap": [ - -0.24687175452709198, - 6.263266086578369 + -1.2830103635787964, + 3.865669012069702 ], "pca3d": [ - 0.040313705801963806, - -0.0032832117285579443, - 0.24805569648742676 + 0.04319484159350395, + -0.001543440972454846, + 0.24912837147712708 ], "tsne3d": [ - 15.382813453674316, - -37.417327880859375, - 11.66456127166748 + -1.8748751878738403, + 4.98728609085083, + 3.5581350326538086 ], "umap3d": [ - 4.0235114097595215, - -0.3541094660758972, - -1.860532283782959 + 0.9171486496925354, + 0.4093033969402313, + 7.41921854019165 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, + "id": 14, "label": "Clinical Health" } ], @@ -56295,47 +56478,47 @@ "doi": "10.1101/2023.10.16.562533", "projections": { "pca": [ - -0.23060117661952972, - -0.017453867942094803 + -0.23378543555736542, + -0.01684853993356228 ], "tsne": [ - -6.34633207321167, - -30.25728988647461 + -20.897733688354492, + -20.23324203491211 ], "umap": [ - -1.3450654745101929, - 3.1928577423095703 + -0.9921764731407166, + 7.043153285980225 ], "pca3d": [ - -0.23060080409049988, - -0.017459934577345848, - -0.21836040914058685 + -0.23378504812717438, + -0.016856564208865166, + -0.21407252550125122 ], "tsne3d": [ - -38.72732162475586, - -12.571642875671387, - -14.868943214416504 + -30.894535064697266, + -25.788312911987305, + -7.095033168792725 ], "umap3d": [ - 1.4736268520355225, - 1.145318627357483, - -1.3415805101394653 + 0.43236029148101807, + 0.27350762486457825, + 3.9041192531585693 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 19, - "label": "Cell Models" + "id": 20, + "label": "Cell Foundation" } ], "_embedding": [ @@ -56743,47 +56926,47 @@ "abstract": "NextLat extends standard next-token prediction training with self-supervised predictions in the latent space.", "projections": { "pca": [ - -0.08959262818098068, - 0.16476942598819733 + -0.08864514529705048, + 0.16538108885288239 ], "tsne": [ - -21.460744857788086, - 17.318246841430664 + -5.0035400390625, + 28.087430953979492 ], "umap": [ - 2.116802215576172, - 2.8327736854553223 + 1.120582938194275, + 1.6449191570281982 ], "pca3d": [ - -0.08959220349788666, - 0.16477003693580627, - -0.03455556556582451 + -0.08864469826221466, + 0.1653812974691391, + -0.0335843600332737 ], "tsne3d": [ - -3.789947748184204, - -8.491718292236328, - -32.93328094482422 + 16.013545989990234, + -5.031247138977051, + -21.10628318786621 ], "umap3d": [ - 6.4351091384887695, - -0.8509664535522461, - 0.5080526471138 + 4.059569835662842, + -1.0186777114868164, + 6.115719318389893 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 15, - "label": "Latent Models" + "id": 26, + "label": "World Models" } ], "_embedding": [ @@ -57193,47 +57376,47 @@ "abstract": "A research preview of Omnii, our next-generation genome language model.", "projections": { "pca": [ - -0.11774435639381409, - 0.027820564806461334 + -0.11908724159002304, + 0.028625845909118652 ], "tsne": [ - 1.6779083013534546, - -30.945194244384766 + -12.598058700561523, + -24.008686065673828 ], "umap": [ - -1.9580388069152832, - 3.226320266723633 + -1.6134986877441406, + 7.136713027954102 ], "pca3d": [ - -0.11774495244026184, - 0.027815034613013268, - -0.11239241063594818 + -0.11908788233995438, + 0.028620250523090363, + -0.11174702644348145 ], "tsne3d": [ - -41.957611083984375, - 2.7909064292907715, - -0.3776027262210846 + -27.976837158203125, + -14.748368263244629, + -25.633792877197266 ], "umap3d": [ - 1.3868800401687622, - 0.4961470663547516, - -0.8187192678451538 + 0.29301393032073975, + 1.177426815032959, + 3.9063754081726074 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 8, - "label": "Gene Regulation" + "id": 3, + "label": "Genomic Sequences" } ], "_embedding": [ @@ -57641,47 +57824,47 @@ "journal": "Nature Biomedical Engineering", "projections": { "pca": [ - -0.030461085960268974, - -0.005378341302275658 + -0.02907230705022812, + -0.0034142236690968275 ], "tsne": [ - -5.893552780151367, - 1.8351751565933228 + -11.721762657165527, + 8.043553352355957 ], "umap": [ - 0.5356793999671936, - 6.497483253479004 + -0.7482241988182068, + 3.722498655319214 ], "pca3d": [ - -0.030461376532912254, - -0.005377991124987602, - 0.12105053663253784 + -0.029072530567646027, + -0.0034149622078984976, + 0.12394188344478607 ], "tsne3d": [ - 13.745017051696777, - -14.432699203491211, - 10.20368766784668 + 7.864093780517578, + -8.581497192382812, + 7.936606407165527 ], "umap3d": [ - 4.8376078605651855, - -0.5790412425994873, - -1.2919954061508179 + 1.7670371532440186, + 1.0207566022872925, + 7.061208248138428 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 0, - "label": "Clinical LLMs" + "id": 5, + "label": "Health Benchmarks" } ], "_embedding": [ @@ -58089,47 +58272,47 @@ "abstract": "Introducing LifeSciBench, an expert-authored, expert-reviewed benchmark for evaluating how AI systems handle real-world life science research tasks and decisions.", "projections": { "pca": [ - 0.0384126640856266, - -0.049708060920238495 + 0.04197320714592934, + -0.04719814658164978 ], "tsne": [ - -0.054179124534130096, - 0.41662999987602234 + -3.4054596424102783, + -0.4681831896305084 ], "umap": [ - -0.297759473323822, - 5.228862762451172 + -0.7517194747924805, + 4.486878871917725 ], "pca3d": [ - 0.03841296210885048, - -0.04970312863588333, - 0.1877729445695877 + 0.04197356477379799, + -0.04719241335988045, + 0.1869017779827118 ], "tsne3d": [ - 26.215999603271484, - -23.231121063232422, - -0.2782280445098877 + -4.532037734985352, + -10.064988136291504, + -10.10651969909668 ], "umap3d": [ - 4.080313682556152, - 0.6029009819030762, - -0.9424471855163574 + 2.768346071243286, + 0.25225701928138733, + 4.966550827026367 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ - { - "id": 0, - "label": "Medical Data AI" - }, { "id": 2, - "label": "AI Drug Discovery" + "label": "Clinical AI Data" }, { - "id": 13, + "id": 9, "label": "Drug Discovery" + }, + { + "id": 9, + "label": "Biomedical AI" } ], "_embedding": [ @@ -58537,47 +58720,47 @@ "journal": "arXiv", "projections": { "pca": [ - -0.06414053589105606, - 0.14973284304141998 + -0.06336716562509537, + 0.15112340450286865 ], "tsne": [ - -16.580968856811523, - 19.621915817260742 + 3.230034112930298, + 32.666046142578125 ], "umap": [ - 2.8404970169067383, - 2.8387155532836914 + 2.0272774696350098, + 1.9621143341064453 ], "pca3d": [ - -0.06414162367582321, - 0.14973126351833344, - 0.04832889139652252 + -0.06336832046508789, + 0.15112538635730743, + 0.04856492578983307 ], "tsne3d": [ - 28.790002822875977, - 4.422151565551758, - -27.762067794799805 + 4.90407133102417, + 4.876590728759766, + -37.94818878173828 ], "umap3d": [ - 6.5626020431518555, - -0.6037953495979309, - 1.1889114379882812 + 4.655846118927002, + -0.6858983635902405, + 5.955790996551514 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 6, + "label": "Learning Techniques" } ], "_embedding": [ @@ -58991,47 +59174,47 @@ ], "projections": { "pca": [ - -0.10917066037654877, - 0.17301778495311737 + -0.10807202756404877, + 0.17453381419181824 ], "tsne": [ - -8.720870971679688, - 11.366394996643066 + 13.40583610534668, + 23.653745651245117 ], "umap": [ - 2.3551998138427734, - 3.6103410720825195 + 1.3966352939605713, + 2.745788812637329 ], "pca3d": [ - -0.10917051136493683, - 0.17300841212272644, - 0.040809694677591324 + -0.10807199031114578, + 0.17452332377433777, + 0.041635941714048386 ], "tsne3d": [ - 16.347257614135742, - 3.610522985458374, - -11.395256996154785 + 21.07034683227539, + -12.606392860412598, + -27.312719345092773 ], "umap3d": [ - 5.68148136138916, - -0.15603773295879364, - 0.5910717844963074 + 3.9728968143463135, + -0.15375569462776184, + 6.146334171295166 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 27, - "label": "Embeddings" + "id": 18, + "label": "Retrieval Learning" } ], "_embedding": [ @@ -59448,47 +59631,47 @@ ], "projections": { "pca": [ - -0.15879599750041962, - 0.2511802315711975 + -0.15722481906414032, + 0.253240168094635 ], "tsne": [ - -18.482887268066406, - 12.297607421875 + -2.2392940521240234, + 21.729990005493164 ], "umap": [ - 1.6288199424743652, - 2.396415948867798 + 0.6666236519813538, + 1.364894151687622 ], "pca3d": [ - -0.15879561007022858, - 0.251187801361084, - -0.08626408129930496 + -0.15722447633743286, + 0.25324657559394836, + -0.08251231908798218 ], "tsne3d": [ - -9.043418884277344, - 4.923816680908203, - -37.60087585449219 + 5.3556084632873535, + -4.8515214920043945, + -16.890775680541992 ], "umap3d": [ - 6.473433494567871, - -1.0839223861694336, - -0.0581270195543766 + 3.792132616043091, + -1.3940274715423584, + 6.312417030334473 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 15, - "label": "Latent Models" + "id": 8, + "label": "JEPA Video Models" } ], "_embedding": [ @@ -59896,46 +60079,46 @@ "abstract": "Duality of Forget Gates and Position Embeddings in Sequence Modeling", "projections": { "pca": [ - -0.17692549526691437, - 0.2707631289958954 + -0.17536450922489166, + 0.2724303603172302 ], "tsne": [ - -27.28646469116211, - 19.04975128173828 + 0.96571284532547, + 24.87308692932129 ], "umap": [ - 2.219723701477051, - 2.2865469455718994 + 1.1731191873550415, + 1.3526796102523804 ], "pca3d": [ - -0.17692624032497406, - 0.2707541882991791, - -0.10906508564949036 + -0.17536534368991852, + 0.2724214792251587, + -0.10637447237968445 ], "tsne3d": [ - 2.262751340866089, - 22.604398727416992, - -49.65664291381836 + -9.056361198425293, + 15.460275650024414, + -34.18077850341797 ], "umap3d": [ - 6.732973575592041, - -1.291168212890625, - 0.45182740688323975 + 3.744946241378784, + -1.2446669340133667, + 5.682575702667236 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 4, + "id": 26, "label": "World Models" } ], @@ -60346,47 +60529,47 @@ "abstract": "Demonstrating that dense retrieval models learn much more information than they can express through their usual scoring mechanism: they also contain an indexable, natural-language-like sparse vocabulary, which is plug-and-play with BM25.", "projections": { "pca": [ - -0.10779160261154175, - 0.19366337358951569 + -0.1068933829665184, + 0.19333516061306 ], "tsne": [ - -19.453739166259766, - 19.76698112487793 + -2.6895861625671387, + 33.16127014160156 ], "umap": [ - 2.3663322925567627, - 3.25715970993042 + 1.5063978433609009, + 2.1960597038269043 ], "pca3d": [ - -0.1077926903963089, - 0.19364874064922333, - 0.035330332815647125 + -0.10689447075128555, + 0.1933203935623169, + 0.03507237136363983 ], "tsne3d": [ - 17.125253677368164, - -3.3435616493225098, - -15.39397144317627 + 18.013681411743164, + -8.773879051208496, + -32.89792251586914 ], "umap3d": [ - 6.185525417327881, - -0.37078994512557983, - 0.6459097862243652 + 4.273275852203369, + -0.6479811072349548, + 6.343851566314697 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 27, - "label": "Embeddings" + "id": 18, + "label": "Retrieval Learning" } ], "_embedding": [ @@ -60818,47 +61001,47 @@ ], "projections": { "pca": [ - -0.18641327321529388, - -0.010779286734759808 + -0.1862698197364807, + -0.00900608766824007 ], "tsne": [ - -9.608644485473633, - -29.43909454345703 + -21.6573486328125, + -17.599637985229492 ], "umap": [ - -1.3248693943023682, - 3.4863882064819336 + -1.0475306510925293, + 6.70001220703125 ], "pca3d": [ - -0.18641328811645508, - -0.010789038613438606, - -0.11419457197189331 + -0.18626989424228668, + -0.009015282616019249, + -0.11100326478481293 ], "tsne3d": [ - -37.678245544433594, - -29.354198455810547, - -18.370332717895508 + -41.99283981323242, + -26.23509407043457, + -3.2532949447631836 ], "umap3d": [ - 1.5144035816192627, - 1.0691674947738647, - -1.4135022163391113 + 0.3786981403827667, + 0.1672060340642929, + 4.12290620803833 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 19, - "label": "Cell Models" + "id": 20, + "label": "Cell Foundation" } ], "_embedding": [ @@ -61271,47 +61454,47 @@ ], "projections": { "pca": [ - -0.1307828426361084, - 0.24705296754837036 + -0.13043516874313354, + 0.24724462628364563 ], "tsne": [ - -17.07038688659668, - 23.66866111755371 + 6.587940216064453, + 28.3245849609375 ], "umap": [ - 2.871812582015991, - 2.892943859100342 + 2.0647640228271484, + 1.8212474584579468 ], "pca3d": [ - -0.1307833045721054, - 0.24704726040363312, - 0.0009587313397787511 + -0.1304357498884201, + 0.247238427400589, + 0.0002025696012424305 ], "tsne3d": [ - 12.64525032043457, - -1.0074248313903809, - -30.866167068481445 + 19.348161697387695, + 8.87326717376709, + -30.196945190429688 ], "umap3d": [ - 6.615259647369385, - -0.43305957317352295, - 1.1406152248382568 + 4.826260089874268, + -0.7680605053901672, + 5.819003105163574 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 6, + "label": "Learning Techniques" } ], "_embedding": [ @@ -61721,46 +61904,46 @@ "journal": "Nature", "projections": { "pca": [ - -0.18215391039848328, - 0.09948737174272537 + -0.17467541992664337, + 0.10561175644397736 ], "tsne": [ - -16.254674911499023, - -9.019954681396484 + -22.06764793395996, + 13.320530891418457 ], "umap": [ - -0.3688446283340454, - 5.991904258728027 + -1.2838863134384155, + 3.5896642208099365 ], "pca3d": [ - -0.1821535974740982, - 0.099491186439991, - 0.33153069019317627 + -0.17467530071735382, + 0.10561675578355789, + 0.33254674077033997 ], "tsne3d": [ - 12.841004371643066, - -41.415916442871094, - -8.542119026184082 + -20.473960876464844, + 12.414740562438965, + 1.0479317903518677 ], "umap3d": [ - 4.351388931274414, - -0.24274037778377533, - -2.0416455268859863 + 0.6387754678726196, + 0.17526115477085114, + 7.487028121948242 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, + "id": 14, "label": "Clinical Health" } ], @@ -62185,46 +62368,46 @@ "doi": "10.64898/2026.06.09.730991", "projections": { "pca": [ - -0.16857145726680756, - -0.13281244039535522 + -0.1707608997821808, + -0.13195687532424927 ], "tsne": [ - 4.845734596252441, - -27.985544204711914 + -9.59034538269043, + -20.3525333404541 ], "umap": [ - -2.319477081298828, - 4.024559020996094 + -2.1471776962280273, + 6.955748558044434 ], "pca3d": [ - -0.16857095062732697, - -0.13279469311237335, - -0.16039128601551056 + -0.17076043784618378, + -0.1319391429424286, + -0.15828201174736023 ], "tsne3d": [ - -42.659908294677734, - -3.069003105163574, - 14.519611358642578 + -26.549192428588867, + -1.9860916137695312, + -19.640483856201172 ], "umap3d": [ - 1.3459279537200928, - -0.1258368045091629, - -1.1759343147277832 + -0.11670584231615067, + 1.4134631156921387, + 4.527828216552734 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 22, + "id": 27, "label": "Genetic Variants" } ], @@ -62635,47 +62818,47 @@ "abstract": "Zhipu and some Chinese peers aim to capture users changing from US rivals\u2019 top models amid high prices, geopolitical bans.", "projections": { "pca": [ - 0.22526252269744873, - 0.05064833164215088 + 0.22549569606781006, + 0.05095207318663597 ], "tsne": [ - 18.324092864990234, - 28.802846908569336 + 30.21110725402832, + 12.99321460723877 ], "umap": [ - 4.756523132324219, - 5.343473434448242 + 4.053442001342773, + 3.8983190059661865 ], "pca3d": [ - 0.22526155412197113, - 0.0506499744951725, - 0.08508434146642685 + 0.2254948616027832, + 0.05095544084906578, + 0.0827743336558342 ], "tsne3d": [ - 51.02942657470703, - 6.779738903045654, - -6.690219402313232 + 12.6494779586792, + 44.51335906982422, + 13.860031127929688 ], "umap3d": [ - 5.279162883758545, - 1.114253282546997, - 2.258878469467163 + 5.0550994873046875, + 1.9807404279708862, + 5.416937351226807 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { - "id": 7, - "label": "Open AI" + "id": 11, + "label": "Code Tools" } ], "_embedding": [ @@ -63083,46 +63266,46 @@ "journal": "Nature Medicine", "projections": { "pca": [ - -0.2208329141139984, - 0.07485434412956238 + -0.216332346200943, + 0.07870500534772873 ], "tsne": [ - -10.498037338256836, - -0.30217328667640686 + -17.718019485473633, + 9.551025390625 ], "umap": [ - 0.018117694184184074, - 6.4089508056640625 + -1.4946593046188354, + 3.4540975093841553 ], "pca3d": [ - -0.22083468735218048, - 0.07482903450727463, - 0.22648420929908752 + -0.21633419394493103, + 0.07868289947509766, + 0.23009133338928223 ], "tsne3d": [ - 11.855525970458984, - -21.830331802368164, - 0.7345334887504578 + -6.2751312255859375, + -4.176647186279297, + 1.4955790042877197 ], "umap3d": [ - 4.471766471862793, - -0.6121605038642883, - -1.6154075860977173 + 1.2702521085739136, + 0.48503947257995605, + 7.383111953735352 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, + "id": 14, "label": "Clinical Health" } ], @@ -63537,47 +63720,47 @@ ], "projections": { "pca": [ - 0.01786251738667488, - 0.1878383308649063 + 0.020175307989120483, + 0.18990308046340942 ], "tsne": [ - -1.0749088525772095, - 15.417379379272461 + 0.41555890440940857, + 3.3858449459075928 ], "umap": [ - 3.754042387008667, - 3.774773597717285 + 2.8039309978485107, + 2.5454273223876953 ], "pca3d": [ - 0.017862187698483467, - 0.1878385692834854, - 0.18134884536266327 + 0.020174937322735786, + 0.18990406394004822, + 0.17900288105010986 ], "tsne3d": [ - 31.713977813720703, - -9.644472122192383, - -15.246495246887207 + 1.6670273542404175, + 24.23763656616211, + -14.017417907714844 ], "umap3d": [ - 5.669274806976318, - 0.8546220660209656, - 1.024685263633728 + 4.076608180999756, + 0.21997812390327454, + 5.1088409423828125 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 0, - "label": "AI Agents" + "id": 8, + "label": "Open AI" }, { - "id": 1, - "label": "AI Agents" + "id": 22, + "label": "AI Gemini" } ], "_embedding": [ @@ -63985,47 +64168,47 @@ "abstract": "Tilde Research is a moonshot AI lab advancing mechanistic interpretability, new architectures, and pretraining science.", "projections": { "pca": [ - 0.10056598484516144, - 0.06497986614704132 + 0.10093915462493896, + 0.06629928946495056 ], "tsne": [ - 17.152822494506836, - 4.286367416381836 + 18.076879501342773, + -3.046638250350952 ], "umap": [ - 2.7598769664764404, - 5.872290134429932 + 2.2212061882019043, + 4.734722137451172 ], "pca3d": [ - 0.10056550800800323, - 0.06497261673212051, - -0.0021058537531644106 + 0.10093867778778076, + 0.06629277020692825, + -0.0011076253140345216 ], "tsne3d": [ - 17.560699462890625, - 16.85135841369629, - 8.363059043884277 + 14.841248512268066, + 9.20831298828125, + 7.526566505432129 ], "umap3d": [ - 3.790295362472534, - 0.04478510469198227, - 1.5875493288040161 + 3.379682779312134, + 1.7690789699554443, + 6.090816974639893 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 4, + "label": "AI Agents" }, { - "id": 3, - "label": "SMB Data" + "id": 24, + "label": "SMB Models" } ], "_embedding": [ @@ -64441,47 +64624,47 @@ ], "projections": { "pca": [ - -0.2038312554359436, - -0.0646228939294815 + -0.2066393792629242, + -0.06450189650058746 ], "tsne": [ - -1.312627911567688, - -29.176809310913086 + -16.665937423706055, + -22.12965965270996 ], "umap": [ - -1.9209328889846802, - 3.3102409839630127 + -1.5034030675888062, + 7.305180549621582 ], "pca3d": [ - -0.20383135974407196, - -0.0646258294582367, - -0.1926967054605484 + -0.20663955807685852, + -0.06450555473566055, + -0.1887742280960083 ], "tsne3d": [ - -49.84587860107422, - -16.9809513092041, - -2.814516305923462 + -38.102378845214844, + -10.363395690917969, + -14.807790756225586 ], "umap3d": [ - 1.1700947284698486, - 0.5456392765045166, - -1.2449146509170532 + 0.049040790647268295, + 0.8837909698486328, + 3.8795905113220215 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 8, - "label": "Gene Regulation" + "id": 3, + "label": "Genomic Sequences" } ], "_embedding": [ @@ -64896,46 +65079,46 @@ "doi": "10.64898/2026.05.06.723362", "projections": { "pca": [ - -0.23688270151615143, - -0.11291000992059708 + -0.239920973777771, + -0.11211591958999634 ], "tsne": [ - 2.949232339859009, - -28.079442977905273 + -11.8510160446167, + -20.552026748657227 ], "umap": [ - -2.2949256896972656, - 3.976346015930176 + -2.1806867122650146, + 6.9119343757629395 ], "pca3d": [ - -0.2368823140859604, - -0.11290621757507324, - -0.21059292554855347 + -0.23992064595222473, + -0.11211224645376205, + -0.20659726858139038 ], "tsne3d": [ - -39.10850143432617, - -6.223182678222656, - 7.290956974029541 + -31.79208755493164, + -7.368464469909668, + -19.462261199951172 ], "umap3d": [ - 1.4086436033248901, - -0.04337240010499954, - -1.351970911026001 + -0.08757872134447098, + 1.2495101690292358, + 4.379608154296875 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 22, + "id": 27, "label": "Genetic Variants" } ], @@ -65342,47 +65525,47 @@ ], "projections": { "pca": [ - 0.11811134964227676, - 0.1474904865026474 + 0.11840739101171494, + 0.1475144475698471 ], "tsne": [ - 10.810546875, - 3.1444642543792725 + 12.510390281677246, + 4.113299369812012 ], "umap": [ - 2.0978121757507324, - 5.181918621063232 + 1.2431128025054932, + 4.620178699493408 ], "pca3d": [ - 0.11811106652021408, - 0.14749491214752197, - 0.0830460786819458 + 0.11840695142745972, + 0.14751772582530975, + 0.07848645001649857 ], "tsne3d": [ - 10.355100631713867, - 5.553648948669434, - 0.15031202137470245 + 25.57115364074707, + -6.78831672668457, + -3.1657066345214844 ], "umap3d": [ - 3.735138416290283, - 0.8752577304840088, - 1.175368070602417 + 3.0759501457214355, + 1.6982711553573608, + 5.288580894470215 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 4, - "label": "Com Area Amp" + "id": 6, + "label": "Data Needs" }, { - "id": 26, - "label": "AI Information" + "id": 10, + "label": "Research Data" } ], "_embedding": [ @@ -65795,47 +65978,47 @@ ], "projections": { "pca": [ - -0.06592586636543274, - 0.17946940660476685 + -0.06615861505270004, + 0.178910031914711 ], "tsne": [ - -20.32833480834961, - 23.083730697631836 + 4.472056865692139, + 25.76912498474121 ], "umap": [ - 2.5066986083984375, - 3.152695417404175 + 1.6945723295211792, + 2.106494665145874 ], "pca3d": [ - -0.06592604517936707, - 0.1794733852148056, - -0.012833816930651665 + -0.06615882366895676, + 0.17891345918178558, + -0.011922530829906464 ], "tsne3d": [ - 14.529376029968262, - 8.871488571166992, - -22.333450317382812 + 13.852232933044434, + -1.8612442016601562, + -36.993038177490234 ], "umap3d": [ - 6.2801513671875, - -0.24299566447734833, - 0.8265442848205566 + 4.631106376647949, + -0.7203462719917297, + 6.236165523529053 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 18, + "label": "Retrieval Learning" } ], "_embedding": [ @@ -66252,47 +66435,47 @@ "doi": "10.64898/2026.05.27.728319", "projections": { "pca": [ - -0.2535851299762726, - -0.21087798476219177 + -0.25500544905662537, + -0.2095952033996582 ], "tsne": [ - -9.796646118164062, - -19.28253936767578 + -20.622894287109375, + -10.912443161010742 ], "umap": [ - -1.970643401145935, - 5.648468494415283 + -1.952088713645935, + 5.474360466003418 ], "pca3d": [ - -0.2535845935344696, - -0.21087484061717987, - -0.11638881266117096 + -0.2550049424171448, + -0.20959220826625824, + -0.10989868640899658 ], "tsne3d": [ - -26.620698928833008, - -23.564577102661133, - -7.520520210266113 + -28.050477981567383, + -8.722392082214355, + 8.999205589294434 ], "umap3d": [ - 2.674806594848633, - -0.47970908880233765, - -0.725459635257721 + 0.46794337034225464, + 0.11878452450037003, + 5.4538774490356445 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 1, "label": "Cancer Data" }, { - "id": 9, - "label": "Cancer Biobank" + "id": 1, + "label": "Cancer Data" } ], "_embedding": [ @@ -66700,47 +66883,47 @@ "abstract": "An overview of Gemma 4 12B, a model designed to bring high-performance multimodal intelligence directly to your laptop.", "projections": { "pca": [ - 0.07062247395515442, - 0.10354190319776535 + 0.07311394810676575, + 0.10421712696552277 ], "tsne": [ - -21.297883987426758, - 7.219460487365723 + -13.358291625976562, + 21.881502151489258 ], "umap": [ - 1.4547145366668701, - 3.5429468154907227 + 0.5309971570968628, + 2.64900541305542 ], "pca3d": [ - 0.07062096148729324, - 0.10353577136993408, - 0.005089334677904844 + 0.07311233878135681, + 0.10421371459960938, + 0.006634783931076527 ], "tsne3d": [ - 45.52859115600586, - 7.57278299331665, - -30.28251838684082 + -19.81051254272461, + 29.464160919189453, + 6.157892227172852 ], "umap3d": [ - 5.3782172203063965, - -0.8111065030097961, - 0.03404156118631363 + 3.1426589488983154, + -0.5055446028709412, + 6.19832706451416 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, - "label": "Model Training" + "id": 2, + "label": "Medical Models" }, { - "id": 27, - "label": "Embeddings" + "id": 0, + "label": "Multimodal Models" } ], "_embedding": [ @@ -67148,47 +67331,47 @@ "journal": "Nature", "projections": { "pca": [ - -0.17917361855506897, - -0.03600628301501274 + -0.1735966056585312, + -0.03199253976345062 ], "tsne": [ - -11.827391624450684, - 2.6262173652648926 + -19.85179328918457, + 5.474462509155273 ], "umap": [ - -0.07417908310890198, - 6.024628639221191 + -1.0331918001174927, + 3.273237943649292 ], "pca3d": [ - -0.17917299270629883, - -0.036002691835165024, - 0.23317795991897583 + -0.17359603941440582, + -0.03198942914605141, + 0.23634374141693115 ], "tsne3d": [ - 0.5838412642478943, - -22.149023056030273, - -16.226945877075195 + 3.083728551864624, + -8.331609725952148, + -5.9901299476623535 ], "umap3d": [ - 4.5217413902282715, - -0.8642079830169678, - -1.4574205875396729 + 1.1697973012924194, + 0.8497692942619324, + 7.059170246124268 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 6, - "label": "Clinical Health" + "id": 5, + "label": "Health Benchmarks" } ], "_embedding": [ @@ -67603,47 +67786,47 @@ ], "projections": { "pca": [ - -0.1809154897928238, - 0.024315839633345604 + -0.18241126835346222, + 0.02571474388241768 ], "tsne": [ - -11.900286674499512, - -26.782203674316406 + -25.093326568603516, + -16.44931983947754 ], "umap": [ - -0.8971822261810303, - 3.0814597606658936 + -0.996396541595459, + 6.223763465881348 ], "pca3d": [ - -0.18091437220573425, - 0.024321136996150017, - -0.19291600584983826 + -0.18241015076637268, + 0.02571813389658928, + -0.18819810450077057 ], "tsne3d": [ - -35.374446868896484, - -15.434091567993164, - -23.886606216430664 + -42.804893493652344, + -22.53890037536621, + 5.117976665496826 ], "umap3d": [ - 1.6893144845962524, - 1.177239179611206, - -1.8361772298812866 + 0.39168328046798706, + -0.255058616399765, + 4.403966426849365 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 19, - "label": "Cell Models" + "id": 28, + "label": "Spatial Drug" } ], "_embedding": [ @@ -68058,47 +68241,47 @@ ], "projections": { "pca": [ - -0.18925070762634277, - 0.22097241878509521 + -0.1906406134366989, + 0.21998080611228943 ], "tsne": [ - -19.76807403564453, - 16.898998260498047 + -3.6059579849243164, + 27.133150100708008 ], "umap": [ - 2.252518653869629, - 2.9348971843719482 + 1.220577359199524, + 1.9291801452636719 ], "pca3d": [ - -0.18925027549266815, - 0.2209729254245758, - -0.09313899278640747 + -0.1906401664018631, + 0.21997851133346558, + -0.08963239192962646 ], "tsne3d": [ - 2.1840970516204834, - -5.226821422576904, - -29.672348022460938 + 11.641500473022461, + -2.472777843475342, + -25.505870819091797 ], "umap3d": [ - 6.372929096221924, - -0.6539146900177002, - 0.4857068657875061 + 4.187696933746338, + -0.9612094759941101, + 6.228848934173584 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 15, - "label": "Latent Models" + "id": 18, + "label": "Retrieval Learning" } ], "_embedding": [ @@ -68521,47 +68704,47 @@ "doi": "10.64898/2026.05.29.728868", "projections": { "pca": [ - -0.12625859677791595, - -0.07316715270280838 + -0.12829409539699554, + -0.07151114195585251 ], "tsne": [ - 5.940279483795166, - -27.81400489807129 + -7.98946475982666, + -20.212350845336914 ], "umap": [ - -1.9530441761016846, - 4.161865711212158 + -1.8285635709762573, + 6.672386169433594 ], "pca3d": [ - -0.12625758349895477, - -0.07314464449882507, - -0.15371696650981903 + -0.12829318642616272, + -0.07148904353380203, + -0.15173517167568207 ], "tsne3d": [ - -39.96078109741211, - 1.760561466217041, - 13.999838829040527 + -22.35687828063965, + -2.3493521213531494, + -21.559547424316406 ], "umap3d": [ - 1.6073158979415894, - -0.1193908080458641, - -0.839204728603363 + 0.20362488925457, + 1.3260570764541626, + 4.611445903778076 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 2, - "label": "Genetic Disease" + "id": 27, + "label": "Genetic Variants" } ], "_embedding": [ @@ -68969,47 +69152,47 @@ "abstract": "Research lab and product company building the platform for continual learning.", "projections": { "pca": [ - -0.05392645671963692, - 0.1635814756155014 + -0.050717733800411224, + 0.16642434895038605 ], "tsne": [ - -14.894624710083008, - 28.329639434814453 + 7.077490329742432, + 35.17033004760742 ], "umap": [ - 3.548074722290039, - 2.819471597671509 + 2.2121100425720215, + 2.1274356842041016 ], "pca3d": [ - -0.05392611026763916, - 0.16359077394008636, - 0.13867494463920593 + -0.050717342644929886, + 0.16643399000167847, + 0.13760115206241608 ], "tsne3d": [ - 19.48384666442871, - -16.63248634338379, - -38.68998718261719 + 9.121883392333984, + 22.47216796875, + -39.768463134765625 ], "umap3d": [ - 6.616944789886475, - -0.29877591133117676, - 1.4827467203140259 + 4.9500861167907715, + -0.40172305703163147, + 5.758766174316406 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 6, + "label": "Learning Techniques" } ], "_embedding": [ @@ -69423,47 +69606,47 @@ ], "projections": { "pca": [ - -0.17638340592384338, - -0.0945378914475441 + -0.17801322042942047, + -0.09362288564443588 ], "tsne": [ - -11.75548267364502, - -25.916290283203125 + -24.745603561401367, + -15.674083709716797 ], "umap": [ - -0.8515238761901855, - 3.188135862350464 + -1.0555164813995361, + 6.165097713470459 ], "pca3d": [ - -0.1763826608657837, - -0.0945362076163292, - -0.1443985551595688 + -0.178012415766716, + -0.09362324327230453, + -0.138987734913826 ], "tsne3d": [ - -29.61555290222168, - -17.506179809570312, - -23.45096778869629 + -37.510494232177734, + -20.978235244750977, + 6.440146446228027 ], "umap3d": [ - 1.706750512123108, - 1.1174267530441284, - -1.8731352090835571 + 0.3864102363586426, + -0.30524373054504395, + 4.461488723754883 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 19, - "label": "Cell Models" + "id": 28, + "label": "Spatial Drug" } ], "_embedding": [ @@ -69875,47 +70058,47 @@ ], "projections": { "pca": [ - -0.17591069638729095, - 0.27869823575019836 + -0.17438039183616638, + 0.28044724464416504 ], "tsne": [ - -17.264822006225586, - 14.01064682006836 + -0.0027018235996365547, + 21.86040496826172 ], "umap": [ - 1.7859357595443726, - 2.2097458839416504 + 0.8461182713508606, + 1.164582371711731 ], "pca3d": [ - -0.17591017484664917, - 0.27871906757354736, - -0.020923644304275513 + -0.17437992990016937, + 0.2804658114910126, + -0.018758529797196388 ], "tsne3d": [ - -0.6828634738922119, - 0.7536460161209106, - -43.070709228515625 + 8.4638090133667, + 4.424647331237793, + -15.161158561706543 ], "umap3d": [ - 6.726841926574707, - -0.862956166267395, - -0.02865363471210003 + 3.9914681911468506, + -1.5054010152816772, + 6.191612243652344 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 4, - "label": "World Models" + "id": 8, + "label": "JEPA Video Models" } ], "_embedding": [ @@ -70322,47 +70505,47 @@ "abstract": "Proteins are fundamental to life. The full extent of their biology is beyond our ability to characterize with experimental approaches in the physical laboratory. Accurate digital representations could accelerate the discovery of protein biology through virtual experiments. We propose language modeling to learn unified and general representations that can be scaled to all of protein biology. Building on these representations, we develop a structure prediction model that exceeds the performance of established methods for biomolecular complex prediction across benchmarks, including for the interactions of antibodies with their targets. A simple search procedure yields high experimental success rates for the discovery of proteins with nanomolar binding affinities for both miniproteins and single-chain antibodies, a modality critical for therapeutic design. Study of the concepts in the language model\u2019s representation space reveals a systematic organization aligned with the reductionist understanding of proteins developed through empirical science. Leveraging this organization, we generate a comprehensive map of protein biology encompassing over 6.8 billion sequences and 1.1 billion predicted structures, identifying connections across known and unknown biology. As a whole, this shows language modeling as a powerful substrate for representing the biology of proteins, operating across scales from the prediction and design of protein interactions at the atomic level, to identifying properties of proteins at different levels of granularity and abstraction, to the scale of mapping connections between proteins across billions of years of evolution.", "projections": { "pca": [ - -0.18024252355098724, - 0.034750282764434814 + -0.1822385936975479, + 0.03571509197354317 ], "tsne": [ - 1.9387797117233276, - -36.35871124267578 + -12.036105155944824, + -29.143526077270508 ], "umap": [ - -1.379183292388916, - 2.471407413482666 + -0.5673558115959167, + 7.6146063804626465 ], "pca3d": [ - -0.18024341762065887, - 0.03473746031522751, - -0.19476419687271118 + -0.18223950266838074, + 0.03570105880498886, + -0.19025272130966187 ], "tsne3d": [ - -45.3135871887207, - 12.140447616577148, - -15.61734390258789 + -31.14525032043457, + -34.77040100097656, + -19.38187026977539 ], "umap3d": [ - 1.4874389171600342, - 1.258004903793335, - -0.48085376620292664 + 1.0219073295593262, + 0.9088523387908936, + 3.4891433715820312 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 21, - "label": "Protein RNA" + "id": 12, + "label": "Protein Language" } ], "_embedding": [ @@ -70781,47 +70964,47 @@ "doi": "10.64898/2025.12.05.692665", "projections": { "pca": [ - -0.1512674242258072, - -0.18640953302383423 + -0.1554345041513443, + -0.1871265321969986 ], "tsne": [ - 4.4976935386657715, - -27.166133880615234 + -9.780136108398438, + -19.648632049560547 ], "umap": [ - -2.2878074645996094, - 3.9814934730529785 + -1.9912794828414917, + 7.153388977050781 ], "pca3d": [ - -0.15126633644104004, - -0.18638838827610016, - -0.20799003541469574 + -0.15543349087238312, + -0.18710649013519287, + -0.2052449882030487 ], "tsne3d": [ - -43.84654998779297, - -6.269589900970459, - 20.39402961730957 + -28.817630767822266, + 3.1531918048858643, + -17.9599666595459 ], "umap3d": [ - 1.4681026935577393, - -0.10860005021095276, - -1.2692523002624512 + -0.17951002717018127, + 1.4224337339401245, + 4.335682392120361 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 22, - "label": "Genetic Variants" + "id": 3, + "label": "Genomic Sequences" } ], "_embedding": [ @@ -71229,47 +71412,47 @@ "journal": "Nature Methods", "projections": { "pca": [ - -0.28331458568573, - -0.07752829790115356 + -0.28510236740112305, + -0.07680731266736984 ], "tsne": [ - -3.9272921085357666, - -31.185924530029297 + -16.566295623779297, + -23.534902572631836 ], "umap": [ - -1.81003999710083, - 3.288759231567383 + -1.4584466218948364, + 7.190223217010498 ], "pca3d": [ - -0.283314049243927, - -0.07753244787454605, - -0.16471968591213226 + -0.28510192036628723, + -0.07681217789649963, + -0.1620212346315384 ], "tsne3d": [ - -45.91670608520508, - -11.686192512512207, - -9.225876808166504 + -33.23536682128906, + -19.125518798828125, + -10.583099365234375 ], "umap3d": [ - 1.329392433166504, - 0.7101824879646301, - -1.0665079355239868 + 0.18685108423233032, + 0.7638751864433289, + 3.8505191802978516 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 8, - "label": "Gene Regulation" + "id": 3, + "label": "Genomic Sequences" } ], "_embedding": [ @@ -71679,47 +71862,47 @@ "abstract": "Code, data and weights for the paper **What drives success in physical planning with Joint-Embedding Predictive World Models?** - facebookresearch/jepa-wms", "projections": { "pca": [ - 0.08596146106719971, - 0.08212050050497055 + 0.086821548640728, + 0.08362038433551788 ], "tsne": [ - -15.059989929199219, - 13.460012435913086 + 0.9378007650375366, + 19.575674057006836 ], "umap": [ - 1.7457889318466187, - 2.2269208431243896 + 0.7895803451538086, + 1.134049654006958 ], "pca3d": [ - 0.08596275746822357, - 0.08215035498142242, - -0.05549316108226776 + 0.08682303130626678, + 0.08364686369895935, + -0.05483528599143028 ], "tsne3d": [ - -12.837808609008789, - 0.6909187436103821, - -46.24897003173828 + 12.35033893585205, + 2.5515806674957275, + -8.762744903564453 ], "umap3d": [ - 6.707563400268555, - -0.8017160892486572, - -0.12367302924394608 + 4.019418239593506, + -1.5757250785827637, + 6.183262348175049 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 4, - "label": "World Models" + "id": 8, + "label": "JEPA Video Models" } ], "_embedding": [ @@ -72150,46 +72333,46 @@ "openalex_url": "https://openalex.org/W7162240333", "projections": { "pca": [ - -0.13819319009780884, - -0.1315331906080246 + -0.14164909720420837, + -0.1311892867088318 ], "tsne": [ - 3.9412107467651367, - -28.502883911132812 + -10.884892463684082, + -20.6932430267334 ], "umap": [ - -2.2986044883728027, - 3.8085439205169678 + -2.2712488174438477, + 7.031482219696045 ], "pca3d": [ - -0.1381930261850357, - -0.1315343827009201, - -0.19572699069976807 + -0.14164915680885315, + -0.13119064271450043, + -0.19383308291435242 ], "tsne3d": [ - -45.861637115478516, - -6.957801342010498, - 11.071415901184082 + -31.022214889526367, + -2.9073171615600586, + -17.264245986938477 ], "umap3d": [ - 1.2307801246643066, - 0.0046389480121433735, - -1.1993708610534668 + -0.19609250128269196, + 1.238844871520996, + 4.3483076095581055 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 22, + "id": 27, "label": "Genetic Variants" } ], @@ -72607,47 +72790,47 @@ ], "projections": { "pca": [ - -0.14770889282226562, - 0.1882002204656601 + -0.14612945914268494, + 0.19028906524181366 ], "tsne": [ - -18.07379150390625, - 11.80270767211914 + -2.2151432037353516, + 21.018712997436523 ], "umap": [ - 1.6527165174484253, - 2.455815315246582 + 0.5205010771751404, + 1.408712387084961 ], "pca3d": [ - -0.1477082073688507, - 0.18820978701114655, - -0.0668959692120552 + -0.14612874388694763, + 0.19029748439788818, + -0.06126264110207558 ], "tsne3d": [ - -8.683476448059082, - -0.033280640840530396, - -32.684326171875 + 6.821018218994141, + -1.947484016418457, + -12.66500473022461 ], "umap3d": [ - 6.45987606048584, - -1.1034456491470337, - -0.11336452513933182 + 3.7935924530029297, + -1.4585074186325073, + 6.366513729095459 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 15, - "label": "Latent Models" + "id": 8, + "label": "JEPA Video Models" } ], "_embedding": [ @@ -73068,47 +73251,47 @@ "doi": "10.48550/arxiv.2605.18324", "projections": { "pca": [ - -0.10254623740911484, - 0.21953672170639038 + -0.10217297822237015, + 0.22081522643566132 ], "tsne": [ - -21.39017677307129, - 12.200236320495605 + -5.4087300300598145, + 23.61174774169922 ], "umap": [ - 1.5799555778503418, - 2.8486320972442627 + 0.605688750743866, + 1.8708280324935913 ], "pca3d": [ - -0.10254614800214767, - 0.21953751146793365, - -0.066688172519207 + -0.10217292606830597, + 0.22081559896469116, + -0.06151355430483818 ], "tsne3d": [ - -6.128280162811279, - 11.204483985900879, - -28.986013412475586 + -1.6578760147094727, + -0.8727179169654846, + -20.77654266357422 ], "umap3d": [ - 6.0803141593933105, - -1.1346577405929565, - 0.11654341965913773 + 3.5051581859588623, + -1.0484743118286133, + 6.39130973815918 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 15, - "label": "Latent Models" + "id": 8, + "label": "JEPA Video Models" } ], "_embedding": [ @@ -73515,47 +73698,47 @@ "title": "Frontiers | Using GPT-4 to annotate the severity of all phenotypic abnormalities within the human phenotype ontology", "projections": { "pca": [ - 0.02751000225543976, - -0.0771290585398674 + 0.025604311376810074, + -0.07661184668540955 ], "tsne": [ - 10.985011100769043, - -4.812019348144531 + 6.2210493087768555, + -2.465421438217163 ], "umap": [ - 1.3036749362945557, - 5.107486724853516 + 0.607052743434906, + 5.324210166931152 ], "pca3d": [ - 0.0275103896856308, - -0.07713638991117477, - -0.031847033649683 + 0.025604678317904472, + -0.07662107795476913, + -0.03132820501923561 ], "tsne3d": [ - -5.905048847198486, - 12.456957817077637, - 14.752111434936523 + 18.63251495361328, + -16.48064613342285, + 16.5426082611084 ], "umap3d": [ - 3.065854787826538, - 0.39126020669937134, - 0.5786540508270264 + 2.1433894634246826, + 1.3499962091445923, + 5.41873836517334 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 3, + "label": "Open Source Tools" }, { - "id": 4, - "label": "Com Area Amp" + "id": 6, + "label": "Data Needs" }, { - "id": 23, - "label": "Research Authors" + "id": 16, + "label": "Lab Research" } ], "_embedding": [ @@ -73972,47 +74155,47 @@ "doi": "10.64898/2026.02.02.703349", "projections": { "pca": [ - -0.19506077468395233, - 0.06656105816364288 + -0.19817963242530823, + 0.06654674559831619 ], "tsne": [ - 1.519539475440979, - -35.83025360107422 + -12.615124702453613, + -28.79090690612793 ], "umap": [ - -1.3863667249679565, - 2.4938313961029053 + -0.5972139835357666, + 7.66126823425293 ], "pca3d": [ - -0.19506123661994934, - 0.06655068695545197, - -0.24233533442020416 + -0.19818031787872314, + 0.06653625518083572, + -0.23901593685150146 ], "tsne3d": [ - -48.320919036865234, - 6.995689868927002, - -14.478449821472168 + -36.4459342956543, + -30.568248748779297, + -22.62299156188965 ], "umap3d": [ - 1.396411657333374, - 1.2671183347702026, - -0.4347882866859436 + 1.055871844291687, + 0.9200538992881775, + 3.37019419670105 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 21, - "label": "Protein RNA" + "id": 12, + "label": "Protein Language" } ], "_embedding": [ @@ -74425,47 +74608,47 @@ "doi": "10.1038/s41562-026-02475-8", "projections": { "pca": [ - -0.0961737409234047, - -0.24588340520858765 + -0.0977998822927475, + -0.2454797923564911 ], "tsne": [ - 3.246572256088257, - -20.376583099365234 + -10.275609970092773, + -13.195585250854492 ], "umap": [ - -1.5723192691802979, - 5.341443061828613 + -2.6760449409484863, + 5.889523506164551 ], "pca3d": [ - -0.09617201238870621, - -0.24586105346679688, - -0.01512679923325777 + -0.09779807180166245, + -0.24545979499816895, + -0.013515232130885124 ], "tsne3d": [ - -19.232219696044922, - -17.32282257080078, - 13.707185745239258 + -13.788077354431152, + -14.854507446289062, + -0.7840914726257324 ], "umap3d": [ - 2.6117844581604004, - -0.6392698884010315, - -1.9097745418548584 + 0.2389269769191742, + 1.2667425870895386, + 5.869874000549316 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 1, "label": "Cancer Data" }, { - "id": 9, - "label": "Cancer Biobank" + "id": 1, + "label": "Cancer Data" } ], "_embedding": [ @@ -74879,46 +75062,46 @@ ], "projections": { "pca": [ - -0.16905350983142853, - 0.2924574911594391 + -0.16745135188102722, + 0.29357776045799255 ], "tsne": [ - -24.972003936767578, - 15.974405288696289 + -0.7484926581382751, + 25.862180709838867 ], "umap": [ - 2.0959298610687256, - 2.3873677253723145 + 1.2496509552001953, + 1.3847949504852295 ], "pca3d": [ - -0.16905368864536285, - 0.29245901107788086, - -0.0479121170938015 + -0.1674516797065735, + 0.2935793101787567, + -0.04327651485800743 ], "tsne3d": [ - -5.175107002258301, - 17.58342742919922, - -39.29262161254883 + -5.9527201652526855, + 2.8573379516601562, + -31.501651763916016 ], "umap3d": [ - 6.633602619171143, - -1.2113449573516846, - 0.5232152938842773 + 3.7654662132263184, + -1.1548206806182861, + 5.81959342956543 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 4, + "id": 26, "label": "World Models" } ], @@ -75327,47 +75510,47 @@ "journal": "Nature", "projections": { "pca": [ - -0.05435005947947502, - 0.17607051134109497 + -0.049795977771282196, + 0.17976264655590057 ], "tsne": [ - -2.7774746417999268, - 10.159172058105469 + -3.5150396823883057, + 1.6339409351348877 ], "umap": [ - -0.10813402384519577, - 5.223391056060791 + -0.5608041882514954, + 4.226684093475342 ], "pca3d": [ - -0.05434968322515488, - 0.17607258260250092, - 0.12997587025165558 + -0.04979567602276802, + 0.17976480722427368, + 0.12593616545200348 ], "tsne3d": [ - 22.20482063293457, - -19.102882385253906, - -13.543902397155762 + 9.336322784423828, + 21.41117286682129, + -10.111655235290527 ], "umap3d": [ - 4.328500270843506, - 0.5124437808990479, - -0.7404329180717468 + 2.964421033859253, + 0.25182268023490906, + 5.14778470993042 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ - { - "id": 0, - "label": "Medical Data AI" - }, { "id": 2, - "label": "AI Drug Discovery" + "label": "Clinical AI Data" }, { - "id": 13, + "id": 9, "label": "Drug Discovery" + }, + { + "id": 9, + "label": "Biomedical AI" } ], "_embedding": [ @@ -75781,47 +75964,47 @@ ], "projections": { "pca": [ - -0.1312233954668045, - -0.06649579107761383 + -0.1298951655626297, + -0.06479816883802414 ], "tsne": [ - -5.892706871032715, - -1.0901628732681274 + -10.590961456298828, + 5.515861511230469 ], "umap": [ - 0.27127352356910706, - 6.445908069610596 + -1.3434606790542603, + 3.7846131324768066 ], "pca3d": [ - -0.13122376799583435, - -0.06650491058826447, - 0.10561155527830124 + -0.1298956274986267, + -0.06480707973241806, + 0.10969895124435425 ], "tsne3d": [ - 13.599722862243652, - -20.165029525756836, - 16.86920928955078 + 5.685301780700684, + -16.27199363708496, + 7.471807956695557 ], "umap3d": [ - 4.438284873962402, - -0.7426373362541199, - -1.6785213947296143 + 1.5146445035934448, + 0.7770000696182251, + 7.1756486892700195 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 0, - "label": "Clinical LLMs" + "id": 14, + "label": "Clinical Health" } ], "_embedding": [ @@ -76236,47 +76419,47 @@ ], "projections": { "pca": [ - -0.21750304102897644, - -0.005155801773071289 + -0.21678097546100616, + -0.0027593434788286686 ], "tsne": [ - -0.006985858082771301, - -7.936710834503174 + -7.8412766456604, + -5.236969470977783 ], "umap": [ - -0.4908929467201233, - 5.061550140380859 + -1.0108182430267334, + 4.585564613342285 ], "pca3d": [ - -0.2175028771162033, - -0.005167766008526087, - 0.005232133902609348 + -0.2167808562517166, + -0.002772647188976407, + 0.00988028198480606 ], "tsne3d": [ - -11.28830337524414, - -6.5156121253967285, - 16.003381729125977 + 2.1271607875823975, + -16.389673233032227, + -2.761742115020752 ], "umap3d": [ - 3.617859125137329, - 0.3192339837551117, - -1.1826980113983154 + 1.9125456809997559, + 0.13276250660419464, + 5.251245021820068 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ - { - "id": 0, - "label": "Medical Data AI" - }, { "id": 2, - "label": "AI Drug Discovery" + "label": "Clinical AI Data" }, { - "id": 13, + "id": 9, "label": "Drug Discovery" + }, + { + "id": 9, + "label": "Biomedical AI" } ], "_embedding": [ @@ -76688,47 +76871,47 @@ ], "projections": { "pca": [ - -0.18603011965751648, - -0.06248975917696953 + -0.18575860559940338, + -0.06083128973841667 ], "tsne": [ - -8.208662986755371, - -1.0291109085083008 + -12.5440034866333, + 6.683225154876709 ], "umap": [ - 0.2854507565498352, - 6.24497127532959 + -1.2936276197433472, + 3.952501058578491 ], "pca3d": [ - -0.18603020906448364, - -0.06250441819429398, - 0.04167657718062401 + -0.18575860559940338, + -0.06084601953625679, + 0.04600132256746292 ], "tsne3d": [ - -0.42983749508857727, - -11.602574348449707, - 18.689300537109375 + 2.3430914878845215, + -12.246013641357422, + 2.10239315032959 ], "umap3d": [ - 4.5504469871521, - -0.4421146810054779, - -1.519209623336792 + 1.558931827545166, + 0.8193421959877014, + 7.1830854415893555 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 0, - "label": "Clinical LLMs" + "id": 14, + "label": "Clinical Health" } ], "_embedding": [ @@ -77144,47 +77327,47 @@ "doi": "10.64898/2026.05.13.725007", "projections": { "pca": [ - -0.2535073757171631, - -0.0293267909437418 + -0.2571771740913391, + -0.029621552675962448 ], "tsne": [ - -0.26333820819854736, - -30.796621322631836 + -14.566817283630371, + -23.4863338470459 ], "umap": [ - -1.7527356147766113, - 3.253953456878662 + -1.214807391166687, + 7.165866851806641 ], "pca3d": [ - -0.2535077631473541, - -0.02934144251048565, - -0.22210626304149628 + -0.2571776211261749, + -0.029636502265930176, + -0.21678586304187775 ], "tsne3d": [ - -41.60490798950195, - -6.722787857055664, - -6.821850299835205 + -30.382848739624023, + -21.484209060668945, + -15.714885711669922 ], "umap3d": [ - 1.290562391281128, - 0.8176501393318176, - -1.0056774616241455 + 0.4718029499053955, + 0.8109495639801025, + 3.888435125350952 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 8, - "label": "Gene Regulation" + "id": 3, + "label": "Genomic Sequences" } ], "_embedding": [ @@ -77604,47 +77787,47 @@ "doi": "10.64898/2026.05.07.723544", "projections": { "pca": [ - -0.19560813903808594, - 0.044762104749679565 + -0.196693554520607, + 0.045657116919755936 ], "tsne": [ - -7.1971001625061035, - -35.98624801635742 + -23.89000701904297, + -26.846267700195312 ], "umap": [ - -1.564339280128479, - 3.129408121109009 + -1.0283581018447876, + 7.178999423980713 ], "pca3d": [ - -0.19560812413692474, - 0.0447518415749073, - -0.14301162958145142 + -0.19669361412525177, + 0.045646149665117264, + -0.13863007724285126 ], "tsne3d": [ - -59.83988952636719, - -20.13737678527832, - -10.00903034210205 + -42.14193344116211, + -0.4981693625450134, + -7.128508567810059 ], "umap3d": [ - 1.4035221338272095, - 0.9147626757621765, - -0.9694791436195374 + 0.660398006439209, + 0.7712827920913696, + 3.891572952270508 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 8, - "label": "Gene Regulation" + "id": 20, + "label": "Cell Foundation" } ], "_embedding": [ @@ -78058,47 +78241,47 @@ ], "projections": { "pca": [ - -0.13946332037448883, - 0.24890777468681335 + -0.13889935612678528, + 0.25070440769195557 ], "tsne": [ - -15.066142082214355, - 26.65349769592285 + 6.6095290184021, + 32.89026641845703 ], "umap": [ - 3.480369806289673, - 2.689000368118286 + 2.4243853092193604, + 1.7407677173614502 ], "pca3d": [ - -0.13946324586868286, - 0.24891632795333862, - -0.030118092894554138 + -0.13889943063259125, + 0.25071319937705994, + -0.02981449104845524 ], "tsne3d": [ - 19.559057235717773, - -10.013962745666504, - -39.9796142578125 + 11.07767391204834, + 19.480792999267578, + -35.95472717285156 ], "umap3d": [ - 6.859687805175781, - -0.362868994474411, - 1.30339515209198 + 4.998797416687012, + -0.5560858845710754, + 5.63189172744751 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 6, + "label": "Learning Techniques" } ], "_embedding": [ @@ -78508,47 +78691,47 @@ "journal": "Nature Genetics", "projections": { "pca": [ - -0.16079694032669067, - -0.22026370465755463 + -0.16001471877098083, + -0.2172965407371521 ], "tsne": [ - 3.8001365661621094, - -19.618061065673828 + -9.669699668884277, + -12.500847816467285 ], "umap": [ - -1.4849162101745605, - 5.424965858459473 + -2.6571459770202637, + 5.899997234344482 ], "pca3d": [ - -0.16079527139663696, - -0.22024878859519958, - 0.07619445770978928 + -0.16001290082931519, + -0.21728263795375824, + 0.07846475392580032 ], "tsne3d": [ - -14.321752548217773, - -15.271484375, - 10.750141143798828 + -15.224020957946777, + -13.61410903930664, + 3.896632671356201 ], "umap3d": [ - 2.69073748588562, - -0.6858193874359131, - -1.903946876525879 + 0.21336814761161804, + 1.2589646577835083, + 5.8676605224609375 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 1, "label": "Cancer Data" }, { - "id": 9, - "label": "Cancer Biobank" + "id": 1, + "label": "Cancer Data" } ], "_embedding": [ @@ -78973,47 +79156,47 @@ ], "projections": { "pca": [ - -0.22697602212429047, - 0.022086961194872856 + -0.22356131672859192, + 0.026139836758375168 ], "tsne": [ - -11.410948753356934, - -4.753985404968262 + -14.483847618103027, + 6.156245708465576 ], "umap": [ - -0.2805480360984802, - 6.9214630126953125 + -1.6666574478149414, + 2.917512893676758 ], "pca3d": [ - -0.22697466611862183, - 0.022095385938882828, - 0.14060211181640625 + -0.22356009483337402, + 0.026147084310650826, + 0.1462181955575943 ], "tsne3d": [ - 0.21372365951538086, - -23.88796615600586, - -0.46576252579689026 + -5.2064738273620605, + -1.387155294418335, + 16.46036720275879 ], "umap3d": [ - 4.545775890350342, - -1.3746693134307861, - -1.5333243608474731 + 1.362899661064148, + 0.39117947220802307, + 7.250082015991211 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, - "label": "Clinical Health" + "id": 13, + "label": "ECG AI Models" } ], "_embedding": [ @@ -79428,47 +79611,47 @@ ], "projections": { "pca": [ - -0.13231326639652252, - -0.14616677165031433 + -0.1310415416955948, + -0.14350825548171997 ], "tsne": [ - -8.261503219604492, - -10.316913604736328 + -13.263199806213379, + -1.3585293292999268 ], "umap": [ - -1.238106369972229, - 6.159242153167725 + -2.2453975677490234, + 4.255369186401367 ], "pca3d": [ - -0.13231204450130463, - -0.14615410566329956, - 0.09380016475915909 + -0.13104037940502167, + -0.14349564909934998, + 0.09801943600177765 ], "tsne3d": [ - -2.4397501945495605, - -35.84892272949219, - 11.167276382446289 + -5.233545780181885, + -22.960786819458008, + 11.660018920898438 ], "umap3d": [ - 3.245669364929199, - -0.7700586318969727, - -1.5523196458816528 + 0.7383422255516052, + 0.6925470232963562, + 6.440046310424805 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 1, - "label": "Cancer Data" + "id": 5, + "label": "Clinical Data" }, { - "id": 28, - "label": "Clinical AI" + "id": 21, + "label": "Clinical Trials" } ], "_embedding": [ @@ -79876,47 +80059,47 @@ "journal": "Nature Machine Intelligence", "projections": { "pca": [ - -0.16422763466835022, - 0.10630477964878082 + -0.16467410326004028, + 0.10708928108215332 ], "tsne": [ - 1.1844072341918945, - -37.657108306884766 + -11.228886604309082, + -27.91521644592285 ], "umap": [ - -1.3587414026260376, - 2.43101167678833 + -0.49027442932128906, + 7.696465492248535 ], "pca3d": [ - -0.16422918438911438, - 0.10628484189510345, - -0.16362453997135162 + -0.16467568278312683, + 0.10706930607557297, + -0.16150034964084625 ], "tsne3d": [ - -41.058292388916016, - 13.929947853088379, - -11.635186195373535 + -30.55691909790039, + -33.725364685058594, + -24.560697555541992 ], "umap3d": [ - 1.4069815874099731, - 1.2680283784866333, - -0.41596725583076477 + 1.0410137176513672, + 0.9317724704742432, + 3.4130992889404297 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 21, - "label": "Protein RNA" + "id": 12, + "label": "Protein Language" } ], "_embedding": [ @@ -80335,47 +80518,47 @@ "doi": "10.64898/2026.05.11.724344", "projections": { "pca": [ - -0.19848990440368652, - 0.05227816104888916 + -0.20191019773483276, + 0.051694341003894806 ], "tsne": [ - -0.19015498459339142, - -35.38197326660156 + -14.03143310546875, + -27.856077194213867 ], "umap": [ - -1.5558850765228271, - 2.7409632205963135 + -0.7984068393707275, + 7.524857044219971 ], "pca3d": [ - -0.19849064946174622, - 0.0522717610001564, - -0.25364160537719727 + -0.20191097259521484, + 0.051686953753232956, + -0.24903982877731323 ], "tsne3d": [ - -40.55723571777344, - 1.7576472759246826, - -14.965173721313477 + -28.826486587524414, + -28.056217193603516, + -16.909832000732422 ], "umap3d": [ - 1.2807859182357788, - 1.2255104780197144, - -0.7089842557907104 + 0.735615611076355, + 0.8489854335784912, + 3.4687938690185547 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 21, - "label": "Protein RNA" + "id": 12, + "label": "Protein Language" } ], "_embedding": [ @@ -80793,46 +80976,46 @@ ], "projections": { "pca": [ - -0.11664119362831116, - 0.2504007816314697 + -0.11543499678373337, + 0.25097376108169556 ], "tsne": [ - -22.987863540649414, - 20.173099517822266 + -1.2401798963546753, + 29.414968490600586 ], "umap": [ - 2.216045618057251, - 2.527092933654785 + 1.1702913045883179, + 1.5689467191696167 ], "pca3d": [ - -0.11664176732301712, - 0.25039899349212646, - -0.047099146991968155 + -0.11543549597263336, + 0.25097042322158813, + -0.0434168316423893 ], "tsne3d": [ - 2.9634764194488525, - 12.88374137878418, - -32.46440887451172 + -1.1875890493392944, + 4.256691932678223, + -25.956247329711914 ], "umap3d": [ - 6.472714900970459, - -1.0565837621688843, - 0.5448254346847534 + 3.977656602859497, + -1.1078290939331055, + 5.9754462242126465 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 4, + "id": 26, "label": "World Models" } ], @@ -81250,47 +81433,47 @@ ], "projections": { "pca": [ - -0.2603471875190735, - -0.07129878550767899 + -0.2627975642681122, + -0.07036226987838745 ], "tsne": [ - 2.239356279373169, - -24.16157341003418 + -11.903105735778809, + -16.836009979248047 ], "umap": [ - -2.0786523818969727, - 4.432473182678223 + -2.299733877182007, + 6.5634636878967285 ], "pca3d": [ - -0.2603464722633362, - -0.07129466533660889, - -0.14634184539318085 + -0.26279687881469727, + -0.07035995274782181, + -0.14079806208610535 ], "tsne3d": [ - -30.990968704223633, - -9.500505447387695, - 13.223331451416016 + -19.169830322265625, + -8.25763988494873, + -9.060094833374023 ], "umap3d": [ - 1.7880468368530273, - -0.18967559933662415, - -1.6862711906433105 + 0.085407555103302, + 1.4578857421875, + 4.859403133392334 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 2, - "label": "Genetic Disease" + "id": 27, + "label": "Genetic Variants" } ], "_embedding": [ @@ -81709,47 +81892,47 @@ ], "projections": { "pca": [ - -0.05655168369412422, - -0.14896172285079956 + -0.05421119183301926, + -0.14619359374046326 ], "tsne": [ - -7.2593889236450195, - -0.5709906816482544 + -11.582186698913574, + 6.432466983795166 ], "umap": [ - 0.2735992670059204, - 6.441237926483154 + -1.3482813835144043, + 3.7803399562835693 ], "pca3d": [ - -0.056551456451416016, - -0.14896328747272491, - 0.1137445792555809 + -0.05421098321676254, + -0.14619572460651398, + 0.11772016435861588 ], "tsne3d": [ - 4.382447242736816, - -17.436735153198242, - 16.50925636291504 + 1.83856201171875, + -11.666910171508789, + 8.553374290466309 ], "umap3d": [ - 4.609004974365234, - -0.5861648321151733, - -1.640790343284607 + 1.3917012214660645, + 0.8701650500297546, + 7.179629802703857 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 0, - "label": "Clinical LLMs" + "id": 14, + "label": "Clinical Health" } ], "_embedding": [ @@ -82158,47 +82341,47 @@ "title": "Aurora: A Leverage-Aware Optimizer for Rectangular Matrices | Tilde", "projections": { "pca": [ - -0.07058678567409515, - 0.2095857560634613 + -0.06639724224805832, + 0.21452267467975616 ], "tsne": [ - -8.667737007141113, - 17.501300811767578 + 8.234743118286133, + 19.367006301879883 ], "umap": [ - 3.01784086227417, - 3.3130831718444824 + 2.067054033279419, + 2.4699552059173584 ], "pca3d": [ - -0.07058679312467575, - 0.2095983922481537, - -0.005496802274137735 + -0.06639747321605682, + 0.21453627943992615, + -0.007483761291950941 ], "tsne3d": [ - 19.949424743652344, - 18.428913116455078, - -27.163715362548828 + -2.488497734069824, + 24.34144401550293, + -36.21279525756836 ], "umap3d": [ - 6.050833225250244, - -0.2840639650821686, - 1.2780746221542358 + 4.48695182800293, + -0.24064432084560394, + 5.936041355133057 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 6, + "label": "Learning Techniques" } ], "_embedding": [ @@ -82661,47 +82844,47 @@ "openalex_url": "https://openalex.org/W7160658183", "projections": { "pca": [ - -0.24078594148159027, - -0.04189354553818703 + -0.2387283444404602, + -0.03921478986740112 ], "tsne": [ - -12.8845796585083, - -25.08867073059082 + -25.431224822998047, + -14.465753555297852 ], "umap": [ - -0.9029883146286011, - 3.1803321838378906 + -1.1010602712631226, + 6.123239040374756 ], "pca3d": [ - -0.24078577756881714, - -0.0418948233127594, - 0.026099003851413727 + -0.23872819542884827, + -0.039215318858623505, + 0.02970764972269535 ], "tsne3d": [ - -25.741199493408203, - -20.7934627532959, - -28.143550872802734 + -42.37080764770508, + -18.533475875854492, + 9.261919021606445 ], "umap3d": [ - 1.677579641342163, - 1.1196399927139282, - -1.906650185585022 + 0.40781211853027344, + -0.26005345582962036, + 4.503810405731201 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 19, - "label": "Cell Models" + "id": 28, + "label": "Spatial Drug" } ], "_embedding": [ @@ -83111,46 +83294,46 @@ "doi": "10.1038/s41746-026-02625-2", "projections": { "pca": [ - -0.11295764893293381, - 0.02673114836215973 + -0.11052948236465454, + 0.029160883277654648 ], "tsne": [ - -11.248907089233398, - -6.558496952056885 + -16.6451473236084, + 2.9372568130493164 ], "umap": [ - -0.43828773498535156, - 6.628873348236084 + -1.8345063924789429, + 3.6822385787963867 ], "pca3d": [ - -0.11295654624700546, - 0.02674289420247078, - 0.12441396713256836 + -0.11052832752466202, + 0.029170697554945946, + 0.12747304141521454 ], "tsne3d": [ - 4.056951522827148, - -31.77926254272461, - 0.030731866136193275 + -1.2680789232254028, + -22.143659591674805, + 4.224851608276367 ], "umap3d": [ - 4.010882377624512, - -0.8093287348747253, - -2.059781789779663 + 0.7674161195755005, + 0.7090747952461243, + 7.219349384307861 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, + "id": 14, "label": "Clinical Health" } ], @@ -83588,46 +83771,46 @@ ], "projections": { "pca": [ - -0.1329927295446396, - -0.1295277178287506 + -0.13009129464626312, + -0.12577275931835175 ], "tsne": [ - -8.645889282226562, - -5.744044303894043 + -15.592830657958984, + 3.5883917808532715 ], "umap": [ - -0.40651053190231323, - 6.647593975067139 + -2.004399299621582, + 3.786006212234497 ], "pca3d": [ - -0.13299116492271423, - -0.12950222194194794, - 0.16111508011817932 + -0.13008980453014374, + -0.12575067579746246, + 0.166580468416214 ], "tsne3d": [ - -3.457507371902466, - -21.960987091064453, - 13.483697891235352 + -3.4279987812042236, + -13.136163711547852, + 17.100866317749023 ], "umap3d": [ - 4.047553539276123, - -1.0648213624954224, - -1.775376558303833 + 0.8343693614006042, + 0.9141463041305542, + 6.928690433502197 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, + "id": 14, "label": "Clinical Health" } ], @@ -84043,46 +84226,46 @@ ], "projections": { "pca": [ - -0.1396508365869522, - 0.19315724074840546 + -0.13884060084819794, + 0.19392047822475433 ], "tsne": [ - -24.915390014648438, - 16.414447784423828 + -0.7365854978561401, + 26.316287994384766 ], "umap": [ - 2.3796558380126953, - 2.2170917987823486 + 1.3334370851516724, + 1.4045429229736328 ], "pca3d": [ - -0.13965147733688354, - 0.19315341114997864, - -0.09031368792057037 + -0.1388414204120636, + 0.19391803443431854, + -0.08675415068864822 ], "tsne3d": [ - -3.58964467048645, - 13.358189582824707, - -41.77003479003906 + -4.141590595245361, + 5.347788333892822, + -34.77696990966797 ], "umap3d": [ - 6.677573204040527, - -1.2085859775543213, - 0.5994182825088501 + 3.8590617179870605, + -1.1674991846084595, + 5.6706461906433105 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 4, + "id": 26, "label": "World Models" } ], @@ -84492,47 +84675,47 @@ "journal": "Nature", "projections": { "pca": [ - -0.20187725126743317, - 0.049158912152051926 + -0.19877810776233673, + 0.05301526561379433 ], "tsne": [ - 1.5549510717391968, - -6.511401176452637 + -6.048884868621826, + -4.915857315063477 ], "umap": [ - -0.5953449010848999, - 4.6552581787109375 + -0.7995702624320984, + 5.157285213470459 ], "pca3d": [ - -0.20187784731388092, - 0.049145717173814774, - 0.008097796700894833 + -0.1987789273262024, + 0.053002018481492996, + 0.009681252762675285 ], "tsne3d": [ - -19.57870864868164, - -9.986331939697266, - 23.782329559326172 + 0.2251604050397873, + -25.756549835205078, + -5.608236789703369 ], "umap3d": [ - 3.4743406772613525, - 0.6614539623260498, - -1.0774701833724976 + 2.106915235519409, + 0.11128733307123184, + 4.930157661437988 ] }, "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_label": "Clinical AI Data", "cluster_levels": [ { "id": 2, - "label": "Bio Models Gene" + "label": "Clinical AI Data" }, { - "id": 2, - "label": "AI Drug Discovery" + "id": 9, + "label": "Drug Discovery" }, { - "id": 13, - "label": "Drug Discovery" + "id": 9, + "label": "Biomedical AI" } ], "_embedding": [ @@ -84942,47 +85125,47 @@ "journal": "Nature Biotechnology", "projections": { "pca": [ - -0.019667543470859528, - -0.1484212875366211 + -0.018579041585326195, + -0.14815953373908997 ], "tsne": [ - -9.146524429321289, - -11.711604118347168 + -12.367395401000977, + -2.35835862159729 ], "umap": [ - -1.3013402223587036, - 6.102278232574463 + -2.119565010070801, + 4.4333624839782715 ], "pca3d": [ - -0.0196674857288599, - -0.14842845499515533, - 0.059412881731987 + -0.018579026684165, + -0.14816541969776154, + 0.06245901435613632 ], "tsne3d": [ - -1.8004882335662842, - -42.17069625854492, - 14.079461097717285 + -7.030959606170654, + -24.895959854125977, + 31.363616943359375 ], "umap3d": [ - 3.2207722663879395, - -0.9376567006111145, - -1.0569710731506348 + 0.8504438996315002, + 0.4735197424888611, + 6.234492778778076 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 28, - "label": "Clinical AI" + "id": 21, + "label": "Clinical Trials" } ], "_embedding": [ @@ -85404,47 +85587,47 @@ ], "projections": { "pca": [ - -0.22476376593112946, - -0.08278227597475052 + -0.22321608662605286, + -0.07938376069068909 ], "tsne": [ - -8.474689483642578, - -7.329450607299805 + -14.97292709350586, + 1.8375307321548462 ], "umap": [ - -0.7243579030036926, - 6.294422626495361 + -2.0844907760620117, + 3.795790672302246 ], "pca3d": [ - -0.22476227581501007, - -0.082765594124794, - 0.12814302742481232 + -0.22321467101573944, + -0.07936853170394897, + 0.13307218253612518 ], "tsne3d": [ - -9.084397315979004, - -25.820850372314453, - 9.836337089538574 + -9.150533676147461, + -16.752363204956055, + 10.609075546264648 ], "umap3d": [ - 3.6359126567840576, - -0.9389175176620483, - -1.8955289125442505 + 0.737837553024292, + 1.0381157398223877, + 6.785421848297119 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 28, - "label": "Clinical AI" + "id": 14, + "label": "Clinical Health" } ], "_embedding": [ @@ -85854,47 +86037,47 @@ "journal": "Nature Methods", "projections": { "pca": [ - -0.1979319006204605, - 0.044696398079395294 + -0.19844384491443634, + 0.04647860676050186 ], "tsne": [ - 1.0995627641677856, - -38.5100212097168 + -10.626408576965332, + -27.400663375854492 ], "umap": [ - -1.430161714553833, - 2.4927775859832764 + -0.5260524153709412, + 7.7147440910339355 ], "pca3d": [ - -0.19793246686458588, - 0.04468751326203346, - -0.16762502491474152 + -0.19844458997249603, + 0.04646977037191391, + -0.16736657917499542 ], "tsne3d": [ - -43.91084671020508, - 18.552480697631836, - -10.473011016845703 + -32.474552154541016, + -32.95018768310547, + -28.668411254882812 ], "umap3d": [ - 1.4933850765228271, - 1.1448003053665161, - -0.5218660831451416 + 1.0130794048309326, + 1.0578999519348145, + 3.4474940299987793 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 21, - "label": "Protein RNA" + "id": 12, + "label": "Protein Language" } ], "_embedding": [ @@ -86304,46 +86487,46 @@ "journal": "Nature", "projections": { "pca": [ - -0.16614070534706116, - -0.010499224998056889 + -0.16267846524715424, + -0.008270525373518467 ], "tsne": [ - -15.631902694702148, - -0.6540072560310364 + -9.201290130615234, + 9.292929649353027 ], "umap": [ - 0.17850248515605927, - 7.220301628112793 + -0.9281682968139648, + 2.9235565662384033 ], "pca3d": [ - -0.16614177823066711, - -0.010508651845157146, - 0.1327466368675232 + -0.1626795083284378, + -0.008278579451143742, + 0.13910949230194092 ], "tsne3d": [ - -0.8428678512573242, - -7.079894542694092, - -6.443862438201904 + -3.9618916511535645, + 14.213835716247559, + 16.800365447998047 ], "umap3d": [ - 4.57456636428833, - -1.2602958679199219, - -0.9159783720970154 + 1.7319964170455933, + 0.0016533798770979047, + 7.082988739013672 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 11, + "id": 19, "label": "Medical Imaging" } ], @@ -86759,47 +86942,47 @@ ], "projections": { "pca": [ - -0.18077559769153595, - -0.13247369229793549 + -0.17911861836910248, + -0.13043910264968872 ], "tsne": [ - -6.8966898918151855, - -11.603142738342285 + -16.377355575561523, + -2.61063814163208 ], "umap": [ - -1.454451084136963, - 6.3136820793151855 + -2.4139909744262695, + 4.3899946212768555 ], "pca3d": [ - -0.18077409267425537, - -0.13245773315429688, - 0.02801997773349285 + -0.17911718785762787, + -0.1304250806570053, + 0.03279636800289154 ], "tsne3d": [ - -10.368096351623535, - -37.08539581298828, - 12.085235595703125 + -9.528305053710938, + -18.14203643798828, + 22.006319046020508 ], "umap3d": [ - 3.0031652450561523, - -0.9604060053825378, - -1.4911614656448364 + 0.5625819563865662, + 0.7963997721672058, + 6.276454448699951 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 28, - "label": "Clinical AI" + "id": 21, + "label": "Clinical Trials" } ], "_embedding": [ @@ -87206,47 +87389,47 @@ "title": "Covariance-based Sequence Pooling", "projections": { "pca": [ - -0.18508750200271606, - 0.12573425471782684 + -0.18448756635189056, + 0.1266692727804184 ], "tsne": [ - -3.6095566749572754, - -36.26010513305664 + -17.511634826660156, + -28.999244689941406 ], "umap": [ - -1.9640984535217285, - 2.811401844024658 + -1.2790573835372925, + 7.641988277435303 ], "pca3d": [ - -0.18508745729923248, - 0.12573762238025665, - -0.1375841349363327 + -0.1844874620437622, + 0.1266735941171646, + -0.13832800090312958 ], "tsne3d": [ - -57.386207580566406, - 2.3036835193634033, - -16.467342376708984 + -41.07600784301758, + -23.928695678710938, + -29.120189666748047 ], "umap3d": [ - 1.0097795724868774, - 0.8586391806602478, - -0.8590705394744873 + 0.2726703882217407, + 0.9449982643127441, + 3.5327675342559814 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 8, - "label": "Gene Regulation" + "id": 3, + "label": "Genomic Sequences" } ], "_embedding": [ @@ -87662,47 +87845,47 @@ ], "projections": { "pca": [ - -0.0795406699180603, - 0.13413649797439575 + -0.08011528104543686, + 0.13598118722438812 ], "tsne": [ - -15.025313377380371, - 19.98879051208496 + 4.369839668273926, + 30.669979095458984 ], "umap": [ - 3.0377514362335205, - 3.0505435466766357 + 2.1596996784210205, + 2.1149425506591797 ], "pca3d": [ - -0.07954048365354538, - 0.13414940237998962, - -0.106422558426857 + -0.08011516183614731, + 0.13599419593811035, + -0.10471193492412567 ], "tsne3d": [ - 27.133148193359375, - 3.6370930671691895, - -36.11396789550781 + 5.219634532928467, + 12.002243041992188, + -35.65554428100586 ], "umap3d": [ - 6.209620475769043, - -0.5400134325027466, - 1.3423477411270142 + 4.581676483154297, + -0.3508794903755188, + 5.856394290924072 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 6, + "label": "Learning Techniques" } ], "_embedding": [ @@ -88110,46 +88293,46 @@ "journal": "Nature Medicine", "projections": { "pca": [ - -0.02184240333735943, - -0.13465812802314758 + -0.01925530470907688, + -0.13239046931266785 ], "tsne": [ - -21.577091217041016, - -4.347360134124756 + -7.115706443786621, + 7.532695293426514 ], "umap": [ - -0.44650864601135254, - 7.562010765075684 + -1.5855904817581177, + 2.521418809890747 ], "pca3d": [ - -0.02184235118329525, - -0.13465873897075653, - 0.14576312899589539 + -0.019255267456173897, + -0.13238921761512756, + 0.14987142384052277 ], "tsne3d": [ - -17.298208236694336, - -9.282180786132812, - -5.369961261749268 + -21.789539337158203, + 9.879798889160156, + 32.23089599609375 ], "umap3d": [ - 4.109149932861328, - -1.6451046466827393, - -0.8491724729537964 + 1.5086009502410889, + -0.32161951065063477, + 6.780554294586182 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 11, + "id": 19, "label": "Medical Imaging" } ], @@ -88631,47 +88814,47 @@ "openalex_url": "https://openalex.org/W4415407738", "projections": { "pca": [ - -0.18954798579216003, - -0.09447603672742844 + -0.19274504482746124, + -0.0937955230474472 ], "tsne": [ - 2.004531145095825, - -28.802993774414062 + -12.820609092712402, + -21.801342010498047 ], "umap": [ - -2.198288917541504, - 3.6987154483795166 + -1.9079604148864746, + 7.0775580406188965 ], "pca3d": [ - -0.1895473301410675, - -0.09445875138044357, - -0.15014921128749847 + -0.19274450838565826, + -0.0937790721654892, + -0.14750446379184723 ], "tsne3d": [ - -44.31298828125, - -0.225879967212677, - 6.189206123352051 + -24.808923721313477, + -9.997159004211426, + -19.692302703857422 ], "umap3d": [ - 1.3252449035644531, - 0.25494512915611267, - -1.0657517910003662 + 0.04412074759602547, + 1.2877002954483032, + 4.159201622009277 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 22, - "label": "Genetic Variants" + "id": 3, + "label": "Genomic Sequences" } ], "_embedding": [ @@ -89079,47 +89262,47 @@ "abstract": "Official release of Genos models. Contribute to zhejianglab/Genos development by creating an account on GitHub.", "projections": { "pca": [ - 0.3345886468887329, - -0.06597321480512619 + 0.332648903131485, + -0.06671721488237381 ], "tsne": [ - 24.74825096130371, - 9.833634376525879 + 27.047273635864258, + -7.745692729949951 ], "umap": [ - 3.3096158504486084, - 6.772671222686768 + 2.8858280181884766, + 5.543422698974609 ], "pca3d": [ - 0.33458995819091797, - -0.06594720482826233, - -0.1940927654504776 + 0.33265039324760437, + -0.06669292598962784, + -0.19712162017822266 ], "tsne3d": [ - 22.730924606323242, - 42.032772064208984, - 8.923763275146484 + 38.142311096191406, + 5.051857948303223, + 12.177948951721191 ], "umap3d": [ - 3.3747851848602295, - -0.07633546739816666, - 2.525146722793579 + 3.5955498218536377, + 2.7602622509002686, + 6.578734397888184 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 18, - "label": "Github Tools" + "id": 17, + "label": "GitHub Creation" } ], "_embedding": [ @@ -89535,46 +89718,46 @@ ], "projections": { "pca": [ - 0.025707710534334183, - 0.12122061848640442 + 0.02633582055568695, + 0.12212023884057999 ], "tsne": [ - -23.30961799621582, - 17.354280471801758 + 4.090999603271484, + 17.09634017944336 ], "umap": [ - 2.316544771194458, - 2.5355355739593506 + 1.517179250717163, + 1.5825860500335693 ], "pca3d": [ - 0.02570742927491665, - 0.12122536450624466, - -0.0935981422662735 + 0.02633538469672203, + 0.12212549149990082, + -0.09128040075302124 ], "tsne3d": [ - 3.9422638416290283, - 28.502721786499023, - -31.53970718383789 + -11.563501358032227, + 13.778634071350098, + -22.693376541137695 ], "umap3d": [ - 6.46182918548584, - -1.1918888092041016, - 0.7689570188522339 + 3.7649612426757812, + -0.8325807452201843, + 5.586215019226074 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 4, + "id": 26, "label": "World Models" } ], @@ -90006,47 +90189,47 @@ "doi": "10.64898/2026.04.07.717039", "projections": { "pca": [ - -0.2097281962633133, - -0.055165186524391174 + -0.21292267739772797, + -0.05460408702492714 ], "tsne": [ - -2.1713597774505615, - -30.020925521850586 + -17.37834358215332, + -22.60857582092285 ], "umap": [ - -1.8594673871994019, - 3.410679340362549 + -1.4935274124145508, + 7.324792385101318 ], "pca3d": [ - -0.20972807705402374, - -0.05516209825873375, - -0.2124275267124176 + -0.21292245388031006, + -0.05460117757320404, + -0.20903892815113068 ], "tsne3d": [ - -52.84519958496094, - -11.872976303100586, - -0.7651070952415466 + -39.73196029663086, + -15.4830904006958, + -13.754109382629395 ], "umap3d": [ - 1.2539997100830078, - 0.6990521550178528, - -1.3146699666976929 + 0.05398770421743393, + 0.8288931250572205, + 3.7433505058288574 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 8, - "label": "Gene Regulation" + "id": 3, + "label": "Genomic Sequences" } ], "_embedding": [ @@ -90463,47 +90646,47 @@ "doi": "10.64898/2026.03.02.709002", "projections": { "pca": [ - -0.22805871069431305, - -0.1148105040192604 + -0.2299044132232666, + -0.1147952601313591 ], "tsne": [ - -11.665407180786133, - -25.87456703186035 + -24.543136596679688, + -15.623438835144043 ], "umap": [ - -0.9715514779090881, - 3.2798726558685303 + -1.0747894048690796, + 6.124997615814209 ], "pca3d": [ - -0.22805775701999664, - -0.11481126397848129, - -0.13001404702663422 + -0.22990354895591736, + -0.114796943962574, + -0.1231202483177185 ], "tsne3d": [ - -30.7579402923584, - -21.778217315673828, - -23.2314510345459 + -39.074459075927734, + -18.824264526367188, + 3.980398416519165 ], "umap3d": [ - 1.6649724245071411, - 1.032731294631958, - -1.8214952945709229 + 0.4131438732147217, + -0.20777082443237305, + 4.448866844177246 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 19, - "label": "Cell Models" + "id": 28, + "label": "Spatial Drug" } ], "_embedding": [ @@ -90919,46 +91102,46 @@ ], "projections": { "pca": [ - -0.12465451657772064, - 0.2185945361852646 + -0.12458066642284393, + 0.2189253866672516 ], "tsne": [ - -22.611309051513672, - 20.190486907958984 + -0.5745561122894287, + 29.249740600585938 ], "umap": [ - 2.2603745460510254, - 2.572185754776001 + 1.1343756914138794, + 1.5828940868377686 ], "pca3d": [ - -0.12465491890907288, - 0.21859963238239288, - -0.0912972092628479 + -0.12458109110593796, + 0.2189294844865799, + -0.08857131749391556 ], "tsne3d": [ - 4.32883882522583, - 10.862738609313965, - -37.33379364013672 + 1.7504887580871582, + 2.0201377868652344, + -28.725078582763672 ], "umap3d": [ - 6.425805568695068, - -1.031467318534851, - 0.6495768427848816 + 3.9539544582366943, + -1.030572533607483, + 5.860273361206055 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 4, + "id": 26, "label": "World Models" } ], @@ -91375,47 +91558,47 @@ ], "projections": { "pca": [ - -0.15033438801765442, - 0.21401555836200714 + -0.14916586875915527, + 0.2152881771326065 ], "tsne": [ - -19.441204071044922, - 16.637727737426758 + -3.4032158851623535, + 26.83039665222168 ], "umap": [ - 2.120110273361206, - 3.0215682983398438 + 1.1910829544067383, + 2.0411760807037354 ], "pca3d": [ - -0.1503339409828186, - 0.21402092278003693, - 0.024474801495671272 + -0.14916525781154633, + 0.2152913510799408, + 0.027400191873311996 ], "tsne3d": [ - 5.61720609664917, - -5.4033589363098145, - -25.95842170715332 + 12.564367294311523, + -4.101993560791016, + -29.248151779174805 ], "umap3d": [ - 6.236236572265625, - -0.5766169428825378, - 0.44838571548461914 + 4.180054187774658, + -0.9828510880470276, + 6.397023677825928 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 15, - "label": "Latent Models" + "id": 18, + "label": "Retrieval Learning" } ], "_embedding": [ @@ -91860,47 +92043,47 @@ "openalex_url": "https://openalex.org/W4404628441", "projections": { "pca": [ - -0.0910436138510704, - -0.273886114358902 + -0.09270463138818741, + -0.2727576196193695 ], "tsne": [ - 1.8799184560775757, - -18.34569549560547 + -11.555673599243164, + -11.001019477844238 ], "umap": [ - -1.55423104763031, - 4.972808361053467 + -2.3036186695098877, + 5.8257951736450195 ], "pca3d": [ - -0.09104258567094803, - -0.2738792598247528, - -0.06312831491231918 + -0.09270338714122772, + -0.27275222539901733, + -0.059600744396448135 ], "tsne3d": [ - -26.422555923461914, - -20.492713928222656, - 20.83171272277832 + -15.471174240112305, + -29.89505386352539, + 6.884178161621094 ], "umap3d": [ - 2.3446872234344482, - -0.3244078755378723, - -1.447573184967041 + 0.4270516633987427, + 1.109326720237732, + 5.381888389587402 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 2, - "label": "AI Drug Discovery" + "id": 1, + "label": "Cancer Data" }, { - "id": 9, - "label": "Cancer Biobank" + "id": 1, + "label": "Cancer Data" } ], "_embedding": [ @@ -92314,47 +92497,47 @@ ], "projections": { "pca": [ - -0.18345366418361664, - -0.019025184214115143 + -0.18576659262180328, + -0.018795166164636612 ], "tsne": [ - -2.3052361011505127, - -31.370878219604492 + -16.949939727783203, + -24.91657066345215 ], "umap": [ - -1.9543864727020264, - 3.0618598461151123 + -1.3091773986816406, + 7.503360271453857 ], "pca3d": [ - -0.18345417082309723, - -0.019031742587685585, - -0.22397102415561676 + -0.18576718866825104, + -0.018801525235176086, + -0.22137384116649628 ], "tsne3d": [ - -53.666446685791016, - -10.374117851257324, - -7.053890705108643 + -39.18217086791992, + -17.307754516601562, + -18.71026039123535 ], "umap3d": [ - 1.046519160270691, - 0.8707690238952637, - -1.1182060241699219 + 0.22484511137008667, + 0.9235997796058655, + 3.4707047939300537 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 8, - "label": "Gene Regulation" + "id": 3, + "label": "Genomic Sequences" } ], "_embedding": [ @@ -92770,47 +92953,47 @@ "doi": "10.1101/2024.07.31.606098", "projections": { "pca": [ - -0.11091829091310501, - -0.03840602561831474 + -0.11263524740934372, + -0.03608543798327446 ], "tsne": [ - 4.258543491363525, - -35.90444564819336 + -11.732686042785645, + -31.34920883178711 ], "umap": [ - -1.4785164594650269, - 2.5857036113739014 + -0.5805104970932007, + 7.590592384338379 ], "pca3d": [ - -0.11091861128807068, - -0.03841570019721985, - -0.18667735159397125 + -0.11263559013605118, + -0.03609476238489151, + -0.18344055116176605 ], "tsne3d": [ - -49.97655487060547, - 13.675172805786133, - -20.85062599182129 + -30.14129638671875, + -39.692447662353516, + -17.450572967529297 ], "umap3d": [ - 1.604995608329773, - 1.3166240453720093, - -0.506172776222229 + 1.0483479499816895, + 0.8387693762779236, + 3.537475347518921 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 21, - "label": "Protein RNA" + "id": 12, + "label": "Protein Language" } ], "_embedding": [ @@ -93218,47 +93401,47 @@ "journal": "Nature Machine Intelligence", "projections": { "pca": [ - -0.13634544610977173, - 0.12909956276416779 + -0.13479891419410706, + 0.1306677907705307 ], "tsne": [ - -24.2254695892334, - 21.301483154296875 + -0.11538977175951004, + -26.969934463500977 ], "umap": [ - 0.9808601140975952, - 2.652205228805542 + 1.1044847965240479, + 1.6223852634429932 ], "pca3d": [ - -0.13634523749351501, - 0.1291111558675766, - -0.0897655189037323 + -0.1347988098859787, + 0.13067959249019623, + -0.08809232711791992 ], "tsne3d": [ - -17.57379913330078, - 27.882631301879883, - -24.671388626098633 + -2.903287649154663, + -2.094740152359009, + -36.989437103271484 ], "umap3d": [ - 6.169428825378418, - -1.1204524040222168, - 0.7321867942810059 + 3.604367733001709, + -0.8355421423912048, + 5.647240161895752 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 15, - "label": "Latent Models" + "id": 26, + "label": "World Models" } ], "_embedding": [ @@ -93677,47 +93860,47 @@ "doi": "10.64898/2025.12.03.25341576", "projections": { "pca": [ - -0.2619284689426422, - -0.1369161307811737 + -0.26302191615104675, + -0.13521847128868103 ], "tsne": [ - 1.7751802206039429, - -23.415283203125 + -12.230443954467773, + -16.037260055541992 ], "umap": [ - -1.937004804611206, - 4.690309047698975 + -2.396488666534424, + 6.402191638946533 ], "pca3d": [ - -0.26192736625671387, - -0.13690119981765747, - -0.05881263315677643 + -0.26302069425582886, + -0.13520553708076477, + -0.05408276990056038 ], "tsne3d": [ - -28.95207405090332, - -15.103900909423828, - 11.052576065063477 + -19.607254028320312, + -10.05606460571289, + -4.071578025817871 ], "umap3d": [ - 1.8748939037322998, - -0.2765200138092041, - -1.6708877086639404 + 0.036494676023721695, + 1.3270978927612305, + 5.002986907958984 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 2, - "label": "Genetic Disease" + "id": 27, + "label": "Genetic Variants" } ], "_embedding": [ @@ -94135,47 +94318,47 @@ "doi": "10.1177/11779322251358314", "projections": { "pca": [ - -0.14862169325351715, - -0.1470445990562439 + -0.15075305104255676, + -0.14657846093177795 ], "tsne": [ - 3.2552382946014404, - -24.269874572753906 + -10.971295356750488, + -17.137727737426758 ], "umap": [ - -1.9895058870315552, - 4.193165302276611 + -2.0537755489349365, + 6.647874355316162 ], "pca3d": [ - -0.14862170815467834, - -0.1470387876033783, - -0.06325692683458328 + -0.1507529616355896, + -0.14657272398471832, + -0.05864085629582405 ], "tsne3d": [ - -31.62603187561035, - -3.198878526687622, - 15.259637832641602 + -17.122100830078125, + -4.986425876617432, + -13.296425819396973 ], "umap3d": [ - 1.7102876901626587, - 0.054161060601472855, - -1.357085108757019 + 0.3267041742801666, + 1.3461109399795532, + 4.627004146575928 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 2, - "label": "Genetic Disease" + "id": 27, + "label": "Genetic Variants" } ], "_embedding": [ @@ -94593,47 +94776,47 @@ "doi": "10.64898/2026.04.10.26350624", "projections": { "pca": [ - -0.23996666073799133, - -0.12016665935516357 + -0.24061372876167297, + -0.11733225733041763 ], "tsne": [ - 3.1345808506011963, - -22.5712833404541 + -10.743636131286621, + -15.398869514465332 ], "umap": [ - -2.152984619140625, - 4.520637035369873 + -2.424536943435669, + 6.4852375984191895 ], "pca3d": [ - -0.2399647831916809, - -0.12015607208013535, - -0.07628964632749557 + -0.24061191082000732, + -0.1173233911395073, + -0.07210425287485123 ], "tsne3d": [ - -31.915781021118164, - -14.732230186462402, - 18.942481994628906 + -18.822097778320312, + -3.6982598304748535, + -5.109629154205322 ], "umap3d": [ - 1.7801611423492432, - -0.24720288813114166, - -1.707776427268982 + -0.05052388459444046, + 1.3711422681808472, + 4.874770164489746 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 2, - "label": "Genetic Disease" + "id": 27, + "label": "Genetic Variants" } ], "_embedding": [ @@ -95059,47 +95242,47 @@ ], "projections": { "pca": [ - -0.2135583609342575, - -0.1971680372953415 + -0.21187150478363037, + -0.19361314177513123 ], "tsne": [ - -9.200918197631836, - -6.763378143310547 + -13.959614753723145, + 1.5480587482452393 ], "umap": [ - -0.7336832880973816, - 6.4324517250061035 + -2.183946132659912, + 3.724757671356201 ], "pca3d": [ - -0.21355675160884857, - -0.1971493810415268, - 0.1412319391965866 + -0.21187002956867218, + -0.19359566271305084, + 0.14723478257656097 ], "tsne3d": [ - -8.985069274902344, - -17.467426300048828, - 10.536133766174316 + -11.53808879852295, + -12.49646282196045, + 6.747946262359619 ], "umap3d": [ - 3.7200653553009033, - -1.0318067073822021, - -1.795929193496704 + 0.8071649074554443, + 1.1060690879821777, + 6.764151573181152 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 28, - "label": "Clinical AI" + "id": 14, + "label": "Clinical Health" } ], "_embedding": [ @@ -95505,47 +95688,47 @@ ], "projections": { "pca": [ - 0.20566557347774506, - -0.01750340312719345 + 0.20650798082351685, + -0.017666185274720192 ], "tsne": [ - 31.340856552124023, - 14.372465133666992 + 34.879676818847656, + -6.928349018096924 ], "umap": [ - 3.4823672771453857, - 6.503322601318359 + 2.06968092918396, + 5.215112686157227 ], "pca3d": [ - 0.20566557347774506, - -0.017494546249508858, - -0.036038950085639954 + 0.20650815963745117, + -0.017657434567809105, + -0.03876853361725807 ], "tsne3d": [ - 42.17325210571289, - 39.92291259765625, - 28.63363265991211 + 51.964271545410156, + 17.75316619873047, + 4.03372049331665 ], "umap3d": [ - 3.506061553955078, - 0.2222006916999817, - 2.3250620365142822 + 3.546505928039551, + 2.5868325233459473, + 6.277835369110107 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 18, - "label": "Github Tools" + "id": 24, + "label": "SMB Models" } ], "_embedding": [ @@ -95951,47 +96134,47 @@ ], "projections": { "pca": [ - -0.08324556052684784, - 0.08620483428239822 + -0.08186148852109909, + 0.08738691359758377 ], "tsne": [ - 7.124743461608887, - -28.553979873657227 + -6.744103908538818, + -20.429916381835938 ], "umap": [ - -1.926754117012024, - 4.183966636657715 + -1.821050763130188, + 6.722349643707275 ], "pca3d": [ - -0.08324481546878815, - 0.08622120320796967, - 0.0398431122303009 + -0.08186070621013641, + 0.08740241825580597, + 0.03979840874671936 ], "tsne3d": [ - -36.3033447265625, - 5.664495944976807, - 9.918024063110352 + -19.05950927734375, + -3.838141918182373, + -25.970979690551758 ], "umap3d": [ - 1.700249433517456, - 0.017542459070682526, - -0.9613603353500366 + 0.2337062656879425, + 1.078187346458435, + 4.568737030029297 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 2, - "label": "Genetic Disease" + "id": 27, + "label": "Genetic Variants" } ], "_embedding": [ @@ -96413,47 +96596,47 @@ ], "projections": { "pca": [ - -0.23243415355682373, - -0.009385108016431332 + -0.23555652797222137, + -0.008830755949020386 ], "tsne": [ - -0.7328084707260132, - -32.935951232910156 + -15.113410949707031, + -25.78054428100586 ], "umap": [ - -2.0498151779174805, - 2.9422717094421387 + -1.3017499446868896, + 7.616640090942383 ], "pca3d": [ - -0.23243436217308044, - -0.009386379271745682, - -0.2661210000514984 + -0.23555685579776764, + -0.00883227214217186, + -0.2625373899936676 ], "tsne3d": [ - -48.152767181396484, - -3.6038706302642822, - -10.715843200683594 + -34.298484802246094, + -19.248815536499023, + -22.76863670349121 ], "umap3d": [ - 1.0043785572052002, - 0.7459422945976257, - -0.9246385097503662 + 0.3182772099971771, + 0.9800757765769958, + 3.503575325012207 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 8, - "label": "Gene Regulation" + "id": 3, + "label": "Genomic Sequences" } ], "_embedding": [ @@ -96861,47 +97044,47 @@ "journal": "Nature Computational Science", "projections": { "pca": [ - -0.23381374776363373, - 0.10888026654720306 + -0.23359176516532898, + 0.11183499544858932 ], "tsne": [ - -7.119822025299072, - -32.514122009277344 + -24.737579345703125, + -23.56700897216797 ], "umap": [ - -1.3337047100067139, - 3.116121768951416 + -0.8725476264953613, + 6.963833332061768 ], "pca3d": [ - -0.23381328582763672, - 0.1088968813419342, - -0.13432781398296356 + -0.23359140753746033, + 0.11184965819120407, + -0.13273343443870544 ], "tsne3d": [ - -45.88427734375, - -10.682570457458496, - -21.36750602722168 + -32.7646369934082, + -31.304929733276367, + -1.779369592666626 ], "umap3d": [ - 1.3369815349578857, - 1.2212210893630981, - -1.2265527248382568 + 0.48478731513023376, + 0.2512468993663788, + 3.7839972972869873 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 19, - "label": "Cell Models" + "id": 20, + "label": "Cell Foundation" } ], "_embedding": [ @@ -97319,47 +97502,47 @@ "doi": "10.1101/2025.08.21.671613", "projections": { "pca": [ - -0.27585217356681824, - -0.05533859506249428 + -0.2764829993247986, + -0.054942816495895386 ], "tsne": [ - -8.537595748901367, - -29.085323333740234 + -21.410938262939453, + -18.49800682067871 ], "umap": [ - -1.2881066799163818, - 3.393242597579956 + -1.0330078601837158, + 6.654348850250244 ], "pca3d": [ - -0.27585211396217346, - -0.05535130575299263, - -0.06899949908256531 + -0.2764829099178314, + -0.054954737424850464, + -0.0626598373055458 ], "tsne3d": [ - -36.33259201049805, - -22.85727310180664, - -17.60078239440918 + -36.65376281738281, + -22.736093521118164, + -2.15021014213562 ], "umap3d": [ - 1.5239335298538208, - 1.1341633796691895, - -1.4643630981445312 + 0.42246848344802856, + 0.14806993305683136, + 4.111310958862305 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 19, - "label": "Cell Models" + "id": 20, + "label": "Cell Foundation" } ], "_embedding": [ @@ -97778,47 +97961,47 @@ "doi": "10.48550/arxiv.2602.10885", "projections": { "pca": [ - -0.1345808207988739, - 0.24226821959018707 + -0.13352511823177338, + 0.243569478392601 ], "tsne": [ - -13.230581283569336, - 23.97661590576172 + 8.776951789855957, + 30.062761306762695 ], "umap": [ - 3.326072931289673, - 2.7684881687164307 + 2.3239870071411133, + 1.393066167831421 ], "pca3d": [ - -0.13458077609539032, - 0.24227049946784973, - 0.013230115175247192 + -0.13352514803409576, + 0.2435707449913025, + 0.012790469452738762 ], "tsne3d": [ - 12.376136779785156, - 4.5639166831970215, - -43.97884750366211 + 10.186844825744629, + 21.447999954223633, + -27.444677352905273 ], "umap3d": [ - 6.970623970031738, - -0.3043925166130066, - 1.0984770059585571 + 4.793323993682861, + -0.6275259852409363, + 5.41463565826416 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 6, + "label": "Learning Techniques" } ], "_embedding": [ @@ -98232,46 +98415,46 @@ ], "projections": { "pca": [ - -0.19311293959617615, - 0.011666465550661087 + -0.1909475475549698, + 0.014235789887607098 ], "tsne": [ - -8.26339340209961, - -1.353784441947937 + -12.651019096374512, + 6.396463394165039 ], "umap": [ - 0.09511791914701462, - 6.369390487670898 + -1.2958731651306152, + 3.7760119438171387 ], "pca3d": [ - -0.1931123286485672, - 0.011662153527140617, - 0.10836590081453323 + -0.1909470111131668, + 0.014230509288609028, + 0.1135750561952591 ], "tsne3d": [ - 3.5170910358428955, - -10.726569175720215, - 15.409845352172852 + 0.8916040062904358, + -8.505962371826172, + 3.6856138706207275 ], "umap3d": [ - 4.574516296386719, - -0.5227013826370239, - -1.6202976703643799 + 1.485899806022644, + 0.7728980183601379, + 7.305596351623535 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, + "id": 14, "label": "Clinical Health" } ], @@ -98680,47 +98863,47 @@ "journal": "Nature", "projections": { "pca": [ - -0.1790696084499359, - 0.049614500254392624 + -0.17497971653938293, + 0.052987731993198395 ], "tsne": [ - -11.125198364257812, - 2.6416938304901123 + -19.30490493774414, + 5.000400066375732 ], "umap": [ - 0.2137039452791214, - 5.8945207595825195 + -0.7288051247596741, + 3.372109889984131 ], "pca3d": [ - -0.17906887829303741, - 0.04962124302983284, - 0.11771360784769058 + -0.17497916519641876, + 0.0529930517077446, + 0.12028233706951141 ], "tsne3d": [ - -1.5360057353973389, - -18.992177963256836, - -12.461812973022461 + 7.2941203117370605, + -8.160175323486328, + -4.575744152069092 ], "umap3d": [ - 4.683264255523682, - -0.8942456245422363, - -1.3712561130523682 + 1.1500920057296753, + 1.2299214601516724, + 6.841522693634033 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 0, - "label": "Clinical LLMs" + "id": 5, + "label": "Health Benchmarks" } ], "_embedding": [ @@ -99130,47 +99313,47 @@ "journal": "Nature Methods", "projections": { "pca": [ - -0.14598768949508667, - 0.009612206369638443 + -0.14924000203609467, + 0.009683240205049515 ], "tsne": [ - -1.0996545553207397, - -35.02830123901367 + -15.044007301330566, + -27.933368682861328 ], "umap": [ - -1.5681216716766357, - 2.8784074783325195 + -0.9621172547340393, + 7.443020820617676 ], "pca3d": [ - -0.1459878832101822, - 0.009609056636691093, - -0.23279155790805817 + -0.14924024045467377, + 0.009679185226559639, + -0.23008759319782257 ], "tsne3d": [ - -38.66965866088867, - -2.8269031047821045, - -19.32203483581543 + -25.038616180419922, + -26.423986434936523, + -20.868803024291992 ], "umap3d": [ - 1.262917160987854, - 1.1576461791992188, - -0.9452524185180664 + 0.5271711945533752, + 0.8527334928512573, + 3.634726047515869 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 21, - "label": "Protein RNA" + "id": 12, + "label": "Protein Language" } ], "_embedding": [ @@ -99596,47 +99779,47 @@ "openalex_url": "https://openalex.org/W4417312688", "projections": { "pca": [ - -0.25098463892936707, - -0.1292579025030136 + -0.24994178116321564, + -0.12607035040855408 ], "tsne": [ - -8.689788818359375, - -7.501490116119385 + -14.888169288635254, + 1.472000002861023 ], "umap": [ - -1.0762903690338135, - 6.046300888061523 + -2.5445985794067383, + 4.057170391082764 ], "pca3d": [ - -0.25098252296447754, - -0.1292298287153244, - 0.11315378546714783 + -0.24993978440761566, + -0.12604455649852753, + 0.11803307384252548 ], "tsne3d": [ - -12.393401145935059, - -22.40448570251465, - 9.9281644821167 + -12.080172538757324, + -17.049293518066406, + 7.989648342132568 ], "umap3d": [ - 3.169806718826294, - -0.834793746471405, - -2.0305228233337402 + 0.45834311842918396, + 1.1495832204818726, + 6.465193271636963 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 1, - "label": "Cancer Data" + "id": 5, + "label": "Clinical Data" }, { - "id": 28, - "label": "Clinical AI" + "id": 21, + "label": "Clinical Trials" } ], "_embedding": [ @@ -100044,47 +100227,47 @@ "journal": "Nature", "projections": { "pca": [ - -0.17473390698432922, - 0.2055605798959732 + -0.17384526133537292, + 0.20572425425052643 ], "tsne": [ - -20.256561279296875, - 25.665664672851562 + 6.32216215133667, + 24.34746742248535 ], "umap": [ - 2.564119338989258, - 3.1153082847595215 + 1.4988027811050415, + 1.9921422004699707 ], "pca3d": [ - -0.17473427951335907, - 0.20556700229644775, - -0.004494315013289452 + -0.17384572327136993, + 0.20572808384895325, + -0.004480342380702496 ], "tsne3d": [ - 13.151180267333984, - -14.718823432922363, - -26.10849380493164 + 23.561838150024414, + 0.23146125674247742, + -38.35573196411133 ], "umap3d": [ - 6.434278964996338, - -0.28369495272636414, - 0.6122819185256958 + 4.685178279876709, + -1.0351148843765259, + 6.190805912017822 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 18, + "label": "Retrieval Learning" } ], "_embedding": [ @@ -100519,46 +100702,46 @@ "openalex_url": "https://openalex.org/W7151599444", "projections": { "pca": [ - -0.2311859130859375, - -0.15133893489837646 + -0.23308065533638, + -0.1499284952878952 ], "tsne": [ - 3.0505340099334717, - -25.819746017456055 + -11.404017448425293, + -18.396568298339844 ], "umap": [ - -2.3389487266540527, - 4.225818157196045 + -2.336486577987671, + 6.827523231506348 ], "pca3d": [ - -0.23118479549884796, - -0.1513255089521408, - -0.10482843965291977 + -0.23307955265045166, + -0.14991460740566254, + -0.10161590576171875 ], "tsne3d": [ - -36.60091018676758, - -15.580025672912598, - 11.56315803527832 + -23.58263397216797, + -2.548567295074463, + -12.25303840637207 ], "umap3d": [ - 1.3908839225769043, - -0.15309284627437592, - -1.4504369497299194 + -0.21766012907028198, + 1.234718680381775, + 4.677939414978027 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 22, + "id": 27, "label": "Genetic Variants" } ], @@ -100970,46 +101153,46 @@ "journal": "Nature Biomedical Engineering", "projections": { "pca": [ - -0.2351660579442978, - 0.06146957725286484 + -0.23267361521720886, + 0.0645780861377716 ], "tsne": [ - 4.859961032867432, - -24.81031608581543 + -9.219111442565918, + -17.581693649291992 ], "umap": [ - -2.4076883792877197, - 4.180600643157959 + -2.3545172214508057, + 6.844598770141602 ], "pca3d": [ - -0.23516592383384705, - 0.061473965644836426, - -0.035802703350782394 + -0.23267340660095215, + 0.06458400189876556, + -0.03469708561897278 ], "tsne3d": [ - -43.653724670410156, - -18.32361602783203, - 20.13853645324707 + -27.117469787597656, + -1.4204739332199097, + -4.955957889556885 ], "umap3d": [ - 1.4039138555526733, - -0.18246887624263763, - -1.5229027271270752 + -0.2031877189874649, + 1.3250328302383423, + 4.589048862457275 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 22, + "id": 27, "label": "Genetic Variants" } ], @@ -101426,46 +101609,46 @@ ], "projections": { "pca": [ - -0.19990302622318268, - -0.014594543725252151 + -0.19710135459899902, + -0.011021778918802738 ], "tsne": [ - -21.41119384765625, - -0.4242660105228424 + -5.568748474121094, + 12.663342475891113 ], "umap": [ - -0.20199480652809143, - 7.82159948348999 + -1.1578755378723145, + 2.2540109157562256 ], "pca3d": [ - -0.19990259408950806, - -0.01459246315062046, - 0.0873299241065979 + -0.19710099697113037, + -0.011020065285265446, + 0.09348684549331665 ], "tsne3d": [ - -16.976713180541992, - 6.559067726135254, - -7.501992702484131 + -7.540132522583008, + 14.087687492370605, + 25.153812408447266 ], "umap3d": [ - 4.459250450134277, - -1.6244165897369385, - -0.3906034827232361 + 1.914192795753479, + -0.4949708878993988, + 6.563117980957031 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 11, + "id": 19, "label": "Medical Imaging" } ], @@ -101909,47 +102092,47 @@ ], "projections": { "pca": [ - -0.1354764997959137, - 0.24228008091449738 + -0.13579384982585907, + 0.24216260015964508 ], "tsne": [ - -12.881758689880371, - 22.58240509033203 + 8.622673034667969, + 28.606271743774414 ], "umap": [ - 3.2024929523468018, - 2.6878206729888916 + 2.172490119934082, + 1.4622385501861572 ], "pca3d": [ - -0.1354772448539734, - 0.24228055775165558, - -0.008851162157952785 + -0.13579462468624115, + 0.24216219782829285, + -0.007324533071368933 ], "tsne3d": [ - 14.68708324432373, - 10.497014999389648, - -41.645381927490234 + 8.42299747467041, + 12.797112464904785, + -24.414949417114258 ], "umap3d": [ - 6.858745098114014, - -0.34339627623558044, - 0.8621479272842407 + 4.584827423095703, + -0.6818628907203674, + 5.543496608734131 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 6, + "label": "Learning Techniques" } ], "_embedding": [ @@ -102380,47 +102563,47 @@ ], "projections": { "pca": [ - -1.2738275472656824e-05, - 0.2940962612628937 + 0.0022963699884712696, + 0.29582974314689636 ], "tsne": [ - -9.29566764831543, - 21.69832420349121 + 12.518573760986328, + 28.44647789001465 ], "umap": [ - 2.8788626194000244, - 2.534101724624634 + 1.907141089439392, + 1.495270013809204 ], "pca3d": [ - -1.4048138837097213e-05, - 0.29408279061317444, - -0.02569795399904251 + 0.0022948619443923235, + 0.2958190441131592, + -0.025994956493377686 ], "tsne3d": [ - 24.38918113708496, - 11.876834869384766, - -50.960060119628906 + 0.5169156193733215, + 23.098876953125, + -22.92815399169922 ], "umap3d": [ - 6.804623126983643, - -0.4796397387981415, - 0.642448902130127 + 4.225711345672607, + -0.7109596133232117, + 5.541594505310059 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 6, + "label": "Learning Techniques" } ], "_embedding": [ @@ -102830,47 +103013,47 @@ "journal": "Nature Methods", "projections": { "pca": [ - -0.045074522495269775, - 0.09031542390584946 + -0.046014491468667984, + 0.0903981551527977 ], "tsne": [ - 8.744207382202148, - -4.189235687255859 + 5.349374771118164, + -0.11257603764533997 ], "umap": [ - 1.410626769065857, - 4.8336076736450195 + 0.6800939440727234, + 5.074561595916748 ], "pca3d": [ - -0.04507501795887947, - 0.09030601382255554, - -0.013140790164470673 + -0.04601508378982544, + 0.09038665890693665, + -0.01331708300858736 ], "tsne3d": [ - -8.001103401184082, - 13.19374942779541, - 5.258602142333984 + 22.63071060180664, + -21.29924774169922, + 11.197193145751953 ], "umap3d": [ - 3.1448607444763184, - 0.6021947264671326, - 0.5042266845703125 + 2.328967332839966, + 1.3772152662277222, + 5.163331031799316 ] }, "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_label": "Open Source Tools", "cluster_levels": [ { "id": 3, - "label": "AI Training Models" + "label": "Open Source Tools" }, { - "id": 4, - "label": "Com Area Amp" + "id": 6, + "label": "Data Needs" }, { - "id": 23, - "label": "Research Authors" + "id": 10, + "label": "Research Data" } ], "_embedding": [ @@ -103290,47 +103473,47 @@ "doi": "10.64898/2026.02.19.706898", "projections": { "pca": [ - -0.1567985713481903, - 0.03395905718207359 + -0.15897752344608307, + 0.03475140035152435 ], "tsne": [ - 2.3452987670898438, - -35.2012939453125 + -12.878413200378418, + -29.82370376586914 ], "umap": [ - -1.4300897121429443, - 2.466510772705078 + -0.5272762775421143, + 7.6844587326049805 ], "pca3d": [ - -0.1567985713481903, - 0.033956948667764664, - -0.20209266245365143 + -0.15897762775421143, + 0.03474913537502289, + -0.19919434189796448 ], "tsne3d": [ - -51.17557144165039, - 12.363304138183594, - -12.739455223083496 + -40.39762878417969, + -32.198760986328125, + -19.02802085876465 ], "umap3d": [ - 1.3588781356811523, - 1.3257710933685303, - -0.4525197744369507 + 1.0726546049118042, + 1.005325198173523, + 3.3333728313446045 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 21, - "label": "Protein RNA" + "id": 12, + "label": "Protein Language" } ], "_embedding": [ @@ -103765,46 +103948,46 @@ "doi": "10.64898/2026.04.10.717844", "projections": { "pca": [ - -0.26644468307495117, - -0.14227861166000366 + -0.26813051104545593, + -0.14005038142204285 ], "tsne": [ - 2.51416277885437, - -25.402645111083984 + -11.931535720825195, + -17.942581176757812 ], "umap": [ - -2.3981282711029053, - 4.247751712799072 + -2.252206325531006, + 6.811133861541748 ], "pca3d": [ - -0.26644378900527954, - -0.14227357506752014, - -0.14238393306732178 + -0.26812979578971863, + -0.14004558324813843, + -0.13741327822208405 ], "tsne3d": [ - -35.51919937133789, - -10.041473388671875, - 10.087418556213379 + -23.540771484375, + -6.1383185386657715, + -8.719352722167969 ], "umap3d": [ - 1.4583402872085571, - -0.14189037680625916, - -1.4692132472991943 + -0.12937302887439728, + 1.1643149852752686, + 4.749032497406006 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 22, + "id": 27, "label": "Genetic Variants" } ], @@ -104225,47 +104408,47 @@ ], "projections": { "pca": [ - -0.2431277483701706, - 0.004348143003880978 + -0.24548807740211487, + 0.004656524863094091 ], "tsne": [ - 0.08669772744178772, - -32.81545639038086 + -14.280828475952148, + -25.54688835144043 ], "umap": [ - -1.9612994194030762, - 3.063603639602661 + -1.1673600673675537, + 7.555013656616211 ], "pca3d": [ - -0.24312841892242432, - 0.0043348451144993305, - -0.22428405284881592 + -0.24548886716365814, + 0.004643792752176523, + -0.21951726078987122 ], "tsne3d": [ - -45.509056091308594, - 0.558746337890625, - -7.459805011749268 + -30.326492309570312, + -21.575220108032227, + -24.57709312438965 ], "umap3d": [ - 1.0490758419036865, - 0.8047791123390198, - -0.834810733795166 + 0.3726014494895935, + 1.0149832963943481, + 3.5398805141448975 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 8, - "label": "Gene Regulation" + "id": 12, + "label": "Protein Language" } ], "_embedding": [ @@ -104674,47 +104857,47 @@ "title": "Gemma 4 \u00e2\u0080\u0094 Google DeepMind", "projections": { "pca": [ - 0.051701001822948456, - 0.16968587040901184 + 0.05532708019018173, + 0.17145945131778717 ], "tsne": [ - -4.353661060333252, - 27.795671463012695 + -15.099677085876465, + 21.901472091674805 ], "umap": [ - 1.607565999031067, - 4.073812007904053 + 0.6606870293617249, + 3.1374454498291016 ], "pca3d": [ - 0.05170033499598503, - 0.1696784347295761, - 0.08100811392068863 + 0.055326372385025024, + 0.17145337164402008, + 0.07682938873767853 ], "tsne3d": [ - 42.57628631591797, - 7.3308305740356445, - -35.913265228271484 + -17.27145004272461, + 26.15410804748535, + 2.673647880554199 ], "umap3d": [ - 4.971285820007324, - -0.3737254738807678, - 0.05230529233813286 + 3.180976152420044, + -0.06666740775108337, + 6.119254112243652 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 4, - "label": "Com Area Amp" + "id": 2, + "label": "Medical Models" }, { - "id": 27, - "label": "Embeddings" + "id": 0, + "label": "Multimodal Models" } ], "_embedding": [ @@ -105137,46 +105320,46 @@ ], "projections": { "pca": [ - -0.2250296175479889, - -0.05783729627728462 + -0.22338812053203583, + -0.055184006690979004 ], "tsne": [ - -10.092870712280273, - -4.2057600021362305 + -15.06805419921875, + 5.186773777008057 ], "umap": [ - -0.21034862101078033, - 6.80773401260376 + -1.924206256866455, + 3.459731340408325 ], "pca3d": [ - -0.22502920031547546, - -0.05783224478363991, - 0.07816680520772934 + -0.22338783740997314, + -0.055179569870233536, + 0.08421206474304199 ], "tsne3d": [ - 0.7201506495475769, - -19.842330932617188, - 7.536798477172852 + -2.8830459117889404, + -8.752522468566895, + 12.846258163452148 ], "umap3d": [ - 4.376336574554443, - -1.1192400455474854, - -1.9032093286514282 + 0.9974058270454407, + 0.8708861470222473, + 7.121759414672852 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, + "id": 14, "label": "Clinical Health" } ], @@ -105583,47 +105766,47 @@ ], "projections": { "pca": [ - 0.20637989044189453, - -0.003289887448772788 + 0.20577217638492584, + -0.005169782321900129 ], "tsne": [ - 15.246766090393066, - -14.972884178161621 + 2.268059253692627, + -15.390159606933594 ], "umap": [ - 0.5161105394363403, - 4.941939353942871 + -0.0007422285852953792, + 4.831144332885742 ], "pca3d": [ - 0.2063804566860199, - -0.0032771893311291933, - 0.05037238821387291 + 0.20577280223369598, + -0.005159663036465645, + 0.046293582767248154 ], "tsne3d": [ - -32.33329391479492, - 27.4899845123291, - 16.498565673828125 + 15.032785415649414, + -23.740535736083984, + -3.456153154373169 ], "umap3d": [ - 3.028740882873535, - 1.2783633470535278, - -0.012246659956872463 + 1.6376547813415527, + 1.9596314430236816, + 4.988305568695068 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 4, - "label": "Com Area Amp" + "id": 9, + "label": "Drug Discovery" }, { - "id": 17, - "label": "Literature AI" + "id": 23, + "label": "Peer Review" } ], "_embedding": [ @@ -106031,47 +106214,47 @@ "journal": "Nature Reviews Cancer", "projections": { "pca": [ - -0.025438254699110985, - -0.08914216607809067 + -0.02048044092953205, + -0.08611235022544861 ], "tsne": [ - -15.81961441040039, - -12.793778419494629 + -24.777267456054688, + 10.436205863952637 ], "umap": [ - -0.968217670917511, - 5.551196098327637 + -1.3419675827026367, + 4.505974769592285 ], "pca3d": [ - -0.02543928101658821, - -0.08916135877370834, - 0.26946768164634705 + -0.020481592044234276, + -0.08612759411334991, + 0.27122312784194946 ], "tsne3d": [ - 4.1050262451171875, - -35.90808868408203, - -14.950193405151367 + -27.541006088256836, + 4.550325393676758, + 6.402474403381348 ], "umap3d": [ - 3.4832615852355957, - -0.10167843103408813, - -1.3954002857208252 + 0.3753954768180847, + -0.12815231084823608, + 6.685217380523682 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 2, - "label": "AI Drug Discovery" + "id": 9, + "label": "Drug Discovery" }, { - "id": 28, - "label": "Clinical AI" + "id": 9, + "label": "Biomedical AI" } ], "_embedding": [ @@ -106491,47 +106674,47 @@ "doi": "10.1101/2025.06.11.659222", "projections": { "pca": [ - -0.24010325968265533, - -0.05783601850271225 + -0.24314193427562714, + -0.05742594599723816 ], "tsne": [ - -6.232590198516846, - -29.1112117767334 + -19.812768936157227, + -20.118427276611328 ], "umap": [ - -1.3342185020446777, - 3.4031295776367188 + -0.9399881958961487, + 6.953101634979248 ], "pca3d": [ - -0.24010342359542847, - -0.057840745896101, - -0.1620633453130722 + -0.24314211308956146, + -0.057430438697338104, + -0.15779298543930054 ], "tsne3d": [ - -42.54584503173828, - -18.873306274414062, - -13.474194526672363 + -28.914899826049805, + -21.63434410095215, + -3.735365390777588 ], "umap3d": [ - 1.5708118677139282, - 1.0528292655944824, - -1.2998582124710083 + 0.46126481890678406, + 0.3029667139053345, + 3.982921838760376 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 19, - "label": "Cell Models" + "id": 20, + "label": "Cell Foundation" } ], "_embedding": [ @@ -106939,47 +107122,47 @@ "abstract": "In Part 1, I described how duplicating a block of seven middle layers in Qwen2-72B \u2014 no weight changes, no training \u2014 produced the #1 model on the HuggingFace Open LLM Leaderboard. The method, which I called RYS (Repeat Your Self), was discovered using nothing but hard math probes and EQ-Bench on a pair of RTX 4090s.", "projections": { "pca": [ - 0.051380008459091187, - 0.11300497502088547 + 0.051289189606904984, + 0.1116785779595375 ], "tsne": [ - -12.656401634216309, - 19.568933486938477 + 10.235376358032227, + 26.31157112121582 ], "umap": [ - 3.125009536743164, - 3.0028364658355713 + 2.323060989379883, + 1.8465710878372192 ], "pca3d": [ - 0.05137896537780762, - 0.11299517750740051, - 0.007432471960783005 + 0.051288120448589325, + 0.11166912317276001, + 0.0074713877402246 ], "tsne3d": [ - 30.3868408203125, - -14.654047966003418, - -35.02121353149414 + 18.889949798583984, + 17.857744216918945, + -41.82469940185547 ], "umap3d": [ - 6.587240695953369, - -0.24374395608901978, - 1.1782134771347046 + 4.787576198577881, + -0.417074054479599, + 5.646259307861328 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 6, + "label": "Learning Techniques" } ], "_embedding": [ @@ -107387,47 +107570,47 @@ "abstract": "How we're building indexes for regular expression search so agents can find text in large monorepos without the 15-second ripgrep waits.", "projections": { "pca": [ - 0.07844217121601105, - 0.09055886417627335 + 0.0790514126420021, + 0.09191308170557022 ], "tsne": [ - -8.39982795715332, - 10.82279109954834 + 13.852455139160156, + 23.21186065673828 ], "umap": [ - 2.769350528717041, - 4.094908714294434 + 1.8824260234832764, + 3.110342025756836 ], "pca3d": [ - 0.07844100892543793, - 0.0905366986989975, - -0.04100533202290535 + 0.07905025035142899, + 0.0918925553560257, + -0.04189315438270569 ], "tsne3d": [ - 19.544504165649414, - 8.077457427978516, - -11.626094818115234 + 25.067703247070312, + -14.706500053405762, + -28.053199768066406 ], "umap3d": [ - 5.287088871002197, - 0.09172213077545166, - 1.4452519416809082 + 4.214430332183838, + 0.3433464765548706, + 6.08573055267334 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 5, - "label": "GPU Inference" + "id": 15, + "label": "PyTorch Models" } ], "_embedding": [ @@ -107835,47 +108018,47 @@ "journal": "Nature", "projections": { "pca": [ - -0.2733292281627655, - -0.03243416175246239 + -0.27439209818840027, + -0.031678296625614166 ], "tsne": [ - -5.4277119636535645, - -31.93879508972168 + -18.465471267700195, + -23.661096572875977 ], "umap": [ - -1.7777125835418701, - 3.1602189540863037 + -1.4440515041351318, + 7.332503795623779 ], "pca3d": [ - -0.2733288109302521, - -0.03242673724889755, - -0.17418165504932404 + -0.27439191937446594, + -0.03167135268449783, + -0.17284749448299408 ], "tsne3d": [ - -51.249549865722656, - -14.826234817504883, - -14.54335880279541 + -38.843101501464844, + -21.4840030670166, + -11.118681907653809 ], "umap3d": [ - 1.081449031829834, - 0.9418720602989197, - -1.2705315351486206 + 0.12046562880277634, + 0.7832649350166321, + 3.725262403488159 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 8, - "label": "Gene Regulation" + "id": 3, + "label": "Genomic Sequences" } ], "_embedding": [ @@ -108285,47 +108468,47 @@ "abstract": "We\u2019re on a journey to advance and democratize artificial intelligence through open source and open science.", "projections": { "pca": [ - 0.10989387333393097, - 0.11985450237989426 + 0.11270596832036972, + 0.12170035392045975 ], "tsne": [ - 8.354103088378906, - 17.9322509765625 + 19.592159271240234, + 13.447515487670898 ], "umap": [ - 4.84592866897583, - 4.635445594787598 + 3.9850189685821533, + 3.293792724609375 ], "pca3d": [ - 0.10989371687173843, - 0.11986076086759567, - 0.15549753606319427 + 0.112705759704113, + 0.12170454114675522, + 0.15377451479434967 ], "tsne3d": [ - 36.84156799316406, - -9.761876106262207, - 16.35086441040039 + -3.3872480392456055, + 30.58485984802246, + 9.336167335510254 ], "umap3d": [ - 5.062882423400879, - 1.640885353088379, - 2.017443895339966 + 4.611769676208496, + 2.2645301818847656, + 4.667184829711914 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { "id": 7, - "label": "Open AI" + "label": "Hugging Face Models" } ], "_embedding": [ @@ -108733,47 +108916,47 @@ "journal": "Nature Medicine", "projections": { "pca": [ - -0.15385450422763824, - -0.23483456671237946 + -0.15339651703834534, + -0.23301057517528534 ], "tsne": [ - -3.7990477085113525, - -19.270706176757812 + -16.435144424438477, + -11.059798240661621 ], "umap": [ - -2.108630895614624, - 5.489655017852783 + -2.2055325508117676, + 5.470545291900635 ], "pca3d": [ - -0.15385526418685913, - -0.23484447598457336, - -0.04130254313349724 + -0.15339744091033936, + -0.23301684856414795, + -0.03940827026963234 ], "tsne3d": [ - -20.67050552368164, - -17.44152069091797, - 2.6197688579559326 + -17.068212509155273, + -40.69330978393555, + 13.97399616241455 ], "umap3d": [ - 2.4565134048461914, - -0.6945697069168091, - -0.7291347980499268 + 0.2353915572166443, + 0.494157612323761, + 5.550785064697266 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 1, "label": "Cancer Data" }, { - "id": 9, - "label": "Cancer Biobank" + "id": 1, + "label": "Cancer Data" } ], "_embedding": [ @@ -109188,47 +109371,47 @@ ], "projections": { "pca": [ - -0.10486476868391037, - 0.19794012606143951 + -0.10319516807794571, + 0.1990824192762375 ], "tsne": [ - -18.061159133911133, - 9.959651947021484 + -3.502561092376709, + 19.563386917114258 ], "umap": [ - 1.5861399173736572, - 2.440855026245117 + 0.5873216986656189, + 1.6069639921188354 ], "pca3d": [ - -0.10486484318971634, - 0.19794593751430511, - -0.021682580932974815 + -0.10319523513317108, + 0.19908805191516876, + -0.019349118694663048 ], "tsne3d": [ - -5.67026948928833, - -12.115495681762695, - -43.680747985839844 + 2.6843981742858887, + 2.226454257965088, + -8.662781715393066 ], "umap3d": [ - 6.27635383605957, - -1.0426644086837769, - -0.16217465698719025 + 3.690502405166626, + -1.3247957229614258, + 6.3742218017578125 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 15, - "label": "Latent Models" + "id": 8, + "label": "JEPA Video Models" } ], "_embedding": [ @@ -109649,47 +109832,47 @@ "doi": "10.64898/2026.02.04.703804", "projections": { "pca": [ - -0.1976865977048874, - 0.05211929976940155 + -0.19873428344726562, + 0.05314159765839577 ], "tsne": [ - -7.555525779724121, - -23.77532196044922 + -20.025192260742188, + -16.392915725708008 ], "umap": [ - -0.8002840280532837, - 3.732372760772705 + -0.5075370073318481, + 6.644855499267578 ], "pca3d": [ - -0.19768667221069336, - 0.0521213561296463, - -0.07367679476737976 + -0.19873428344726562, + 0.05314216390252113, + -0.06850104033946991 ], "tsne3d": [ - -38.69608688354492, - -7.152332782745361, - -31.241193771362305 + -17.469148635864258, + -36.35276794433594, + -8.810163497924805 ], "umap3d": [ - 2.065401315689087, - 1.1669747829437256, - -1.080806016921997 + 1.008078932762146, + 0.2948720455169678, + 3.983114004135132 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 19, - "label": "Cell Models" + "id": 20, + "label": "Cell Foundation" } ], "_embedding": [ @@ -110097,47 +110280,47 @@ "journal": "Nature Biotechnology", "projections": { "pca": [ - -0.11866451054811478, - 0.03436042368412018 + -0.11636670678853989, + 0.037224896252155304 ], "tsne": [ - -3.9230222702026367, - -33.30978012084961 + -17.72252082824707, + -26.17991065979004 ], "umap": [ - -1.9603782892227173, - 2.9763684272766113 + -1.3702820539474487, + 7.5383381843566895 ], "pca3d": [ - -0.11866496503353119, - 0.03436350077390671, - -0.10206999629735947 + -0.11636736989021301, + 0.037229012697935104, + -0.10218716412782669 ], "tsne3d": [ - -57.87771987915039, - -9.270157814025879, - -12.29799747467041 + -42.895164489746094, + -16.432315826416016, + -22.964445114135742 ], "umap3d": [ - 0.8996312618255615, - 0.9338427186012268, - -1.0453754663467407 + 0.12878932058811188, + 1.0094679594039917, + 3.5120794773101807 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 8, - "label": "Gene Regulation" + "id": 3, + "label": "Genomic Sequences" } ], "_embedding": [ @@ -110545,47 +110728,47 @@ "journal": "Nature", "projections": { "pca": [ - -0.11542780697345734, - -0.0146992402151227 + -0.10769293457269669, + -0.008104673586785793 ], "tsne": [ - -15.732580184936523, - -9.166779518127441 + -21.77324104309082, + 13.021856307983398 ], "umap": [ - -0.4991261959075928, - 5.895768642425537 + -1.3290965557098389, + 3.5907697677612305 ], "pca3d": [ - -0.11542757600545883, - -0.014696337282657623, - 0.3826345205307007 + -0.10769280791282654, + -0.008099214173853397, + 0.38319146633148193 ], "tsne3d": [ - 10.196660041809082, - -37.79143142700195, - -6.288763999938965 + -17.852170944213867, + 9.345365524291992, + 2.052584648132324 ], "umap3d": [ - 4.311649322509766, - -0.1632709503173828, - -2.0032453536987305 + 0.608834445476532, + 0.13776350021362305, + 7.448342323303223 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 2, - "label": "AI Drug Discovery" + "id": 5, + "label": "Clinical Data" }, { - "id": 28, - "label": "Clinical AI" + "id": 14, + "label": "Clinical Health" } ], "_embedding": [ @@ -110997,47 +111180,47 @@ ], "projections": { "pca": [ - -0.14057405292987823, - 0.20430505275726318 + -0.14004141092300415, + 0.20508286356925964 ], "tsne": [ - -12.09290885925293, - 11.651586532592773 + -5.371384143829346, + 31.911808013916016 ], "umap": [ - 1.9990065097808838, - 3.2053606510162354 + 1.0215519666671753, + 2.3410844802856445 ], "pca3d": [ - -0.14057399332523346, - 0.2042979747056961, - -0.03760774806141853 + -0.1400412619113922, + 0.20507441461086273, + -0.033723022788763046 ], "tsne3d": [ - 9.598931312561035, - -0.9782096147537231, - -18.018709182739258 + 12.661853790283203, + -14.481873512268066, + -31.039024353027344 ], "umap3d": [ - 5.880784511566162, - -0.41843292117118835, - 0.3701382279396057 + 3.975292205810547, + -0.7768880724906921, + 6.491986274719238 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 27, - "label": "Embeddings" + "id": 18, + "label": "Retrieval Learning" } ], "_embedding": [ @@ -111454,47 +111637,47 @@ ], "projections": { "pca": [ - -0.10117638111114502, - 0.2925708293914795 + -0.09941981732845306, + 0.2940843999385834 ], "tsne": [ - -17.57286262512207, - 14.901397705078125 + 0.059111256152391434, + 22.682823181152344 ], "umap": [ - 1.7588131427764893, - 2.2613961696624756 + 0.7916743755340576, + 1.1465702056884766 ], "pca3d": [ - -0.10117592662572861, - 0.29258859157562256, - 0.002074559684842825 + -0.09941926598548889, + 0.29410088062286377, + 0.004591172095388174 ], "tsne3d": [ - 0.4343063235282898, - -4.672933578491211, - -40.85133743286133 + 11.543783187866211, + 5.173219680786133, + -19.264938354492188 ], "umap3d": [ - 6.641512870788574, - -0.9229316711425781, - 0.035285212099552155 + 3.995861530303955, + -1.4694122076034546, + 6.243754863739014 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 4, - "label": "World Models" + "id": 8, + "label": "JEPA Video Models" } ], "_embedding": [ @@ -111906,47 +112089,47 @@ ], "projections": { "pca": [ - -0.2348071187734604, - -0.06975116580724716 + -0.23679287731647491, + -0.06832516938447952 ], "tsne": [ - -6.207956314086914, - -25.575136184692383 + -22.34469985961914, + -23.114986419677734 ], "umap": [ - -0.884682834148407, - 3.5326619148254395 + -0.5528522729873657, + 6.647596836090088 ], "pca3d": [ - -0.2348061203956604, - -0.06974465399980545, - -0.17756980657577515 + -0.23679198324680328, + -0.06832002848386765, + -0.17293818295001984 ], "tsne3d": [ - -47.15690231323242, - -33.938297271728516, - -9.336333274841309 + -19.973115921020508, + -32.63313293457031, + -3.933469533920288 ], "umap3d": [ - 1.9107197523117065, - 1.3496501445770264, - -1.337252140045166 + 0.8267114162445068, + 0.12450086325407028, + 3.7976276874542236 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 19, - "label": "Cell Models" + "id": 20, + "label": "Cell Foundation" } ], "_embedding": [ @@ -112365,47 +112548,47 @@ "doi": "10.48550/arxiv.2602.19685", "projections": { "pca": [ - -0.15573588013648987, - 0.011419788002967834 + -0.15768752992153168, + 0.011779706925153732 ], "tsne": [ - -7.024749755859375, - -26.1709041595459 + -22.984115600585938, + -22.430099487304688 ], "umap": [ - -0.9685477018356323, - 3.5886781215667725 + -0.5405844449996948, + 6.6665191650390625 ], "pca3d": [ - -0.15573512017726898, - 0.011432887054979801, - -0.20876279473304749 + -0.15768694877624512, + 0.011791244149208069, + -0.20585434138774872 ], "tsne3d": [ - -52.64461135864258, - -30.396465301513672, - -8.924444198608398 + -23.913278579711914, + -33.31937789916992, + -7.607210159301758 ], "umap3d": [ - 1.9329233169555664, - 1.3563296794891357, - -1.2952667474746704 + 0.7924600839614868, + 0.06824137270450592, + 3.777998924255371 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 19, - "label": "Cell Models" + "id": 20, + "label": "Cell Foundation" } ], "_embedding": [ @@ -112824,47 +113007,47 @@ "doi": "10.64898/2026.03.13.710968", "projections": { "pca": [ - -0.21968448162078857, - -0.0654510036110878 + -0.21989265084266663, + -0.06367714703083038 ], "tsne": [ - -5.316615104675293, - -23.983583450317383 + -22.41852569580078, + -21.200164794921875 ], "umap": [ - -0.8309758305549622, - 4.057158470153809 + -0.7449482083320618, + 5.98554801940918 ], "pca3d": [ - -0.21968458592891693, - -0.0654575452208519, - -0.08807332813739777 + -0.21989287436008453, + -0.06368374824523926, + -0.08383095264434814 ], "tsne3d": [ - -48.163455963134766, - -31.6929874420166, - -0.38976743817329407 + -14.490785598754883, + -28.492246627807617, + -6.426008701324463 ], "umap3d": [ - 2.500546932220459, - 1.0756858587265015, - -1.2707756757736206 + 1.3788293600082397, + 0.1463804841041565, + 4.238056182861328 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 2, - "label": "AI Drug Discovery" + "id": 9, + "label": "Drug Discovery" }, { - "id": 19, - "label": "Cell Models" + "id": 28, + "label": "Spatial Drug" } ], "_embedding": [ @@ -113282,47 +113465,47 @@ "doi": "10.1101/2025.06.25.661237", "projections": { "pca": [ - -0.30407631397247314, - -0.042536649852991104 + -0.3050401508808136, + -0.04232729971408844 ], "tsne": [ - -7.285403728485107, - -28.70469093322754 + -19.647974014282227, + -18.602380752563477 ], "umap": [ - -1.4387378692626953, - 3.6662216186523438 + -1.2066800594329834, + 6.474060535430908 ], "pca3d": [ - -0.3040769696235657, - -0.042553484439849854, - -0.09646240621805191 + -0.3050409257411957, + -0.04234331101179123, + -0.09030959755182266 ], "tsne3d": [ - -36.49019241333008, - -21.062944412231445, - -11.765277862548828 + -33.572479248046875, + -18.674835205078125, + -3.2404842376708984 ], "umap3d": [ - 1.613613247871399, - 0.9179717898368835, - -1.3017598390579224 + 0.5048307776451111, + 0.3224693536758423, + 4.311270713806152 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 19, - "label": "Cell Models" + "id": 20, + "label": "Cell Foundation" } ], "_embedding": [ @@ -113728,47 +113911,47 @@ ], "projections": { "pca": [ - -0.08812862634658813, - -0.15101569890975952 + -0.08901059627532959, + -0.14982959628105164 ], "tsne": [ - 13.562016487121582, - -14.638989448547363 + 0.7500280737876892, + -14.886781692504883 ], "umap": [ - 0.3728964030742645, - 4.717385292053223 + -0.13910290598869324, + 4.970146179199219 ], "pca3d": [ - -0.08812765032052994, - -0.15100878477096558, - -0.005599398631602526 + -0.0890096127986908, + -0.1498250663280487, + -0.004689883440732956 ], "tsne3d": [ - -27.985729217529297, - 18.775402069091797, - 15.455120086669922 + 12.548894882202148, + -28.845369338989258, + -8.787297248840332 ], "umap3d": [ - 2.8393194675445557, - 1.2185873985290527, - -0.15249355137348175 + 1.5069018602371216, + 1.8101602792739868, + 4.926970958709717 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 4, - "label": "Com Area Amp" + "id": 9, + "label": "Drug Discovery" }, { - "id": 17, - "label": "Literature AI" + "id": 23, + "label": "Peer Review" } ], "_embedding": [ @@ -114182,47 +114365,47 @@ ], "projections": { "pca": [ - -0.17762114107608795, - 0.2569468915462494 + -0.17830054461956024, + 0.25714579224586487 ], "tsne": [ - -18.061901092529297, - 20.75286293029785 + 4.029558181762695, + 27.56861686706543 ], "umap": [ - 2.6076903343200684, - 2.989854097366333 + 1.681981086730957, + 2.003941535949707 ], "pca3d": [ - -0.1776210218667984, - 0.25694650411605835, - -0.02867470495402813 + -0.17830026149749756, + 0.25714561343193054, + -0.02758539654314518 ], "tsne3d": [ - 17.8872127532959, - 5.17083215713501, - -26.400272369384766 + 11.548297882080078, + 2.1660492420196533, + -33.2788200378418 ], "umap3d": [ - 6.320047378540039, - -0.49133193492889404, - 0.9345554113388062 + 4.542150974273682, + -0.7076612710952759, + 6.192052364349365 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 18, + "label": "Retrieval Learning" } ], "_embedding": [ @@ -114632,46 +114815,46 @@ "journal": "Nature", "projections": { "pca": [ - 0.04619082435965538, - 0.24482648074626923 + 0.04892914742231369, + 0.24682755768299103 ], "tsne": [ - -8.600382804870605, - 25.481250762939453 + 5.120457172393799, + 20.394731521606445 ], "umap": [ - 2.0456619262695312, - 1.9186753034591675 + 1.3175603151321411, + 0.9370726346969604 ], "pca3d": [ - 0.04619075730443001, - 0.24484996497631073, - 0.04806693643331528 + 0.04892905801534653, + 0.24685105681419373, + 0.04507850483059883 ], "tsne3d": [ - -1.6889451742172241, - 3.521536111831665, - -54.953582763671875 + 12.834580421447754, + 11.957722663879395, + -11.422409057617188 ], "umap3d": [ - 6.981973648071289, - -0.5178261995315552, - 0.19504909217357635 + 4.40562105178833, + -1.494759202003479, + 5.810503005981445 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 4, + "id": 26, "label": "World Models" } ], @@ -115080,47 +115263,47 @@ "journal": "Nature", "projections": { "pca": [ - -0.13119696080684662, - -0.16741596162319183 + -0.127205029129982, + -0.16384154558181763 ], "tsne": [ - -8.635652542114258, - -9.649785041809082 + -13.86473274230957, + -0.7344540953636169 ], "umap": [ - -1.0455007553100586, - 6.248167514801025 + -2.159970760345459, + 4.196592330932617 ], "pca3d": [ - -0.1311955451965332, - -0.16739290952682495, - 0.20515108108520508 + -0.12720361351966858, + -0.1638188511133194, + 0.2061127871274948 ], "tsne3d": [ - -2.7452151775360107, - -38.45511245727539, - 5.564448356628418 + -1.185857892036438, + -26.10396385192871, + 12.179414749145508 ], "umap3d": [ - 3.492746114730835, - -0.8636598587036133, - -1.5497690439224243 + 0.7933770418167114, + 0.6476637721061707, + 6.595812797546387 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 1, - "label": "Cancer Data" + "id": 5, + "label": "Clinical Data" }, { - "id": 28, - "label": "Clinical AI" + "id": 21, + "label": "Clinical Trials" } ], "_embedding": [ @@ -115539,47 +115722,47 @@ ], "projections": { "pca": [ - -0.18645453453063965, - -0.0747191309928894 + -0.1863749921321869, + -0.07281741499900818 ], "tsne": [ - -10.182031631469727, - -7.273232460021973 + -16.00765037536621, + 2.0468828678131104 ], "umap": [ - -0.8412525057792664, - 6.284521102905273 + -2.359029531478882, + 3.869657278060913 ], "pca3d": [ - -0.1864534467458725, - -0.07470319420099258, - 0.002145711798220873 + -0.1863739788532257, + -0.07280284911394119, + 0.006395484320819378 ], "tsne3d": [ - -8.263904571533203, - -23.949111938476562, - 3.37261962890625 + -7.807107925415039, + -20.6246395111084, + 5.806018352508545 ], "umap3d": [ - 3.5817811489105225, - -0.8932221531867981, - -2.055171012878418 + 0.5039445757865906, + 1.0719587802886963, + 6.763154029846191 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 28, - "label": "Clinical AI" + "id": 21, + "label": "Clinical Trials" } ], "_embedding": [ @@ -115994,47 +116177,47 @@ ], "projections": { "pca": [ - -0.13370846211910248, - 0.22577783465385437 + -0.13260290026664734, + 0.22677864134311676 ], "tsne": [ - -24.85965347290039, - 12.870530128479004 + -6.71539831161499, + 25.28826904296875 ], "umap": [ - 1.6886900663375854, - 2.7486793994903564 + 0.7034545540809631, + 1.7385729551315308 ], "pca3d": [ - -0.1337084025144577, - 0.22578006982803345, - -0.07159434258937836 + -0.13260287046432495, + 0.22678057849407196, + -0.06693602353334427 ], "tsne3d": [ - -8.609604835510254, - 17.4061336517334, - -30.2674503326416 + -3.5437607765197754, + -3.667506694793701, + -25.275760650634766 ], "umap3d": [ - 6.194963455200195, - -1.2597743272781372, - 0.28960850834846497 + 3.3955795764923096, + -1.149935245513916, + 6.153980731964111 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 15, - "label": "Latent Models" + "id": 8, + "label": "JEPA Video Models" } ], "_embedding": [ @@ -116452,46 +116635,46 @@ ], "projections": { "pca": [ - -0.2053377479314804, - -0.03562554717063904 + -0.20353242754936218, + -0.03356289491057396 ], "tsne": [ - -19.071491241455078, - 1.5299180746078491 + -9.83836841583252, + 12.927679061889648 ], "umap": [ - -0.4641539752483368, - 7.417804718017578 + -1.3336044549942017, + 2.512678861618042 ], "pca3d": [ - -0.20533698797225952, - -0.035619791597127914, - 0.06092316657304764 + -0.20353145897388458, + -0.033557843416929245, + 0.06827976554632187 ], "tsne3d": [ - -9.053937911987305, - -9.117618560791016, - -21.031951904296875 + -6.261744976043701, + 2.123002767562866, + 21.02618980407715 ], "umap3d": [ - 4.565884113311768, - -1.6358160972595215, - -0.8726266026496887 + 1.7162457704544067, + -0.4487471878528595, + 6.976560592651367 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 11, + "id": 19, "label": "Medical Imaging" } ], @@ -116916,47 +117099,47 @@ "doi": "10.64898/2025.12.18.694875", "projections": { "pca": [ - -0.19278915226459503, - -0.02759256213903427 + -0.19585362076759338, + -0.026752397418022156 ], "tsne": [ - -0.3192920982837677, - -29.81273651123047 + -15.058755874633789, + -22.77427864074707 ], "umap": [ - -1.960005760192871, - 3.504345178604126 + -1.5542964935302734, + 7.283170700073242 ], "pca3d": [ - -0.19278940558433533, - -0.027596423402428627, - -0.19793646037578583 + -0.19585396349430084, + -0.02675618790090084, + -0.19528409838676453 ], "tsne3d": [ - -47.30061340332031, - -5.842087745666504, - 0.9201971888542175 + -34.20667266845703, + -14.103687286376953, + -17.569833755493164 ], "umap3d": [ - 1.1895745992660522, - 0.4719061553478241, - -1.1226791143417358 + 0.21105411648750305, + 1.1307172775268555, + 3.8218491077423096 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 8, - "label": "Gene Regulation" + "id": 3, + "label": "Genomic Sequences" } ], "_embedding": [ @@ -117365,47 +117548,47 @@ "abstract": "AI agents running research on single-GPU nanochat training automatically - karpathy/autoresearch", "projections": { "pca": [ - 0.11206824332475662, - 0.13816669583320618 + 0.1134529858827591, + 0.13923975825309753 ], "tsne": [ - 14.093189239501953, - 21.139408111572266 + 21.09495735168457, + 6.674575328826904 ], "umap": [ - 3.619189977645874, - 5.469925403594971 + 2.962939977645874, + 4.263742923736572 ], "pca3d": [ - 0.11206870526075363, - 0.13816605508327484, - 0.03222963586449623 + 0.11345352232456207, + 0.1392378807067871, + 0.02925017662346363 ], "tsne3d": [ - 34.969444274902344, - 11.678189277648926, - -9.2406005859375 + 16.010225296020508, + 26.733739852905273, + -7.284698009490967 ], "umap3d": [ - 4.5509796142578125, - 0.47211310267448425, - 2.1433894634246826 + 4.148540019989014, + 1.891062617301941, + 5.765432834625244 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 20, - "label": "AI Agents" + "id": 4, + "label": "Open Agent Tools" } ], "_embedding": [ @@ -117833,46 +118016,46 @@ "openalex_url": "https://openalex.org/W7133902476", "projections": { "pca": [ - -0.05556996166706085, - 0.006683478597551584 + -0.05086471885442734, + 0.01129083801060915 ], "tsne": [ - -17.72869873046875, - -3.4352946281433105 + -9.896503448486328, + 15.250617027282715 ], "umap": [ - -0.24186556041240692, - 7.542396545410156 + -0.9986089468002319, + 2.7313873767852783 ], "pca3d": [ - -0.05556957796216011, - 0.006692510563880205, - 0.19798442721366882 + -0.0508643314242363, + 0.011298881843686104, + 0.20088118314743042 ], "tsne3d": [ - -16.633792877197266, - -8.103131294250488, - -15.086251258850098 + -17.193662643432617, + 5.065942764282227, + 20.537057876586914 ], "umap3d": [ - 4.357411861419678, - -1.4323961734771729, - -0.5991981625556946 + 1.6234588623046875, + -0.4491020441055298, + 6.5984206199646 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 11, + "id": 19, "label": "Medical Imaging" } ], @@ -118394,47 +118577,47 @@ "openalex_url": "https://openalex.org/W7117104805", "projections": { "pca": [ - -0.11934303492307663, - 0.006151043809950352 + -0.11433670669794083, + 0.010187008418142796 ], "tsne": [ - -15.064443588256836, - -4.769598960876465 + -17.015613555908203, + 13.307491302490234 ], "umap": [ - -0.19140681624412537, - 7.045246124267578 + -1.5480836629867554, + 3.0131263732910156 ], "pca3d": [ - -0.11934269964694977, - 0.006154385861009359, - 0.25846245884895325 + -0.11433634161949158, + 0.01019140426069498, + 0.26307564973831177 ], "tsne3d": [ - 12.805279731750488, - -29.736591339111328, - -23.234712600708008 + -14.062800407409668, + -0.942040741443634, + 17.02071189880371 ], "umap3d": [ - 4.566158294677734, - -1.3455731868743896, - -1.3051389455795288 + 1.1845118999481201, + 0.16461803019046783, + 7.039984226226807 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 11, - "label": "Medical Imaging" + "id": 13, + "label": "ECG AI Models" } ], "_embedding": [ @@ -118842,46 +119025,46 @@ "journal": "Nature", "projections": { "pca": [ - -0.12421906739473343, - -0.014081676490604877 + -0.12132060527801514, + -0.01108894869685173 ], "tsne": [ - -14.70340347290039, - -4.812128067016602 + -16.990798950195312, + 13.172731399536133 ], "umap": [ - -0.3290623724460602, - 6.940196514129639 + -1.8979508876800537, + 3.396737575531006 ], "pca3d": [ - -0.12421772629022598, - -0.014061146415770054, - 0.18603916466236115 + -0.1213192418217659, + -0.011069935746490955, + 0.18984554708003998 ], "tsne3d": [ - 8.838516235351562, - -28.150327682495117, - -20.967866897583008 + -12.352283477783203, + -4.334808826446533, + 18.2076416015625 ], "umap3d": [ - 4.307575225830078, - -1.236944556236267, - -1.7523982524871826 + 0.9603262543678284, + 0.6490527391433716, + 7.034576416015625 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, + "id": 14, "label": "Clinical Health" } ], @@ -119313,47 +119496,47 @@ ], "projections": { "pca": [ - -0.19654692709445953, - 0.2705168128013611 + -0.19636954367160797, + 0.2709566652774811 ], "tsne": [ - -20.76022720336914, - 10.633111000061035 + -5.17345666885376, + 21.78488540649414 ], "umap": [ - 1.5672999620437622, - 2.982962131500244 + 0.5702596306800842, + 1.9025092124938965 ], "pca3d": [ - -0.19654785096645355, - 0.27051302790641785, - -0.031160647049546242 + -0.19637031853199005, + 0.2709518373012543, + -0.02597852237522602 ], "tsne3d": [ - -2.7725203037261963, - 10.725188255310059, - -24.052549362182617 + -1.4633574485778809, + 3.2348103523254395, + -17.186365127563477 ], "umap3d": [ - 5.952137470245361, - -1.1080529689788818, - 0.05630231276154518 + 3.4776782989501953, + -1.022037148475647, + 6.349725246429443 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 15, - "label": "Latent Models" + "id": 8, + "label": "JEPA Video Models" } ], "_embedding": [ @@ -119761,46 +119944,46 @@ "journal": "Nature", "projections": { "pca": [ - -0.19719691574573517, - 0.07403538376092911 + -0.19072476029396057, + 0.0788319855928421 ], "tsne": [ - -18.558330535888672, - -2.8880128860473633 + -8.761926651000977, + 15.129374504089355 ], "umap": [ - -0.22955100238323212, - 7.675512313842773 + -0.8575716614723206, + 2.584001302719116 ], "pca3d": [ - -0.19719766080379486, - 0.07403428107500076, - 0.21366648375988007 + -0.1907256543636322, + 0.07883194088935852, + 0.2193414270877838 ], "tsne3d": [ - -15.472230911254883, - -1.6880712509155273, - -19.07025146484375 + -16.626901626586914, + 11.685037612915039, + 19.06623649597168 ], "umap3d": [ - 4.405398368835449, - -1.4601387977600098, - -0.6274107694625854 + 1.6788705587387085, + -0.5768041610717773, + 6.645840644836426 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 11, + "id": 19, "label": "Medical Imaging" } ], @@ -120218,47 +120401,47 @@ "doi": "10.64898/2026.02.28.708160", "projections": { "pca": [ - -0.2783088684082031, - 0.01612081192433834 + -0.28063589334487915, + 0.01688017137348652 ], "tsne": [ - -8.310919761657715, - -29.588178634643555 + -21.76365089416504, + -19.09311866760254 ], "umap": [ - -1.2285295724868774, - 3.2609543800354004 + -0.9402636289596558, + 6.766684532165527 ], "pca3d": [ - -0.2783086895942688, - 0.016121037304401398, - -0.1994265615940094 + -0.28063565492630005, + 0.01688040792942047, + -0.19511881470680237 ], "tsne3d": [ - -40.2220458984375, - -21.57486915588379, - -20.7366886138916 + -36.365264892578125, + -25.879493713378906, + -5.151805400848389 ], "umap3d": [ - 1.4628452062606812, - 1.2032124996185303, - -1.4216364622116089 + 0.451490581035614, + 0.08458246290683746, + 3.9270567893981934 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 19, - "label": "Cell Models" + "id": 20, + "label": "Cell Foundation" } ], "_embedding": [ @@ -120679,47 +120862,47 @@ ], "projections": { "pca": [ - -0.0724378451704979, - 0.1779050976037979 + -0.07121957838535309, + 0.1791243702173233 ], "tsne": [ - -14.229032516479492, - 21.101179122924805 + 5.381383419036865, + 32.06159210205078 ], "umap": [ - 3.5447545051574707, - 3.087495803833008 + 2.6360015869140625, + 2.1522772312164307 ], "pca3d": [ - -0.07243825495243073, - 0.17789627611637115, - 0.02073204144835472 + -0.0712200328707695, + 0.1791168749332428, + 0.022827794775366783 ], "tsne3d": [ - 27.24393081665039, - -5.902962684631348, - -36.365909576416016 + 9.570318222045898, + 15.287128448486328, + -39.50325012207031 ], "umap3d": [ - 6.533252716064453, - -0.03202549368143082, - 1.2606309652328491 + 4.666980266571045, + -0.22023306787014008, + 5.50191593170166 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 6, + "label": "Learning Techniques" } ], "_embedding": [ @@ -121137,47 +121320,47 @@ ], "projections": { "pca": [ - -0.17148612439632416, - 0.22470015287399292 + -0.17133021354675293, + 0.22543276846408844 ], "tsne": [ - -12.777142524719238, - 22.58922576904297 + 8.504953384399414, + 28.65093421936035 ], "umap": [ - 3.3322649002075195, - 2.9883129596710205 + 2.1413025856018066, + 1.4692765474319458 ], "pca3d": [ - -0.17148712277412415, - 0.22469623386859894, - -0.02791210636496544 + -0.1713312566280365, + 0.22542835772037506, + -0.02637438289821148 ], "tsne3d": [ - 19.135555267333984, - 8.813457489013672, - -40.77572250366211 + 11.51233959197998, + 13.921767234802246, + -22.085086822509766 ], "umap3d": [ - 6.737804889678955, - -0.21465396881103516, - 0.8447537422180176 + 4.444751739501953, + -0.5774835348129272, + 5.442078590393066 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 6, + "label": "Learning Techniques" } ], "_embedding": [ @@ -121599,46 +121782,46 @@ ], "projections": { "pca": [ - -0.26540717482566833, - -0.10955344140529633 + -0.26789626479148865, + -0.10890748351812363 ], "tsne": [ - 1.0821282863616943, - -26.236888885498047 + -13.249680519104004, + -18.883270263671875 ], "umap": [ - -2.108168601989746, - 4.073551654815674 + -2.054530382156372, + 6.865209579467773 ], "pca3d": [ - -0.26540666818618774, - -0.10955296456813812, - -0.14688797295093536 + -0.26789605617523193, + -0.1089070737361908, + -0.14261914789676666 ], "tsne3d": [ - -32.425498962402344, - -11.589079856872559, - 4.428308010101318 + -23.263639450073242, + -13.34461784362793, + -8.626677513122559 ], "umap3d": [ - 1.5302293300628662, - 0.10290989279747009, - -1.2674564123153687 + 0.016317525878548622, + 1.0133508443832397, + 4.4210429191589355 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 22, + "id": 27, "label": "Genetic Variants" } ], @@ -122057,47 +122240,47 @@ "doi": "10.1101/2025.10.26.684578", "projections": { "pca": [ - -0.2890746295452118, - -0.06382283568382263 + -0.2900528311729431, + -0.062105223536491394 ], "tsne": [ - 0.38427525758743286, - -24.44768714904785 + -14.02209186553955, + -16.821855545043945 ], "umap": [ - -1.877914309501648, - 4.20686674118042 + -1.845016360282898, + 6.64646577835083 ], "pca3d": [ - -0.28907355666160583, - -0.06381376832723618, - -0.07695727050304413 + -0.29005196690559387, + -0.062096938490867615, + -0.07325835525989532 ], "tsne3d": [ - -30.911584854125977, - -17.671598434448242, - 5.469146728515625 + -22.367843627929688, + -14.749801635742188, + -3.5910823345184326 ], "umap3d": [ - 1.773244857788086, - 0.10257934033870697, - -1.3435590267181396 + 0.14314255118370056, + 0.8278858661651611, + 4.572137832641602 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 2, - "label": "Genetic Disease" + "id": 27, + "label": "Genetic Variants" } ], "_embedding": [ @@ -122521,47 +122704,47 @@ "doi": "10.1101/2025.09.09.25335438", "projections": { "pca": [ - -0.20170184969902039, - -0.17371132969856262 + -0.20466230809688568, + -0.17282287776470184 ], "tsne": [ - 2.885660171508789, - -26.709796905517578 + -11.36230754852295, + -19.147754669189453 ], "umap": [ - -2.128157377243042, - 4.30365514755249 + -2.2063047885894775, + 6.7621612548828125 ], "pca3d": [ - -0.20170074701309204, - -0.17369118332862854, - -0.0983283743262291 + -0.20466114580631256, + -0.17280429601669312, + -0.0944376215338707 ], "tsne3d": [ - -39.49327850341797, - -9.730908393859863, - 15.202179908752441 + -24.312171936035156, + -6.1323018074035645, + -15.810133934020996 ], "umap3d": [ - 1.5847442150115967, - -0.1634935438632965, - -1.2183091640472412 + 0.041193753480911255, + 1.3094948530197144, + 4.608176231384277 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 2, - "label": "Genetic Disease" + "id": 27, + "label": "Genetic Variants" } ], "_embedding": [ @@ -122969,47 +123152,47 @@ "journal": "Nature Methods", "projections": { "pca": [ - -0.1951264888048172, - 0.016097141429781914 + -0.19379550218582153, + 0.01752883568406105 ], "tsne": [ - -12.133490562438965, - -30.075902938842773 + -26.0950870513916, + -19.523847579956055 ], "umap": [ - -1.384273648262024, - 3.783083438873291 + -1.2491761445999146, + 6.5910820960998535 ], "pca3d": [ - -0.19512659311294556, - 0.01607959344983101, - -0.050154250115156174 + -0.19379565119743347, + 0.017511945217847824, + -0.046700619161129 ], "tsne3d": [ - -32.35482406616211, - -6.2280049324035645, - -12.246111869812012 + -30.515766143798828, + -16.190959930419922, + 2.3784923553466797 ], "umap3d": [ - 1.784080147743225, - 0.6064353585243225, - -1.1256645917892456 + 0.33528512716293335, + 0.3829939067363739, + 4.425455093383789 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 19, - "label": "Cell Models" + "id": 20, + "label": "Cell Foundation" } ], "_embedding": [ @@ -123458,47 +123641,47 @@ "openalex_url": "https://openalex.org/W7131126599", "projections": { "pca": [ - -0.11361666023731232, - -0.15847808122634888 + -0.11128056049346924, + -0.1565019190311432 ], "tsne": [ - -24.106565475463867, - -10.974159240722656 + -25.808835983276367, + 3.629099130630493 ], "umap": [ - -1.10456120967865, - 7.466281890869141 + -2.369138240814209, + 2.818436861038208 ], "pca3d": [ - -0.11361677199602127, - -0.1584906131029129, - 0.1974482238292694 + -0.1112806424498558, + -0.15651123225688934, + 0.20242412388324738 ], "tsne3d": [ - -6.789694309234619, - -29.496612548828125, - -29.352031707763672 + -34.79539489746094, + 14.174275398254395, + 24.891841888427734 ], "umap3d": [ - 3.646549701690674, - -1.812099814414978, - -1.6893600225448608 + 1.1125978231430054, + -0.3467162847518921, + 7.7852606773376465 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 12, - "label": "ECG Models" + "id": 13, + "label": "ECG AI Models" } ], "_embedding": [ @@ -123918,47 +124101,47 @@ "cited_by_count": 83, "projections": { "pca": [ - -0.18220704793930054, - -0.024260438978672028 + -0.18068179488182068, + -0.022434266284108162 ], "tsne": [ - -23.77364730834961, - -9.326889038085938 + -25.267114639282227, + 5.328608512878418 ], "umap": [ - -0.9818557500839233, - 7.4012556076049805 + -2.27799916267395, + 2.74711012840271 ], "pca3d": [ - -0.1822069138288498, - -0.024273265153169632, - 0.03616892173886299 + -0.18068167567253113, + -0.022445738315582275, + 0.04345840960741043 ], "tsne3d": [ - -10.220521926879883, - -18.368513107299805, - -30.247028350830078 + -30.85038185119629, + 11.419622421264648, + 16.733983993530273 ], "umap3d": [ - 3.8049416542053223, - -1.841469168663025, - -1.5891833305358887 + 1.2623037099838257, + -0.4252895414829254, + 7.631824016571045 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 12, - "label": "ECG Models" + "id": 13, + "label": "ECG AI Models" } ], "_embedding": [ @@ -124376,47 +124559,47 @@ "doi": "10.64898/2026.02.23.707551", "projections": { "pca": [ - -0.1499566286802292, - -0.14224763214588165 + -0.14905808866024017, + -0.13882488012313843 ], "tsne": [ - 1.118882656097412, - -4.216636657714844 + -4.877358436584473, + -3.0492396354675293 ], "umap": [ - -0.4539903700351715, - 4.8099684715271 + -0.7697347402572632, + 5.04751443862915 ], "pca3d": [ - -0.14995640516281128, - -0.14225956797599792, - 0.0587378591299057 + -0.1490577906370163, + -0.13883554935455322, + 0.06207308545708656 ], "tsne3d": [ - -14.333925247192383, - -10.714836120605469, - 30.879724502563477 + -7.05138635635376, + -22.699331283569336, + -9.950630187988281 ], "umap3d": [ - 3.646982192993164, - 0.7905266880989075, - -1.0766254663467407 + 2.3444406986236572, + 0.13481076061725616, + 4.808323860168457 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ - { - "id": 0, - "label": "Medical Data AI" - }, { "id": 2, - "label": "AI Drug Discovery" + "label": "Clinical AI Data" }, { - "id": 13, + "id": 9, "label": "Drug Discovery" + }, + { + "id": 9, + "label": "Biomedical AI" } ], "_embedding": [ @@ -124844,46 +125027,46 @@ ], "projections": { "pca": [ - -0.17082174122333527, - -0.15317969024181366 + -0.17144176363945007, + -0.1512952595949173 ], "tsne": [ - -11.72846508026123, - -18.736495971679688 + -23.243112564086914, + -10.307193756103516 ], "umap": [ - -1.7992920875549316, - 5.953620910644531 + -1.7900021076202393, + 5.189013957977295 ], "pca3d": [ - -0.1708216667175293, - -0.15317849814891815, - -0.09071578085422516 + -0.17144159972667694, + -0.1512940675020218, + -0.08489389717578888 ], "tsne3d": [ - -22.59479331970215, - -23.790298461914062, - -13.869503021240234 + -28.94475746154785, + -5.905447006225586, + 15.152872085571289 ], "umap3d": [ - 2.947557210922241, - -0.693872332572937, - -0.5936821103096008 + 0.7906975746154785, + -0.08914123475551605, + 5.625502586364746 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 24, + "id": 1, "label": "Cancer Data" } ], @@ -125312,46 +125495,46 @@ "doi": "10.64898/2026.02.19.26346539", "projections": { "pca": [ - -0.10715608298778534, - -0.15315373241901398 + -0.10385267436504364, + -0.14820422232151031 ], "tsne": [ - -6.309803485870361, - -4.621409893035889 + -11.712008476257324, + 2.1124634742736816 ], "umap": [ - -0.5229024887084961, - 6.291655540466309 + -1.562023401260376, + 4.107819557189941 ], "pca3d": [ - -0.10715486109256744, - -0.15314927697181702, - 0.200778529047966 + -0.1038515493273735, + -0.14820004999637604, + 0.2052897810935974 ], "tsne3d": [ - 2.252498149871826, - -32.574012756347656, - 13.916101455688477 + -2.480649471282959, + -18.73158073425293, + 11.664298057556152 ], "umap3d": [ - 3.808795213699341, - -0.533860445022583, - -1.7398263216018677 + 0.7832635045051575, + 0.6160323023796082, + 6.866006851196289 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, + "id": 14, "label": "Clinical Health" } ], @@ -125760,47 +125943,47 @@ "journal": "Nature Medicine", "projections": { "pca": [ - 0.001407149713486433, - -0.14788436889648438 + 0.0033154012635350227, + -0.14580652117729187 ], "tsne": [ - -4.593149662017822, - -0.3936273753643036 + -9.459794044494629, + 4.941100597381592 ], "umap": [ - 0.23342108726501465, - 6.262112140655518 + -1.3818507194519043, + 3.932258129119873 ], "pca3d": [ - 0.0014076379593461752, - -0.14788700640201569, - 0.23634468019008636 + 0.0033159113954752684, + -0.14580944180488586, + 0.23850105702877045 ], "tsne3d": [ - 9.142306327819824, - -16.9979305267334, - 24.123302459716797 + 6.675994873046875, + -19.422534942626953, + 14.283063888549805 ], "umap3d": [ - 4.254154205322266, - -0.7366762757301331, - -1.553510069847107 + 1.3250550031661987, + 0.8253181576728821, + 6.802084922790527 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 0, - "label": "Clinical LLMs" + "id": 14, + "label": "Clinical Health" } ], "_embedding": [ @@ -126233,47 +126416,47 @@ "doi": "10.64898/2026.02.18.706454", "projections": { "pca": [ - -0.17394980788230896, - -0.03434653580188751 + -0.177083358168602, + -0.03340756893157959 ], "tsne": [ - -5.843080997467041, - -26.89197540283203 + -22.046228408813477, + -24.098875045776367 ], "umap": [ - -1.0516459941864014, - 3.5004656314849854 + -0.6384111046791077, + 6.785029411315918 ], "pca3d": [ - -0.17394942045211792, - -0.034326668828725815, - -0.21077509224414825 + -0.17708280682563782, + -0.03338872641324997, + -0.2085302621126175 ], "tsne3d": [ - -47.505863189697266, - -29.925207138061523, - -14.336950302124023 + -24.81083869934082, + -31.602733612060547, + -1.8826699256896973 ], "umap3d": [ - 1.8082754611968994, - 1.3609074354171753, - -1.3119454383850098 + 0.7003523707389832, + 0.07727787643671036, + 3.7090024948120117 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 19, - "label": "Cell Models" + "id": 20, + "label": "Cell Foundation" } ], "_embedding": [ @@ -126681,46 +126864,46 @@ "journal": "Nature", "projections": { "pca": [ - -0.22286851704120636, - -0.08022190630435944 + -0.21947699785232544, + -0.0772513672709465 ], "tsne": [ - -11.842719078063965, - -11.032118797302246 + -19.467899322509766, + 8.360794067382812 ], "umap": [ - -0.23798580467700958, - 6.146137714385986 + -1.747719407081604, + 3.6689252853393555 ], "pca3d": [ - -0.2228693664073944, - -0.08023173362016678, - 0.09675554931163788 + -0.21947796642780304, + -0.07726054638624191, + 0.10068070888519287 ], "tsne3d": [ - 7.715350151062012, - -22.90191650390625, - -5.5894389152526855 + -12.34579086303711, + -4.783795356750488, + 2.516514778137207 ], "umap3d": [ - 4.35832405090332, - -0.5363614559173584, - -1.485425353050232 + 1.25663161277771, + 0.42777788639068604, + 7.137004852294922 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, + "id": 14, "label": "Clinical Health" } ], @@ -127137,47 +127320,47 @@ ], "projections": { "pca": [ - -0.05852200463414192, - 0.2546122670173645 + -0.05693911761045456, + 0.25598904490470886 ], "tsne": [ - -13.828604698181152, - 24.97366714477539 + 8.751131057739258, + 31.399717330932617 ], "umap": [ - 3.438425064086914, - 2.5029802322387695 + 2.4726064205169678, + 1.3529541492462158 ], "pca3d": [ - -0.05852197855710983, - 0.25461840629577637, - 0.040080759674310684 + -0.05693914741277695, + 0.2559945285320282, + 0.038631852716207504 ], "tsne3d": [ - 16.378402709960938, - -2.47418475151062, - -48.58062744140625 + 15.5587158203125, + 20.76618766784668, + -25.870105743408203 ], "umap3d": [ - 7.018159866333008, - -0.3741823434829712, - 1.1500630378723145 + 4.998790740966797, + -0.8255746364593506, + 5.456686496734619 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 6, + "label": "Learning Techniques" } ], "_embedding": [ @@ -127584,46 +127767,46 @@ "title": "Consistency diffusion language models: Up to 14x faster inference without sacrificing quality", "projections": { "pca": [ - -0.20289300382137299, - 0.26317450404167175 + -0.20047305524349213, + 0.26464661955833435 ], "tsne": [ - -22.182104110717773, - 21.755821228027344 + -0.5225045084953308, + 30.868776321411133 ], "umap": [ - 2.6221702098846436, - 2.470219373703003 + 1.6059917211532593, + 1.4741450548171997 ], "pca3d": [ - -0.2028934359550476, - 0.26318085193634033, - -0.015330511145293713 + -0.20047356188297272, + 0.264651358127594, + -0.014744496904313564 ], "tsne3d": [ - 10.141853332519531, - 15.263775825500488, - -32.69369125366211 + -0.35438627004623413, + 9.839214324951172, + -26.977371215820312 ], "umap3d": [ - 6.690499305725098, - -0.902573823928833, - 0.7443287968635559 + 4.339774131774902, + -1.013343095779419, + 5.789164066314697 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 4, + "id": 26, "label": "World Models" } ], @@ -128036,46 +128219,46 @@ ], "projections": { "pca": [ - -0.11688865721225739, - 0.23294271528720856 + -0.11557739973068237, + 0.23358562588691711 ], "tsne": [ - -24.416112899780273, - 14.907259941101074 + -1.6728663444519043, + 25.395851135253906 ], "umap": [ - 1.9910526275634766, - 2.454437017440796 + 1.0840550661087036, + 1.5477714538574219 ], "pca3d": [ - -0.116888627409935, - 0.23295186460018158, - -0.07133013755083084 + -0.11557735502719879, + 0.2335936725139618, + -0.06721660494804382 ], "tsne3d": [ - -9.872655868530273, - 16.570358276367188, - -36.1342658996582 + -3.7860634326934814, + -1.4439197778701782, + -30.226966857910156 ], "umap3d": [ - 6.481021404266357, - -1.2941838502883911, - 0.4512037932872772 + 3.6639461517333984, + -1.1824911832809448, + 5.866179466247559 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 4, + "id": 26, "label": "World Models" } ], @@ -128492,47 +128675,47 @@ ], "projections": { "pca": [ - -0.10749302804470062, - 0.1921660453081131 + -0.1045255959033966, + 0.19459407031536102 ], "tsne": [ - -23.267742156982422, - 12.140609741210938 + -7.418086528778076, + 23.262737274169922 ], "umap": [ - 1.4578721523284912, - 2.9012134075164795 + 0.3646204471588135, + 1.8726924657821655 ], "pca3d": [ - -0.10749370604753494, - 0.19215723872184753, - 0.03758495673537254 + -0.10452617704868317, + 0.1945868730545044, + 0.04307449236512184 ], "tsne3d": [ - -15.428407669067383, - 9.881266593933105, - -27.060760498046875 + -9.917027473449707, + -0.9341087937355042, + -20.646100997924805 ], "umap3d": [ - 5.926321506500244, - -1.3737789392471313, - 0.05232067033648491 + 3.2053275108337402, + -1.1457995176315308, + 6.444888114929199 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 15, - "label": "Latent Models" + "id": 8, + "label": "JEPA Video Models" } ], "_embedding": [ @@ -128939,47 +129122,47 @@ "title": "Paper page - Unified Latents (UL): How to train your latents", "projections": { "pca": [ - 0.04766324907541275, - 0.14883582293987274 + 0.04806378856301308, + 0.14923253655433655 ], "tsne": [ - -19.958133697509766, - 17.818706512451172 + -3.5596790313720703, + 28.405567169189453 ], "umap": [ - 2.264807939529419, - 2.9785854816436768 + 1.2189687490463257, + 1.9560613632202148 ], "pca3d": [ - 0.047663021832704544, - 0.14882947504520416, - -0.007641473785042763 + 0.04806370288133621, + 0.14922618865966797, + -0.009190534241497517 ], "tsne3d": [ - 3.612262725830078, - -9.54572868347168, - -33.407718658447266 + 14.773439407348633, + 0.7313593029975891, + -23.7835636138916 ], "umap3d": [ - 6.460235118865967, - -0.6622199416160583, - 0.4262963533401489 + 4.179453372955322, + -1.0586280822753906, + 6.327565670013428 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 15, - "label": "Latent Models" + "id": 18, + "label": "Retrieval Learning" } ], "_embedding": [ @@ -129405,46 +129588,46 @@ "openalex_url": "https://openalex.org/W7125424904", "projections": { "pca": [ - -0.0579858236014843, - -0.06376206874847412 + -0.05364067107439041, + -0.05940539017319679 ], "tsne": [ - -13.658379554748535, - -2.7463290691375732 + -22.418916702270508, + 5.934679985046387 ], "umap": [ - -0.22727398574352264, - 6.403297424316406 + -1.4946643114089966, + 3.452820062637329 ], "pca3d": [ - -0.057986147701740265, - -0.06377345323562622, - 0.26715442538261414 + -0.05364100635051727, + -0.05941466614603996, + 0.27021366357803345 ], "tsne3d": [ - 15.784587860107422, - -27.839061737060547, - 11.280415534973145 + -7.045496463775635, + 10.520059585571289, + 7.212488174438477 ], "umap3d": [ - 4.0670552253723145, - -0.7201518416404724, - -1.7455673217773438 + 1.0317574739456177, + 0.2607293725013733, + 7.623500823974609 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, + "id": 14, "label": "Clinical Health" } ], @@ -129853,47 +130036,47 @@ "journal": "Nature", "projections": { "pca": [ - -0.13715623319149017, - 0.0456828847527504 + -0.13060705363750458, + 0.04984468221664429 ], "tsne": [ - -3.2900216579437256, - 9.643355369567871 + -4.270347595214844, + 1.7997196912765503 ], "umap": [ - -0.1863301694393158, - 5.527052879333496 + -0.8961594700813293, + 4.0795979499816895 ], "pca3d": [ - -0.13715600967407227, - 0.04568372294306755, - 0.21395519375801086 + -0.1306069791316986, + 0.04984525963664055, + 0.2136492282152176 ], "tsne3d": [ - 18.952077865600586, - -23.03929328918457, - -13.188138961791992 + -8.679574966430664, + 6.151625156402588, + -4.006970405578613 ], "umap3d": [ - 4.245569705963135, - 0.0027116844430565834, - -1.5143420696258545 + 2.5452733039855957, + 0.15972498059272766, + 5.428558349609375 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ - { - "id": 0, - "label": "Medical Data AI" - }, { "id": 2, - "label": "AI Drug Discovery" + "label": "Clinical AI Data" }, { - "id": 13, + "id": 9, "label": "Drug Discovery" + }, + { + "id": 5, + "label": "Health Benchmarks" } ], "_embedding": [ @@ -130301,46 +130484,46 @@ "journal": "Nature Biomedical Engineering", "projections": { "pca": [ - -0.026127999648451805, - -0.0029762182384729385 + -0.022558774799108505, + 0.0003851023211609572 ], "tsne": [ - -18.222570419311523, - -4.426682949066162 + -9.625009536743164, + 16.58026695251465 ], "umap": [ - -0.44315147399902344, - 7.554288387298584 + -0.8721446394920349, + 2.634752035140991 ], "pca3d": [ - -0.026127461344003677, - -0.002956620417535305, - 0.11928226798772812 + -0.022558216005563736, + 0.00040382673614658415, + 0.12330260872840881 ], "tsne3d": [ - -20.156740188598633, - -4.73954439163208, - -22.094263076782227 + -21.116376876831055, + 8.796903610229492, + 17.340099334716797 ], "umap3d": [ - 4.374722003936768, - -1.4134231805801392, - -0.5969576239585876 + 1.6057116985321045, + -0.5665670037269592, + 6.533724784851074 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 11, + "id": 19, "label": "Medical Imaging" } ], @@ -130749,47 +130932,47 @@ "abstract": "Modelling human health trajectories using generative transformers - gerstung-lab/Delphi", "projections": { "pca": [ - -0.06902893632650375, - -0.08262386918067932 + -0.06840259581804276, + -0.08117935806512833 ], "tsne": [ - -10.116429328918457, - 2.558168411254883 + -18.410032272338867, + 4.4741902351379395 ], "umap": [ - 0.4916963577270508, - 6.189389705657959 + -0.681233823299408, + 3.511120557785034 ], "pca3d": [ - -0.06902789324522018, - -0.08260837197303772, - 0.07653024792671204 + -0.06840161234140396, + -0.08116614818572998, + 0.08099566400051117 ], "tsne3d": [ - -4.758656024932861, - -20.576242446899414, - -8.318394660949707 + 9.588968276977539, + -5.183448791503906, + -2.1422579288482666 ], "umap3d": [ - 4.904855728149414, - -0.9466351270675659, - -1.3995541334152222 + 1.4171935319900513, + 1.3451675176620483, + 6.849095821380615 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 0, - "label": "Clinical LLMs" + "id": 5, + "label": "Health Benchmarks" } ], "_embedding": [ @@ -131199,46 +131382,46 @@ "journal": "Nature Medicine", "projections": { "pca": [ - 0.015267946757376194, - -0.2725984454154968 + 0.017603708431124687, + -0.2707184851169586 ], "tsne": [ - -3.6519083976745605, - -16.423086166381836 + -25.17292022705078, + -3.957552909851074 ], "umap": [ - -2.326280117034912, - 6.179431438446045 + -2.569275379180908, + 5.021993637084961 ], "pca3d": [ - 0.015268257819116116, - -0.2725985050201416, - 0.0940854474902153 + 0.01760404370725155, + -0.2707173526287079, + 0.09586413204669952 ], "tsne3d": [ - -25.033565521240234, - -48.15074157714844, - 3.093202829360962 + -28.627788543701172, + -30.960695266723633, + 20.6444149017334 ], "umap3d": [ - 2.6339335441589355, - -1.2338361740112305, - -1.0109151601791382 + -0.0006734967464581132, + 0.11973872780799866, + 5.950428485870361 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 24, + "id": 1, "label": "Cancer Data" } ], @@ -131667,46 +131850,46 @@ "doi": "10.1001/jama.2024.28047", "projections": { "pca": [ - -0.0865071564912796, - -0.07010983675718307 + -0.08219079673290253, + -0.0669798031449318 ], "tsne": [ - -5.975820064544678, - -2.9712910652160645 + -11.061043739318848, + 3.504687786102295 ], "umap": [ - -0.3214852213859558, - 6.211479187011719 + -1.7294918298721313, + 3.975334405899048 ], "pca3d": [ - -0.08650771528482437, - -0.07011960446834564, - 0.2485647201538086 + -0.0821913331747055, + -0.0669860914349556, + 0.252407044172287 ], "tsne3d": [ - 9.0463285446167, - -28.84799575805664, - 16.771774291992188 + -6.224945068359375, + -11.02318000793457, + 0.46531960368156433 ], "umap3d": [ - 3.912719249725342, - -0.5510472059249878, - -1.6800658702850342 + 0.9490615129470825, + 0.3920038938522339, + 7.168248653411865 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, + "id": 14, "label": "Clinical Health" } ], @@ -132138,47 +132321,47 @@ "openalex_url": "https://openalex.org/W4399738410", "projections": { "pca": [ - -0.1298477053642273, - -0.12546458840370178 + -0.12826509773731232, + -0.1226767897605896 ], "tsne": [ - -5.7894110679626465, - -1.3515127897262573 + -10.544631958007812, + 5.1692681312561035 ], "umap": [ - 0.19450438022613525, - 6.251794338226318 + -1.5681710243225098, + 4.123592376708984 ], "pca3d": [ - -0.12984713912010193, - -0.12546978890895844, - 0.10752714425325394 + -0.12826456129550934, + -0.12268232554197311, + 0.11227381229400635 ], "tsne3d": [ - 10.094990730285645, - -21.68221664428711, - 19.370018005371094 + 3.5252208709716797, + -17.525409698486328, + 10.335616111755371 ], "umap3d": [ - 3.981391668319702, - -0.8087867498397827, - -1.5454702377319336 + 1.2565996646881104, + 0.6498021483421326, + 6.860281944274902 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 0, - "label": "Clinical LLMs" + "id": 14, + "label": "Clinical Health" } ], "_embedding": [ @@ -132635,47 +132818,47 @@ "doi": "10.64898/2026.02.04.26343575", "projections": { "pca": [ - -0.21175295114517212, - -0.1778494119644165 + -0.21619167923927307, + -0.17711839079856873 ], "tsne": [ - -2.4421699047088623, - -28.83708953857422 + -17.126062393188477, + -20.79023551940918 ], "umap": [ - -2.12075138092041, - 3.545954942703247 + -1.7286831140518188, + 7.393255233764648 ], "pca3d": [ - -0.21175187826156616, - -0.1778429001569748, - -0.20412194728851318 + -0.21619094908237457, + -0.17711329460144043, + -0.20060504972934723 ], "tsne3d": [ - -52.842872619628906, - -10.548129081726074, - 6.3372626304626465 + -43.06623077392578, + -10.203898429870605, + -10.865478515625 ], "umap3d": [ - 1.087640643119812, - 0.3577125370502472, - -1.3601611852645874 + -0.12870751321315765, + 0.9660483598709106, + 3.7795865535736084 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 8, - "label": "Gene Regulation" + "id": 3, + "label": "Genomic Sequences" } ], "_embedding": [ @@ -133124,47 +133307,47 @@ "openalex_url": "https://openalex.org/W7128380964", "projections": { "pca": [ - -0.1962168663740158, - -0.08718305826187134 + -0.1966215968132019, + -0.08656837046146393 ], "tsne": [ - 0.43064701557159424, - -22.530410766601562 + -13.095181465148926, + -14.856691360473633 ], "umap": [ - -1.6943947076797485, - 4.436223030090332 + -2.1538729667663574, + 6.472855567932129 ], "pca3d": [ - -0.1962176114320755, - -0.08719661831855774, - -0.0012808043975383043 + -0.19662240147590637, + -0.08657985925674438, + 0.0019061076454818249 ], "tsne3d": [ - -23.964723587036133, - -7.237617015838623, - 10.959784507751465 + -16.20774269104004, + -14.792264938354492, + -9.018726348876953 ], "umap3d": [ - 2.0703470706939697, - 0.054314304143190384, - -1.5244569778442383 + 0.24963657557964325, + 1.021069049835205, + 4.76684045791626 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 2, - "label": "Genetic Disease" + "id": 27, + "label": "Genetic Variants" } ], "_embedding": [ @@ -133570,47 +133753,47 @@ ], "projections": { "pca": [ - 0.07307585328817368, - -0.041346676647663116 + 0.07186464965343475, + -0.04162226617336273 ], "tsne": [ - 13.02534294128418, - -13.430466651916504 + -30.88992691040039, + -15.85655689239502 ], "umap": [ - 0.4889106750488281, - 4.925777912139893 + -0.03699421137571335, + 4.933436870574951 ], "pca3d": [ - 0.07307563722133636, - -0.041352659463882446, - -0.10839029401540756 + 0.07186449319124222, + -0.04162899777293205, + -0.10917523503303528 ], "tsne3d": [ - -23.28435707092285, - 19.001298904418945, - 12.74267292022705 + 15.072144508361816, + -30.572023391723633, + -12.206814765930176 ], "umap3d": [ - 3.00380539894104, - 1.1392216682434082, - 0.039891548454761505 + 1.6761435270309448, + 1.9878426790237427, + 5.066535949707031 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 4, - "label": "Com Area Amp" + "id": 9, + "label": "Drug Discovery" }, { - "id": 17, - "label": "Literature AI" + "id": 23, + "label": "Peer Review" } ], "_embedding": [ @@ -134026,46 +134209,46 @@ ], "projections": { "pca": [ - -0.1945648044347763, - -0.08534888923168182 + -0.1916518658399582, + -0.08183982223272324 ], "tsne": [ - -9.112242698669434, - -3.7663733959198 + -14.125904083251953, + 4.992301940917969 ], "umap": [ - -0.054748546332120895, - 6.627526760101318 + -1.7459543943405151, + 3.610934257507324 ], "pca3d": [ - -0.19456426799297333, - -0.08533773571252823, - 0.21880246698856354 + -0.19165143370628357, + -0.0818302184343338, + 0.22419023513793945 ], "tsne3d": [ - 4.873916149139404, - -24.927215576171875, - 7.101595401763916 + -5.3980512619018555, + -5.793868064880371, + 9.564412117004395 ], "umap3d": [ - 4.316095352172852, - -0.8470228910446167, - -1.962807536125183 + 1.1219359636306763, + 0.7708638310432434, + 7.202637195587158 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, + "id": 14, "label": "Clinical Health" } ], @@ -134474,47 +134657,47 @@ "journal": "Nature Biomedical Engineering", "projections": { "pca": [ - -0.10442623496055603, - 0.019900651648640633 + -0.10166230797767639, + 0.022229718044400215 ], "tsne": [ - -14.54956340789795, - 1.4309055805206299 + -12.954391479492188, + 15.09044361114502 ], "umap": [ - -0.33912965655326843, - 7.2054948806762695 + -1.4371435642242432, + 2.9929111003875732 ], "pca3d": [ - -0.10442639887332916, - 0.019897500053048134, - 0.205329030752182 + -0.10166237503290176, + 0.022227391600608826, + 0.21025682985782623 ], "tsne3d": [ - 2.3921971321105957, - -24.005361557006836, - 35.885108947753906 + -0.7333797812461853, + -1.8702752590179443, + 31.16623306274414 ], "umap3d": [ - 4.159966945648193, - -1.5518195629119873, - -1.0345368385314941 + 1.3156633377075195, + -0.1633671373128891, + 6.999264240264893 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 11, - "label": "Medical Imaging" + "id": 13, + "label": "ECG AI Models" } ], "_embedding": [ @@ -134922,47 +135105,47 @@ "journal": "Nature", "projections": { "pca": [ - -0.08835598826408386, - 0.048301033675670624 + -0.08614246547222137, + 0.050043728202581406 ], "tsne": [ - -14.523223876953125, - 1.385481357574463 + -12.95812702178955, + 15.095142364501953 ], "umap": [ - -0.49765005707740784, - 7.284220218658447 + -1.296052098274231, + 2.8250670433044434 ], "pca3d": [ - -0.08835629373788834, - 0.04830187186598778, - 0.1629028618335724 + -0.08614275604486465, + 0.05004490911960602, + 0.16711173951625824 ], "tsne3d": [ - 3.7669661045074463, - -27.82480812072754, - 35.808624267578125 + 1.1364848613739014, + 0.2996346950531006, + 29.478384017944336 ], "umap3d": [ - 4.190999507904053, - -1.5026129484176636, - -0.9570536613464355 + 1.3645782470703125, + -0.30349746346473694, + 6.926072120666504 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 12, - "label": "ECG Models" + "id": 19, + "label": "Medical Imaging" } ], "_embedding": [ @@ -135371,47 +135554,47 @@ "abstract": "EchoJEPA: A Latent Predictive Foundation Model for Echocardiography - bowang-lab/EchoJEPA", "projections": { "pca": [ - -0.09441597759723663, - -0.0598980151116848 + -0.09322385489940643, + -0.058448489755392075 ], "tsne": [ - -19.471555709838867, - 2.982517719268799 + -11.057284355163574, + 13.385321617126465 ], "umap": [ - -0.5788345336914062, - 7.435971736907959 + -1.351730465888977, + 2.5115928649902344 ], "pca3d": [ - -0.09441550076007843, - -0.059895146638154984, - -0.0011382638476788998 + -0.09322339296340942, + -0.058445438742637634, + 0.004758003167808056 ], "tsne3d": [ - -6.312428951263428, - -14.65178394317627, - -22.820035934448242 + -1.0922622680664062, + 4.546258926391602, + 19.2504825592041 ], "umap3d": [ - 4.511889457702637, - -1.6829071044921875, - -0.9265874028205872 + 1.7233874797821045, + -0.5206067562103271, + 7.121076583862305 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 12, - "label": "ECG Models" + "id": 19, + "label": "Medical Imaging" } ], "_embedding": [ @@ -135819,47 +136002,47 @@ "abstract": "Contribute to AartikSarma/pubmed_slackbot development by creating an account on GitHub.", "projections": { "pca": [ - 0.2737165093421936, - -0.0678563341498375 + 0.27511337399482727, + -0.06598753482103348 ], "tsne": [ - 27.718055725097656, - 12.188389778137207 + 30.818477630615234, + -7.490835189819336 ], "umap": [ - 3.4929046630859375, - 6.849733829498291 + 3.241583824157715, + 5.609743595123291 ], "pca3d": [ - 0.2737181484699249, - -0.06784764677286148, - -0.024223312735557556 + 0.27511516213417053, + -0.06598210334777832, + -0.027946162968873978 ], "tsne3d": [ - 30.069740295410156, - 43.12563705444336, - 20.65694236755371 + 42.48072814941406, + 13.666661262512207, + 11.674004554748535 ], "umap3d": [ - 3.51680326461792, - -0.04545123502612114, - 2.878758430480957 + 4.017455577850342, + 2.7695400714874268, + 6.608368873596191 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 18, - "label": "Github Tools" + "id": 17, + "label": "GitHub Creation" } ], "_embedding": [ @@ -136267,46 +136450,46 @@ "journal": "Nature Medicine", "projections": { "pca": [ - -0.13973461091518402, - -0.00017105178267229348 + -0.13477985560894012, + 0.0038404352962970734 ], "tsne": [ - -11.484171867370605, - -0.5485478639602661 + -18.380733489990234, + 8.871112823486328 ], "umap": [ - 0.13618461787700653, - 6.40435266494751 + -1.6320016384124756, + 3.3911967277526855 ], "pca3d": [ - -0.139736145734787, - -0.00018854168592952192, - 0.22272562980651855 + -0.13478146493434906, + 0.003825685242190957, + 0.22519846260547638 ], "tsne3d": [ - 11.019364356994629, - -17.1198673248291, - -3.737734794616699 + -9.582728385925293, + -5.076745986938477, + -2.7791404724121094 ], "umap3d": [ - 4.521698474884033, - -0.6771770715713501, - -1.598891258239746 + 1.3883529901504517, + 0.531104326248169, + 7.341563701629639 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, + "id": 14, "label": "Clinical Health" } ], @@ -136729,47 +136912,47 @@ ], "projections": { "pca": [ - -0.1984786093235016, - -0.10153453797101974 + -0.19611792266368866, + -0.09791562706232071 ], "tsne": [ - -12.398836135864258, - -16.326854705810547 + -21.00164222717285, + -0.07061497867107391 ], "umap": [ - -1.2251663208007812, - 6.6672163009643555 + -1.713571310043335, + 4.5833048820495605 ], "pca3d": [ - -0.1984783262014389, - -0.10152631998062134, - 0.08683520555496216 + -0.19611769914627075, + -0.09790729731321335, + 0.09300563484430313 ], "tsne3d": [ - -13.446297645568848, - -23.08290672302246, - -14.635916709899902 + -21.21904945373535, + -4.589573860168457, + 20.90699577331543 ], "umap3d": [ - 3.535470962524414, - -1.1162601709365845, - -0.8957718014717102 + 0.9603083729743958, + -0.12532393634319305, + 6.140481948852539 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 28, - "label": "Clinical AI" + "id": 21, + "label": "Clinical Trials" } ], "_embedding": [ @@ -137193,47 +137376,47 @@ ], "projections": { "pca": [ - -0.20764446258544922, - -0.13298027217388153 + -0.20598505437374115, + -0.1305130124092102 ], "tsne": [ - -9.30782413482666, - -10.305156707763672 + -14.906450271606445, + -0.7951895594596863 ], "umap": [ - -1.4360053539276123, - 6.231744289398193 + -2.1857967376708984, + 4.441269397735596 ], "pca3d": [ - -0.20764398574829102, - -0.1329723447561264, - 0.05949735268950462 + -0.20598450303077698, + -0.13050530850887299, + 0.06430214643478394 ], "tsne3d": [ - -6.2191009521484375, - -33.36260223388672, - 5.561047554016113 + -14.264400482177734, + -12.633910179138184, + 14.268024444580078 ], "umap3d": [ - 3.193674325942993, - -0.8733906149864197, - -1.213966965675354 + 0.7378553152084351, + 0.39153504371643066, + 6.231699466705322 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 28, - "label": "Clinical AI" + "id": 21, + "label": "Clinical Trials" } ], "_embedding": [ @@ -137639,47 +137822,47 @@ ], "projections": { "pca": [ - 0.1451137512922287, - 0.0629788488149643 + 0.14526395499706268, + 0.06112534925341606 ], "tsne": [ - 8.72655200958252, - -3.035773515701294 + 6.417191505432129, + 1.0873914957046509 ], "umap": [ - 1.540380835533142, - 4.9190239906311035 + 0.7545319199562073, + 4.9840264320373535 ], "pca3d": [ - 0.14511388540267944, - 0.06298500299453735, - 0.10398943722248077 + 0.1452641487121582, + 0.06112737953662872, + 0.09895657747983932 ], "tsne3d": [ - -6.167430877685547, - 15.816908836364746, - -0.6728709936141968 + 25.86855697631836, + -26.302827835083008, + 12.944356918334961 ], "umap3d": [ - 3.104121685028076, - 0.809898853302002, - 0.6526778936386108 + 2.379031181335449, + 1.477102518081665, + 5.118880748748779 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 4, - "label": "Com Area Amp" + "id": 6, + "label": "Data Needs" }, { - "id": 23, - "label": "Research Authors" + "id": 10, + "label": "Research Data" } ], "_embedding": [ @@ -138090,46 +138273,46 @@ "doi": "10.1038/s41591-025-04184-7", "projections": { "pca": [ - -0.20141157507896423, - -0.01789787784218788 + -0.19800874590873718, + -0.01447372417896986 ], "tsne": [ - -7.791187286376953, - -4.202157974243164 + -13.304247856140137, + 3.90082049369812 ], "umap": [ - -0.3116307854652405, - 6.406162738800049 + -1.5574069023132324, + 3.70127534866333 ], "pca3d": [ - -0.20141175389289856, - -0.017904382199048996, - 0.24350053071975708 + -0.1980089396238327, + -0.014479046687483788, + 0.2488650679588318 ], "tsne3d": [ - 10.731066703796387, - -29.204448699951172, - 4.937788963317871 + -7.43251895904541, + -0.1901482492685318, + 6.794912815093994 ], "umap3d": [ - 4.122183799743652, - -0.43215593695640564, - -1.9427289962768555 + 0.8362734317779541, + 0.48602330684661865, + 7.338412761688232 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, + "id": 14, "label": "Clinical Health" } ], @@ -138545,47 +138728,47 @@ ], "projections": { "pca": [ - -0.14030557870864868, - 0.2829395830631256 + -0.1406027227640152, + 0.28379306197166443 ], "tsne": [ - -14.465176582336426, - 23.253671646118164 + 7.504882335662842, + 30.17816162109375 ], "umap": [ - 3.215075731277466, - 2.4933831691741943 + 2.2291929721832275, + 1.500815510749817 ], "pca3d": [ - -0.14030587673187256, - 0.2829400897026062, - -0.03180989623069763 + -0.14060290157794952, + 0.2837934195995331, + -0.02966620959341526 ], "tsne3d": [ - 9.539693832397461, - -1.5612266063690186, - -42.91489791870117 + 7.88106107711792, + 17.089128494262695, + -30.84745979309082 ], "umap3d": [ - 6.974996089935303, - -0.5023676753044128, - 1.0972825288772583 + 4.953558444976807, + -0.7427497506141663, + 5.6619553565979 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 6, + "label": "Learning Techniques" } ], "_embedding": [ @@ -138992,47 +139175,47 @@ "title": "MedGemma \u00e2\u0080\u0094 Google DeepMind", "projections": { "pca": [ - 0.16452361643314362, - -0.0012154803844168782 + 0.16833749413490295, + 0.00040638918289914727 ], "tsne": [ - -10.067636489868164, - 6.304275035858154 + -16.69509506225586, + 18.11298942565918 ], "umap": [ - 0.30554503202438354, - 6.055111408233643 + -0.6112927198410034, + 3.1975467205047607 ], "pca3d": [ - 0.1645239293575287, - -0.0012161381309852004, - 0.22111180424690247 + 0.16833758354187012, + 0.000405263650463894, + 0.21753720939159393 ], "tsne3d": [ - 26.36293601989746, - -37.86063766479492, - -10.561399459838867 + -12.919906616210938, + 21.529367446899414, + 5.727447509765625 ], "umap3d": [ - 4.657812118530273, - -0.6926124095916748, - -0.7311022281646729 + 1.8555147647857666, + 0.22280465066432953, + 7.05184268951416 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 0, - "label": "Clinical LLMs" + "id": 5, + "label": "Health Benchmarks" } ], "_embedding": [ @@ -139439,47 +139622,47 @@ "title": "Next generation medical image interpretation with MedGemma 1.5 and medical speech to text with MedASR", "projections": { "pca": [ - -0.055490195751190186, - -0.07997462898492813 + -0.051814112812280655, + -0.0768403634428978 ], "tsne": [ - -10.753400802612305, - 4.9875168800354 + -16.23430061340332, + 16.642202377319336 ], "umap": [ - 0.266999751329422, - 7.0448689460754395 + -0.7554318904876709, + 3.055299997329712 ], "pca3d": [ - -0.05549105629324913, - -0.07998388260602951, - 0.2276461124420166 + -0.0518149733543396, + -0.07684803009033203, + 0.23233619332313538 ], "tsne3d": [ - 27.999935150146484, - -32.20083236694336, - -7.3754658699035645 + -10.209029197692871, + 18.379987716674805, + 10.47533130645752 ], "umap3d": [ - 4.651923179626465, - -1.0183476209640503, - -0.9105190634727478 + 1.843619465827942, + 0.14357317984104156, + 7.068785667419434 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 11, - "label": "Medical Imaging" + "id": 5, + "label": "Health Benchmarks" } ], "_embedding": [ @@ -139887,47 +140070,47 @@ "abstract": "Groups models released for use in health AI by Google. Read more about HAI-DEF at http://goo.gle/hai-def", "projections": { "pca": [ - 0.019402727484703064, - -0.01595865935087204 + 0.022343246266245842, + -0.013226361013948917 ], "tsne": [ - -13.502464294433594, - -0.7588340640068054 + -14.46296501159668, + 13.654264450073242 ], "umap": [ - -0.4519343078136444, - 6.8842267990112305 + -1.8620171546936035, + 3.2996132373809814 ], "pca3d": [ - 0.01940249092876911, - -0.01595528982579708, - 0.1572585105895996 + 0.022343017160892487, + -0.013222533278167248, + 0.15908914804458618 ], "tsne3d": [ - 8.880766868591309, - -28.504472732543945, - 29.286731719970703 + 5.603384971618652, + -2.7275874614715576, + 25.156097412109375 ], "umap3d": [ - 4.271242618560791, - -1.131170392036438, - -1.4868921041488647 + 1.1871365308761597, + 0.5879967212677002, + 7.276476860046387 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, - "label": "Clinical Health" + "id": 13, + "label": "ECG AI Models" } ], "_embedding": [ @@ -140334,47 +140517,47 @@ "title": "Using Interpretability to Identify a Novel Class of Alzheimer's Biomarkers", "projections": { "pca": [ - -0.20062699913978577, - -0.02692379429936409 + -0.1985643059015274, + -0.02401607111096382 ], "tsne": [ - -7.803494930267334, - -36.82670974731445 + -24.76613426208496, + -27.365461349487305 ], "umap": [ - -1.893193006515503, - 3.9439220428466797 + -1.8543710708618164, + 6.612950801849365 ], "pca3d": [ - -0.20062653720378876, - -0.026929492130875587, - 0.02127787284553051 + -0.1985638588666916, + -0.024022087454795837, + 0.024045925587415695 ], "tsne3d": [ - -64.33512115478516, - -21.95627212524414, - -14.210858345031738 + -37.6965217590332, + 2.4637768268585205, + -6.274338722229004 ], "umap3d": [ - 1.5627009868621826, - 0.23534321784973145, - -1.4144262075424194 + 0.19668684899806976, + 1.166877031326294, + 4.511475086212158 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 2, - "label": "Genetic Disease" + "id": 27, + "label": "Genetic Variants" } ], "_embedding": [ @@ -140784,46 +140967,46 @@ "journal": "Nature", "projections": { "pca": [ - -0.25434643030166626, - -0.062206756323575974 + -0.2548484206199646, + -0.05923629552125931 ], "tsne": [ - 4.205970764160156, - -25.5710506439209 + -10.045350074768066, + -18.201583862304688 ], "umap": [ - -2.494187116622925, - 4.16126823425293 + -2.330314874649048, + 6.889782905578613 ], "pca3d": [ - -0.25434544682502747, - -0.062192466109991074, - -0.12154146283864975 + -0.25484758615493774, + -0.05922186002135277, + -0.12100531160831451 ], "tsne3d": [ - -43.346683502197266, - -16.55682945251465, - 13.053740501403809 + -29.112751007080078, + -2.9003992080688477, + -9.973469734191895 ], "umap3d": [ - 1.3676968812942505, - -0.09281085431575775, - -1.4990379810333252 + -0.1753627061843872, + 1.2675859928131104, + 4.471470355987549 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 22, + "id": 27, "label": "Genetic Variants" } ], @@ -141230,46 +141413,46 @@ ], "projections": { "pca": [ - -0.01995265670120716, - -0.05757205933332443 + -0.017427612096071243, + -0.05630997568368912 ], "tsne": [ - -6.402782440185547, - -6.512807369232178 + -19.719810485839844, + 2.923523426055908 ], "umap": [ - -0.5318590998649597, - 6.5359015464782715 + -1.7189645767211914, + 3.873474597930908 ], "pca3d": [ - -0.019952677190303802, - -0.05757632106542587, - 0.16370797157287598 + -0.017427733168005943, + -0.05631463602185249, + 0.16413122415542603 ], "tsne3d": [ - 9.170744895935059, - -44.59139633178711, - 15.8324613571167 + 10.989786148071289, + -25.03554344177246, + 8.628890037536621 ], "umap3d": [ - 3.9622297286987305, - -0.6782733201980591, - -2.1636345386505127 + 0.6552332639694214, + 0.8139591813087463, + 7.219343185424805 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, + "id": 14, "label": "Clinical Health" } ], @@ -141684,47 +141867,47 @@ ], "projections": { "pca": [ - -0.13844087719917297, - 0.21666313707828522 + -0.1397435963153839, + 0.21627765893936157 ], "tsne": [ - -21.84213638305664, - 20.387556076049805 + 0.007326730992645025, + 29.56280517578125 ], "umap": [ - 2.8172662258148193, - 2.5248334407806396 + 1.7509915828704834, + 1.5374068021774292 ], "pca3d": [ - -0.1384415477514267, - 0.21666319668293, - -0.10734797269105911 + -0.13974431157112122, + 0.21627745032310486, + -0.10425230115652084 ], "tsne3d": [ - 8.04410171508789, - 8.281580924987793, - -35.478397369384766 + 4.264358997344971, + 4.786174297332764, + -26.930444717407227 ], "umap3d": [ - 6.678139686584473, - -0.7894608974456787, - 0.9505152106285095 + 4.341868877410889, + -0.9539223313331604, + 5.724515914916992 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 26, + "label": "World Models" } ], "_embedding": [ @@ -142132,47 +142315,47 @@ "journal": "Nature Communications", "projections": { "pca": [ - -0.24505291879177094, - -0.00614430196583271 + -0.2443213164806366, + -0.002209238475188613 ], "tsne": [ - -4.057983875274658, - -34.609012603759766 + -17.916608810424805, + -27.431819915771484 ], "umap": [ - -1.9669489860534668, - 2.981555461883545 + -1.3248529434204102, + 7.617578029632568 ], "pca3d": [ - -0.24505281448364258, - -0.006124861538410187, - -0.1255779266357422 + -0.24432136118412018, + -0.002189192920923233, + -0.12505272030830383 ], "tsne3d": [ - -51.62705612182617, - -6.416625022888184, - -17.050561904907227 + -37.5009651184082, + -15.964116096496582, + -27.253433227539062 ], "umap3d": [ - 0.8888645768165588, - 0.9559096693992615, - -1.0454658269882202 + 0.17861905694007874, + 0.8984062075614929, + 3.4907169342041016 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 8, - "label": "Gene Regulation" + "id": 3, + "label": "Genomic Sequences" } ], "_embedding": [ @@ -142578,47 +142761,47 @@ ], "projections": { "pca": [ - 0.2280767560005188, - 0.06011602655053139 + 0.22826342284679413, + 0.06053050979971886 ], "tsne": [ - 12.036425590515137, - -1.4985315799713135 + 10.01297664642334, + 0.7577409148216248 ], "umap": [ - 1.8538174629211426, - 5.434427738189697 + 1.2217439413070679, + 4.948597431182861 ], "pca3d": [ - 0.2280767261981964, - 0.06011281535029411, - -0.003193735843524337 + 0.22826336324214935, + 0.060526251792907715, + -0.008889690041542053 ], "tsne3d": [ - 0.4622313976287842, - 16.50467872619629, - 7.148664474487305 + 21.450645446777344, + -15.450443267822266, + 6.583184242248535 ], "umap3d": [ - 3.2785394191741943, - 0.8656473159790039, - 1.1854017972946167 + 2.8059847354888916, + 1.7500057220458984, + 5.360361099243164 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 4, - "label": "Com Area Amp" + "id": 6, + "label": "Data Needs" }, { - "id": 26, - "label": "AI Information" + "id": 10, + "label": "Research Data" } ], "_embedding": [ @@ -143026,47 +143209,47 @@ "abstract": "This report introduces new metrics of AI usage to provide a rich portrait of interactions with Claude in November 2025, just prior to the release of Opus 4.5.", "projections": { "pca": [ - 0.20118607580661774, - 0.0593874566257 + 0.2033105194568634, + 0.061046697199344635 ], "tsne": [ - 21.610139846801758, - 26.789478302001953 + 34.297977447509766, + 9.805487632751465 ], "umap": [ - 4.885593891143799, - 5.852833271026611 + 4.367441654205322, + 4.460601806640625 ], "pca3d": [ - 0.20118620991706848, - 0.059394944459199905, - 0.1600044071674347 + 0.2033110111951828, + 0.06105383113026619, + 0.15512877702713013 ], "tsne3d": [ - 57.52449417114258, - 8.01371955871582, - 10.00676155090332 + 17.609025955200195, + 38.374610900878906, + 2.947237491607666 ], "umap3d": [ - 5.284520149230957, - 0.6857316493988037, - 3.024437665939331 + 5.471050262451172, + 2.2088375091552734, + 6.0786004066467285 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { - "id": 16, - "label": "Claude Code" + "id": 11, + "label": "Code Tools" } ], "_embedding": [ @@ -143476,47 +143659,47 @@ "abstract": "Holistic Evaluation of Language Models (HELM) is an open source Python framework created by the Center for Research on Foundation Models (CRFM) at Stanford for holistic, reproducible and transparent evaluation of foundation models, including large language models (LLMs) and multimodal models. - stanford-crfm/helm", "projections": { "pca": [ - 0.10442952066659927, - -0.022937191650271416 + 0.1045028567314148, + -0.022972755134105682 ], "tsne": [ - -7.133891582489014, - 4.748621463775635 + -13.90357780456543, + 11.013801574707031 ], "umap": [ - 0.7210670113563538, - 6.596526145935059 + -0.5855879187583923, + 3.5960514545440674 ], "pca3d": [ - 0.10442879796028137, - -0.022923996672034264, - -0.01295844092965126 + 0.10450217127799988, + -0.022960202768445015, + -0.011701353825628757 ], "tsne3d": [ - 28.327699661254883, - 34.2052001953125, - -29.91324806213379 + 3.833613395690918, + 5.79351282119751, + 8.96850872039795 ], "umap3d": [ - 5.014400959014893, - -0.6847034096717834, - -1.2086617946624756 + 1.9217910766601562, + 0.9724656343460083, + 7.151613235473633 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 0, - "label": "Clinical LLMs" + "id": 5, + "label": "Health Benchmarks" } ], "_embedding": [ @@ -143924,46 +144107,46 @@ "journal": "Nature Medicine", "projections": { "pca": [ - -0.14572541415691376, - 0.06406976282596588 + -0.14141367375850677, + 0.06752549111843109 ], "tsne": [ - -10.795886039733887, - 0.09654789417982101 + -17.579404830932617, + 9.52295207977295 ], "umap": [ - 0.16148249804973602, - 6.505414009094238 + -1.4451266527175903, + 3.3685948848724365 ], "pca3d": [ - -0.14572690427303314, - 0.06405854225158691, - 0.16899670660495758 + -0.14141522347927094, + 0.06751611083745956, + 0.17085634171962738 ], "tsne3d": [ - 15.281103134155273, - -18.24687385559082, - 1.0584650039672852 + -7.533089637756348, + -0.7709910273551941, + -0.49667349457740784 ], "umap3d": [ - 4.593656063079834, - -0.7179595828056335, - -1.525528907775879 + 1.4202873706817627, + 0.5363634824752808, + 7.360724449157715 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, + "id": 14, "label": "Clinical Health" } ], @@ -144391,47 +144574,47 @@ "doi": "10.1101/2024.11.16.623974", "projections": { "pca": [ - -0.18611003458499908, - -0.08306561410427094 + -0.18764245510101318, + -0.0823555439710617 ], "tsne": [ - -8.005738258361816, - -26.454435348510742 + -23.602750778198242, + -21.453649520874023 ], "umap": [ - -1.085174560546875, - 3.5760715007781982 + -0.7087854146957397, + 6.59070348739624 ], "pca3d": [ - -0.18610961735248566, - -0.08306621015071869, - -0.16425834596157074 + -0.18764221668243408, + -0.08235597610473633, + -0.16030408442020416 ], "tsne3d": [ - -54.87646484375, - -28.96014976501465, - -2.2372450828552246 + -29.291221618652344, + -35.14490509033203, + -8.045701026916504 ], "umap3d": [ - 1.8882650136947632, - 1.2434834241867065, - -1.4338558912277222 + 0.7869543433189392, + 0.09241551160812378, + 3.8899524211883545 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 19, - "label": "Cell Models" + "id": 20, + "label": "Cell Foundation" } ], "_embedding": [ @@ -144846,47 +145029,47 @@ "doi": "10.3390/w18080899", "projections": { "pca": [ - 0.055656611919403076, - -0.11685438454151154 + 0.05622011050581932, + -0.11584276705980301 ], "tsne": [ - 12.386799812316895, - -15.30575942993164 + -0.33660218119621277, + -15.202312469482422 ], "umap": [ - -0.05521082878112793, - 4.587103366851807 + -0.31421756744384766, + 5.171623229980469 ], "pca3d": [ - 0.05565692111849785, - -0.11684325337409973, - -0.0402783639729023 + 0.05622049421072006, + -0.11583184450864792, + -0.04124885052442551 ], "tsne3d": [ - -28.326143264770508, - 13.507149696350098, - 18.362863540649414 + 8.272789001464844, + -28.708389282226562, + -11.407980918884277 ], "umap3d": [ - 2.7608706951141357, - 1.0557587146759033, - -0.4283069968223572 + 1.3705610036849976, + 1.4663550853729248, + 4.736150741577148 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ - { - "id": 0, - "label": "Medical Data AI" - }, { "id": 2, - "label": "AI Drug Discovery" + "label": "Clinical AI Data" }, { - "id": 13, + "id": 9, "label": "Drug Discovery" + }, + { + "id": 23, + "label": "Peer Review" } ], "_embedding": [ @@ -145294,47 +145477,47 @@ ], "projections": { "pca": [ - -0.16008153557777405, - 0.20833101868629456 + -0.1572752147912979, + 0.20955398678779602 ], "tsne": [ - -25.912321090698242, - 7.083156585693359 + -0.33977824449539185, + 16.28972053527832 ], "umap": [ - 1.9114235639572144, - 3.673668384552002 + 1.180742859840393, + 2.3226518630981445 ], "pca3d": [ - -0.16008155047893524, - 0.20832806825637817, - 0.044128675013780594 + -0.15727540850639343, + 0.20955078303813934, + 0.04563720524311066 ], "tsne3d": [ - -26.337635040283203, - 19.159671783447266, - -32.01971435546875 + -10.831278800964355, + 23.19107437133789, + -29.293201446533203 ], "umap3d": [ - 5.290022850036621, - -0.9855412840843201, - 0.4264592230319977 + 3.6026179790496826, + -0.28573793172836304, + 6.341897010803223 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 27, - "label": "Embeddings" + "id": 18, + "label": "Retrieval Learning" } ], "_embedding": [ @@ -145742,47 +145925,47 @@ "journal": "Nature", "projections": { "pca": [ - -0.2372879832983017, - 0.004878162872046232 + -0.23060759902000427, + 0.008812809363007545 ], "tsne": [ - -13.950090408325195, - -10.747822761535645 + -21.459247589111328, + 10.736823081970215 ], "umap": [ - -0.7791730761528015, - 6.89185905456543 + -1.322964072227478, + 2.8667218685150146 ], "pca3d": [ - -0.23728764057159424, - 0.004875496029853821, - 0.2119215428829193 + -0.2306075394153595, + 0.008810682222247124, + 0.21553967893123627 ], "tsne3d": [ - -6.2070817947387695, - -41.407325744628906, - -4.916131019592285 + -15.88479995727539, + 7.477467060089111, + 11.425024032592773 ], "umap3d": [ - 4.165867805480957, - -1.0883150100708008, - -1.0436906814575195 + 1.3844362497329712, + -0.254391610622406, + 7.121883392333984 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 12, - "label": "ECG Models" + "id": 19, + "label": "Medical Imaging" } ], "_embedding": [ @@ -146214,47 +146397,47 @@ ], "projections": { "pca": [ - -0.04721252992749214, - -0.057001058012247086 + -0.04348232224583626, + -0.053044579923152924 ], "tsne": [ - -5.535311222076416, - -4.182568550109863 + -10.15943717956543, + 2.2083728313446045 ], "umap": [ - -0.2548857033252716, - 6.29161262512207 + -1.1060601472854614, + 3.9101479053497314 ], "pca3d": [ - -0.04721193388104439, - -0.05700010433793068, - 0.22278545796871185 + -0.04348175227642059, + -0.053043439984321594, + 0.2257470190525055 ], "tsne3d": [ - 11.667844772338867, - -33.531558990478516, - 10.43846321105957 + -5.729142189025879, + 5.078296661376953, + 5.722995758056641 ], "umap3d": [ - 4.005138397216797, - -0.3789690136909485, - -1.9285852909088135 + 0.8232943415641785, + 0.48060867190361023, + 7.396062850952148 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, - "label": "Clinical Health" + "id": 5, + "label": "Health Benchmarks" } ], "_embedding": [ @@ -146672,47 +146855,47 @@ "doi": "10.1101/2025.02.06.636901", "projections": { "pca": [ - -0.2084675431251526, - 0.05605060234665871 + -0.21116098761558533, + 0.05665760859847069 ], "tsne": [ - 1.518115758895874, - -36.72837829589844 + -11.871325492858887, + -28.561677932739258 ], "umap": [ - -1.4447051286697388, - 2.521383762359619 + -0.5945974588394165, + 7.655855655670166 ], "pca3d": [ - -0.20846834778785706, - 0.05603233724832535, - -0.2332802563905716 + -0.21116185188293457, + 0.05663970112800598, + -0.22940978407859802 ], "tsne3d": [ - -39.86035919189453, - 9.84016227722168, - -16.810256958007812 + -34.548828125, + -31.589752197265625, + -17.53681182861328 ], "umap3d": [ - 1.3472895622253418, - 1.3181899785995483, - -0.47144585847854614 + 0.9876024723052979, + 0.9254406094551086, + 3.4005095958709717 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 21, - "label": "Protein RNA" + "id": 12, + "label": "Protein Language" } ], "_embedding": [ @@ -147120,46 +147303,46 @@ "journal": "Nature", "projections": { "pca": [ - 0.032634150236845016, - -0.19792361557483673 + 0.03347281366586685, + -0.1970282793045044 ], "tsne": [ - -6.2978668212890625, - -19.723154067993164 + -25.157075881958008, + -6.265655994415283 ], "umap": [ - -2.4176766872406006, - 6.155457973480225 + -2.610478401184082, + 5.262317180633545 ], "pca3d": [ - 0.03263422101736069, - -0.19792255759239197, - 0.0366433821618557 + 0.03347296640276909, + -0.19702771306037903, + 0.03832964971661568 ], "tsne3d": [ - -30.481189727783203, - -43.27326583862305, - -2.0807430744171143 + -33.633724212646484, + -24.396595001220703, + 17.592487335205078 ], "umap3d": [ - 2.5741922855377197, - -1.1620322465896606, - -1.0160146951675415 + 0.06751003116369247, + -0.08556940406560898, + 5.7828216552734375 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 24, + "id": 1, "label": "Cancer Data" } ], @@ -147606,47 +147789,47 @@ "openalex_url": "https://openalex.org/W7119529911", "projections": { "pca": [ - -0.11797772347927094, - -0.0627259910106659 + -0.11802342534065247, + -0.060750894248485565 ], "tsne": [ - 0.35325226187705994, - -15.542927742004395 + -8.528608322143555, + -9.394852638244629 ], "umap": [ - -0.7285881638526917, - 4.387211799621582 + -0.7882277369499207, + 5.70621395111084 ], "pca3d": [ - -0.11797818541526794, - -0.0627380758523941, - -0.08029679208993912 + -0.11802396923303604, + -0.06076131761074066, + -0.07669930160045624 ], "tsne3d": [ - -45.399757385253906, - -38.38703918457031, - 5.550037860870361 + -9.858494758605957, + -30.35679054260254, + -11.238039016723633 ], "umap3d": [ - 2.8908634185791016, - 0.9277870059013367, - -1.1777384281158447 + 1.657639980316162, + 0.22701814770698547, + 4.359741687774658 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 2, - "label": "AI Drug Discovery" + "id": 9, + "label": "Drug Discovery" }, { - "id": 13, - "label": "Drug Discovery" + "id": 28, + "label": "Spatial Drug" } ], "_embedding": [ @@ -148065,47 +148248,47 @@ ], "projections": { "pca": [ - -0.11888227611780167, - -0.12137452512979507 + -0.11650163680315018, + -0.1183527335524559 ], "tsne": [ - -6.793760299682617, - -0.2887679636478424 + -11.21822738647461, + 6.615148544311523 ], "umap": [ - 0.45467522740364075, - 6.476388931274414 + -1.367809772491455, + 3.878434658050537 ], "pca3d": [ - -0.11888207495212555, - -0.12137399613857269, - 0.154622882604599 + -0.11650144308805466, + -0.11835286766290665, + 0.15846040844917297 ], "tsne3d": [ - 9.27280330657959, - -15.672318458557129, - 14.93761157989502 + 4.7660417556762695, + -11.302332878112793, + 11.792027473449707 ], "umap3d": [ - 4.497703552246094, - -0.7365779876708984, - -1.7112760543823242 + 1.4303545951843262, + 0.9703966379165649, + 7.198522090911865 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 0, - "label": "Clinical LLMs" + "id": 14, + "label": "Clinical Health" } ], "_embedding": [ @@ -148513,47 +148696,47 @@ "journal": "Nature Medicine", "projections": { "pca": [ - -0.14898942410945892, - -0.24487009644508362 + -0.14844569563865662, + -0.2424822598695755 ], "tsne": [ - -4.621514320373535, - -18.758914947509766 + -16.85468864440918, + -10.285649299621582 ], "umap": [ - -2.0761704444885254, - 5.634236812591553 + -2.1873340606689453, + 5.200939178466797 ], "pca3d": [ - -0.14898884296417236, - -0.2448650300502777, - 0.04041372239589691 + -0.148445263504982, + -0.24247680604457855, + 0.04187057912349701 ], "tsne3d": [ - -16.335430145263672, - -19.523649215698242, - -0.30258652567863464 + -19.353527069091797, + -7.5035319328308105, + 6.079402446746826 ], "umap3d": [ - 2.588850259780884, - -0.6788244247436523, - -0.7957382798194885 + 0.2821348011493683, + 0.42581072449684143, + 5.631068229675293 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 9, - "label": "Cancer Biobank" + "id": 1, + "label": "Cancer Data" } ], "_embedding": [ @@ -148961,47 +149144,47 @@ "journal": "Nature Machine Intelligence", "projections": { "pca": [ - -0.2284112572669983, - 0.18885308504104614 + -0.22893281280994415, + 0.18929454684257507 ], "tsne": [ - -13.5171480178833, - -32.662109375 + -29.29621696472168, + -20.926523208618164 ], "umap": [ - -1.014790654182434, - 2.9373092651367188 + 0.48014673590660095, + 2.855379581451416 ], "pca3d": [ - -0.22841286659240723, - 0.1888386607170105, - -0.12184829264879227 + -0.2289346307516098, + 0.18928207457065582, + -0.12171543389558792 ], "tsne3d": [ - -50.345603942871094, - -17.643413543701172, - -24.29758071899414 + -20.163097381591797, + 9.099625587463379, + -28.95088768005371 ], "umap3d": [ - 1.453382134437561, - 1.4717670679092407, - -1.3956495523452759 + 0.8548480272293091, + -0.2226928472518921, + 4.154306888580322 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 0, + "label": "Multimodal Models" }, { - "id": 7, - "label": "Cell Models" + "id": 2, + "label": "Medical Models" }, { - "id": 19, - "label": "Cell Models" + "id": 0, + "label": "Multimodal Models" } ], "_embedding": [ @@ -149426,47 +149609,47 @@ "openalex_url": "https://openalex.org/W7117329996", "projections": { "pca": [ - -0.256529301404953, - -0.02193799614906311 + -0.25925779342651367, + -0.021710097789764404 ], "tsne": [ - -7.2212114334106445, - -26.579357147216797 + -22.936603546142578, + -21.988271713256836 ], "umap": [ - -1.0917476415634155, - 3.4730217456817627 + -0.6167600750923157, + 6.6754679679870605 ], "pca3d": [ - -0.25652870535850525, - -0.021931685507297516, - -0.21414721012115479 + -0.2592572569847107, + -0.02170509845018387, + -0.21042375266551971 ], "tsne3d": [ - -52.299400329589844, - -26.227724075317383, - -6.347330093383789 + -27.09980010986328, + -31.0557918548584, + -8.791191101074219 ], "umap3d": [ - 1.7838491201400757, - 1.2851868867874146, - -1.3440308570861816 + 0.7252430319786072, + 0.18306253850460052, + 3.8650708198547363 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 19, - "label": "Cell Models" + "id": 20, + "label": "Cell Foundation" } ], "_embedding": [ @@ -149876,47 +150059,47 @@ "abstract": "First published in 1869, Nature is the world\u2019s leading multidisciplinary science journal. Nature publishes the finest peer-reviewed research that drives ground-breaking discovery, and is read by thought-leaders and decision-makers around the world.", "projections": { "pca": [ - 0.06425444036722183, - -0.2917204797267914 + 0.06487245112657547, + -0.28987765312194824 ], "tsne": [ - -3.7329940795898438, - -9.356290817260742 + -19.14777374267578, + -1.6731288433074951 ], "umap": [ - -1.1967425346374512, - 6.274199485778809 + -2.0850038528442383, + 4.361879825592041 ], "pca3d": [ - 0.0642550140619278, - -0.29171222448349, - 0.11603552103042603 + 0.06487303972244263, + -0.2898693084716797, + 0.11751005798578262 ], "tsne3d": [ - -1.9694215059280396, - -34.39541244506836, - 21.74349021911621 + -4.849950790405273, + -30.505170822143555, + 19.971315383911133 ], "umap3d": [ - 3.3500781059265137, - -0.7282232046127319, - -1.617495059967041 + 0.5790529251098633, + 0.5906420350074768, + 6.460447311401367 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 1, - "label": "Cancer Data" + "id": 5, + "label": "Clinical Data" }, { - "id": 28, - "label": "Clinical AI" + "id": 21, + "label": "Clinical Trials" } ], "_embedding": [ @@ -150325,47 +150508,47 @@ "cited_by_count": 4, "projections": { "pca": [ - -0.15836238861083984, - 0.08380135148763657 + -0.15687178075313568, + 0.08688880503177643 ], "tsne": [ - -17.82990837097168, - -5.503955364227295 + -9.789353370666504, + 18.077512741088867 ], "umap": [ - 0.23068088293075562, - 3.754242181777954 + -0.31212806701660156, + 2.639241933822632 ], "pca3d": [ - -0.15836215019226074, - 0.08381704241037369, - -0.03983260691165924 + -0.15687157213687897, + 0.08690401166677475, + -0.036484234035015106 ], "tsne3d": [ - -21.61052894592285, - -8.329405784606934, - -26.286813735961914 + -23.497922897338867, + 5.366331577301025, + 14.723925590515137 ], "umap3d": [ - 4.36237096786499, - -1.1781797409057617, - -0.4561988115310669 + 1.600240707397461, + -0.6065104603767395, + 6.136836528778076 ] }, "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_label": "Clinical AI Data", "cluster_levels": [ { "id": 2, - "label": "Bio Models Gene" + "label": "Clinical AI Data" }, { "id": 2, - "label": "AI Drug Discovery" + "label": "Medical Models" }, { - "id": 17, - "label": "Literature AI" + "id": 19, + "label": "Medical Imaging" } ], "_embedding": [ @@ -150785,47 +150968,47 @@ ], "projections": { "pca": [ - -0.17033062875270844, - 0.2755906581878662 + -0.16882379353046417, + 0.2770017981529236 ], "tsne": [ - -19.067554473876953, - 12.631649017333984 + -2.5186233520507812, + 22.360153198242188 ], "umap": [ - 1.676855206489563, - 2.6323626041412354 + 0.6118810772895813, + 1.5611417293548584 ], "pca3d": [ - -0.17033137381076813, - 0.2755803167819977, - -0.0644112154841423 + -0.16882441937923431, + 0.276991069316864, + -0.060112789273262024 ], "tsne3d": [ - -4.463741779327393, - 5.670792102813721, - -33.816349029541016 + 3.4549930095672607, + -0.33357998728752136, + -17.213159561157227 ], "umap3d": [ - 6.302570343017578, - -1.0968650579452515, - -0.03895826265215874 + 3.7097890377044678, + -1.349879264831543, + 6.413546562194824 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 15, - "label": "Latent Models" + "id": 8, + "label": "JEPA Video Models" } ], "_embedding": [ @@ -151231,47 +151414,47 @@ ], "projections": { "pca": [ - 0.03417959809303284, - -0.0034221939276903868 + 0.033031247556209564, + -0.0043958090245723724 ], "tsne": [ - -1.19647216796875, - -37.10740661621094 + -14.975543975830078, + -29.959922790527344 ], "umap": [ - -1.548418402671814, - 2.5758750438690186 + -0.8677645921707153, + 7.618666172027588 ], "pca3d": [ - 0.0341782346367836, - -0.003441903041675687, - -0.15552788972854614 + 0.03302977979183197, + -0.004414142109453678, + -0.15748177468776703 ], "tsne3d": [ - -62.55228805541992, - -1.4393309354782104, - -4.722342491149902 + -24.474464416503906, + -33.52128601074219, + -19.197404861450195 ], "umap3d": [ - 1.1933311223983765, - 1.2295196056365967, - -0.7666563391685486 + 0.6013258695602417, + 1.0086644887924194, + 3.548584222793579 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 21, - "label": "Protein RNA" + "id": 12, + "label": "Protein Language" } ], "_embedding": [ @@ -151683,47 +151866,47 @@ "doi": "10.1186/s41512-025-00214-7", "projections": { "pca": [ - 0.08460602909326553, - -0.14661851525306702 + 0.08549705147743225, + -0.1461525708436966 ], "tsne": [ - -4.775247573852539, - -7.335872173309326 + -21.39765739440918, + 1.3028090000152588 ], "umap": [ - -0.9624090790748596, - 6.5779805183410645 + -1.8316222429275513, + 4.128361701965332 ], "pca3d": [ - 0.08460593968629837, - -0.1466146558523178, - 0.06387166678905487 + 0.08549704402685165, + -0.14614784717559814, + 0.06626307964324951 ], "tsne3d": [ - 7.836859226226807, - -40.38588333129883, - 24.45792007446289 + -17.246801376342773, + -6.56266975402832, + 26.368602752685547 ], "umap3d": [ - 3.5898914337158203, - -1.0300389528274536, - -1.7212085723876953 + 0.5556222796440125, + 0.6282796263694763, + 6.936060428619385 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 28, - "label": "Clinical AI" + "id": 14, + "label": "Clinical Health" } ], "_embedding": [ @@ -152140,47 +152323,47 @@ ], "projections": { "pca": [ - -0.06465699523687363, - 0.18978488445281982 + -0.06509195268154144, + 0.18952973186969757 ], "tsne": [ - -18.808429718017578, - 23.841455459594727 + 6.974814414978027, + 26.291976928710938 ], "umap": [ - 2.7489614486694336, - 3.1028528213500977 + 1.6983774900436401, + 2.002455472946167 ], "pca3d": [ - -0.06465655565261841, - 0.18979592621326447, - -0.006117661949247122 + -0.0650915801525116, + 0.18953640758991241, + -0.007078973576426506 ], "tsne3d": [ - 18.382314682006836, - -7.468029022216797, - -23.646060943603516 + 20.848146438598633, + 7.044778347015381, + -39.30842971801758 ], "umap3d": [ - 6.405750751495361, - -0.289635568857193, - 0.8470682501792908 + 4.702960968017578, + -0.8062358498573303, + 6.131385803222656 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 18, + "label": "Retrieval Learning" } ], "_embedding": [ @@ -152596,47 +152779,47 @@ "doi": "10.1101/2025.11.20.689462", "projections": { "pca": [ - -0.20470356941223145, - -0.023287333548069 + -0.20598746836185455, + -0.021036040037870407 ], "tsne": [ - -9.837142944335938, - -26.082761764526367 + -24.416135787963867, + -18.068225860595703 ], "umap": [ - -1.1703500747680664, - 3.2954859733581543 + -1.0888402462005615, + 6.507840633392334 ], "pca3d": [ - -0.20470255613327026, - -0.023279139772057533, - -0.16328616440296173 + -0.20598646998405457, + -0.021030321717262268, + -0.157859206199646 ], "tsne3d": [ - -30.917524337768555, - -15.464483261108398, - -16.54422950744629 + -32.10335159301758, + -23.89824104309082, + 3.6954355239868164 ], "umap3d": [ - 1.6414254903793335, - 1.0452840328216553, - -1.594128131866455 + 0.4414697289466858, + -0.08262801170349121, + 4.288218975067139 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 19, - "label": "Cell Models" + "id": 20, + "label": "Cell Foundation" } ], "_embedding": [ @@ -153058,46 +153241,46 @@ "openalex_url": "https://openalex.org/W4417274608", "projections": { "pca": [ - 0.05233693867921829, - -0.25571370124816895 + 0.05189242213964462, + -0.2556168735027313 ], "tsne": [ - -2.4348397254943848, - -15.896379470825195 + -24.35045051574707, + -4.929599761962891 ], "umap": [ - -2.3307454586029053, - 6.207690238952637 + -2.7078311443328857, + 5.029236793518066 ], "pca3d": [ - 0.0523364394903183, - -0.25573021173477173, - 0.003112474922090769 + 0.05189209803938866, + -0.25563159584999084, + 0.006044671405106783 ], "tsne3d": [ - -31.170276641845703, - -41.465423583984375, - 8.220320701599121 + -26.136489868164062, + -28.5416259765625, + 14.214301109313965 ], "umap3d": [ - 2.518026828765869, - -1.1807856559753418, - -1.1156338453292847 + -0.029249288141727448, + 0.10755715519189835, + 5.877739429473877 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 24, + "id": 1, "label": "Cancer Data" } ], @@ -153510,46 +153693,46 @@ ], "projections": { "pca": [ - -0.07763488590717316, - 0.24927212297916412 + -0.07770600914955139, + 0.24848563969135284 ], "tsne": [ - -26.61449432373047, - 17.56088638305664 + 1.2248928546905518, + 26.330495834350586 ], "umap": [ - 2.37674880027771, - 2.217498779296875 + 1.3272351026535034, + 1.3250898122787476 ], "pca3d": [ - -0.07763570547103882, - 0.2492694854736328, - -0.10926437377929688 + -0.0777069628238678, + 0.2484823763370514, + -0.10713425278663635 ], "tsne3d": [ - -0.9543960690498352, - 24.533414840698242, - -41.158180236816406 + -10.105082511901855, + 8.017735481262207, + -33.187591552734375 ], "umap3d": [ - 6.7357587814331055, - -1.3135703802108765, - 0.5639830231666565 + 3.747272253036499, + -1.1595423221588135, + 5.598519325256348 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 4, + "id": 26, "label": "World Models" } ], @@ -153960,46 +154143,46 @@ "doi": "10.1016/s2589-7500(21)00206-5", "projections": { "pca": [ - 0.025519665330648422, - -0.005939388182014227 + 0.02741621434688568, + -0.004366825334727764 ], "tsne": [ - -5.449250221252441, - -5.861965656280518 + -20.55099105834961, + 2.2548182010650635 ], "umap": [ - -0.543135941028595, - 6.534393310546875 + -1.6807278394699097, + 3.9099607467651367 ], "pca3d": [ - 0.025519469752907753, - -0.005944177974015474, - 0.16447970271110535 + 0.027416056022047997, + -0.004371974151581526, + 0.16743561625480652 ], "tsne3d": [ - 9.342630386352539, - -37.693748474121094, - 18.195110321044922 + -12.367531776428223, + 0.767539381980896, + 9.231582641601562 ], "umap3d": [ - 3.9845409393310547, - -0.6538445353507996, - -2.0890839099884033 + 0.6593905687332153, + 0.6690162420272827, + 7.23568058013916 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, + "id": 14, "label": "Clinical Health" } ], @@ -154415,47 +154598,47 @@ ], "projections": { "pca": [ - -0.17027924954891205, - 0.22341902554035187 + -0.1702108234167099, + 0.22438618540763855 ], "tsne": [ - -21.074058532714844, - 14.906791687011719 + -3.125826120376587, + 24.52370834350586 ], "umap": [ - 1.9392790794372559, - 2.8626294136047363 + 1.023531198501587, + 1.9927321672439575 ], "pca3d": [ - -0.17027918994426727, - 0.22341933846473694, - -0.06328696012496948 + -0.17021070420742035, + 0.22438624501228333, + -0.0594257153570652 ], "tsne3d": [ - 3.361063241958618, - 4.5478434562683105, - -26.76091766357422 + 5.941758632659912, + -3.47597599029541, + -32.21206283569336 ], "umap3d": [ - 6.22637414932251, - -0.7675442099571228, - 0.37039217352867126 + 4.081262111663818, + -0.9561141729354858, + 6.289393901824951 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 15, - "label": "Latent Models" + "id": 18, + "label": "Retrieval Learning" } ], "_embedding": [ @@ -154861,47 +155044,47 @@ ], "projections": { "pca": [ - 0.1463647037744522, - 0.050643883645534515 + 0.14876367151737213, + 0.052194152027368546 ], "tsne": [ - 14.782073020935059, - -14.021774291992188 + 1.9181435108184814, + -14.176694869995117 ], "umap": [ - 0.5481387972831726, - 4.909036636352539 + 0.06104602664709091, + 4.827303886413574 ], "pca3d": [ - 0.14636480808258057, - 0.05064556747674942, - 0.159544438123703 + 0.14876392483711243, + 0.05219462886452675, + 0.15593098104000092 ], "tsne3d": [ - -25.660419464111328, - 25.04819107055664, - 18.11607551574707 + 17.060062408447266, + -29.545909881591797, + -4.948039531707764 ], "umap3d": [ - 3.0800724029541016, - 1.2727056741714478, - 0.05034227296710014 + 1.6663568019866943, + 1.8798892498016357, + 4.929661273956299 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 4, - "label": "Com Area Amp" + "id": 9, + "label": "Drug Discovery" }, { - "id": 17, - "label": "Literature AI" + "id": 23, + "label": "Peer Review" } ], "_embedding": [ @@ -155308,47 +155491,47 @@ "title": "Titans + MIRAS: Helping AI have long-term memory", "projections": { "pca": [ - -0.0814150869846344, - 0.352804571390152 + -0.07728441804647446, + 0.3553028404712677 ], "tsne": [ - -9.72364330291748, - 21.79010009765625 + 12.110913276672363, + 28.307632446289062 ], "umap": [ - 3.3005712032318115, - 2.9445667266845703 + 2.288210868835449, + 1.5906217098236084 ], "pca3d": [ - -0.08141588419675827, - 0.3528015911579132, - 0.06227452680468559 + -0.0772852674126625, + 0.35530126094818115, + 0.058888744562864304 ], "tsne3d": [ - 23.729337692260742, - 6.904697418212891, - -48.51688003540039 + 5.077018737792969, + 23.75310707092285, + -23.291397094726562 ], "umap3d": [ - 6.817286968231201, - -0.11302562057971954, - 0.801449179649353 + 4.4083571434021, + -0.502971351146698, + 5.4480814933776855 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 6, + "label": "Learning Techniques" } ], "_embedding": [ @@ -155754,47 +155937,47 @@ ], "projections": { "pca": [ - 0.11531201004981995, - -0.03550085425376892 + 0.11575515568256378, + -0.034544140100479126 ], "tsne": [ - -2.121201515197754, - 22.133230209350586 + 2.6200480461120605, + 6.86516809463501 ], "umap": [ - 1.4377877712249756, - 5.729869842529297 + 0.8432470560073853, + 4.556701183319092 ], "pca3d": [ - 0.11531227827072144, - -0.03550155088305473, - 0.16660599410533905 + 0.11575546860694885, + -0.03454696014523506, + 0.16603603959083557 ], "tsne3d": [ - 25.925350189208984, - -29.405357360839844, - 14.45177936553955 + -5.337080001831055, + 14.706435203552246, + -0.45086896419525146 ], "umap3d": [ - 3.5516958236694336, - 0.629030704498291, - 0.8865881562232971 + 2.44758939743042, + 1.494360089302063, + 5.83612585067749 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 3, + "label": "Open Source Tools" }, { - "id": 4, - "label": "Com Area Amp" + "id": 6, + "label": "Data Needs" }, { - "id": 26, - "label": "AI Information" + "id": 10, + "label": "Research Data" } ], "_embedding": [ @@ -156226,47 +156409,47 @@ "openalex_url": "https://openalex.org/W4416695734", "projections": { "pca": [ - -0.18727679550647736, - -0.09558893740177155 + -0.18548738956451416, + -0.09281139075756073 ], "tsne": [ - -24.03150749206543, - -10.38179874420166 + -25.614599227905273, + 4.360037326812744 ], "umap": [ - -1.080937147140503, - 7.505516052246094 + -2.4052011966705322, + 2.7743747234344482 ], "pca3d": [ - -0.1872768998146057, - -0.09560476988554001, - 0.10052713751792908 + -0.18548767268657684, + -0.09282427281141281, + 0.10735465586185455 ], "tsne3d": [ - -8.110832214355469, - -23.265859603881836, - -28.676603317260742 + -31.710161209106445, + 12.653011322021484, + 21.087739944458008 ], "umap3d": [ - 3.736077070236206, - -1.9020991325378418, - -1.686575174331665 + 1.1687284708023071, + -0.3520359694957733, + 7.774763107299805 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 12, - "label": "ECG Models" + "id": 13, + "label": "ECG AI Models" } ], "_embedding": [ @@ -156689,47 +156872,47 @@ "doi": "10.1101/2025.11.24.685470", "projections": { "pca": [ - -0.18023577332496643, - -0.10380624234676361 + -0.18028901517391205, + -0.10186795145273209 ], "tsne": [ - -5.947445869445801, - -22.136966705322266 + -19.302518844604492, + -14.458366394042969 ], "umap": [ - -1.3727444410324097, - 4.414546489715576 + -1.5145294666290283, + 6.072380065917969 ], "pca3d": [ - -0.18023528158664703, - -0.10380120575428009, - -0.039479732513427734 + -0.18028850853443146, + -0.101863794028759, + -0.03516753762960434 ], "tsne3d": [ - -38.575958251953125, - -34.12432861328125, - -2.7627029418945312 + -20.911630630493164, + -19.304059982299805, + -0.7021127939224243 ], "umap3d": [ - 2.225931406021118, - 0.26736438274383545, - -1.2867358922958374 + 0.42142820358276367, + 0.3078058362007141, + 4.793887615203857 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 2, - "label": "AI Drug Discovery" + "id": 7, + "label": "Cell Genes" }, { - "id": 2, - "label": "Genetic Disease" + "id": 28, + "label": "Spatial Drug" } ], "_embedding": [ @@ -157139,46 +157322,46 @@ "journal": "Nature", "projections": { "pca": [ - -0.09416653960943222, - -0.21268302202224731 + -0.09371752291917801, + -0.21279403567314148 ], "tsne": [ - -6.964034557342529, - -14.39376163482666 + -20.324934005737305, + -3.9759020805358887 ], "umap": [ - -1.9480137825012207, - 6.174205780029297 + -2.475062847137451, + 4.985488414764404 ], "pca3d": [ - -0.09416729211807251, - -0.21268761157989502, - 0.050910890102386475 + -0.09371829032897949, + -0.21279706060886383, + 0.05635964870452881 ], "tsne3d": [ - -13.055176734924316, - -30.65912628173828, - 2.5178370475769043 + -25.74898338317871, + -25.078216552734375, + 28.861875534057617 ], "umap3d": [ - 2.8839073181152344, - -0.9976920485496521, - -0.8515883684158325 + 0.253495991230011, + 0.35641181468963623, + 6.087535858154297 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 24, + "id": 1, "label": "Cancer Data" } ], @@ -157596,47 +157779,47 @@ ], "projections": { "pca": [ - -0.20223037898540497, - 0.1651492416858673 + -0.20201747119426727, + 0.16627097129821777 ], "tsne": [ - -19.677339553833008, - 14.359394073486328 + -4.258230209350586, + 25.028581619262695 ], "umap": [ - 1.893197774887085, - 2.9269440174102783 + 0.8696498870849609, + 2.064728260040283 ], "pca3d": [ - -0.20222926139831543, - 0.16515561938285828, - -0.0679728239774704 + -0.20201627910137177, + 0.16627421975135803, + -0.06262152642011642 ], "tsne3d": [ - -2.7211437225341797, - 0.2814897894859314, - -28.257417678833008 + 6.348552227020264, + -5.207949638366699, + -22.832788467407227 ], "umap3d": [ - 6.138255596160889, - -0.7161906361579895, - 0.22800607979297638 + 3.889653205871582, + -1.1285643577575684, + 6.501623630523682 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 15, - "label": "Latent Models" + "id": 18, + "label": "Retrieval Learning" } ], "_embedding": [ @@ -158050,47 +158233,47 @@ ], "projections": { "pca": [ - -0.21109451353549957, - -0.05626746267080307 + -0.20805402100086212, + -0.05306931585073471 ], "tsne": [ - -7.192636966705322, - -12.0389986038208 + -17.044795989990234, + -2.716644525527954 ], "umap": [ - -1.37501859664917, - 6.208723545074463 + -2.347832679748535, + 4.458686828613281 ], "pca3d": [ - -0.21109339594841003, - -0.0562543049454689, - 0.08796148002147675 + -0.2080528438091278, + -0.05305716395378113, + 0.09237273782491684 ], "tsne3d": [ - -12.294629096984863, - -39.99026107788086, - 7.892857551574707 + -11.241623878479004, + -15.832441329956055, + 18.661767959594727 ], "umap3d": [ - 3.1254680156707764, - -0.9141183495521545, - -1.5114867687225342 + 0.6133655309677124, + 0.7625896334648132, + 6.275207042694092 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 28, - "label": "Clinical AI" + "id": 21, + "label": "Clinical Trials" } ], "_embedding": [ @@ -158500,46 +158683,46 @@ "journal": "arXiv", "projections": { "pca": [ - -0.15583255887031555, - 0.28388354182243347 + -0.15390585362911224, + 0.28434649109840393 ], "tsne": [ - -10.705723762512207, - 26.54292106628418 + 4.96273946762085, + 21.828994750976562 ], "umap": [ - 2.1961417198181152, - 1.9805299043655396 + 1.4397685527801514, + 0.9894720315933228 ], "pca3d": [ - -0.1558326780796051, - 0.2838956117630005, - 0.07837957888841629 + -0.1539059728384018, + 0.2843571901321411, + 0.07791747152805328 ], "tsne3d": [ - 7.638736724853516, - 2.1991162300109863, - -56.95161437988281 + 3.1916065216064453, + 14.392916679382324, + -14.012002944946289 ], "umap3d": [ - 6.94598388671875, - -0.45950180292129517, - 0.21478702127933502 + 4.46341609954834, + -1.443934679031372, + 5.846356391906738 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 4, + "id": 26, "label": "World Models" } ], @@ -158948,47 +159131,47 @@ "journal": "Nature Communications", "projections": { "pca": [ - 0.035517025738954544, - -0.02199053391814232 + 0.03733224421739578, + -0.021544352173805237 ], "tsne": [ - 7.526163578033447, - -4.803404808044434 + 4.230194568634033, + -0.5679686665534973 ], "umap": [ - 1.0528068542480469, - 4.642148494720459 + 0.3131817579269409, + 5.488030433654785 ], "pca3d": [ - 0.03551619127392769, - -0.0220076534897089, - 0.013377114199101925 + 0.037331316620111465, + -0.02156047150492668, + 0.013250363059341908 ], "tsne3d": [ - -13.664793968200684, - 10.961380004882812, - 6.15355110168457 + -30.979816436767578, + 20.344724655151367, + -7.871456146240234 ], "umap3d": [ - 2.885572671890259, - 0.27019038796424866, - 0.24689818918704987 + 1.9055824279785156, + 1.071478009223938, + 5.272500514984131 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 4, - "label": "Com Area Amp" + "id": 6, + "label": "Data Needs" }, { - "id": 23, - "label": "Research Authors" + "id": 16, + "label": "Lab Research" } ], "_embedding": [ @@ -159405,46 +159588,46 @@ ], "projections": { "pca": [ - -0.18563328683376312, - 0.016869261860847473 + -0.1825459897518158, + 0.02092437632381916 ], "tsne": [ - -16.026538848876953, - -2.004631757736206 + -10.040258407592773, + 10.649492263793945 ], "umap": [ - -0.05654209107160568, - 7.216552257537842 + -1.1193547248840332, + 2.8126587867736816 ], "pca3d": [ - -0.1856333464384079, - 0.016865259036421776, - 0.14080442488193512 + -0.1825462430715561, + 0.020920420065522194, + 0.1464957594871521 ], "tsne3d": [ - -8.15412712097168, - -11.27381706237793, - -12.461819648742676 + -9.435847282409668, + 4.698798656463623, + 14.546114921569824 ], "umap3d": [ - 4.538750648498535, - -1.1833511590957642, - -0.947433352470398 + 1.640104055404663, + -0.015926148742437363, + 6.918756484985352 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 11, + "id": 19, "label": "Medical Imaging" } ], @@ -159863,46 +160046,46 @@ "doi": "10.5281/zenodo.20711012", "projections": { "pca": [ - 0.06744996458292007, - -0.0824914500117302 + 0.0686071515083313, + -0.08029627799987793 ], "tsne": [ - -13.826106071472168, - -19.540042877197266 + -25.158924102783203, + -11.181702613830566 ], "umap": [ - -1.5452054738998413, - 6.590641498565674 + -1.7292499542236328, + 4.988086223602295 ], "pca3d": [ - 0.06744980067014694, - -0.08248157799243927, - 0.02686508744955063 + 0.06860709935426712, + -0.08028610050678253, + 0.028368595987558365 ], "tsne3d": [ - -19.945968627929688, - -25.927881240844727, - -19.475038528442383 + -31.58608055114746, + -1.1730397939682007, + 15.093585014343262 ], "umap3d": [ - 3.2976653575897217, - -0.918954074382782, - -0.6005938053131104 + 0.9618144035339355, + -0.2445673942565918, + 5.879006862640381 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 24, + "id": 1, "label": "Cancer Data" } ], @@ -160311,47 +160494,47 @@ "journal": "Nature", "projections": { "pca": [ - -0.09957680106163025, - -0.14016340672969818 + -0.09510639309883118, + -0.13782747089862823 ], "tsne": [ - -7.668175220489502, - -8.958616256713867 + -13.373437881469727, + 0.08456659317016602 ], "umap": [ - -1.0747934579849243, - 6.290611743927002 + -2.213251829147339, + 4.142006874084473 ], "pca3d": [ - -0.0995761901140213, - -0.14015144109725952, - 0.18577812612056732 + -0.0951058641076088, + -0.13781535625457764, + 0.18757084012031555 ], "tsne3d": [ - -3.9455835819244385, - -45.0012321472168, - 4.79945707321167 + 1.9044256210327148, + -26.623117446899414, + 16.40357208251953 ], "umap3d": [ - 3.446927309036255, - -0.861083447933197, - -1.5436652898788452 + 0.7106655240058899, + 0.7273718118667603, + 6.577991485595703 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 1, - "label": "Cancer Data" + "id": 5, + "label": "Clinical Data" }, { - "id": 28, - "label": "Clinical AI" + "id": 21, + "label": "Clinical Trials" } ], "_embedding": [ @@ -160759,47 +160942,47 @@ "abstract": "Contribute to Jianing-Qiu/Nano_Banana_CXR_Classifier development by creating an account on GitHub.", "projections": { "pca": [ - 0.2680295407772064, - -0.0663866400718689 + 0.26747411489486694, + -0.0664825364947319 ], "tsne": [ - 25.729408264160156, - 10.026103973388672 + 27.80352783203125, + -8.32947826385498 ], "umap": [ - 3.2582015991210938, - 6.837080478668213 + 2.8434619903564453, + 5.625576972961426 ], "pca3d": [ - 0.26803058385849, - -0.06637722998857498, - -0.12171393632888794 + 0.2674751877784729, + -0.06647471338510513, + -0.12369093298912048 ], "tsne3d": [ - 24.69403839111328, - 47.3284797668457, - 6.559822082519531 + 41.93115997314453, + 3.126120090484619, + 9.271560668945312 ], "umap3d": [ - 3.3487446308135986, - -0.2065853476524353, - 2.523498296737671 + 3.5853703022003174, + 2.795502185821533, + 6.710348606109619 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 18, - "label": "Github Tools" + "id": 17, + "label": "GitHub Creation" } ], "_embedding": [ @@ -161213,47 +161396,47 @@ ], "projections": { "pca": [ - -0.16115806996822357, - 0.26303383708000183 + -0.15947246551513672, + 0.265019953250885 ], "tsne": [ - -23.274303436279297, - 12.425921440124512 + -7.139105796813965, + 23.42534065246582 ], "umap": [ - 1.673984408378601, - 2.6879806518554688 + 0.531634509563446, + 1.6787446737289429 ], "pca3d": [ - -0.1611582636833191, - 0.2630273103713989, - -0.05909198522567749 + -0.15947259962558746, + 0.26501354575157166, + -0.054220736026763916 ], "tsne3d": [ - -13.371443748474121, - 10.462716102600098, - -31.132301330566406 + -7.766094207763672, + 0.5972663164138794, + -23.360336303710938 ], "umap3d": [ - 6.207376003265381, - -1.3978224992752075, - 0.15580029785633087 + 3.4026362895965576, + -1.2163314819335938, + 6.298745632171631 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 15, - "label": "Latent Models" + "id": 8, + "label": "JEPA Video Models" } ], "_embedding": [ @@ -161661,46 +161844,46 @@ "journal": "Nature Communications", "projections": { "pca": [ - -0.17933019995689392, - 0.03867557644844055 + -0.17451021075248718, + 0.04252288118004799 ], "tsne": [ - -9.915976524353027, - 0.0840078666806221 + -17.926435470581055, + 10.307766914367676 ], "umap": [ - 0.03276749327778816, - 6.4242987632751465 + -1.532451868057251, + 3.5382091999053955 ], "pca3d": [ - -0.17933034896850586, - 0.03866296261548996, - 0.18455594778060913 + -0.17451055347919464, + 0.04251116141676903, + 0.18560460209846497 ], "tsne3d": [ - 15.438899040222168, - -24.740493774414062, - -2.073796272277832 + -2.8408093452453613, + -3.7954165935516357, + -1.4262325763702393 ], "umap3d": [ - 4.362380504608154, - -0.509822428226471, - -1.6565200090408325 + 1.3135985136032104, + 0.5841996073722839, + 7.381627082824707 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, + "id": 14, "label": "Clinical Health" } ], @@ -162119,46 +162302,46 @@ ], "projections": { "pca": [ - -0.21590706706047058, - -0.07395726442337036 + -0.21374250948429108, + -0.07110284268856049 ], "tsne": [ - -7.625304222106934, - -2.970623731613159 + -12.490805625915527, + 4.770342826843262 ], "umap": [ - -0.2875196933746338, - 6.112485408782959 + -1.611520528793335, + 3.9362268447875977 ], "pca3d": [ - -0.2159070372581482, - -0.07396003603935242, - 0.14339490234851837 + -0.21374261379241943, + -0.07110556215047836, + 0.14860397577285767 ], "tsne3d": [ - 4.594979763031006, - -25.047449111938477, - 13.718911170959473 + -4.730499744415283, + -9.35753059387207, + 5.188889503479004 ], "umap3d": [ - 3.9890010356903076, - -0.4835256338119507, - -1.5437344312667847 + 1.0169919729232788, + 0.49410247802734375, + 6.995776653289795 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, + "id": 14, "label": "Clinical Health" } ], @@ -162603,46 +162786,46 @@ ], "projections": { "pca": [ - -0.09603282064199448, - 0.23609846830368042 + -0.0952954813838005, + 0.23719340562820435 ], "tsne": [ - -9.689614295959473, - 26.19434356689453 + 3.752840280532837, + 21.281761169433594 ], "umap": [ - 1.9940154552459717, - 1.9838145971298218 + 1.2564127445220947, + 0.9844455718994141 ], "pca3d": [ - -0.09603230655193329, - 0.2361234724521637, - 0.017978539690375328 + -0.09529498964548111, + 0.23721568286418915, + 0.019389739260077477 ], "tsne3d": [ - 2.6304569244384766, - -1.6507006883621216, - -48.96124267578125 + 6.462523937225342, + 9.136054039001465, + -13.194500923156738 ], "umap3d": [ - 6.959273815155029, - -0.5789262652397156, - 0.13826143741607666 + 4.398989200592041, + -1.5153855085372925, + 5.855385780334473 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 4, + "id": 26, "label": "World Models" } ], @@ -163059,47 +163242,47 @@ ], "projections": { "pca": [ - -0.2524474561214447, - 0.05385533347725868 + -0.2550468444824219, + 0.05348682031035423 ], "tsne": [ - 0.5219733715057373, - -31.31287384033203 + -13.755983352661133, + -23.91224479675293 ], "umap": [ - -1.7626233100891113, - 3.2292134761810303 + -1.2719242572784424, + 7.324762344360352 ], "pca3d": [ - -0.25244784355163574, - 0.05384981632232666, - -0.1361357718706131 + -0.25504735112190247, + 0.05348093435168266, + -0.1323481947183609 ], "tsne3d": [ - -39.419795989990234, - -1.2693191766738892, - -5.712579727172852 + -28.622766494750977, + -18.712371826171875, + -20.0403995513916 ], "umap3d": [ - 1.3272329568862915, - 0.6361703276634216, - -0.8028281331062317 + 0.32847005128860474, + 1.0785874128341675, + 3.7574074268341064 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 8, - "label": "Gene Regulation" + "id": 3, + "label": "Genomic Sequences" } ], "_embedding": [ @@ -163513,47 +163696,47 @@ ], "projections": { "pca": [ - -0.18322400748729706, - 0.06927490234375 + -0.1834578514099121, + 0.07007825374603271 ], "tsne": [ - -18.376771926879883, - 23.049211502075195 + 5.908431053161621, + 27.0074520111084 ], "umap": [ - 2.8388025760650635, - 2.9423491954803467 + 1.7842310667037964, + 1.9465819597244263 ], "pca3d": [ - -0.1832236498594284, - 0.06928448379039764, - -0.011118489317595959 + -0.18345756828784943, + 0.07008571922779083, + -0.0074720862321555614 ], "tsne3d": [ - 18.498292922973633, - -8.704483985900879, - -29.49587059020996 + 17.505273818969727, + 5.383692264556885, + -36.09300994873047 ], "umap3d": [ - 6.595272541046143, - -0.3285377621650696, - 0.9073055982589722 + 4.821627140045166, + -0.8297888040542603, + 6.157329082489014 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 6, + "label": "Learning Techniques" } ], "_embedding": [ @@ -163975,47 +164158,47 @@ "doi": "10.1101/2024.12.19.629561", "projections": { "pca": [ - -0.21282735466957092, - -0.0644713044166565 + -0.21477530896663666, + -0.06289947777986526 ], "tsne": [ - -4.8024001121521, - -26.041603088378906 + -20.55430793762207, + -23.120494842529297 ], "umap": [ - -1.016417145729065, - 3.5453648567199707 + -0.578266441822052, + 6.698606967926025 ], "pca3d": [ - -0.21282702684402466, - -0.0644674077630043, - -0.18543781340122223 + -0.21477530896663666, + -0.06289506703615189, + -0.1825145184993744 ], "tsne3d": [ - -41.22989273071289, - -29.434661865234375, - -9.516095161437988 + -21.46284294128418, + -28.519948959350586, + -10.569660186767578 ], "umap3d": [ - 1.8843034505844116, - 1.3361835479736328, - -1.304160237312317 + 0.7011489272117615, + 0.10394962131977081, + 3.705620050430298 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 19, - "label": "Cell Models" + "id": 20, + "label": "Cell Foundation" } ], "_embedding": [ @@ -164423,47 +164606,47 @@ "abstract": "Contribute to galilai-group/lejepa development by creating an account on GitHub.", "projections": { "pca": [ - 0.36232155561447144, - 0.04702726751565933 + 0.36146116256713867, + 0.04664338752627373 ], "tsne": [ - 29.05080223083496, - 13.713460922241211 + 32.5909423828125, + -6.600458145141602 ], "umap": [ - 3.653156280517578, - 6.896467685699463 + 3.2124218940734863, + 5.711359024047852 ], "pca3d": [ - 0.3623230457305908, - 0.047044750303030014, - -0.1689021736383438 + 0.3614630401134491, + 0.04665727913379669, + -0.17461413145065308 ], "tsne3d": [ - 38.0794677734375, - 41.84833908081055, - 23.519771575927734 + 47.60161209106445, + 15.154354095458984, + 6.801178455352783 ], "umap3d": [ - 3.4599974155426025, - -0.025315502658486366, - 2.911909580230713 + 3.93559193611145, + 2.9077322483062744, + 6.73189640045166 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 18, - "label": "Github Tools" + "id": 17, + "label": "GitHub Creation" } ], "_embedding": [ @@ -164875,47 +165058,47 @@ ], "projections": { "pca": [ - -0.17072542011737823, - 0.2726287245750427 + -0.16927959024906158, + 0.2749309539794922 ], "tsne": [ - -17.734861373901367, - 12.898340225219727 + -1.293929934501648, + 21.733747482299805 ], "umap": [ - 1.6963156461715698, - 2.3569610118865967 + 0.8136768341064453, + 1.304154396057129 ], "pca3d": [ - -0.1707247942686081, - 0.2726377844810486, - -0.06123615801334381 + -0.16927888989448547, + 0.2749388515949249, + -0.05790555477142334 ], "tsne3d": [ - -5.8942718505859375, - -0.22267970442771912, - -37.902801513671875 + 9.293529510498047, + -1.0909773111343384, + -16.628820419311523 ], "umap3d": [ - 6.59348201751709, - -0.9931162595748901, - 0.030281178653240204 + 3.9198813438415527, + -1.4792399406433105, + 6.187613487243652 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 4, - "label": "World Models" + "id": 8, + "label": "JEPA Video Models" } ], "_embedding": [ @@ -165323,47 +165506,47 @@ "journal": "Nature", "projections": { "pca": [ - -0.22997519373893738, - -0.1005287915468216 + -0.22910882532596588, + -0.0973091647028923 ], "tsne": [ - 4.091423511505127, - -22.675779342651367 + -9.868091583251953, + -15.668713569641113 ], "umap": [ - -2.202565908432007, - 4.330453872680664 + -2.3774478435516357, + 6.89049768447876 ], "pca3d": [ - -0.22997325658798218, - -0.10049048066139221, - -0.018790878355503082 + -0.2291068136692047, + -0.09727364778518677, + -0.017387904226779938 ], "tsne3d": [ - -37.05794906616211, - -14.718792915344238, - 21.811344146728516 + -20.582040786743164, + 0.6525100469589233, + -6.276475429534912 ], "umap3d": [ - 1.5244178771972656, - -0.21538780629634857, - -1.590694546699524 + -0.15443381667137146, + 1.3899019956588745, + 4.715860843658447 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 2, - "label": "Genetic Disease" + "id": 27, + "label": "Genetic Variants" } ], "_embedding": [ @@ -165781,47 +165964,47 @@ "doi": "10.1101/2025.11.10.687627", "projections": { "pca": [ - -0.2621211111545563, - -0.05668831989169121 + -0.26587069034576416, + -0.056356389075517654 ], "tsne": [ - -1.3768439292907715, - -30.45937156677246 + -15.878262519836426, + -23.16019058227539 ], "umap": [ - -1.9088259935379028, - 3.2159910202026367 + -1.4808214902877808, + 7.405098915100098 ], "pca3d": [ - -0.2621210217475891, - -0.05669359862804413, - -0.22639305889606476 + -0.26587074995040894, + -0.056361790746450424, + -0.22353599965572357 ], "tsne3d": [ - -47.77147674560547, - -9.187495231628418, - -4.224242687225342 + -34.39188003540039, + -18.13376808166504, + -15.028227806091309 ], "umap3d": [ - 1.0908383131027222, - 0.7366366982460022, - -1.1047619581222534 + 0.1014324203133583, + 0.910996675491333, + 3.7260990142822266 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 8, - "label": "Gene Regulation" + "id": 3, + "label": "Genomic Sequences" } ], "_embedding": [ @@ -166229,47 +166412,47 @@ "journal": "Nature", "projections": { "pca": [ - -0.16347654163837433, - -0.043825551867485046 + -0.15669676661491394, + -0.0389786958694458 ], "tsne": [ - -14.929689407348633, - -12.8092041015625 + -23.977752685546875, + 10.218650817871094 ], "umap": [ - -1.0034102201461792, - 5.520930767059326 + -1.2914681434631348, + 4.455560684204102 ], "pca3d": [ - -0.16347701847553253, - -0.043830033391714096, - 0.26457950472831726 + -0.15669728815555573, + -0.03898043930530548, + 0.26661166548728943 ], "tsne3d": [ - 0.9007396101951599, - -38.663856506347656, - -10.401495933532715 + -22.78885269165039, + 4.411838531494141, + 4.555210113525391 ], "umap3d": [ - 3.4394655227661133, - -0.09995439648628235, - -1.3800715208053589 + 0.3782219886779785, + -0.15595701336860657, + 6.698038578033447 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 2, - "label": "AI Drug Discovery" + "id": 9, + "label": "Drug Discovery" }, { - "id": 28, - "label": "Clinical AI" + "id": 9, + "label": "Biomedical AI" } ], "_embedding": [ @@ -166677,47 +166860,47 @@ "journal": "Nature Biotechnology", "projections": { "pca": [ - -0.17240890860557556, - 0.11903877556324005 + -0.17040526866912842, + 0.12033843994140625 ], "tsne": [ - -13.882951736450195, - -32.44486618041992 + -29.289535522460938, + -20.840734481811523 ], "umap": [ - -1.0558462142944336, - 3.0350728034973145 + -0.8918136954307556, + 6.312308311462402 ], "pca3d": [ - -0.1724098175764084, - 0.1190168559551239, - 0.053679440170526505 + -0.17040613293647766, + 0.12031698226928711, + 0.053131330758333206 ], "tsne3d": [ - -48.94026565551758, - -17.653066635131836, - -29.51442527770996 + -23.99553108215332, + 10.72605037689209, + -29.860610961914062 ], "umap3d": [ - 1.4150046110153198, - 1.4260339736938477, - -1.4326356649398804 + 0.8075961470603943, + -0.17871634662151337, + 4.155925273895264 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 19, - "label": "Cell Models" + "id": 28, + "label": "Spatial Drug" } ], "_embedding": [ @@ -167125,47 +167308,47 @@ "journal": "Nature", "projections": { "pca": [ - 0.05104760080575943, - -0.2479073405265808 + 0.0498872771859169, + -0.2479408234357834 ], "tsne": [ - 5.538187026977539, - -31.786218643188477 + -21.40023422241211, + -29.782062530517578 ], "umap": [ - -1.7935607433319092, - 3.924675226211548 + -1.5989563465118408, + 6.6449151039123535 ], "pca3d": [ - 0.051047466695308685, - -0.24790345132350922, - -0.11080493777990341 + 0.049887146800756454, + -0.24793699383735657, + -0.10933657735586166 ], "tsne3d": [ - -54.21348571777344, - -12.697197914123535, - 18.209228515625 + -40.35441207885742, + -0.7265440225601196, + -18.157020568847656 ], "umap3d": [ - 1.5951684713363647, - 0.30932366847991943, - -1.2074077129364014 + 0.14528119564056396, + 0.9524654150009155, + 4.11081600189209 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 2, - "label": "Genetic Disease" + "id": 27, + "label": "Genetic Variants" } ], "_embedding": [ @@ -167572,47 +167755,47 @@ "title": "Introducing Nested Learning: A new ML paradigm for continual learning", "projections": { "pca": [ - -0.1789352148771286, - 0.24652941524982452 + -0.17512452602386475, + 0.24962082505226135 ], "tsne": [ - -15.017655372619629, - 28.607038497924805 + 6.86336088180542, + 35.147544860839844 ], "umap": [ - 3.471039056777954, - 2.6899194717407227 + 2.246325731277466, + 2.1714670658111572 ], "pca3d": [ - -0.17893505096435547, - 0.2465379685163498, - 0.05781559646129608 + -0.17512445151805878, + 0.24963019788265228, + 0.05771750211715698 ], "tsne3d": [ - 16.523712158203125, - -17.485401153564453, - -42.681217193603516 + 9.540846824645996, + 26.284008026123047, + -38.187957763671875 ], "umap3d": [ - 6.5973896980285645, - -0.2868698835372925, - 1.46799898147583 + 4.920190334320068, + -0.4271538555622101, + 5.760176658630371 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 6, + "label": "Learning Techniques" } ], "_embedding": [ @@ -168062,47 +168245,47 @@ ], "projections": { "pca": [ - -0.05426675081253052, - 0.1194724440574646 + -0.05358440801501274, + 0.12071425467729568 ], "tsne": [ - -2.057800769805908, - 10.622049331665039 + -2.7561402320861816, + 1.3005297183990479 ], "umap": [ - -0.03148989379405975, - 5.103992938995361 + -0.41072165966033936, + 4.365593433380127 ], "pca3d": [ - -0.05426656827330589, - 0.1194709911942482, - 0.05328381806612015 + -0.05358429625630379, + 0.12071272730827332, + 0.052340585738420486 ], "tsne3d": [ - 24.885175704956055, - -14.948863983154297, - -11.982068061828613 + 10.241738319396973, + 25.5177059173584, + -10.585369110107422 ], "umap3d": [ - 4.316633224487305, - 0.6219321489334106, - -0.5515864491462708 + 3.0552241802215576, + 0.373797208070755, + 5.041766166687012 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ - { - "id": 0, - "label": "Medical Data AI" - }, { "id": 2, - "label": "AI Drug Discovery" + "label": "Clinical AI Data" }, { - "id": 13, + "id": 9, "label": "Drug Discovery" + }, + { + "id": 23, + "label": "Peer Review" } ], "_embedding": [ @@ -168510,47 +168693,47 @@ "journal": "Nature Medicine", "projections": { "pca": [ - -0.15377260744571686, - 0.003883390687406063 + -0.14835990965366364, + 0.00932893343269825 ], "tsne": [ - -15.06442928314209, - -17.507888793945312 + -26.2188663482666, + -9.396723747253418 ], "umap": [ - -1.3788022994995117, - 6.916806697845459 + -1.548229455947876, + 4.727810859680176 ], "pca3d": [ - -0.1537724882364273, - 0.003899647854268551, - 0.12117110937833786 + -0.1483597755432129, + 0.009344901889562607, + 0.12443549931049347 ], "tsne3d": [ - -19.90030288696289, - -12.898307800292969, - -20.94569206237793 + -24.536327362060547, + 2.2904109954833984, + 18.951141357421875 ], "umap3d": [ - 3.8076257705688477, - -1.179244041442871, - -0.5820949673652649 + 1.264318585395813, + -0.5122312307357788, + 6.103928565979004 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 12, - "label": "ECG Models" + "id": 9, + "label": "Biomedical AI" } ], "_embedding": [ @@ -168973,46 +169156,46 @@ "cited_by_count": 1, "projections": { "pca": [ - -0.2585136294364929, - -0.107113778591156 + -0.26148760318756104, + -0.10651949793100357 ], "tsne": [ - 1.290047287940979, - -26.560344696044922 + -13.094416618347168, + -19.18527603149414 ], "umap": [ - -2.378892421722412, - 4.0106072425842285 + -2.118746757507324, + 7.125993251800537 ], "pca3d": [ - -0.25851279497146606, - -0.10710550844669342, - -0.1975661665201187 + -0.2614869475364685, + -0.1065111979842186, + -0.1948680877685547 ], "tsne3d": [ - -37.126487731933594, - -12.749174118041992, - 3.8652570247650146 + -24.030494689941406, + -10.944122314453125, + -11.875592231750488 ], "umap3d": [ - 1.2183822393417358, - 0.06479102373123169, - -1.2967512607574463 + -0.17679335176944733, + 1.0460286140441895, + 4.2655253410339355 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 22, + "id": 27, "label": "Genetic Variants" } ], @@ -169425,47 +169608,47 @@ "journal": "OpenReview", "projections": { "pca": [ - -0.25546857714653015, - 0.027422459796071053 + -0.2553364336490631, + 0.029335645958781242 ], "tsne": [ - 1.3975706100463867, - -7.694086074829102 + -6.266695022583008, + -5.800510883331299 ], "umap": [ - -0.5405963063240051, - 4.846282958984375 + -0.7769532203674316, + 5.010056495666504 ], "pca3d": [ - -0.25546932220458984, - 0.027403922751545906, - -0.037762485444545746 + -0.2553371787071228, + 0.029316477477550507, + -0.03243749588727951 ], "tsne3d": [ - -16.90415382385254, - -5.407815456390381, - 19.220558166503906 + 3.094667911529541, + -20.490520477294922, + -6.282158851623535 ], "umap3d": [ - 3.5575642585754395, - 0.510869026184082, - -1.0010216236114502 + 2.0651872158050537, + 0.05481361597776413, + 5.100849628448486 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ - { - "id": 0, - "label": "Medical Data AI" - }, { "id": 2, - "label": "AI Drug Discovery" + "label": "Clinical AI Data" }, { - "id": 13, + "id": 9, "label": "Drug Discovery" + }, + { + "id": 9, + "label": "Biomedical AI" } ], "_embedding": [ @@ -169881,46 +170064,46 @@ ], "projections": { "pca": [ - -0.10742823779582977, - 0.2710855007171631 + -0.10691177099943161, + 0.27142781019210815 ], "tsne": [ - -25.40460205078125, - 17.816490173339844 + 0.49795886874198914, + 26.983488082885742 ], "umap": [ - 2.3307840824127197, - 2.3045637607574463 + 1.2859559059143066, + 1.290457844734192 ], "pca3d": [ - -0.10742932558059692, - 0.27107879519462585, - -0.09397583454847336 + -0.10691298544406891, + 0.2714197635650635, + -0.09023512154817581 ], "tsne3d": [ - 1.0853233337402344, - 20.147672653198242, - -37.28641891479492 + -7.922330856323242, + 6.681352615356445, + -28.36886215209961 ], "umap3d": [ - 6.670217037200928, - -1.253086805343628, - 0.6350060105323792 + 3.7116293907165527, + -1.0797967910766602, + 5.544429779052734 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 4, + "id": 26, "label": "World Models" } ], @@ -170338,47 +170521,47 @@ ], "projections": { "pca": [ - -0.12190588563680649, - 0.26731356978416443 + -0.12285792827606201, + 0.26774415373802185 ], "tsne": [ - -15.43948745727539, - 24.211702346801758 + 6.6692328453063965, + 30.96255874633789 ], "umap": [ - 3.262765645980835, - 2.568081855773926 + 2.3428752422332764, + 1.6819602251052856 ], "pca3d": [ - -0.12190662324428558, - 0.26731574535369873, - -0.09720495343208313 + -0.12285880744457245, + 0.26774701476097107, + -0.09632018208503723 ], "tsne3d": [ - 19.402067184448242, - -0.9880073666572571, - -38.31293487548828 + 14.39594841003418, + 14.301010131835938, + -32.797454833984375 ], "umap3d": [ - 6.8028059005737305, - -0.5001835823059082, - 1.3170349597930908 + 4.9297990798950195, + -0.6806807518005371, + 5.70171594619751 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 6, + "label": "Learning Techniques" } ], "_embedding": [ @@ -170799,46 +170982,46 @@ "doi": "10.1101/2024.01.08.574756", "projections": { "pca": [ - -0.23614855110645294, - -0.26784324645996094 + -0.2368355542421341, + -0.2672855854034424 ], "tsne": [ - -8.725885391235352, - -14.742938995361328 + -18.4103946685791, + -5.822963714599609 ], "umap": [ - -1.7508150339126587, - 5.996525287628174 + -2.2319161891937256, + 5.053180694580078 ], "pca3d": [ - -0.2361478954553604, - -0.267842173576355, - -0.023921672254800797 + -0.23683485388755798, + -0.2672837972640991, + -0.018198566511273384 ], "tsne3d": [ - -16.225427627563477, - -37.10654067993164, - -0.4766146242618561 + -19.654531478881836, + -17.537694931030273, + 18.351974487304688 ], "umap3d": [ - 2.783372640609741, - -0.7833237051963806, - -1.087787389755249 + 0.42565837502479553, + 0.42746803164482117, + 5.742341995239258 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 24, + "id": 1, "label": "Cancer Data" } ], @@ -171246,47 +171429,47 @@ "title": "Model Spec (2024/05/08)", "projections": { "pca": [ - 0.0978885218501091, - 0.09822443872690201 + 0.09937138855457306, + 0.10030855983495712 ], "tsne": [ - -0.8159013986587524, - 5.896226406097412 + 4.77900505065918, + 10.448551177978516 ], "umap": [ - 1.5510473251342773, - 4.428554058074951 + 0.20374439656734467, + 3.1726491451263428 ], "pca3d": [ - 0.0978885293006897, - 0.09823913872241974, - 0.028684597462415695 + 0.09937136620283127, + 0.10032189637422562, + 0.025952858850359917 ], "tsne3d": [ - 5.085750579833984, - -2.1021664142608643, - 24.425140380859375 + 15.334595680236816, + -19.233301162719727, + 21.93439292907715 ], "umap3d": [ - 5.166872501373291, - -0.5338067412376404, - -0.2554088532924652 + 2.6811716556549072, + 0.5920976996421814, + 6.468743801116943 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 4, - "label": "Com Area Amp" + "id": 2, + "label": "Medical Models" }, { - "id": 23, - "label": "Research Authors" + "id": 0, + "label": "Multimodal Models" } ], "_embedding": [ @@ -171700,47 +171883,47 @@ ], "projections": { "pca": [ - -0.15575329959392548, - 0.24673472344875336 + -0.15529821813106537, + 0.24760232865810394 ], "tsne": [ - -18.515846252441406, - 13.259954452514648 + -1.747668981552124, + 22.566360473632812 ], "umap": [ - 1.810975432395935, - 2.4359652996063232 + 0.7627311944961548, + 1.2467762231826782 ], "pca3d": [ - -0.15575356781482697, - 0.24673809111118317, - -0.06829778105020523 + -0.1552984118461609, + 0.24760431051254272, + -0.0643424466252327 ], "tsne3d": [ - -0.8491057753562927, - 3.1385385990142822, - -36.371822357177734 + 6.208838939666748, + 1.4290481805801392, + -19.8148250579834 ], "umap3d": [ - 6.571671485900879, - -1.0163390636444092, - 0.012628485448658466 + 3.8297035694122314, + -1.438685655593872, + 6.283407211303711 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 4, - "label": "World Models" + "id": 8, + "label": "JEPA Video Models" } ], "_embedding": [ @@ -172176,47 +172359,47 @@ "openalex_url": "https://openalex.org/W4415443081", "projections": { "pca": [ - -0.07653755694627762, - -0.3447735607624054 + -0.07866837829351425, + -0.34586191177368164 ], "tsne": [ - 0.006449700798839331, - -19.904680252075195 + -13.39288330078125, + -12.099663734436035 ], "umap": [ - -2.0636184215545654, - 4.832916259765625 + -2.5323684215545654, + 5.993709564208984 ], "pca3d": [ - -0.07653667032718658, - -0.34476715326309204, - -0.020501865074038506 + -0.07866763323545456, + -0.345855712890625, + -0.016682028770446777 ], "tsne3d": [ - -28.65559959411621, - -27.32614517211914, - 12.420098304748535 + -19.45404624938965, + -23.158689498901367, + 6.588889122009277 ], "umap3d": [ - 2.1638102531433105, - -0.3973837196826935, - -1.5868366956710815 + 0.038741063326597214, + 0.9624660015106201, + 5.426523685455322 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 1, + "label": "Cancer Data" }, { - "id": 2, - "label": "Genetic Disease" + "id": 27, + "label": "Genetic Variants" } ], "_embedding": [ @@ -172624,47 +172807,47 @@ "abstract": "We\u2019re on a journey to advance and democratize artificial intelligence through open source and open science.", "projections": { "pca": [ - 0.13526825606822968, - 0.137147456407547 + 0.13801448047161102, + 0.13887985050678253 ], "tsne": [ - 7.250422954559326, - 18.03696060180664 + 21.371007919311523, + 14.771849632263184 ], "umap": [ - 5.131701469421387, - 4.79882287979126 + 4.1457295417785645, + 3.0216734409332275 ], "pca3d": [ - 0.13526791334152222, - 0.13715671002864838, - 0.1558077186346054 + 0.13801400363445282, + 0.13888727128505707, + 0.15145033597946167 ], "tsne3d": [ - 49.644222259521484, - -7.219424724578857, - 15.763985633850098 + -5.7661452293396, + 37.334232330322266, + 10.347567558288574 ], "umap3d": [ - 5.399909019470215, - 1.7944964170455933, - 2.079010486602783 + 4.928348541259766, + 2.1861371994018555, + 4.532614231109619 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { "id": 7, - "label": "Open AI" + "label": "Hugging Face Models" } ], "_embedding": [ @@ -173084,47 +173267,47 @@ "doi": "10.1101/2024.09.29.24314557", "projections": { "pca": [ - -0.19451019167900085, - -0.24362321197986603 + -0.1958540380001068, + -0.24228034913539886 ], "tsne": [ - 0.7418231964111328, - -20.148496627807617 + -12.703044891357422, + -12.352002143859863 ], "umap": [ - -1.8757261037826538, - 4.9012980461120605 + -2.4775936603546143, + 6.0640764236450195 ], "pca3d": [ - -0.19450850784778595, - -0.24360278248786926, - -0.00622932193800807 + -0.19585248827934265, + -0.2422613650560379, + -0.001141035696491599 ], "tsne3d": [ - -24.823497772216797, - -22.898408889770508, - 13.321305274963379 + -15.300613403320312, + -20.548521041870117, + 5.874379634857178 ], "umap3d": [ - 2.222449779510498, - -0.40846070647239685, - -1.6994119882583618 + 0.08869247138500214, + 1.129662275314331, + 5.490567207336426 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 1, + "label": "Cancer Data" }, { - "id": 2, - "label": "Genetic Disease" + "id": 27, + "label": "Genetic Variants" } ], "_embedding": [ @@ -173571,47 +173754,47 @@ "openalex_url": "https://openalex.org/W4415482059", "projections": { "pca": [ - -0.20296406745910645, - -0.1063925251364708 + -0.2041836678981781, + -0.10503238439559937 ], "tsne": [ - -3.5886828899383545, - -22.757503509521484 + -8.987349510192871, + -9.767356872558594 ], "umap": [ - -0.8888638019561768, - 4.141069412231445 + -0.816489577293396, + 5.941833019256592 ], "pca3d": [ - -0.20296408236026764, - -0.10640530288219452, - -0.10618661344051361 + -0.2041839361190796, + -0.10504332929849625, + -0.1018180251121521 ], "tsne3d": [ - -46.626983642578125, - -33.09097671508789, - 5.765985012054443 + -12.574426651000977, + -26.868886947631836, + -10.775588035583496 ], "umap3d": [ - 2.620961904525757, - 0.9236186742782593, - -1.205007553100586 + 1.5154355764389038, + 0.2680732309818268, + 4.259746551513672 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 2, - "label": "AI Drug Discovery" + "id": 9, + "label": "Drug Discovery" }, { - "id": 19, - "label": "Cell Models" + "id": 28, + "label": "Spatial Drug" } ], "_embedding": [ @@ -174029,47 +174212,47 @@ "doi": "10.1101/2025.04.25.650676", "projections": { "pca": [ - -0.16453245282173157, - 0.04599421098828316 + -0.16407275199890137, + 0.04767915606498718 ], "tsne": [ - -19.57396125793457, - 15.676395416259766 + -4.106253623962402, + 26.130598068237305 ], "umap": [ - 2.0399646759033203, - 3.141002655029297 + 0.9757130742073059, + 2.172055721282959 ], "pca3d": [ - -0.16453108191490173, - 0.04600667953491211, - 0.06681893765926361 + -0.16407126188278198, + 0.04768751189112663, + 0.07106848061084747 ], "tsne3d": [ - -0.9058733582496643, - -4.308657646179199, - -25.135337829589844 + 10.343920707702637, + -6.753508567810059, + -24.726476669311523 ], "umap3d": [ - 6.078892230987549, - -0.6019025444984436, - 0.21701394021511078 + 3.976196765899658, + -1.081545114517212, + 6.543076992034912 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 15, - "label": "Latent Models" + "id": 18, + "label": "Retrieval Learning" } ], "_embedding": [ @@ -174477,47 +174660,47 @@ "abstract": "Featured Update: July 2025\n\n\nWhat\u2019s New in II-Medical-8B-1706 and II-Medical-32B-Preview\n\nOur newest state-of-the-art medical models bring major upgrades across the board, setting a new benchmark for trustworthy, open-source medical AI:\n\n * Best open-source model under 32B for multi-turn medical dialogues and precise compliance\n * Reinforced safety through a third RL training phase focused on producing clinically cautious, user-friendly answers\n * Improved real-world clinical reasoning \u2014 not jus", "projections": { "pca": [ - 0.09291277080774307, - -0.09424791485071182 + 0.09546732902526855, + -0.09165620803833008 ], "tsne": [ - -3.8334288597106934, - 1.5593713521957397 + -10.82779598236084, + 8.96644401550293 ], "umap": [ - 0.469880610704422, - 6.7111287117004395 + -0.9090526700019836, + 3.417027473449707 ], "pca3d": [ - 0.09291167557239532, - -0.09426456689834595, - 0.21667712926864624 + 0.09546614438295364, + -0.09167080372571945, + 0.21816842257976532 ], "tsne3d": [ - 20.557146072387695, - -17.186676025390625, - 10.940863609313965 + 9.20833683013916, + -5.35498571395874, + 15.581531524658203 ], "umap3d": [ - 4.817470073699951, - -0.8154153227806091, - -1.474022626876831 + 1.6193814277648926, + 0.9300055503845215, + 7.19444465637207 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 0, - "label": "Clinical LLMs" + "id": 5, + "label": "Health Benchmarks" } ], "_embedding": [ @@ -174925,46 +175108,46 @@ "abstract": "DeepVariant turns variant-calling into an image classification task. Here we explore what these pileup images look like and try to do the same classification task ourselves. We show easy and difficult examples, including multiallelics. By the end, we have a better intuition for how DeepVariant works.", "projections": { "pca": [ - -0.12843570113182068, - -0.10035178810358047 + -0.12929938733577728, + -0.10036361962556839 ], "tsne": [ - 10.470134735107422, - -32.00570297241211 + -18.536426544189453, + -34.68935775756836 ], "umap": [ - -2.8143858909606934, - 4.199151992797852 + -2.577259063720703, + 7.124425411224365 ], "pca3d": [ - -0.1284361183643341, - -0.10036531835794449, - -0.13118170201778412 + -0.12929974496364594, + -0.10037484019994736, + -0.1262662410736084 ], "tsne3d": [ - -54.29803466796875, - 10.404655456542969, - 17.963056564331055 + -29.53439712524414, + -6.038793087005615, + -35.989742279052734 ], "umap3d": [ - 1.0776551961898804, - -0.3741171658039093, - -1.3618040084838867 + -0.4993784427642822, + 0.9552740454673767, + 4.553066253662109 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 22, + "id": 27, "label": "Genetic Variants" } ], @@ -175373,46 +175556,46 @@ "abstract": "Posted by Mark DePristo and Ryan Poplin, Google Brain Team(Crossposted on the Google Open Source Blog)Across many scientific disciplines, but in pa...", "projections": { "pca": [ - -0.10727962851524353, - -0.13202698528766632 + -0.10849574208259583, + -0.13222408294677734 ], "tsne": [ - 10.58580207824707, - -32.17173385620117 + -18.670608520507812, + -34.82185363769531 ], "umap": [ - -2.803224802017212, - 4.2023186683654785 + -2.527540445327759, + 7.126150608062744 ], "pca3d": [ - -0.10728015750646591, - -0.1320454478263855, - -0.11870395392179489 + -0.10849632322788239, + -0.13223983347415924, + -0.1139005646109581 ], "tsne3d": [ - -56.4072151184082, - 11.270036697387695, - 13.52593994140625 + -26.028118133544922, + -5.633606910705566, + -38.124305725097656 ], "umap3d": [ - 1.0325634479522705, - -0.36254340410232544, - -1.3321388959884644 + -0.4620635211467743, + 0.950954020023346, + 4.5123982429504395 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 22, + "id": 27, "label": "Genetic Variants" } ], @@ -175820,46 +176003,46 @@ "title": "Using AI to identify genetic variants in tumors with DeepSomatic", "projections": { "pca": [ - -0.134873166680336, - -0.12844301760196686 + -0.1350296139717102, + -0.1282951533794403 ], "tsne": [ - 10.43399429321289, - -32.309226989746094 + -18.56246566772461, + -34.97428512573242 ], "umap": [ - -2.7712435722351074, - 4.217700481414795 + -2.6279144287109375, + 7.101628303527832 ], "pca3d": [ - -0.13487370312213898, - -0.12846022844314575, - -0.06951270997524261 + -0.13503031432628632, + -0.12830868363380432, + -0.0651615709066391 ], "tsne3d": [ - -57.55207061767578, - 7.1965837478637695, - 15.932746887207031 + -27.304771423339844, + -2.5555098056793213, + -35.86990737915039 ], "umap3d": [ - 1.0965467691421509, - -0.33360838890075684, - -1.3079862594604492 + -0.44907233119010925, + 0.9468216896057129, + 4.577824115753174 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 22, + "id": 27, "label": "Genetic Variants" } ], @@ -176268,47 +176451,47 @@ "abstract": "Contexts Optical Compression. Contribute to deepseek-ai/DeepSeek-OCR development by creating an account on GitHub.", "projections": { "pca": [ - 0.2116742879152298, - 0.11580125987529755 + 0.21222540736198425, + 0.11611439287662506 ], "tsne": [ - 11.093504905700684, - 23.513166427612305 + 25.467681884765625, + -1.5650839805603027 ], "umap": [ - 3.1543712615966797, - 5.341958045959473 + 2.1694798469543457, + 3.5462098121643066 ], "pca3d": [ - 0.21167394518852234, - 0.11579451709985733, - -0.10854341089725494 + 0.21222516894340515, + 0.11610749363899231, + -0.10860124230384827 ], "tsne3d": [ - 21.476882934570312, - 40.58959197998047, - -13.268304824829102 + 39.66215896606445, + 19.71101951599121, + -16.040672302246094 ], "umap3d": [ - 4.723033905029297, - -0.30482736229896545, - 1.3025872707366943 + 3.745471477508545, + 1.1281673908233643, + 6.405642986297607 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 20, - "label": "AI Agents" + "id": 15, + "label": "PyTorch Models" } ], "_embedding": [ @@ -176728,47 +176911,47 @@ ], "projections": { "pca": [ - -0.23114925622940063, - -0.06127430498600006 + -0.23532254993915558, + -0.0613681897521019 ], "tsne": [ - -5.371312141418457, - -27.076927185058594 + -21.320222854614258, + -23.298128128051758 ], "umap": [ - -1.1014631986618042, - 3.5664114952087402 + -0.7254176139831543, + 6.823357582092285 ], "pca3d": [ - -0.23114868998527527, - -0.06126701086759567, - -0.20306728780269623 + -0.2353219836950302, + -0.06136146932840347, + -0.19895465672016144 ], "tsne3d": [ - -46.278968811035156, - -24.571617126464844, - -11.475367546081543 + -25.163726806640625, + -26.790311813354492, + -4.155909538269043 ], "umap3d": [ - 1.8327745199203491, - 1.3246499300003052, - -1.2830628156661987 + 0.6644566059112549, + 0.12494029104709625, + 3.8027045726776123 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 19, - "label": "Cell Models" + "id": 20, + "label": "Cell Foundation" } ], "_embedding": [ @@ -177193,47 +177376,47 @@ "openalex_url": "https://openalex.org/W4409705874", "projections": { "pca": [ - -0.08096615225076675, - -0.10564472526311874 + -0.08480780571699142, + -0.10674681514501572 ], "tsne": [ - -5.634835720062256, - -27.275169372558594 + -21.49894905090332, + -24.216419219970703 ], "umap": [ - -0.9560152292251587, - 3.5328948497772217 + -0.6059003472328186, + 6.796380043029785 ], "pca3d": [ - -0.08096633106470108, - -0.10563826560974121, - -0.1866629272699356 + -0.08480796217918396, + -0.10673978924751282, + -0.18378594517707825 ], "tsne3d": [ - -50.54408645629883, - -25.202247619628906, - -14.593460083007812 + -23.82120132446289, + -27.34703254699707, + 0.12661822140216827 ], "umap3d": [ - 1.914435863494873, - 1.423960566520691, - -1.3778997659683228 + 0.7442570328712463, + 0.017152704298496246, + 3.7090163230895996 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 19, - "label": "Cell Models" + "id": 20, + "label": "Cell Foundation" } ], "_embedding": [ @@ -177641,47 +177824,47 @@ "abstract": "We\u2019re on a journey to advance and democratize artificial intelligence through open source and open science.", "projections": { "pca": [ - 0.11821474879980087, - 0.1577766090631485 + 0.12215611338615417, + 0.1602056324481964 ], "tsne": [ - 8.251808166503906, - 20.42205047607422 + 22.058870315551758, + 13.853233337402344 ], "umap": [ - 5.181281089782715, - 4.737300395965576 + 4.161068916320801, + 2.9246625900268555 ], "pca3d": [ - 0.11821460723876953, - 0.1577879935503006, - 0.19789329171180725 + 0.12215586006641388, + 0.16021470725536346, + 0.1938294917345047 ], "tsne3d": [ - 44.78599548339844, - -0.47550877928733826, - 14.501358032226562 + 4.900365829467773, + 33.21870422363281, + 11.599383354187012 ], "umap3d": [ - 5.491867542266846, - 1.7657639980316162, - 2.044889211654663 + 4.923094749450684, + 2.252444267272949, + 4.539612293243408 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { "id": 7, - "label": "Open AI" + "label": "Hugging Face Models" } ], "_embedding": [ @@ -178103,47 +178286,47 @@ "doi": "10.1101/2025.10.14.682378", "projections": { "pca": [ - -0.29471850395202637, - 0.07473349571228027 + -0.2967659831047058, + 0.0757780447602272 ], "tsne": [ - -6.649737358093262, - -30.280590057373047 + -21.106704711914062, + -19.967485427856445 ], "umap": [ - -1.3870809078216553, - 3.0823709964752197 + -0.8982715010643005, + 7.0463690757751465 ], "pca3d": [ - -0.29471784830093384, - 0.07473582029342651, - -0.2188427746295929 + -0.2967655062675476, + 0.07577846944332123, + -0.21532763540744781 ], "tsne3d": [ - -39.71781539916992, - -14.719694137573242, - -19.372486114501953 + -33.171085357666016, + -25.139270782470703, + -10.486062049865723 ], "umap3d": [ - 1.390514850616455, - 1.1883138418197632, - -1.2411805391311646 + 0.42043614387512207, + 0.3998318314552307, + 3.769228458404541 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 19, - "label": "Cell Models" + "id": 20, + "label": "Cell Foundation" } ], "_embedding": [ @@ -178560,47 +178743,47 @@ ], "projections": { "pca": [ - -0.1405375748872757, - 0.1902751475572586 + -0.1410062462091446, + 0.18962666392326355 ], "tsne": [ - -18.167404174804688, - 23.96050262451172 + 6.7633185386657715, + 26.919445037841797 ], "umap": [ - 2.70099139213562, - 3.078949451446533 + 1.5902410745620728, + 2.031158208847046 ], "pca3d": [ - -0.1405373364686966, - 0.19028528034687042, - -0.003754253266379237 + -0.1410059779882431, + 0.1896345317363739, + -0.0032424665987491608 ], "tsne3d": [ - 13.796364784240723, - -5.4690704345703125, - -27.025747299194336 + 21.69232940673828, + 7.750207901000977, + -34.36240768432617 ], "umap3d": [ - 6.481090068817139, - -0.269528329372406, - 0.776375412940979 + 4.777658939361572, + -0.8831091523170471, + 6.09008264541626 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 18, + "label": "Retrieval Learning" } ], "_embedding": [ @@ -179012,46 +179195,46 @@ ], "projections": { "pca": [ - -0.0689351037144661, - -0.12666113674640656 + -0.07214135676622391, + -0.12746745347976685 ], "tsne": [ - 5.158425331115723, - -29.13492202758789 + -9.402576446533203, + -21.26736068725586 ], "umap": [ - -2.4088189601898193, - 3.914472818374634 + -2.1807751655578613, + 6.969363212585449 ], "pca3d": [ - -0.06893560290336609, - -0.12666237354278564, - -0.19755515456199646 + -0.07214201241731644, + -0.12746740877628326, + -0.19462527334690094 ], "tsne3d": [ - -48.76316452026367, - -1.01271390914917, - 13.83910846710205 + -30.138357162475586, + -1.312498927116394, + -23.591711044311523 ], "umap3d": [ - 1.2598292827606201, - -0.022989442571997643, - -1.204535961151123 + -0.14462104439735413, + 1.3095834255218506, + 4.394199848175049 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 22, + "id": 27, "label": "Genetic Variants" } ], @@ -179460,46 +179643,46 @@ "abstract": "This post investigates the importance of various features of sequencing data to the ability to accurately call variants. Channel ablation experiments reveal how DeepVariant uses specific representational information to correctly call specific genotypes.", "projections": { "pca": [ - -0.09694676846265793, - 0.00028223422123119235 + -0.09866952896118164, + -0.00038705504266545177 ], "tsne": [ - 10.586363792419434, - -31.120553970336914 + -18.515073776245117, + -33.832706451416016 ], "umap": [ - -2.8513922691345215, - 4.096712112426758 + -2.5401153564453125, + 7.207866191864014 ], "pca3d": [ - -0.09694699943065643, - 0.0002697508316487074, - -0.1580076515674591 + -0.09866984188556671, + -0.0003985077782999724, + -0.1560731679201126 ], "tsne3d": [ - -50.022926330566406, - 10.90703296661377, - 14.092920303344727 + -26.678211212158203, + -9.75185489654541, + -34.789337158203125 ], "umap3d": [ - 0.9756425023078918, - -0.29118573665618896, - -1.239267349243164 + -0.5174545049667358, + 1.0172326564788818, + 4.38214111328125 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 22, + "id": 27, "label": "Genetic Variants" } ], @@ -179929,47 +180112,47 @@ "doi": "10.1101/2025.09.08.674723", "projections": { "pca": [ - -0.2296333909034729, - -0.11979232728481293 + -0.22940830886363983, + -0.11746703833341599 ], "tsne": [ - -9.029830932617188, - -19.724275588989258 + -19.703048706054688, + -11.458063125610352 ], "umap": [ - -1.967181921005249, - 5.427634239196777 + -1.9947930574417114, + 5.489625930786133 ], "pca3d": [ - -0.22963334619998932, - -0.11979588866233826, - -0.06837783753871918 + -0.2294081747531891, + -0.11746909469366074, + -0.0631674975156784 ], "tsne3d": [ - -30.580780029296875, - -21.01630401611328, - -4.15265417098999 + -27.161510467529297, + -7.174023151397705, + 4.243860244750977 ], "umap3d": [ - 2.602992534637451, - -0.2948332726955414, - -0.9202196598052979 + 0.19528281688690186, + 0.159129336476326, + 5.322697639465332 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 1, "label": "Cancer Data" }, { - "id": 9, - "label": "Cancer Biobank" + "id": 1, + "label": "Cancer Data" } ], "_embedding": [ @@ -180377,47 +180560,47 @@ "abstract": "We propose Recursive Language Models (RLMs), an inference strategy where language models can decompose and recursively interact with input context of unbounded length through REPL environments.", "projections": { "pca": [ - -0.12692590057849884, - 0.1854608952999115 + -0.12759453058242798, + 0.1837937831878662 ], "tsne": [ - -17.853626251220703, - 27.723196029663086 + 3.744523048400879, + 34.89214324951172 ], "umap": [ - 3.03733229637146, - 2.31457257270813 + 2.073667287826538, + 1.3915964365005493 ], "pca3d": [ - -0.12692679464817047, - 0.18546162545681, - -0.07690180093050003 + -0.12759554386138916, + 0.18379493057727814, + -0.07473903894424438 ], "tsne3d": [ - 15.50257396697998, - 19.827659606933594, - -42.863338470458984 + 3.6762502193450928, + 9.4435453414917, + -21.43446159362793 ], "umap3d": [ - 7.034181118011475, - -0.6203908324241638, - 0.9455744028091431 + 4.685117244720459, + -1.034295678138733, + 5.501245498657227 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 6, + "label": "Learning Techniques" } ], "_embedding": [ @@ -180824,47 +181007,47 @@ "title": "Teaching machines the language of biology: Scaling large language models for next-generation single-cell analysis", "projections": { "pca": [ - -0.12627314031124115, - 0.026303280144929886 + -0.12860213220119476, + 0.025875067338347435 ], "tsne": [ - -7.725273132324219, - -23.471534729003906 + -19.851655960083008, + -16.233461380004883 ], "umap": [ - -0.762840211391449, - 3.721312999725342 + -0.3891978859901428, + 6.930705547332764 ], "pca3d": [ - -0.12627460062503815, - 0.026294682174921036, - -0.061521489173173904 + -0.12860356271266937, + 0.025867143645882607, + -0.05711332708597183 ], "tsne3d": [ - -37.620506286621094, - -2.630495309829712, - -32.057891845703125 + -15.803470611572266, + -39.89211654663086, + -8.883452415466309 ], "umap3d": [ - 1.9667059183120728, - 0.979982316493988, - -0.8979164361953735 + 1.0593196153640747, + 0.702620267868042, + 4.061111927032471 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 19, - "label": "Cell Models" + "id": 20, + "label": "Cell Foundation" } ], "_embedding": [ @@ -181274,47 +181457,47 @@ "journal": "Nature Methods", "projections": { "pca": [ - -0.18475107848644257, - 0.006188616622239351 + -0.18524277210235596, + 0.007971306331455708 ], "tsne": [ - -3.564692735671997, - -35.1382942199707 + -17.473573684692383, + -27.85697364807129 ], "umap": [ - -1.934998631477356, - 2.839754104614258 + -1.217450737953186, + 7.692234516143799 ], "pca3d": [ - -0.18475143611431122, - 0.006189051084220409, - -0.19558320939540863 + -0.18524327874183655, + 0.007972285151481628, + -0.19472210109233856 ], "tsne3d": [ - -54.10763168334961, - -2.2311010360717773, - -14.752189636230469 + -38.6265983581543, + -20.266769409179688, + -27.01690673828125 ], "umap3d": [ - 0.9067692756652832, - 0.9132371544837952, - -0.927983283996582 + 0.25896936655044556, + 0.9210216999053955, + 3.456357002258301 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 8, - "label": "Gene Regulation" + "id": 12, + "label": "Protein Language" } ], "_embedding": [ @@ -181730,47 +181913,47 @@ ], "projections": { "pca": [ - -0.12656687200069427, - 0.1798199564218521 + -0.12570632994174957, + 0.18046697974205017 ], "tsne": [ - -18.635801315307617, - 21.451765060424805 + 4.459371089935303, + 27.0150203704834 ], "umap": [ - 2.7337424755096436, - 3.082653760910034 + 1.8193243741989136, + 1.930985927581787 ], "pca3d": [ - -0.12656563520431519, - 0.17984041571617126, - -0.02318139187991619 + -0.12570509314537048, + 0.1804850697517395, + -0.02211744524538517 ], "tsne3d": [ - 19.601755142211914, - 1.977599024772644, - -21.959842681884766 + 16.766111373901367, + 1.5259835720062256, + -31.434255599975586 ], "umap3d": [ - 6.409562587738037, - -0.4607963562011719, - 1.0370312929153442 + 4.585188388824463, + -0.7386166453361511, + 6.180967330932617 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 6, + "label": "Learning Techniques" } ], "_embedding": [ @@ -182178,47 +182361,47 @@ "abstract": "C2S-Scale 27B, a new foundation model built on Gemma AI and validated in the lab, is transforming single-cell analysis and may help provide a novel pathway for cancer therapy.", "projections": { "pca": [ - -0.029431665316224098, - -0.10900066047906876 + -0.02833888866007328, + -0.10738782584667206 ], "tsne": [ - -11.677213668823242, - -20.031925201416016 + -22.22254753112793, + -11.262897491455078 ], "umap": [ - -1.8647713661193848, - 5.715073585510254 + -1.7143970727920532, + 5.2014570236206055 ], "pca3d": [ - -0.029432246461510658, - -0.10900276899337769, - 0.043584030121564865 + -0.028339507058262825, + -0.10738784819841385, + 0.048105619847774506 ], "tsne3d": [ - -23.913511276245117, - -33.30473327636719, - -16.457897186279297 + -33.22591018676758, + -8.195979118347168, + 11.705747604370117 ], "umap3d": [ - 2.9699206352233887, - -0.6370648145675659, - -0.5942063331604004 + 0.7581270933151245, + -0.14341288805007935, + 5.518805027008057 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 9, - "label": "Cancer Biobank" + "id": 1, + "label": "Cancer Data" } ], "_embedding": [ @@ -182634,46 +182817,46 @@ "doi": "10.1007/s00285-015-0888-x", "projections": { "pca": [ - -0.07032639533281326, - -0.0454898327589035 + -0.0682835802435875, + -0.04476195201277733 ], "tsne": [ - -16.688940048217773, - -21.220060348510742 + -28.14923095703125, + -6.405613422393799 ], "umap": [ - -1.7092516422271729, - 6.25322961807251 + -1.8048884868621826, + 5.264184951782227 ], "pca3d": [ - -0.07032591104507446, - -0.04547656327486038, - 0.01913483627140522 + -0.06828318536281586, + -0.04474951699376106, + 0.023294810205698013 ], "tsne3d": [ - -23.019548416137695, - -46.452293395996094, - -14.04985523223877 + -38.105655670166016, + -9.480138778686523, + 22.669328689575195 ], "umap3d": [ - 3.0275306701660156, - -1.008514642715454, - -0.9713789820671082 + 0.7274790406227112, + -0.21448588371276855, + 5.869337558746338 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 24, + "id": 1, "label": "Cancer Data" } ], @@ -183090,46 +183273,46 @@ "doi": "10.1007/s00285-019-01383-y", "projections": { "pca": [ - -0.023338118568062782, - -0.14250271022319794 + -0.022422993555665016, + -0.14173869788646698 ], "tsne": [ - -16.654691696166992, - -21.221355438232422 + -28.0875301361084, + -6.401869773864746 ], "umap": [ - -1.87601900100708, - 6.103682041168213 + -2.0191335678100586, + 5.2208685874938965 ], "pca3d": [ - -0.023338114842772484, - -0.14248818159103394, - 0.01776166632771492 + -0.022422967478632927, + -0.14172396063804626, + 0.02154609188437462 ], "tsne3d": [ - -23.58675193786621, - -45.24262619018555, - -10.07003402709961 + -38.38715744018555, + -12.585384368896484, + 21.184600830078125 ], "umap3d": [ - 2.7713382244110107, - -0.9615206718444824, - -0.9790807366371155 + 0.5373939275741577, + -0.1751360148191452, + 5.753059387207031 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 24, + "id": 1, "label": "Cancer Data" } ], @@ -183538,46 +183721,46 @@ "abstract": "We release Code World Model (CWM), a 32-billion-parameter open-weights LLM, to advance research\non code generation with world models. To improve code...", "projections": { "pca": [ - 0.058088891208171844, - 0.09732811897993088 + 0.05829332768917084, + 0.09765463322401047 ], "tsne": [ - -13.958660125732422, - 13.983566284179688 + 1.8347365856170654, + 18.849273681640625 ], "umap": [ - 2.42055344581604, - 2.396765947341919 + 1.6072419881820679, + 1.156584620475769 ], "pca3d": [ - 0.058088406920433044, - 0.09734292328357697, - -0.030668066814541817 + 0.058292873203754425, + 0.09766882658004761, + -0.030424408614635468 ], "tsne3d": [ - -15.773219108581543, - 2.4324822425842285, - -51.025115966796875 + 12.479866027832031, + 6.458349704742432, + -5.945641994476318 ], "umap3d": [ - 6.628658771514893, - -0.3302437365055084, - 0.03755403310060501 + 4.4109296798706055, + -1.1741430759429932, + 5.8013014793396 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 4, + "id": 26, "label": "World Models" } ], @@ -183986,47 +184169,47 @@ "abstract": "Built for data scientists and bioinformaticians, NVIDIA Parabricks is a scalable genomics software suite for secondary analysis.", "projections": { "pca": [ - 0.08564414083957672, - -0.05927979573607445 + 0.08622005581855774, + -0.057265304028987885 ], "tsne": [ - 35.11743927001953, - -5.502194404602051 + 18.92020606994629, + -21.01513671875 ], "umap": [ - -1.662735939025879, - 4.272225379943848 + -1.4891616106033325, + 6.5088605880737305 ], "pca3d": [ - 0.08564358204603195, - -0.05928143486380577, - -0.09983833879232407 + 0.08621944487094879, + -0.0572628453373909, + -0.09960208088159561 ], "tsne3d": [ - 15.47987174987793, - 50.5338134765625, - -6.774517059326172 + 43.620479583740234, + 3.4576222896575928, + -6.92464017868042 ], "umap3d": [ - 1.5750865936279297, - -0.15452738106250763, - -0.697994589805603 + 3.986696481704712, + 1.6497184038162231, + 6.828053951263428 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 2, - "label": "Genetic Disease" + "id": 20, + "label": "Cell Foundation" } ], "_embedding": [ @@ -184440,47 +184623,47 @@ ], "projections": { "pca": [ - -0.15419791638851166, - 0.21376648545265198 + -0.1558385193347931, + 0.21339520812034607 ], "tsne": [ - -16.879545211791992, - 20.978042602539062 + 4.101245403289795, + 29.357332229614258 ], "umap": [ - 2.9181699752807617, - 2.8135271072387695 + 1.942910075187683, + 1.8843084573745728 ], "pca3d": [ - -0.15419869124889374, - 0.21376203000545502, - -0.08718989044427872 + -0.15583932399749756, + 0.2133917510509491, + -0.08420976996421814 ], "tsne3d": [ - 21.421497344970703, - 5.463037014007568, - -32.15166473388672 + 7.5890421867370605, + 7.184847831726074, + -32.544769287109375 ], "umap3d": [ - 6.512843132019043, - -0.5638723373413086, - 1.1569992303848267 + 4.558389663696289, + -0.6757166385650635, + 5.9434309005737305 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 6, + "label": "Learning Techniques" } ], "_embedding": [ @@ -184900,47 +185083,47 @@ "doi": "10.48550/arxiv.2510.06557", "projections": { "pca": [ - -0.16146330535411835, - 0.32113927602767944 + -0.1594839245080948, + 0.32337889075279236 ], "tsne": [ - -13.005115509033203, - 23.615097045898438 + 8.823159217834473, + 29.894561767578125 ], "umap": [ - 3.463376760482788, - 2.6706433296203613 + 2.299163818359375, + 1.3972225189208984 ], "pca3d": [ - -0.16146405041217804, - 0.3211418092250824, - 0.030535656958818436 + -0.15948466956615448, + 0.32338085770606995, + 0.03174389526247978 ], "tsne3d": [ - 17.93018913269043, - 4.226095676422119, - -44.92049026489258 + 10.542380332946777, + 17.775110244750977, + -24.876644134521484 ], "umap3d": [ - 7.005405902862549, - -0.2737366557121277, - 1.0583947896957397 + 4.817200660705566, + -0.7168654203414917, + 5.404438495635986 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 6, + "label": "Learning Techniques" } ], "_embedding": [ @@ -185356,47 +185539,47 @@ ], "projections": { "pca": [ - -0.19514665007591248, - 0.23638147115707397 + -0.19444438815116882, + 0.23732931911945343 ], "tsne": [ - -11.431217193603516, - 21.07967758178711 + 2.1042368412017822, + 29.749713897705078 ], "umap": [ - 2.6846885681152344, - 2.5418081283569336 + 1.7416664361953735, + 1.7317240238189697 ], "pca3d": [ - -0.19514766335487366, - 0.2363676279783249, - -0.010149678215384483 + -0.19444526731967926, + 0.2373160719871521, + -0.005265435203909874 ], "tsne3d": [ - 7.774620056152344, - 12.916138648986816, - -48.65829086303711 + 0.8778314590454102, + 17.697010040283203, + -28.556026458740234 ], "umap3d": [ - 6.7377753257751465, - -0.5766337513923645, - 0.7411624193191528 + 4.2515645027160645, + -0.6901483535766602, + 5.735313892364502 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 6, + "label": "Learning Techniques" } ], "_embedding": [ @@ -185804,47 +185987,47 @@ "journal": "Nature", "projections": { "pca": [ - -0.05152714625000954, - 0.011745186522603035 + -0.05334534868597984, + 0.012264386750757694 ], "tsne": [ - -2.620570659637451, - -32.42327117919922 + -17.950119018554688, + -24.78311538696289 ], "umap": [ - -1.8087860345840454, - 2.8680901527404785 + -1.2261751890182495, + 7.543479919433594 ], "pca3d": [ - -0.05152762681245804, - 0.011738150380551815, - -0.18436187505722046 + -0.053345851600170135, + 0.012258126400411129, + -0.18364016711711884 ], "tsne3d": [ - -59.038352966308594, - -8.52407455444336, - -2.7595837116241455 + -42.93096160888672, + -21.01349639892578, + -16.372047424316406 ], "umap3d": [ - 1.1130188703536987, - 1.0045117139816284, - -1.0469645261764526 + 0.3957194685935974, + 1.0832934379577637, + 3.5539095401763916 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 8, - "label": "Gene Regulation" + "id": 3, + "label": "Genomic Sequences" } ], "_embedding": [ @@ -186260,47 +186443,47 @@ ], "projections": { "pca": [ - -0.13695618510246277, - 0.12991832196712494 + -0.1359596699476242, + 0.13100644946098328 ], "tsne": [ - -17.3518009185791, - 12.273375511169434 + -1.4576865434646606, + 21.15195083618164 ], "umap": [ - 1.616259217262268, - 2.415321111679077 + 0.5786164999008179, + 1.3028241395950317 ], "pca3d": [ - -0.13695506751537323, - 0.12992942333221436, - -0.09730219841003418 + -0.1359584480524063, + 0.13101531565189362, + -0.0932123214006424 ], "tsne3d": [ - -11.277173042297363, - -1.5441123247146606, - -38.404541015625 + 10.418404579162598, + -5.363870620727539, + -15.709813117980957 ], "umap3d": [ - 6.53094482421875, - -1.0561836957931519, - -0.18224960565567017 + 3.8412725925445557, + -1.482872486114502, + 6.339571475982666 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 15, - "label": "Latent Models" + "id": 8, + "label": "JEPA Video Models" } ], "_embedding": [ @@ -186726,47 +186909,47 @@ "openalex_url": "https://openalex.org/W4414944240", "projections": { "pca": [ - 0.1342046707868576, - -0.19372150301933289 + 0.13551992177963257, + -0.1937706470489502 ], "tsne": [ - 14.18017578125, - -4.52940559387207 + -4.887819766998291, + 4.518769264221191 ], "umap": [ - 1.013407588005066, - 5.994932651519775 + -1.8602683544158936, + 2.7484230995178223 ], "pca3d": [ - 0.13420486450195312, - -0.1937292516231537, - 0.16392073035240173 + 0.13552002608776093, + -0.1937789022922516, + 0.16363175213336945 ], "tsne3d": [ - 1.3234108686447144, - 6.945682048797607, - 17.322959899902344 + -25.832460403442383, + -0.9973278045654297, + 35.56242370605469 ], "umap3d": [ - 3.724754810333252, - -1.061007022857666, - -0.7586765885353088 + 1.4902385473251343, + 0.35137608647346497, + 6.384970664978027 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 4, - "label": "Com Area Amp" + "id": 5, + "label": "Clinical Data" }, { - "id": 0, - "label": "Clinical LLMs" + "id": 13, + "label": "ECG AI Models" } ], "_embedding": [ @@ -187177,47 +187360,47 @@ ], "projections": { "pca": [ - -0.13385431468486786, - 0.2010137140750885 + -0.13305650651454926, + 0.20180939137935638 ], "tsne": [ - -11.231695175170898, - 22.950023651123047 + 10.194437026977539, + 28.573101043701172 ], "umap": [ - 3.329521894454956, - 2.7749621868133545 + 2.171687126159668, + 1.4127205610275269 ], "pca3d": [ - -0.1338549256324768, - 0.20101501047611237, - 0.018342336639761925 + -0.13305725157260895, + 0.20181088149547577, + 0.019872063770890236 ], "tsne3d": [ - 15.847209930419922, - 14.051907539367676, - -49.0007209777832 + 2.6749536991119385, + 16.095129013061523, + -21.860675811767578 ], "umap3d": [ - 6.994422912597656, - -0.24466466903686523, - 0.8102182149887085 + 4.609853744506836, + -0.6736623048782349, + 5.352501392364502 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 6, + "label": "Learning Techniques" } ], "_embedding": [ @@ -187625,46 +187808,46 @@ "journal": "Nature Communications", "projections": { "pca": [ - -0.1124061569571495, - -0.23727628588676453 + -0.1112975925207138, + -0.23417583107948303 ], "tsne": [ - 5.519529819488525, - -23.37147331237793 + -8.484695434570312, + -16.20712661743164 ], "umap": [ - -2.3919644355773926, - 4.268344402313232 + -2.4434587955474854, + 6.654201507568359 ], "pca3d": [ - -0.11240451782941818, - -0.2372559905052185, - 0.046744659543037415 + -0.11129596084356308, + -0.23415625095367432, + 0.04606693610548973 ], "tsne3d": [ - -37.21196365356445, - -21.790122985839844, - 14.200778007507324 + -25.902029037475586, + 2.6795833110809326, + -10.943500518798828 ], "umap3d": [ - 1.4797797203063965, - -0.1754712462425232, - -1.5423898696899414 + -0.22099314630031586, + 1.2782456874847412, + 4.637578010559082 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 22, + "id": 27, "label": "Genetic Variants" } ], @@ -188091,47 +188274,47 @@ "openalex_url": "https://openalex.org/W2781444321", "projections": { "pca": [ - -0.04992211237549782, - -0.1907913237810135 + -0.04756830632686615, + -0.18804332613945007 ], "tsne": [ - -6.383802890777588, - -11.08242416381836 + -15.881115913391113, + -2.5906405448913574 ], "umap": [ - -1.3574512004852295, - 6.295806884765625 + -2.429001808166504, + 4.341511249542236 ], "pca3d": [ - -0.049920640885829926, - -0.19078150391578674, - 0.11132805794477463 + -0.047566719353199005, + -0.1880352646112442, + 0.11444663256406784 ], "tsne3d": [ - -11.56717586517334, - -42.165550231933594, - 15.175683975219727 + -12.796097755432129, + -19.573671340942383, + 25.265533447265625 ], "umap3d": [ - 3.1029410362243652, - -0.9829058647155762, - -1.5639621019363403 + 0.5060186386108398, + 0.8098646998405457, + 6.390117168426514 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 28, - "label": "Clinical AI" + "id": 21, + "label": "Clinical Trials" } ], "_embedding": [ @@ -188548,47 +188731,47 @@ ], "projections": { "pca": [ - -0.06931460648775101, - -0.13700929284095764 + -0.0664871409535408, + -0.13396267592906952 ], "tsne": [ - -12.775345802307129, - -7.859236717224121 + -7.4088664054870605, + 3.4814891815185547 ], "umap": [ - -0.5795993208885193, - 7.180113315582275 + -1.777215600013733, + 2.6905221939086914 ], "pca3d": [ - -0.06931477785110474, - -0.13701336085796356, - 0.11902157962322235 + -0.06648742407560349, + -0.13396413624286652, + 0.12414155155420303 ], "tsne3d": [ - 6.6058878898620605, - -42.02080535888672, - 5.833267688751221 + -25.495622634887695, + 5.7737579345703125, + 31.767288208007812 ], "umap3d": [ - 3.958137035369873, - -1.4503378868103027, - -0.9985771775245667 + 1.3033781051635742, + -0.10382252931594849, + 6.74964714050293 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 12, - "label": "ECG Models" + "id": 13, + "label": "ECG AI Models" } ], "_embedding": [ @@ -188996,46 +189179,46 @@ "journal": "Nature", "projections": { "pca": [ - 0.10137990117073059, - -0.22591263055801392 + 0.1011810153722763, + -0.22575125098228455 ], "tsne": [ - -3.5186262130737305, - -16.352033615112305 + -25.07796859741211, + -4.1304707527160645 ], "umap": [ - -2.3477749824523926, - 6.123097896575928 + -2.7782552242279053, + 5.01989221572876 ], "pca3d": [ - 0.101380355656147, - -0.2259121537208557, - 0.04474883899092674 + 0.10118158161640167, + -0.22575169801712036, + 0.045949537307024 ], "tsne3d": [ - -27.687911987304688, - -44.826393127441406, - 3.9990057945251465 + -29.94835662841797, + -28.719396591186523, + 18.160491943359375 ], "umap3d": [ - 2.563565254211426, - -1.2625218629837036, - -1.0575674772262573 + -0.054279595613479614, + 0.09675170481204987, + 5.918159008026123 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 24, + "id": 1, "label": "Cancer Data" } ], @@ -189443,47 +189626,47 @@ "title": "A Shift from Animal Testing", "projections": { "pca": [ - 0.11350492388010025, - -0.22236521542072296 + 0.11496646702289581, + -0.2214365154504776 ], "tsne": [ - -3.262761116027832, - -9.261540412902832 + -19.6158504486084, + -1.587203860282898 ], "umap": [ - -1.1669559478759766, - 6.303921222686768 + -2.1452548503875732, + 4.237863540649414 ], "pca3d": [ - 0.11350513994693756, - -0.22236430644989014, - 0.12695088982582092 + 0.1149667501449585, + -0.22143514454364777, + 0.12747441232204437 ], "tsne3d": [ - -3.2063169479370117, - -37.04121017456055, - 26.148258209228516 + -4.016805648803711, + -33.94533920288086, + 22.49341583251953 ], "umap3d": [ - 3.3160364627838135, - -0.672768771648407, - -1.707673192024231 + 0.6308455467224121, + 0.5892060995101929, + 6.555977821350098 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 1, - "label": "Cancer Data" + "id": 5, + "label": "Clinical Data" }, { - "id": 28, - "label": "Clinical AI" + "id": 21, + "label": "Clinical Trials" } ], "_embedding": [ @@ -189893,46 +190076,46 @@ "doi": "10.1109/RBME.2025.3577587", "projections": { "pca": [ - -0.1442621350288391, - -0.2347964346408844 + -0.1428079903125763, + -0.23273588716983795 ], "tsne": [ - -8.014455795288086, - -17.126222610473633 + -21.783308029174805, + -5.393745422363281 ], "umap": [ - -2.0929555892944336, - 5.85014533996582 + -2.3429555892944336, + 5.165887832641602 ], "pca3d": [ - -0.14426180720329285, - -0.23478755354881287, - 0.03865792602300644 + -0.14280763268470764, + -0.2327265590429306, + 0.0439564511179924 ], "tsne3d": [ - -20.919368743896484, - -40.758113861083984, - -4.794665813446045 + -22.18449592590332, + -20.556272506713867, + 13.899640083312988 ], "umap3d": [ - 2.637129783630371, - -0.8971882462501526, - -1.000380277633667 + 0.2116534411907196, + 0.08170966058969498, + 5.730720520019531 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 24, + "id": 1, "label": "Cancer Data" } ], @@ -190341,46 +190524,46 @@ "journal": "Nature", "projections": { "pca": [ - -0.1336566060781479, - -0.05141947418451309 + -0.13092777132987976, + -0.04942130669951439 ], "tsne": [ - -9.991776466369629, - -9.455245971679688 + -15.066330909729004, + 0.0032262629829347134 ], "umap": [ - -0.46605053544044495, - 6.425282955169678 + -2.0641226768493652, + 3.811464548110962 ], "pca3d": [ - -0.13365638256072998, - -0.05140768736600876, - 0.1513323187828064 + -0.13092754781246185, + -0.049409471452236176, + 0.15525224804878235 ], "tsne3d": [ - -4.187671184539795, - -31.326812744140625, - 1.1540889739990234 + -13.399986267089844, + -8.87595272064209, + 12.396867752075195 ], "umap3d": [ - 4.006417751312256, - -0.9873586297035217, - -1.467002034187317 + 1.0609803199768066, + 0.8183981776237488, + 6.775275230407715 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, + "id": 14, "label": "Clinical Health" } ], @@ -190789,46 +190972,46 @@ "journal": "Nature Communications", "projections": { "pca": [ - -0.1618167906999588, - -0.08404365181922913 + -0.15541237592697144, + -0.07860908657312393 ], "tsne": [ - -19.429306030273438, - -3.8473000526428223 + -8.343147277832031, + 16.08997344970703 ], "umap": [ - -0.2852250933647156, - 7.744869232177734 + -1.0131531953811646, + 2.4014036655426025 ], "pca3d": [ - -0.16181619465351105, - -0.08403179049491882, - 0.17345917224884033 + -0.15541183948516846, + -0.0785965621471405, + 0.17852184176445007 ], "tsne3d": [ - -22.989561080932617, - -0.5644610524177551, - -15.27928352355957 + -20.321184158325195, + 12.135574340820312, + 23.987262725830078 ], "umap3d": [ - 4.36527681350708, - -1.638815999031067, - -0.5603057146072388 + 1.6675851345062256, + -0.565459132194519, + 6.64219331741333 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 11, + "id": 19, "label": "Medical Imaging" } ], @@ -191239,46 +191422,46 @@ "journal": "Nature Communications", "projections": { "pca": [ - -0.11593864858150482, - -0.055964987725019455 + -0.11044898629188538, + -0.049969032406806946 ], "tsne": [ - -18.54566192626953, - -3.9382083415985107 + -9.307541847229004, + 16.023460388183594 ], "umap": [ - -0.4228842258453369, - 7.639886856079102 + -0.9260650873184204, + 2.629051923751831 ], "pca3d": [ - -0.11593770980834961, - -0.05594474822282791, - 0.20218458771705627 + -0.11044808477163315, + -0.049950070679187775, + 0.20604504644870758 ], "tsne3d": [ - -20.852153778076172, - -5.127182960510254, - -17.119924545288086 + -20.378414154052734, + 8.354900360107422, + 21.45306396484375 ], "umap3d": [ - 4.293676853179932, - -1.5276333093643188, - -0.6754278540611267 + 1.5842148065567017, + -0.5796691179275513, + 6.568499565124512 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 11, + "id": 19, "label": "Medical Imaging" } ], @@ -191687,47 +191870,47 @@ "abstract": "Kernels, of the mega variety :). Contribute to HazyResearch/Megakernels development by creating an account on GitHub.", "projections": { "pca": [ - 0.06392527371644974, - 0.11627417057752609 + 0.061961520463228226, + 0.11534342169761658 ], "tsne": [ - 10.114200592041016, - 8.029947280883789 + 14.88603687286377, + -7.779788017272949 ], "umap": [ - 2.9497599601745605, - 4.796500205993652 + 2.1093997955322266, + 4.018096446990967 ], "pca3d": [ - 0.06392426788806915, - 0.11626973748207092, - -0.1817595660686493 + 0.06196046248078346, + 0.11534124612808228, + -0.18161962926387787 ], "tsne3d": [ - 0.6227023005485535, - 43.46784210205078, - -8.117191314697266 + 32.66427993774414, + 5.128180027008057, + -9.925298690795898 ], "umap3d": [ - 4.728055000305176, - -0.5317589640617371, - 2.045151948928833 + 4.222469806671143, + 1.2799128293991089, + 6.710747241973877 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 5, - "label": "GPU Inference" + "id": 15, + "label": "PyTorch Models" } ], "_embedding": [ @@ -192134,47 +192317,47 @@ "title": "We Bought the Whole GPU, So We're Damn Well Going to Use the Whole GPU", "projections": { "pca": [ - 0.1061486080288887, - 0.12546546757221222 + 0.10443202406167984, + 0.12523463368415833 ], "tsne": [ - 10.198004722595215, - 7.971161842346191 + 14.844304084777832, + -7.691100597381592 ], "umap": [ - 2.8445379734039307, - 4.825606822967529 + 2.075422763824463, + 4.081358432769775 ], "pca3d": [ - 0.1061476618051529, - 0.12545999884605408, - -0.1429021805524826 + 0.10443095862865448, + 0.12523111701011658, + -0.14360883831977844 ], "tsne3d": [ - 4.522538661956787, - 42.43122482299805, - -8.994879722595215 + 31.82642936706543, + 2.314648389816284, + -11.564836502075195 ], "umap3d": [ - 4.6499552726745605, - -0.45125123858451843, - 2.0560262203216553 + 4.315520763397217, + 1.3862966299057007, + 6.651209831237793 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 5, - "label": "GPU Inference" + "id": 15, + "label": "PyTorch Models" } ], "_embedding": [ @@ -192582,47 +192765,47 @@ "journal": "Nature Communications", "projections": { "pca": [ - -0.21994544565677643, - -0.057726725935935974 + -0.2162473350763321, + -0.0538000613451004 ], "tsne": [ - -14.123880386352539, - -13.947225570678711 + -24.0081729888916, + 9.026971817016602 ], "umap": [ - -1.2954891920089722, - 5.379105091094971 + -1.5104849338531494, + 4.659907341003418 ], "pca3d": [ - -0.21994538605213165, - -0.057722609490156174, - 0.11376897245645523 + -0.21624749898910522, + -0.053792912513017654, + 0.11527373641729355 ], "tsne3d": [ - -4.183193683624268, - -36.493690490722656, - -14.017963409423828 + -24.85084342956543, + -1.0339490175247192, + 3.100156784057617 ], "umap3d": [ - 3.012322187423706, - -0.05641842633485794, - -1.2495863437652588 + 0.25822392106056213, + -0.09886268526315689, + 6.432474613189697 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 2, - "label": "AI Drug Discovery" + "id": 1, + "label": "Cancer Data" }, { "id": 9, - "label": "Cancer Biobank" + "label": "Biomedical AI" } ], "_embedding": [ @@ -193030,47 +193213,47 @@ "abstract": "OpenTSLM: Time-Series Language Models for Reasoning over Multivariate Medical Text- and Time-Series Data - SchmiedmayerLab/OpenTSLM", "projections": { "pca": [ - -0.005010101944208145, - -0.040136441588401794 + -0.004195771645754576, + -0.03912452235817909 ], "tsne": [ - -6.646814823150635, - 4.240822792053223 + -13.192582130432129, + 10.515955924987793 ], "umap": [ - 0.7381198406219482, - 6.568391799926758 + -0.3181026577949524, + 3.6038620471954346 ], "pca3d": [ - -0.005010682623833418, - -0.04013983905315399, - 0.048060331493616104 + -0.004196380265057087, + -0.03912879154086113, + 0.051379479467868805 ], "tsne3d": [ - 26.76087760925293, - 31.264493942260742, - -25.271015167236328 + 5.814756393432617, + 1.5483051538467407, + 8.944280624389648 ], "umap3d": [ - 5.129356384277344, - -0.6208680272102356, - -1.1785740852355957 + 1.8748286962509155, + 1.0230451822280884, + 7.179196357727051 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 0, - "label": "Clinical LLMs" + "id": 5, + "label": "Health Benchmarks" } ], "_embedding": [ @@ -193489,47 +193672,47 @@ "doi": "10.1101/2025.08.02.668173", "projections": { "pca": [ - -0.19728511571884155, - 0.03379543870687485 + -0.2008601576089859, + 0.03346869722008705 ], "tsne": [ - 2.439526319503784, - -36.81149673461914 + -11.315814971923828, + -29.217525482177734 ], "umap": [ - -1.4767475128173828, - 2.457185983657837 + -0.6224994659423828, + 7.694460391998291 ], "pca3d": [ - -0.1972861886024475, - 0.033781517297029495, - -0.23379506170749664 + -0.20086123049259186, + 0.03345588967204094, + -0.2295156717300415 ], "tsne3d": [ - -44.67641830444336, - 7.076332092285156, - -19.273523330688477 + -35.787689208984375, + -35.3528938293457, + -21.27499008178711 ], "umap3d": [ - 1.3991175889968872, - 1.2887276411056519, - -0.44439786672592163 + 1.0856103897094727, + 0.9628760814666748, + 3.3784260749816895 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 21, - "label": "Protein RNA" + "id": 12, + "label": "Protein Language" } ], "_embedding": [ @@ -193937,47 +194120,47 @@ "journal": "Nature", "projections": { "pca": [ - -0.014277344569563866, - -0.054797083139419556 + -0.017010487616062164, + -0.05539175122976303 ], "tsne": [ - 3.2309305667877197, - -37.8687858581543 + -10.263525009155273, + -29.70248794555664 ], "umap": [ - -1.4695196151733398, - 2.4668359756469727 + -0.5575962662696838, + 7.760355472564697 ], "pca3d": [ - -0.014277957379817963, - -0.0548076406121254, - -0.1854398250579834 + -0.01701112650334835, + -0.05540144443511963, + -0.1824309229850769 ], "tsne3d": [ - -44.278297424316406, - 7.027276992797852, - -25.011091232299805 + -36.73966598510742, + -39.73301696777344, + -22.97556495666504 ], "umap3d": [ - 1.3444690704345703, - 1.2315449714660645, - -0.40907368063926697 + 1.1128923892974854, + 1.0487101078033447, + 3.2850122451782227 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 21, - "label": "Protein RNA" + "id": 12, + "label": "Protein Language" } ], "_embedding": [ @@ -194389,46 +194572,46 @@ ], "projections": { "pca": [ - -0.06591841578483582, - 0.21686606109142303 + -0.06464461237192154, + 0.21807098388671875 ], "tsne": [ - -25.67525863647461, - 15.043170928955078 + -1.0198270082473755, + 24.652389526367188 ], "umap": [ - 2.1688530445098877, - 2.319559335708618 + 1.151757001876831, + 1.4603676795959473 ], "pca3d": [ - -0.06591864675283432, - 0.21687079966068268, - -0.04008961096405983 + -0.06464491784572601, + 0.21807557344436646, + -0.038301192224025726 ], "tsne3d": [ - -10.443917274475098, - 19.237707138061523, - -42.7658576965332 + -9.366440773010254, + -1.1728756427764893, + -32.774906158447266 ], "umap3d": [ - 6.577774524688721, - -1.224548578262329, - 0.48576265573501587 + 3.807023048400879, + -1.1864964962005615, + 5.866140842437744 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 4, + "id": 26, "label": "World Models" } ], @@ -194844,46 +195027,46 @@ ], "projections": { "pca": [ - -0.12608779966831207, - 0.19874975085258484 + -0.12644055485725403, + 0.19862307608127594 ], "tsne": [ - -21.652196884155273, - 21.275144577026367 + -0.10668781399726868, + 30.515714645385742 ], "umap": [ - 2.615844488143921, - 2.537914991378784 + 1.6273456811904907, + 1.5807452201843262 ], "pca3d": [ - -0.12608744204044342, - 0.1987612098455429, - -0.010123364627361298 + -0.12644030153751373, + 0.19863273203372955, + -0.007244345732033253 ], "tsne3d": [ - 9.70612621307373, - 10.523575782775879, - -30.13158416748047 + 2.307152271270752, + 8.024921417236328, + -30.092384338378906 ], "umap3d": [ - 6.633809566497803, - -0.9609158635139465, - 0.8102003931999207 + 4.277706623077393, + -0.9735141396522522, + 5.8275909423828125 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 4, + "id": 26, "label": "World Models" } ], @@ -195309,46 +195492,46 @@ ], "projections": { "pca": [ - -0.1583375185728073, - -0.042290277779102325 + -0.15616928040981293, + -0.038694366812705994 ], "tsne": [ - -20.073383331298828, - -1.4014371633529663 + -7.132491588592529, + 13.007871627807617 ], "umap": [ - 0.062478236854076385, - 7.801546096801758 + -1.0741560459136963, + 2.29613995552063 ], "pca3d": [ - -0.1583375632762909, - -0.042290426790714264, - 0.07198021560907364 + -0.1561693549156189, + -0.03869299218058586, + 0.07970160245895386 ], "tsne3d": [ - -14.621238708496094, - 1.3857553005218506, - -11.133565902709961 + -12.020119667053223, + 10.384262084960938, + 27.18681526184082 ], "umap3d": [ - 4.505814552307129, - -1.7424629926681519, - -0.5497106909751892 + 1.8856033086776733, + -0.5037796497344971, + 6.847163200378418 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 11, + "id": 19, "label": "Medical Imaging" } ], @@ -195757,47 +195940,47 @@ "journal": "Nature", "projections": { "pca": [ - -0.21258306503295898, - -0.060581814497709274 + -0.20763860642910004, + -0.05615418404340744 ], "tsne": [ - -23.59787940979004, - -12.269606590270996 + -27.187070846557617, + 4.571639060974121 ], "umap": [ - -1.118011236190796, - 7.530562877655029 + -2.489863872528076, + 2.7048354148864746 ], "pca3d": [ - -0.2125830054283142, - -0.06059686094522476, - 0.1945723593235016 + -0.2076386958360672, + -0.056166041642427444, + 0.19839943945407867 ], "tsne3d": [ - -1.2361900806427002, - -25.09966468811035, - -27.880348205566406 + -31.152334213256836, + 18.623132705688477, + 22.24254035949707 ], "umap3d": [ - 3.7273030281066895, - -1.9451346397399902, - -1.7423641681671143 + 1.0954281091690063, + -0.447133332490921, + 7.87925910949707 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 12, - "label": "ECG Models" + "id": 13, + "label": "ECG AI Models" } ], "_embedding": [ @@ -196233,47 +196416,47 @@ "openalex_url": "https://openalex.org/W4414408253", "projections": { "pca": [ - -0.11501535028219223, - -0.24828782677650452 + -0.1136019378900528, + -0.2458374798297882 ], "tsne": [ - -6.072085857391357, - -8.978703498840332 + -17.2309627532959, + -1.0958144664764404 ], "umap": [ - -1.1207846403121948, - 6.2557053565979 + -2.0729870796203613, + 4.293054103851318 ], "pca3d": [ - -0.11501402407884598, - -0.24827316403388977, - 0.06501825153827667 + -0.11360061168670654, + -0.2458236813545227, + 0.07106411457061768 ], "tsne3d": [ - -3.1992743015289307, - -29.678449630737305, - 16.158367156982422 + -5.588588237762451, + -19.03022575378418, + 16.47602081298828 ], "umap3d": [ - 3.409745454788208, - -0.8549468517303467, - -1.4322313070297241 + 0.6360860466957092, + 0.6036902666091919, + 6.540518283843994 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 1, - "label": "Cancer Data" + "id": 5, + "label": "Clinical Data" }, { - "id": 28, - "label": "Clinical AI" + "id": 21, + "label": "Clinical Trials" } ], "_embedding": [ @@ -196694,47 +196877,47 @@ "doi": "10.1101/2025.09.21.677619", "projections": { "pca": [ - -0.21031337976455688, - -0.027428822591900826 + -0.21392221748828888, + -0.02719251438975334 ], "tsne": [ - 1.6020832061767578, - -29.831640243530273 + -12.639695167541504, + -22.75566291809082 ], "umap": [ - -1.9979009628295898, - 3.4964654445648193 + -1.6430633068084717, + 7.071639537811279 ], "pca3d": [ - -0.21031340956687927, - -0.02742348238825798, - -0.19742701947689056 + -0.2139221429824829, + -0.02718770131468773, + -0.19414155185222626 ], "tsne3d": [ - -39.71714782714844, - -3.4309496879577637, - 1.4936928749084473 + -30.093406677246094, + -11.784371376037598, + -21.710617065429688 ], "umap3d": [ - 1.4813168048858643, - 0.3226960301399231, - -0.9997039437294006 + 0.2539357841014862, + 1.133009672164917, + 3.984563112258911 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 8, - "label": "Gene Regulation" + "id": 3, + "label": "Genomic Sequences" } ], "_embedding": [ @@ -197157,46 +197340,46 @@ "openalex_url": "https://openalex.org/W4414314433", "projections": { "pca": [ - -0.04481267184019089, - -0.01976061426103115 + -0.041754350066185, + -0.01765495166182518 ], "tsne": [ - -20.181468963623047, - 0.4719577133655548 + -6.890097618103027, + 14.259666442871094 ], "umap": [ - 0.029524385929107666, - 7.766914367675781 + -1.0840266942977905, + 2.292436122894287 ], "pca3d": [ - -0.04481307417154312, - -0.019761210307478905, - 0.12665480375289917 + -0.04175462946295738, + -0.017653783783316612, + 0.1322917938232422 ], "tsne3d": [ - -12.487358093261719, - 5.243043422698975, - -16.56040382385254 + -14.841859817504883, + 14.018985748291016, + 30.505449295043945 ], "umap3d": [ - 4.571456432342529, - -1.7568289041519165, - -0.580650269985199 + 1.819331407546997, + -0.5080274939537048, + 6.907299041748047 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 11, + "id": 19, "label": "Medical Imaging" } ], @@ -197603,47 +197786,47 @@ ], "projections": { "pca": [ - -0.00920610036700964, - -0.12010156363248825 + -0.012470986694097519, + -0.12189298868179321 ], "tsne": [ - 9.391162872314453, - -5.025252342224121 + 6.152258396148682, + -0.18291838467121124 ], "umap": [ - 1.2833774089813232, - 4.880096912384033 + 0.6096585988998413, + 5.2680158615112305 ], "pca3d": [ - -0.00920587033033371, - -0.12010980397462845, - -0.08245141059160233 + -0.012470750138163567, + -0.12190322577953339, + -0.08334758877754211 ], "tsne3d": [ - -10.085468292236328, - 18.85979461669922, - 3.9453976154327393 + 23.410499572753906, + -23.16597557067871, + 16.199922561645508 ], "umap3d": [ - 2.926213026046753, - 0.6529667377471924, - 0.5059489607810974 + 2.148531436920166, + 1.4653983116149902, + 5.049219131469727 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 3, + "label": "Open Source Tools" }, { - "id": 4, - "label": "Com Area Amp" + "id": 6, + "label": "Data Needs" }, { - "id": 23, - "label": "Research Authors" + "id": 16, + "label": "Lab Research" } ], "_embedding": [ @@ -198051,47 +198234,47 @@ "journal": "Nature", "projections": { "pca": [ - -0.13297680020332336, - -0.13326002657413483 + -0.12707115709781647, + -0.1279059201478958 ], "tsne": [ - -13.365201950073242, - -13.102449417114258 + -23.117656707763672, + 8.67226791381836 ], "umap": [ - -0.8777843713760376, - 5.766550064086914 + -1.5816453695297241, + 4.367594242095947 ], "pca3d": [ - -0.13297608494758606, - -0.13325290381908417, - 0.23661763966083527 + -0.12707053124904633, + -0.12789730727672577, + 0.23769496381282806 ], "tsne3d": [ - -3.0789098739624023, - -31.889413833618164, - -10.124598503112793 + -19.8150691986084, + -0.5618709325790405, + 3.105485677719116 ], "umap3d": [ - 3.452296257019043, - -0.24050897359848022, - -1.4078413248062134 + 0.4457077980041504, + 0.036571767181158066, + 6.695005893707275 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 2, - "label": "AI Drug Discovery" + "id": 5, + "label": "Clinical Data" }, { - "id": 28, - "label": "Clinical AI" + "id": 14, + "label": "Clinical Health" } ], "_embedding": [ @@ -198510,47 +198693,47 @@ ], "projections": { "pca": [ - -0.22104518115520477, - -0.18860168755054474 + -0.22050641477108002, + -0.18671953678131104 ], "tsne": [ - -5.817483901977539, - -12.30500602722168 + -16.60911750793457, + -3.8930647373199463 ], "umap": [ - -1.4783154726028442, - 6.173317909240723 + -2.4763543605804443, + 4.426541328430176 ], "pca3d": [ - -0.22104397416114807, - -0.18858793377876282, - 0.05351362004876137 + -0.22050543129444122, + -0.18670561909675598, + 0.06001414731144905 ], "tsne3d": [ - -15.20410442352295, - -34.43160629272461, - 9.88327693939209 + -13.250330924987793, + -20.616352081298828, + 18.498027801513672 ], "umap3d": [ - 2.976029634475708, - -0.8819191455841064, - -1.5756064653396606 + 0.5059552192687988, + 0.9491634964942932, + 6.289199352264404 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 28, - "label": "Clinical AI" + "id": 21, + "label": "Clinical Trials" } ], "_embedding": [ @@ -198958,47 +199141,47 @@ ], "projections": { "pca": [ - -0.0368555411696434, - -0.2602231204509735 + -0.03777452930808067, + -0.26000431180000305 ], "tsne": [ - 3.4078593254089355, - -15.993168830871582 + -13.779471397399902, + -5.599812030792236 ], "umap": [ - -1.5499727725982666, - 5.456257343292236 + -2.2351109981536865, + 5.456395626068115 ], "pca3d": [ - -0.03685501962900162, - -0.260206013917923, - 0.0229426808655262 + -0.03777382895350456, + -0.2599877417087555, + 0.027415581047534943 ], "tsne3d": [ - -29.948848724365234, - 17.61054229736328, - 34.266685485839844 + -11.982479095458984, + -32.4195556640625, + 14.080635070800781 ], "umap3d": [ - 2.5067009925842285, - -0.5084131956100464, - -1.134783387184143 + 0.5978888273239136, + 1.055938482284546, + 5.780906677246094 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 1, "label": "Cancer Data" }, { - "id": 9, - "label": "Cancer Biobank" + "id": 1, + "label": "Cancer Data" } ], "_embedding": [ @@ -199444,47 +199627,47 @@ "openalex_url": "https://openalex.org/W4400906056", "projections": { "pca": [ - -0.0856490433216095, - -0.3427267074584961 + -0.08601408451795578, + -0.3418969511985779 ], "tsne": [ - -5.18851375579834, - -12.686887741088867 + -16.6593017578125, + -4.3003363609313965 ], "umap": [ - -1.6230727434158325, - 6.119014739990234 + -2.675359010696411, + 4.70707893371582 ], "pca3d": [ - -0.08564810454845428, - -0.34272029995918274, - 0.04032023623585701 + -0.08601293712854385, + -0.3418903946876526, + 0.04576632380485535 ], "tsne3d": [ - -19.313735961914062, - -35.06507873535156, - 12.080934524536133 + -15.55225944519043, + -23.802589416503906, + 18.36656951904297 ], "umap3d": [ - 2.780643939971924, - -1.0110982656478882, - -1.44179105758667 + 0.2766134440898895, + 0.7394213676452637, + 6.231875896453857 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 24, - "label": "Cancer Data" + "id": 21, + "label": "Clinical Trials" } ], "_embedding": [ @@ -199894,47 +200077,47 @@ "journal": "Nature Communications", "projections": { "pca": [ - -0.07635374367237091, - -0.27886754274368286 + -0.07646948099136353, + -0.27852749824523926 ], "tsne": [ - 3.3137638568878174, - -15.983470916748047 + -13.822606086730957, + -5.567898750305176 ], "umap": [ - -1.5643051862716675, - 5.519105911254883 + -2.310593605041504, + 5.342533111572266 ], "pca3d": [ - -0.07635291665792465, - -0.2788490355014801, - 0.030920041725039482 + -0.07646865397691727, + -0.2785094082355499, + 0.0363786518573761 ], "tsne3d": [ - -34.13039016723633, - 17.72551727294922, - 33.524017333984375 + -12.550896644592285, + -29.846729278564453, + 16.34109878540039 ], "umap3d": [ - 2.5443320274353027, - -0.6557108163833618, - -1.3344851732254028 + 0.5425087213516235, + 1.0774738788604736, + 5.823225975036621 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 1, "label": "Cancer Data" }, { - "id": 9, - "label": "Cancer Biobank" + "id": 1, + "label": "Cancer Data" } ], "_embedding": [ @@ -200340,47 +200523,47 @@ ], "projections": { "pca": [ - 0.2643603980541229, - -0.01502862386405468 + 0.2619461417198181, + -0.01596035622060299 ], "tsne": [ - 12.91731071472168, - -0.4570060968399048 + 10.60012435913086, + -0.00520513067021966 ], "umap": [ - 1.7114883661270142, - 5.415492534637451 + 1.025217890739441, + 4.913132190704346 ], "pca3d": [ - 0.26436084508895874, - -0.015031402930617332, - 0.03329051658511162 + 0.2619466483592987, + -0.015965238213539124, + 0.027692627161741257 ], "tsne3d": [ - 4.948122024536133, - 16.895395278930664, - 2.483457088470459 + 26.863405227661133, + -15.060811996459961, + 5.196549415588379 ], "umap3d": [ - 3.127511501312256, - 0.8850618600845337, - 1.054169774055481 + 2.6873104572296143, + 1.7522014379501343, + 5.3905487060546875 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 4, - "label": "Com Area Amp" + "id": 6, + "label": "Data Needs" }, { - "id": 26, - "label": "AI Information" + "id": 10, + "label": "Research Data" } ], "_embedding": [ @@ -200789,47 +200972,47 @@ "journal": "Nature", "projections": { "pca": [ - -0.03210761025547981, - -0.016795629635453224 + -0.024919116869568825, + -0.012321997433900833 ], "tsne": [ - -15.305988311767578, - -10.326326370239258 + -22.22900390625, + 11.658064842224121 ], "umap": [ - -0.5597564578056335, - 5.92802619934082 + -1.3633681535720825, + 3.448819398880005 ], "pca3d": [ - -0.03210795670747757, - -0.016800355166196823, - 0.3632309138774872 + -0.02491961233317852, + -0.012324796058237553, + 0.364550918340683 ], "tsne3d": [ - 4.555309295654297, - -40.2879524230957, - -5.5656843185424805 + -18.300004959106445, + 7.1037774085998535, + 6.52285099029541 ], "umap3d": [ - 4.257060527801514, - -0.15300196409225464, - -1.9159568548202515 + 0.6082935929298401, + 0.015578875318169594, + 7.308151721954346 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 2, - "label": "AI Drug Discovery" + "id": 5, + "label": "Clinical Data" }, { - "id": 28, - "label": "Clinical AI" + "id": 14, + "label": "Clinical Health" } ], "_embedding": [ @@ -201237,47 +201420,47 @@ "abstract": "Contribute to sinai-computational-pathology/SSL_tile_benchmarks development by creating an account on GitHub.", "projections": { "pca": [ - 0.1915382593870163, - -0.0587075874209404 + 0.19197502732276917, + -0.05759336054325104 ], "tsne": [ - 28.770679473876953, - 10.685223579406738 + 30.846223831176758, + -9.437914848327637 ], "umap": [ - 3.4409027099609375, - 7.076323509216309 + 3.0677335262298584, + 5.848388671875 ], "pca3d": [ - 0.19153916835784912, - -0.058693770319223404, - -0.12727178633213043 + 0.1919761747121811, + -0.05758156627416611, + -0.12923844158649445 ], "tsne3d": [ - 31.22455596923828, - 52.2849235534668, - 17.5345458984375 + 42.780792236328125, + 15.155623435974121, + 17.99375343322754 ], "umap3d": [ - 3.351118803024292, - -0.24729299545288086, - 2.7396867275238037 + 3.7395474910736084, + 2.817720413208008, + 6.810764312744141 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 18, - "label": "Github Tools" + "id": 17, + "label": "GitHub Creation" } ], "_embedding": [ @@ -201685,47 +201868,47 @@ "journal": "Nature Communications", "projections": { "pca": [ - -0.2260999083518982, - -0.02373429760336876 + -0.22165758907794952, + -0.01883477158844471 ], "tsne": [ - -14.665628433227539, - -17.759403228759766 + -25.919897079467773, + -9.497368812561035 ], "umap": [ - -1.4382550716400146, - 6.822760105133057 + -1.6653480529785156, + 4.87802791595459 ], "pca3d": [ - -0.22609896957874298, - -0.023716893047094345, - 0.13724496960639954 + -0.22165673971176147, + -0.018817441537976265, + 0.13857029378414154 ], "tsne3d": [ - -17.2362003326416, - -17.54637336730957, - -20.841960906982422 + -27.37852668762207, + 0.16716785728931427, + 21.57073402404785 ], "umap3d": [ - 3.556058168411255, - -1.1521154642105103, - -0.5918055176734924 + 1.063508152961731, + -0.38387393951416016, + 5.965939521789551 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 12, - "label": "ECG Models" + "id": 1, + "label": "Cancer Data" } ], "_embedding": [ @@ -202133,47 +202316,47 @@ "abstract": "SIGnature is a Python package that empowers researchers to rapidly query gene sets across diverse single-cell RNA sequencing (scRNA-seq) datasets through precomputed gene attribution scores. - Genentech/SIGnature", "projections": { "pca": [ - -0.05858032777905464, - -0.16424579918384552 + -0.06194644793868065, + -0.16501262784004211 ], "tsne": [ - -10.397889137268066, - -34.03653335571289 + -17.009035110473633, + -14.903783798217773 ], "umap": [ - -1.7507860660552979, - 3.624061107635498 + -1.269301176071167, + 7.032077312469482 ], "pca3d": [ - -0.05858001112937927, - -0.16424499452114105, - -0.20238129794597626 + -0.061946138739585876, + -0.16501249372959137, + -0.20023685693740845 ], "tsne3d": [ - -39.07141876220703, - -42.51847839355469, - -16.89946746826172 + -29.82494354248047, + -36.05799865722656, + 6.380086421966553 ], "umap3d": [ - 1.5266286134719849, - 0.717420220375061, - -1.304840087890625 + 0.1759946644306183, + 0.5278677940368652, + 4.0856757164001465 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 8, - "label": "Gene Regulation" + "id": 20, + "label": "Cell Foundation" } ], "_embedding": [ @@ -202601,47 +202784,47 @@ "doi": "10.1101/2025.06.14.659567", "projections": { "pca": [ - -0.16251839697360992, - -0.1917116492986679 + -0.16535718739032745, + -0.19153355062007904 ], "tsne": [ - -10.398508071899414, - -33.99116897583008 + -17.015548706054688, + -14.857124328613281 ], "umap": [ - -1.4939336776733398, - 3.6790082454681396 + -1.192888855934143, + 6.947199821472168 ], "pca3d": [ - -0.1625175029039383, - -0.1917070597410202, - -0.10706666111946106 + -0.16535623371601105, + -0.1915297657251358, + -0.10269403457641602 ], "tsne3d": [ - -37.00646209716797, - -40.02578353881836, - -14.367108345031738 + -27.836862564086914, + -33.316871643066406, + 6.620970726013184 ], "umap3d": [ - 1.741376280784607, - 0.7049806118011475, - -1.4684354066848755 + 0.20175163447856903, + 0.4422948658466339, + 4.084542274475098 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 19, - "label": "Cell Models" + "id": 20, + "label": "Cell Foundation" } ], "_embedding": [ @@ -203058,47 +203241,47 @@ "doi": "10.1145/3712256.3726329", "projections": { "pca": [ - -0.10285554081201553, - 0.13677851855754852 + -0.10300763696432114, + 0.13818493485450745 ], "tsne": [ - -14.271927833557129, - 17.90306282043457 + -0.6645052433013916, + -25.782821655273438 ], "umap": [ - 0.6353200078010559, - 2.784069299697876 + -1.3079252243041992, + 6.720313549041748 ], "pca3d": [ - -0.10285516828298569, - 0.1367935687303543, - -0.04552561417222023 + -0.1030072346329689, + 0.13820037245750427, + -0.04385941103100777 ], "tsne3d": [ - -21.491065979003906, - 28.726524353027344, - -18.51251983642578 + -3.8425345420837402, + 0.8211618661880493, + -42.98167037963867 ], "umap3d": [ - 5.824594020843506, - -0.7137556076049805, - 1.0565440654754639 + 3.3981292247772217, + -0.4756793677806854, + 5.400777816772461 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 5, - "label": "Model Training" + "id": 7, + "label": "Cell Genes" }, { - "id": 15, - "label": "Latent Models" + "id": 20, + "label": "Cell Foundation" } ], "_embedding": [ @@ -203521,46 +203704,46 @@ ], "projections": { "pca": [ - -0.1627698689699173, - -0.13226470351219177 + -0.15993715822696686, + -0.12801723182201385 ], "tsne": [ - -10.757993698120117, - -3.499882698059082 + -15.638839721679688, + 5.899913787841797 ], "umap": [ - -0.11659485101699829, - 6.846226215362549 + -1.8543154001235962, + 3.4119884967803955 ], "pca3d": [ - -0.1627688705921173, - -0.13224871456623077, - 0.17311622202396393 + -0.15993617475032806, + -0.12800289690494537, + 0.17915287613868713 ], "tsne3d": [ - -2.147010564804077, - -16.411548614501953, - 11.347651481628418 + 0.07590978592634201, + -8.97291088104248, + 16.931255340576172 ], "umap3d": [ - 4.404602527618408, - -1.1691926717758179, - -1.7811957597732544 + 1.0984266996383667, + 0.9499354958534241, + 7.299848556518555 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, + "id": 14, "label": "Clinical Health" } ], @@ -203971,46 +204154,46 @@ "journal": "Nature", "projections": { "pca": [ - -0.16380593180656433, - -0.039387498050928116 + -0.15954582393169403, + -0.0356876440346241 ], "tsne": [ - -10.815597534179688, - -2.075977325439453 + -17.390625, + 7.4474921226501465 ], "umap": [ - -0.028350312262773514, - 6.659043312072754 + -1.8393523693084717, + 3.4387054443359375 ], "pca3d": [ - -0.16380636394023895, - -0.03938138484954834, - 0.19522523880004883 + -0.15954634547233582, + -0.03568258509039879, + 0.19927695393562317 ], "tsne3d": [ - 7.774139881134033, - -15.185530662536621, - 2.852585554122925 + 1.9868899583816528, + -3.6906330585479736, + 13.574740409851074 ], "umap3d": [ - 4.543014049530029, - -0.9278432130813599, - -1.9442131519317627 + 1.1861611604690552, + 0.8559823036193848, + 7.43388032913208 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, + "id": 14, "label": "Clinical Health" } ], @@ -204419,47 +204602,47 @@ "abstract": "Writing effective tools for AI agents\u2014using AI agents", "projections": { "pca": [ - 0.057916294783353806, - 0.3143855333328247 + 0.0629117339849472, + 0.3174186050891876 ], "tsne": [ - -3.145061492919922, - 17.706308364868164 + 11.473592758178711, + 15.251153945922852 ], "umap": [ - 3.310256242752075, - 4.099949359893799 + 2.4451019763946533, + 2.8550455570220947 ], "pca3d": [ - 0.05791576951742172, - 0.31437742710113525, - 0.17986100912094116 + 0.06291123479604721, + 0.3174112141132355, + 0.17446474730968475 ], "tsne3d": [ - 38.71980285644531, - -2.1933157444000244, - -16.892959594726562 + 4.8772969245910645, + 19.715232849121094, + -6.022256374359131 ], "umap3d": [ - 5.5393385887146, - 0.4134804904460907, - 1.2530854940414429 + 4.27404260635376, + 0.4956055283546448, + 5.588491916656494 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 1, - "label": "AI Agents" + "id": 22, + "label": "AI Gemini" } ], "_embedding": [ @@ -204872,46 +205055,46 @@ "doi": "10.1007/s10549-025-07772-w", "projections": { "pca": [ - -0.014720330014824867, - -0.21779431402683258 + -0.016896739602088928, + -0.2189241647720337 ], "tsne": [ - -3.0024540424346924, - -14.541519165039062 + -23.15206527709961, + -4.383600234985352 ], "umap": [ - -2.2186832427978516, - 6.02454948425293 + -2.778315782546997, + 4.834384918212891 ], "pca3d": [ - -0.014720476232469082, - -0.21780286729335785, - -0.0301640834659338 + -0.016896743327379227, + -0.21893106400966644, + -0.026054594665765762 ], "tsne3d": [ - -24.4634952545166, - -40.31673049926758, - 10.871570587158203 + -20.49456024169922, + -27.19192886352539, + 15.677131652832031 ], "umap3d": [ - 2.6105968952178955, - -1.0904568433761597, - -1.221078872680664 + -0.015431482344865799, + 0.2256353795528412, + 5.958151340484619 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 24, + "id": 1, "label": "Cancer Data" } ], @@ -205320,47 +205503,47 @@ "abstract": "We\u2019re on a journey to advance and democratize artificial intelligence through open source and open science.", "projections": { "pca": [ - 0.2060711532831192, - 0.1767384558916092 + 0.20959390699863434, + 0.17922857403755188 ], "tsne": [ - 5.358494281768799, - 18.331087112426758 + 23.046302795410156, + 10.842681884765625 ], "umap": [ - 4.829582214355469, - 4.787045955657959 + 3.8136398792266846, + 2.959120512008667 ], "pca3d": [ - 0.2060709297657013, - 0.17673538625240326, - 0.1895904541015625 + 0.20959357917308807, + 0.17922544479370117, + 0.186562642455101 ], "tsne3d": [ - 36.11282730102539, - -4.120110034942627, - 0.2190839648246765 + 5.438422679901123, + 37.86495590209961, + -1.828271508216858 ], "umap3d": [ - 5.45457649230957, - 1.5905132293701172, - 1.8261562585830688 + 4.902149677276611, + 1.9528985023498535, + 4.817240238189697 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { "id": 7, - "label": "Open AI" + "label": "Hugging Face Models" } ], "_embedding": [ @@ -205790,47 +205973,47 @@ ], "projections": { "pca": [ - -0.18322476744651794, - -0.11358024924993515 + -0.1863263100385666, + -0.11348138749599457 ], "tsne": [ - -3.250248432159424, - -27.137277603149414 + -15.5748872756958, + -21.52076530456543 ], "umap": [ - -1.349157691001892, - 3.6068832874298096 + -1.0870345830917358, + 6.875751972198486 ], "pca3d": [ - -0.18322540819644928, - -0.1135890930891037, - -0.15510717034339905 + -0.18632687628269196, + -0.11348938196897507, + -0.15227706730365753 ], "tsne3d": [ - -41.79079055786133, - -17.867494583129883, - -5.215688228607178 + -25.27055549621582, + -21.5233211517334, + -9.877692222595215 ], "umap3d": [ - 1.7192212343215942, - 0.7267076373100281, - -1.1211756467819214 + 0.5811909437179565, + 0.8992182016372681, + 4.18826961517334 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 19, - "label": "Cell Models" + "id": 20, + "label": "Cell Foundation" } ], "_embedding": [ @@ -206257,47 +206440,47 @@ "openalex_url": "https://openalex.org/W3136047647", "projections": { "pca": [ - -0.19743047654628754, - -0.07038923352956772 + -0.19459503889083862, + -0.06648312509059906 ], "tsne": [ - -11.805315971374512, - -3.9219837188720703 + -15.70526123046875, + 6.793424129486084 ], "umap": [ - -0.032800812274217606, - 6.812985420227051 + -2.0238749980926514, + 3.366368532180786 ], "pca3d": [ - -0.19742928445339203, - -0.07037744671106339, - 0.17544689774513245 + -0.19459401071071625, + -0.0664716437458992, + 0.18115635216236115 ], "tsne3d": [ - -1.9682108163833618, - -12.939797401428223, - 6.4384684562683105 + -2.8013362884521484, + -6.795267581939697, + 20.25432586669922 ], "umap3d": [ - 4.437644958496094, - -1.2527506351470947, - -1.893083930015564 + 1.0311168432235718, + 1.125583291053772, + 7.310371398925781 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, - "label": "Clinical Health" + "id": 13, + "label": "ECG AI Models" } ], "_embedding": [ @@ -206705,46 +206888,46 @@ "journal": "Nature", "projections": { "pca": [ - -0.1259034425020218, - -0.016064723953604698 + -0.12032318115234375, + -0.01085576880723238 ], "tsne": [ - -11.904024124145508, - -2.785726547241211 + -17.177528381347656, + 6.299674987792969 ], "umap": [ - -0.13560646772384644, - 6.837620258331299 + -2.024348020553589, + 3.4347965717315674 ], "pca3d": [ - -0.12590180337429047, - -0.016027871519327164, - 0.20507216453552246 + -0.12032163888216019, + -0.010821491479873657, + 0.20624509453773499 ], "tsne3d": [ - 3.381819725036621, - -9.456293106079102, - 3.4095003604888916 + 1.8046561479568481, + -3.4007937908172607, + 18.904571533203125 ], "umap3d": [ - 4.434680938720703, - -1.153304100036621, - -2.1069915294647217 + 1.0876096487045288, + 1.0295625925064087, + 7.378532886505127 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, + "id": 14, "label": "Clinical Health" } ], @@ -207156,46 +207339,46 @@ "cited_by_count": 1220, "projections": { "pca": [ - 0.014492496848106384, - -0.23991405963897705 + 0.01736368238925934, + -0.23753966391086578 ], "tsne": [ - -3.402926445007324, - -5.898055076599121 + -17.877647399902344, + 1.8402427434921265 ], "umap": [ - -0.41111037135124207, - 6.816225528717041 + -1.958146095275879, + 3.8035216331481934 ], "pca3d": [ - 0.014492700807750225, - -0.2399170696735382, - 0.23010283708572388 + 0.01736397296190262, + -0.23754222691059113, + 0.23385770618915558 ], "tsne3d": [ - -5.100025653839111, - -20.869016647338867, - 19.965896606445312 + 1.0842885971069336, + -18.76861572265625, + 25.409326553344727 ], "umap3d": [ - 3.9856600761413574, - -1.173857569694519, - -1.7395095825195312 + 0.8505390286445618, + 1.019565463066101, + 6.92158317565918 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, + "id": 14, "label": "Clinical Health" } ], @@ -207606,46 +207789,46 @@ "journal": "Nature", "projections": { "pca": [ - -0.1954740434885025, - 0.0008907165611162782 + -0.18939542770385742, + 0.005990156903862953 ], "tsne": [ - -12.733315467834473, - -4.566273212432861 + -16.127099990844727, + 7.4005913734436035 ], "umap": [ - -0.34613120555877686, - 6.700753211975098 + -2.0585687160491943, + 3.472327470779419 ], "pca3d": [ - -0.19547344744205475, - 0.0008932449272833765, - 0.24880607426166534 + -0.18939505517482758, + 0.005993936210870743, + 0.25080734491348267 ], "tsne3d": [ - -6.628469467163086, - -11.456106185913086, - 3.5357351303100586 + -6.39653205871582, + -6.458206653594971, + 23.327838897705078 ], "umap3d": [ - 4.304661273956299, - -1.2585053443908691, - -1.8831100463867188 + 0.9991872906684875, + 0.9435129761695862, + 7.310247898101807 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, + "id": 14, "label": "Clinical Health" } ], @@ -208071,46 +208254,46 @@ "openalex_url": "https://openalex.org/W4410631600", "projections": { "pca": [ - -0.1832420378923416, - -0.19509363174438477 + -0.1824333518743515, + -0.1933583915233612 ], "tsne": [ - -8.466585159301758, - -18.240320205688477 + -21.954008102416992, + -7.433682918548584 ], "umap": [ - -2.247764825820923, - 5.892091274261475 + -2.3322834968566895, + 5.1679840087890625 ], "pca3d": [ - -0.1832418143749237, - -0.1950976550579071, - -0.04154888913035393 + -0.18243321776390076, + -0.19336088001728058, + -0.03570772334933281 ], "tsne3d": [ - -23.83277702331543, - -33.35659408569336, - -5.282291412353516 + -27.63445472717285, + -15.770447731018066, + 14.725210189819336 ], "umap3d": [ - 2.6397268772125244, - -0.9673796892166138, - -0.8891782760620117 + 0.2187061458826065, + -0.024983961135149002, + 5.634511470794678 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 24, + "id": 1, "label": "Cancer Data" } ], @@ -208547,46 +208730,46 @@ ], "projections": { "pca": [ - -0.1580887734889984, - -0.031237391754984856 + -0.15591613948345184, + -0.027799775823950768 ], "tsne": [ - -21.391637802124023, - 0.9358568787574768 + -5.851800441741943, + 14.30029582977295 ], "umap": [ - -0.0034790695644915104, - 7.838012218475342 + -0.9359766244888306, + 2.1789445877075195 ], "pca3d": [ - -0.158088818192482, - -0.031240316107869148, - 0.04921583831310272 + -0.15591618418693542, + -0.0278011504560709, + 0.05745787173509598 ], "tsne3d": [ - -18.355398178100586, - 6.317687034606934, - -13.779014587402344 + -12.557975769042969, + 15.515369415283203, + 25.239425659179688 ], "umap3d": [ - 4.5819807052612305, - -1.6592588424682617, - -0.38656115531921387 + 2.1511154174804688, + -0.5634103417396545, + 6.738393306732178 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 11, + "id": 19, "label": "Medical Imaging" } ], @@ -209004,47 +209187,47 @@ "doi": "10.1101/2025.09.01.673319", "projections": { "pca": [ - -0.17866796255111694, - 0.0023449391592293978 + -0.17736928164958954, + 0.0056920601055026054 ], "tsne": [ - 0.5677364468574524, - -4.994289398193359 + -5.818023681640625, + -3.260226249694824 ], "umap": [ - -0.4694596230983734, - 4.909693241119385 + -0.8332728743553162, + 4.915915489196777 ], "pca3d": [ - -0.17866823077201843, - 0.002327623311430216, - 0.05787105858325958 + -0.17736968398094177, + 0.005675899796187878, + 0.06108582019805908 ], "tsne3d": [ - -12.728302955627441, - -12.172126770019531, - 25.265718460083008 + -4.013288974761963, + -21.538375854492188, + -6.126059055328369 ], "umap3d": [ - 3.7312467098236084, - 0.5339747667312622, - -1.0297167301177979 + 2.2903099060058594, + 0.1934833526611328, + 4.988322734832764 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ - { - "id": 0, - "label": "Medical Data AI" - }, { "id": 2, - "label": "AI Drug Discovery" + "label": "Clinical AI Data" }, { - "id": 13, + "id": 9, "label": "Drug Discovery" + }, + { + "id": 9, + "label": "Biomedical AI" } ], "_embedding": [ @@ -209468,46 +209651,46 @@ ], "projections": { "pca": [ - -0.1752631664276123, - -0.06954152882099152 + -0.17304149270057678, + -0.06588198989629745 ], "tsne": [ - -19.698963165283203, - -1.9916529655456543 + -7.957081317901611, + 12.926741600036621 ], "umap": [ - -0.28821590542793274, - 7.61117696762085 + -1.242287516593933, + 2.452113628387451 ], "pca3d": [ - -0.17526239156723022, - -0.06953350454568863, - 0.07193825393915176 + -0.17304082214832306, + -0.06587446480989456, + 0.08012094348669052 ], "tsne3d": [ - -15.915721893310547, - -3.3717312812805176, - -9.441967010498047 + -13.711130142211914, + 7.084153652191162, + 25.083364486694336 ], "umap3d": [ - 4.295556545257568, - -1.5788040161132812, - -0.6480842232704163 + 1.6066796779632568, + -0.42246997356414795, + 6.655429363250732 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 11, + "id": 19, "label": "Medical Imaging" } ], @@ -209927,47 +210110,47 @@ ], "projections": { "pca": [ - -0.238104909658432, - -0.17666411399841309 + -0.23939411342144012, + -0.1752053052186966 ], "tsne": [ - 1.5802682638168335, - -22.43335723876953 + -12.124670028686523, + -14.990896224975586 ], "umap": [ - -1.9477365016937256, - 4.893344879150391 + -2.4866552352905273, + 6.125580310821533 ], "pca3d": [ - -0.23810452222824097, - -0.17667192220687866, - -0.01921684294939041 + -0.23939380049705505, + -0.17521238327026367, + -0.013425975106656551 ], "tsne3d": [ - -25.704214096069336, - -11.719202041625977, - 14.816411018371582 + -15.307269096374512, + -10.250463485717773, + -6.657524585723877 ], "umap3d": [ - 2.0631656646728516, - -0.3996204137802124, - -1.7625999450683594 + 0.09822048991918564, + 1.3379050493240356, + 5.123867511749268 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 1, + "label": "Cancer Data" }, { - "id": 2, - "label": "Genetic Disease" + "id": 27, + "label": "Genetic Variants" } ], "_embedding": [ @@ -210381,47 +210564,47 @@ ], "projections": { "pca": [ - -0.13806553184986115, - 0.2562173306941986 + -0.1397712677717209, + 0.2556436061859131 ], "tsne": [ - -16.805395126342773, - 21.407236099243164 + 4.6032257080078125, + 29.193330764770508 ], "umap": [ - 2.9076755046844482, - 2.774958848953247 + 1.9561963081359863, + 1.8586772680282593 ], "pca3d": [ - -0.13806641101837158, - 0.2562098503112793, - -0.09118544310331345 + -0.13977216184139252, + 0.2556377649307251, + -0.08904676139354706 ], "tsne3d": [ - 19.198881149291992, - 1.3785024881362915, - -31.60280990600586 + 11.133296012878418, + 8.152726173400879, + -33.627655029296875 ], "umap3d": [ - 6.576268672943115, - -0.6125136613845825, - 1.2303491830825806 + 4.697848320007324, + -0.6010496616363525, + 5.910011291503906 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 6, + "label": "Learning Techniques" } ], "_embedding": [ @@ -210836,46 +211019,46 @@ ], "projections": { "pca": [ - -0.12639372050762177, - 0.32210513949394226 + -0.12600596249103546, + 0.32251212000846863 ], "tsne": [ - -10.580581665039062, - 25.581424713134766 + 4.365166664123535, + 22.102859497070312 ], "umap": [ - 2.608588933944702, - 2.1924078464508057 + 1.7457504272460938, + 1.1299785375595093 ], "pca3d": [ - -0.12639391422271729, - 0.32212239503860474, - -0.01733574829995632 + -0.12600621581077576, + 0.32252612709999084, + -0.016616998240351677 ], "tsne3d": [ - 7.893718242645264, - 2.9886372089385986, - -50.80956268310547 + 7.464130401611328, + 13.565814018249512, + -16.706260681152344 ], "umap3d": [ - 6.924814701080322, - -0.3885681927204132, - 0.4991028308868408 + 4.523583889007568, + -1.2137410640716553, + 5.691515922546387 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 4, + "id": 26, "label": "World Models" } ], @@ -211286,47 +211469,47 @@ "journal": "Nature", "projections": { "pca": [ - -0.267110675573349, - 0.05102921649813652 + -0.2654370665550232, + 0.053674615919589996 ], "tsne": [ - -4.97334623336792, - -35.13168716430664 + -18.91762351989746, + -27.835250854492188 ], "umap": [ - -1.972817301750183, - 2.888019323348999 + -1.24127995967865, + 7.587783336639404 ], "pca3d": [ - -0.267111212015152, - 0.0510115846991539, - -0.09721824526786804 + -0.2654377818107605, + 0.05365990474820137, + -0.09540790319442749 ], "tsne3d": [ - -54.25828170776367, - -4.238346576690674, - -22.403705596923828 + -35.82967758178711, + -17.665525436401367, + -32.05257797241211 ], "umap3d": [ - 0.904914915561676, - 0.9199116230010986, - -0.9738339781761169 + 0.21808063983917236, + 0.7818102240562439, + 3.488872528076172 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 8, - "label": "Gene Regulation" + "id": 3, + "label": "Genomic Sequences" } ], "_embedding": [ @@ -211733,46 +211916,46 @@ "title": "Curiosity: A New Medical Intelligence for Clinical and Operational Insights", "projections": { "pca": [ - -0.15220744907855988, - -0.016009489074349403 + -0.14488473534584045, + -0.010617959313094616 ], "tsne": [ - -15.147441864013672, - -6.9921441078186035 + -19.955747604370117, + 12.210881233215332 ], "umap": [ - -0.4112250804901123, - 5.991081714630127 + -1.3093221187591553, + 3.6235451698303223 ], "pca3d": [ - -0.15220773220062256, - -0.016025360673666, - 0.33203843235969543 + -0.14488519728183746, + -0.010632006451487541, + 0.3315507173538208 ], "tsne3d": [ - 14.335807800292969, - -30.986827850341797, - -10.449995994567871 + -14.925468444824219, + 6.235307216644287, + -2.805901288986206 ], "umap3d": [ - 4.365480899810791, - -0.2358621209859848, - -2.0936715602874756 + 0.5959216356277466, + 0.30300694704055786, + 7.4624409675598145 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, + "id": 14, "label": "Clinical Health" } ], @@ -212182,47 +212365,47 @@ ], "projections": { "pca": [ - 0.27528372406959534, - 0.1193891316652298 + 0.27541106939315796, + 0.11767435073852539 ], "tsne": [ - 9.492080688476562, - 0.38966870307922363 + 9.776914596557617, + 3.7368550300598145 ], "umap": [ - 1.9843429327011108, - 5.659092426300049 + 1.3405177593231201, + 4.709188938140869 ], "pca3d": [ - 0.2752837538719177, - 0.11938677728176117, - 0.03423694148659706 + 0.2754111886024475, + 0.11766910552978516, + 0.028400450944900513 ], "tsne3d": [ - 5.48077917098999, - 23.56693458557129, - -12.611053466796875 + 21.827848434448242, + -25.523147583007812, + 3.758497714996338 ], "umap3d": [ - 3.393256187438965, - 0.9411346316337585, - 1.1505489349365234 + 2.9454214572906494, + 1.9853062629699707, + 5.376439094543457 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 4, - "label": "Com Area Amp" + "id": 6, + "label": "Data Needs" }, { - "id": 26, - "label": "AI Information" + "id": 10, + "label": "Research Data" } ], "_embedding": [ @@ -212630,46 +212813,46 @@ "journal": "Nature", "projections": { "pca": [ - -0.04632283374667168, - 0.3571145534515381 + -0.043438222259283066, + 0.3584301769733429 ], "tsne": [ - -15.786023139953613, - 15.307619094848633 + 1.6736341714859009, + 22.12654685974121 ], "umap": [ - 2.0299713611602783, - 2.1360130310058594 + 0.9945427775382996, + 1.1210843324661255 ], "pca3d": [ - -0.0463234968483448, - 0.3571118116378784, - 0.0555182509124279 + -0.043439049273729324, + 0.35842815041542053, + 0.05392630770802498 ], "tsne3d": [ - -4.799351215362549, - 6.43458366394043, - -47.951515197753906 + 15.773368835449219, + 6.597146511077881, + -14.24736499786377 ], "umap3d": [ - 6.812239170074463, - -0.8484178185462952, - 0.20787213742733002 + 4.009040355682373, + -1.3294380903244019, + 5.849365234375 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 4, + "id": 26, "label": "World Models" } ], @@ -213080,47 +213263,47 @@ "abstract": "Discover how a specialized AI model, GPT-4b micro, helped OpenAI and Retro Bio engineer more effective proteins for stem cell therapy and longevity research.", "projections": { "pca": [ - -0.03833587467670441, - -0.0007512756274081767 + -0.036227647215127945, + 0.0025557808112353086 ], "tsne": [ - 1.2936747074127197, - 12.397161483764648 + -0.7124793529510498, + 0.1241731271147728 ], "umap": [ - -0.17282681167125702, - 5.004961967468262 + -0.5461642742156982, + 4.600218296051025 ], "pca3d": [ - -0.038336798548698425, - -0.000770444399677217, - 0.12502779066562653 + -0.036228641867637634, + 0.0025394915137439966, + 0.1262865662574768 ], "tsne3d": [ - 29.173051834106445, - -14.548651695251465, - -3.845209836959839 + 4.9413957595825195, + 34.333030700683594, + -14.642804145812988 ], "umap3d": [ - 4.264387607574463, - 0.8134946227073669, - -0.6410555243492126 + 3.227351665496826, + 0.3276025056838989, + 4.859304904937744 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ - { - "id": 0, - "label": "Medical Data AI" - }, { "id": 2, - "label": "AI Drug Discovery" + "label": "Clinical AI Data" }, { - "id": 13, + "id": 9, "label": "Drug Discovery" + }, + { + "id": 9, + "label": "Biomedical AI" } ], "_embedding": [ @@ -213534,47 +213717,47 @@ ], "projections": { "pca": [ - -0.16172584891319275, - 0.1567443460226059 + -0.16212429106235504, + 0.1570976972579956 ], "tsne": [ - -11.827275276184082, - 11.65346622467041 + -5.28498649597168, + 32.146461486816406 ], "umap": [ - 2.1354877948760986, - 3.4501218795776367 + 1.1182929277420044, + 2.4347593784332275 ], "pca3d": [ - -0.16172567009925842, - 0.15673421323299408, - -0.05646919459104538 + -0.16212435066699982, + 0.15708644688129425, + -0.052574221044778824 ], "tsne3d": [ - 9.243207931518555, - 0.14241981506347656, - -13.40400505065918 + 14.109807968139648, + -15.020801544189453, + -27.295883178710938 ], "umap3d": [ - 5.79763126373291, - -0.33522868156433105, - 0.40900251269340515 + 3.988865852355957, + -0.5422998070716858, + 6.531925678253174 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 27, - "label": "Embeddings" + "id": 18, + "label": "Retrieval Learning" } ], "_embedding": [ @@ -213982,47 +214165,47 @@ "abstract": "On the Theoretical Limitations of Embedding-Based Retrieval - google-deepmind/limit", "projections": { "pca": [ - 0.015207466669380665, - 0.21013452112674713 + 0.015819665044546127, + 0.21030090749263763 ], "tsne": [ - -11.479002952575684, - 12.95142936706543 + -4.872024059295654, + 33.276145935058594 ], "umap": [ - 2.2698891162872314, - 3.4045279026031494 + 1.3740646839141846, + 2.419713258743286 ], "pca3d": [ - 0.015207579359412193, - 0.21013471484184265, - -0.04881421476602554 + 0.015819815918803215, + 0.2102992981672287, + -0.05054143816232681 ], "tsne3d": [ - 10.45510196685791, - -4.019408702850342, - -10.274991035461426 + 15.54278564453125, + -14.651505470275879, + -23.023666381835938 ], "umap3d": [ - 5.984677314758301, - -0.30719679594039917, - 0.6392467617988586 + 4.112234592437744, + -0.4879690408706665, + 6.334500312805176 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 27, - "label": "Embeddings" + "id": 18, + "label": "Retrieval Learning" } ], "_embedding": [ @@ -214430,47 +214613,47 @@ "abstract": "Official Pytorch implementation of "OmniMRI: A Unified Vision\u2013Language Foundation Model for Generalist MRI Interpretation" - I3Tlab/OmniMRI", "projections": { "pca": [ - 0.09442488104104996, - 0.02658531628549099 + 0.09487877786159515, + 0.027941854670643806 ], "tsne": [ - 13.723908424377441, - 24.743196487426758 + 19.167741775512695, + -12.785694122314453 ], "umap": [ - 2.4681832790374756, - 6.088729381561279 + 1.7511950731277466, + 3.6760189533233643 ], "pca3d": [ - 0.09442444145679474, - 0.026576770469546318, - -0.04960964247584343 + 0.0948784127831459, + 0.027933569625020027, + -0.04731012508273125 ], "tsne3d": [ - -10.645811080932617, - 38.486595153808594, - 6.960962295532227 + 29.633176803588867, + 23.12895965576172, + -12.702156066894531 ], "umap3d": [ - 4.3400444984436035, - -0.8132994174957275, - 1.2797881364822388 + 3.4539284706115723, + 1.100703477859497, + 6.671792030334473 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 4, + "label": "AI Agents" }, { - "id": 3, - "label": "SMB Data" + "id": 15, + "label": "PyTorch Models" } ], "_embedding": [ @@ -214887,47 +215070,47 @@ ], "projections": { "pca": [ - -0.09898622334003448, - 0.148859441280365 + -0.09748783707618713, + 0.14870963990688324 ], "tsne": [ - -14.19235610961914, - 7.017354965209961 + 10.225906372070312, + 23.740060806274414 ], "umap": [ - 2.7127490043640137, - 3.3448288440704346 + 1.8168144226074219, + 2.040818452835083 ], "pca3d": [ - -0.09898586571216583, - 0.14887070655822754, - -0.0342290922999382 + -0.097487673163414, + 0.14871785044670105, + -0.03551063686609268 ], "tsne3d": [ - 24.80166244506836, - -31.115232467651367, - 30.907184600830078 + 21.36405372619629, + 1.9266082048416138, + -28.792253494262695 ], "umap3d": [ - 6.268794059753418, - -0.42270246148109436, - 0.9776244759559631 + 4.579828262329102, + -0.5838222503662109, + 6.0071892738342285 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 6, + "label": "Learning Techniques" } ], "_embedding": [ @@ -215344,47 +215527,47 @@ "doi": "10.1101/2025.05.08.652944", "projections": { "pca": [ - -0.16934220492839813, - -0.10377049446105957 + -0.17129892110824585, + -0.1028633862733841 ], "tsne": [ - -10.105263710021973, - -30.330110549926758 + -22.96467399597168, + -18.32448387145996 ], "umap": [ - -1.4774119853973389, - 3.3277950286865234 + -1.0755443572998047, + 7.02017068862915 ], "pca3d": [ - -0.16934190690517426, - -0.10377433151006699, - -0.18810924887657166 + -0.17129860818386078, + -0.10286829620599747, + -0.18358705937862396 ], "tsne3d": [ - -38.914546966552734, - -31.178098678588867, - -25.33497428894043 + -43.86353302001953, + -20.594465255737305, + -5.811323165893555 ], "umap3d": [ - 1.4000496864318848, - 0.9690084457397461, - -1.3682043552398682 + 0.36021867394447327, + 0.2896025478839874, + 3.8602962493896484 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 19, - "label": "Cell Models" + "id": 20, + "label": "Cell Foundation" } ], "_embedding": [ @@ -215800,47 +215983,47 @@ ], "projections": { "pca": [ - -0.19343271851539612, - 0.1558687686920166 + -0.1933332085609436, + 0.15655165910720825 ], "tsne": [ - -11.309558868408203, - 11.586527824401855 + -4.831358432769775, + 31.821962356567383 ], "umap": [ - 2.0694072246551514, - 3.3238561153411865 + 1.1041011810302734, + 2.4299681186676025 ], "pca3d": [ - -0.19343262910842896, - 0.15585952997207642, - -0.03923973813652992 + -0.19333328306674957, + 0.15654228627681732, + -0.03552473708987236 ], "tsne3d": [ - 10.330024719238281, - 4.5513834953308105, - -15.521871566772461 + 16.742565155029297, + -17.16619110107422, + -29.746232986450195 ], "umap3d": [ - 5.794190406799316, - -0.31513354182243347, - 0.3919720947742462 + 3.990009069442749, + -0.6740316152572632, + 6.562565803527832 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 27, - "label": "Embeddings" + "id": 18, + "label": "Retrieval Learning" } ], "_embedding": [ @@ -216250,46 +216433,46 @@ "abstract": "A pipeline that accurately simulates high quality publicly cancer genomes (VCFs, CNAs and SVs). - LincolnSteinLab/oncoGAN", "projections": { "pca": [ - 0.03405793383717537, - -0.18775524199008942 + 0.03465896472334862, + -0.18620669841766357 ], "tsne": [ - -10.164118766784668, - -20.41628074645996 + -21.06816291809082, + -10.182418823242188 ], "umap": [ - -1.987083911895752, - 5.790953159332275 + -1.9368367195129395, + 5.495676517486572 ], "pca3d": [ - 0.03405768424272537, - -0.18776103854179382, - -0.05632589012384415 + 0.03465870022773743, + -0.18621030449867249, + -0.05312134698033333 ], "tsne3d": [ - -24.249208450317383, - -17.62077522277832, - -6.071565628051758 + -24.509078979492188, + -4.626614093780518, + 10.423299789428711 ], "umap3d": [ - 2.7484216690063477, - -0.5749089121818542, - -0.7342230677604675 + 0.4477664828300476, + 0.09955465793609619, + 5.444977760314941 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 1, "label": "Cancer Data" }, { - "id": 24, + "id": 1, "label": "Cancer Data" } ], @@ -216707,47 +216890,47 @@ "doi": "10.1101/2025.08.12.669924", "projections": { "pca": [ - -0.17126643657684326, - -0.10498189181089401 + -0.17628097534179688, + -0.10501691699028015 ], "tsne": [ - -2.7222204208374023, - -28.944454193115234 + -17.394088745117188, + -20.78329849243164 ], "umap": [ - -2.0402262210845947, - 3.383521318435669 + -1.5980442762374878, + 7.517781734466553 ], "pca3d": [ - -0.17126570641994476, - -0.10497414320707321, - -0.24401913583278656 + -0.17628014087677002, + -0.1050112172961235, + -0.23992696404457092 ], "tsne3d": [ - -57.27540969848633, - -12.281767845153809, - 6.915184497833252 + -44.650634765625, + -11.152287483215332, + -7.389636039733887 ], "umap3d": [ - 1.076573371887207, - 0.5886273384094238, - -1.328212022781372 + -0.05808393284678459, + 0.8501726388931274, + 3.6199870109558105 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 8, - "label": "Gene Regulation" + "id": 3, + "label": "Genomic Sequences" } ], "_embedding": [ @@ -217155,47 +217338,47 @@ "abstract": "DINOv3 scales self-supervised learning for images to create universal vision backbones that achieve absolute state-of-the-art performance across diverse...", "projections": { "pca": [ - -0.07545532286167145, - 0.09628419578075409 + -0.07473624497652054, + 0.09795032441616058 ], "tsne": [ - -22.121248245239258, - 2.399137496948242 + -5.2001729011535645, + 15.614121437072754 ], "umap": [ - 1.1483949422836304, - 3.270505905151367 + -0.2892085611820221, + 1.9676814079284668 ], "pca3d": [ - -0.07545558363199234, - 0.09628167748451233, - -0.03651844710111618 + -0.07473649829626083, + 0.09794944524765015, + -0.03267321363091469 ], "tsne3d": [ - -20.812755584716797, - 9.190536499023438, - -17.0526065826416 + -13.686388969421387, + 19.528902053833008, + 25.11765480041504 ], "umap3d": [ - 5.18039608001709, - -1.5325822830200195, - -0.16793809831142426 + 2.5372941493988037, + -0.7844945192337036, + 6.648064136505127 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, - "label": "Model Training" + "id": 2, + "label": "Medical Models" }, { - "id": 15, - "label": "Latent Models" + "id": 19, + "label": "Medical Imaging" } ], "_embedding": [ @@ -217603,47 +217786,47 @@ "abstract": "Self-supervised learning holds the promise of eliminating the need for manual data annotation, enabling models to scale effortlessly to massive datasets...", "projections": { "pca": [ - -0.07389505952596664, - 0.12185093760490417 + -0.07320446521043777, + 0.12331562489271164 ], "tsne": [ - -22.29490852355957, - 2.7713136672973633 + -4.971044540405273, + 15.937798500061035 ], "umap": [ - 1.1560996770858765, - 3.2254867553710938 + -0.25225287675857544, + 1.9627898931503296 ], "pca3d": [ - -0.07389488816261292, - 0.12184737622737885, - 0.04044437035918236 + -0.07320431619882584, + 0.12331356853246689, + 0.04174312576651573 ], "tsne3d": [ - -23.938261032104492, - 11.927603721618652, - -18.930622100830078 + -14.665793418884277, + 23.160900115966797, + 24.780338287353516 ], "umap3d": [ - 5.292586803436279, - -1.4362493753433228, - -0.1702834665775299 + 2.599860191345215, + -0.7848772406578064, + 6.609019756317139 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, - "label": "Model Training" + "id": 2, + "label": "Medical Models" }, { - "id": 15, - "label": "Latent Models" + "id": 19, + "label": "Medical Imaging" } ], "_embedding": [ @@ -218051,47 +218234,47 @@ "journal": "Nature Communications", "projections": { "pca": [ - 0.002118866192176938, - -0.22181321680545807 + 0.002380992751568556, + -0.2202075570821762 ], "tsne": [ - -3.6683762073516846, - -12.126051902770996 + -14.056354522705078, + -3.1355836391448975 ], "umap": [ - -1.0318777561187744, - 6.043593883514404 + -2.556063175201416, + 4.136689186096191 ], "pca3d": [ - 0.0021206969395279884, - -0.22178925573825836, - 0.12611551582813263 + 0.002383048180490732, + -0.22018563747406006, + 0.12751001119613647 ], "tsne3d": [ - -16.191059112548828, - -27.161773681640625, - 13.314465522766113 + -13.089866638183594, + -21.85967254638672, + 12.188741683959961 ], "umap3d": [ - 3.16416597366333, - -0.9388518929481506, - -2.0033650398254395 + 0.3757067620754242, + 1.0888545513153076, + 6.554324626922607 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 1, - "label": "Cancer Data" + "id": 5, + "label": "Clinical Data" }, { - "id": 28, - "label": "Clinical AI" + "id": 21, + "label": "Clinical Trials" } ], "_embedding": [ @@ -218507,46 +218690,46 @@ "journal": "OpenReview", "projections": { "pca": [ - -0.17589996755123138, - 0.030050596222281456 + -0.17329832911491394, + 0.03361746296286583 ], "tsne": [ - -9.34620475769043, - -2.224189043045044 + -13.789131164550781, + 7.212519645690918 ], "umap": [ - 0.18513168394565582, - 6.650771617889404 + -1.5656036138534546, + 3.456676483154297 ], "pca3d": [ - -0.17590050399303436, - 0.030035020783543587, - 0.16198574006557465 + -0.17329902946949005, + 0.03360341116786003, + 0.166947141289711 ], "tsne3d": [ - 8.469244956970215, - -20.026226043701172, - 8.794903755187988 + -1.314570426940918, + -2.919919490814209, + 7.312828540802002 ], "umap3d": [ - 4.530541896820068, - -0.8303408622741699, - -1.7029497623443604 + 1.2901149988174438, + 0.7312944531440735, + 7.403504371643066 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, + "id": 14, "label": "Clinical Health" } ], @@ -218959,47 +219142,47 @@ "journal": "Nature", "projections": { "pca": [ - -0.048981182277202606, - -0.23450349271297455 + -0.051299799233675, + -0.23431871831417084 ], "tsne": [ - -2.981995105743408, - -19.673877716064453 + -15.81273078918457, + -11.671462059020996 ], "umap": [ - -1.8864649534225464, - 5.1366047859191895 + -2.1329963207244873, + 6.015731334686279 ], "pca3d": [ - -0.04898086190223694, - -0.23449715971946716, - -0.05656607821583748 + -0.05129944905638695, + -0.2343112826347351, + -0.05688919499516487 ], "tsne3d": [ - -22.103527069091797, - -12.602407455444336, - 3.713738441467285 + -13.288952827453613, + -41.96707534790039, + 12.449618339538574 ], "umap3d": [ - 2.108153820037842, - -0.4806043207645416, - -0.8053281903266907 + 0.37994417548179626, + 1.2524385452270508, + 5.301728248596191 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 1, "label": "Cancer Data" }, { - "id": 9, - "label": "Cancer Biobank" + "id": 27, + "label": "Genetic Variants" } ], "_embedding": [ @@ -219459,47 +219642,47 @@ ], "projections": { "pca": [ - -0.017550567165017128, - 0.14887094497680664 + -0.01707964763045311, + 0.14977307617664337 ], "tsne": [ - -21.390148162841797, - 9.578314781188965 + -4.87986946105957, + 20.511564254760742 ], "umap": [ - 1.5247613191604614, - 3.258223533630371 + 0.2832511067390442, + 2.1200995445251465 ], "pca3d": [ - -0.017550943419337273, - 0.14887449145317078, - -0.0058323354460299015 + -0.01708000712096691, + 0.14977623522281647, + -0.002652933122590184 ], "tsne3d": [ - -4.204253673553467, - 18.93332290649414, - -20.14835548400879 + -4.138928413391113, + 0.6306556463241577, + -12.07576847076416 ], "umap3d": [ - 5.649597644805908, - -1.1618399620056152, - 0.06453505158424377 + 3.146648645401001, + -0.7535406947135925, + 6.522127628326416 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, - "label": "Model Training" + "id": 2, + "label": "Medical Models" }, { - "id": 15, - "label": "Latent Models" + "id": 8, + "label": "JEPA Video Models" } ], "_embedding": [ @@ -219920,47 +220103,47 @@ "doi": "10.1101/2024.12.31.24319813", "projections": { "pca": [ - -0.13610108196735382, - -0.08970010280609131 + -0.13380147516727448, + -0.08659575879573822 ], "tsne": [ - -22.917259216308594, - -10.893915176391602 + -26.619630813598633, + 3.0698866844177246 ], "umap": [ - -1.1154171228408813, - 7.43074893951416 + -2.4304370880126953, + 2.710338830947876 ], "pca3d": [ - -0.13610103726387024, - -0.08971482515335083, - 0.12185712158679962 + -0.13380156457424164, + -0.08660782128572464, + 0.12677638232707977 ], "tsne3d": [ - -8.186119079589844, - -27.82120132446289, - -23.695322036743164 + -30.03264617919922, + 13.287837028503418, + 26.571073532104492 ], "umap3d": [ - 3.6759860515594482, - -1.8498679399490356, - -1.642741084098816 + 1.124685525894165, + -0.37613990902900696, + 7.7567524909973145 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 12, - "label": "ECG Models" + "id": 13, + "label": "ECG AI Models" } ], "_embedding": [ @@ -220376,47 +220559,47 @@ "doi": "10.18637/jss.v107.i02", "projections": { "pca": [ - -0.11240831017494202, - -0.1664850115776062 + -0.11145222932100296, + -0.16403578221797943 ], "tsne": [ - -5.729501724243164, - -9.484256744384766 + -16.65776824951172, + -0.7767595648765564 ], "umap": [ - -1.200413465499878, - 6.416723728179932 + -2.192690134048462, + 4.2592926025390625 ], "pca3d": [ - -0.11240675300359726, - -0.16646991670131683, - 0.04833251237869263 + -0.11145052313804626, + -0.16402186453342438, + 0.052148569375276566 ], "tsne3d": [ - -8.797622680664062, - -31.656831741333008, - 17.54314422607422 + -4.010219573974609, + -23.041444778442383, + 18.924365997314453 ], "umap3d": [ - 3.3442535400390625, - -0.9082682728767395, - -1.633489727973938 + 0.58359295129776, + 0.8060657382011414, + 6.505568504333496 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 1, - "label": "Cancer Data" + "id": 5, + "label": "Clinical Data" }, { - "id": 28, - "label": "Clinical AI" + "id": 21, + "label": "Clinical Trials" } ], "_embedding": [ @@ -220824,47 +221007,47 @@ "abstract": "A paper from Anthropic describing persona vectors and their applications to monitoring and controlling model behavior", "projections": { "pca": [ - -0.1047627255320549, - 0.2132844179868698 + -0.10333292186260223, + 0.21454232931137085 ], "tsne": [ - -20.20956802368164, - 26.21251678466797 + 6.533960819244385, + 23.82379722595215 ], "umap": [ - 2.460905075073242, - 2.955439329147339 + 1.4837738275527954, + 1.9456080198287964 ], "pca3d": [ - -0.10476234555244446, - 0.213300883769989, - 0.04535597935318947 + -0.10333241522312164, + 0.21455611288547516, + 0.04186021536588669 ], "tsne3d": [ - 15.745624542236328, - -17.900529861450195, - -23.669368743896484 + 26.9537353515625, + -1.672127604484558, + -37.46661376953125 ], "umap3d": [ - 6.519757270812988, - -0.35886144638061523, - 0.5304580926895142 + 4.646945953369141, + -1.1058201789855957, + 6.162384510040283 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 18, + "label": "Retrieval Learning" } ], "_embedding": [ @@ -221283,47 +221466,47 @@ ], "projections": { "pca": [ - -0.1667090803384781, - 0.2860550284385681 + -0.1652376800775528, + 0.2875872254371643 ], "tsne": [ - -11.422386169433594, - 23.07763671875 + 10.008395195007324, + 28.826919555664062 ], "umap": [ - 3.297711133956909, - 2.721675157546997 + 2.1683406829833984, + 1.3832848072052002 ], "pca3d": [ - -0.16671022772789001, - 0.28605616092681885, - 0.05427797511219978 + -0.16523884236812592, + 0.2875889837741852, + 0.05596664920449257 ], "tsne3d": [ - 16.0908203125, - 9.54257583618164, - -48.047428131103516 + 6.0772786140441895, + 17.674545288085938, + -22.282421112060547 ], "umap3d": [ - 6.9918975830078125, - -0.21000735461711884, - 0.7551240921020508 + 4.566993713378906, + -0.725328266620636, + 5.379014015197754 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 6, + "label": "Learning Techniques" } ], "_embedding": [ @@ -221731,47 +221914,47 @@ "abstract": "Hierarchical Reasoning Model Official Release. Contribute to sapientinc/HRM development by creating an account on GitHub.", "projections": { "pca": [ - 0.12684530019760132, - 0.05439791455864906 + 0.1271086037158966, + 0.054942987859249115 ], "tsne": [ - 7.482337951660156, - 26.82625389099121 + 16.337203979492188, + 18.765100479125977 ], "umap": [ - 3.3928565979003906, - 3.1556053161621094 + 2.4738123416900635, + 1.7333402633666992 ], "pca3d": [ - 0.12684497237205505, - 0.05441877245903015, - -0.028461461886763573 + 0.1271083503961563, + 0.05496269837021828, + -0.029621299356222153 ], "tsne3d": [ - 46.44873046875, - -9.913029670715332, - -32.09016418457031 + 16.716020584106445, + 31.283918380737305, + -20.978832244873047 ], "umap3d": [ - 6.696799278259277, - 0.11707784980535507, - 0.6148195862770081 + 4.501588821411133, + -0.5140642523765564, + 5.229332447052002 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 6, + "label": "Learning Techniques" } ], "_embedding": [ @@ -222179,47 +222362,47 @@ "abstract": "Org profile for Sapient AI on Hugging Face, the AI community building the future.", "projections": { "pca": [ - 0.08852929621934891, - 0.11119157820940018 + 0.09020605683326721, + 0.11244438588619232 ], "tsne": [ - 7.510936260223389, - 26.760305404663086 + 16.421836853027344, + 18.641756057739258 ], "umap": [ - 4.668802261352539, - 4.701780319213867 + 3.779891014099121, + 3.1307480335235596 ], "pca3d": [ - 0.08852843195199966, - 0.11119219660758972, - 0.07069407403469086 + 0.09020516276359558, + 0.1124456450343132, + 0.06964629143476486 ], "tsne3d": [ - 47.633888244628906, - -11.324614524841309, - -28.320213317871094 + 17.018163681030273, + 34.41461944580078, + -19.54667091369629 ], "umap3d": [ - 5.06391716003418, - 1.424399733543396, - 1.9840716123580933 + 4.4615678787231445, + 2.151996374130249, + 4.758090972900391 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { "id": 7, - "label": "Open AI" + "label": "Hugging Face Models" } ], "_embedding": [ @@ -222627,47 +222810,47 @@ "journal": "Nature Biotechnology", "projections": { "pca": [ - 0.04560032859444618, - 0.11828015744686127 + 0.04735179990530014, + 0.11656534671783447 ], "tsne": [ - -4.6301188468933105, - 29.11199378967285 + -16.307077407836914, + 22.62860870361328 ], "umap": [ - 1.681150197982788, - 4.506325721740723 + 0.8860016465187073, + 3.6096692085266113 ], "pca3d": [ - 0.045599404722452164, - 0.11828559637069702, - 0.12341486662626266 + 0.04735084995627403, + 0.11657021194696426, + 0.11916177719831467 ], "tsne3d": [ - 37.71817398071289, - 11.352272033691406, - -43.456600189208984 + -19.21422004699707, + 25.16971778869629, + -5.110236167907715 ], "umap3d": [ - 4.639461040496826, - 0.0870494544506073, - 0.2508123219013214 + 3.356545925140381, + 0.3502263128757477, + 5.663758277893066 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { "id": 4, - "label": "Com Area Amp" + "label": "AI Agents" }, { - "id": 23, - "label": "Research Authors" + "id": 0, + "label": "Multimodal Models" } ], "_embedding": [ @@ -223074,47 +223257,47 @@ "title": "Clarification on Availability of Public ECG Dataset \u00b7 Issue #8 \u00b7 PierreElias/IntroECG \u00b7 GitHub", "projections": { "pca": [ - 0.2617809474468231, - -0.16827166080474854 + 0.26233384013175964, + -0.16798362135887146 ], "tsne": [ - 20.391855239868164, - -2.2029452323913574 + 12.126233100891113, + -12.937028884887695 ], "umap": [ - 1.970802903175354, - 6.315833568572998 + 1.485266089439392, + 5.120023727416992 ], "pca3d": [ - 0.26178112626075745, - -0.1682867854833603, - 0.08572264015674591 + 0.26233431696891785, + -0.1679995357990265, + 0.0847112387418747 ], "tsne3d": [ - 4.508370399475098, - 15.982721328735352, - 26.482481002807617 + 21.768672943115234, + -6.5748162269592285, + 19.354516983032227 ], "umap3d": [ - 3.2168972492218018, - -0.14756326377391815, - 1.2667949199676514 + 2.5623390674591064, + 1.9729578495025635, + 6.178915500640869 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 6, + "label": "Data Needs" }, { - "id": 25, - "label": "Python Data" + "id": 10, + "label": "Research Data" } ], "_embedding": [ @@ -223541,47 +223724,47 @@ ], "projections": { "pca": [ - -0.10155118256807327, - 0.24225187301635742 + -0.10075128078460693, + 0.2432931512594223 ], "tsne": [ - -15.675007820129395, - 22.92566680908203 + 6.225502967834473, + 29.90162467956543 ], "umap": [ - 3.2587954998016357, - 2.870577573776245 + 2.3008480072021484, + 1.7452590465545654 ], "pca3d": [ - -0.10155140608549118, - 0.24225658178329468, - -0.0020288359373807907 + -0.10075157135725021, + 0.2432974874973297, + -0.001378337386995554 ], "tsne3d": [ - 12.300880432128906, - -4.873480319976807, - -36.86563491821289 + 18.27845001220703, + 13.064826965332031, + -25.719985961914062 ], "umap3d": [ - 6.660459995269775, - -0.47917255759239197, - 1.390414834022522 + 4.909000873565674, + -0.6236056685447693, + 5.675300121307373 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 6, + "label": "Learning Techniques" } ], "_embedding": [ @@ -223987,47 +224170,47 @@ ], "projections": { "pca": [ - 0.028251152485609055, - -0.1943274736404419 + 0.02981254830956459, + -0.19238872826099396 ], "tsne": [ - 14.136921882629395, - -14.595205307006836 + 1.2761199474334717, + -14.701041221618652 ], "umap": [ - 0.4304180145263672, - 5.016356468200684 + -0.28655490279197693, + 4.678157806396484 ], "pca3d": [ - 0.028252040967345238, - -0.19432274997234344, - 0.1617489904165268 + 0.029813552275300026, + -0.19238664209842682, + 0.16102023422718048 ], "tsne3d": [ - -30.421655654907227, - 21.837583541870117, - 18.223613739013672 + 12.563822746276855, + -27.947694778442383, + -4.954367160797119 ], "umap3d": [ - 3.046782970428467, - 1.1924203634262085, - -0.09991426765918732 + 1.5359007120132446, + 1.920212984085083, + 5.0585784912109375 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 4, - "label": "Com Area Amp" + "id": 9, + "label": "Drug Discovery" }, { - "id": 17, - "label": "Literature AI" + "id": 23, + "label": "Peer Review" } ], "_embedding": [ @@ -224442,47 +224625,47 @@ ], "projections": { "pca": [ - -0.0879351794719696, - 0.21427598595619202 + -0.08689367771148682, + 0.2154766172170639 ], "tsne": [ - -25.599641799926758, - 12.727201461791992 + -7.006349563598633, + 25.9068603515625 ], "umap": [ - 1.626882791519165, - 2.755180835723877 + 0.7682387232780457, + 1.7471885681152344 ], "pca3d": [ - -0.08793570846319199, - 0.21426968276500702, - -0.11120625585317612 + -0.08689436316490173, + 0.215470552444458, + -0.10821298509836197 ], "tsne3d": [ - -7.528448581695557, - 22.153888702392578, - -30.378135681152344 + -1.7663862705230713, + -7.12205696105957, + -26.175216674804688 ], "umap3d": [ - 6.202470779418945, - -1.2665729522705078, - 0.32757002115249634 + 3.505568742752075, + -1.0788078308105469, + 6.064087867736816 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 15, - "label": "Latent Models" + "id": 8, + "label": "JEPA Video Models" } ], "_embedding": [ @@ -224900,47 +225083,47 @@ ], "projections": { "pca": [ - -0.12083576619625092, - 0.21782186627388 + -0.12243658304214478, + 0.21605680882930756 ], "tsne": [ - -20.10527801513672, - 24.75579261779785 + 5.678668022155762, + 25.018959045410156 ], "umap": [ - 2.540637254714966, - 3.028104066848755 + 1.59685480594635, + 1.985993504524231 ], "pca3d": [ - -0.12083613127470016, - 0.21781966090202332, - -0.04173734411597252 + -0.12243703007698059, + 0.21605350077152252, + -0.039190344512462616 ], "tsne3d": [ - 11.391643524169922, - -12.935579299926758, - -30.717493057250977 + 19.32245635986328, + -0.3756486177444458, + -39.72569274902344 ], "umap3d": [ - 6.438162803649902, - -0.31745341420173645, - 0.6636018753051758 + 4.674019813537598, + -0.9511987566947937, + 6.233614444732666 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 18, + "label": "Retrieval Learning" } ], "_embedding": [ @@ -225347,47 +225530,47 @@ "abstract": "Gene regulation in humans extends beyond the four letter genetic code. Cytosine methylation, in particular, functions as a critical epigenetic switchboard, dynamically programming cellular identity, adapting gene expression in response to environmen- tal cues, and underpinning the onset and progression of numerous diseases. Here we present Pleiades, a series of whole-genome epigenetic foundation models spanning three sizes: 90M, 600M, and 7B parameters. Pleiades is trained upon an extensive proprietary dataset of methylated and unmethylated human DNA sequences totalling 1.9T tokens. We introduce alignment embeddings and stacked hierarchical attention techniques to provide precise epigenetic modelling without the need for extended context lengths. Collectively, these advances enable Pleiades to perform a diverse range of downstream biological and clinical tasks, including nucleotide-level regulatory 1 prediction, realistic generation of cell-free DNA fragments and fragment-level cell- type-of-origin classification, within a unified and scalable computational framework. We specifically apply Pleiades to the early detection of real-world cohorts of clinical Alzheimer\u2019s disease and Parkinson\u2019s disease, achieving high-accuracy. We integrate Pleiades with leading protein biomarkers, achieving state-of-the-art results, underscor- ing the complementary value of epigenomic and proteomic multi-modal approaches. By advancing beyond the modelling of pure DNA sequences and relying on limited genomic regions, Pleiades establishes genome-wide epigenomic modelling as a new paradigm for clinical diagnostics, synthetic biology, and precision medicine.", "projections": { "pca": [ - -0.20636522769927979, - -0.13708224892616272 + -0.20812247693538666, + -0.13546469807624817 ], "tsne": [ - -1.468888521194458, - -26.772159576416016 + -16.40398406982422, + -18.65748405456543 ], "umap": [ - -2.0906190872192383, - 3.7235803604125977 + -1.6732020378112793, + 7.149720668792725 ], "pca3d": [ - -0.2063652127981186, - -0.13708260655403137, - -0.10806727409362793 + -0.20812244713306427, + -0.13546471297740936, + -0.10483134537935257 ], "tsne3d": [ - -46.305477142333984, - -20.642780303955078, - 3.1116833686828613 + -36.347557067871094, + -8.547779083251953, + -8.245110511779785 ], "umap3d": [ - 1.208437204360962, - 0.4183766543865204, - -1.2781212329864502 + -0.08742669969797134, + 0.8526187539100647, + 3.9683644771575928 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 22, - "label": "Genetic Variants" + "id": 3, + "label": "Genomic Sequences" } ], "_embedding": [ @@ -225795,47 +225978,47 @@ "abstract": "Resource library for getting started with deep learning work using electrocardiograms - PierreElias/IntroECG", "projections": { "pca": [ - 0.16343800723552704, - -0.021481914445757866 + 0.16389407217502594, + -0.020658789202570915 ], "tsne": [ - 20.386777877807617, - -2.02431321144104 + 12.486979484558105, + -12.795947074890137 ], "umap": [ - 2.4755396842956543, - 5.463680744171143 + 1.4953190088272095, + 4.507298946380615 ], "pca3d": [ - 0.16343806684017181, - -0.02150033973157406, - -0.019222760573029518 + 0.1638941615819931, + -0.020676273852586746, + -0.019310154020786285 ], "tsne3d": [ - 2.2666163444519043, - 19.557777404785156, - 27.837636947631836 + 23.9709529876709, + -7.894564628601074, + 21.912334442138672 ], "umap3d": [ - 3.996896743774414, - -0.38202232122421265, - 1.1163029670715332 + 3.028834342956543, + 1.6294009685516357, + 6.4572577476501465 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 26, - "label": "AI Information" + "id": 10, + "label": "Research Data" } ], "_embedding": [ @@ -226245,47 +226428,47 @@ "journal": "Nature", "projections": { "pca": [ - -0.1645582616329193, - -0.012352493591606617 + -0.15807028114795685, + -0.007155335042625666 ], "tsne": [ - -24.142539978027344, - -12.321863174438477 + -26.872817993164062, + 5.0805864334106445 ], "umap": [ - -1.1054686307907104, - 7.533933162689209 + -2.4655230045318604, + 2.690539598464966 ], "pca3d": [ - -0.16455860435962677, - -0.012362617999315262, - 0.22999539971351624 + -0.15807078778743744, + -0.007162358611822128, + 0.2339893877506256 ], "tsne3d": [ - -2.830174684524536, - -23.434207916259766, - -33.07725524902344 + -34.14316177368164, + 17.30233383178711, + 18.82866096496582 ], "umap3d": [ - 3.7105581760406494, - -1.9216680526733398, - -1.7030211687088013 + 1.1312594413757324, + -0.40774333477020264, + 7.814274311065674 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 12, - "label": "ECG Models" + "id": 13, + "label": "ECG AI Models" } ], "_embedding": [ @@ -226702,47 +226885,47 @@ ], "projections": { "pca": [ - -0.16420547664165497, - 0.13293245434761047 + -0.16314074397087097, + 0.1326354742050171 ], "tsne": [ - -12.364495277404785, - 16.460468292236328 + -9.124780654907227, + 29.550411224365234 ], "umap": [ - 2.6567223072052, - 2.807213544845581 + 1.8369462490081787, + 2.3169138431549072 ], "pca3d": [ - -0.16420605778694153, - 0.1329190731048584, - -0.007928458973765373 + -0.1631413847208023, + 0.13262315094470978, + -0.0036245782393962145 ], "tsne3d": [ - -24.109859466552734, - 6.4082159996032715, - -39.31140899658203 + 7.647894382476807, + -1.6279337406158447, + -42.85041427612305 ], "umap3d": [ - 6.444841384887695, - -0.40880274772644043, - 1.0392637252807617 + 4.66182804107666, + -0.6542759537696838, + 6.309704303741455 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 18, + "label": "Retrieval Learning" } ], "_embedding": [ @@ -227151,47 +227334,47 @@ "journal": "METR Blog", "projections": { "pca": [ - 0.1370159387588501, - 0.09994717687368393 + 0.13898883759975433, + 0.10276660323143005 ], "tsne": [ - -1.058465838432312, - 18.443891525268555 + 10.082930564880371, + 13.583624839782715 ], "umap": [ - 3.676638126373291, - 3.994277000427246 + 2.7519283294677734, + 2.9159607887268066 ], "pca3d": [ - 0.13701674342155457, - 0.09996630996465683, - 0.12087435275316238 + 0.13898982107639313, + 0.10278616100549698, + 0.11723293364048004 ], "tsne3d": [ - 41.46406555175781, - -11.91452693939209, - -13.402100563049316 + -2.779968023300171, + 23.37053680419922, + -6.963143825531006 ], "umap3d": [ - 5.574216842651367, - 0.883975625038147, - 1.0708247423171997 + 4.352577209472656, + 0.6660735011100769, + 5.221651554107666 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 0, - "label": "AI Agents" + "id": 8, + "label": "Open AI" }, { - "id": 1, - "label": "AI Agents" + "id": 22, + "label": "AI Gemini" } ], "_embedding": [ @@ -227604,46 +227787,46 @@ ], "projections": { "pca": [ - -0.10341892391443253, - 0.2358947992324829 + -0.10236100107431412, + 0.23728100955486298 ], "tsne": [ - -9.859200477600098, - 26.001344680786133 + 4.180363178253174, + 21.346630096435547 ], "umap": [ - 2.0987842082977295, - 1.922328233718872 + 1.4119175672531128, + 0.9374493956565857 ], "pca3d": [ - -0.10341832041740417, - 0.2359178513288498, - 0.06256446987390518 + -0.10236038267612457, + 0.23730142414569855, + 0.06372793018817902 ], "tsne3d": [ - 3.5142061710357666, - 1.189825415611267, - -52.668575286865234 + 7.927774906158447, + 12.809070587158203, + -12.572322845458984 ], "umap3d": [ - 6.96607780456543, - -0.4499424993991852, - 0.19940973818302155 + 4.4715495109558105, + -1.474948763847351, + 5.827707767486572 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 4, + "id": 26, "label": "World Models" } ], @@ -228052,46 +228235,46 @@ "journal": "Nature", "projections": { "pca": [ - -0.19063986837863922, - -0.23214130103588104 + -0.18965595960617065, + -0.22997838258743286 ], "tsne": [ - -7.795041084289551, - -19.200822830200195 + -22.76589012145996, + -7.515392303466797 ], "umap": [ - -2.300292491912842, - 5.899720191955566 + -2.3636133670806885, + 5.238095760345459 ], "pca3d": [ - -0.1906392127275467, - -0.23213805258274078, - 0.015433716587722301 + -0.1896553635597229, + -0.22997449338436127, + 0.02000313811004162 ], "tsne3d": [ - -29.660564422607422, - -30.62421417236328, - -3.6229541301727295 + -32.779483795166016, + -16.794782638549805, + 13.658245086669922 ], "umap3d": [ - 2.483616590499878, - -0.8348375558853149, - -0.9445803165435791 + 0.0753258615732193, + -0.03097524493932724, + 5.531515598297119 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 24, + "id": 1, "label": "Cancer Data" } ], @@ -228509,46 +228692,46 @@ ], "projections": { "pca": [ - -0.08627398312091827, - -0.08731646835803986 + -0.08373593538999557, + -0.08431252837181091 ], "tsne": [ - -21.577856063842773, - -1.541991949081421 + -6.2565999031066895, + 12.286588668823242 ], "umap": [ - -0.16399788856506348, - 7.867366790771484 + -1.1253973245620728, + 2.2074718475341797 ], "pca3d": [ - -0.08627381920814514, - -0.08731278777122498, - 0.04491998255252838 + -0.08373574167490005, + -0.08430808782577515, + 0.05059221014380455 ], "tsne3d": [ - -10.192763328552246, - 8.080419540405273, - -8.614392280578613 + -3.351689338684082, + 10.637258529663086, + 27.677413940429688 ], "umap3d": [ - 4.419913291931152, - -1.795151710510254, - -0.440511018037796 + 1.8386610746383667, + -0.6299546957015991, + 6.7018046379089355 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 11, + "id": 19, "label": "Medical Imaging" } ], @@ -228983,46 +229166,46 @@ ], "projections": { "pca": [ - -0.19571079313755035, - -0.03924822807312012 + -0.19333025813102722, + -0.036275435239076614 ], "tsne": [ - -17.176105499267578, - -1.776924967765808 + -8.997980117797852, + 11.325480461120605 ], "umap": [ - -0.05015300586819649, - 7.421128273010254 + -0.9389646053314209, + 2.6367454528808594 ], "pca3d": [ - -0.19571121037006378, - -0.03926378861069679, - 0.11226961761713028 + -0.19333064556121826, + -0.03629065677523613, + 0.11896617710590363 ], "tsne3d": [ - -9.58505630493164, - -5.743549346923828, - -13.647873878479004 + -10.763678550720215, + 6.72500467300415, + 18.715877532958984 ], "umap3d": [ - 4.464783191680908, - -1.2733359336853027, - -0.5914607048034668 + 1.900951862335205, + -0.2387404590845108, + 6.665607929229736 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 11, + "id": 19, "label": "Medical Imaging" } ], @@ -229433,47 +229616,47 @@ "abstract": "BiomedCLIP data pipeline. Contribute to microsoft/BiomedCLIP_data_pipeline development by creating an account on GitHub.", "projections": { "pca": [ - 0.2293584942817688, - -0.15809781849384308 + 0.2293320894241333, + -0.15734153985977173 ], "tsne": [ - 27.803871154785156, - 10.693147659301758 + 30.156599044799805, + -8.88364028930664 ], "umap": [ - 3.330357551574707, - 7.0383830070495605 + 2.9894425868988037, + 5.840600967407227 ], "pca3d": [ - 0.22935979068279266, - -0.15808816254138947, - -0.11599265038967133 + 0.22933362424373627, + -0.15733419358730316, + -0.11596061289310455 ], "tsne3d": [ - 26.33759880065918, - 48.0988655090332, - 18.944129943847656 + 43.52202224731445, + 10.2708740234375, + 15.754473686218262 ], "umap3d": [ - 3.3597185611724854, - -0.13301551342010498, - 2.7514328956604004 + 3.613264560699463, + 2.8227527141571045, + 6.781207084655762 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 18, - "label": "Github Tools" + "id": 17, + "label": "GitHub Creation" } ], "_embedding": [ @@ -229888,47 +230071,47 @@ ], "projections": { "pca": [ - -0.11328493803739548, - -0.038610488176345825 + -0.1139661893248558, + -0.03780984878540039 ], "tsne": [ - -12.828878402709961, - -26.98651885986328 + -26.003204345703125, + -16.307836532592773 ], "umap": [ - -0.7908362746238708, - 3.0725064277648926 + -1.0665336847305298, + 6.108221054077148 ], "pca3d": [ - -0.11328498274087906, - -0.03861289843916893, - -0.14195939898490906 + -0.11396623402833939, + -0.03781222179532051, + -0.13640256226062775 ], "tsne3d": [ - -35.14592361450195, - -20.449960708618164, - -28.556537628173828 + -38.86563491821289, + -26.386348724365234, + 4.926332950592041 ], "umap3d": [ - 1.6837862730026245, - 1.1554234027862549, - -1.8542948961257935 + 0.40695682168006897, + -0.30225375294685364, + 4.43040657043457 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 19, - "label": "Cell Models" + "id": 28, + "label": "Spatial Drug" } ], "_embedding": [ @@ -230336,47 +230519,47 @@ "abstract": "Microsoft researchers achieved a breakthrough in the accuracy of DFT, a method for predicting the properties of molecules and materials, by using deep learning. This work can lead to better batteries, green fertilizers, precision drug discovery, and more.", "projections": { "pca": [ - -0.040351610630750656, - 0.08805237710475922 + -0.038480907678604126, + 0.0885922759771347 ], "tsne": [ - -4.02567720413208, - 13.9544038772583 + 0.663063645362854, + -3.024397134780884 ], "umap": [ - 3.6275057792663574, - 3.5617430210113525 + 2.764580249786377, + 2.388380527496338 ], "pca3d": [ - -0.04035244882106781, - 0.08804238587617874, - 0.049441538751125336 + -0.03848184272646904, + 0.0885840505361557, + 0.050966791808605194 ], "tsne3d": [ - -12.630890846252441, - 40.13458251953125, - -30.6091365814209 + 4.775490760803223, + 6.255997180938721, + -50.81861114501953 ], "umap3d": [ - 5.833538055419922, - 0.7138911485671997, - 0.7728857398033142 + 3.7973015308380127, + 0.08302009105682373, + 4.982400417327881 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 1, - "label": "AI Agents" + "id": 22, + "label": "AI Gemini" } ], "_embedding": [ @@ -230807,47 +230990,47 @@ "doi": "10.1101/2025.06.11.659105", "projections": { "pca": [ - -0.1230337992310524, - -0.13482503592967987 + -0.12644018232822418, + -0.13478276133537292 ], "tsne": [ - -5.041190147399902, - -25.365371704101562 + -21.14376449584961, + -22.403400421142578 ], "umap": [ - -0.9655141830444336, - 3.654951810836792 + -0.6513588428497314, + 6.6477837562561035 ], "pca3d": [ - -0.12303419411182404, - -0.13482943177223206, - -0.1834077388048172 + -0.12644055485725403, + -0.13478603959083557, + -0.18038253486156464 ], "tsne3d": [ - -44.78849411010742, - -24.97281265258789, - -4.871242046356201 + -20.37445068359375, + -24.527711868286133, + -6.058330535888672 ], "umap3d": [ - 1.9939627647399902, - 1.2212324142456055, - -1.2429345846176147 + 0.7170578241348267, + 0.1663980484008789, + 3.817350387573242 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 19, - "label": "Cell Models" + "id": 20, + "label": "Cell Foundation" } ], "_embedding": [ @@ -231258,47 +231441,47 @@ ], "projections": { "pca": [ - -0.04884229600429535, - 0.29317960143089294 + -0.046730898320674896, + 0.2946743965148926 ], "tsne": [ - -11.509257316589355, - 24.244888305664062 + 10.341475486755371, + 29.958559036254883 ], "umap": [ - 3.407212257385254, - 2.887637138366699 + 2.2835352420806885, + 1.416592001914978 ], "pca3d": [ - -0.04884285479784012, - 0.29319098591804504, - 0.05916089192032814 + -0.04673151299357414, + 0.2946849763393402, + 0.058069951832294464 ], "tsne3d": [ - 16.06484603881836, - 4.574538707733154, - -51.97172927856445 + 10.24271297454834, + 19.317302703857422, + -19.242738723754883 ], "umap3d": [ - 6.983845233917236, - -0.12398220598697662, - 0.781630277633667 + 4.581000328063965, + -0.6565847396850586, + 5.300004482269287 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 6, + "label": "Learning Techniques" } ], "_embedding": [ @@ -231714,47 +231897,47 @@ ], "projections": { "pca": [ - -0.15412157773971558, - 0.284513384103775 + -0.15511281788349152, + 0.28457391262054443 ], "tsne": [ - -16.302993774414062, - 23.563385009765625 + 6.632632255554199, + 29.179887771606445 ], "umap": [ - 3.1712777614593506, - 2.615248441696167 + 2.170905351638794, + 1.641264796257019 ], "pca3d": [ - -0.15412212908267975, - 0.28451400995254517, - -0.07575302571058273 + -0.15511348843574524, + 0.2845745384693146, + -0.07549947500228882 ], "tsne3d": [ - 11.46410083770752, - 1.463060736656189, - -36.10435485839844 + 15.129426002502441, + 9.938173294067383, + -28.106664657592773 ], "umap3d": [ - 6.692952632904053, - -0.4742218852043152, - 1.1972941160202026 + 4.955007553100586, + -0.5981643199920654, + 5.7713093757629395 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 6, + "label": "Learning Techniques" } ], "_embedding": [ @@ -232162,47 +232345,47 @@ "abstract": "We’re excited to share V-JEPA 2, the first world model trained on video that enables state-of-the-art understanding and prediction, as well as zero-shot...", "projections": { "pca": [ - 0.027605213224887848, - 0.13594987988471985 + 0.02938658744096756, + 0.1376177966594696 ], "tsne": [ - -15.930447578430176, - 12.91185474395752 + 0.12316322326660156, + 20.279767990112305 ], "umap": [ - 1.6562321186065674, - 2.2389814853668213 + 0.7157472968101501, + 1.156222939491272 ], "pca3d": [ - 0.02760503999888897, - 0.13596418499946594, - 0.009723218157887459 + 0.02938651852309704, + 0.13763178884983063, + 0.01226336695253849 ], "tsne3d": [ - -7.616517543792725, - -3.2191765308380127, - -46.91460037231445 + 15.246150016784668, + -0.8526812195777893, + -12.04059886932373 ], "umap3d": [ - 6.638285160064697, - -0.8862690925598145, - -0.18081313371658325 + 4.015076160430908, + -1.6044410467147827, + 6.230358600616455 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 4, - "label": "World Models" + "id": 8, + "label": "JEPA Video Models" } ], "_embedding": [ @@ -232617,46 +232800,46 @@ ], "projections": { "pca": [ - -0.10420029610395432, - -0.019760169088840485 + -0.09960217028856277, + -0.015508735552430153 ], "tsne": [ - -12.2665433883667, - -7.480280876159668 + -8.061629295349121, + 2.9850480556488037 ], "umap": [ - -0.45794445276260376, - 6.522234916687012 + -1.4120656251907349, + 3.876615524291992 ], "pca3d": [ - -0.10419992357492447, - -0.01976088248193264, - 0.22410909831523895 + -0.09960197657346725, + -0.01550827082246542, + 0.2269047647714615 ], "tsne3d": [ - 8.106273651123047, - -36.7308464050293, - 5.612697601318359 + -10.629562377929688, + 4.460724830627441, + 3.774106979370117 ], "umap3d": [ - 4.021040439605713, - -0.34973299503326416, - -1.9357928037643433 + 0.7294368743896484, + 0.3457009196281433, + 7.199062347412109 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, + "id": 14, "label": "Clinical Health" } ], @@ -233079,47 +233262,47 @@ ], "projections": { "pca": [ - -0.12901808321475983, - 0.22979630529880524 + -0.12692931294441223, + 0.2314666360616684 ], "tsne": [ - -20.287351608276367, - 10.032296180725098 + -5.70834493637085, + 21.277385711669922 ], "umap": [ - 1.5153841972351074, - 3.2353601455688477 + 0.37917405366897583, + 2.1656064987182617 ], "pca3d": [ - -0.12901845574378967, - 0.2297978550195694, - 0.03859544172883034 + -0.1269298642873764, + 0.23146742582321167, + 0.04107994586229324 ], "tsne3d": [ - -5.272672176361084, - 11.420310974121094, - -19.830123901367188 + -0.9928643107414246, + 6.295562267303467, + -14.504267692565918 ], "umap3d": [ - 5.735162734985352, - -1.001145362854004, - 0.020404759794473648 + 3.3005447387695312, + -0.817077100276947, + 6.307922840118408 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 15, - "label": "Latent Models" + "id": 8, + "label": "JEPA Video Models" } ], "_embedding": [ @@ -233536,47 +233719,47 @@ ], "projections": { "pca": [ - -0.15801456570625305, - 0.28630563616752625 + -0.15806615352630615, + 0.28743210434913635 ], "tsne": [ - -14.716666221618652, - 23.969993591308594 + 7.436553478240967, + 30.7243595123291 ], "umap": [ - 3.318249464035034, - 2.4834964275360107 + 2.3590152263641357, + 1.4692213535308838 ], "pca3d": [ - -0.15801504254341125, - 0.2863074243068695, - -0.04674782231450081 + -0.1580667942762375, + 0.287433385848999, + -0.04507311433553696 ], "tsne3d": [ - 15.257301330566406, - -1.133357048034668, - -41.91265869140625 + 11.98632526397705, + 15.510709762573242, + -29.103469848632812 ], "umap3d": [ - 7.027694225311279, - -0.5150774121284485, - 1.2150975465774536 + 5.007948875427246, + -0.7459340691566467, + 5.5764851570129395 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 6, + "label": "Learning Techniques" } ], "_embedding": [ @@ -233995,47 +234178,47 @@ ], "projections": { "pca": [ - -0.15734978020191193, - 0.16208061575889587 + -0.15650752186775208, + 0.16300950944423676 ], "tsne": [ - -25.831401824951172, - 7.417654037475586 + -0.32531601190567017, + 16.26752471923828 ], "umap": [ - 2.0740463733673096, - 3.8500099182128906 + 1.3510862588882446, + 2.8635520935058594 ], "pca3d": [ - -0.15735016763210297, - 0.162076935172081, - -0.05211751163005829 + -0.15650781989097595, + 0.16300582885742188, + -0.04889902099967003 ], "tsne3d": [ - -23.221925735473633, - 22.08173370361328, - -32.520450592041016 + -8.79605770111084, + 22.65481948852539, + -26.67603302001953 ], "umap3d": [ - 5.117462158203125, - -0.8575794100761414, - 0.72174471616745 + 3.6016576290130615, + 0.032101310789585114, + 6.435919284820557 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 27, - "label": "Embeddings" + "id": 18, + "label": "Retrieval Learning" } ], "_embedding": [ @@ -234453,47 +234636,47 @@ ], "projections": { "pca": [ - -0.12790222465991974, - 0.25131654739379883 + -0.1285567730665207, + 0.25072795152664185 ], "tsne": [ - -19.76877784729004, - 21.007129669189453 + 3.056337356567383, + 28.748271942138672 ], "umap": [ - 2.6489148139953613, - 2.7519094944000244 + 1.6682194471359253, + 1.7485027313232422 ], "pca3d": [ - -0.1279030293226242, - 0.25131475925445557, - -0.06844490021467209 + -0.12855756282806396, + 0.2507251501083374, + -0.06564551591873169 ], "tsne3d": [ - 16.110109329223633, - 8.63235092163086, - -34.43501663208008 + 9.610563278198242, + 7.024807929992676, + -27.073974609375 ], "umap3d": [ - 6.5873494148254395, - -0.5077348351478577, - 0.8288173675537109 + 4.454556465148926, + -0.821588933467865, + 5.889477729797363 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 6, + "label": "Learning Techniques" } ], "_embedding": [ @@ -234912,46 +235095,46 @@ ], "projections": { "pca": [ - -0.1480035036802292, - -0.018715450540184975 + -0.14565666019916534, + -0.017278483137488365 ], "tsne": [ - -17.711896896362305, - -0.4333323538303375 + -8.048232078552246, + 10.452204704284668 ], "umap": [ - 0.1667395979166031, - 7.483893871307373 + -0.739909291267395, + 2.5275752544403076 ], "pca3d": [ - -0.14800389111042023, - -0.018724864348769188, - 0.10624932497739792 + -0.1456572562456131, + -0.01728668250143528, + 0.11383762955665588 ], "tsne3d": [ - -7.182055950164795, - -5.154105186462402, - -6.839298248291016 + -8.619776725769043, + 12.766226768493652, + 19.62441062927246 ], "umap3d": [ - 4.682649612426758, - -1.4317586421966553, - -0.5953347086906433 + 1.9794782400131226, + -0.20192523300647736, + 6.9424729347229 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 11, + "id": 19, "label": "Medical Imaging" } ], @@ -235368,46 +235551,46 @@ ], "projections": { "pca": [ - -0.15614239871501923, - 0.2529751658439636 + -0.15578749775886536, + 0.25309574604034424 ], "tsne": [ - -26.86146354675293, - 18.964534759521484 + 1.1312516927719116, + 25.209131240844727 ], "umap": [ - 2.3347864151000977, - 2.2217226028442383 + 1.2561142444610596, + 1.3766988515853882 ], "pca3d": [ - -0.15614351630210876, - 0.2529660761356354, - -0.10859840363264084 + -0.15578867495059967, + 0.25308749079704285, + -0.10531605780124664 ], "tsne3d": [ - 1.221028447151184, - 18.013967514038086, - -48.07877731323242 + -5.9415106773376465, + 12.730973243713379, + -34.112457275390625 ], "umap3d": [ - 6.86484432220459, - -1.175317406654358, - 0.5601636171340942 + 3.7586777210235596, + -1.2254136800765991, + 5.571666240692139 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 4, + "id": 26, "label": "World Models" } ], @@ -235878,47 +236061,47 @@ ], "projections": { "pca": [ - -0.12750062346458435, - -0.06918156892061234 + -0.1286783367395401, + -0.06751540303230286 ], "tsne": [ - -13.030202865600586, - -26.33861541748047 + -26.03459930419922, + -15.681944847106934 ], "umap": [ - -0.7856987118721008, - 3.0598161220550537 + -1.0409510135650635, + 6.1521220207214355 ], "pca3d": [ - -0.12749993801116943, - -0.06917267292737961, - -0.15640509128570557 + -0.1286775916814804, + -0.06750742346048355, + -0.15100686252117157 ], "tsne3d": [ - -31.397663116455078, - -16.210155487060547, - -29.910097122192383 + -39.96556854248047, + -24.935415267944336, + 9.526900291442871 ], "umap3d": [ - 1.713813066482544, - 1.1596636772155762, - -1.9088854789733887 + 0.37928494811058044, + -0.34473466873168945, + 4.483379364013672 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 19, - "label": "Cell Models" + "id": 28, + "label": "Spatial Drug" } ], "_embedding": [ @@ -236331,47 +236514,47 @@ ], "projections": { "pca": [ - -0.1389765590429306, - 0.23300211131572723 + -0.1390545517206192, + 0.23305492103099823 ], "tsne": [ - -14.8131685256958, - 24.730594635009766 + 7.743642330169678, + 31.279006958007812 ], "umap": [ - 3.3840973377227783, - 2.545443534851074 + 2.369746685028076, + 1.3742278814315796 ], "pca3d": [ - -0.13897667825222015, - 0.23301619291305542, - -0.043223168700933456 + -0.13905476033687592, + 0.2330668568611145, + -0.04179835692048073 ], "tsne3d": [ - 20.2601318359375, - -2.11102294921875, - -43.734230041503906 + 16.274192810058594, + 17.10248374938965, + -29.509063720703125 ], "umap3d": [ - 7.070561408996582, - -0.4842916429042816, - 1.1220364570617676 + 4.976561546325684, + -0.8338426947593689, + 5.5169854164123535 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 6, + "label": "Learning Techniques" } ], "_embedding": [ @@ -236779,47 +236962,47 @@ "abstract": "When impressive AI biology results are full of errors", "projections": { "pca": [ - -0.06493736058473587, - 0.12920597195625305 + -0.06292738765478134, + 0.1302901953458786 ], "tsne": [ - -5.433618545532227, - 14.851699829101562 + -0.5768910050392151, + -2.8481285572052 ], "umap": [ - 3.4085705280303955, - 3.196972131729126 + 2.575800895690918, + 2.127166986465454 ], "pca3d": [ - -0.06493806093931198, - 0.12918679416179657, - 0.1054973378777504 + -0.06292823702096939, + 0.13027217984199524, + 0.10530412942171097 ], "tsne3d": [ - 34.99393081665039, - -4.392899513244629, - -31.078712463378906 + 6.997862815856934, + 11.020892143249512, + -46.68152618408203 ], "umap3d": [ - 6.329351425170898, - 0.29692792892456055, - 0.8396971225738525 + 4.159035682678223, + -0.32723352313041687, + 5.255178928375244 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 6, + "label": "Learning Techniques" } ], "_embedding": [ @@ -237240,47 +237423,47 @@ ], "projections": { "pca": [ - -0.18793870508670807, - -0.05244546756148338 + -0.18860967457294464, + -0.0510229617357254 ], "tsne": [ - 2.70068359375, - -8.566835403442383 + -4.9950032234191895, + -6.956329345703125 ], "umap": [ - -0.528701901435852, - 4.64115571975708 + -0.6802137494087219, + 5.176412105560303 ], "pca3d": [ - -0.18793907761573792, - -0.05245410278439522, - -0.08162712305784225 + -0.18861019611358643, + -0.051031190901994705, + -0.07782668620347977 ], "tsne3d": [ - -19.0665283203125, - -0.07325877249240875, - 21.19114112854004 + 4.921760559082031, + -19.094318389892578, + -10.941712379455566 ], "umap3d": [ - 3.428755283355713, - 0.6294029951095581, - -0.9041548371315002 + 2.106875419616699, + 0.22045280039310455, + 4.879019260406494 ] }, "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_label": "Clinical AI Data", "cluster_levels": [ { "id": 2, - "label": "Bio Models Gene" + "label": "Clinical AI Data" }, { - "id": 2, - "label": "AI Drug Discovery" + "id": 9, + "label": "Drug Discovery" }, { - "id": 13, - "label": "Drug Discovery" + "id": 9, + "label": "Biomedical AI" } ], "_embedding": [ @@ -237688,47 +237871,47 @@ "abstract": "BioReason: Incentivizing Multimodal Biological Reasoning within a DNA-LLM Model | NeurIPS '25 - bowang-lab/BioReason", "projections": { "pca": [ - 0.040948834270238876, - -0.05110018700361252 + 0.04077078402042389, + -0.05005670711398125 ], "tsne": [ - 2.8987607955932617, - -8.646807670593262 + -4.8237080574035645, + -7.080373287200928 ], "umap": [ - -0.38999149203300476, - 4.761218070983887 + -0.5828412175178528, + 5.2480058670043945 ], "pca3d": [ - 0.040949102491140366, - -0.051098860800266266, - -0.08885717391967773 + 0.040771130472421646, + -0.05005697160959244, + -0.08821609616279602 ], "tsne3d": [ - -19.436933517456055, - 3.5886893272399902, - 23.2819766998291 + 6.770899772644043, + -18.990835189819336, + -13.893013000488281 ], "umap3d": [ - 3.4210009574890137, - 0.539118766784668, - -0.6032412648200989 + 2.125148296356201, + 0.41739386320114136, + 4.93172025680542 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ - { - "id": 0, - "label": "Medical Data AI" - }, { "id": 2, - "label": "AI Drug Discovery" + "label": "Clinical AI Data" }, { - "id": 13, + "id": 9, "label": "Drug Discovery" + }, + { + "id": 9, + "label": "Biomedical AI" } ], "_embedding": [ @@ -238192,46 +238375,46 @@ "openalex_url": "https://openalex.org/W4410851723", "projections": { "pca": [ - -0.23134970664978027, - -0.08307638764381409 + -0.23340480029582977, + -0.08283133059740067 ], "tsne": [ - 1.5770432949066162, - -25.36437225341797 + -13.069171905517578, + -17.875776290893555 ], "umap": [ - -2.5482382774353027, - 4.037774085998535 + -2.213658571243286, + 7.05144739151001 ], "pca3d": [ - -0.23134927451610565, - -0.08308496326208115, - -0.10969622433185577 + -0.23340439796447754, + -0.08283887803554535, + -0.10656458139419556 ], "tsne3d": [ - -31.498037338256836, - -4.646322727203369, - 6.481098651885986 + -27.91194725036621, + -8.573114395141602, + -6.398238658905029 ], "umap3d": [ - 1.1250075101852417, - -0.14044685661792755, - -1.4069139957427979 + -0.3399513065814972, + 1.1367766857147217, + 4.428276062011719 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 22, + "id": 27, "label": "Genetic Variants" } ], @@ -238640,47 +238823,47 @@ "abstract": "Subset-Norm and Subset-Momentum. This repo is built on top of https://github.com/jiaweizzhao/GaLore. - timmytonga/sn-sm", "projections": { "pca": [ - 0.1522640436887741, - 0.045086540281772614 + 0.14932309091091156, + 0.04421394318342209 ], "tsne": [ - 6.3611321449279785, - 4.32958984375 + 11.09805679321289, + -9.227009773254395 ], "umap": [ - 2.3800995349884033, - 4.503005027770996 + 1.5580974817276, + 3.9314424991607666 ], "pca3d": [ - 0.15226446092128754, - 0.04509618133306503, - -0.21277068555355072 + 0.14932361245155334, + 0.04422244802117348, + -0.21306025981903076 ], "tsne3d": [ - -8.870988845825195, - 27.637081146240234, - 21.004898071289062 + 38.92107009887695, + -8.56264877319336, + -11.94859790802002 ], "umap3d": [ - 4.455610275268555, - -0.6824391484260559, - 1.650941252708435 + 3.984468460083008, + 0.9356486201286316, + 6.589876651763916 ] }, "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_label": "Open Source Tools", "cluster_levels": [ { "id": 3, - "label": "AI Training Models" + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 5, - "label": "GPU Inference" + "id": 15, + "label": "PyTorch Models" } ], "_embedding": [ @@ -239156,47 +239339,47 @@ "openalex_url": "https://openalex.org/W4410396192", "projections": { "pca": [ - 0.011926302686333656, - -0.2819075286388397 + 0.011024895124137402, + -0.28142815828323364 ], "tsne": [ - -0.7756674885749817, - -18.04534912109375 + -12.315694808959961, + -8.70765209197998 ], "umap": [ - -1.2807626724243164, - 5.87445068359375 + -2.5472054481506348, + 4.326111793518066 ], "pca3d": [ - 0.011926599778234959, - -0.2819097638130188, - -0.022701792418956757 + 0.011025131680071354, + -0.2814289331436157, + -0.020056387409567833 ], "tsne3d": [ - -31.876522064208984, - -32.989524841308594, - 18.315502166748047 + -8.13438892364502, + -25.87264060974121, + 6.980416297912598 ], "umap3d": [ - 2.8352134227752686, - -0.5679184198379517, - -1.8527841567993164 + 0.20330949127674103, + 0.9491593241691589, + 5.950226306915283 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 28, - "label": "Clinical AI" + "id": 21, + "label": "Clinical Trials" } ], "_embedding": [ @@ -239611,47 +239794,47 @@ "doi": "10.1101/2025.04.30.651545", "projections": { "pca": [ - -0.25421229004859924, - 0.026290366426110268 + -0.2563835382461548, + 0.02690625563263893 ], "tsne": [ - 1.189117670059204, - -36.56924819946289 + -12.093156814575195, + -28.257984161376953 ], "umap": [ - -1.4094399213790894, - 2.5251522064208984 + -0.5967451930046082, + 7.628546237945557 ], "pca3d": [ - -0.25421297550201416, - 0.026272129267454147, - -0.2019176185131073 + -0.2563841938972473, + 0.02688695304095745, + -0.19615121185779572 ], "tsne3d": [ - -42.84382247924805, - 8.464853286743164, - -12.356544494628906 + -31.695594787597656, + -30.781803131103516, + -21.000810623168945 ], "umap3d": [ - 1.442297339439392, - 1.3040928840637207, - -0.5009323954582214 + 0.9137707352638245, + 0.8157672882080078, + 3.4566540718078613 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 21, - "label": "Protein RNA" + "id": 12, + "label": "Protein Language" } ], "_embedding": [ @@ -240071,47 +240254,47 @@ ], "projections": { "pca": [ - -0.19320078194141388, - -0.177438423037529 + -0.19211840629577637, + -0.1747511774301529 ], "tsne": [ - -0.9718664288520813, - -12.828821182250977 + -11.09018325805664, + -6.1150078773498535 ], "umap": [ - -0.9651395082473755, - 4.619123458862305 + -1.0482628345489502, + 5.538854598999023 ], "pca3d": [ - -0.1932002454996109, - -0.17744065821170807, - 0.011416135355830193 + -0.19211772084236145, + -0.17475241422653198, + 0.01575210876762867 ], "tsne3d": [ - -17.473312377929688, - -26.274856567382812, - 23.626243591308594 + -10.94598388671875, + -27.8034725189209, + -1.172843098640442 ], "umap3d": [ - 2.878138780593872, - 0.5995304584503174, - -1.3128948211669922 + 1.4589157104492188, + 0.1707608997821808, + 4.689580917358398 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 2, - "label": "AI Drug Discovery" + "id": 9, + "label": "Drug Discovery" }, { - "id": 13, - "label": "Drug Discovery" + "id": 28, + "label": "Spatial Drug" } ], "_embedding": [ @@ -240526,46 +240709,46 @@ "doi": "10.1364/BOE.541570", "projections": { "pca": [ - -0.08118949830532074, - -0.1492408812046051 + -0.07853177934885025, + -0.14809063076972961 ], "tsne": [ - -9.411015510559082, - -13.911169052124023 + -18.57366180419922, + -4.760775089263916 ], "umap": [ - -1.7091217041015625, - 6.021597862243652 + -2.2391061782836914, + 4.925929546356201 ], "pca3d": [ - -0.0811891257762909, - -0.14923889935016632, - 0.07273845374584198 + -0.078531414270401, + -0.14808784425258636, + 0.07741893827915192 ], "tsne3d": [ - -14.467326164245605, - -43.526180267333984, - 0.3980428874492645 + -16.18198013305664, + -13.284307479858398, + 22.26197624206543 ], "umap3d": [ - 2.91157865524292, - -0.8333873748779297, - -1.168342113494873 + 0.5424373149871826, + 0.3040109872817993, + 5.791574954986572 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 24, + "id": 1, "label": "Cancer Data" } ], @@ -240972,47 +241155,47 @@ ], "projections": { "pca": [ - 0.1674758642911911, - -0.06315828859806061 + 0.16756218671798706, + -0.06330730020999908 ], "tsne": [ - 14.60484790802002, - -14.885391235351562 + 1.7027376890182495, + -15.093424797058105 ], "umap": [ - 0.5311527252197266, - 4.8861985206604 + -0.013464169576764107, + 4.850636959075928 ], "pca3d": [ - 0.16747668385505676, - -0.06313928961753845, - 0.09449030458927155 + 0.16756325960159302, + -0.0632912814617157, + 0.09110032767057419 ], "tsne3d": [ - -29.691301345825195, - 23.878097534179688, - 12.912860870361328 + 15.625556945800781, + -25.26820182800293, + -7.843945026397705 ], "umap3d": [ - 3.013601779937744, - 1.2736679315567017, - -0.011193133890628815 + 1.662683367729187, + 1.9183803796768188, + 4.934468746185303 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 4, - "label": "Com Area Amp" + "id": 9, + "label": "Drug Discovery" }, { - "id": 17, - "label": "Literature AI" + "id": 23, + "label": "Peer Review" } ], "_embedding": [ @@ -241427,46 +241610,46 @@ ], "projections": { "pca": [ - -0.17360612750053406, - -0.15119682252407074 + -0.17165786027908325, + -0.14864158630371094 ], "tsne": [ - -9.978789329528809, - -17.545927047729492 + -20.824310302734375, + -7.449681282043457 ], "umap": [ - -1.9451578855514526, - 5.877408504486084 + -1.9476438760757446, + 5.196696758270264 ], "pca3d": [ - -0.17360542714595795, - -0.1511966735124588, - 0.01907716505229473 + -0.17165742814540863, + -0.148641899228096, + 0.02455630525946617 ], "tsne3d": [ - -20.790340423583984, - -27.466983795166016, - -6.739796161651611 + -25.395925521850586, + -10.995800971984863, + 14.506994247436523 ], "umap3d": [ - 2.9474146366119385, - -0.7405499219894409, - -0.5611879825592041 + 0.6307004690170288, + 0.007560458034276962, + 5.694024085998535 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 24, + "id": 1, "label": "Cancer Data" } ], @@ -241875,47 +242058,47 @@ "abstract": "HealthBench is a new evaluation benchmark for AI in healthcare which evaluates models in realistic scenarios. Built with input from 250+ physicians, it aims to provide a shared standard for model performance and safety in health.", "projections": { "pca": [ - 0.022837918251752853, - -0.1051197424530983 + 0.027765914797782898, + -0.10130045562982559 ], "tsne": [ - -2.0420496463775635, - 1.1698333024978638 + -15.621871948242188, + 11.181750297546387 ], "umap": [ - 0.18754078447818756, - 6.727658271789551 + -0.9834140539169312, + 3.5087876319885254 ], "pca3d": [ - 0.02283765934407711, - -0.10511499643325806, - 0.27824166417121887 + 0.027765627950429916, + -0.10129433870315552, + 0.2811983823776245 ], "tsne3d": [ - 23.244483947753906, - -22.648544311523438, - 6.202073574066162 + 8.053766250610352, + -9.354986190795898, + 20.421586990356445 ], "umap3d": [ - 4.707745552062988, - -0.8973016142845154, - -1.6042894124984741 + 1.477013349533081, + 1.0053319931030273, + 7.238807678222656 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 6, - "label": "Clinical Health" + "id": 5, + "label": "Health Benchmarks" } ], "_embedding": [ @@ -242334,47 +242517,47 @@ ], "projections": { "pca": [ - -0.15246964991092682, - -0.03928331285715103 + -0.15228532254695892, + -0.037703707814216614 ], "tsne": [ - 0.007954799570143223, - -5.490548133850098 + -6.6824188232421875, + -3.3338468074798584 ], "umap": [ - -0.6467105150222778, - 4.7277445793151855 + -0.8434560298919678, + 5.137837886810303 ], "pca3d": [ - -0.15247008204460144, - -0.039296213537454605, - -0.03829719498753548 + -0.15228594839572906, + -0.03771716728806496, + -0.03470813110470772 ], "tsne3d": [ - -15.00227165222168, - -16.920345306396484, - 24.119962692260742 + -5.788400650024414, + -24.10932159423828, + -2.9519925117492676 ], "umap3d": [ - 3.5315632820129395, - 0.6003209948539734, - -1.0540870428085327 + 1.966123104095459, + 0.1297849714756012, + 4.933500289916992 ] }, "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_label": "Clinical AI Data", "cluster_levels": [ { "id": 2, - "label": "Bio Models Gene" + "label": "Clinical AI Data" }, { - "id": 2, - "label": "AI Drug Discovery" + "id": 9, + "label": "Drug Discovery" }, { - "id": 13, - "label": "Drug Discovery" + "id": 9, + "label": "Biomedical AI" } ], "_embedding": [ @@ -242783,46 +242966,46 @@ "title": "Muon: An optimizer for hidden layers in neural networks | Keller Jordan blog", "projections": { "pca": [ - -0.08288687467575073, - 0.2710046172142029 + -0.08120507001876831, + 0.27176496386528015 ], "tsne": [ - -26.944252014160156, - 20.679874420166016 + 2.253499984741211, + 24.250690460205078 ], "umap": [ - 2.2942495346069336, - 2.186173915863037 + 1.386755347251892, + 1.2879632711410522 ], "pca3d": [ - -0.08288776129484177, - 0.2709960341453552, - -0.05898358300328255 + -0.08120614290237427, + 0.2717573642730713, + -0.059372927993535995 ], "tsne3d": [ - -2.840214252471924, - 14.107417106628418, - -51.03903579711914 + -2.4258337020874023, + 12.963852882385254, + -38.20515441894531 ], "umap3d": [ - 6.864887237548828, - -1.1442112922668457, - 0.5584235787391663 + 4.033228874206543, + -1.2002341747283936, + 5.747969627380371 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 4, + "id": 26, "label": "World Models" } ], @@ -243229,47 +243412,47 @@ ], "projections": { "pca": [ - 0.23584480583667755, - -0.04652602598071098 + 0.2374962568283081, + -0.045459941029548645 ], "tsne": [ - 17.98853874206543, - 0.46941787004470825 + 10.826238632202148, + -4.230527877807617 ], "umap": [ - 2.081041097640991, - 5.906017780303955 + 1.6843336820602417, + 5.076474189758301 ], "pca3d": [ - 0.23584458231925964, - -0.04652366787195206, - 0.11288760602474213 + 0.23749612271785736, + -0.0454571507871151, + 0.1071564331650734 ], "tsne3d": [ - 10.914495468139648, - 9.67790699005127, - 20.459903717041016 + 25.573450088500977, + -2.206282377243042, + 12.849089622497559 ], "umap3d": [ - 3.375920534133911, - 0.40534529089927673, - 1.4605404138565063 + 2.918525457382202, + 2.1215062141418457, + 5.877621650695801 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 6, + "label": "Data Needs" }, { - "id": 26, - "label": "AI Information" + "id": 24, + "label": "SMB Models" } ], "_embedding": [ @@ -243677,47 +243860,47 @@ "abstract": "\ud83e\udde0 | Multimodal Integration of Oncology Data System - GitHub - lab-rasool/MINDS: \ud83e\udde0 | Multimodal Integration of Oncology Data System", "projections": { "pca": [ - 0.12499582022428513, - -0.14993160963058472 + 0.12682059407234192, + -0.148794025182724 ], "tsne": [ - 28.33743667602539, - 8.905330657958984 + 29.069887161254883, + -10.456881523132324 ], "umap": [ - 2.428640604019165, - 7.031891345977783 + 2.022947072982788, + 5.949418067932129 ], "pca3d": [ - 0.12499599158763885, - -0.14993666112422943, - 0.021452458575367928 + 0.12682081758975983, + -0.14879922568798065, + 0.02273000404238701 ], "tsne3d": [ - 20.713598251342773, - 54.943885803222656, - 13.830490112304688 + 46.40083694458008, + 10.881498336791992, + 23.609722137451172 ], "umap3d": [ - 2.9484522342681885, - -0.8610420227050781, - 0.1799154430627823 + 2.76485538482666, + 2.1538448333740234, + 6.83111047744751 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 25, - "label": "Python Data" + "id": 2, + "label": "Bioinformatics Tools" } ], "_embedding": [ @@ -244131,47 +244314,47 @@ ], "projections": { "pca": [ - -0.24145980179309845, - 0.0022968051489442587 + -0.24288897216320038, + 0.002685066545382142 ], "tsne": [ - -8.733297348022461, - -31.273517608642578 + -23.355554580688477, + -19.600597381591797 ], "umap": [ - -1.3766406774520874, - 2.8431921005249023 + -0.7436185479164124, + 7.066217422485352 ], "pca3d": [ - -0.241459459066391, - 0.0022941904608160257, - -0.13889797031879425 + -0.2428886443376541, + 0.0026810120325535536, + -0.13501112163066864 ], "tsne3d": [ - -44.53116989135742, - -25.84953498840332, - -24.63088607788086 + -36.892372131347656, + -30.845510482788086, + -9.779248237609863 ], "umap3d": [ - 1.5283783674240112, - 1.224648118019104, - -1.0268670320510864 + 0.6082406640052795, + 0.3704470992088318, + 3.7010700702667236 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 21, - "label": "Protein RNA" + "id": 20, + "label": "Cell Foundation" } ], "_embedding": [ @@ -244577,47 +244760,47 @@ ], "projections": { "pca": [ - -0.08566492050886154, - -0.1411084085702896 + -0.08749566972255707, + -0.1393141895532608 ], "tsne": [ - 7.8619561195373535, - -20.464237213134766 + -5.343305587768555, + -16.483665466308594 ], "umap": [ - -2.0395705699920654, - 4.572062969207764 + -2.628633737564087, + 6.420995712280273 ], "pca3d": [ - -0.08566439896821976, - -0.14110791683197021, - -0.0769028440117836 + -0.08749516308307648, + -0.1393146812915802, + -0.07304190844297409 ], "tsne3d": [ - -32.34193420410156, - -6.092344760894775, - 27.79237937927246 + -9.227704048156738, + -17.33261489868164, + -28.689739227294922 ], "umap3d": [ - 1.8352627754211426, - -0.2613556683063507, - -1.8546571731567383 + 0.14162032306194305, + 1.6626628637313843, + 4.986133575439453 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 1, + "label": "Cancer Data" }, { - "id": 2, - "label": "Genetic Disease" + "id": 27, + "label": "Genetic Variants" } ], "_embedding": [ @@ -245023,47 +245206,47 @@ ], "projections": { "pca": [ - -0.08566492050886154, - -0.1411084085702896 + -0.08749566972255707, + -0.1393141895532608 ], "tsne": [ - 7.862046718597412, - -20.464479446411133 + -5.3418755531311035, + -16.481945037841797 ], "umap": [ - -2.0740458965301514, - 4.559239864349365 + -2.6821229457855225, + 6.432106018066406 ], "pca3d": [ - -0.08566439896821976, - -0.14110791683197021, - -0.0769028440117836 + -0.08749516308307648, + -0.1393146812915802, + -0.07304190844297409 ], "tsne3d": [ - -30.810020446777344, - -1.6875226497650146, - 26.35645294189453 + -9.211771011352539, + -17.183828353881836, + -24.646541595458984 ], "umap3d": [ - 1.8688807487487793, - -0.30166521668434143, - -1.8787190914154053 + 0.12349763512611389, + 1.6639378070831299, + 4.965734958648682 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 1, + "label": "Cancer Data" }, { - "id": 2, - "label": "Genetic Disease" + "id": 27, + "label": "Genetic Variants" } ], "_embedding": [ @@ -245469,47 +245652,47 @@ ], "projections": { "pca": [ - -0.08566492050886154, - -0.1411084085702896 + -0.08749566972255707, + -0.1393141895532608 ], "tsne": [ - 7.862046718597412, - -20.464479446411133 + -5.342509746551514, + -16.48314666748047 ], "umap": [ - -2.05696439743042, - 4.5825700759887695 + -2.672938823699951, + 6.4170403480529785 ], "pca3d": [ - -0.08566439896821976, - -0.14110791683197021, - -0.0769028440117836 + -0.08749516308307648, + -0.1393146812915802, + -0.07304190844297409 ], "tsne3d": [ - -30.810020446777344, - -1.6875226497650146, - 26.35645294189453 + -10.938423156738281, + -14.205835342407227, + -26.773406982421875 ], "umap3d": [ - 1.8436893224716187, - -0.2731499969959259, - -1.8614163398742676 + 0.1788732409477234, + 1.6573600769042969, + 5.024969577789307 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 1, + "label": "Cancer Data" }, { - "id": 2, - "label": "Genetic Disease" + "id": 27, + "label": "Genetic Variants" } ], "_embedding": [ @@ -245918,47 +246101,47 @@ "abstract": "Org profile for Standard Model Biomedicine, Inc. on Hugging Face, the AI community building the future.", "projections": { "pca": [ - 0.05386574938893318, - -0.1199304610490799 + 0.054074231535196304, + -0.11815182864665985 ], "tsne": [ - 19.203609466552734, - 8.486560821533203 + 15.970799446105957, + 12.056568145751953 ], "umap": [ - 3.1066629886627197, - 5.818823337554932 + 3.9383580684661865, + 3.6364545822143555 ], "pca3d": [ - 0.05386573448777199, - -0.11993227899074554, - 0.09298492968082428 + 0.05407419428229332, + -0.1181541308760643, + 0.0949796512722969 ], "tsne3d": [ - 26.551496505737305, - -4.445894718170166, - 18.211366653442383 + 35.91763687133789, + -5.952513694763184, + 15.614931106567383 ], "umap3d": [ - 4.622398376464844, - 1.473807454109192, - 1.9400092363357544 + 4.0352983474731445, + 2.2878854274749756, + 4.897219181060791 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, - "label": "AI Agents" + "id": 8, + "label": "Open AI" }, { - "id": 20, - "label": "AI Agents" + "id": 7, + "label": "Hugging Face Models" } ], "_embedding": [ @@ -246369,47 +246552,47 @@ "abstract": "A Collection of EHR World Model variants, developed by Standard Model Biomedicine. Note that these are patient encoders, not generative models.", "projections": { "pca": [ - 0.08569694310426712, - -0.11610133200883865 + 0.08419037610292435, + -0.11577166616916656 ], "tsne": [ - 16.419408798217773, - 6.628256320953369 + 16.371593475341797, + -1.7229739427566528 ], "umap": [ - 2.569481611251831, - 6.21246862411499 + 2.045422077178955, + 4.949549674987793 ], "pca3d": [ - 0.08569707721471786, - -0.1161021739244461, - 0.0298052579164505 + 0.08419044315814972, + -0.11577318608760834, + 0.03353894129395485 ], "tsne3d": [ - 8.52813720703125, - 27.363914489746094, - 5.286116600036621 + 11.719980239868164, + 6.520261764526367, + 17.95146369934082 ], "umap3d": [ - 3.7618496417999268, - -0.09422553330659866, - 1.50846529006958 + 3.1428308486938477, + 1.715841293334961, + 6.2756500244140625 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 3, - "label": "SMB Data" + "id": 24, + "label": "SMB Models" } ], "_embedding": [ @@ -246817,47 +247000,47 @@ ], "projections": { "pca": [ - 0.18354937434196472, - -0.10944142192602158 + 0.18338410556316376, + -0.1085352674126625 ], "tsne": [ - 18.63875961303711, - 3.0415091514587402 + 18.491138458251953, + -4.660003185272217 ], "umap": [ - 2.636756181716919, - 6.153090476989746 + 2.1796367168426514, + 5.036770343780518 ], "pca3d": [ - 0.18354950845241547, - -0.10944823920726776, - -0.04587237536907196 + 0.18338419497013092, + -0.10854203253984451, + -0.04632044956088066 ], "tsne3d": [ - 14.644218444824219, - 17.269947052001953, - 17.415660858154297 + 20.05411148071289, + 3.2478535175323486, + 14.251850128173828 ], "umap3d": [ - 3.591642379760742, - 0.03323257341980934, - 1.6978371143341064 + 3.2170979976654053, + 2.0640475749969482, + 6.205141067504883 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 3, - "label": "SMB Data" + "id": 24, + "label": "SMB Models" } ], "_embedding": [ @@ -247265,47 +247448,47 @@ ], "projections": { "pca": [ - 0.18354937434196472, - -0.10944142192602158 + 0.18338410556316376, + -0.1085352674126625 ], "tsne": [ - 18.63863754272461, - 3.0413901805877686 + 18.49117660522461, + -4.659988880157471 ], "umap": [ - 2.649940013885498, - 6.111561298370361 + 2.2429237365722656, + 4.930813789367676 ], "pca3d": [ - 0.18354950845241547, - -0.10944823920726776, - -0.04587237536907196 + 0.18338419497013092, + -0.10854203253984451, + -0.04632044956088066 ], "tsne3d": [ - 10.758821487426758, - 18.69247817993164, - 16.859018325805664 + 21.090795516967773, + 5.069501876831055, + 11.543143272399902 ], "umap3d": [ - 3.5974478721618652, - 0.004819263704121113, - 1.7297768592834473 + 3.2470881938934326, + 2.0433359146118164, + 6.238978862762451 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 3, - "label": "SMB Data" + "id": 24, + "label": "SMB Models" } ], "_embedding": [ @@ -247726,46 +247909,46 @@ ], "projections": { "pca": [ - -0.26315149664878845, - -0.13932111859321594 + -0.2654609978199005, + -0.13855184614658356 ], "tsne": [ - 0.15239356458187103, - -25.684967041015625 + -14.272502899169922, + -18.103565216064453 ], "umap": [ - -2.2964978218078613, - 4.078354835510254 + -2.128140449523926, + 6.922111511230469 ], "pca3d": [ - -0.2631509006023407, - -0.13931366801261902, - -0.194743350148201 + -0.2654604911804199, + -0.13854466378688812, + -0.19025129079818726 ], "tsne3d": [ - -33.104488372802734, - -16.33371353149414, - -1.6113344430923462 + -26.460838317871094, + -8.71206283569336, + -0.47190597653388977 ], "umap3d": [ - 1.6637909412384033, - -0.002154221525415778, - -1.0903102159500122 + -0.0768035277724266, + 0.7050408124923706, + 4.631619930267334 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 22, + "id": 27, "label": "Genetic Variants" } ], @@ -248183,46 +248366,46 @@ ], "projections": { "pca": [ - -0.14542251825332642, - -0.07796339690685272 + -0.14327400922775269, + -0.07517077028751373 ], "tsne": [ - -10.193709373474121, - -5.2883381843566895 + -15.727654457092285, + 4.612339973449707 ], "umap": [ - -0.23918132483959198, - 6.699917793273926 + -2.053929090499878, + 3.555208206176758 ], "pca3d": [ - -0.14542168378829956, - -0.0779508650302887, - 0.1280103176832199 + -0.1432732194662094, + -0.0751594752073288, + 0.13385164737701416 ], "tsne3d": [ - -3.839749574661255, - -18.96013069152832, - 3.4563963413238525 + -7.274370193481445, + -8.600462913513184, + 16.538509368896484 ], "umap3d": [ - 4.219224452972412, - -1.1278440952301025, - -1.9871858358383179 + 0.9916744232177734, + 1.1373788118362427, + 7.169694900512695 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, + "id": 14, "label": "Clinical Health" } ], @@ -248640,47 +248823,47 @@ ], "projections": { "pca": [ - -0.15188045799732208, - 0.09174060821533203 + -0.15175698697566986, + 0.09281406551599503 ], "tsne": [ - -8.069347381591797, - 2.874645709991455 + -12.283037185668945, + 9.578827857971191 ], "umap": [ - 0.713306725025177, - 6.253571033477783 + -0.13658231496810913, + 3.3029327392578125 ], "pca3d": [ - -0.1518801748752594, - 0.09174419194459915, - 0.023665986955165863 + -0.15175671875476837, + 0.09281617403030396, + 0.027287589386105537 ], "tsne3d": [ - 1.486749529838562, - -14.71091079711914, - -2.7720224857330322 + 4.394430637359619, + 0.8487816452980042, + -2.0947020053863525 ], "umap3d": [ - 5.353687286376953, - -0.8284794092178345, - -0.8958761692047119 + 1.975935697555542, + 0.9112918376922607, + 6.885439395904541 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { "id": 0, - "label": "Clinical LLMs" + "label": "Multimodal Models" } ], "_embedding": [ @@ -249104,47 +249287,47 @@ ], "projections": { "pca": [ - -0.09731069207191467, - -0.07436179369688034 + -0.09483328461647034, + -0.07172869890928268 ], "tsne": [ - -9.165434837341309, - 2.269178628921509 + -17.47907066345215, + 4.607950210571289 ], "umap": [ - 0.7595071196556091, - 6.415838718414307 + -0.4335935413837433, + 3.5119752883911133 ], "pca3d": [ - -0.09730925410985947, - -0.07435266673564911, - 0.15375468134880066 + -0.09483206272125244, + -0.07172141224145889, + 0.1589566171169281 ], "tsne3d": [ - -3.592778205871582, - -17.402835845947266, - -3.83929705619812 + 5.870253562927246, + -3.3210487365722656, + 0.31091248989105225 ], "umap3d": [ - 4.910658836364746, - -0.9482282400131226, - -1.3932017087936401 + 1.5471763610839844, + 1.2668722867965698, + 6.906746864318848 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 0, - "label": "Clinical LLMs" + "id": 5, + "label": "Health Benchmarks" } ], "_embedding": [ @@ -249555,47 +249738,47 @@ "journal": "Nature Communications", "projections": { "pca": [ - -0.05591008439660072, - -0.013556201942265034 + -0.0550059974193573, + -0.01140409056097269 ], "tsne": [ - 16.548158645629883, - -2.77247953414917 + 8.106456756591797, + -6.036503791809082 ], "umap": [ - 0.9019086956977844, - 6.3837971687316895 + -0.4240802824497223, + 3.521510124206543 ], "pca3d": [ - -0.05590960010886192, - -0.013555224053561687, - 0.0581640787422657 + -0.05500559136271477, + -0.011403794400393963, + 0.06152306869626045 ], "tsne3d": [ - 10.725105285644531, - -2.8570873737335205, - 9.16783618927002 + 9.781882286071777, + -1.014562964439392, + 2.6977486610412598 ], "umap3d": [ - 5.066905975341797, - -0.9385402798652649, - -1.3067704439163208 + 1.7481281757354736, + 1.3271281719207764, + 6.880212306976318 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 0, - "label": "Clinical LLMs" + "id": 5, + "label": "Health Benchmarks" } ], "_embedding": [ @@ -250006,47 +250189,47 @@ "journal": "Nature", "projections": { "pca": [ - 0.11270005255937576, - -0.019483447074890137 + 0.11373482644557953, + -0.017925776541233063 ], "tsne": [ - 16.73430824279785, - -2.4061741828918457 + 8.512051582336426, + -5.889011859893799 ], "umap": [ - 1.118423342704773, - 6.3157734870910645 + -0.28487464785575867, + 3.5707225799560547 ], "pca3d": [ - 0.11269998550415039, - -0.019486967474222183, - 0.10770589858293533 + 0.11373481154441833, + -0.017929930239915848, + 0.10797534883022308 ], "tsne3d": [ - 15.226621627807617, - -1.365283727645874, - 11.004079818725586 + 13.731831550598145, + -2.183180570602417, + 3.3604700565338135 ], "umap3d": [ - 5.151039123535156, - -0.8913995623588562, - -1.231125831604004 + 1.8871469497680664, + 1.3847867250442505, + 6.81667947769165 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 4, - "label": "Com Area Amp" + "id": 2, + "label": "Medical Models" }, { - "id": 0, - "label": "Clinical LLMs" + "id": 5, + "label": "Health Benchmarks" } ], "_embedding": [ @@ -250457,47 +250640,47 @@ "journal": "Nature", "projections": { "pca": [ - 0.030734967440366745, - -0.029155300930142403 + 0.031997572630643845, + -0.027297986671328545 ], "tsne": [ - 17.002803802490234, - -2.6964807510375977 + 8.496072769165039, + -6.26836633682251 ], "umap": [ - 1.0466668605804443, - 6.29378080368042 + -0.364112913608551, + 3.550016403198242 ], "pca3d": [ - 0.030735336244106293, - -0.029150834307074547, - 0.08859453350305557 + 0.03199787810444832, + -0.02729322575032711, + 0.0906657725572586 ], "tsne3d": [ - 10.838361740112305, - -1.0942096710205078, - 14.291509628295898 + 12.908050537109375, + 1.4971498250961304, + 0.785175085067749 ], "umap3d": [ - 5.1534552574157715, - -0.9618768095970154, - -1.2785924673080444 + 1.8307064771652222, + 1.369744896888733, + 6.811748027801514 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 4, - "label": "Com Area Amp" + "id": 2, + "label": "Medical Models" }, { - "id": 0, - "label": "Clinical LLMs" + "id": 5, + "label": "Health Benchmarks" } ], "_embedding": [ @@ -250918,47 +251101,47 @@ "doi": "10.1016/j.jbi.2020.103637", "projections": { "pca": [ - -0.045668087899684906, - 0.0028728495817631483 + -0.04484514892101288, + 0.0038723042234778404 ], "tsne": [ - -6.647653102874756, - 2.4463682174682617 + -12.829147338867188, + 8.822702407836914 ], "umap": [ - 0.7831214666366577, - 6.507475852966309 + -0.4283396899700165, + 3.59474778175354 ], "pca3d": [ - -0.045668940991163254, - 0.0028605556581169367, - 0.14534524083137512 + -0.04484600946307182, + 0.0038599828258156776, + 0.14958618581295013 ], "tsne3d": [ - 11.868708610534668, - -8.981356620788574, - 8.245200157165527 + 6.809122085571289, + -3.2788784503936768, + 6.637289524078369 ], "umap3d": [ - 5.0348429679870605, - -0.8163781762123108, - -1.301042079925537 + 1.7972335815429688, + 1.1506587266921997, + 7.069467544555664 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 0, - "label": "Clinical LLMs" + "id": 5, + "label": "Health Benchmarks" } ], "_embedding": [ @@ -251371,47 +251554,47 @@ "doi": "10.1016/s2589-7500(24)00220-6", "projections": { "pca": [ - 0.21473973989486694, - -0.011614187620580196 + 0.2133696973323822, + -0.01106706727296114 ], "tsne": [ - 16.970783233642578, - -1.7806092500686646 + 9.171123504638672, + -5.73007869720459 ], "umap": [ - 1.6210435628890991, - 6.126091003417969 + 1.8024876117706299, + 4.792649745941162 ], "pca3d": [ - 0.21473954617977142, - -0.011616209521889687, - 0.02802262455224991 + 0.21336963772773743, + -0.011069120839238167, + 0.025601539760828018 ], "tsne3d": [ - 14.610004425048828, - 3.3894999027252197, - 13.890247344970703 + 16.165889739990234, + 1.504133939743042, + 4.355258464813232 ], "umap3d": [ - 3.862017869949341, - 0.33159443736076355, - 1.4040186405181885 + 2.623396635055542, + 1.7746633291244507, + 6.3466010093688965 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 4, - "label": "Com Area Amp" + "label": "AI Agents" }, { - "id": 26, - "label": "AI Information" + "id": 24, + "label": "SMB Models" } ], "_embedding": [ @@ -251819,47 +252002,47 @@ "abstract": "A minimal, ML-oriented data standard for medical record data to improve reproducibility, robustness, and computational performance. - Medical Event Data Standard", "projections": { "pca": [ - 0.10965663939714432, - -0.24751318991184235 + 0.11141757667064667, + -0.24556347727775574 ], "tsne": [ - 30.036701202392578, - -6.845835208892822 + 13.49210262298584, + -22.99827003479004 ], "umap": [ - 1.429588794708252, - 7.202818393707275 + 1.425554633140564, + 6.145063877105713 ], "pca3d": [ - 0.10965639352798462, - -0.24752484261989594, - 0.15414433181285858 + 0.11141736805438995, + -0.24557369947433472, + 0.15709103643894196 ], "tsne3d": [ - 4.589186668395996, - -3.6319994926452637, - 44.94101333618164 + 7.101908206939697, + 2.1944706439971924, + 42.12821578979492 ], "umap3d": [ - 3.131347894668579, - -1.4383392333984375, - 0.9988777041435242 + 1.8078426122665405, + 1.8380815982818604, + 7.380505561828613 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 6, + "label": "Data Needs" }, { - "id": 10, - "label": "Medical Data" + "id": 25, + "label": "Data Standards" } ], "_embedding": [ @@ -252273,46 +252456,46 @@ ], "projections": { "pca": [ - -0.15307283401489258, - 0.046023622155189514 + -0.15114940702915192, + 0.04852218180894852 ], "tsne": [ - -16.953617095947266, - -1.0388351678848267 + -8.886354446411133, + 10.77390193939209 ], "umap": [ - 0.20813187956809998, - 7.326508045196533 + -0.7094138264656067, + 2.78503155708313 ], "pca3d": [ - -0.15307432413101196, - 0.045989640057086945, - 0.11729145795106888 + -0.15115085244178772, + 0.04849078878760338, + 0.12420434504747391 ], "tsne3d": [ - -4.412203788757324, - -5.25615119934082, - -12.147480964660645 + -7.199375629425049, + 9.15947151184082, + 16.887483596801758 ], "umap3d": [ - 4.690652847290039, - -1.2093943357467651, - -0.6757896542549133 + 2.050779104232788, + -0.0747922733426094, + 6.903204441070557 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 11, + "id": 19, "label": "Medical Imaging" } ], @@ -252753,47 +252936,47 @@ ], "projections": { "pca": [ - -0.0965455174446106, - 0.2595441937446594 + -0.09468647092580795, + 0.26202309131622314 ], "tsne": [ - -16.610332489013672, - 13.578608512878418 + 0.019410504028201103, + 21.173715591430664 ], "umap": [ - 1.6869759559631348, - 2.2192676067352295 + 0.7543497681617737, + 1.11423659324646 ], "pca3d": [ - -0.09654517471790314, - 0.2595541775226593, - 0.024640552699565887 + -0.09468603134155273, + 0.26203250885009766, + 0.026199324056506157 ], "tsne3d": [ - -6.090272426605225, - 1.2552565336227417, - -43.898773193359375 + 12.402748107910156, + 2.155412435531616, + -14.187015533447266 ], "umap3d": [ - 6.711268901824951, - -0.9454663395881653, - -0.058152392506599426 + 3.9665796756744385, + -1.5245740413665771, + 6.169637680053711 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 4, - "label": "World Models" + "id": 8, + "label": "JEPA Video Models" } ], "_embedding": [ @@ -253201,47 +253384,47 @@ "abstract": "Renderer for the harmony response format to be used with gpt-oss - openai/harmony", "projections": { "pca": [ - 0.18896465003490448, - 0.11437319219112396 + 0.18904119729995728, + 0.11520985513925552 ], "tsne": [ - 15.70722484588623, - 13.644820213317871 + 20.822324752807617, + 1.555202603340149 ], "umap": [ - 3.4643092155456543, - 5.611389636993408 + 2.583247184753418, + 4.506282806396484 ], "pca3d": [ - 0.1889646053314209, - 0.11436990648508072, - -0.016298439353704453 + 0.1890411674976349, + 0.11520735919475555, + -0.017795683816075325 ], "tsne3d": [ - 23.85927391052246, - 18.109241485595703, - 2.6361403465270996 + 15.967437744140625, + 18.01380729675293, + 12.569009780883789 ], "umap3d": [ - 4.300704002380371, - 0.4621901214122772, - 1.7361810207366943 + 3.841231346130371, + 1.7932186126708984, + 5.774383544921875 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 20, - "label": "AI Agents" + "id": 4, + "label": "Open Agent Tools" } ], "_embedding": [ @@ -253650,47 +253833,47 @@ "abstract": "GitHub is where people build software. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects.", "projections": { "pca": [ - 0.2615725100040436, - -0.009787307120859623 + 0.2609241008758545, + -0.009313109330832958 ], "tsne": [ - 23.696308135986328, - 11.897587776184082 + 27.572481155395508, + -5.079628944396973 ], "umap": [ - 3.4174728393554688, - 6.649024486541748 + 2.9223976135253906, + 5.316539764404297 ], "pca3d": [ - 0.2615727484226227, - -0.009794185869395733, - -0.12215163558721542 + 0.2609245479106903, + -0.009320386685431004, + -0.12354165315628052 ], "tsne3d": [ - 23.91916847229004, - 38.92234420776367, - 22.33153533935547 + 37.791656494140625, + 11.207514762878418, + 6.84650993347168 ], "umap3d": [ - 3.8002614974975586, - -0.19942183792591095, - 2.5151829719543457 + 3.8816003799438477, + 2.4420032501220703, + 6.659940242767334 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 18, - "label": "Github Tools" + "id": 17, + "label": "GitHub Creation" } ], "_embedding": [ @@ -254098,47 +254281,47 @@ "journal": "Nature", "projections": { "pca": [ - -0.16320055723190308, - -0.10334061831235886 + -0.16649457812309265, + -0.10406716167926788 ], "tsne": [ - -1.7692776918411255, - -24.600536346435547 + -17.02011489868164, + -17.145641326904297 ], "umap": [ - -1.3173447847366333, - 4.415247440338135 + -1.3511956930160522, + 6.137810707092285 ], "pca3d": [ - -0.16320070624351501, - -0.10334984213113785, - -0.150907501578331 + -0.166494682431221, + -0.10407577455043793, + -0.14693453907966614 ], "tsne3d": [ - -37.17568588256836, - -24.818302154541016, - 1.4816814661026 + -35.808189392089844, + -10.04074478149414, + -1.609749674797058 ], "umap3d": [ - 2.291351795196533, - 0.026815049350261688, - -0.4353214204311371 + 0.6067630052566528, + 0.5287286639213562, + 4.65342903137207 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 2, - "label": "AI Drug Discovery" + "id": 7, + "label": "Cell Genes" }, { - "id": 2, - "label": "Genetic Disease" + "id": 28, + "label": "Spatial Drug" } ], "_embedding": [ @@ -254546,47 +254729,47 @@ "abstract": "This cookbook demonstrates how to build Agentic research workflows using the OpenAI Deep Research API and the OpenAI Agents SDK. It is a con", "projections": { "pca": [ - 0.17627356946468353, - 0.09632302075624466 + 0.1776961088180542, + 0.09783653169870377 ], "tsne": [ - 14.434420585632324, - 14.706560134887695 + 20.66450309753418, + 3.58689022064209 ], "umap": [ - 3.621068000793457, - 5.354030132293701 + 2.835381269454956, + 4.289407730102539 ], "pca3d": [ - 0.17627348005771637, - 0.09631003439426422, - 0.07698581367731094 + 0.1776961088180542, + 0.09782472252845764, + 0.074648916721344 ], "tsne3d": [ - 31.535385131835938, - 12.565994262695312, - 0.3500358462333679 + 13.32589054107666, + 20.444412231445312, + -1.473800778388977 ], "umap3d": [ - 4.455433368682861, - 0.6195490956306458, - 1.7728973627090454 + 3.9599006175994873, + 1.7052762508392334, + 5.581189155578613 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 20, - "label": "AI Agents" + "id": 4, + "label": "Open Agent Tools" } ], "_embedding": [ @@ -255008,46 +255191,46 @@ ], "projections": { "pca": [ - -0.08805285394191742, - -0.1631060093641281 + -0.08439670503139496, + -0.15972045063972473 ], "tsne": [ - -21.362092971801758, - -3.1066627502441406 + -5.956613063812256, + 9.998659133911133 ], "umap": [ - -0.12087633460760117, - 7.6245808601379395 + -1.3680299520492554, + 2.5554957389831543 ], "pca3d": [ - -0.08805256336927414, - -0.1631082445383072, - 0.19466367363929749 + -0.08439657837152481, + -0.15972259640693665, + 0.2009977251291275 ], "tsne3d": [ - -8.037537574768066, - -1.6136314868927002, - 4.218193054199219 + -9.704873085021973, + 1.977316975593567, + 30.96818733215332 ], "umap3d": [ - 4.318906307220459, - -1.7137089967727661, - -0.81197190284729 + 1.7019447088241577, + -0.3628618121147156, + 6.806445598602295 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 11, + "id": 19, "label": "Medical Imaging" } ], @@ -255469,47 +255652,47 @@ "doi": "10.1016/j.cell.2012.12.034", "projections": { "pca": [ - -0.0576203390955925, - -0.1408195048570633 + -0.06183025613427162, + -0.14211255311965942 ], "tsne": [ - 8.984482765197754, - -8.313177108764648 + 4.306764125823975, + -5.800860404968262 ], "umap": [ - 0.8780516982078552, - 4.801504135131836 + 0.3776609003543854, + 5.535072326660156 ], "pca3d": [ - -0.05762014165520668, - -0.14082859456539154, - -0.1497868448495865 + -0.061830051243305206, + -0.14212176203727722, + -0.14646883308887482 ], "tsne3d": [ - -9.263482093811035, - 16.20123863220215, - 19.883960723876953 + -21.23275375366211, + 11.150266647338867, + -14.770147323608398 ], "umap3d": [ - 2.6960179805755615, - 0.07953804731369019, - 0.36099082231521606 + 1.7050446271896362, + 1.1652363538742065, + 5.351364612579346 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 4, - "label": "Com Area Amp" + "id": 6, + "label": "Data Needs" }, { - "id": 17, - "label": "Literature AI" + "id": 16, + "label": "Lab Research" } ], "_embedding": [ @@ -255917,47 +256100,47 @@ "journal": "Nature Genetics", "projections": { "pca": [ - -0.084749735891819, - -0.11150380223989487 + -0.08903904259204865, + -0.11276144534349442 ], "tsne": [ - 9.00820255279541, - -8.172059059143066 + 4.298235893249512, + -5.684298515319824 ], "umap": [ - 1.1152573823928833, - 4.825695991516113 + 0.4293203055858612, + 5.530028820037842 ], "pca3d": [ - -0.08474956452846527, - -0.11151256412267685, - -0.17612726986408234 + -0.08903902769088745, + -0.11277022957801819, + -0.1732288897037506 ], "tsne3d": [ - -11.411698341369629, - 18.312070846557617, - 17.103734970092773 + -19.924354553222656, + 8.09678840637207, + -15.321737289428711 ], "umap3d": [ - 2.647742748260498, - 0.06306327879428864, - 0.30016443133354187 + 1.7711156606674194, + 1.2016650438308716, + 5.240046501159668 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 4, - "label": "Com Area Amp" + "id": 6, + "label": "Data Needs" }, { - "id": 23, - "label": "Research Authors" + "id": 16, + "label": "Lab Research" } ], "_embedding": [ @@ -256365,47 +256548,47 @@ "abstract": "Contribute to standardmodelbio/smb-vision development by creating an account on GitHub.", "projections": { "pca": [ - 0.1353234201669693, - 0.02663242630660534 + 0.13441544771194458, + 0.027189843356609344 ], "tsne": [ - 17.60148811340332, - 5.1152801513671875 + 18.839815139770508, + -2.411336898803711 ], "umap": [ - 2.9737069606781006, - 6.0163679122924805 + 2.3041491508483887, + 4.859439373016357 ], "pca3d": [ - 0.1353234350681305, - 0.026621371507644653, - -0.01985582895576954 + 0.1344154328107834, + 0.027179403230547905, + -0.0192116666585207 ], "tsne3d": [ - 15.919625282287598, - 22.0401611328125, - 10.867383003234863 + 18.316133499145508, + 11.946683883666992, + 10.931805610656738 ], "umap3d": [ - 3.7592391967773438, - 0.0663321316242218, - 1.7093424797058105 + 3.4582395553588867, + 1.8899506330490112, + 6.134520530700684 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 3, - "label": "SMB Data" + "id": 24, + "label": "SMB Models" } ], "_embedding": [ @@ -256813,47 +256996,47 @@ "abstract": "Contribute to standardmodelbio/smb-vision development by creating an account on GitHub.", "projections": { "pca": [ - 0.12665241956710815, - 0.029152797535061836 + 0.12561127543449402, + 0.029895728453993797 ], "tsne": [ - 17.48267936706543, - 5.119282245635986 + 18.793764114379883, + -2.311439037322998 ], "umap": [ - 2.7640342712402344, - 5.964250087738037 + 2.1927285194396973, + 4.780731201171875 ], "pca3d": [ - 0.12665224075317383, - 0.029141990467905998, - -0.01992155984044075 + 0.1256110668182373, + 0.029885856434702873, + -0.019658397883176804 ], "tsne3d": [ - 12.222397804260254, - 21.227813720703125, - 9.027375221252441 + 16.151479721069336, + 10.021692276000977, + 12.84042739868164 ], "umap3d": [ - 3.713991165161133, - 0.09700404852628708, - 1.5902384519577026 + 3.326188802719116, + 1.8274472951889038, + 6.075952053070068 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 4, + "label": "AI Agents" }, { - "id": 3, - "label": "SMB Data" + "id": 24, + "label": "SMB Models" } ], "_embedding": [ @@ -257272,47 +257455,47 @@ ], "projections": { "pca": [ - -0.2693300247192383, - 0.010550186038017273 + -0.27060067653656006, + 0.011977811343967915 ], "tsne": [ - -2.6792218685150146, - -25.53392791748047 + -18.202011108398438, + -18.166316986083984 ], "umap": [ - -1.5222409963607788, - 3.9704270362854004 + -1.419974446296692, + 6.582526683807373 ], "pca3d": [ - -0.26933014392852783, - 0.010541433468461037, - -0.1355522871017456 + -0.2706008553504944, + 0.01197004783898592, + -0.1294519007205963 ], "tsne3d": [ - -33.247127532958984, - -11.367613792419434, - -8.475285530090332 + -29.078699111938477, + -13.603666305541992, + -1.8398101329803467 ], "umap3d": [ - 1.894931435585022, - 0.368400514125824, - -0.9434491395950317 + 0.29236963391304016, + 0.48881372809410095, + 4.523294448852539 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 2, - "label": "Genetic Disease" + "id": 20, + "label": "Cell Foundation" } ], "_embedding": [ @@ -257720,47 +257903,47 @@ "abstract": "We\u2019re on a journey to advance and democratize artificial intelligence through open source and open science.", "projections": { "pca": [ - 0.223214253783226, - 0.1511976718902588 + 0.22285392880439758, + 0.15210814774036407 ], "tsne": [ - 15.541431427001953, - 15.448558807373047 + 21.868789672851562, + 3.1203737258911133 ], "umap": [ - 3.5895068645477295, - 5.473255157470703 + 2.7113964557647705, + 4.339563369750977 ], "pca3d": [ - 0.22321410477161407, - 0.1511886566877365, - 0.04362642019987106 + 0.22285372018814087, + 0.15209878981113434, + 0.04041228070855141 ], "tsne3d": [ - 30.780033111572266, - 12.532904624938965, - 6.608292102813721 + 15.357383728027344, + 19.47624969482422, + 3.1066765785217285 ], "umap3d": [ - 4.387640953063965, - 0.5717652440071106, - 1.852563738822937 + 3.9822449684143066, + 1.8823397159576416, + 5.706980228424072 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 20, - "label": "AI Agents" + "id": 4, + "label": "Open Agent Tools" } ], "_embedding": [ @@ -258168,47 +258351,47 @@ "abstract": "We\u2019re on a journey to advance and democratize artificial intelligence through open source and open science.", "projections": { "pca": [ - 0.2306191623210907, - 0.10481356084346771 + 0.23026110231876373, + 0.10541251301765442 ], "tsne": [ - 16.348726272583008, - 15.585498809814453 + 22.40622329711914, + 2.740034341812134 ], "umap": [ - 3.651150941848755, - 5.747355937957764 + 2.7951595783233643, + 4.41204309463501 ], "pca3d": [ - 0.23061925172805786, - 0.10480960458517075, - 0.04689313843846321 + 0.23026111721992493, + 0.10540744662284851, + 0.04350089654326439 ], "tsne3d": [ - 29.472829818725586, - 17.58905601501465, - 8.318635940551758 + 18.071950912475586, + 16.13195037841797, + 5.055406093597412 ], "umap3d": [ - 4.319581985473633, - 0.5479934215545654, - 1.9254682064056396 + 3.9700353145599365, + 1.938676118850708, + 5.791481971740723 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 20, - "label": "AI Agents" + "id": 4, + "label": "Open Agent Tools" } ], "_embedding": [ @@ -258616,47 +258799,47 @@ "abstract": "We\u2019re on a journey to advance and democratize artificial intelligence through open source and open science.", "projections": { "pca": [ - 0.2332112342119217, - 0.13691183924674988 + 0.232728973031044, + 0.13740603625774384 ], "tsne": [ - 15.809303283691406, - 15.486260414123535 + 22.016401290893555, + 2.86401629447937 ], "umap": [ - 3.561548948287964, - 5.464606761932373 + 2.72329044342041, + 4.458315372467041 ], "pca3d": [ - 0.23321104049682617, - 0.13690485060214996, - 0.041237689554691315 + 0.23272891342639923, + 0.13739824295043945, + 0.03752901777625084 ], "tsne3d": [ - 25.747791290283203, - 13.606002807617188, - 7.7008538246154785 + 14.965738296508789, + 18.86005401611328, + 7.324618816375732 ], "umap3d": [ - 4.329713821411133, - 0.6667802333831787, - 1.8463362455368042 + 3.8954854011535645, + 1.895251750946045, + 5.6235551834106445 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 20, - "label": "AI Agents" + "id": 4, + "label": "Open Agent Tools" } ], "_embedding": [ @@ -259064,47 +259247,47 @@ "abstract": "We\u2019re on a journey to advance and democratize artificial intelligence through open source and open science.", "projections": { "pca": [ - 0.2338877022266388, - 0.1364719718694687 + 0.2340318113565445, + 0.13779166340827942 ], "tsne": [ - 16.041423797607422, - 15.844915390014648 + 22.408212661743164, + 3.0288162231445312 ], "umap": [ - 3.530189275741577, - 5.648220062255859 + 2.787287473678589, + 4.447120666503906 ], "pca3d": [ - 0.23388786613941193, - 0.13646678626537323, - 0.044105470180511475 + 0.23403190076351166, + 0.1377856284379959, + 0.04022512584924698 ], "tsne3d": [ - 29.072711944580078, - 14.025894165039062, - 11.891589164733887 + 18.832935333251953, + 20.286888122558594, + 5.989960670471191 ], "umap3d": [ - 4.317072868347168, - 0.47254666686058044, - 2.017683267593384 + 3.920474052429199, + 2.05517578125, + 5.78783655166626 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 20, - "label": "AI Agents" + "id": 4, + "label": "Open Agent Tools" } ], "_embedding": [ @@ -259514,47 +259697,47 @@ "abstract": "Build resilient agents. Contribute to langchain-ai/langgraph development by creating an account on GitHub.", "projections": { "pca": [ - 0.15890872478485107, - 0.09217362850904465 + 0.1592721939086914, + 0.09284938871860504 ], "tsne": [ - 18.445711135864258, - 15.491371154785156 + 24.0494441986084, + 1.6883803606033325 ], "umap": [ - 3.6791512966156006, - 5.73162317276001 + 2.9094388484954834, + 4.605565071105957 ], "pca3d": [ - 0.15890833735466003, - 0.09215619415044785, - 0.01208356861025095 + 0.15927168726921082, + 0.09283341467380524, + 0.01178108248859644 ], "tsne3d": [ - 31.843097686767578, - 24.000919342041016, - 2.8508036136627197 + 22.74785041809082, + 19.486385345458984, + 0.471019446849823 ], "umap3d": [ - 4.2284064292907715, - 0.4391119182109833, - 2.075645923614502 + 3.9814646244049072, + 1.9647353887557983, + 5.798241138458252 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 20, - "label": "AI Agents" + "id": 4, + "label": "Open Agent Tools" } ], "_embedding": [ @@ -259964,47 +260147,47 @@ "abstract": "Open AI data scientist agent that automates complex data analysis tasks using the ReAct framework. Execute Python code locally or in the cloud, upload datasets, and generate detailed analytical reports with minimal setup. - togethercomputer/open-data-scientist", "projections": { "pca": [ - 0.16138340532779694, - 0.005555578973144293 + 0.16264574229717255, + 0.006705265957862139 ], "tsne": [ - 18.170705795288086, - 15.52973747253418 + 23.853641510009766, + 1.8258963823318481 ], "umap": [ - 3.674034833908081, - 5.595726013183594 + 2.88948130607605, + 4.569009780883789 ], "pca3d": [ - 0.16138312220573425, - 0.005542899947613478, - 0.04973568767309189 + 0.1626453995704651, + 0.006694150157272816, + 0.048011451959609985 ], "tsne3d": [ - 32.00861740112305, - 20.510847091674805, - -0.24051834642887115 + 20.013551712036133, + 20.454090118408203, + -2.123018980026245 ], "umap3d": [ - 4.269019603729248, - 0.5798973441123962, - 1.9524939060211182 + 3.9800143241882324, + 1.8656870126724243, + 5.6893415451049805 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 20, - "label": "AI Agents" + "id": 4, + "label": "Open Agent Tools" } ], "_embedding": [ @@ -260412,495 +260595,509 @@ ], "projections": { "pca": [ - 0.26040390133857727, - -0.22873200476169586 + 0.2513560652732849, + -0.22828440368175507 + ], + "tsne": [ + 10.658280372619629, + -17.907365798950195 + ], + "umap": [ + 1.4872220754623413, + 5.655119895935059 + ], + "pca3d": [ + 0.25135713815689087, + -0.2282692939043045, + 0.0752204954624176 + ], + "tsne3d": [ + 26.505664825439453, + -3.0918633937835693, + 31.231586456298828 + ], + "umap3d": [ + 2.0287842750549316, + 2.303778886795044, + 6.239681243896484 + ] + }, + "cluster_id": 3, + "cluster_label": "Open Source Tools", + "cluster_levels": [ + { + "id": 3, + "label": "Open Source Tools" + }, + { + "id": 6, + "label": "Data Needs" + }, + { + "id": 25, + "label": "Data Standards" + } + ], + "_embedding": [ + -0.0086, + -0.0849, + -0.0208, + -0.0668, + 0.0315, + -0.0377, + -0.0037, + -0.0081, + -0.039, + 0.0332, + 0.0262, + 0.0125, + -0.0073, + 0.0077, + 0.0232, + 0.0131, + -0.0593, + -0.04, + -0.0111, + 0.0769, + 0.0081, + -0.0056, + -0.0498, + -0.0368, + -0.0664, + 0.0757, + -0.0208, + -0.1075, + -0.0395, + -0.1533, + -0.0094, + -0.0539, + 0.0116, + 0.0015, + 0.0062, + -0.0266, + 0.0393, + 0.0193, + -0.0715, + 0.0266, + 0.0246, + 0.0039, + 0.0542, + 0.0638, + -0.0463, + -0.0247, + -0.0469, + 0.0131, + -0.0296, + 0.0361, + 0.0267, + 0.0024, + -0.0521, + 0.0363, + 0.0005, + 0.0664, + 0.0048, + 0.0479, + 0.0291, + 0.0237, + 0.0169, + -0.0046, + -0.0992, + 0.0913, + 0.0498, + 0.0833, + -0.0544, + -0.0685, + 0.0503, + -0.011, + 0.0119, + 0.0324, + -0.0025, + 0.0626, + 0.0662, + -0.0015, + -0.0293, + -0.0267, + 0.0139, + -0.0534, + -0.0523, + 0.0278, + 0.0295, + 0.0115, + -0.0346, + -0.0412, + 0.0015, + 0.0248, + -0.0526, + 0.0187, + -0.0229, + 0.0068, + 0.0364, + 0.0584, + -0.0325, + -0.0512, + 0.0109, + 0.0489, + -0.0134, + 0.3623, + -0.0977, + 0.0159, + -0.0307, + 0.0498, + -0.064, + -0.0135, + -0.0005, + -0.0244, + 0.0389, + 0.0511, + 0.0203, + -0.031, + -0.0525, + -0.0376, + 0.0062, + 0.0084, + -0.0119, + 0.0443, + 0.0121, + -0.0416, + 0.0006, + 0.0157, + 0.0397, + 0.033, + 0.0045, + 0.0679, + 0.023, + 0.0218, + 0.035, + 0.0083, + 0.0297, + -0.0139, + -0.074, + -0.0604, + -0.0023, + -0.0292, + 0.0153, + 0.0092, + 0.0479, + 0.0408, + -0.0051, + -0.0382, + 0.0363, + -0.014, + -0.0468, + 0.1142, + 0.0429, + 0.0646, + -0.0885, + -0.01, + 0.0518, + 0.0735, + -0.0244, + -0.0331, + -0.0145, + -0.0015, + 0.0204, + -0.0093, + -0.0191, + -0.0304, + 0.0054, + -0.0042, + 0.0614, + 0.1512, + -0.0187, + -0.1213, + -0.0008, + 0.0608, + 0.0669, + -0.0261, + 0.0283, + 0.0109, + -0.0292, + 0.0105, + -0.0223, + -0.0569, + -0.0012, + 0.0004, + -0.0262, + -0.0017, + 0.0225, + -0.0241, + -0.0178, + -0.0189, + -0.0486, + -0.0162, + -0.0306, + 0.0537, + 0.0183, + -0.0558, + -0.0455, + 0.0917, + -0.0496, + 0.0596, + -0.0311, + -0.0484, + -0.0072, + -0.0121, + -0.0446, + 0.0116, + -0.0038, + 0.0863, + -0.031, + 0.0756, + 0.02, + 0.0489, + 0.016, + -0.023, + 0.0328, + 0.0176, + -0.0308, + 0.0305, + -0.0321, + -0.0105, + -0.0442, + 0.0098, + -0.0198, + -0.021, + -0.0549, + 0.0457, + -0.0284, + 0.074, + -0.0116, + -0.3411, + 0.0038, + -0.0756, + 0.0144, + -0.0105, + 0.0303, + 0.0171, + 0.0362, + -0.0832, + 0.0618, + 0.1003, + -0.0297, + -0.0574, + -0.0272, + -0.085, + -0.0499, + 0.0101, + 0.0018, + -0.0742, + -0.0061, + 0.0161, + -0.0384, + -0.0014, + -0.0035, + 0.0607, + 0.0582, + 0.1012, + -0.0764, + 0.0227, + -0.0232, + 0.048, + 0.022, + -0.0404, + -0.1162, + -0.012, + -0.0532, + -0.053, + 0.0122, + -0.0307, + -0.05, + 0.0224, + 0.0003, + 0.0167, + -0.0389, + 0.0267, + -0.0103, + -0.0365, + -0.0057, + -0.0214, + 0.0228, + -0.0235, + 0.0102, + -0.0298, + 0.0578, + 0.0047, + 0.0508, + -0.0226, + 0.0407, + 0.0136, + 0.0682, + 0.0309, + 0.0498, + -0.0036, + -0.0453, + 0.0561, + -0.0489, + -0.046, + -0.0006, + 0.0381, + -0.0317, + -0.0135, + 0.0891, + -0.0379, + 0.03, + 0.0525, + 0.0555, + 0.007, + 0.0056, + -0.0114, + 0.0118, + 0.0117, + -0.0471, + -0.0154, + 0.0097, + 0.0497, + 0.0637, + -0.0152, + 0.0295, + -0.0332, + -0.0534, + -0.0074, + -0.0035, + -0.0627, + 0.0025, + 0.0133, + 0.0288, + -0.212, + 0.0219, + 0.0075, + 0.0315, + -0.0176, + -0.0208, + 0.0146, + -0.0053, + -0.0454, + -0.0515, + 0.0062, + 0.0878, + 0.0409, + -0.0122, + 0.0362, + 0.0432, + 0.0965, + 0.043, + -0.0339, + -0.0382, + 0.0863, + -0.0412, + 0.1332, + 0.0318, + -0.0267, + 0.045, + 0.0007, + 0.0686, + 0.0037, + 0.0324, + -0.0131, + 0.0051, + 0.02, + 0.0022, + 0.0048, + -0.0443, + 0.0214, + 0.03, + 0.0148, + 0.0179, + 0.0006, + -0.0599, + 0.0045, + 0.0204, + 0.0437, + 0.0102, + -0.01, + -0.0737, + -0.0355, + 0.0164, + 0.0038, + -0.0608, + -0.0117, + -0.0242, + 0.0177, + 0.0244, + 0.0181, + 0.0028, + 0.0011, + -0.0604, + -0.0401, + 0.0742, + -0.033, + 0.0441, + 0.0427 + ], + "title": "A Data Sharing Agreement Framework", + "authors": [ + "Vipin Swarup", + "Len Seligman", + "Arnon Rosenthal" + ], + "affiliations": [ + "Mitre (United States)" + ], + "year": 2006, + "journal": "Lecture notes in computer science", + "doi": "10.1007/11961635_2", + "cited_by_count": 23, + "openalex_url": "https://openalex.org/W1514960352" + }, + { + "channel": "project-big-beautiful-database", + "shared_by": "ryan", + "timestamp": "2026-05-13T18:32:42.216969+00:00", + "first_shared": "2026-05-13T18:32:42.216969+00:00", + "slack_url": "https://standardmodelbio.slack.com/archives/C091GD124L8/p1778697162216969", + "url": "https://data.projectdatasphere.org/documents/resources_data_user_agreement.pdf", + "text": "Hey guys. I applied for access to Project Data Sphere (). It has some older oncology clinical trial data--mostly control arm but also ~20 studies with both control and treatment arms. This would be a valuable resource as we think about applications. raised the question about bringing the data in house. Do we have governance set up in our AWS environment. The DUA states: \"You understand and agree that the Platform is for your personal use and that, except as permitted by Articles IV, VII and VIII, you are prohibited from disclosing any portion of the Data available through the Platform to, or otherwise sharing any portion of the Data with, any third party.\" So it sounds like anyone who wants to access the data should complete an application--and we'd want to ensure the data is only available to those who have", + "reactions_count": 1, + "reply_count": 2, + "reaction_details": { + "raised_hands": 1 + }, + "channels": [ + "project-big-beautiful-database" + ], + "projections": { + "pca": [ + 0.2612887918949127, + -0.22775031626224518 ], "tsne": [ - 24.050752639770508, - -0.9360953569412231 + 10.693028450012207, + -17.88092613220215 ], "umap": [ - 2.010429620742798, - 6.693185329437256 + 1.5026867389678955, + 5.627987861633301 ], "pca3d": [ - 0.2604052424430847, - -0.22871653735637665, - 0.0735284760594368 + 0.26128992438316345, + -0.2277364283800125, + 0.07208899408578873 ], "tsne3d": [ - 16.952390670776367, - 15.184197425842285, - 34.98450469970703 + 25.548288345336914, + -1.0453808307647705, + 28.855226516723633 ], "umap3d": [ - 2.765042304992676, - -0.2603970766067505, - 1.5006550550460815 + 2.156804323196411, + 2.4067482948303223, + 6.193238735198975 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ - { - "id": 1, - "label": "Open Agent Tools" - }, { "id": 3, - "label": "Github Data" - }, - { - "id": 25, - "label": "Python Data" - } - ], - "_embedding": [ - 0.0102, - -0.0663, - -0.0049, - -0.0792, - 0.0343, - -0.0243, - -0.0103, - -0.005, - -0.0407, - 0.0276, - 0.0338, - 0.0144, - -0.0145, - -0.0055, - 0.0154, - 0.0079, - -0.0525, - -0.0257, - 0.0073, - 0.0852, - -0.0088, - -0.0012, - -0.0438, - -0.0326, - -0.0639, - 0.0707, - -0.0142, - -0.0939, - -0.053, - -0.1775, - -0.0131, - -0.066, - 0.0189, - -0.0031, - -0.0043, - -0.0337, - 0.0303, - 0.0127, - -0.0469, - 0.0229, - 0.0347, - -0.0016, - 0.0327, - 0.056, - -0.0258, - -0.0308, - -0.0344, - 0.0082, - -0.0153, - 0.0453, - 0.0198, - 0.0158, - -0.0308, - 0.0394, - 0.0104, - 0.0417, - 0.0202, - 0.0486, - 0.0269, - 0.0156, - 0.017, - 0.0043, - -0.1063, - 0.08, - 0.0504, - 0.0864, - -0.0571, - -0.0588, - 0.0459, - -0.009, - -0.0097, - 0.018, - -0.0052, - 0.0758, - 0.0598, - 0.0031, - -0.0245, - -0.0271, - 0.0235, - -0.053, - -0.0361, - 0.0313, - 0.0226, - 0.0341, - -0.0426, - -0.0251, - -0.0233, - 0.0396, - -0.0333, - 0.0351, - -0.007, - 0.0059, - 0.0463, - 0.0642, - -0.047, - -0.0499, - -0.0024, - 0.0441, - -0.0218, - 0.4303, - -0.0793, - 0.0104, - -0.0386, - 0.0643, - -0.037, - -0.0238, - 0.0072, - -0.0054, - 0.0261, - 0.0331, - 0.029, - -0.0218, - -0.0377, - -0.0466, - -0.0026, - 0.008, - -0.008, - 0.0465, - -0.0055, - -0.0224, - -0.0007, - 0.0166, - 0.0455, - 0.013, - 0.0176, - 0.0338, - 0.0312, - 0.0344, - 0.0262, - 0.0087, - 0.0296, - -0.0087, - -0.0938, - -0.0422, - 0.0126, - -0.032, - 0.0123, - 0.0029, - 0.0375, - 0.0375, - -0.0137, - -0.0286, - 0.0248, - -0.0183, - -0.0484, - 0.102, - 0.0274, - 0.0519, - -0.062, - -0.0354, - 0.0316, - 0.0746, - -0.0275, - -0.0193, - -0.0008, - -0.0066, - 0.0075, - -0.0045, - -0.0245, - -0.0453, - -0.0046, - -0.0147, - 0.0452, - 0.1466, - -0.014, - -0.0888, - -0.0109, - 0.0602, - 0.0578, - -0.0134, - 0.0295, - 0.0004, - -0.0109, - 0.0177, - -0.0055, - -0.0698, - 0.0037, - 0.009, - -0.0236, - 0.0134, - 0.0307, - -0.0285, - -0.0076, - -0.0248, - -0.0621, - -0.0179, - -0.0311, - 0.0631, - -0.0065, - -0.0496, - -0.0672, - 0.0834, - -0.052, - 0.026, - -0.02, - -0.0588, - 0.0007, - -0.0152, - -0.0429, - -0.0137, - 0.0305, - 0.0556, - -0.0356, - 0.0897, - 0.0242, - 0.0561, - 0.024, - -0.0174, - 0.0575, - 0.0114, - -0.0445, - 0.0353, - -0.0038, - -0.0198, - -0.0315, - 0.0256, - -0.0156, - -0.0272, - -0.0357, - 0.0477, - -0.0206, - 0.0908, - -0.0179, - -0.3442, - -0.0105, - -0.0712, - 0.0036, - -0.0212, - 0.012, - 0.0173, - 0.0293, - -0.0604, - 0.0523, - 0.0927, - -0.0163, - -0.0514, - -0.0126, - -0.0819, - -0.0611, - 0.0159, - 0.0001, - -0.0701, - -0.001, - 0.01, - -0.0443, - 0.003, - -0.0066, - 0.0468, - 0.0521, - 0.1063, - -0.0624, - 0.0284, - -0.016, - 0.0345, - 0.0049, - -0.0234, - -0.1211, - -0.018, - -0.0448, - -0.0445, - 0.0345, - -0.0323, - -0.0401, - 0.0091, - 0.0047, - 0.0154, - -0.0321, - 0.0281, - -0.007, - -0.014, - 0.0126, - -0.0258, - -0.012, - -0.0328, - -0.0032, - -0.0201, - 0.0531, - -0.0045, - 0.0356, - -0.0296, - 0.0272, - 0.0109, - 0.0858, - 0.0248, - 0.0492, - -0.0096, - -0.0297, - 0.0337, - -0.0678, - -0.0494, - 0.0092, - 0.0453, - -0.0621, - -0.0175, - 0.0769, - -0.0306, - 0.0213, - 0.045, - 0.0389, - -0.0107, - -0.003, - 0.0001, - 0.008, - -0.0134, - -0.0474, - -0.007, - 0.007, - 0.0356, - 0.0492, - -0.0098, - 0.0006, - -0.0271, - -0.0458, - -0.0223, - 0.0038, - -0.0803, - 0.0094, - 0.03, - 0.0357, - -0.2428, - 0.0234, - 0.0121, - 0.0423, - 0.0014, - -0.0287, - 0.0071, - -0.0023, - -0.0312, - -0.0331, - 0.0057, - 0.0965, - 0.0328, - -0.0093, - 0.036, - 0.057, - 0.0949, - 0.0434, - -0.028, - -0.0397, - 0.0766, - -0.0297, - 0.1215, - 0.0198, - -0.0345, - 0.0555, - 0.0008, - 0.0676, - -0.0138, - 0.044, - 0.0026, - -0.0071, - 0.009, - 0.0085, - 0.0203, - -0.0541, - 0.0138, - 0.0318, - 0.0234, - 0.003, - 0.0129, - -0.0469, - -0.0088, - 0.0233, - 0.0204, - -0.0008, - -0.0164, - -0.0529, - -0.0254, - 0.0179, - 0.0072, - -0.0456, - -0.0235, - -0.0232, - 0.0138, - 0.0285, - 0.0102, - 0.0048, - 0.0146, - -0.0646, - -0.0296, - 0.0495, - -0.0052, - 0.0272, - 0.0399 - ] - }, - { - "channel": "project-big-beautiful-database", - "shared_by": "ryan", - "timestamp": "2026-05-13T18:32:42.216969+00:00", - "first_shared": "2026-05-13T18:32:42.216969+00:00", - "slack_url": "https://standardmodelbio.slack.com/archives/C091GD124L8/p1778697162216969", - "url": "https://data.projectdatasphere.org/documents/resources_data_user_agreement.pdf", - "text": "Hey guys. I applied for access to Project Data Sphere (). It has some older oncology clinical trial data--mostly control arm but also ~20 studies with both control and treatment arms. This would be a valuable resource as we think about applications. raised the question about bringing the data in house. Do we have governance set up in our AWS environment. The DUA states: \"You understand and agree that the Platform is for your personal use and that, except as permitted by Articles IV, VII and VIII, you are prohibited from disclosing any portion of the Data available through the Platform to, or otherwise sharing any portion of the Data with, any third party.\" So it sounds like anyone who wants to access the data should complete an application--and we'd want to ensure the data is only available to those who have", - "reactions_count": 1, - "reply_count": 2, - "reaction_details": { - "raised_hands": 1 - }, - "channels": [ - "project-big-beautiful-database" - ], - "projections": { - "pca": [ - 0.26040390133857727, - -0.22873200476169586 - ], - "tsne": [ - 24.050752639770508, - -0.9360953569412231 - ], - "umap": [ - 2.0539121627807617, - 6.7806267738342285 - ], - "pca3d": [ - 0.2604052424430847, - -0.22871653735637665, - 0.0735284760594368 - ], - "tsne3d": [ - 16.952390670776367, - 15.184197425842285, - 34.98450469970703 - ], - "umap3d": [ - 2.7658114433288574, - -0.2369544506072998, - 1.4964028596878052 - ] - }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", - "cluster_levels": [ - { - "id": 1, - "label": "Open Agent Tools" + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 6, + "label": "Data Needs" }, { "id": 25, - "label": "Python Data" + "label": "Data Standards" } ], "_embedding": [ @@ -261319,47 +261516,47 @@ ], "projections": { "pca": [ - -0.10574797540903091, - -0.107964888215065 + -0.1030779629945755, + -0.10506875813007355 ], "tsne": [ - -24.63392448425293, - -10.638276100158691 + -25.242935180664062, + 3.8163645267486572 ], "umap": [ - -1.0635541677474976, - 7.506362438201904 + -2.3919925689697266, + 2.825838804244995 ], "pca3d": [ - -0.10574829578399658, - -0.10797914117574692, - 0.1831144094467163 + -0.10307835042476654, + -0.10507999360561371, + 0.18772976100444794 ], "tsne3d": [ - -9.93028736114502, - -26.339574813842773, - -31.889219284057617 + -34.88056945800781, + 10.643630027770996, + 22.479745864868164 ], "umap3d": [ - 3.7075483798980713, - -1.8522939682006836, - -1.6855363845825195 + 1.0828520059585571, + -0.28398022055625916, + 7.8305864334106445 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 12, - "label": "ECG Models" + "id": 13, + "label": "ECG AI Models" } ], "_embedding": [ @@ -261767,47 +261964,47 @@ "abstract": "[NEJM AI 2025] An Electrocardiogram Foundation Model Built on over 10 Million Recordings - ECGFounder/csv/LVEF.csv at master \u00b7 PKUDigitalHealth/ECGFounder", "projections": { "pca": [ - 0.0025929429102689028, - -0.19966910779476166 + 0.004011731129139662, + -0.19937050342559814 ], "tsne": [ - -25.312252044677734, - -10.402497291564941 + -24.613473892211914, + 3.5182852745056152 ], "umap": [ - -1.0151023864746094, - 7.515493392944336 + -2.3381521701812744, + 2.8895418643951416 ], "pca3d": [ - 0.002592685166746378, - -0.1996840536594391, - 0.11038123071193695 + 0.004011404234915972, + -0.19938375055789948, + 0.11494600027799606 ], "tsne3d": [ - -12.551608085632324, - -29.773527145385742, - -34.43962860107422 + -38.92437744140625, + 10.065065383911133, + 21.584583282470703 ], "umap3d": [ - 3.690319299697876, - -1.8003604412078857, - -1.6900824308395386 + 1.0974822044372559, + -0.21084368228912354, + 7.856665134429932 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 12, - "label": "ECG Models" + "id": 13, + "label": "ECG AI Models" } ], "_embedding": [ @@ -262213,47 +262410,47 @@ ], "projections": { "pca": [ - 0.3113698661327362, - -0.137053981423378 + 0.3094311058521271, + -0.1375729888677597 ], "tsne": [ - 25.932003021240234, - -4.699375152587891 + 13.911681175231934, + -18.351734161376953 ], "umap": [ - 1.8390647172927856, - 7.651845932006836 + 0.9581683278083801, + 6.2205400466918945 ], "pca3d": [ - 0.31136977672576904, - -0.13705942034721375, - -0.12669637799263 + 0.3094310462474823, + -0.1375778466463089, + -0.12922737002372742 ], "tsne3d": [ - 8.703052520751953, - 13.244059562683105, - 43.00825119018555 + 14.148309707641602, + 4.257363319396973, + 33.188507080078125 ], "umap3d": [ - 2.191361904144287, - -0.43181610107421875, - 1.5477361679077148 + 1.8055369853973389, + 2.7199206352233887, + 6.106637477874756 ] }, "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { "id": 1, - "label": "Open Agent Tools" + "label": "Bioinformatics Models" }, { - "id": 3, - "label": "Github Data" + "id": 6, + "label": "Data Needs" }, { - "id": 10, - "label": "Medical Data" + "id": 25, + "label": "Data Standards" } ], "_embedding": [ @@ -262659,47 +262856,47 @@ ], "projections": { "pca": [ - 0.3113698661327362, - -0.137053981423378 + 0.3094311058521271, + -0.1375729888677597 ], "tsne": [ - 25.932003021240234, - -4.699375152587891 + 14.408160209655762, + -18.805049896240234 ], "umap": [ - 1.8419121503829956, - 7.690821647644043 + 0.9373185038566589, + 6.237232208251953 ], "pca3d": [ - 0.31136977672576904, - -0.13705942034721375, - -0.12669637799263 + 0.3094310462474823, + -0.1375778466463089, + -0.12922737002372742 ], "tsne3d": [ - 8.703052520751953, - 13.244059562683105, - 43.00825119018555 + 12.574893951416016, + 0.25804463028907776, + 31.32213592529297 ], "umap3d": [ - 2.1915500164031982, - -0.42156606912612915, - 1.5323222875595093 + 1.8178998231887817, + 2.723318576812744, + 6.131594657897949 ] }, "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { "id": 1, - "label": "Open Agent Tools" + "label": "Bioinformatics Models" }, { - "id": 3, - "label": "Github Data" + "id": 6, + "label": "Data Needs" }, { - "id": 10, - "label": "Medical Data" + "id": 25, + "label": "Data Standards" } ], "_embedding": [ @@ -263105,47 +263302,47 @@ ], "projections": { "pca": [ - 0.3113698661327362, - -0.137053981423378 + 0.3094311058521271, + -0.1375729888677597 ], "tsne": [ - 25.426532745361328, - -4.936324596405029 + 13.94180965423584, + -18.852642059326172 ], "umap": [ - 1.8571250438690186, - 7.673513412475586 + 0.9684473872184753, + 6.216691493988037 ], "pca3d": [ - 0.31136977672576904, - -0.13705942034721375, - -0.12669637799263 + 0.3094310462474823, + -0.1375778466463089, + -0.12922737002372742 ], "tsne3d": [ - 5.414345741271973, - 16.746238708496094, - 46.13040542602539 + 16.66474723815918, + 0.20255433022975922, + 33.70359420776367 ], "umap3d": [ - 2.171797037124634, - -0.4480500817298889, - 1.564159631729126 + 1.8137825727462769, + 2.707153081893921, + 6.115677356719971 ] }, "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { "id": 1, - "label": "Open Agent Tools" + "label": "Bioinformatics Models" }, { - "id": 3, - "label": "Github Data" + "id": 6, + "label": "Data Needs" }, { - "id": 10, - "label": "Medical Data" + "id": 25, + "label": "Data Standards" } ], "_embedding": [ @@ -263551,47 +263748,47 @@ ], "projections": { "pca": [ - 0.3113698661327362, - -0.137053981423378 + 0.3094311058521271, + -0.1375729888677597 ], "tsne": [ - 25.932003021240234, - -4.699375152587891 + 13.94180965423584, + -18.852642059326172 ], "umap": [ - 1.8401135206222534, - 7.659642696380615 + 0.9466188549995422, + 6.223644256591797 ], "pca3d": [ - 0.31136977672576904, - -0.13705942034721375, - -0.12669637799263 + 0.3094310462474823, + -0.1375778466463089, + -0.12922737002372742 ], "tsne3d": [ - 8.703052520751953, - 13.244059562683105, - 43.00825119018555 + 16.66474723815918, + 0.20255433022975922, + 33.70359420776367 ], "umap3d": [ - 2.186249017715454, - -0.42432278394699097, - 1.5589842796325684 + 1.8258191347122192, + 2.712071418762207, + 6.128132343292236 ] }, "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { "id": 1, - "label": "Open Agent Tools" + "label": "Bioinformatics Models" }, { - "id": 3, - "label": "Github Data" + "id": 6, + "label": "Data Needs" }, { - "id": 10, - "label": "Medical Data" + "id": 25, + "label": "Data Standards" } ], "_embedding": [ @@ -263997,47 +264194,47 @@ ], "projections": { "pca": [ - 0.3113698661327362, - -0.137053981423378 + 0.3094311058521271, + -0.1375729888677597 ], "tsne": [ - 25.43185043334961, - -4.416138172149658 + 14.253373146057129, + -18.3784122467041 ], "umap": [ - 1.859276294708252, - 7.673519134521484 + 0.9530037045478821, + 6.235297203063965 ], "pca3d": [ - 0.31136977672576904, - -0.13705942034721375, - -0.12669637799263 + 0.3094310462474823, + -0.1375778466463089, + -0.12922737002372742 ], "tsne3d": [ - 5.508009910583496, - 17.247249603271484, - 40.44511413574219 + 16.383697509765625, + 2.3750739097595215, + 29.463865280151367 ], "umap3d": [ - 2.1799843311309814, - -0.4289015233516693, - 1.548526644706726 + 1.8167369365692139, + 2.738496780395508, + 6.132290363311768 ] }, "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { "id": 1, - "label": "Open Agent Tools" + "label": "Bioinformatics Models" }, { - "id": 3, - "label": "Github Data" + "id": 6, + "label": "Data Needs" }, { - "id": 10, - "label": "Medical Data" + "id": 25, + "label": "Data Standards" } ], "_embedding": [ @@ -264446,46 +264643,46 @@ "abstract": "UK Biobank is well aware of the many recent AI-related developments in clinical research. UK Biobank considers that the use of AI...", "projections": { "pca": [ - 0.050413455814123154, - -0.09828905761241913 + 0.05332394689321518, + -0.09618581086397171 ], "tsne": [ - -1.9808034896850586, - -2.8558037281036377 + -1.6411787271499634, + 6.380080699920654 ], "umap": [ - -0.3565359115600586, - 6.4114556312561035 + -1.4094737768173218, + 3.530636787414551 ], "pca3d": [ - 0.050413262099027634, - -0.09829291701316833, - 0.2333223521709442 + 0.05332378298044205, + -0.09618715941905975, + 0.23498670756816864 ], "tsne3d": [ - 8.866925239562988, - -21.078006744384766, - 42.9720458984375 + -1.4181735515594482, + -6.977072715759277, + 38.608192443847656 ], "umap3d": [ - 3.929990768432617, - -0.5253769755363464, - -1.326252818107605 + 1.0679363012313843, + -0.03246127441525459, + 7.230957508087158 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, + "id": 14, "label": "Clinical Health" } ], @@ -264892,47 +265089,47 @@ ], "projections": { "pca": [ - 0.17261280119419098, - -0.16203781962394714 + 0.17258314788341522, + -0.16190560162067413 ], "tsne": [ - 18.1204891204834, - -7.89716911315918 + 6.438600540161133, + -19.412887573242188 ], "umap": [ - -1.611586570739746, - 5.264750957489014 + -2.034968852996826, + 5.746689319610596 ], "pca3d": [ - 0.172612726688385, - -0.16203369200229645, - -0.001968441531062126 + 0.17258316278457642, + -0.1619015336036682, + -0.002305334433913231 ], "tsne3d": [ - -21.98640251159668, - 21.8951416015625, - 37.70903015136719 + 13.325946807861328, + -13.562251091003418, + 34.89960861206055 ], "umap3d": [ - 2.219118356704712, - -0.4698355495929718, - -0.7368149161338806 + 0.8171266317367554, + 1.43641996383667, + 5.6023664474487305 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 1, "label": "Cancer Data" }, { - "id": 9, - "label": "Cancer Biobank" + "id": 1, + "label": "Cancer Data" } ], "_embedding": [ @@ -265339,47 +265536,47 @@ "title": "Automated bot check in progress", "projections": { "pca": [ - 0.17723533511161804, - 0.08400728553533554 + 0.1768130362033844, + 0.08388054370880127 ], "tsne": [ - 9.702744483947754, - 6.523534774780273 + 13.672239303588867, + -6.055520057678223 ], "umap": [ - 2.856454372406006, - 4.937951564788818 + 1.9903438091278076, + 4.06300687789917 ], "pca3d": [ - 0.17723460495471954, - 0.08400137722492218, - -0.04799004644155502 + 0.17681214213371277, + 0.08387500047683716, + -0.051303233951330185 ], "tsne3d": [ - 3.0615599155426025, - 37.6983528137207, - -12.842233657836914 + 30.05951690673828, + 5.484314441680908, + -15.78842544555664 ], "umap3d": [ - 4.691918849945068, - -0.26285114884376526, - 1.993354082107544 + 4.225529193878174, + 1.3743696212768555, + 6.511882305145264 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 5, - "label": "GPU Inference" + "id": 15, + "label": "PyTorch Models" } ], "_embedding": [ @@ -265787,47 +265984,47 @@ "abstract": "We\u2019re on a journey to advance and democratize artificial intelligence through open source and open science.", "projections": { "pca": [ - 0.16563493013381958, - 0.11504001170396805 + 0.1679190695285797, + 0.11631949245929718 ], "tsne": [ - 8.132729530334473, - 22.05342674255371 + 22.59711456298828, + 14.822893142700195 ], "umap": [ - 5.133968830108643, - 4.762076377868652 + 4.205001354217529, + 2.949188470840454 ], "pca3d": [ - 0.16563451290130615, - 0.11505039036273956, - 0.09453914314508438 + 0.16791853308677673, + 0.11632752418518066, + 0.09133859723806381 ], "tsne3d": [ - 43.932830810546875, - -15.310737609863281, - 7.266188621520996 + 1.8953784704208374, + 39.34053421020508, + 14.839337348937988 ], "umap3d": [ - 5.472170352935791, - 1.6822158098220825, - 2.001633882522583 + 4.91562032699585, + 2.2312071323394775, + 4.611334800720215 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { "id": 7, - "label": "Open AI" + "label": "Hugging Face Models" } ], "_embedding": [ @@ -266233,47 +266430,47 @@ ], "projections": { "pca": [ - 0.09586470574140549, - -0.07186028361320496 + 0.09478190541267395, + -0.07234502583742142 ], "tsne": [ - 19.681188583374023, - -20.8796443939209 + 1.2294690608978271, + -20.681547164916992 ], "umap": [ - 0.48833659291267395, - 4.568505764007568 + 0.025772353634238243, + 4.970685958862305 ], "pca3d": [ - 0.09586527198553085, - -0.071841299533844, - -0.07687131315469742 + 0.09478256851434708, + -0.07232832908630371, + -0.08139899373054504 ], "tsne3d": [ - -36.078060150146484, - 29.401153564453125, - 2.6134281158447266 + 3.319214105606079, + -39.23392868041992, + -11.4706449508667 ], "umap3d": [ - 2.8547003269195557, - 1.4097572565078735, - 0.07036274671554565 + 1.7134654521942139, + 1.8792256116867065, + 4.90150785446167 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 4, - "label": "Com Area Amp" + "id": 9, + "label": "Drug Discovery" }, { - "id": 17, - "label": "Literature AI" + "id": 23, + "label": "Peer Review" } ], "_embedding": [ @@ -266679,47 +266876,47 @@ ], "projections": { "pca": [ - 0.32742130756378174, - 0.03395402804017067 + 0.3270334303379059, + 0.03250433877110481 ], "tsne": [ - 15.581541061401367, - 0.2014780342578888 + 12.132140159606934, + -3.3717689514160156 ], "umap": [ - 2.123948574066162, - 5.517406940460205 + 1.5340416431427002, + 4.690042018890381 ], "pca3d": [ - 0.3274204730987549, - 0.03394721448421478, - 0.013295483775436878 + 0.3270327150821686, + 0.032500073313713074, + 0.006184982601553202 ], "tsne3d": [ - 2.098205327987671, - 31.551925659179688, - -6.596129894256592 + 33.52912139892578, + -11.64542293548584, + -0.9777458906173706 ], "umap3d": [ - 3.5385918617248535, - 0.5286722779273987, - 1.421484351158142 + 2.9494714736938477, + 1.8934102058410645, + 5.581387996673584 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 4, - "label": "Com Area Amp" + "id": 6, + "label": "Data Needs" }, { - "id": 26, - "label": "AI Information" + "id": 10, + "label": "Research Data" } ], "_embedding": [ @@ -267127,47 +267324,47 @@ "abstract": "Share your terminal over the web. Contribute to tsl0922/ttyd development by creating an account on GitHub.", "projections": { "pca": [ - 0.34903964400291443, - -0.020462580025196075 + 0.348087877035141, + -0.022201577201485634 ], "tsne": [ - 27.19835662841797, - 14.60137939453125 + 31.342761993408203, + -4.762588024139404 ], "umap": [ - 3.54038143157959, - 6.9786200523376465 + 3.1206510066986084, + 5.727980613708496 ], "pca3d": [ - 0.34904009103775024, - -0.02045322023332119, - -0.22178591787815094 + 0.34808844327926636, + -0.022194748744368553, + -0.22498822212219238 ], "tsne3d": [ - 28.00567054748535, - 37.081993103027344, - 28.53014373779297 + 42.86563491821289, + 22.126338958740234, + 14.89388656616211 ], "umap3d": [ - 3.6521387100219727, - -0.3363001048564911, - 2.7776942253112793 + 3.953368902206421, + 2.7062270641326904, + 6.81196403503418 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 18, - "label": "Github Tools" + "id": 17, + "label": "GitHub Creation" } ], "_embedding": [ @@ -267573,47 +267770,47 @@ ], "projections": { "pca": [ - 0.09586470574140549, - -0.07186028361320496 + 0.09478190541267395, + -0.07234502583742142 ], "tsne": [ - 19.681188583374023, - -20.8796443939209 + 1.2294424772262573, + -20.681594848632812 ], "umap": [ - 0.5332330465316772, - 4.582920074462891 + 0.0032093231566250324, + 4.912321090698242 ], "pca3d": [ - 0.09586527198553085, - -0.071841299533844, - -0.07687131315469742 + 0.09478256851434708, + -0.07232832908630371, + -0.08139899373054504 ], "tsne3d": [ - -36.078060150146484, - 29.401153564453125, - 2.6134281158447266 + 5.031013488769531, + -39.2407112121582, + -14.221747398376465 ], "umap3d": [ - 2.768307685852051, - 1.486767053604126, - 0.14852535724639893 + 1.7321840524673462, + 1.8695578575134277, + 4.880801677703857 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 4, - "label": "Com Area Amp" + "id": 9, + "label": "Drug Discovery" }, { - "id": 17, - "label": "Literature AI" + "id": 23, + "label": "Peer Review" } ], "_embedding": [ @@ -268021,47 +268218,47 @@ "abstract": "How can we have virtual machines with GPU cards to allow us run our deep learning models? Since the DNAnexus builds on top of AMS anyway,...", "projections": { "pca": [ - 0.16492028534412384, - 0.09454336017370224 + 0.16523350775241852, + 0.0944218561053276 ], "tsne": [ - 9.877625465393066, - 8.970001220703125 + 14.669519424438477, + -8.635151863098145 ], "umap": [ - 2.988990306854248, - 4.996937274932861 + 2.1296029090881348, + 4.219719409942627 ], "pca3d": [ - 0.16491897404193878, - 0.09452558308839798, - 0.006057403050363064 + 0.16523213684558868, + 0.09440785646438599, + 0.004834514576941729 ], "tsne3d": [ - 7.704294681549072, - 46.98390579223633, - -8.820439338684082 + 36.52619171142578, + 0.975407600402832, + -11.195893287658691 ], "umap3d": [ - 4.556095123291016, - -0.3469958007335663, - 1.9191656112670898 + 4.13596773147583, + 1.438292145729065, + 6.6440935134887695 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 5, - "label": "GPU Inference" + "id": 15, + "label": "PyTorch Models" } ], "_embedding": [ @@ -268469,47 +268666,47 @@ "abstract": "FEMR (Framework for Electronic Medical Records) provides tooling for large-scale, self-supervised learning using electronic health records - som-shahlab/femr", "projections": { "pca": [ - 0.04750283062458038, - -0.12840399146080017 + 0.04938508942723274, + -0.12633195519447327 ], "tsne": [ - -27.563251495361328, - -5.034473419189453 + -14.889466285705566, + 8.995745658874512 ], "umap": [ - 0.3356632888317108, - 6.866413116455078 + -1.8684320449829102, + 3.3210699558258057 ], "pca3d": [ - 0.04750223830342293, - -0.1284121423959732, - 0.07881039381027222 + 0.049384552985429764, + -0.12633861601352692, + 0.0828663557767868 ], "tsne3d": [ - -0.8274391889572144, - -2.9329288005828857, - 10.780023574829102 + 2.6054165363311768, + -10.576872825622559, + 27.79389190673828 ], "umap3d": [ - 4.584654808044434, - -1.269246220588684, - -1.7997392416000366 + 1.1654574871063232, + 1.0114121437072754, + 7.382146835327148 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, - "label": "Clinical Health" + "id": 13, + "label": "ECG AI Models" } ], "_embedding": [ @@ -268917,47 +269114,47 @@ "abstract": "A terminal-based Dicom/NRRD/Nifti image viewer built on textual and textual-image - apleynes/pydcmview", "projections": { "pca": [ - 0.19723628461360931, - -0.013416685163974762 + 0.2001371532678604, + -0.010817092843353748 ], "tsne": [ - 34.74960708618164, - 6.496923923492432 + 20.978626251220703, + -15.413176536560059 ], "umap": [ - 2.4212703704833984, - 6.779786586761475 + 2.0809779167175293, + 5.768959999084473 ], "pca3d": [ - 0.19723573327064514, - -0.013418457470834255, - -0.040401265025138855 + 0.20013660192489624, + -0.01081743836402893, + -0.040580760687589645 ], "tsne3d": [ - 9.920014381408691, - 36.6704216003418, - 40.51389694213867 + 20.417163848876953, + 16.99073600769043, + 30.22260093688965 ], "umap3d": [ - 3.7248778343200684, - -0.9897161722183228, - 1.6743561029434204 + 3.238816261291504, + 1.870672345161438, + 7.133784294128418 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 25, - "label": "Python Data" + "id": 2, + "label": "Bioinformatics Tools" } ], "_embedding": [ @@ -269367,47 +269564,47 @@ "abstract": "We\u2019re on a journey to advance and democratize artificial intelligence through open source and open science.", "projections": { "pca": [ - 0.07836461067199707, - 0.029551014304161072 + 0.08156439661979675, + 0.031791359186172485 ], "tsne": [ - 8.95654296875, - 18.56609344482422 + 20.099510192871094, + 14.333610534667969 ], "umap": [ - 5.034316062927246, - 4.659903049468994 + 4.0869011878967285, + 3.2002906799316406 ], "pca3d": [ - 0.07836432754993439, - 0.029550060629844666, - 0.1518254280090332 + 0.08156395703554153, + 0.031789809465408325, + 0.15135318040847778 ], "tsne3d": [ - 42.065956115722656, - -5.23142671585083, - 18.867673873901367 + -0.13575410842895508, + 32.10683059692383, + 13.927660942077637 ], "umap3d": [ - 5.16879415512085, - 1.754093885421753, - 2.1055643558502197 + 4.614564895629883, + 2.258913040161133, + 4.5490546226501465 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { "id": 7, - "label": "Open AI" + "label": "Hugging Face Models" } ], "_embedding": [ @@ -269813,47 +270010,47 @@ ], "projections": { "pca": [ - 0.1862333118915558, - 0.00021212549472693354 + 0.1874089390039444, + 0.0011393006425350904 ], "tsne": [ - 18.273279190063477, - -7.525484085083008 + 6.7373881340026855, + -19.17368507385254 ], "umap": [ - 2.5093178749084473, - 5.538497447967529 + 1.4572139978408813, + 5.071999549865723 ], "pca3d": [ - 0.18623322248458862, - 0.00021513660612981766, - 0.01001722738146782 + 0.18740878999233246, + 0.0011416348861530423, + 0.005812857765704393 ], "tsne3d": [ - -17.412559509277344, - 22.52907371520996, - 38.32437515258789 + 16.865236282348633, + -12.201570510864258, + 34.40431594848633 ], "umap3d": [ - 3.317917585372925, - 0.0328269824385643, - 0.9510225057601929 + 2.5680644512176514, + 1.9643678665161133, + 5.8245697021484375 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, - "label": "AI Agents" + "id": 6, + "label": "Data Needs" }, { - "id": 26, - "label": "AI Information" + "id": 10, + "label": "Research Data" } ], "_embedding": [ @@ -270261,47 +270458,47 @@ "abstract": "Python package to simplify access to the data available in NCI Imaging Data Commons - idc-index/idc_index/index.py at main \u00b7 ImagingDataCommons/idc-index", "projections": { "pca": [ - 0.2866429090499878, - -0.1556049883365631 + 0.28810423612594604, + -0.15448729693889618 ], "tsne": [ - 35.283016204833984, - 5.022707939147949 + 20.661962509155273, + -16.99492645263672 ], "umap": [ - 2.3732657432556152, - 6.808754920959473 + 2.0097811222076416, + 5.916604518890381 ], "pca3d": [ - 0.2866418957710266, - -0.1556183397769928, - 0.00920894742012024 + 0.2881034314632416, + -0.15449722111225128, + 0.009204929694533348 ], "tsne3d": [ - 4.6676506996154785, - 31.767139434814453, - 42.88395690917969 + 20.959758758544922, + 17.81500816345215, + 36.683738708496094 ], "umap3d": [ - 3.6360297203063965, - -1.0836100578308105, - 1.590358853340149 + 3.1028528213500977, + 1.8430262804031372, + 7.208789825439453 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 25, - "label": "Python Data" + "id": 2, + "label": "Bioinformatics Tools" } ], "_embedding": [ @@ -270709,47 +270906,47 @@ "abstract": "Browse the MTEB Leaderboard to see which language\u2011embedding models achieve the highest scores on many different tasks. You don\u2019t need to provide any input\u2014just pick a category and view the rankings...", "projections": { "pca": [ - 0.14941538870334625, - 0.06229967623949051 + 0.14846648275852203, + 0.06139242276549339 ], "tsne": [ - 7.507941722869873, - 14.683996200561523 + 16.405014038085938, + 15.750173568725586 ], "umap": [ - 2.299253225326538, - 3.9693520069122314 + 2.991741180419922, + 3.444230318069458 ], "pca3d": [ - 0.1494145393371582, - 0.06228741258382797, - -0.020104529336094856 + 0.14846572279930115, + 0.061380770057439804, + -0.02056591399013996 ], "tsne3d": [ - 44.91475296020508, - -18.47752571105957, - 24.98206901550293 + -10.558908462524414, + 32.886627197265625, + 19.421539306640625 ], "umap3d": [ - 5.318720817565918, - -0.28988683223724365, - 0.6271991729736328 + 3.6992998123168945, + 1.0214394330978394, + 5.925190448760986 ] }, "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_label": "Open Source Tools", "cluster_levels": [ { "id": 3, - "label": "AI Training Models" + "label": "Open Source Tools" }, { - "id": 5, - "label": "Model Training" + "id": 8, + "label": "Open AI" }, { - "id": 27, - "label": "Embeddings" + "id": 22, + "label": "AI Gemini" } ], "_embedding": [ @@ -271157,47 +271354,47 @@ "abstract": "DICOM to Nifti conversion with meta data preservation - moloney/dcmstack", "projections": { "pca": [ - 0.2269565314054489, - -0.027997534722089767 + 0.22718557715415955, + -0.028211981058120728 ], "tsne": [ - 33.171417236328125, - 7.135876655578613 + 22.570301055908203, + -14.873698234558105 ], "umap": [ - 2.7234034538269043, - 6.648313045501709 + 2.2702107429504395, + 5.6408209800720215 ], "pca3d": [ - 0.22695638239383698, - -0.027998825535178185, - -0.12070408463478088 + 0.22718529403209686, + -0.02821294777095318, + -0.12275365740060806 ], "tsne3d": [ - 13.199477195739746, - 29.286733627319336, - 38.55110549926758 + 23.867645263671875, + 22.703460693359375, + 31.37179183959961 ], "umap3d": [ - 3.6696882247924805, - -0.7693200707435608, - 1.9546787738800049 + 3.3906452655792236, + 1.9704924821853638, + 7.029563903808594 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 3, - "label": "SMB Data" + "id": 2, + "label": "Bioinformatics Tools" } ], "_embedding": [ @@ -271605,47 +271802,47 @@ "abstract": "Connect Supabase to your AI assistants. Contribute to supabase/mcp development by creating an account on GitHub.", "projections": { "pca": [ - 0.26530149579048157, - 0.020522357895970345 + 0.26468512415885925, + 0.020075660198926926 ], "tsne": [ - 24.404417037963867, - 13.950824737548828 + 28.929595947265625, + -6.042746543884277 ], "umap": [ - 3.661709785461426, - 6.573758125305176 + 3.239508628845215, + 5.371513366699219 ], "pca3d": [ - 0.26530107855796814, - 0.020517634227871895, - -0.11774726957082748 + 0.2646850049495697, + 0.020073072984814644, + -0.12041772156953812 ], "tsne3d": [ - 36.143577575683594, - 38.11057662963867, - 10.022111892700195 + 38.40909957885742, + 17.375415802001953, + 3.084336042404175 ], "umap3d": [ - 3.758639335632324, - 0.006266344804316759, - 2.7675020694732666 + 4.009731769561768, + 2.5306835174560547, + 6.4901323318481445 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 18, - "label": "Github Tools" + "id": 17, + "label": "GitHub Creation" } ], "_embedding": [ @@ -272053,47 +272250,47 @@ "abstract": "Contribute to icometrix/dicom2nifti development by creating an account on GitHub.", "projections": { "pca": [ - 0.30419012904167175, - -0.0038466225378215313 + 0.30377644300460815, + -0.004220965318381786 ], "tsne": [ - 32.57138442993164, - 7.28900671005249 + 22.722511291503906, + -14.245320320129395 ], "umap": [ - 2.9446427822113037, - 6.711715221405029 + 2.458402633666992, + 5.567061901092529 ], "pca3d": [ - 0.30419015884399414, - -0.0038464542012661695, - -0.15302908420562744 + 0.3037763833999634, + -0.004221200477331877, + -0.15605150163173676 ], "tsne3d": [ - 13.816473960876465, - 33.08124542236328, - 35.13185119628906 + 24.671310424804688, + 21.032020568847656, + 27.53977394104004 ], "umap3d": [ - 3.7024991512298584, - -0.6950531601905823, - 2.1075186729431152 + 3.5205326080322266, + 2.1217572689056396, + 7.015844345092773 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 18, - "label": "Github Tools" + "id": 2, + "label": "Bioinformatics Tools" } ], "_embedding": [ @@ -272501,47 +272698,47 @@ "abstract": "Contribute to icometrix/dicom2nifti development by creating an account on GitHub.", "projections": { "pca": [ - 0.2201797068119049, - -0.028465906158089638 + 0.21875883638858795, + -0.028840405866503716 ], "tsne": [ - 32.422386169433594, - 7.18065071105957 + 22.670207977294922, + -14.115413665771484 ], "umap": [ - 2.9072282314300537, - 6.618841171264648 + 2.258023738861084, + 5.567052841186523 ], "pca3d": [ - 0.22017954289913177, - -0.0284649059176445, - -0.18752865493297577 + 0.21875852346420288, + -0.028837235644459724, + -0.188630610704422 ], "tsne3d": [ - 10.865286827087402, - 31.401243209838867, - 32.69110870361328 + 28.047395706176758, + 20.86393928527832, + 28.08041000366211 ], "umap3d": [ - 3.766723394393921, - -0.7443756461143494, - 2.0568490028381348 + 3.5225515365600586, + 1.9318597316741943, + 7.014052867889404 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 3, - "label": "SMB Data" + "id": 2, + "label": "Bioinformatics Tools" } ], "_embedding": [ @@ -272951,47 +273148,47 @@ "abstract": "Python package to simplify access to the data available in NCI Imaging Data Commons - ImagingDataCommons/idc-index", "projections": { "pca": [ - 0.24904398620128632, - -0.18353478610515594 + 0.2518653869628906, + -0.18129760026931763 ], "tsne": [ - 35.22196578979492, - 4.95228385925293 + 20.55643081665039, + -16.9703369140625 ], "umap": [ - 2.2315452098846436, - 6.86457633972168 + 1.9949991703033447, + 5.967254638671875 ], "pca3d": [ - 0.2490435242652893, - -0.18354418873786926, - 0.03502693772315979 + 0.2518649697303772, + -0.18130382895469666, + 0.03634043037891388 ], "tsne3d": [ - 2.590461492538452, - 34.7459831237793, - 40.97350311279297 + 21.793100357055664, + 14.6058931350708, + 36.072723388671875 ], "umap3d": [ - 3.5562820434570312, - -1.129994511604309, - 1.5066817998886108 + 2.974774122238159, + 1.8624199628829956, + 7.259421348571777 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 25, - "label": "Python Data" + "id": 2, + "label": "Bioinformatics Tools" } ], "_embedding": [ @@ -273399,47 +273596,47 @@ "abstract": "We\u2019re on a journey to advance and democratize artificial intelligence through open source and open science.", "projections": { "pca": [ - 0.0798177495598793, - 0.03031456097960472 + 0.08170587569475174, + 0.03172402083873749 ], "tsne": [ - 9.142375946044922, - 17.179100036621094 + 18.80216407775879, + 14.285365104675293 ], "umap": [ - 4.744757652282715, - 4.560044288635254 + 3.955622434616089, + 3.3966689109802246 ], "pca3d": [ - 0.07981722056865692, - 0.030307522043585777, - 0.15345899760723114 + 0.0817052572965622, + 0.031717073172330856, + 0.15467968583106995 ], "tsne3d": [ - 36.35919952392578, - -7.313458442687988, - 22.1384334564209 + -2.0201046466827393, + 26.77547836303711, + 12.644430160522461 ], "umap3d": [ - 4.93049955368042, - 1.6165415048599243, - 2.003746271133423 + 4.457334041595459, + 2.225843906402588, + 4.686280250549316 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { "id": 7, - "label": "Open AI" + "label": "Hugging Face Models" } ], "_embedding": [ @@ -273847,46 +274044,46 @@ "abstract": "AACR Project GENIE is a public cancer registry of real-world data available for use in genomics projects to power precision medicine.", "projections": { "pca": [ - 0.10822991281747818, - -0.3117809593677521 + 0.10853210836648941, + -0.31069639325141907 ], "tsne": [ - 6.79610538482666, - -13.878776550292969 + -14.68386173248291, + -8.597827911376953 ], "umap": [ - -2.3268909454345703, - 6.388296604156494 + -2.583613634109497, + 5.160649299621582 ], "pca3d": [ - 0.10823005437850952, - -0.311786413192749, - 0.005694075487554073 + 0.10853228718042374, + -0.3106999695301056, + 0.007767840754240751 ], "tsne3d": [ - -29.846195220947266, - -24.459569931030273, - 35.748687744140625 + -26.864709854125977, + -15.392602920532227, + 34.91462707519531 ], "umap3d": [ - 2.5897276401519775, - -1.0345338582992554, - -0.3302961587905884 + 0.1066947877407074, + 0.40097931027412415, + 5.838738918304443 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 24, + "id": 1, "label": "Cancer Data" } ], @@ -274295,46 +274492,46 @@ "abstract": "The Cancer Genome Atlas (TCGA) is a landmark cancer genomics program that sequenced and molecularly characterized over 11,000 cases of primary cancer samples. Learn more about how the program transformed the cancer research community and beyond.", "projections": { "pca": [ - 0.028307588770985603, - -0.3941870331764221 + 0.026123130694031715, + -0.39489203691482544 ], "tsne": [ - 5.540289402008057, - -13.462546348571777 + -15.843061447143555, + -7.798413276672363 ], "umap": [ - -2.2193539142608643, - 6.336735248565674 + -2.3701670169830322, + 5.129493236541748 ], "pca3d": [ - 0.02830793336033821, - -0.3941948115825653, - -0.07377012819051743 + 0.026123598217964172, + -0.3948988616466522, + -0.06935620307922363 ], "tsne3d": [ - -24.847187042236328, - -27.139278411865234, - 36.153411865234375 + -23.567970275878906, + -16.31597900390625, + 31.40932846069336 ], "umap3d": [ - 2.6237335205078125, - -0.9984627366065979, - -0.38799822330474854 + 0.20599645376205444, + 0.25747373700141907, + 5.854739189147949 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 24, + "id": 1, "label": "Cancer Data" } ], @@ -274762,47 +274959,47 @@ "openalex_url": "https://openalex.org/W4406532053", "projections": { "pca": [ - -0.18135906755924225, - -0.020202701911330223 + -0.18362049758434296, + -0.01912209391593933 ], "tsne": [ - 7.166500091552734, - -25.426755905151367 + -8.546894073486328, + -23.199220657348633 ], "umap": [ - -2.5473711490631104, - 3.8521859645843506 + -2.054326057434082, + 7.338817119598389 ], "pca3d": [ - -0.18135856091976166, - -0.020199649035930634, - -0.17583049833774567 + -0.1836201548576355, + -0.01911761611700058, + -0.1745937317609787 ], "tsne3d": [ - -32.40508270263672, - -7.008782863616943, - -1.1928306818008423 + -18.538166046142578, + -11.858741760253906, + -16.543346405029297 ], "umap3d": [ - 1.098463535308838, - -0.017710721120238304, - -1.0781816244125366 + -0.2954448163509369, + 1.1343363523483276, + 4.177462577819824 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 22, - "label": "Genetic Variants" + "id": 3, + "label": "Genomic Sequences" } ], "_embedding": [ @@ -275210,46 +275407,46 @@ "journal": "Nature Communications", "projections": { "pca": [ - -0.008380305022001266, - -0.23897597193717957 + -0.01047419011592865, + -0.24063025414943695 ], "tsne": [ - 8.620194435119629, - -31.34125518798828 + -16.694625854492188, + -34.516746520996094 ], "umap": [ - -2.8405165672302246, - 4.160918235778809 + -2.617572784423828, + 7.107453346252441 ], "pca3d": [ - -0.008380865678191185, - -0.23898856341838837, - -0.1160496324300766 + -0.01047484204173088, + -0.24063941836357117, + -0.11452522873878479 ], "tsne3d": [ - -52.05274963378906, - 3.0609567165374756, - 20.245059967041016 + -31.028249740600586, + -2.838590383529663, + -30.35179901123047 ], "umap3d": [ - 1.1042178869247437, - -0.29112401604652405, - -1.2936315536499023 + -0.4799480140209198, + 1.0375964641571045, + 4.5098876953125 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 22, + "id": 27, "label": "Genetic Variants" } ], @@ -275660,47 +275857,47 @@ "abstract": "GitHub Gist: instantly share code, notes, and snippets.", "projections": { "pca": [ - 0.17287789285182953, - -0.10560186952352524 + 0.17131997644901276, + -0.10620541125535965 ], "tsne": [ - 30.917884826660156, - 9.747319221496582 + 26.8856143951416, + -14.447179794311523 ], "umap": [ - 3.171996831893921, - 7.054409503936768 + 2.437497854232788, + 6.136814117431641 ], "pca3d": [ - 0.1728784590959549, - -0.10559991002082825, - -0.23350749909877777 + 0.17132055759429932, + -0.10620345175266266, + -0.23535798490047455 ], "tsne3d": [ - 21.075647354125977, - 39.32997131347656, - 28.468507766723633 + 44.67190170288086, + 22.743024826049805, + 20.805450439453125 ], "umap3d": [ - 3.5096120834350586, - -0.6117438077926636, - 2.4684901237487793 + 3.449709415435791, + 2.5141615867614746, + 7.035922050476074 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 18, - "label": "Github Tools" + "id": 2, + "label": "Bioinformatics Tools" } ], "_embedding": [ @@ -276108,47 +276305,47 @@ "abstract": "Researchers have used contorted, inefficient, and arbitrary analyses to demonstrated added value in biomarkers, genes, and new lab measurements. Traditional statistical measures have always been up to the task, and are more powerful and more flexible. It\u2019s time to revisit them, and to add a few slight twists to make them more helpful.", "projections": { "pca": [ - -0.011393766850233078, - -0.11950894445180893 + -0.011080658994615078, + -0.11803745478391647 ], "tsne": [ - -2.766695737838745, - -6.824896335601807 + -18.471248626708984, + 0.8153948187828064 ], "umap": [ - -0.8672208786010742, - 6.58906364440918 + -1.7950109243392944, + 4.233015537261963 ], "pca3d": [ - -0.011393415741622448, - -0.11951129883527756, - 0.012640911154448986 + -0.011080175638198853, + -0.11803887784481049, + 0.013399395160377026 ], "tsne3d": [ - -7.875679969787598, - -24.843162536621094, - 24.448331832885742 + 1.8992475271224976, + -23.68305015563965, + 27.153240203857422 ], "umap3d": [ - 3.548243284225464, - -1.2510626316070557, - -1.5050498247146606 + 0.6593841314315796, + 1.1734845638275146, + 6.262602806091309 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 28, - "label": "Clinical AI" + "id": 21, + "label": "Clinical Trials" } ], "_embedding": [ @@ -276563,47 +276760,47 @@ "journal": "Nature", "projections": { "pca": [ - 0.1348007470369339, - -0.06861977279186249 + 0.13335640728473663, + -0.06884663552045822 ], "tsne": [ - 8.985991477966309, - -9.542062759399414 + 4.66780948638916, + -6.862658500671387 ], "umap": [ - 0.9844605326652527, - 4.709011077880859 + 0.5234752893447876, + 5.535660266876221 ], "pca3d": [ - 0.13480105996131897, - -0.06862086057662964, - -0.09191127866506577 + 0.13335685431957245, + -0.06884949654340744, + -0.09066585451364517 ], "tsne3d": [ - -9.576756477355957, - 18.58646011352539, - 25.40806007385254 + -24.638093948364258, + 14.726297378540039, + -14.650200843811035 ], "umap3d": [ - 2.8010735511779785, - 0.0001730806689010933, - 0.4432103931903839 + 1.7919869422912598, + 1.3579010963439941, + 5.614013195037842 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 4, - "label": "Com Area Amp" + "id": 6, + "label": "Data Needs" }, { - "id": 23, - "label": "Research Authors" + "id": 16, + "label": "Lab Research" } ], "_embedding": [ @@ -277009,47 +277206,47 @@ ], "projections": { "pca": [ - 0.06345038115978241, - -0.18724463880062103 + 0.062364645302295685, + -0.18757854402065277 ], "tsne": [ - -5.152415752410889, - -10.983966827392578 + -15.51002025604248, + -3.7164669036865234 ], "umap": [ - -1.3658231496810913, - 6.338599681854248 + -2.4460971355438232, + 4.345918655395508 ], "pca3d": [ - 0.0634516030550003, - -0.1872287541627884, - 0.005212143063545227 + 0.062366027384996414, + -0.18756364285945892, + 0.0072669521905481815 ], "tsne3d": [ - -14.689794540405273, - -36.6312370300293, - 18.84259033203125 + -9.579317092895508, + -24.284961700439453, + 22.45075798034668 ], "umap3d": [ - 3.0207746028900146, - -1.0006052255630493, - -1.6163958311080933 + 0.4849458634853363, + 0.8271675109863281, + 6.384559631347656 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 28, - "label": "Clinical AI" + "id": 21, + "label": "Clinical Trials" } ], "_embedding": [ @@ -277457,47 +277654,47 @@ "abstract": "Fast Data Science, AKA fds, is a CLI for Data Scientists to version control data and code at once, by conveniently wrapping git and dvc - DagsHub/fds", "projections": { "pca": [ - 0.22536055743694305, - -0.03914105147123337 + 0.22514300048351288, + -0.03830764442682266 ], "tsne": [ - 20.38117027282715, - 3.6641557216644287 + 20.34937858581543, + -4.5507612228393555 ], "umap": [ - 2.811373710632324, - 6.156452655792236 + 2.215400457382202, + 5.07961893081665 ], "pca3d": [ - 0.22536063194274902, - -0.0391346700489521, - -0.09610976278781891 + 0.2251432090997696, + -0.03830081969499588, + -0.09576691687107086 ], "tsne3d": [ - 13.999981880187988, - 22.41868019104004, - 23.52924919128418 + 22.730745315551758, + 11.352173805236816, + 15.17702865600586 ], "umap3d": [ - 3.537975311279297, - -0.2631676197052002, - 1.8947166204452515 + 3.317859172821045, + 2.0043225288391113, + 6.270055770874023 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 3, - "label": "SMB Data" + "id": 24, + "label": "SMB Models" } ], "_embedding": [ @@ -277939,46 +278136,46 @@ "doi": "10.1101/2025.06.25.661532", "projections": { "pca": [ - -0.25179725885391235, - -0.16657906770706177 + -0.2547804117202759, + -0.16534504294395447 ], "tsne": [ - 2.0936455726623535, - -26.9748477935791 + -12.175433158874512, + -19.37468147277832 ], "umap": [ - -2.30084228515625, - 3.9742658138275146 + -2.0843658447265625, + 6.953226566314697 ], "pca3d": [ - -0.251796692609787, - -0.16658061742782593, - -0.15150001645088196 + -0.2547798752784729, + -0.16534601151943207, + -0.14723530411720276 ], "tsne3d": [ - -41.74666213989258, - -12.657211303710938, - 8.889248847961426 + -28.13582420349121, + -6.9955925941467285, + -12.495847702026367 ], "umap3d": [ - 1.30075204372406, - 0.04951416328549385, - -1.3750762939453125 + -0.15652652084827423, + 1.0036219358444214, + 4.249910831451416 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 22, + "id": 27, "label": "Genetic Variants" } ], @@ -278415,47 +278612,47 @@ "doi": "10.1101/2025.07.16.665231", "projections": { "pca": [ - -0.19270476698875427, - -0.17278635501861572 + -0.1944427341222763, + -0.17129407823085785 ], "tsne": [ - -1.4431010484695435, - -26.675724029541016 + -16.370769500732422, + -18.57172203063965 ], "umap": [ - -2.136706829071045, - 3.8862242698669434 + -1.7913436889648438, + 6.993119239807129 ], "pca3d": [ - -0.19270451366901398, - -0.17278675734996796, - -0.09304624050855637 + -0.19444267451763153, + -0.1712941825389862, + -0.08909866958856583 ], "tsne3d": [ - -42.45170593261719, - -20.576644897460938, - 4.889074802398682 + -35.22494888305664, + -6.473210334777832, + -10.828470230102539 ], "umap3d": [ - 1.2789334058761597, - 0.39756712317466736, - -1.3016977310180664 + -0.060197606682777405, + 0.869304895401001, + 4.036293029785156 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 22, - "label": "Genetic Variants" + "id": 3, + "label": "Genomic Sequences" } ], "_embedding": [ @@ -278863,47 +279060,47 @@ "journal": "Nature Biotechnology", "projections": { "pca": [ - -0.02301882393658161, - -0.09373465180397034 + -0.025105832144618034, + -0.09408682584762573 ], "tsne": [ - 5.727199554443359, - -9.11501693725586 + 0.3830272853374481, + -7.3797736167907715 ], "umap": [ - -0.9362552762031555, - 4.760553359985352 + -0.8780544996261597, + 5.670536518096924 ], "pca3d": [ - -0.023018868640065193, - -0.09374207258224487, - -0.11574114114046097 + -0.025105847045779228, + -0.09409433603286743, + -0.11060258001089096 ], "tsne3d": [ - -7.528133869171143, - 7.2590413093566895, - 24.68594741821289 + 1.7582647800445557, + -27.172473907470703, + -21.667957305908203 ], "umap3d": [ - 2.750047445297241, - -0.13199004530906677, - -0.17122815549373627 + 1.389949917793274, + 0.63486248254776, + 5.222211837768555 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 2, - "label": "AI Drug Discovery" + "id": 9, + "label": "Drug Discovery" }, { - "id": 13, - "label": "Drug Discovery" + "id": 28, + "label": "Spatial Drug" } ], "_embedding": [ @@ -279311,47 +279508,47 @@ "journal": "Nature", "projections": { "pca": [ - -0.046880125999450684, - -0.16356261074543 + -0.04915950447320938, + -0.16412270069122314 ], "tsne": [ - 5.674742698669434, - -9.182729721069336 + 0.3098617196083069, + -7.417933940887451 ], "umap": [ - -1.0456604957580566, - 4.819645404815674 + -1.016450047492981, + 5.71748161315918 ], "pca3d": [ - -0.04688010737299919, - -0.16357450187206268, - -0.09887868165969849 + -0.0491594634950161, + -0.16413436830043793, + -0.09334073960781097 ], "tsne3d": [ - -9.647836685180664, - 8.692649841308594, - 28.09055519104004 + -1.0898948907852173, + -25.684057235717773, + -23.102169036865234 ], "umap3d": [ - 2.730294942855835, - -0.22785145044326782, - -0.2270856648683548 + 1.2304877042770386, + 0.5788470506668091, + 5.321575164794922 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 2, - "label": "AI Drug Discovery" + "id": 9, + "label": "Drug Discovery" }, { - "id": 13, - "label": "Drug Discovery" + "id": 28, + "label": "Spatial Drug" } ], "_embedding": [ @@ -279773,47 +279970,47 @@ "openalex_url": "https://openalex.org/W3144696998", "projections": { "pca": [ - -0.15332381427288055, - -0.10685638338327408 + -0.15656821429729462, + -0.10781746357679367 ], "tsne": [ - -10.190722465515137, - -27.847583770751953 + -22.85926055908203, + -16.540821075439453 ], "umap": [ - -1.4414840936660767, - 3.523024082183838 + -1.2710775136947632, + 6.751047611236572 ], "pca3d": [ - -0.15332436561584473, - -0.10687194019556046, - -0.19201454520225525 + -0.1565687507390976, + -0.1078319326043129, + -0.18810273706912994 ], "tsne3d": [ - -31.38592529296875, - -28.63039779663086, - -24.889863967895508 + -41.254268646240234, + -16.765731811523438, + -1.3268656730651855 ], "umap3d": [ - 1.6284782886505127, - 0.9509984254837036, - -1.6132563352584839 + 0.3021880090236664, + 0.337786465883255, + 3.9995665550231934 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 19, - "label": "Cell Models" + "id": 20, + "label": "Cell Foundation" } ], "_embedding": [ @@ -280253,47 +280450,47 @@ "openalex_url": "https://openalex.org/W4399390205", "projections": { "pca": [ - -0.21071496605873108, - -0.09461982548236847 + -0.2120540738105774, + -0.09473506361246109 ], "tsne": [ - -9.968608856201172, - -22.072513580322266 + -21.205589294433594, + -12.956987380981445 ], "umap": [ - -1.1865966320037842, - 4.701521873474121 + -1.3799482583999634, + 5.755324363708496 ], "pca3d": [ - -0.2107149213552475, - -0.09463394433259964, - -0.07860597968101501 + -0.21205393970012665, + -0.09474919736385345, + -0.0723661407828331 ], "tsne3d": [ - -30.054311752319336, - -28.07955551147461, - -12.528274536132812 + -35.60346603393555, + -11.94240951538086, + 4.520936489105225 ], "umap3d": [ - 2.6024675369262695, - -0.2101985365152359, - -0.4258297085762024 + 0.7918096780776978, + 0.3115658462047577, + 5.17706823348999 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 2, - "label": "AI Drug Discovery" + "id": 1, + "label": "Cancer Data" }, { - "id": 2, - "label": "Genetic Disease" + "id": 28, + "label": "Spatial Drug" } ], "_embedding": [ @@ -280701,47 +280898,47 @@ "abstract": "Contribute to standardmodelbio/Llama3-Med development by creating an account on GitHub.", "projections": { "pca": [ - 0.25207334756851196, - -0.15457285940647125 + 0.25206562876701355, + -0.15352429449558258 ], "tsne": [ - 25.567855834960938, - 8.16313648223877 + 26.260644912719727, + -9.739143371582031 ], "umap": [ - 3.147702217102051, - 6.899269104003906 + 2.799017906188965, + 5.596050262451172 ], "pca3d": [ - 0.25207385420799255, - -0.1545657217502594, - -0.08118559420108795 + 0.25206631422042847, + -0.1535177379846573, + -0.08035992085933685 ], "tsne3d": [ - 13.643593788146973, - 46.11776351928711, - 14.049577713012695 + 38.876991271972656, + 3.4337165355682373, + 23.218896865844727 ], "umap3d": [ - 3.287015914916992, - -0.150105282664299, - 2.3356428146362305 + 3.3344907760620117, + 2.638540267944336, + 6.607826232910156 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 18, - "label": "Github Tools" + "id": 17, + "label": "GitHub Creation" } ], "_embedding": [ @@ -281157,47 +281354,47 @@ ], "projections": { "pca": [ - -0.1243417039513588, - 0.24749493598937988 + -0.12270025163888931, + 0.24837511777877808 ], "tsne": [ - -22.407119750976562, - 10.640571594238281 + -4.303341865539551, + 22.544034957885742 ], "umap": [ - 1.5257972478866577, - 2.979526996612549 + 0.5516242980957031, + 1.8320573568344116 ], "pca3d": [ - -0.12434257566928864, - 0.2474847435951233, - 0.0023643015883862972 + -0.12270118296146393, + 0.24836575984954834, + 0.004862151108682156 ], "tsne3d": [ - 4.128048419952393, - 11.923640251159668, - -21.76847267150879 + -9.795193672180176, + 4.325925827026367, + -15.995755195617676 ], "umap3d": [ - 6.001396179199219, - -1.1387892961502075, - 0.03170204162597656 + 3.3786237239837646, + -1.1701266765594482, + 6.327287197113037 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 15, - "label": "Latent Models" + "id": 8, + "label": "JEPA Video Models" } ], "_embedding": [ @@ -281623,47 +281820,47 @@ "openalex_url": "https://openalex.org/W4409274838", "projections": { "pca": [ - -0.19839654862880707, - -0.08753570169210434 + -0.20069560408592224, + -0.08809832483530045 ], "tsne": [ - -0.8779768347740173, - -27.709779739379883 + -15.831014633178711, + -19.654306411743164 ], "umap": [ - -2.206545829772949, - 3.3922369480133057 + -1.7684056758880615, + 7.332082748413086 ], "pca3d": [ - -0.19839662313461304, - -0.08754180371761322, - -0.15154853463172913 + -0.20069566369056702, + -0.08810348808765411, + -0.1492440104484558 ], "tsne3d": [ - -49.4908447265625, - -22.502901077270508, - 9.452265739440918 + -32.992027282714844, + -13.230029106140137, + -8.188716888427734 ], "umap3d": [ - 1.070540189743042, - 0.4378369152545929, - -1.2221146821975708 + -0.06568869203329086, + 0.948620617389679, + 3.894052267074585 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 8, - "label": "Gene Regulation" + "id": 3, + "label": "Genomic Sequences" } ], "_embedding": [ @@ -282079,47 +282276,47 @@ ], "projections": { "pca": [ - -0.10397069156169891, - 0.26003316044807434 + -0.10353503376245499, + 0.26031988859176636 ], "tsne": [ - -17.238285064697266, - 18.908489227294922 + 2.5306737422943115, + 31.760452270507812 ], "umap": [ - 2.7845072746276855, - 2.8300793170928955 + 1.927115559577942, + 1.9609614610671997 ], "pca3d": [ - -0.10397104918956757, - 0.2600330710411072, - -0.0032616727985441685 + -0.10353559255599976, + 0.26031965017318726, + -0.0012697301572188735 ], "tsne3d": [ - 27.33177947998047, - -1.9710512161254883, - -26.893871307373047 + 9.387325286865234, + 5.8284831047058105, + -41.1168098449707 ], "umap3d": [ - 6.50613260269165, - -0.656417191028595, - 1.1009818315505981 + 4.479604244232178, + -0.678044855594635, + 6.00662899017334 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 6, + "label": "Learning Techniques" } ], "_embedding": [ @@ -282527,47 +282724,47 @@ "abstract": "Fast and memory-efficient exact attention. Contribute to Dao-AILab/flash-attention development by creating an account on GitHub.", "projections": { "pca": [ - 0.1735927313566208, - 0.03932677209377289 + 0.17448067665100098, + 0.040495097637176514 ], "tsne": [ - 10.703575134277344, - 30.204742431640625 + 19.85881233215332, + 26.24256134033203 ], "umap": [ - 2.8560574054718018, - 5.099609851837158 + 1.8544442653656006, + 3.5771727561950684 ], "pca3d": [ - 0.1735914945602417, - 0.03932322561740875, - -0.049757830798625946 + 0.1744796484708786, + 0.040494002401828766, + -0.049813125282526016 ], "tsne3d": [ - 14.72860050201416, - 38.02111053466797, - -25.139982223510742 + -18.158430099487305, + 29.204822540283203, + -18.76752281188965 ], "umap3d": [ - 4.886959075927734, - -0.48261910676956177, - 1.227953553199768 + 3.750898838043213, + 0.8515976667404175, + 6.424221038818359 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 5, - "label": "GPU Inference" + "id": 15, + "label": "PyTorch Models" } ], "_embedding": [ @@ -282987,47 +283184,47 @@ "doi": "10.1101/2025.02.21.639494", "projections": { "pca": [ - -0.2585403025150299, - -0.06427421420812607 + -0.263966828584671, + -0.0646563395857811 ], "tsne": [ - 0.3934919536113739, - -28.245132446289062 + -14.361347198486328, + -21.148239135742188 ], "umap": [ - -2.2223849296569824, - 3.450744152069092 + -1.8168452978134155, + 7.331958293914795 ], "pca3d": [ - -0.258539617061615, - -0.06426463276147842, - -0.2410930097103119 + -0.26396626234054565, + -0.06464672833681107, + -0.2404293715953827 ], "tsne3d": [ - -43.74489212036133, - -11.952414512634277, - 0.10814236104488373 + -28.616180419921875, + -15.405570983886719, + -12.890618324279785 ], "umap3d": [ - 1.0687516927719116, - 0.4158145785331726, - -1.1447182893753052 + -0.10311201214790344, + 1.0192334651947021, + 3.87196683883667 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 8, - "label": "Gene Regulation" + "id": 3, + "label": "Genomic Sequences" } ], "_embedding": [ @@ -283444,47 +283641,47 @@ "doi": "10.1101/2024.09.23.614632", "projections": { "pca": [ - -0.238323375582695, - -0.02734038420021534 + -0.2436501532793045, + -0.028277965262532234 ], "tsne": [ - 0.6361435055732727, - -27.408693313598633 + -14.230045318603516, + -19.944429397583008 ], "umap": [ - -2.4887681007385254, - 3.5619120597839355 + -1.975902795791626, + 7.3698015213012695 ], "pca3d": [ - -0.23832324147224426, - -0.027340540662407875, - -0.182295560836792 + -0.2436499297618866, + -0.028277074918150902, + -0.18205028772354126 ], "tsne3d": [ - -38.544071197509766, - -10.85035514831543, - -1.4457581043243408 + -22.533374786376953, + -16.595354080200195, + -13.673761367797852 ], "umap3d": [ - 1.0262266397476196, - 0.2706531882286072, - -1.159168004989624 + -0.38421183824539185, + 1.194510579109192, + 4.059082508087158 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 22, - "label": "Genetic Variants" + "id": 3, + "label": "Genomic Sequences" } ], "_embedding": [ @@ -283892,46 +284089,46 @@ "abstract": "LLM inference at SotA speeds and Modal quality, now available to everyone.", "projections": { "pca": [ - 0.07878176867961884, - 0.166385680437088 + 0.08003357797861099, + 0.16650362312793732 ], "tsne": [ - -16.09145164489746, - 17.171483993530273 + 0.43287068605422974, + 34.34831237792969 ], "umap": [ - 2.3872945308685303, - 2.7917661666870117 + 1.839260220527649, + 1.3441283702850342 ], "pca3d": [ - 0.07878094911575317, - 0.16638244688510895, - -0.01388427522033453 + 0.08003275096416473, + 0.16650095582008362, + -0.014512309804558754 ], "tsne3d": [ - 6.024643898010254, - 21.573827743530273, - -24.742773056030273 + -5.143988609313965, + 12.459672927856445, + -18.474323272705078 ], "umap3d": [ - 6.43839693069458, - -0.9287182092666626, - 0.8019388318061829 + 3.9400782585144043, + -0.797827959060669, + 5.663778781890869 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 4, + "id": 26, "label": "World Models" } ], @@ -284340,47 +284537,47 @@ "abstract": "A linear estimator on top of clip to predict the aesthetic quality of pictures - LAION-AI/aesthetic-predictor", "projections": { "pca": [ - 0.055057551711797714, - 0.14495909214019775 + 0.05818260833621025, + 0.14699631929397583 ], "tsne": [ - -24.682653427124023, - 10.876077651977539 + -8.483073234558105, + 24.610572814941406 ], "umap": [ - 1.473338007926941, - 2.924532890319824 + 0.49970221519470215, + 1.8508193492889404 ], "pca3d": [ - 0.05505719035863876, - 0.14496652781963348, - 0.060938552021980286 + 0.05818233639001846, + 0.1470053344964981, + 0.06108100339770317 ], "tsne3d": [ - -12.046278953552246, - 17.50475311279297, - -24.38165855407715 + -6.6691389083862305, + -6.540570259094238, + -20.849349975585938 ], "umap3d": [ - 6.005436420440674, - -1.2039854526519775, - 0.141697496175766 + 3.212015151977539, + -1.1371068954467773, + 6.411100387573242 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 15, - "label": "Latent Models" + "id": 8, + "label": "JEPA Video Models" } ], "_embedding": [ @@ -284790,47 +284987,47 @@ "abstract": "A plain-language writing skill for AI agents, with a revision view that shows what changed. - shreyashankar/plain-writing-skill", "projections": { "pca": [ - 0.16602858901023865, - 0.16727367043495178 + 0.16709686815738678, + 0.16677220165729523 ], "tsne": [ - 19.861536026000977, - 20.38383674621582 + 26.78998565673828, + 2.7333242893218994 ], "umap": [ - 3.4076292514801025, - 4.468026161193848 + 2.550752878189087, + 3.2063207626342773 ], "pca3d": [ - 0.1660277247428894, - 0.1672661006450653, - 0.00262532290071249 + 0.16709625720977783, + 0.16676519811153412, + 0.0011986554600298405 ], "tsne3d": [ - 40.827796936035156, - 16.038738250732422, - 2.58624267578125 + 21.504308700561523, + 28.225080490112305, + 2.3175125122070312 ], "umap3d": [ - 5.131372451782227, - 0.2777855396270752, - 1.6842142343521118 + 4.314687252044678, + 0.8617551326751709, + 5.811572074890137 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 1, - "label": "AI Agents" + "id": 22, + "label": "AI Gemini" } ], "_embedding": [ @@ -285238,47 +285435,47 @@ "abstract": "This system card describes Claude Mythos 5 and Claude Fable 5, two configurations of a new large language model from Anthropic. Mythos 5 is the most capable model Anthropic has trained, released to trusted partners with lifted safeguards, while Fable 5 is available for general use with additional safeguards blocking high-risk domain tasks. The document presents pre-deployment evaluations covering responsible scaling policy, chemical and biological risks, cybersecurity capabilities, safeguards, agentic safety, alignment, model welfare, and general capabilities.", "projections": { "pca": [ - 0.13796471059322357, - -0.007943029515445232 + 0.13825823366641998, + -0.007809941656887531 ], "tsne": [ - 20.39325523376465, - 28.432340621948242 + 32.225189208984375, + 11.77254867553711 ], "umap": [ - 4.893860340118408, - 5.862563610076904 + 4.389588356018066, + 4.440353870391846 ], "pca3d": [ - 0.13796377182006836, - -0.007936887443065643, - 0.012447341345250607 + 0.13825735449790955, + -0.0078022959642112255, + 0.011826424859464169 ], "tsne3d": [ - 54.8238410949707, - 10.504332542419434, - -2.8056812286376953 + 17.578500747680664, + 42.20288848876953, + 11.733479499816895 ], "umap3d": [ - 5.261229991912842, - 0.7714893817901611, - 3.1317262649536133 + 5.462380409240723, + 2.234332323074341, + 6.045451641082764 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { - "id": 16, - "label": "Claude Code" + "id": 11, + "label": "Code Tools" } ], "_embedding": [ @@ -285686,47 +285883,47 @@ "abstract": "Goals are persistent objectives in Codex that keep a thread working toward a defined outcome across turns. A Goal gives Codex a completion c", "projections": { "pca": [ - 0.2660166323184967, - 0.021293746307492256 + 0.26611924171447754, + 0.02197929471731186 ], "tsne": [ - 4.469442844390869, - 8.042048454284668 + 12.624871253967285, + 9.71910285949707 ], "umap": [ - 3.152620792388916, - 4.428790092468262 + 2.4145030975341797, + 3.810365676879883 ], "pca3d": [ - 0.2660156786441803, - 0.021294236183166504, - -0.011932763271033764 + 0.26611852645874023, + 0.021981000900268555, + -0.013900686986744404 ], "tsne3d": [ - 38.07952117919922, - 20.635568618774414, - -24.622310638427734 + 27.142776489257812, + 14.546772956848145, + -19.75579833984375 ], "umap3d": [ - 5.158710479736328, - 0.16200228035449982, - 2.0659031867980957 + 4.490032196044922, + 1.3170784711837769, + 6.1479387283325195 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 5, - "label": "GPU Inference" + "id": 15, + "label": "PyTorch Models" } ], "_embedding": [ @@ -286134,46 +286331,46 @@ "abstract": "AutoScientist automates the full model training loop, co-optimizing data and recipes until your model converges. Outperforms human-configured training by 35% on average.", "projections": { "pca": [ - -0.018826114013791084, - 0.10488349944353104 + -0.016019154340028763, + 0.10807841271162033 ], "tsne": [ - -3.806802749633789, - -3.586228132247925 + -8.815319061279297, + 1.0171477794647217 ], "umap": [ - -0.3917860686779022, - 6.269406318664551 + -1.2401727437973022, + 4.012728214263916 ], "pca3d": [ - -0.018826134502887726, - 0.10488690435886383, - 0.16483961045742035 + -0.01601913571357727, + 0.10808476060628891, + 0.1652822643518448 ], "tsne3d": [ - 19.904285430908203, - -40.6246452331543, - 13.373016357421875 + -0.8349429368972778, + 6.545624732971191, + -0.7654356956481934 ], "umap3d": [ - 4.057600498199463, - -0.3398882746696472, - -1.8598986864089966 + 0.9845718741416931, + 0.5503618121147156, + 7.34932279586792 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, + "id": 14, "label": "Clinical Health" } ], @@ -286582,47 +286779,47 @@ "abstract": "Contribute to jmschrei/cherimoya development by creating an account on GitHub.", "projections": { "pca": [ - 0.15473657846450806, - 0.15336622297763824 + 0.15304891765117645, + 0.15228785574436188 ], "tsne": [ - 11.771283149719238, - 9.6256742477417 + 16.946958541870117, + -8.704803466796875 ], "umap": [ - 3.0630078315734863, - 4.867611408233643 + 2.188666820526123, + 4.064835071563721 ], "pca3d": [ - 0.15473626554012299, - 0.1533762514591217, - -0.19016990065574646 + 0.15304861962795258, + 0.15229861438274384, + -0.19237564504146576 ], "tsne3d": [ - -3.374501943588257, - 44.81894302368164, - -2.661903142929077 + 33.299598693847656, + 7.814087390899658, + -5.031084060668945 ], "umap3d": [ - 4.568540573120117, - -0.5576052069664001, - 2.08565354347229 + 4.121242523193359, + 1.2676656246185303, + 6.8069844245910645 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 5, - "label": "GPU Inference" + "id": 15, + "label": "PyTorch Models" } ], "_embedding": [ @@ -287038,47 +287235,47 @@ ], "projections": { "pca": [ - 0.007203838787972927, - 0.1858695149421692 + 0.008340396918356419, + 0.18806760013103485 ], "tsne": [ - -6.825789451599121, - 19.883472442626953 + 10.910359382629395, + 18.738121032714844 ], "umap": [ - 3.1839792728424072, - 3.8696136474609375 + 2.294773817062378, + 3.086430072784424 ], "pca3d": [ - 0.007203842978924513, - 0.18588127195835114, - -0.06362264603376389 + 0.008340311236679554, + 0.1880790740251541, + -0.06595391035079956 ], "tsne3d": [ - 24.55240821838379, - 14.094801902770996, - -38.05622482299805 + 16.90445899963379, + 14.805997848510742, + -18.3341121673584 ], "umap3d": [ - 5.665319919586182, - -0.07809866964817047, - 1.7226321697235107 + 4.546181678771973, + 0.5982474088668823, + 5.9810309410095215 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 1, - "label": "AI Agents" + "id": 22, + "label": "AI Gemini" } ], "_embedding": [ @@ -287486,47 +287683,47 @@ "abstract": "A vector index built on TurboQuant, written in Rust with Python bindings - RyanCodrai/turbovec", "projections": { "pca": [ - 0.19237291812896729, - 0.02672611176967621 + 0.19263684749603271, + 0.027504799887537956 ], "tsne": [ - 34.29692459106445, - 4.299304485321045 + 19.5639591217041, + -16.527978897094727 ], "umap": [ - 2.411334276199341, - 6.746329307556152 + 2.135895013809204, + 5.740316867828369 ], "pca3d": [ - 0.19237256050109863, - 0.0267240758985281, - -0.10716499388217926 + 0.19263657927513123, + 0.02750418707728386, + -0.10808944702148438 ], "tsne3d": [ - 0.39958006143569946, - 32.536155700683594, - 35.631107330322266 + 26.97858428955078, + 14.442164421081543, + 35.79396438598633 ], "umap3d": [ - 3.8597476482391357, - -0.911592960357666, - 1.7285850048065186 + 3.246575117111206, + 1.777647614479065, + 7.167849063873291 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 25, - "label": "Python Data" + "id": 2, + "label": "Bioinformatics Tools" } ], "_embedding": [ @@ -287934,47 +288131,47 @@ "abstract": "High Bandwidth Flash (HBF)\u2014a new form of NAND\u2014delivers performance within 2.2% of unlimited-capacity HBM, while offering a significant increase in memory capacity\u2014unlocking AI services that \r\ntraditional HBM simply can\u2019t support. Powered by Sandisk\u2019s CBA (CMOS directly Bonded to Array) technology, HBF sets a new standard for high-bandwidth memory, combining exceptional speed, density, \r\nand energy efficiency in a single architecture.", "projections": { "pca": [ - 0.14379549026489258, - 0.01146270614117384 + 0.14576050639152527, + 0.012661073356866837 ], "tsne": [ - 8.556062698364258, - 6.297523021697998 + 12.681132316589355, + -6.750350475311279 ], "umap": [ - 2.8890738487243652, - 4.958294868469238 + 1.9635155200958252, + 3.816767692565918 ], "pca3d": [ - 0.14379386603832245, - 0.011455992236733437, - -0.010188686661422253 + 0.1457587033510208, + 0.012657956220209599, + -0.009992390871047974 ], "tsne3d": [ - 7.228740215301514, - 39.311927795410156, - -18.709583282470703 + 36.284584045410156, + 6.393368721008301, + -16.7507381439209 ], "umap3d": [ - 4.719497203826904, - -0.3358174264431, - 1.8066235780715942 + 4.119067668914795, + 1.2166346311569214, + 6.503357887268066 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 5, - "label": "GPU Inference" + "id": 15, + "label": "PyTorch Models" } ], "_embedding": [ @@ -288382,47 +288579,47 @@ "abstract": "We\u2019re on a journey to advance and democratize artificial intelligence through open source and open science.", "projections": { "pca": [ - 0.2488609105348587, - 0.12990739941596985 + 0.2492208033800125, + 0.1299310028553009 ], "tsne": [ - 10.51416301727295, - 19.79764747619629 + 20.547664642333984, + 9.811308860778809 ], "umap": [ - 4.607880592346191, - 4.957365036010742 + 3.5207877159118652, + 3.3961117267608643 ], "pca3d": [ - 0.24886064231395721, - 0.1299085021018982, - 0.07303432375192642 + 0.24922052025794983, + 0.12993009388446808, + 0.06900396943092346 ], "tsne3d": [ - 37.85271072387695, - 4.92405891418457, - 3.6422624588012695 + 6.693446159362793, + 27.652626037597656, + 3.041865587234497 ], "umap3d": [ - 5.05324125289917, - 1.2038755416870117, - 1.9036835432052612 + 4.549707889556885, + 2.0020463466644287, + 4.9912567138671875 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { "id": 7, - "label": "Open AI" + "label": "Hugging Face Models" } ], "_embedding": [ @@ -288829,47 +289026,47 @@ "title": "Tokenized Tokens", "projections": { "pca": [ - 0.10105927288532257, - 0.11958328634500504 + 0.10259941220283508, + 0.12031767517328262 ], "tsne": [ - -26.630615234375, - 12.385330200195312 + -7.640261173248291, + 26.701370239257812 ], "umap": [ - 1.6224267482757568, - 2.7998485565185547 + 0.7989121675491333, + 1.794904112815857 ], "pca3d": [ - 0.10105787962675095, - 0.11956992745399475, - -0.06793037801980972 + 0.10259801149368286, + 0.12030395865440369, + -0.07083357125520706 ], "tsne3d": [ - -7.736552715301514, - 27.559202194213867, - -30.235979080200195 + -0.9969366788864136, + -11.376303672790527, + -27.325462341308594 ], "umap3d": [ - 6.134030342102051, - -1.2792420387268066, - 0.43424710631370544 + 3.544713258743286, + -1.0214978456497192, + 5.947591781616211 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 15, - "label": "Latent Models" + "id": 8, + "label": "JEPA Video Models" } ], "_embedding": [ @@ -289277,47 +289474,47 @@ "abstract": "We\u2019re on a journey to advance and democratize artificial intelligence through open source and open science.", "projections": { "pca": [ - 0.11156048625707626, - 0.026774119585752487 + 0.11369266360998154, + 0.027873799204826355 ], "tsne": [ - 5.061470031738281, - 19.818004608154297 + 23.96713638305664, + 14.206342697143555 ], "umap": [ - 5.090231895446777, - 4.804272174835205 + 4.236278057098389, + 2.882997751235962 ], "pca3d": [ - 0.11155956983566284, - 0.026761429384350777, - 0.15777668356895447 + 0.11369165033102036, + 0.027861768379807472, + 0.15681639313697815 ], "tsne3d": [ - 53.9179573059082, - -5.870429039001465, - 4.727041721343994 + -6.617772579193115, + 36.351383209228516, + 2.842090606689453 ], "umap3d": [ - 5.407974720001221, - 1.6913702487945557, - 2.0742247104644775 + 4.966950416564941, + 2.242342948913574, + 4.626859188079834 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { "id": 7, - "label": "Open AI" + "label": "Hugging Face Models" } ], "_embedding": [ @@ -289732,46 +289929,46 @@ ], "projections": { "pca": [ - -0.10806283354759216, - 0.2635817229747772 + -0.10680079460144043, + 0.2647880017757416 ], "tsne": [ - -24.90994644165039, - 18.766448974609375 + 0.09173671901226044, + 27.726308822631836 ], "umap": [ - 2.3209331035614014, - 2.363044261932373 + 1.2348133325576782, + 1.283286452293396 ], "pca3d": [ - -0.108063705265522, - 0.2635737657546997, - -0.019634490832686424 + -0.10680174082517624, + 0.2647797763347626, + -0.01789078861474991 ], "tsne3d": [ - 6.824713230133057, - 20.130373001098633, - -39.09885025024414 + -12.617899894714355, + 7.358256816864014, + -26.662872314453125 ], "umap3d": [ - 6.685151100158691, - -1.2641017436981201, - 0.5614466071128845 + 3.8112754821777344, + -1.2257471084594727, + 5.523890972137451 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 4, + "id": 26, "label": "World Models" } ], @@ -290185,46 +290382,46 @@ ], "projections": { "pca": [ - -0.12846516072750092, - 0.27301540970802307 + -0.12659898400306702, + 0.274563729763031 ], "tsne": [ - -26.075586318969727, - 17.862770080566406 + 0.6033610105514526, + 26.192121505737305 ], "umap": [ - 2.2828528881073, - 2.2290008068084717 + 1.281457543373108, + 1.2894997596740723 ], "pca3d": [ - -0.12846580147743225, - 0.2730121314525604, - -0.08703871071338654 + -0.12659987807273865, + 0.2745612859725952, + -0.08411593735218048 ], "tsne3d": [ - 0.3965253531932831, - 19.806081771850586, - -42.8072624206543 + -8.24829387664795, + 10.740455627441406, + -30.382619857788086 ], "umap3d": [ - 6.771991729736328, - -1.3715486526489258, - 0.531132698059082 + 3.631746530532837, + -1.2717074155807495, + 5.642452716827393 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 4, + "id": 26, "label": "World Models" } ], @@ -290633,47 +290830,47 @@ "abstract": "cuVS - a library for vector search and clustering on the GPU - rapidsai/cuvs", "projections": { "pca": [ - 0.0882144570350647, - 0.13305926322937012 + 0.08905553817749023, + 0.13494306802749634 ], "tsne": [ - 7.790740966796875, - 11.414891242980957 + 13.673955917358398, + -11.965738296508789 ], "umap": [ - 2.514127492904663, - 4.813051700592041 + 1.6249115467071533, + 4.0501179695129395 ], "pca3d": [ - 0.08821438997983932, - 0.13305853307247162, - -0.07888051122426987 + 0.08905554562807083, + 0.1349431872367859, + -0.07961168140172958 ], "tsne3d": [ - -1.4606791734695435, - 24.148740768432617, - 30.096126556396484 + 27.056978225708008, + -9.054948806762695, + -14.276424407958984 ], "umap3d": [ - 4.424378871917725, - -0.5717282891273499, - 1.3260349035263062 + 3.661518096923828, + 1.0999650955200195, + 6.7573466300964355 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 5, - "label": "GPU Inference" + "id": 15, + "label": "PyTorch Models" } ], "_embedding": [ @@ -291083,47 +291280,47 @@ "abstract": "We\u2019ve raised Claude's usage limits and agreed a new compute partnership with SpaceX that will substantially increase our capacity in the near term.", "projections": { "pca": [ - 0.2179463654756546, - 0.023695748299360275 + 0.21764814853668213, + 0.02411336824297905 ], "tsne": [ - 11.277010917663574, - 6.561909198760986 + 14.963971138000488, + -5.89865779876709 ], "umap": [ - 2.957293748855591, - 4.951519012451172 + 2.236405372619629, + 4.266674518585205 ], "pca3d": [ - 0.21794532239437103, - 0.023682748898863792, - -0.045876480638980865 + 0.21764709055423737, + 0.024102965369820595, + -0.0492771677672863 ], "tsne3d": [ - 9.69027042388916, - 37.76951217651367, - -9.848915100097656 + 25.818336486816406, + 4.269601821899414, + -11.352802276611328 ], "umap3d": [ - 4.709884166717529, - -0.19528988003730774, - 2.3601181507110596 + 4.521160125732422, + 1.6218571662902832, + 6.5287604331970215 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 5, - "label": "GPU Inference" + "id": 15, + "label": "PyTorch Models" } ], "_embedding": [ @@ -291531,47 +291728,47 @@ "abstract": "GUI tool for managing archived sessions in Claude Desktop (Cowork) - SugaCrypto/cowork-archive-manager", "projections": { "pca": [ - 0.3332984745502472, - 0.02551627717912197 + 0.33236560225486755, + 0.025312334299087524 ], "tsne": [ - 25.133760452270508, - 18.578418731689453 + 31.515790939331055, + -0.02915697731077671 ], "umap": [ - 3.924300193786621, - 6.26609992980957 + 3.448488235473633, + 4.945943355560303 ], "pca3d": [ - 0.3332989513874054, - 0.025521039962768555, - -0.14635305106639862 + 0.3323661684989929, + 0.025315988808870316, + -0.15058015286922455 ], "tsne3d": [ - 37.85456848144531, - 29.762529373168945, - 20.301408767700195 + 32.341102600097656, + 23.75322151184082, + 14.685043334960938 ], "umap3d": [ - 4.278287410736084, - 0.17426493763923645, - 2.8349123001098633 + 4.48613977432251, + 2.414339542388916, + 6.36738920211792 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 16, - "label": "Claude Code" + "id": 11, + "label": "Code Tools" } ], "_embedding": [ @@ -291979,47 +292176,47 @@ "abstract": "Contribute to Frayo44/agent-view development by creating an account on GitHub.", "projections": { "pca": [ - 0.33824029564857483, - 0.08404936641454697 + 0.3387681841850281, + 0.08481845259666443 ], "tsne": [ - 29.23638343811035, - 13.0887451171875 + 32.4925651550293, + -7.29071044921875 ], "umap": [ - 3.7003140449523926, - 6.916215896606445 + 3.2285706996917725, + 5.693546772003174 ], "pca3d": [ - 0.33824145793914795, - 0.0840592011809349, - -0.08110444247722626 + 0.3387698531150818, + 0.0848257914185524, + -0.08680617064237595 ], "tsne3d": [ - 36.02960205078125, - 46.21023178100586, - 26.39305305480957 + 50.0908203125, + 13.049260139465332, + 10.12597370147705 ], "umap3d": [ - 3.498377799987793, - -0.03003346174955368, - 2.9653208255767822 + 4.000513076782227, + 2.847501754760742, + 6.633712291717529 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 18, - "label": "Github Tools" + "id": 17, + "label": "GitHub Creation" } ], "_embedding": [ @@ -292425,47 +292622,47 @@ ], "projections": { "pca": [ - 0.2244240641593933, - 0.14763212203979492 + 0.22614987194538116, + 0.1501023918390274 ], "tsne": [ - 16.534927368164062, - 19.64322853088379 + 25.60011100769043, + 6.005133152008057 ], "umap": [ - 3.6112663745880127, - 5.330766201019287 + 3.0898513793945312, + 4.195727825164795 ], "pca3d": [ - 0.22442440688610077, - 0.1476324051618576, - 0.048517268151044846 + 0.2261504977941513, + 0.15010173618793488, + 0.0423746295273304 ], "tsne3d": [ - 36.19274139404297, - 11.144474029541016, - 27.32025718688965 + 12.573728561401367, + 28.485300064086914, + 18.767366409301758 ], "umap3d": [ - 4.190131187438965, - 0.806184709072113, - 2.242558717727661 + 4.124888896942139, + 2.310781240463257, + 5.620100975036621 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 20, - "label": "AI Agents" + "id": 4, + "label": "Open Agent Tools" } ], "_embedding": [ @@ -292873,47 +293070,47 @@ "abstract": "Contribute to runpod/runpod-mcp development by creating an account on GitHub.", "projections": { "pca": [ - 0.4051174223423004, - 0.018550056964159012 + 0.403639554977417, + 0.01805168017745018 ], "tsne": [ - 26.11358642578125, - 12.718608856201172 + 29.672863006591797, + -6.094522476196289 ], "umap": [ - 3.645967960357666, - 6.766213893890381 + 3.3427326679229736, + 5.536609172821045 ], "pca3d": [ - 0.4051191508769989, - 0.0185687392950058, - -0.2122412472963333 + 0.40364137291908264, + 0.018068116158246994, + -0.21754798293113708 ], "tsne3d": [ - 33.0544548034668, - 40.282466888427734, - 14.436634063720703 + 40.70149612426758, + 17.326753616333008, + 7.3864336013793945 ], "umap3d": [ - 3.605349540710449, - -0.1385435312986374, - 2.8106722831726074 + 3.87992000579834, + 2.7172179222106934, + 6.784028053283691 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 18, - "label": "Github Tools" + "id": 17, + "label": "GitHub Creation" } ], "_embedding": [ @@ -293321,47 +293518,47 @@ "abstract": "From the Zed Blog: The editor we set out to build is now 1.0.", "projections": { "pca": [ - 0.3107410669326782, - 0.0962584838271141 + 0.3097229301929474, + 0.09568527340888977 ], "tsne": [ - 20.6156005859375, - 11.125945091247559 + 23.810884475708008, + -3.1112165451049805 ], "umap": [ - 3.3952293395996094, - 6.2048492431640625 + 2.5148425102233887, + 4.980528831481934 ], "pca3d": [ - 0.31074100732803345, - 0.09626013785600662, - -0.10263848304748535 + 0.30972278118133545, + 0.09568718075752258, + -0.1076728105545044 ], "tsne3d": [ - 20.064001083374023, - 9.375714302062988, - 17.16876792907715 + 20.852275848388672, + 6.0994696617126465, + 3.296706438064575 ], "umap3d": [ - 3.8321897983551025, - 0.3671117424964905, - 1.8865488767623901 + 3.4784209728240967, + 2.3128256797790527, + 6.138594627380371 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 18, - "label": "Github Tools" + "id": 24, + "label": "SMB Models" } ], "_embedding": [ @@ -293769,47 +293966,47 @@ "abstract": "Claude Code is an agentic coding tool that reads your codebase, edits files, runs commands, and integrates with your development tools. Available in your terminal, IDE, desktop app, and browser.", "projections": { "pca": [ - 0.23634815216064453, - 0.06820893287658691 + 0.23733662068843842, + 0.06831923872232437 ], "tsne": [ - 24.481107711791992, - 23.400938034057617 + 33.5708122253418, + 4.972947120666504 ], "umap": [ - 4.434330940246582, - 6.0687971115112305 + 3.8663625717163086, + 4.595489501953125 ], "pca3d": [ - 0.23634640872478485, - 0.06817910075187683, - -0.09248074889183044 + 0.23733481764793396, + 0.06829207390546799, + -0.09340076893568039 ], "tsne3d": [ - 51.31425857543945, - 22.400693893432617, - 11.750436782836914 + 29.211416244506836, + 36.63508224487305, + 10.76146125793457 ], "umap3d": [ - 4.9351606369018555, - 0.33264490962028503, - 3.00146222114563 + 4.98442268371582, + 2.1671080589294434, + 6.338869094848633 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { - "id": 16, - "label": "Claude Code" + "id": 11, + "label": "Code Tools" } ], "_embedding": [ @@ -294217,47 +294414,47 @@ "abstract": "\ud83e\udea8 why use many token when few token do trick \u2014 Claude Code skill that cuts 65% of tokens by talking like caveman - JuliusBrussee/caveman", "projections": { "pca": [ - 0.17226429283618927, - 0.15762752294540405 + 0.17135988175868988, + 0.15641765296459198 ], "tsne": [ - 24.248462677001953, - 22.697795867919922 + 32.78285598754883, + 4.570207595825195 ], "umap": [ - 4.226647853851318, - 6.023815631866455 + 3.566828966140747, + 4.447281360626221 ], "pca3d": [ - 0.17226295173168182, - 0.15760798752307892, - -0.1512032449245453 + 0.1713586002588272, + 0.156398206949234, + -0.15401627123355865 ], "tsne3d": [ - 47.67970275878906, - 20.968189239501953, - 15.53446102142334 + 31.388866424560547, + 32.889163970947266, + 9.971102714538574 ], "umap3d": [ - 4.941147804260254, - 0.22354155778884888, - 2.852008104324341 + 4.766562461853027, + 2.0165066719055176, + 6.35649299621582 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { - "id": 16, - "label": "Claude Code" + "id": 11, + "label": "Code Tools" } ], "_embedding": [ @@ -294665,47 +294862,47 @@ "abstract": "Show usage stats for OpenAI Codex and Claude Code, without having to login. - steipete/CodexBar", "projections": { "pca": [ - 0.29039520025253296, - -0.034451209008693695 + 0.2917456030845642, + -0.03271692246198654 ], "tsne": [ - 23.285730361938477, - 19.7371883392334 + 30.782672882080078, + 2.123431444168091 ], "umap": [ - 4.148982524871826, - 6.063519477844238 + 3.5127527713775635, + 4.801170825958252 ], "pca3d": [ - 0.29039517045021057, - -0.03445646911859512, - -0.08186937123537064 + 0.29174575209617615, + -0.03272295743227005, + -0.08426635712385178 ], "tsne3d": [ - 40.05269241333008, - 20.58904457092285, - 18.170875549316406 + 24.578283309936523, + 23.91653823852539, + 15.300148010253906 ], "umap3d": [ - 4.621599197387695, - 0.3049863576889038, - 2.7251603603363037 + 4.5208048820495605, + 2.1955761909484863, + 6.231759071350098 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 8, - "label": "Open Models" + "id": 0, + "label": "GitHub Tools" }, { - "id": 16, - "label": "Claude Code" + "id": 11, + "label": "Code Tools" } ], "_embedding": [ @@ -295114,47 +295311,47 @@ "abstract": "OpenAI Privacy Filter is an open-weight model for detecting and redacting personally identifiable information (PII) in text with state-of-the-art accuracy", "projections": { "pca": [ - 0.034242719411849976, - 0.1349666267633438 + 0.03792940080165863, + 0.13711336255073547 ], "tsne": [ - -13.850519180297852, - 7.7384161949157715 + 10.863747596740723, + 23.28956413269043 ], "umap": [ - 2.9193763732910156, - 3.4609780311584473 + 1.9652094841003418, + 2.284187078475952 ], "pca3d": [ - 0.0342429056763649, - 0.13497164845466614, - 0.14122043550014496 + 0.03792954608798027, + 0.13711678981781006, + 0.13743263483047485 ], "tsne3d": [ - 28.932079315185547, - -28.05072784423828, - 28.539274215698242 + 25.470325469970703, + 0.30675533413887024, + -27.46460723876953 ], "umap3d": [ - 6.091465473175049, - -0.14026303589344025, - 0.9570540189743042 + 4.519922733306885, + -0.13880042731761932, + 6.063998699188232 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 6, + "label": "Learning Techniques" } ], "_embedding": [ @@ -295564,47 +295761,47 @@ "abstract": "Introducing GPT-5.5, our smartest model yet\u2014faster, more capable, and built for complex tasks like coding, research, and data analysis across tools.", "projections": { "pca": [ - 0.07758986949920654, - 0.07170214504003525 + 0.07957425713539124, + 0.07400523871183395 ], "tsne": [ - 2.5834131240844727, - 13.694681167602539 + 1.3292239904403687, + 1.0784978866577148 ], "umap": [ - 4.098692417144775, - 4.191763877868652 + 3.2203197479248047, + 2.7889492511749268 ], "pca3d": [ - 0.07758940011262894, - 0.07170439511537552, - 0.013554909266531467 + 0.07957372069358826, + 0.0740095004439354, + 0.013311891816556454 ], "tsne3d": [ - 25.742624282836914, - -5.337248802185059, - -0.4363933801651001 + -0.7714749574661255, + 33.08405685424805, + -8.173837661743164 ], "umap3d": [ - 5.3943281173706055, - 1.1512287855148315, - 1.0469462871551514 + 3.8076717853546143, + 0.6633155941963196, + 4.879425048828125 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, - "label": "AI Agents" + "id": 8, + "label": "Open AI" }, { - "id": 1, - "label": "AI Agents" + "id": 22, + "label": "AI Gemini" } ], "_embedding": [ @@ -296014,47 +296211,47 @@ "abstract": "Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.", "projections": { "pca": [ - 0.15470781922340393, - 0.04005489870905876 + 0.15714947879314423, + 0.04186507686972618 ], "tsne": [ - 21.879331588745117, - 27.806476593017578 + 33.432594299316406, + 10.47861385345459 ], "umap": [ - 4.8871049880981445, - 5.933173179626465 + 4.357064247131348, + 4.504946708679199 ], "pca3d": [ - 0.15470723807811737, - 0.04005030542612076, - 0.10486655682325363 + 0.15714886784553528, + 0.0418609157204628, + 0.10212692618370056 ], "tsne3d": [ - 57.371368408203125, - 13.847107887268066, - 6.81225061416626 + 20.72344398498535, + 40.17498779296875, + 7.216874122619629 ], "umap3d": [ - 5.2436394691467285, - 0.7298962473869324, - 3.1367576122283936 + 5.453386306762695, + 2.252786636352539, + 6.093479633331299 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { - "id": 16, - "label": "Claude Code" + "id": 11, + "label": "Code Tools" } ], "_embedding": [ @@ -296463,47 +296660,47 @@ "title": "Using Claude Code: session management and 1M context | Claude", "projections": { "pca": [ - 0.17737977206707, - 0.08085916936397552 + 0.17598725855350494, + 0.07969684153795242 ], "tsne": [ - 26.631750106811523, - 23.13558578491211 + 35.25475311279297, + 3.669753313064575 ], "umap": [ - 4.465428352355957, - 6.0581583976745605 + 3.809354305267334, + 4.627030372619629 ], "pca3d": [ - 0.17737837135791779, - 0.0808376893401146, - -0.16209676861763 + 0.17598597705364227, + 0.0796779990196228, + -0.16178564727306366 ], "tsne3d": [ - 57.86983108520508, - 30.744173049926758, - 16.320316314697266 + 35.23284149169922, + 37.210750579833984, + 15.382292747497559 ], "umap3d": [ - 4.871067523956299, - 0.22869397699832916, - 2.943049430847168 + 4.896029949188232, + 2.0457229614257812, + 6.438683032989502 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { - "id": 16, - "label": "Claude Code" + "id": 11, + "label": "Code Tools" } ], "_embedding": [ @@ -296910,47 +297107,47 @@ "title": "GitHub Spec Kit | Spec Kit Documentation", "projections": { "pca": [ - 0.3685377836227417, - 0.037193235009908676 + 0.36931928992271423, + 0.03810914605855942 ], "tsne": [ - 23.095083236694336, - 11.150721549987793 + 26.65833282470703, + -4.661428451538086 ], "umap": [ - 3.513606071472168, - 6.566354274749756 + 2.862283706665039, + 5.187346458435059 ], "pca3d": [ - 0.3685382008552551, - 0.03720228746533394, - -0.10022930055856705 + 0.36931994557380676, + 0.03811873123049736, + -0.10521867126226425 ], "tsne3d": [ - 20.4472713470459, - 41.314491271972656, - 17.653194427490234 + 36.95328903198242, + 9.533183097839355, + 1.6509690284729004 ], "umap3d": [ - 3.9538495540618896, - -0.16559740900993347, - 2.57297682762146 + 3.956005096435547, + 2.3895602226257324, + 6.602638244628906 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 18, - "label": "Github Tools" + "id": 17, + "label": "GitHub Creation" } ], "_embedding": [ @@ -297357,47 +297554,47 @@ "title": "Best practices for using Claude Opus 4.7 with Claude Code | Claude", "projections": { "pca": [ - 0.2835003733634949, - 0.048721641302108765 + 0.28527194261550903, + 0.0497940331697464 ], "tsne": [ - 25.754039764404297, - 23.21070671081543 + 34.44390106201172, + 4.196329116821289 ], "umap": [ - 4.476540565490723, - 6.057674407958984 + 3.872948408126831, + 4.618551254272461 ], "pca3d": [ - 0.28349897265434265, - 0.048710934817790985, - -0.10436262935400009 + 0.2852707505226135, + 0.04978692904114723, + -0.10807641595602036 ], "tsne3d": [ - 52.65504455566406, - 27.037294387817383, - 15.917021751403809 + 29.595703125, + 36.36793518066406, + 16.07853889465332 ], "umap3d": [ - 4.888386249542236, - 0.26063597202301025, - 2.930621862411499 + 4.933241844177246, + 2.0158586502075195, + 6.445517539978027 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { - "id": 16, - "label": "Claude Code" + "id": 11, + "label": "Code Tools" } ], "_embedding": [ @@ -297805,47 +298002,47 @@ "abstract": "The original nirholas/claude-code before DMCA and take down. Once everything is cleared, it will return. Working with Anthropic and Github to get everything back. - nirholas/fresh-start", "projections": { "pca": [ - 0.3411208689212799, - -0.04562118649482727 + 0.3411063849925995, + -0.045555878430604935 ], "tsne": [ - 23.22184181213379, - 21.641071319580078 + 33.5032958984375, + 1.9309463500976562 ], "umap": [ - 4.481632709503174, - 6.154532432556152 + 3.818016529083252, + 4.757870674133301 ], "pca3d": [ - 0.34112074971199036, - -0.045616284012794495, - -0.1409338414669037 + 0.3411065340042114, + -0.04555167630314827, + -0.14409935474395752 ], "tsne3d": [ - 45.52839660644531, - 27.5761775970459, - 9.5534029006958 + 23.281360626220703, + 32.55455017089844, + 12.36567211151123 ], "umap3d": [ - 4.757833957672119, - 0.43211570382118225, - 3.0558817386627197 + 4.871583938598633, + 2.2633540630340576, + 6.294969081878662 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { - "id": 16, - "label": "Claude Code" + "id": 11, + "label": "Code Tools" } ], "_embedding": [ @@ -298253,47 +298450,47 @@ "abstract": "Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.", "projections": { "pca": [ - 0.1329837143421173, - 0.08452630043029785 + 0.1354057639837265, + 0.08628197759389877 ], "tsne": [ - 21.67010498046875, - 28.14800262451172 + 33.21886444091797, + 10.897491455078125 ], "umap": [ - 4.970141887664795, - 5.905336380004883 + 4.398497104644775, + 4.529842376708984 ], "pca3d": [ - 0.13298363983631134, - 0.08453365415334702, - 0.1003960371017456 + 0.13540560007095337, + 0.08628834038972855, + 0.0978829637169838 ], "tsne3d": [ - 53.91608810424805, - 12.77177619934082, - 3.2127130031585693 + 21.50764274597168, + 44.297061920166016, + 8.43303108215332 ], "umap3d": [ - 5.300084114074707, - 0.7312822341918945, - 3.166792869567871 + 5.521012783050537, + 2.239461660385132, + 6.066454887390137 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { - "id": 16, - "label": "Claude Code" + "id": 11, + "label": "Code Tools" } ], "_embedding": [ @@ -298701,47 +298898,47 @@ "abstract": "Every meeting, every idea, every voice note \u2014 searchable by your AI. Open-source, privacy-first conversation memory layer. - silverstein/minutes", "projections": { "pca": [ - 0.1837213635444641, - 0.09017765522003174 + 0.18432889878749847, + 0.08897671848535538 ], "tsne": [ - -4.3397626876831055, - 5.2905592918396 + 17.41457748413086, + 25.484949111938477 ], "umap": [ - 0.8653602004051208, - 6.219343662261963 + 0.1644175499677658, + 3.449977159500122 ], "pca3d": [ - 0.18372058868408203, - 0.09016643464565277, - -0.019031427800655365 + 0.1843280792236328, + 0.0889655351638794, + -0.021597152575850487 ], "tsne3d": [ - 17.389774322509766, - 26.92117691040039, - -18.8649845123291 + 14.6658353805542, + -6.8423686027526855, + 9.912569999694824 ], "umap3d": [ - 5.270506858825684, - -0.5312538743019104, - -0.7815654277801514 + 2.224616527557373, + 0.926169753074646, + 6.922321319580078 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 4, - "label": "Com Area Amp" + "id": 2, + "label": "Medical Models" }, { "id": 0, - "label": "Clinical LLMs" + "label": "Multimodal Models" } ], "_embedding": [ @@ -299149,47 +299346,47 @@ "abstract": "Use Garry Tan's exact Claude Code setup: 23 opinionated tools that serve as CEO, Designer, Eng Manager, Release Manager, Doc Engineer, and QA - garrytan/gstack", "projections": { "pca": [ - 0.2704407870769501, - 0.04509279504418373 + 0.26905199885368347, + 0.04519762471318245 ], "tsne": [ - 24.726778030395508, - 19.345561981201172 + 31.405004501342773, + 0.7990646958351135 ], "umap": [ - 4.139476776123047, - 6.092493057250977 + 3.5238094329833984, + 4.759099006652832 ], "pca3d": [ - 0.27044087648391724, - 0.045089174062013626, - -0.14660963416099548 + 0.2690519690513611, + 0.045193862169981, + -0.1498618721961975 ], "tsne3d": [ - 40.082672119140625, - 27.740022659301758, - 15.422089576721191 + 29.291704177856445, + 27.234525680541992, + 13.211490631103516 ], "umap3d": [ - 4.502577781677246, - 0.2361818253993988, - 2.8569390773773193 + 4.695718288421631, + 2.299387216567993, + 6.309149265289307 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 8, - "label": "Open Models" + "id": 0, + "label": "GitHub Tools" }, { - "id": 16, - "label": "Claude Code" + "id": 11, + "label": "Code Tools" } ], "_embedding": [ @@ -299599,47 +299796,47 @@ "abstract": "Twinkle\u2728: Training workbench to make your model glow. - modelscope/twinkle", "projections": { "pca": [ - 0.25283005833625793, - 0.05334038287401199 + 0.25287309288978577, + 0.05278760939836502 ], "tsne": [ - 15.451533317565918, - 12.305768966674805 + 20.1469669342041, + 0.4870785176753998 ], "umap": [ - 3.0789473056793213, - 5.745938301086426 + 2.3524539470672607, + 4.692008018493652 ], "pca3d": [ - 0.2528294324874878, - 0.05334741994738579, - -0.09040319919586182 + 0.25287261605262756, + 0.05279608815908432, + -0.09148437529802322 ], "tsne3d": [ - 21.864274978637695, - 23.898916244506836, - 0.8500057458877563 + 15.273059844970703, + 17.810298919677734, + 17.83156394958496 ], "umap3d": [ - 4.163025379180908, - 0.17981785535812378, - 1.6821357011795044 + 3.6502139568328857, + 1.6476534605026245, + 5.8939595222473145 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 20, - "label": "AI Agents" + "id": 24, + "label": "SMB Models" } ], "_embedding": [ @@ -300047,47 +300244,47 @@ "abstract": "How we built agent sandboxing on macOS, Linux, and Windows to reduce interruptions while improving security.", "projections": { "pca": [ - 0.21574465930461884, - 0.16888639330863953 + 0.2180340588092804, + 0.17059719562530518 ], "tsne": [ - 17.655733108520508, - 17.33908462524414 + 24.38792610168457, + 3.668041229248047 ], "umap": [ - 3.5931875705718994, - 5.607707977294922 + 2.886871814727783, + 4.334677219390869 ], "pca3d": [ - 0.21574486792087555, - 0.168894961476326, - -0.03635222464799881 + 0.21803410351276398, + 0.17060494422912598, + -0.04058120772242546 ], "tsne3d": [ - 37.00249099731445, - 13.340555191040039, - 13.354511260986328 + 17.406152725219727, + 12.975520133972168, + -0.9020887017250061 ], "umap3d": [ - 4.46038293838501, - 0.34646740555763245, - 1.9983159303665161 + 4.23652982711792, + 1.886457085609436, + 6.079313278198242 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 20, - "label": "AI Agents" + "id": 4, + "label": "Open Agent Tools" } ], "_embedding": [ @@ -300495,47 +300692,47 @@ "abstract": "Google Workspace CLI \u2014 one command-line tool for Drive, Gmail, Calendar, Sheets, Docs, Chat, Admin, and more. Dynamically built from Google Discovery Service. Includes AI agent skills. - googleworkspace/cli", "projections": { "pca": [ - 0.2911614775657654, - 0.004562985617667437 + 0.29298579692840576, + 0.005504444241523743 ], "tsne": [ - 20.292278289794922, - 17.28372573852539 + 28.549901962280273, + 4.127223968505859 ], "umap": [ - 3.728365421295166, - 6.250040531158447 + 3.232811689376831, + 4.990866184234619 ], "pca3d": [ - 0.2911611795425415, - 0.004557754844427109, - -0.02933119609951973 + 0.2929856479167938, + 0.005500642117112875, + -0.03231227025389671 ], "tsne3d": [ - 38.31678009033203, - 32.90291213989258, - 2.0720489025115967 + 30.734241485595703, + 18.723121643066406, + -2.5555269718170166 ], "umap3d": [ - 4.121267795562744, - 0.26000848412513733, - 2.6775903701782227 + 4.148500442504883, + 2.432283639907837, + 6.314742088317871 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 18, - "label": "Github Tools" + "id": 17, + "label": "GitHub Creation" } ], "_embedding": [ @@ -300942,47 +301139,47 @@ "title": "Use the Mikado Method to do safe changes in a complex codebase - Change Messy Software Without Breaking It", "projections": { "pca": [ - 0.1678474098443985, - 0.0641498789191246 + 0.17006810009479523, + 0.06598993390798569 ], "tsne": [ - -6.249024391174316, - 19.85262107849121 + 11.363432884216309, + 18.559053421020508 ], "umap": [ - 3.121821880340576, - 4.166282653808594 + 2.2686245441436768, + 3.3755619525909424 ], "pca3d": [ - 0.167847141623497, - 0.06416509300470352, - -0.05801251903176308 + 0.1700679361820221, + 0.06600740551948547, + -0.061852604150772095 ], "tsne3d": [ - 27.58645248413086, - 18.871835708618164, - -37.42461013793945 + 19.870973587036133, + 15.922842025756836, + -14.801422119140625 ], "umap3d": [ - 5.399386405944824, - -0.11666618287563324, - 1.8494088649749756 + 4.456249713897705, + 0.7138543128967285, + 6.131686210632324 ] }, "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_label": "Open Source Tools", "cluster_levels": [ { "id": 3, - "label": "AI Training Models" + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 1, - "label": "AI Agents" + "id": 15, + "label": "PyTorch Models" } ], "_embedding": [ @@ -301391,47 +301588,47 @@ "abstract": "We\u2019re on a journey to advance and democratize artificial intelligence through open source and open science.", "projections": { "pca": [ - 0.207376629114151, - 0.1538875550031662 + 0.20916131138801575, + 0.15477250516414642 ], "tsne": [ - 6.694131851196289, - 22.784038543701172 + 24.82052993774414, + 12.36745834350586 ], "umap": [ - 4.88267183303833, - 4.8780670166015625 + 3.9978160858154297, + 3.143617630004883 ], "pca3d": [ - 0.20737619698047638, - 0.15389618277549744, - 0.09124122560024261 + 0.20916089415550232, + 0.15478070080280304, + 0.08802362531423569 ], "tsne3d": [ - 47.66350173950195, - 0.3637683391571045, - -2.046584129333496 + 3.5295779705047607, + 44.98176574707031, + 7.372729301452637 ], "umap3d": [ - 5.438432693481445, - 1.3574467897415161, - 1.9367567300796509 + 4.979237079620361, + 1.9515496492385864, + 4.8464531898498535 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { "id": 7, - "label": "Open AI" + "label": "Hugging Face Models" } ], "_embedding": [ @@ -301838,47 +302035,47 @@ "title": "Why SambaNova", "projections": { "pca": [ - 0.30172431468963623, - -0.05195922404527664 + 0.3012004494667053, + -0.05326050519943237 ], "tsne": [ - 13.292150497436523, - 3.0780301094055176 + 14.158326148986816, + 2.09733247756958 ], "umap": [ - 1.8307085037231445, - 5.705838203430176 + 1.3258992433547974, + 4.615680694580078 ], "pca3d": [ - 0.30172333121299744, - -0.05197529494762421, - 0.006768320221453905 + 0.3011995851993561, + -0.053274549543857574, + 0.0021969773806631565 ], "tsne3d": [ - 17.0897216796875, - 7.612916469573975, - 4.906497955322266 + 29.996522903442383, + -4.429019451141357, + 3.5374865531921387 ], "umap3d": [ - 3.618256092071533, - 0.7083752751350403, - 1.2018756866455078 + 2.883336305618286, + 1.7438774108886719, + 5.615352630615234 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 4, - "label": "Com Area Amp" + "id": 6, + "label": "Data Needs" }, { - "id": 26, - "label": "AI Information" + "id": 10, + "label": "Research Data" } ], "_embedding": [ @@ -302286,47 +302483,47 @@ "abstract": "Learn how Chai Discovery moves seamlessly from ML experimentation to production antibody pipelines with Modal.", "projections": { "pca": [ - 0.05212007462978363, - -0.016324453055858612 + 0.05182979628443718, + -0.01657608523964882 ], "tsne": [ - 4.206262111663818, - -6.079433441162109 + -1.3121042251586914, + -5.894749164581299 ], "umap": [ - -0.7397043704986572, - 4.683716297149658 + -0.7931030988693237, + 5.425812244415283 ], "pca3d": [ - 0.052119553089141846, - -0.016344187781214714, - -0.049904584884643555 + 0.05182923004031181, + -0.01659487560391426, + -0.04971030354499817 ], "tsne3d": [ - -8.467828750610352, - 1.6766808032989502, - 31.579524993896484 + -3.53556752204895, + -27.119991302490234, + -17.21167755126953 ], "umap3d": [ - 3.32497239112854, - 0.36034226417541504, - -0.49083855748176575 + 2.0460081100463867, + 0.31962066888809204, + 5.002748012542725 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 2, - "label": "AI Drug Discovery" + "id": 9, + "label": "Drug Discovery" }, { - "id": 13, - "label": "Drug Discovery" + "id": 28, + "label": "Spatial Drug" } ], "_embedding": [ @@ -302733,47 +302930,47 @@ "title": "Storage - Hugging Face", "projections": { "pca": [ - 0.08730974793434143, - 0.1419481784105301 + 0.09036801010370255, + 0.14323675632476807 ], "tsne": [ - 2.8691370487213135, - 25.323368072509766 + 20.84510040283203, + 19.212976455688477 ], "umap": [ - 4.5842604637146, - 4.296136379241943 + 3.659799575805664, + 3.4854001998901367 ], "pca3d": [ - 0.0873088613152504, - 0.14195577800273895, - 0.023101085796952248 + 0.09036700427532196, + 0.14324353635311127, + 0.02119540423154831 ], "tsne3d": [ - 45.375240325927734, - -26.418718338012695, - 14.398275375366211 + 8.72459888458252, + 47.05661392211914, + -13.627559661865234 ], "umap3d": [ - 4.799556255340576, - 1.7135764360427856, - 1.9469759464263916 + 4.289639949798584, + 2.053246021270752, + 4.517910957336426 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { "id": 7, - "label": "Open AI" + "label": "Hugging Face Models" } ], "_embedding": [ @@ -303181,47 +303378,47 @@ "abstract": "We\u2019re releasing a major upgrade to Gemini 3 Deep Think, our specialized reasoning mode.", "projections": { "pca": [ - 0.16014420986175537, - 0.10988207161426544 + 0.1628580540418625, + 0.11127857863903046 ], "tsne": [ - 3.771277904510498, - 30.9381160736084 + 27.3326358795166, + 19.061269760131836 ], "umap": [ - 4.3315229415893555, - 4.6198649406433105 + 3.498302698135376, + 2.7186806201934814 ], "pca3d": [ - 0.16014282405376434, - 0.10987737029790878, - 0.12815313041210175 + 0.16285668313503265, + 0.11127693951129913, + 0.12458408623933792 ], "tsne3d": [ - 52.800106048583984, - -1.3466885089874268, - -16.899797439575195 + -10.824063301086426, + 39.65144729614258, + -7.395722389221191 ], "umap3d": [ - 5.479426383972168, - 1.1778044700622559, - 1.5530383586883545 + 4.760444641113281, + 1.4158658981323242, + 4.846768379211426 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { "id": 7, - "label": "Open AI" + "label": "Hugging Face Models" } ], "_embedding": [ @@ -303629,47 +303826,47 @@ "abstract": "Minions are Stripe\u2019s homegrown coding agents, responsible for more than a thousand pull requests merged each week. Though humans review the code, minions write it from start to finish. Learn how they work, and how we built them.", "projections": { "pca": [ - 0.21084485948085785, - 0.11616220325231552 + 0.21087853610515594, + 0.11641994118690491 ], "tsne": [ - 21.108688354492188, - 23.433307647705078 + 30.84950828552246, + 5.927248477935791 ], "umap": [ - 4.071276664733887, - 5.812582015991211 + 3.4487533569335938, + 4.393897533416748 ], "pca3d": [ - 0.21084405481815338, - 0.1161469966173172, - -0.10302746295928955 + 0.2108778953552246, + 0.11640597879886627, + -0.10683005303144455 ], "tsne3d": [ - 48.524662017822266, - 22.693147659301758, - 1.7502894401550293 + 33.652706146240234, + 33.853355407714844, + 2.3820314407348633 ], "umap3d": [ - 5.075658321380615, - 0.3097054958343506, - 2.6322782039642334 + 4.844042778015137, + 1.8541837930679321, + 6.185329437255859 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { - "id": 16, - "label": "Claude Code" + "id": 11, + "label": "Code Tools" } ], "_embedding": [ @@ -304076,47 +304273,47 @@ "title": "Notes on structured concurrency, or: Go statement considered harmful — njs blog", "projections": { "pca": [ - 0.17865411937236786, - 0.05099613592028618 + 0.1783715933561325, + 0.050761736929416656 ], "tsne": [ - 5.130953311920166, - 7.722758769989014 + 12.067306518554688, + 9.394932746887207 ], "umap": [ - 3.0186493396759033, - 4.37632417678833 + 2.248661994934082, + 3.712160110473633 ], "pca3d": [ - 0.1786535233259201, - 0.050999965518713, - -0.09507012367248535 + 0.1783711016178131, + 0.050764698535203934, + -0.09907060116529465 ], "tsne3d": [ - 34.31992721557617, - 20.11534881591797, - -29.10045051574707 + 25.943218231201172, + 10.462210655212402, + -17.27657127380371 ], "umap3d": [ - 5.137925624847412, - -0.018214689567685127, - 1.9298326969146729 + 4.403625011444092, + 1.0900720357894897, + 6.172163963317871 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 5, - "label": "GPU Inference" + "id": 15, + "label": "PyTorch Models" } ], "_embedding": [ @@ -304524,47 +304721,47 @@ "abstract": "The open source coding agent. Contribute to anomalyco/opencode development by creating an account on GitHub.", "projections": { "pca": [ - 0.2887476086616516, - 0.09417121112346649 + 0.29031607508659363, + 0.09505602717399597 ], "tsne": [ - 22.273386001586914, - 20.0678768157959 + 29.88911247253418, + 2.040905237197876 ], "umap": [ - 4.124562740325928, - 6.0904693603515625 + 3.5133907794952393, + 4.824721813201904 ], "pca3d": [ - 0.28874778747558594, - 0.0941714197397232, - -0.08959605544805527 + 0.29031649231910706, + 0.09505469352006912, + -0.0930715799331665 ], "tsne3d": [ - 40.26892852783203, - 22.626224517822266, - 10.846161842346191 + 25.531084060668945, + 27.420862197875977, + 9.170449256896973 ], "umap3d": [ - 4.549587726593018, - 0.315849632024765, - 2.842092990875244 + 4.576884746551514, + 2.2408554553985596, + 6.198751926422119 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 8, - "label": "Open Models" + "id": 0, + "label": "GitHub Tools" }, { - "id": 16, - "label": "Claude Code" + "id": 11, + "label": "Code Tools" } ], "_embedding": [ @@ -304972,47 +305169,47 @@ "abstract": "The Astro Technology Company is joining Cloudflare! Astro remains open-source, MIT-licensed, and platform-agnostic. With Cloudflare", "projections": { "pca": [ - 0.3144623041152954, - -0.0344117246568203 + 0.31668955087661743, + -0.033891309052705765 ], "tsne": [ - 24.935245513916016, - 0.3338720202445984 + 15.971282958984375, + 5.021279811859131 ], "umap": [ - 2.2959299087524414, - 6.443762302398682 + 2.7229197025299072, + 4.601497650146484 ], "pca3d": [ - 0.31446197628974915, - -0.03441210836172104, - 0.039848603308200836 + 0.3166889548301697, + -0.03389047458767891, + 0.03530527278780937 ], "tsne3d": [ - 24.453399658203125, - 12.900045394897461, - 36.68086242675781 + 12.33884334564209, + 33.50908279418945, + 29.310300827026367 ], "umap3d": [ - 3.806678533554077, - 0.6957617998123169, - 1.9687635898590088 + 3.5933167934417725, + 2.3944051265716553, + 5.677678108215332 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 4, + "label": "AI Agents" }, { - "id": 25, - "label": "Python Data" + "id": 4, + "label": "Open Agent Tools" } ], "_embedding": [ @@ -305420,47 +305617,47 @@ "abstract": "Preview GitHub README.md files locally before committing them. - joeyespo/grip", "projections": { "pca": [ - 0.312912255525589, - 0.001512079848907888 + 0.3114334046840668, + 0.0010220715776085854 ], "tsne": [ - 24.716922760009766, - 12.494766235351562 + 28.813879013061523, + -4.258300304412842 ], "umap": [ - 3.506033182144165, - 6.6668701171875 + 3.157855987548828, + 5.509995937347412 ], "pca3d": [ - 0.3129129111766815, - 0.0015252521261572838, - -0.1989717334508896 + 0.3114345073699951, + 0.0010340827284380794, + -0.20094788074493408 ], "tsne3d": [ - 26.600740432739258, - 33.471046447753906, - 18.73705291748047 + 34.28752517700195, + 13.51024055480957, + 11.216139793395996 ], "umap3d": [ - 3.684227466583252, - -0.1543091982603073, - 2.7006895542144775 + 3.921463966369629, + 2.6490321159362793, + 6.738330841064453 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 18, - "label": "Github Tools" + "id": 17, + "label": "GitHub Creation" } ], "_embedding": [ @@ -305867,47 +306064,47 @@ "title": "GitHub - rsatija/biorxiv_preview_bot: When it detects a bioRxiv link in #papers, posts the title, author, and abstract", "projections": { "pca": [ - 0.2004503756761551, - 0.05396046116948128 + 0.2026256024837494, + 0.05661274865269661 ], "tsne": [ - 17.143238067626953, - 19.553176879882812 + 25.991769790649414, + 5.508484840393066 ], "umap": [ - 3.551433563232422, - 5.543468952178955 + 3.114776849746704, + 4.345656394958496 ], "pca3d": [ - 0.20045122504234314, - 0.053964097052812576, - -0.05181541293859482 + 0.2026265263557434, + 0.05661467835307121, + -0.05648702010512352 ], "tsne3d": [ - 34.67777633666992, - 16.421329498291016, - 28.0499267578125 + 13.992850303649902, + 25.322959899902344, + 21.814172744750977 ], "umap3d": [ - 4.073222637176514, - 0.733696460723877, - 2.3543450832366943 + 4.093413352966309, + 2.44114089012146, + 5.787258148193359 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 20, - "label": "AI Agents" + "id": 4, + "label": "Open Agent Tools" } ], "_embedding": [ @@ -306313,47 +306510,47 @@ ], "projections": { "pca": [ - 0.2352113276720047, - 0.06417278200387955 + 0.23742999136447906, + 0.06552514433860779 ], "tsne": [ - 11.760786056518555, - 12.970802307128906 + 16.083375930786133, + 6.1570940017700195 ], "umap": [ - 2.5312647819519043, - 6.119836807250977 + 2.7173640727996826, + 4.508320331573486 ], "pca3d": [ - 0.23521196842193604, - 0.0641842857003212, - 0.0475483238697052 + 0.23743081092834473, + 0.0655350461602211, + 0.0412922240793705 ], "tsne3d": [ - 29.8057861328125, - 9.474323272705078, - 34.974571228027344 + 10.137584686279297, + 33.563865661621094, + 24.441423416137695 ], "umap3d": [ - 3.8731698989868164, - 0.7762638330459595, - 2.0002288818359375 + 3.75127911567688, + 2.410046100616455, + 5.584913730621338 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 4, + "label": "AI Agents" }, { - "id": 3, - "label": "SMB Data" + "id": 4, + "label": "Open Agent Tools" } ], "_embedding": [ @@ -306763,47 +306960,47 @@ "abstract": "We\u2019re on a journey to advance and democratize artificial intelligence through open source and open science.", "projections": { "pca": [ - 0.1503279060125351, - 0.18787583708763123 + 0.15278123319149017, + 0.18982616066932678 ], "tsne": [ - 9.219864845275879, - 21.268516540527344 + 20.817428588867188, + 11.42141342163086 ], "umap": [ - 4.928472518920898, - 4.667347431182861 + 4.040952205657959, + 3.0400960445404053 ], "pca3d": [ - 0.15032745897769928, - 0.18787944316864014, - 0.14048239588737488 + 0.15278059244155884, + 0.18982960283756256, + 0.13855873048305511 ], "tsne3d": [ - 39.16830062866211, - 2.9957337379455566, - 11.427135467529297 + 8.42125129699707, + 32.41158676147461, + 7.174991607666016 ], "umap3d": [ - 5.460255146026611, - 1.5323785543441772, - 1.9728046655654907 + 4.887088298797607, + 2.1024575233459473, + 4.75162935256958 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { "id": 7, - "label": "Open AI" + "label": "Hugging Face Models" } ], "_embedding": [ @@ -307213,47 +307410,47 @@ "abstract": "simple terminal UI for git commands. Contribute to jesseduffield/lazygit development by creating an account on GitHub.", "projections": { "pca": [ - 0.33529603481292725, - 0.026550566777586937 + 0.3345785439014435, + 0.026225075125694275 ], "tsne": [ - 26.28108787536621, - 14.203644752502441 + 30.52962875366211, + -4.268631935119629 ], "umap": [ - 3.6085703372955322, - 6.84372091293335 + 3.2148869037628174, + 5.623592376708984 ], "pca3d": [ - 0.3352968692779541, - 0.02656245417892933, - -0.22515520453453064 + 0.3345794081687927, + 0.026234660297632217, + -0.22816507518291473 ], "tsne3d": [ - 32.745086669921875, - 35.32180404663086, - 23.607269287109375 + 37.91703414916992, + 18.990110397338867, + 14.416962623596191 ], "umap3d": [ - 3.61851167678833, - -0.18827825784683228, - 2.910210132598877 + 4.027615070343018, + 2.7609641551971436, + 6.783237934112549 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 18, - "label": "Github Tools" + "id": 17, + "label": "GitHub Creation" } ], "_embedding": [ @@ -307661,47 +307858,47 @@ "abstract": "In this post, we demonstrate how to build a production-ready biomedical research agent by integrating Biomni", "projections": { "pca": [ - 0.12303279340267181, - -0.07220733910799026 + 0.12377505749464035, + -0.07022592425346375 ], "tsne": [ - 2.433255434036255, - -4.844568252563477 + -4.305666446685791, + -4.221121788024902 ], "umap": [ - -0.202850341796875, - 4.940138339996338 + -0.635309636592865, + 4.928227424621582 ], "pca3d": [ - 0.12303313612937927, - -0.07221426069736481, - 0.002119358628988266 + 0.12377545982599258, + -0.07023285329341888, + 0.00139469257555902 ], "tsne3d": [ - -9.269824028015137, - -3.1566176414489746, - 25.875009536743164 + -0.881036102771759, + -23.54538345336914, + -12.523298263549805 ], "umap3d": [ - 3.735438108444214, - 0.6646676063537598, - -0.7939961552619934 + 2.36141300201416, + 0.2989467978477478, + 4.931800365447998 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ - { - "id": 0, - "label": "Medical Data AI" - }, { "id": 2, - "label": "AI Drug Discovery" + "label": "Clinical AI Data" }, { - "id": 13, + "id": 9, "label": "Drug Discovery" + }, + { + "id": 9, + "label": "Biomedical AI" } ], "_embedding": [ @@ -308109,47 +308306,47 @@ "abstract": "Today, we are introducing Google Scholar Labs, a new feature that explores how generative AI can transform the process of answering detailed scholarly research questions\u2026", "projections": { "pca": [ - 0.07559463381767273, - 0.14781281352043152 + 0.07591760158538818, + 0.1481734663248062 ], "tsne": [ - 12.81485652923584, - 21.081501007080078 + 20.25636100769043, + 7.616418361663818 ], "umap": [ - 4.045340061187744, - 4.615881443023682 + 3.1442532539367676, + 3.2703700065612793 ], "pca3d": [ - 0.07559474557638168, - 0.1478072553873062, - 0.0557638518512249 + 0.07591786235570908, + 0.14816725254058838, + 0.05414824187755585 ], "tsne3d": [ - 35.578224182128906, - 6.2357892990112305, - -7.829059600830078 + 12.560914993286133, + 29.15931510925293, + -5.030619144439697 ], "umap3d": [ - 5.247478008270264, - 0.9876174926757812, - 1.575795292854309 + 4.477705001831055, + 1.4748793840408325, + 5.225233554840088 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, - "label": "AI Agents" + "id": 8, + "label": "Open AI" }, { - "id": 1, - "label": "AI Agents" + "id": 22, + "label": "AI Gemini" } ], "_embedding": [ @@ -308557,47 +308754,47 @@ "abstract": "Today we\u2019re releasing Gemini 3 \u2013 our most intelligent model that helps you bring any idea to life.", "projections": { "pca": [ - 0.2279387265443802, - 0.06329777091741562 + 0.22940990328788757, + 0.06283171474933624 ], "tsne": [ - 3.6873738765716553, - 30.781545639038086 + 27.36231803894043, + 18.88812828063965 ], "umap": [ - 4.32235050201416, - 4.66715669631958 + 3.3545138835906982, + 2.8309032917022705 ], "pca3d": [ - 0.227937713265419, - 0.06329452991485596, - 0.15481692552566528 + 0.22940891981124878, + 0.06283162534236908, + 0.15098510682582855 ], "tsne3d": [ - 55.87335205078125, - -2.495161771774292, - -13.890283584594727 + -13.517071723937988, + 37.752662658691406, + -5.76474666595459 ], "umap3d": [ - 5.373798847198486, - 1.1035616397857666, - 1.5600374937057495 + 4.725700378417969, + 1.4132938385009766, + 4.887567520141602 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { - "id": 7, - "label": "Open AI" + "id": 22, + "label": "AI Gemini" } ], "_embedding": [ @@ -309003,47 +309200,47 @@ ], "projections": { "pca": [ - 0.2670031189918518, - 0.16429463028907776 + 0.2679114043712616, + 0.1647762805223465 ], "tsne": [ - 16.115983963012695, - 19.545331954956055 + 25.184280395507812, + 5.964389801025391 ], "umap": [ - 3.569598913192749, - 5.31028938293457 + 3.0587432384490967, + 4.135824680328369 ], "pca3d": [ - 0.2670038342475891, - 0.16430670022964478, - -0.02729571983218193 + 0.26791226863861084, + 0.16478633880615234, + -0.03485659882426262 ], "tsne3d": [ - 30.872705459594727, - 10.632370948791504, - 27.22654914855957 + 8.86402702331543, + 27.423892974853516, + 20.966487884521484 ], "umap3d": [ - 4.206905841827393, - 0.7918215394020081, - 2.2615489959716797 + 4.092660427093506, + 2.317742109298706, + 5.615503787994385 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 20, - "label": "AI Agents" + "id": 4, + "label": "Open Agent Tools" } ], "_embedding": [ @@ -309450,47 +309647,47 @@ "title": "James Shore: The Accountability Problem", "projections": { "pca": [ - 0.09918546676635742, - 0.062144260853528976 + 0.1029566302895546, + 0.06483840197324753 ], "tsne": [ - -1.7433689832687378, - 20.28624153137207 + 7.96563196182251, + 13.682979583740234 ], "umap": [ - 3.2056946754455566, - 3.697580099105835 + 2.284806251525879, + 2.849912405014038 ], "pca3d": [ - 0.09918643534183502, - 0.062175508588552475, - 0.1626749336719513 + 0.10295771807432175, + 0.06486554443836212, + 0.158341184258461 ], "tsne3d": [ - 38.49098587036133, - -21.805360794067383, - -17.06838035583496 + -9.567334175109863, + 17.52823829650879, + -7.212976455688477 ], "umap3d": [ - 5.627513408660889, - 0.5534007549285889, - 0.819694459438324 + 4.29994010925293, + 0.3489019274711609, + 5.37789249420166 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, - "label": "Model Training" + "id": 4, + "label": "AI Agents" }, { - "id": 1, - "label": "AI Agents" + "id": 22, + "label": "AI Gemini" } ], "_embedding": [ @@ -309897,47 +310094,47 @@ "title": "Anatomy of a Modern Finetuning API \u2014 Benjamin Anderson", "projections": { "pca": [ - 0.042782433331012726, - 0.20760618150234222 + 0.04635730758309364, + 0.21104082465171814 ], "tsne": [ - -8.179093360900879, - 18.356430053710938 + 9.165437698364258, + 19.496915817260742 ], "umap": [ - 3.0147879123687744, - 3.5127179622650146 + 2.0843300819396973, + 2.639239549636841 ], "pca3d": [ - 0.04278199374675751, - 0.20761120319366455, - 0.0470786914229393 + 0.04635678976774216, + 0.21104684472084045, + 0.04287374019622803 ], "tsne3d": [ - 24.342161178588867, - 15.882698059082031, - -30.49582290649414 + -1.3158289194107056, + 28.447059631347656, + -33.488677978515625 ], "umap3d": [ - 5.920727252960205, - -0.1948903501033783, - 1.336775541305542 + 4.476467132568359, + 0.0584292970597744, + 5.926258087158203 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 22, + "label": "AI Gemini" } ], "_embedding": [ @@ -310347,47 +310544,47 @@ "abstract": "Synthetic Patient Population Simulator. Contribute to synthetichealth/synthea development by creating an account on GitHub.", "projections": { "pca": [ - 0.1401733160018921, - -0.13234752416610718 + 0.14220768213272095, + -0.1312871277332306 ], "tsne": [ - 32.323585510253906, - 0.050855398178100586 + 27.1280574798584, + -18.196687698364258 ], "umap": [ - 1.9764060974121094, - 7.139871597290039 + 1.8819222450256348, + 6.502771377563477 ], "pca3d": [ - 0.14017453789710999, - -0.1323324590921402, - 0.04632481932640076 + 0.14220882952213287, + -0.13127397000789642, + 0.04694388061761856 ], "tsne3d": [ - 6.263106346130371, - 53.74794006347656, - 28.94256591796875 + 3.2290308475494385, + 17.03756332397461, + 33.77485656738281 ], "umap3d": [ - 3.074504852294922, - -1.152669072151184, - 1.38872230052948 + 2.2631335258483887, + 2.0261406898498535, + 7.285006046295166 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 10, - "label": "Medical Data" + "id": 25, + "label": "Data Standards" } ], "_embedding": [ @@ -310797,47 +310994,47 @@ "abstract": "Dataloader for applying sequence models to personalized genomics - mcvickerlab/GenVarLoader", "projections": { "pca": [ - 0.0979694277048111, - -0.11969931423664093 + 0.09468308836221695, + -0.1195741519331932 ], "tsne": [ - 7.5267229080200195, - -25.218645095825195 + -8.22860050201416, + -23.337594985961914 ], "umap": [ - -2.6470096111297607, - 3.7580525875091553 + -2.033803701400757, + 7.448639392852783 ], "pca3d": [ - 0.0979696735739708, - -0.11969966441392899, - -0.21841663122177124 + 0.0946834534406662, + -0.11957276612520218, + -0.21841569244861603 ], "tsne3d": [ - -5.6771745681762695, - 48.45248031616211, - 22.30974769592285 + -15.129634857177734, + -11.54745864868164, + -18.60129737854004 ], "umap3d": [ - 1.153140902519226, - -0.038559384644031525, - -0.8824368119239807 + -0.20038069784641266, + 1.2055524587631226, + 4.058047294616699 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 22, - "label": "Genetic Variants" + "id": 3, + "label": "Genomic Sequences" } ], "_embedding": [ @@ -311245,47 +311442,47 @@ "abstract": "The best ChatGPT that $100 can buy. Contribute to karpathy/nanochat development by creating an account on GitHub.", "projections": { "pca": [ - 0.2906494140625, - 0.0008508864557370543 + 0.28971219062805176, + -0.0003126664087176323 ], "tsne": [ - 14.495439529418945, - 21.454072952270508 + 21.24247932434082, + 6.304710865020752 ], "umap": [ - 3.2255136966705322, - 5.633912086486816 + 2.9599335193634033, + 4.710943698883057 ], "pca3d": [ - 0.29064953327178955, - 0.0008494129288010299, - -0.0919974148273468 + 0.2897125780582428, + -0.0003161147178616375, + -0.09619370102882385 ], "tsne3d": [ - 32.35041046142578, - 14.463167190551758, - -12.979762077331543 + 18.42347526550293, + 24.123476028442383, + -10.045713424682617 ], "umap3d": [ - 4.265268802642822, - 0.2708403468132019, - 2.3141047954559326 + 4.159062385559082, + 2.092989206314087, + 6.132969379425049 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 0, - "label": "AI Agents" + "label": "GitHub Tools" }, { - "id": 20, - "label": "AI Agents" + "id": 4, + "label": "Open Agent Tools" } ], "_embedding": [ @@ -311693,47 +311890,47 @@ "abstract": "We built the heap, a 30 petabyte data storage cluster in downtown San Francisco, and spent under $500,000.", "projections": { "pca": [ - 0.22781497240066528, - -0.017595145851373672 + 0.22772805392742157, + -0.018205609172582626 ], "tsne": [ - 9.850706100463867, - 5.6044602394104 + 13.33409309387207, + -5.480970859527588 ], "umap": [ - 2.870335578918457, - 4.865359306335449 + 2.0094666481018066, + 4.127742290496826 ], "pca3d": [ - 0.22781455516815186, - -0.017590930685400963, - -0.10008582472801208 + 0.22772759199142456, + -0.0182016771286726, + -0.10197815299034119 ], "tsne3d": [ - -0.17194105684757233, - 34.98477554321289, - -16.4007568359375 + 30.30398941040039, + 5.057012557983398, + -20.40903091430664 ], "umap3d": [ - 4.587549209594727, - -0.34438571333885193, - 1.9773468971252441 + 4.108586311340332, + 1.4437549114227295, + 6.482564449310303 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 5, - "label": "GPU Inference" + "id": 15, + "label": "PyTorch Models" } ], "_embedding": [ @@ -312143,47 +312340,47 @@ "abstract": "Claude Sonnet 4.5 is the best coding model in the world, strongest model for building complex agents, and best model at using computers.", "projections": { "pca": [ - 0.12327655404806137, - 0.1499885618686676 + 0.12461511790752411, + 0.1499646157026291 ], "tsne": [ - 24.188013076782227, - 25.149415969848633 + 34.40228271484375, + 6.766481399536133 ], "umap": [ - 4.540456295013428, - 6.05312967300415 + 4.052561283111572, + 4.545914649963379 ], "pca3d": [ - 0.12327459454536438, - 0.14997373521327972, - 0.005157798994332552 + 0.12461316585540771, + 0.14995193481445312, + 0.0032706637866795063 ], "tsne3d": [ - 56.76651382446289, - 17.300132751464844, - 14.912997245788574 + 24.510272979736328, + 40.130897521972656, + 14.370692253112793 ], "umap3d": [ - 5.100547790527344, - 0.42610377073287964, - 3.1284079551696777 + 5.1361260414123535, + 2.112337112426758, + 6.297994613647461 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { - "id": 16, - "label": "Claude Code" + "id": 11, + "label": "Code Tools" } ], "_embedding": [ @@ -312591,47 +312788,47 @@ "abstract": "StringZilla v4 brings CUDA acceleration for string processing: 109x faster than Nvidia", "projections": { "pca": [ - 0.08020360767841339, - 0.0825062245130539 + 0.08100086450576782, + 0.08367982506752014 ], "tsne": [ - 8.394207000732422, - 7.713231086730957 + 13.278544425964355, + -8.19255542755127 ], "umap": [ - 2.7924296855926514, - 4.603386878967285 + 2.0107929706573486, + 3.9258222579956055 ], "pca3d": [ - 0.08020186424255371, - 0.08248699456453323, - -0.07732031494379044 + 0.08099906146526337, + 0.08366512507200241, + -0.0785173699259758 ], "tsne3d": [ - 1.6651184558868408, - 45.43522262573242, - -15.295246124267578 + 30.04296875, + -0.9242298603057861, + -15.679671287536621 ], "umap3d": [ - 4.7840800285339355, - -0.4124491214752197, - 1.9356944561004639 + 4.2895050048828125, + 1.0452749729156494, + 6.628046035766602 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 5, - "label": "GPU Inference" + "id": 15, + "label": "PyTorch Models" } ], "_embedding": [ @@ -313039,47 +313236,47 @@ "abstract": "Modin: Scale your Pandas workflows by changing a single line of code - modin-project/modin", "projections": { "pca": [ - 0.24332337081432343, - -0.04555206373333931 + 0.24176234006881714, + -0.045840296894311905 ], "tsne": [ - 28.48965072631836, - 7.64112663269043 + 27.753862380981445, + -11.27027702331543 ], "umap": [ - 2.8894214630126953, - 7.042428016662598 + 2.5421547889709473, + 5.815804958343506 ], "pca3d": [ - 0.24332363903522491, - -0.04554552957415581, - -0.18728527426719666 + 0.24176280200481415, + -0.045833129435777664, + -0.18680040538311005 ], "tsne3d": [ - 15.303430557250977, - 53.55873107910156, - 8.518030166625977 + 42.86833953857422, + 9.043818473815918, + 28.340906143188477 ], "umap3d": [ - 3.384613037109375, - -0.4153592586517334, - 2.2132954597473145 + 3.104607105255127, + 2.4706363677978516, + 6.782437801361084 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 18, - "label": "Github Tools" + "id": 2, + "label": "Bioinformatics Tools" } ], "_embedding": [ @@ -313485,47 +313682,47 @@ ], "projections": { "pca": [ - 0.0923105850815773, - 0.024884069338440895 + 0.09451872110366821, + 0.026474060490727425 ], "tsne": [ - 5.164516925811768, - -0.356166809797287 + 5.778201580047607, + 5.1908416748046875 ], "umap": [ - 1.2429101467132568, - 5.593878746032715 + 0.4297361671924591, + 4.164238929748535 ], "pca3d": [ - 0.09231194108724594, - 0.02490728348493576, - 0.09930238127708435 + 0.09452009946107864, + 0.026493802666664124, + 0.09493857622146606 ], "tsne3d": [ - 21.220352172851562, - -18.367027282714844, - 26.121414184570312 + 20.064210891723633, + -33.621700286865234, + 12.087080001831055 ], "umap3d": [ - 3.6783552169799805, - 0.4756065905094147, - 0.24968314170837402 + 1.7278711795806885, + 1.3045140504837036, + 6.0875115394592285 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 4, - "label": "Com Area Amp" + "id": 9, + "label": "Drug Discovery" }, { "id": 23, - "label": "Research Authors" + "label": "Peer Review" } ], "_embedding": [ @@ -313933,47 +314130,47 @@ "abstract": "Tips and patterns for getting the most out of Claude Code, from configuring your environment to scaling across parallel sessions.", "projections": { "pca": [ - 0.2266755998134613, - 0.071075938642025 + 0.22698664665222168, + 0.07122749090194702 ], "tsne": [ - 26.080066680908203, - 23.233537673950195 + 34.81467056274414, + 4.006815433502197 ], "umap": [ - 4.539287090301514, - 6.059727668762207 + 3.8291308879852295, + 4.635621547698975 ], "pca3d": [ - 0.226674884557724, - 0.07106626033782959, - -0.09184949845075607 + 0.22698596119880676, + 0.07122015953063965, + -0.09467078000307083 ], "tsne3d": [ - 57.48384475708008, - 25.4998722076416, - 15.304366111755371 + 31.75458335876465, + 39.932708740234375, + 15.349334716796875 ], "umap3d": [ - 4.910945415496826, - 0.24843013286590576, - 2.9761080741882324 + 4.947108745574951, + 2.0776875019073486, + 6.394737243652344 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { - "id": 16, - "label": "Claude Code" + "id": 11, + "label": "Code Tools" } ], "_embedding": [ @@ -314381,47 +314578,47 @@ "abstract": "an open source, extensible AI agent that goes beyond code suggestions - install, execute, edit, and test with any LLM - aaif-goose/goose", "projections": { "pca": [ - 0.21536365151405334, - 0.19805008172988892 + 0.21758031845092773, + 0.2004234939813614 ], "tsne": [ - 17.295072555541992, - 22.000885009765625 + 27.401933670043945, + 7.323190212249756 ], "umap": [ - 3.847391366958618, - 5.58074951171875 + 3.2307348251342773, + 4.316784381866455 ], "pca3d": [ - 0.21536274254322052, - 0.19804269075393677, - -0.007916108705103397 + 0.21757958829402924, + 0.20041705667972565, + -0.011541425250470638 ], "tsne3d": [ - 41.314903259277344, - 21.636220932006836, - -6.795853614807129 + 25.632938385009766, + 29.576250076293945, + -3.8037736415863037 ], "umap3d": [ - 4.658454418182373, - 0.5494295358657837, - 2.395301103591919 + 4.396684169769287, + 1.9759933948516846, + 5.78447961807251 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 20, - "label": "AI Agents" + "id": 4, + "label": "Open Agent Tools" } ], "_embedding": [ @@ -314831,47 +315028,47 @@ "abstract": "Autonomous coding agent as an SDK, IDE extension, or CLI assistant. - cline/cline", "projections": { "pca": [ - 0.1660381555557251, - 0.1315739005804062 + 0.16802896559238434, + 0.13266883790493011 ], "tsne": [ - 19.999910354614258, - 17.481149673461914 + 28.470233917236328, + 4.291675567626953 ], "umap": [ - 3.876284599304199, - 5.851055145263672 + 3.287647008895874, + 4.593050956726074 ], "pca3d": [ - 0.16603729128837585, - 0.13155733048915863, - -0.03574441000819206 + 0.16802813112735748, + 0.13265474140644073, + -0.0375637523829937 ], "tsne3d": [ - 39.01179504394531, - 28.37172508239746, - 0.4300660490989685 + 27.9727725982666, + 21.44989585876465, + -3.703061819076538 ], "umap3d": [ - 4.387451171875, - 0.40892159938812256, - 2.54571533203125 + 4.260250568389893, + 2.0941951274871826, + 6.000467777252197 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 8, - "label": "Open Models" + "id": 0, + "label": "GitHub Tools" }, { - "id": 20, - "label": "AI Agents" + "id": 4, + "label": "Open Agent Tools" } ], "_embedding": [ @@ -315284,47 +315481,47 @@ ], "projections": { "pca": [ - -0.10373834520578384, - 0.1961955577135086 + -0.10455873608589172, + 0.1945965737104416 ], "tsne": [ - -17.869293212890625, - 18.584367752075195 + 1.9701488018035889, + 31.64897918701172 ], "umap": [ - 2.572732925415039, - 2.959798574447632 + 1.5940735340118408, + 2.0203857421875 ], "pca3d": [ - -0.10373856127262115, - 0.19619058072566986, - -0.031779974699020386 + -0.10455893725156784, + 0.19459061324596405, + -0.02867181785404682 ], "tsne3d": [ - 24.768882751464844, - -5.003621578216553, - -23.40265655517578 + 13.296481132507324, + 4.184007167816162, + -42.39664840698242 ], "umap3d": [ - 6.400328159332275, - -0.5479888319969177, - 0.7043798565864563 + 4.402299404144287, + -0.7887716293334961, + 6.128880023956299 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 18, + "label": "Retrieval Learning" } ], "_embedding": [ @@ -315732,47 +315929,47 @@ "journal": "Nature Communications", "projections": { "pca": [ - -0.10681368410587311, - -0.0948101133108139 + -0.10597322881221771, + -0.09229467809200287 ], "tsne": [ - -12.369501113891602, - -22.711463928222656 + -22.105527877807617, + -14.209290504455566 ], "umap": [ - -1.2445558309555054, - 4.452408313751221 + -1.4139405488967896, + 5.888050556182861 ], "pca3d": [ - -0.10681307315826416, - -0.09482446312904358, - 0.028623871505260468 + -0.10597268491983414, + -0.0923088863492012, + 0.033177390694618225 ], "tsne3d": [ - -26.21817970275879, - -9.287250518798828, - -8.093199729919434 + -42.324371337890625, + -8.489121437072754, + 5.427679538726807 ], "umap3d": [ - 2.337604284286499, - 0.13331201672554016, - -0.8616887331008911 + 0.7152663469314575, + 0.2454618662595749, + 4.987853527069092 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 2, - "label": "AI Drug Discovery" + "id": 1, + "label": "Cancer Data" }, { - "id": 2, - "label": "Genetic Disease" + "id": 28, + "label": "Spatial Drug" } ], "_embedding": [ @@ -316179,47 +316376,47 @@ "title": "The Pharmome Map: a comprehensive public dataset for drug-target interaction modeling", "projections": { "pca": [ - -0.1228659525513649, - -0.11387617141008377 + -0.12100104987621307, + -0.10999158024787903 ], "tsne": [ - 0.7577686309814453, - -11.895736694335938 + -9.678102493286133, + -7.026257514953613 ], "umap": [ - -0.7701833844184875, - 4.482361793518066 + -0.8691164255142212, + 5.5849528312683105 ], "pca3d": [ - -0.12286495417356491, - -0.11387018114328384, - 0.003943064250051975 + -0.12100011855363846, + -0.10998653620481491, + 0.005026698112487793 ], "tsne3d": [ - -23.532726287841797, - -28.897762298583984, - 24.898622512817383 + -9.848673820495605, + -33.31319046020508, + -0.9731110334396362 ], "umap3d": [ - 3.003415107727051, - 0.7934640645980835, - -1.1579591035842896 + 1.8199169635772705, + 0.33452266454696655, + 4.523400783538818 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 2, - "label": "AI Drug Discovery" + "id": 9, + "label": "Drug Discovery" }, { - "id": 13, - "label": "Drug Discovery" + "id": 28, + "label": "Spatial Drug" } ], "_embedding": [ @@ -316629,47 +316826,47 @@ "abstract": "We\u2019re on a journey to advance and democratize artificial intelligence through open source and open science.", "projections": { "pca": [ - 0.05521613359451294, - -0.14006802439689636 + 0.05649420619010925, + -0.13738113641738892 ], "tsne": [ - 0.9700647592544556, - -11.663475036621094 + -9.455144882202148, + -6.998220920562744 ], "umap": [ - -0.5419442057609558, - 4.781373977661133 + 3.983490228652954, + 3.4070303440093994 ], "pca3d": [ - 0.0552162304520607, - -0.14007215201854706, - 0.06180340051651001 + 0.05649420619010925, + -0.1373857855796814, + 0.0630539134144783 ], "tsne3d": [ - 32.138545989990234, - -15.179265975952148, - 21.216907501220703 + 5.451674938201904, + 51.037681579589844, + -2.1746408939361572 ], "umap3d": [ - 3.145585775375366, - 0.9197958707809448, - -1.003572940826416 + 2.114790916442871, + 0.4256538450717926, + 4.584559440612793 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 3, + "label": "Open Source Tools" }, { - "id": 2, - "label": "AI Drug Discovery" + "id": 8, + "label": "Open AI" }, { - "id": 13, - "label": "Drug Discovery" + "id": 7, + "label": "Hugging Face Models" } ], "_embedding": [ @@ -317076,46 +317273,46 @@ "title": "Enhancing medical image discovery: similarity search in the Imaging Data Commons archive  |  Health AI Developer Foundations  |  Google for Developers", "projections": { "pca": [ - -0.035195719450712204, - -0.12159817665815353 + -0.032749664038419724, + -0.11882457882165909 ], "tsne": [ - -20.454790115356445, - -5.703160762786865 + -4.654409885406494, + 9.667112350463867 ], "umap": [ - -0.11181000620126724, - 7.524658203125 + -1.1498976945877075, + 2.3897337913513184 ], "pca3d": [ - -0.035195447504520416, - -0.12160592526197433, - 0.0906628742814064 + -0.03274928033351898, + -0.118832066655159, + 0.0954543948173523 ], "tsne3d": [ - -21.309934616088867, - -0.346429705619812, - -4.303950786590576 + -17.826190948486328, + 3.50112247467041, + 27.654460906982422 ], "umap3d": [ - 4.094122409820557, - -1.4765148162841797, - -0.3883274495601654 + 1.782080054283142, + -0.1785038411617279, + 6.525166034698486 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 11, + "id": 19, "label": "Medical Imaging" } ], @@ -317569,46 +317766,46 @@ "openalex_url": "https://openalex.org/W7135081182", "projections": { "pca": [ - -0.02725578285753727, - -0.13301672041416168 + -0.024795787408947945, + -0.1304340958595276 ], "tsne": [ - -22.373743057250977, - -0.19164270162582397 + -5.109665393829346, + 11.527233123779297 ], "umap": [ - -0.3344998061656952, - 7.7094502449035645 + -1.2480764389038086, + 2.3970329761505127 ], "pca3d": [ - -0.0272549856454134, - -0.1330094337463379, - 0.12416587769985199 + -0.024794958531856537, + -0.13042889535427094, + 0.12918831408023834 ], "tsne3d": [ - -20.399795532226562, - 8.659821510314941, - -2.7099571228027344 + -11.601004600524902, + 0.4337943494319916, + 25.181339263916016 ], "umap3d": [ - 4.33650541305542, - -1.6637073755264282, - -0.4900311827659607 + 1.8917728662490845, + -0.5196765661239624, + 6.720604419708252 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 11, + "id": 19, "label": "Medical Imaging" } ], @@ -318019,46 +318216,46 @@ "abstract": "We\u2019re on a journey to advance and democratize artificial intelligence through open source and open science.", "projections": { "pca": [ - 0.00048832967877388, - -0.0981321632862091 + 0.0034391155932098627, + -0.09663552790880203 ], "tsne": [ - -24.11949348449707, - -3.421515464782715 + -1.9436798095703125, + 11.061545372009277 ], "umap": [ - 0.06350246071815491, - 8.003225326538086 + -0.9123947620391846, + 2.3123717308044434 ], "pca3d": [ - 0.0004878489417023957, - -0.09813693910837173, - 0.17571528255939484 + 0.0034385276958346367, + -0.09663908928632736, + 0.1793755143880844 ], "tsne3d": [ - 33.52042770385742, - -7.017618179321289, - 27.49293327331543 + -8.433856010437012, + 8.679292678833008, + 38.45448684692383 ], "umap3d": [ - 4.5287652015686035, - -1.8586156368255615, - -0.43031343817710876 + 2.066589117050171, + -0.5166945457458496, + 6.683625221252441 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 11, + "id": 19, "label": "Medical Imaging" } ], @@ -318467,46 +318664,46 @@ "abstract": "CAncer Standards Long-read Evaluation. Contribute to CASTLE-Panel/castle development by creating an account on GitHub.", "projections": { "pca": [ - 0.17365747690200806, - -0.21990284323692322 + 0.17394334077835083, + -0.2200416475534439 ], "tsne": [ - 3.8511886596679688, - -12.341035842895508 + -5.490484237670898, + -10.619056701660156 ], "umap": [ - -1.9409805536270142, - 6.501157760620117 + -2.4531989097595215, + 4.8371100425720215 ], "pca3d": [ - 0.17365773022174835, - -0.21989750862121582, - -0.05048801004886627 + 0.17394371330738068, + -0.22003768384456635, + -0.04954153671860695 ], "tsne3d": [ - -13.390661239624023, - -30.637962341308594, - 37.4857063293457 + -17.83907699584961, + -22.357927322387695, + 32.098411560058594 ], "umap3d": [ - 2.7776167392730713, - -0.9805048704147339, - -0.21764996647834778 + 0.2718353569507599, + 0.6002930402755737, + 6.082283020019531 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 24, + "id": 1, "label": "Cancer Data" } ], @@ -318915,47 +319112,47 @@ ], "projections": { "pca": [ - 0.11613817512989044, - -0.085690438747406 + 0.11380349844694138, + -0.0861511081457138 ], "tsne": [ - 21.29022216796875, - -5.277046203613281 + 9.163289070129395, + -14.384997367858887 ], "umap": [ - 1.8918042182922363, - 7.290739059448242 + 0.8624981641769409, + 5.987436294555664 ], "pca3d": [ - 0.11613866686820984, - -0.08570032566785812, - -0.03588051721453667 + 0.11380405724048615, + -0.08616166561841965, + -0.036089953035116196 ], "tsne3d": [ - 1.5849995613098145, - 10.708845138549805, - 32.94822311401367 + 16.59119987487793, + -4.278111934661865, + 23.94475746154785 ], "umap3d": [ - 2.4290826320648193, - -0.2871953845024109, - 1.350862741470337 + 1.9114272594451904, + 2.394871473312378, + 5.922791957855225 ] }, "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { "id": 1, - "label": "Open Agent Tools" + "label": "Bioinformatics Models" }, { - "id": 3, - "label": "Github Data" + "id": 6, + "label": "Data Needs" }, { - "id": 10, - "label": "Medical Data" + "id": 25, + "label": "Data Standards" } ], "_embedding": [ @@ -319365,47 +319562,47 @@ "abstract": "Gene Expression Omnibus (GEO) is a database repository of high throughput gene expression data and hybridization arrays, chips, microarrays.", "projections": { "pca": [ - 0.04437863454222679, - -0.23904383182525635 + 0.04198601841926575, + -0.2392054796218872 ], "tsne": [ - 21.508697509765625, - -6.3891072273254395 + 8.257121086120605, + -14.978103637695312 ], "umap": [ - 1.540700912475586, - 7.434514999389648 + 0.2758428454399109, + 6.231845855712891 ], "pca3d": [ - 0.04437940940260887, - -0.23904789984226227, - -0.11944743245840073 + 0.041986916214227676, + -0.2392101287841797, + -0.11627349257469177 ], "tsne3d": [ - -5.698192596435547, - 10.633639335632324, - 38.231441497802734 + -3.1868021488189697, + -24.78911781311035, + -34.274879455566406 ], "umap3d": [ - 2.2278859615325928, - -0.24672138690948486, - 0.8300625681877136 + 1.5382044315338135, + 2.2065327167510986, + 5.8428239822387695 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 3, - "label": "Github Data" + "id": 6, + "label": "Data Needs" }, { - "id": 10, - "label": "Medical Data" + "id": 16, + "label": "Lab Research" } ], "_embedding": [ @@ -319814,47 +320011,47 @@ "title": "Home - SRA - NCBI", "projections": { "pca": [ - 0.21252202987670898, - -0.18849633634090424 + 0.21069777011871338, + -0.1888890266418457 ], "tsne": [ - 21.413454055786133, - -5.599363327026367 + 8.922545433044434, + -14.591310501098633 ], "umap": [ - 1.7160382270812988, - 7.5482635498046875 + 0.6608023643493652, + 6.088258743286133 ], "pca3d": [ - 0.21252310276031494, - -0.18849967420101166, - -0.04430204629898071 + 0.21069900691509247, + -0.18889227509498596, + -0.04309394210577011 ], "tsne3d": [ - -1.1524590253829956, - 9.765624046325684, - 35.935462951660156 + 15.677595138549805, + -6.483710765838623, + 26.39400863647461 ], "umap3d": [ - 2.276458263397217, - -0.3236282765865326, - 1.0928730964660645 + 1.6980807781219482, + 2.3854405879974365, + 5.996923923492432 ] }, "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { "id": 1, - "label": "Open Agent Tools" + "label": "Bioinformatics Models" }, { - "id": 3, - "label": "Github Data" + "id": 6, + "label": "Data Needs" }, { - "id": 10, - "label": "Medical Data" + "id": 16, + "label": "Lab Research" } ], "_embedding": [ @@ -320264,46 +320461,46 @@ "doi": "10.1016/j.cell.2022.01.003", "projections": { "pca": [ - -0.07085463404655457, - -0.3173583745956421 + -0.07301490753889084, + -0.3184555172920227 ], "tsne": [ - -5.975490093231201, - -17.05249786376953 + -18.477602005004883, + -8.707107543945312 ], "umap": [ - -2.2679123878479004, - 5.895386695861816 + -2.4754831790924072, + 5.203203201293945 ], "pca3d": [ - -0.07085447758436203, - -0.31735703349113464, - -0.07305142283439636 + -0.0730147510766983, + -0.3184530436992645, + -0.06883396953344345 ], "tsne3d": [ - -26.804126739501953, - -33.353580474853516, - 5.025640487670898 + -25.265958786010742, + -22.705615997314453, + 20.025697708129883 ], "umap3d": [ - 2.539754629135132, - -0.9847754240036011, - -0.7625284790992737 + 0.10619807243347168, + 0.2574789226055145, + 5.766321659088135 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 24, + "id": 1, "label": "Cancer Data" } ], @@ -320727,47 +320924,47 @@ "doi": "10.1016/j.gpb.2022.12.004", "projections": { "pca": [ - 0.0770353302359581, - -0.1957973837852478 + 0.07654578983783722, + -0.19509291648864746 ], "tsne": [ - 8.312848091125488, - -17.220815658569336 + -2.3765976428985596, + -16.478334426879883 ], "umap": [ - -1.6554811000823975, - 4.3418755531311035 + -1.7380199432373047, + 6.28044319152832 ], "pca3d": [ - 0.07703506201505661, - -0.19579873979091644, - -0.04176193103194237 + 0.07654573768377304, + -0.19509504735469818, + -0.04094872251152992 ], "tsne3d": [ - -26.630935668945312, - 3.8388454914093018, - 14.46296215057373 + -4.562310218811035, + -41.62397003173828, + -1.4015074968338013 ], "umap3d": [ - 2.062537670135498, - 0.3802914023399353, - -1.1673214435577393 + 0.7104431986808777, + 1.2127094268798828, + 4.6437764167785645 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 2, - "label": "Genetic Disease" + "id": 27, + "label": "Genetic Variants" } ], "_embedding": [ @@ -321175,47 +321372,47 @@ "abstract": "How can we train AI models on the collective knowledge of the nation", "projections": { "pca": [ - -0.04182629659771919, - 0.049264706671237946 + -0.03906674683094025, + 0.05122947320342064 ], "tsne": [ - -18.214757919311523, - -14.447992324829102 + -27.5333309173584, + 11.145489692687988 ], "umap": [ - -0.8312854170799255, - 5.491027355194092 + -1.283463478088379, + 4.646761417388916 ], "pca3d": [ - -0.04182567447423935, - 0.049267951399087906, - 0.13549195230007172 + -0.039066132158041, + 0.051232218742370605, + 0.1357434242963791 ], "tsne3d": [ - 7.035824775695801, - -37.67884063720703, - -26.449214935302734 + -33.511940002441406, + 13.046435356140137, + 2.903684377670288 ], "umap3d": [ - 3.4994001388549805, - -0.09808798879384995, - -1.32315194606781 + 0.6206058859825134, + -0.14509446918964386, + 6.593048095703125 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 2, - "label": "AI Drug Discovery" + "id": 9, + "label": "Drug Discovery" }, { - "id": 28, - "label": "Clinical AI" + "id": 9, + "label": "Biomedical AI" } ], "_embedding": [ @@ -321636,46 +321833,46 @@ "doi": "10.1007/s10278-024-00982-6", "projections": { "pca": [ - -0.012078799307346344, - -0.11765851080417633 + -0.009304520674049854, + -0.11486943066120148 ], "tsne": [ - -15.923795700073242, - 3.1144137382507324 + -1.314978003501892, + 7.884275436401367 ], "umap": [ - 0.34420642256736755, - 7.451272964477539 + -1.2897220849990845, + 2.874516248703003 ], "pca3d": [ - -0.012078709900379181, - -0.11765673011541367, - 0.2009933888912201 + -0.009304402396082878, + -0.11486606299877167, + 0.20588190853595734 ], "tsne3d": [ - 4.346811771392822, - -13.096343040466309, - 43.13882827758789 + 0.4515558183193207, + 0.2536845803260803, + 39.96804428100586 ], "umap3d": [ - 4.101309776306152, - -1.7532306909561157, - -0.8317886590957642 + 1.5261468887329102, + -0.19052410125732422, + 7.285451412200928 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 11, + "id": 19, "label": "Medical Imaging" } ], @@ -322082,47 +322279,47 @@ ], "projections": { "pca": [ - 0.24237167835235596, - 0.05324520543217659 + 0.2426380217075348, + 0.05206272006034851 ], "tsne": [ - 10.238094329833984, - -1.1714143753051758 + 8.9109525680542, + 1.8469430208206177 ], "umap": [ - 1.5996028184890747, - 5.306115627288818 + 0.9449649453163147, + 4.8390984535217285 ], "pca3d": [ - 0.24237114191055298, - 0.05324011296033859, - 0.029712669551372528 + 0.2426377683877945, + 0.05205579847097397, + 0.02256627194583416 ], "tsne3d": [ - -2.0395328998565674, - 21.88827133178711, - -7.118234634399414 + 27.897052764892578, + -22.033506393432617, + 4.695301532745361 ], "umap3d": [ - 3.1479125022888184, - 1.0183311700820923, - 0.9166513085365295 + 2.5474863052368164, + 1.8176692724227905, + 5.161811351776123 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 4, - "label": "Com Area Amp" + "id": 6, + "label": "Data Needs" }, { - "id": 26, - "label": "AI Information" + "id": 10, + "label": "Research Data" } ], "_embedding": [ @@ -322530,47 +322727,47 @@ "journal": "Nature", "projections": { "pca": [ - -0.10325214266777039, - -0.06581148505210876 + -0.10104561597108841, + -0.06318735331296921 ], "tsne": [ - -1.736968755722046, - -10.544569969177246 + -10.392033576965332, + -3.2989773750305176 ], "umap": [ - -1.1852151155471802, - 6.188331127166748 + -2.5549774169921875, + 4.394912242889404 ], "pca3d": [ - -0.10325198620557785, - -0.06581155210733414, - 0.14884819090366364 + -0.10104542225599289, + -0.06318701058626175, + 0.14962546527385712 ], "tsne3d": [ - -26.377466201782227, - -8.868640899658203, - 39.885799407958984 + -3.1136887073516846, + -33.67765808105469, + 7.504255771636963 ], "umap3d": [ - 2.967599868774414, - -0.7483896017074585, - -1.787536382675171 + 0.5184457302093506, + 1.0431952476501465, + 6.350038051605225 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 28, - "label": "Clinical AI" + "id": 21, + "label": "Clinical Trials" } ], "_embedding": [ @@ -322980,47 +323177,47 @@ "abstract": "A collection of ETLs from common data formats to Medical Event Data Standard - Medical-Event-Data-Standard/meds_etl", "projections": { "pca": [ - 0.27215468883514404, - -0.20619401335716248 + 0.27304941415786743, + -0.20549888908863068 ], "tsne": [ - 29.139970779418945, - -7.121683120727539 + 14.38570499420166, + -22.684362411499023 ], "umap": [ - 1.5156177282333374, - 7.161148548126221 + 1.3672735691070557, + 6.135432720184326 ], "pca3d": [ - 0.27215468883514404, - -0.20620498061180115, - 0.03317542374134064 + 0.27304941415786743, + -0.20551073551177979, + 0.03365431725978851 ], "tsne3d": [ - 8.138458251953125, - 0.8705442547798157, - 46.16663360595703 + 7.175997257232666, + 6.874017238616943, + 40.7324333190918 ], "umap3d": [ - 3.1301422119140625, - -1.3765264749526978, - 1.0200945138931274 + 1.8582453727722168, + 1.884842872619629, + 7.313194751739502 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 6, + "label": "Data Needs" }, { - "id": 10, - "label": "Medical Data" + "id": 25, + "label": "Data Standards" } ], "_embedding": [ @@ -323428,47 +323625,47 @@ "abstract": "A benchmark for few-shot evaluation of foundation models for electronic health records (EHRs) - som-shahlab/ehrshot-benchmark", "projections": { "pca": [ - 0.07522854208946228, - -0.1529480218887329 + 0.07650294154882431, + -0.15058934688568115 ], "tsne": [ - -27.867542266845703, - -4.470757007598877 + -15.328455924987793, + 8.498440742492676 ], "umap": [ - 0.28230515122413635, - 6.90279483795166 + -1.9366044998168945, + 3.2837235927581787 ], "pca3d": [ - 0.07522888481616974, - -0.15293341875076294, - 0.05614517629146576 + 0.07650340348482132, + -0.15057547390460968, + 0.060356464236974716 ], "tsne3d": [ - -2.938196897506714, - -8.250738143920898, - 10.400941848754883 + 0.6032906770706177, + -9.566694259643555, + 23.46531867980957 ], "umap3d": [ - 4.552765846252441, - -1.2749829292297363, - -1.8388968706130981 + 1.1281429529190063, + 1.1763556003570557, + 7.340571403503418 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, - "label": "Clinical Health" + "id": 13, + "label": "ECG AI Models" } ], "_embedding": [ @@ -323876,47 +324073,47 @@ "abstract": "HoX builds systems to make biological data easy to create, use, and share.", "projections": { "pca": [ - 0.1649886816740036, - -0.06283216178417206 + 0.16575540602207184, + -0.061879996210336685 ], "tsne": [ - 13.89450740814209, - 1.2051736116409302 + 12.749419212341309, + 0.67741858959198 ], "umap": [ - 1.8952189683914185, - 5.391305923461914 + 1.1474567651748657, + 5.022647380828857 ], "pca3d": [ - 0.1649886667728424, - -0.06283898651599884, - 0.044791772961616516 + 0.1657554656267166, + -0.06188921630382538, + 0.042835455387830734 ], "tsne3d": [ - 7.0847086906433105, - 7.564809322357178, - 9.277950286865234 + 22.377735137939453, + -6.974536895751953, + 7.707995891571045 ], "umap3d": [ - 3.3972055912017822, - 0.6837170124053955, - 1.163971185684204 + 2.7631795406341553, + 1.694771409034729, + 5.345508575439453 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 4, - "label": "Com Area Amp" + "id": 6, + "label": "Data Needs" }, { - "id": 26, - "label": "AI Information" + "id": 10, + "label": "Research Data" } ], "_embedding": [ @@ -324327,46 +324524,46 @@ "abstract": "How LLM-assisted processing transformed 260,000 unstructured clinical trials into 6.5 million structured components and embeddings", "projections": { "pca": [ - -0.14012348651885986, - -0.11309216916561127 + -0.1365894228219986, + -0.10899969190359116 ], "tsne": [ - -7.366671562194824, - -2.553372383117676 + -12.088438034057617, + 4.558309078216553 ], "umap": [ - -0.29540693759918213, - 6.25830602645874 + -1.7246540784835815, + 3.8818373680114746 ], "pca3d": [ - -0.14012295007705688, - -0.1130952462553978, - 0.12266701459884644 + -0.13658899068832397, + -0.10900302231311798, + 0.12722080945968628 ], "tsne3d": [ - 2.1292872428894043, - -25.28620719909668, - 18.96024513244629 + -3.2017288208007812, + -13.93504810333252, + 6.055965423583984 ], "umap3d": [ - 4.219475746154785, - -0.5607083439826965, - -1.5994888544082642 + 0.9089319109916687, + 0.7516595721244812, + 6.962217807769775 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, + "id": 14, "label": "Clinical Health" } ], @@ -324781,46 +324978,46 @@ ], "projections": { "pca": [ - -0.18888233602046967, - -0.17857716977596283 + -0.1896401345729828, + -0.1777884066104889 ], "tsne": [ - -10.623735427856445, - -16.93578338623047 + -19.138912200927734, + -6.231378078460693 ], "umap": [ - -1.8277102708816528, - 5.957871437072754 + -1.9010484218597412, + 5.093869209289551 ], "pca3d": [ - -0.18888147175312042, - -0.17857344448566437, - -0.008483435027301311 + -0.18963932991027832, + -0.17778567969799042, + -0.0005725376540794969 ], "tsne3d": [ - -18.540084838867188, - -29.382984161376953, - -12.144920349121094 + -21.708871841430664, + -11.295637130737305, + 19.224933624267578 ], "umap3d": [ - 2.876708507537842, - -0.7349410057067871, - -0.665154755115509 + 0.7099018096923828, + 0.11329102516174316, + 5.673513889312744 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 24, + "id": 1, "label": "Cancer Data" } ], @@ -325229,47 +325426,47 @@ "abstract": "MD/PhD candidate at University of Chicago (PhD in Computer Science, Grossman Lab, Khan Lab) | Tufts University BS in Computer Science & Biochemistry - StevenSong", "projections": { "pca": [ - 0.030548347160220146, - -0.1337592899799347 + 0.028071986511349678, + -0.13393281400203705 ], "tsne": [ - 13.167913436889648, - -4.190199375152588 + 7.946042537689209, + -2.924556255340576 ], "umap": [ - 1.1997785568237305, - 5.427921295166016 + 0.5861941576004028, + 5.354347229003906 ], "pca3d": [ - 0.03054872341454029, - -0.1337747722864151, - -0.0021223726216703653 + 0.028072316199541092, + -0.13394851982593536, + -0.000922539213206619 ], "tsne3d": [ - 1.0034072399139404, - 15.10705852508545, - 17.54319190979004 + 26.762296676635742, + -11.737486839294434, + 15.116085052490234 ], "umap3d": [ - 2.893941640853882, - 0.15355005860328674, - 0.6255977749824524 + 2.062204599380493, + 1.419135332107544, + 5.364126682281494 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 3, + "label": "Open Source Tools" }, { - "id": 4, - "label": "Com Area Amp" + "id": 6, + "label": "Data Needs" }, { - "id": 23, - "label": "Research Authors" + "id": 16, + "label": "Lab Research" } ], "_embedding": [ @@ -325675,47 +325872,47 @@ ], "projections": { "pca": [ - 0.0751962885260582, - -0.1151251345872879 + 0.07347536087036133, + -0.11503753811120987 ], "tsne": [ - 13.271097183227539, - -4.059834003448486 + 8.041718482971191, + -2.7426536083221436 ], "umap": [ - 1.5629029273986816, - 5.4540300369262695 + 0.9074633121490479, + 5.206547260284424 ], "pca3d": [ - 0.07519645243883133, - -0.11514053493738174, - 0.027120893821120262 + 0.07347552478313446, + -0.11505326628684998, + 0.02735895663499832 ], "tsne3d": [ - 2.382880210876465, - 12.336024284362793, - 14.567611694335938 + 24.68487548828125, + -12.043743133544922, + 12.279228210449219 ], "umap3d": [ - 3.1169137954711914, - 0.3945479989051819, - 0.9037544131278992 + 2.3969435691833496, + 1.6365207433700562, + 5.4530415534973145 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 4, - "label": "Com Area Amp" + "id": 6, + "label": "Data Needs" }, { - "id": 26, - "label": "AI Information" + "id": 10, + "label": "Research Data" } ], "_embedding": [ @@ -326123,47 +326320,47 @@ ], "projections": { "pca": [ - 0.17464938759803772, - -0.081154003739357 + 0.1744735836982727, + -0.08190927654504776 ], "tsne": [ - 11.900310516357422, - 0.15204185247421265 + 11.790172576904297, + -0.5805977582931519 ], "umap": [ - 1.8070558309555054, - 5.419704437255859 + 1.042799472808838, + 5.1346282958984375 ], "pca3d": [ - 0.17464928328990936, - -0.08116835355758667, - 0.08510006964206696 + 0.17447353899478912, + -0.08192525804042816, + 0.0822027325630188 ], "tsne3d": [ - 2.6491594314575195, - 9.28458023071289, - 3.6455237865448 + 30.363346099853516, + -10.421356201171875, + 7.876389503479004 ], "umap3d": [ - 3.3030574321746826, - 0.6966917514801025, - 1.090240240097046 + 2.694178342819214, + 1.667234182357788, + 5.3592352867126465 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 4, - "label": "Com Area Amp" + "id": 6, + "label": "Data Needs" }, { - "id": 26, - "label": "AI Information" + "id": 10, + "label": "Research Data" } ], "_embedding": [ @@ -326573,47 +326770,47 @@ "abstract": "Discover how Claude accelerates life sciences research with new scientific connectors, skills, and improved performance for drug discovery and clinical work.", "projections": { "pca": [ - 0.07810035347938538, - -0.1373743712902069 + 0.08083170652389526, + -0.13454613089561462 ], "tsne": [ - 1.2586743831634521, - 10.57674789428711 + -1.2721837759017944, + -0.8468833565711975 ], "umap": [ - -0.22613343596458435, - 4.97318696975708 + -0.6001597046852112, + 4.618646144866943 ], "pca3d": [ - 0.0780995562672615, - -0.13739430904388428, - 0.12331786751747131 + 0.08083105832338333, + -0.13456279039382935, + 0.1223350465297699 ], "tsne3d": [ - 33.34241485595703, - -20.14263153076172, - -3.3219668865203857 + 4.107602119445801, + 36.13275146484375, + -19.800289154052734 ], "umap3d": [ - 4.229145526885986, - 0.7949104309082031, - -0.6958029270172119 + 3.100294351577759, + 0.3290194869041443, + 4.832239627838135 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ - { - "id": 0, - "label": "Medical Data AI" - }, { "id": 2, - "label": "AI Drug Discovery" + "label": "Clinical AI Data" }, { - "id": 13, + "id": 9, "label": "Drug Discovery" + }, + { + "id": 9, + "label": "Biomedical AI" } ], "_embedding": [ @@ -327022,47 +327219,47 @@ ], "projections": { "pca": [ - 0.16041207313537598, - 0.02235489711165428 + 0.15847022831439972, + 0.02197784185409546 ], "tsne": [ - 12.976515769958496, - 0.27820318937301636 + 11.403432846069336, + 0.17342281341552734 ], "umap": [ - 1.9259364604949951, - 5.458629131317139 + 1.2779111862182617, + 4.886229991912842 ], "pca3d": [ - 0.16041246056556702, - 0.02234858274459839, - 0.018512867391109467 + 0.15847058594226837, + 0.021969269961118698, + 0.01653135009109974 ], "tsne3d": [ - 6.610837459564209, - 13.847975730895996, - 6.299638271331787 + 25.375825881958008, + -11.227826118469238, + 6.216544151306152 ], "umap3d": [ - 3.276268243789673, - 0.6832894682884216, - 1.2174431085586548 + 2.779299259185791, + 1.7356607913970947, + 5.434905529022217 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 4, - "label": "Com Area Amp" + "id": 6, + "label": "Data Needs" }, { - "id": 26, - "label": "AI Information" + "id": 10, + "label": "Research Data" } ], "_embedding": [ @@ -327470,47 +327667,47 @@ "abstract": "medagent. Contribute to dwgoblue/MedAgent development by creating an account on GitHub.", "projections": { "pca": [ - 0.3212418854236603, - -0.13801266252994537 + 0.32262125611305237, + -0.13677501678466797 ], "tsne": [ - 28.15794563293457, - 12.030655860900879 + 31.136024475097656, + -7.843570709228516 ], "umap": [ - 3.476896286010742, - 7.024880886077881 + 3.112530469894409, + 5.80452299118042 ], "pca3d": [ - 0.3212432861328125, - -0.13800731301307678, - -0.04789925366640091 + 0.32262295484542847, + -0.13677245378494263, + -0.049953892827034 ], "tsne3d": [ - 29.70834732055664, - 45.94283676147461, - 24.606426239013672 + 45.08993148803711, + 10.691082954406738, + 10.663806915283203 ], "umap3d": [ - 3.451254367828369, - -0.07863803952932358, - 2.8507277965545654 + 3.9692389965057373, + 2.9000651836395264, + 6.710732936859131 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 18, - "label": "Github Tools" + "id": 17, + "label": "GitHub Creation" } ], "_embedding": [ @@ -327920,46 +328117,46 @@ "doi": "10.5860/choice.26-6108", "projections": { "pca": [ - -0.0815247893333435, - -0.33267951011657715 + -0.08397669345140457, + -0.3339773416519165 ], "tsne": [ - -5.982585430145264, - -16.704130172729492 + -18.76384162902832, + -8.723746299743652 ], "umap": [ - -2.180866003036499, - 5.937445640563965 + -2.5280628204345703, + 5.2626237869262695 ], "pca3d": [ - -0.08152472227811813, - -0.3326853811740875, - -0.0545860230922699 + -0.0839766189455986, + -0.3339819014072418, + -0.05036879703402519 ], "tsne3d": [ - -23.54366111755371, - -29.348567962646484, - 4.700556755065918 + -27.301671981811523, + -20.80687141418457, + 23.414962768554688 ], "umap3d": [ - 2.5560107231140137, - -0.9386159777641296, - -0.6179904937744141 + 0.12413797527551651, + 0.3481364846229553, + 5.718092441558838 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 24, + "id": 1, "label": "Cancer Data" } ], @@ -328398,46 +328595,46 @@ "openalex_url": "https://openalex.org/W3170030468", "projections": { "pca": [ - -0.10913143306970596, - -0.3046507239341736 + -0.10861057788133621, + -0.3027002811431885 ], "tsne": [ - -6.418291091918945, - -15.506060600280762 + -19.111074447631836, + -7.498457908630371 ], "umap": [ - -2.130736827850342, - 6.277474403381348 + -2.5352118015289307, + 5.1017985343933105 ], "pca3d": [ - -0.10913071036338806, - -0.304640531539917, - 0.028698323294520378 + -0.10860993713140488, + -0.3026901185512543, + 0.034779973328113556 ], "tsne3d": [ - -18.650917053222656, - -28.411182403564453, - 1.8984063863754272 + -22.790733337402344, + -21.240463256835938, + 25.522981643676758 ], "umap3d": [ - 2.7195968627929688, - -1.0702159404754639, - -0.6037160754203796 + 0.264291912317276, + 0.3530983328819275, + 5.993047714233398 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 24, + "id": 1, "label": "Cancer Data" } ], @@ -328847,46 +329044,46 @@ "journal": "Nature", "projections": { "pca": [ - 0.050410497933626175, - -0.18328021466732025 + 0.051063697785139084, + -0.18268492817878723 ], "tsne": [ - 24.815074920654297, - -10.400287628173828 + -2.109341621398926, + -10.831633567810059 ], "umap": [ - -2.0031368732452393, - 6.432454586029053 + -2.281343460083008, + 5.0525617599487305 ], "pca3d": [ - 0.05041040480136871, - -0.1832909733057022, - -0.007355932146310806 + 0.05106358975172043, + -0.18269366025924683, + -0.003313438268378377 ], "tsne3d": [ - -11.91900634765625, - -1.4380512237548828, - 46.4171142578125 + -15.416267395019531, + -13.9445219039917, + 34.53480529785156 ], "umap3d": [ - 2.8771567344665527, - -0.9972861409187317, - -0.23324711620807648 + 0.5005519390106201, + 0.3926908075809479, + 5.906520366668701 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 24, + "id": 1, "label": "Cancer Data" } ], @@ -329295,47 +329492,47 @@ "abstract": "Secure, scalable, multi-cloud platform using federated learning aims to accelerate cancer discoveries and treatments", "projections": { "pca": [ - -0.08526841551065445, - -0.119263656437397 + -0.08208354562520981, + -0.1165739968419075 ], "tsne": [ - -18.10801124572754, - -14.433481216430664 + -27.45624351501465, + 11.096735954284668 ], "umap": [ - -0.9194819927215576, - 5.475865364074707 + -1.2805895805358887, + 4.636662483215332 ], "pca3d": [ - -0.08526812493801117, - -0.11926954984664917, - 0.1601792275905609 + -0.08208328485488892, + -0.1165781319141388, + 0.16290688514709473 ], "tsne3d": [ - 4.41230583190918, - -39.07603454589844, - -23.657665252685547 + -31.828128814697266, + 10.202672958374023, + 3.436194896697998 ], "umap3d": [ - 3.3933188915252686, - -0.0723777636885643, - -1.2635732889175415 + 0.5183107256889343, + -0.19047872722148895, + 6.549376487731934 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 2, - "label": "AI Drug Discovery" + "id": 9, + "label": "Drug Discovery" }, { - "id": 28, - "label": "Clinical AI" + "id": 9, + "label": "Biomedical AI" } ], "_embedding": [ @@ -329759,46 +329956,46 @@ "openalex_url": "https://openalex.org/W3134010398", "projections": { "pca": [ - 0.03309745341539383, - -0.3097210228443146 + 0.034079331904649734, + -0.30916422605514526 ], "tsne": [ - -4.930871486663818, - -14.914872169494629 + -22.867534637451172, + -2.7425620555877686 ], "umap": [ - -2.2258360385894775, - 6.306538105010986 + -2.7345190048217773, + 5.047421932220459 ], "pca3d": [ - 0.033097296953201294, - -0.3097132742404938, - 0.02776958793401718 + 0.034079279750585556, + -0.3091548681259155, + 0.031962163746356964 ], "tsne3d": [ - -18.95253562927246, - -48.981502532958984, - 8.105799674987793 + -22.183910369873047, + -29.92067527770996, + 23.043621063232422 ], "umap3d": [ - 2.6314971446990967, - -1.2433881759643555, - -0.9300886392593384 + -0.04346511512994766, + 0.24035656452178955, + 6.076755523681641 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 24, + "id": 1, "label": "Cancer Data" } ], @@ -330207,46 +330404,46 @@ "journal": "Nature", "projections": { "pca": [ - -0.05945288762450218, - -0.28540414571762085 + -0.06078729405999184, + -0.28632497787475586 ], "tsne": [ - -5.935679912567139, - -17.266332626342773 + -18.251176834106445, + -8.6884183883667 ], "umap": [ - -2.1912729740142822, - 5.871826171875 + -2.5470995903015137, + 5.119079113006592 ], "pca3d": [ - -0.059453047811985016, - -0.2854195535182953, - -0.015382037498056889 + -0.060787592083215714, + -0.28633826971054077, + -0.011095761321485043 ], "tsne3d": [ - -22.86137580871582, - -35.007442474365234, - 2.094172716140747 + -26.021907806396484, + -18.56778907775879, + 19.261947631835938 ], "umap3d": [ - 2.5901472568511963, - -1.0456640720367432, - -0.7086166143417358 + 0.13634300231933594, + 0.2880147397518158, + 5.717950820922852 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 24, + "id": 1, "label": "Cancer Data" } ], @@ -330655,47 +330852,47 @@ "abstract": "Schema definitions and Python types for Medical Event Data Standard, a standard for medical event data such as EHR and claims data - meds/src/meds/schema.py at main \u00b7 Medical-Event-Data-Standard/meds", "projections": { "pca": [ - 0.20392289757728577, - -0.251472532749176 + 0.20509570837020874, + -0.25076568126678467 ], "tsne": [ - 30.616962432861328, - -5.7687907218933105 + 12.934343338012695, + -23.975086212158203 ], "umap": [ - 1.648473858833313, - 7.120357990264893 + 1.4720358848571777, + 6.153838157653809 ], "pca3d": [ - 0.2039228230714798, - -0.2514772415161133, - 0.09789331257343292 + 0.20509551465511322, + -0.2507701516151428, + 0.09937620908021927 ], "tsne3d": [ - 0.3695404827594757, - 2.6741158962249756, - 47.70846176147461 + 13.531290054321289, + 6.9046196937561035, + 43.07648849487305 ], "umap3d": [ - 3.1752254962921143, - -1.3982614278793335, - 1.0405272245407104 + 1.961267113685608, + 1.9763762950897217, + 7.327582836151123 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 6, + "label": "Data Needs" }, { - "id": 10, - "label": "Medical Data" + "id": 25, + "label": "Data Standards" } ], "_embedding": [ @@ -331102,47 +331299,47 @@ "abstract": "View the Nikolaus Schultz Lab page for Kevin M. Boehm.", "projections": { "pca": [ - 0.08593110740184784, - -0.04714929684996605 + 0.08339005708694458, + -0.046967100352048874 ], "tsne": [ - 11.938088417053223, - -23.29712677001953 + -6.356554985046387, + -12.930584907531738 ], "umap": [ - 1.1036394834518433, - 5.246908664703369 + 0.20540876686573029, + 5.69870662689209 ], "pca3d": [ - 0.085931196808815, - -0.0471588633954525, - -0.057871997356414795 + 0.0833902359008789, + -0.04697566106915474, + -0.06154765188694 ], "tsne3d": [ - -30.789703369140625, - 12.943925857543945, - -3.787900447845459 + -35.66623306274414, + -1.189916968345642, + 4.253199577331543 ], "umap3d": [ - 2.708855390548706, - 0.32792118191719055, - 0.5317255854606628 + 1.849326491355896, + 1.3195217847824097, + 5.187202453613281 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 4, - "label": "Com Area Amp" + "id": 6, + "label": "Data Needs" }, { - "id": 23, - "label": "Research Authors" + "id": 16, + "label": "Lab Research" } ], "_embedding": [ @@ -331550,47 +331747,47 @@ "abstract": "The Kushal Dey lab focuses on developing machine learning models and computational pipelines that integrate genomic and epigenomic data.", "projections": { "pca": [ - -0.05328274890780449, - -0.04848824068903923 + -0.05279020965099335, + -0.047370318323373795 ], "tsne": [ - -2.2529795169830322, - -22.603391647338867 + -14.830039024353027, + -14.551678657531738 ], "umap": [ - -1.3865258693695068, - 3.8603689670562744 + -1.2689608335494995, + 6.610560417175293 ], "pca3d": [ - -0.05328318104147911, - -0.048489950597286224, - -0.006089755333960056 + -0.052790649235248566, + -0.04737023264169693, + -0.0060300929471850395 ], "tsne3d": [ - -53.293399810791016, - -32.55437469482422, - 10.461437225341797 + -16.035276412963867, + -21.571462631225586, + -13.473896980285645 ], "umap3d": [ - 2.114400863647461, - 0.6858842968940735, - -1.4271906614303589 + 0.4962970018386841, + 0.5609273910522461, + 4.176956653594971 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 19, - "label": "Cell Models" + "id": 20, + "label": "Cell Foundation" } ], "_embedding": [ @@ -331998,47 +332195,47 @@ "abstract": "Chronicles of training strong LLMs from scratch in the wild", "projections": { "pca": [ - 0.050603583455085754, - 0.12331456691026688 + 0.048728376626968384, + 0.12210698425769806 ], "tsne": [ - -13.481014251708984, - 19.747251510620117 + 9.302271842956543, + 26.371702194213867 ], "umap": [ - 3.030013084411621, - 2.9664621353149414 + 2.2081027030944824, + 2.027282953262329 ], "pca3d": [ - 0.050603460520505905, - 0.12332102656364441, - -0.03095170110464096 + 0.048728249967098236, + 0.12211256474256516, + -0.03212863206863403 ], "tsne3d": [ - 27.771387100219727, - -13.064847946166992, - -29.48642921447754 + 17.598501205444336, + 12.835469245910645, + -42.34016799926758 ], "umap3d": [ - 6.4806060791015625, - -0.29723095893859863, - 1.1740241050720215 + 4.745016574859619, + -0.40036311745643616, + 5.751876354217529 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 6, + "label": "Learning Techniques" } ], "_embedding": [ @@ -332448,47 +332645,47 @@ "abstract": "Tag Long form text using LLMs. Contribute to cocreatelabs-ai/llmtag development by creating an account on GitHub.", "projections": { "pca": [ - 0.2087325155735016, - 0.006629770155996084 + 0.20930014550685883, + 0.006422373931854963 ], "tsne": [ - -5.713107585906982, - 5.778202533721924 + -12.52804946899414, + 11.585478782653809 ], "umap": [ - 0.8265604376792908, - 6.422143936157227 + -0.26245829463005066, + 3.668694496154785 ], "pca3d": [ - 0.20873157680034637, - 0.006615338381379843, - -0.05050695315003395 + 0.20929935574531555, + 0.006408477667719126, + -0.0512978658080101 ], "tsne3d": [ - 26.11579132080078, - 28.005632400512695, - -19.567766189575195 + 10.912274360656738, + 0.635151207447052, + 11.423835754394531 ], "umap3d": [ - 4.993127822875977, - -0.47266829013824463, - -1.149724006652832 + 2.062438488006592, + 1.0567188262939453, + 7.186614513397217 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 0, - "label": "Clinical LLMs" + "id": 5, + "label": "Health Benchmarks" } ], "_embedding": [ @@ -332896,47 +333093,47 @@ "abstract": "Schema definitions and Python types for Medical Event Data Standard, a standard for medical event data such as EHR and claims data - meds/src/meds/schema.py at 0fd1ff6b8686a5ffa249a64716609e0b14531ccf \u00b7 Medical-Event-Data-Standard/meds", "projections": { "pca": [ - 0.15823951363563538, - -0.3111955523490906 + 0.16045138239860535, + -0.30958566069602966 ], "tsne": [ - 30.652917861938477, - -5.82161283493042 + 12.928711891174316, + -23.997966766357422 ], "umap": [ - 1.5159887075424194, - 7.212367057800293 + 1.4193047285079956, + 6.196619987487793 ], "pca3d": [ - 0.15823908150196075, - -0.3111930191516876, - 0.08091790974140167 + 0.1604510247707367, + -0.30958259105682373, + 0.08467494696378708 ], "tsne3d": [ - 2.163076639175415, - 2.389317750930786, - 52.187713623046875 + 12.525431632995605, + 4.422481536865234, + 46.045082092285156 ], "umap3d": [ - 3.1427512168884277, - -1.4108744859695435, - 1.0027347803115845 + 1.863386869430542, + 1.907528281211853, + 7.364584922790527 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 6, + "label": "Data Needs" }, { - "id": 10, - "label": "Medical Data" + "id": 25, + "label": "Data Standards" } ], "_embedding": [ @@ -333344,46 +333541,46 @@ "abstract": "Precision oncology researchers can access AACR Project GENIE data through NCI\u2019s Genomic Data Commons.", "projections": { "pca": [ - 0.11278105527162552, - -0.2522842288017273 + 0.11386463791131973, + -0.25027114152908325 ], "tsne": [ - 6.986844062805176, - -13.936306953430176 + -14.546104431152344, + -8.69922161102295 ], "umap": [ - -2.2851345539093018, - 6.589445114135742 + -2.552088737487793, + 5.239085674285889 ], "pca3d": [ - 0.11278155446052551, - -0.25228238105773926, - 0.012759402394294739 + 0.11386511474847794, + -0.2502674162387848, + 0.013546090573072433 ], "tsne3d": [ - -33.1246223449707, - -21.966957092285156, - 35.41365432739258 + -29.00855827331543, + -14.623452186584473, + 37.4219970703125 ], "umap3d": [ - 2.608046770095825, - -1.072180986404419, - -0.15160372853279114 + 0.16239947080612183, + 0.5375372171401978, + 5.750985145568848 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 24, + "id": 1, "label": "Cancer Data" } ], @@ -333793,47 +333990,47 @@ "abstract": "Cancer biologist Nikolaus Schultz uses computational tools to study the diversity of genomic alterations underlying different cancer types.", "projections": { "pca": [ - -0.07807556539773941, - -0.23677296936511993 + -0.07942243665456772, + -0.2360575646162033 ], "tsne": [ - 11.417418479919434, - -23.351106643676758 + -18.81919288635254, + -11.993229866027832 ], "umap": [ - -2.0055394172668457, - 5.359614849090576 + -1.9614261388778687, + 5.575169086456299 ], "pca3d": [ - -0.07807529717683792, - -0.23677419126033783, - -0.07047180831432343 + -0.07942219823598862, + -0.23605698347091675, + -0.06958575546741486 ], "tsne3d": [ - -31.01069450378418, - 7.959684371948242, - -5.554621696472168 + -32.13385772705078, + -4.04543399810791, + 4.445554256439209 ], "umap3d": [ - 2.5472865104675293, - -0.36556315422058105, - -0.8032628893852234 + 0.2641236484050751, + 0.23160605132579803, + 5.243138313293457 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 1, "label": "Cancer Data" }, { - "id": 9, - "label": "Cancer Biobank" + "id": 1, + "label": "Cancer Data" } ], "_embedding": [ @@ -334241,47 +334438,47 @@ "abstract": "ETL process from FHIR (defined by the German Medical Informatics Initiative) to OMOP - OHDSI/ETL-German-FHIR-Core", "projections": { "pca": [ - 0.22644832730293274, - -0.12233220785856247 + 0.22719517350196838, + -0.1212722584605217 ], "tsne": [ - 28.67719268798828, - -7.519296646118164 + 14.953929901123047, + -22.481687545776367 ], "umap": [ - 1.3736151456832886, - 6.990686416625977 + 1.3385361433029175, + 5.949368000030518 ], "pca3d": [ - 0.22644835710525513, - -0.12233147770166397, - 0.016902713105082512 + 0.227195143699646, + -0.12127198278903961, + 0.017878305166959763 ], "tsne3d": [ - 12.926307678222656, - 1.486717700958252, - 46.51875305175781 + 4.615234851837158, + 9.876118659973145, + 41.42469787597656 ], "umap3d": [ - 3.2556662559509277, - -1.2636685371398926, - 0.889785885810852 + 1.9344476461410522, + 1.7305424213409424, + 7.231348991394043 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 6, + "label": "Data Needs" }, { - "id": 10, - "label": "Medical Data" + "id": 25, + "label": "Data Standards" } ], "_embedding": [ @@ -334695,47 +334892,47 @@ "doi": "10.14309/01.ajg.0001045852.91899.8f", "projections": { "pca": [ - 0.10955683887004852, - -0.17391175031661987 + 0.11178158223628998, + -0.1739685833454132 ], "tsne": [ - -20.96173095703125, - -17.059904098510742 + -5.490922451019287, + 4.036954402923584 ], "umap": [ - -0.8873226642608643, - 6.884061336517334 + -1.968309998512268, + 2.678079843521118 ], "pca3d": [ - 0.10955644398927689, - -0.17391636967658997, - 0.0661270022392273 + 0.11178116500377655, + -0.17397280037403107, + 0.066451296210289 ], "tsne3d": [ - 1.6606345176696777, - -52.543724060058594, - 9.917094230651855 + -29.82111358642578, + -0.506087064743042, + 33.168304443359375 ], "umap3d": [ - 3.642810344696045, - -1.212004542350769, - -0.9001380205154419 + 1.3169496059417725, + 0.12323004752397537, + 6.475827693939209 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 12, - "label": "ECG Models" + "id": 13, + "label": "ECG AI Models" } ], "_embedding": [ @@ -335143,47 +335340,47 @@ "abstract": "A nextflow port of Syed et al.'s plinkQC. Contribute to d-laub/nf-plinkQC development by creating an account on GitHub.", "projections": { "pca": [ - 0.26518166065216064, - -0.03740788996219635 + 0.26355424523353577, + -0.03797675669193268 ], "tsne": [ - 19.962186813354492, - 5.078770637512207 + 21.748489379882812, + -3.951178789138794 ], "umap": [ - 3.1656792163848877, - 6.0913238525390625 + 2.583604097366333, + 4.909656524658203 ], "pca3d": [ - 0.2651814818382263, - -0.03740789368748665, - -0.16785882413387299 + 0.2635541260242462, + -0.037977106869220734, + -0.17014694213867188 ], "tsne3d": [ - 21.037168502807617, - 22.536943435668945, - 19.134489059448242 + 25.688980102539062, + 9.50248908996582, + 9.57717227935791 ], "umap3d": [ - 3.819056510925293, - 0.10879214107990265, - 2.0646562576293945 + 3.6086320877075195, + 2.1520588397979736, + 6.110365390777588 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 3, - "label": "SMB Data" + "id": 24, + "label": "SMB Models" } ], "_embedding": [ @@ -335589,47 +335786,47 @@ ], "projections": { "pca": [ - 0.2740650475025177, - -0.07996129989624023 + 0.2761092782020569, + -0.08011380583047867 ], "tsne": [ - -2.637807607650757, - 30.49917221069336 + -18.459978103637695, + 21.920011520385742 ], "umap": [ - 1.4780843257904053, - 5.225862503051758 + 0.6885153651237488, + 4.6143083572387695 ], "pca3d": [ - 0.27406540513038635, - -0.07995747029781342, - 0.11776745319366455 + 0.27610960602760315, + -0.08011133223772049, + 0.11214618384838104 ], "tsne3d": [ - 38.88016128540039, - 20.117874145507812, - -43.654598236083984 + 14.284934043884277, + -20.480283737182617, + 5.692632675170898 ], "umap3d": [ - 3.5418660640716553, - 0.5574914216995239, - 0.6552474498748779 + 2.409233808517456, + 1.6634159088134766, + 5.584609508514404 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 4, - "label": "Com Area Amp" + "id": 6, + "label": "Data Needs" }, { - "id": 23, - "label": "Research Authors" + "id": 10, + "label": "Research Data" } ], "_embedding": [ @@ -336039,47 +336236,47 @@ "abstract": "Docker image for runpod. Contribute to d-laub/runpod-image development by creating an account on GitHub.", "projections": { "pca": [ - 0.32007667422294617, - -0.03480455279350281 + 0.31910181045532227, + -0.03436945751309395 ], "tsne": [ - 25.335670471191406, - 6.268605709075928 + 23.892335891723633, + -10.39320182800293 ], "umap": [ - 3.349540948867798, - 6.704761505126953 + 2.840116024017334, + 5.517209529876709 ], "pca3d": [ - 0.3200770914554596, - -0.03479081392288208, - -0.20919197797775269 + 0.3191026449203491, + -0.034357257187366486, + -0.21160010993480682 ], "tsne3d": [ - 3.423574686050415, - 43.93960952758789, - 15.285091400146484 + 31.644733428955078, + 17.40181541442871, + 19.992414474487305 ], "umap3d": [ - 3.6795461177825928, - -0.2552139461040497, - 2.382946014404297 + 3.6792876720428467, + 2.516900062561035, + 6.769487380981445 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 18, - "label": "Github Tools" + "id": 17, + "label": "GitHub Creation" } ], "_embedding": [ @@ -336486,47 +336683,47 @@ "title": "The SOC2 starting seven", "projections": { "pca": [ - 0.30628225207328796, - -0.014256742782890797 + 0.3071690797805786, + -0.01337752491235733 ], "tsne": [ - 23.237083435058594, - 0.8736904263496399 + 19.962387084960938, + -6.107193470001221 ], "umap": [ - 2.2977888584136963, - 6.597214698791504 + 2.173555374145508, + 5.205016136169434 ], "pca3d": [ - 0.30628275871276855, - -0.014240672811865807, - -0.06348192691802979 + 0.30716949701309204, + -0.013361415825784206, + -0.06803963333368301 ], "tsne3d": [ - 19.620208740234375, - 19.791030883789062, - 29.880325317382812 + 25.595539093017578, + 1.430467128753662, + 22.96953773498535 ], "umap3d": [ - 2.985363006591797, - -0.1346518099308014, - 1.6814508438110352 + 3.074012279510498, + 2.2694900035858154, + 6.2026753425598145 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 25, - "label": "Python Data" + "id": 24, + "label": "SMB Models" } ], "_embedding": [ @@ -336938,47 +337135,47 @@ "abstract": "The PyPI package lightning was compromised in versions 2.6.2 and 2.6.3 with Mini Shai-Hulud themed malicious code to execute credential-stealing malware on import.", "projections": { "pca": [ - 0.2294379323720932, - 0.07042451947927475 + 0.23034176230430603, + 0.07038887590169907 ], "tsne": [ - 15.691086769104004, - 23.911407470703125 + 20.354877471923828, + -10.536945343017578 ], "umap": [ - 3.381054162979126, - 5.704504013061523 + 2.7097222805023193, + 4.16757869720459 ], "pca3d": [ - 0.22943729162216187, - 0.07041975855827332, - -0.054923657327890396 + 0.2303411215543747, + 0.07038550078868866, + -0.05979400873184204 ], "tsne3d": [ - -13.446809768676758, - 47.78404998779297, - 10.56272029876709 + 24.547452926635742, + 30.390518188476562, + -12.021404266357422 ], "umap3d": [ - 4.484786033630371, - 0.21267445385456085, - 1.9875348806381226 + 4.047396659851074, + 1.710343360900879, + 6.116279125213623 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 20, - "label": "AI Agents" + "id": 4, + "label": "Open Agent Tools" } ], "_embedding": [ @@ -337386,47 +337583,47 @@ "abstract": "A community trust management system based on explicit vouches to participate. - mitchellh/vouch", "projections": { "pca": [ - 0.22830930352210999, - 0.03277691826224327 + 0.22834812104701996, + 0.03253423795104027 ], "tsne": [ - 25.89232063293457, - 15.574456214904785 + 30.986103057861328, + -3.0382399559020996 ], "umap": [ - 3.7520925998687744, - 6.821709156036377 + 3.3377575874328613, + 5.423647880554199 ], "pca3d": [ - 0.2283100187778473, - 0.03279482573270798, - -0.13046316802501678 + 0.22834905982017517, + 0.032547879964113235, + -0.1353543996810913 ], "tsne3d": [ - 32.43510818481445, - 30.122446060180664, - 28.478544235229492 + 38.475982666015625, + 24.734437942504883, + 10.430692672729492 ], "umap3d": [ - 3.6994447708129883, - -0.0278757456690073, - 2.910449266433716 + 4.081724643707275, + 2.7298552989959717, + 6.67213249206543 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 18, - "label": "Github Tools" + "id": 17, + "label": "GitHub Creation" } ], "_embedding": [ @@ -337836,47 +338033,47 @@ "abstract": "A GitHub Action invoking the Gemini CLI. Contribute to google-github-actions/run-gemini-cli development by creating an account on GitHub.", "projections": { "pca": [ - 0.3382527828216553, - -0.025275951251387596 + 0.33848142623901367, + -0.02475956454873085 ], "tsne": [ - 20.80306053161621, - 13.605327606201172 + 25.554622650146484, + -3.7571427822113037 ], "umap": [ - 3.6869401931762695, - 6.435778617858887 + 3.182471752166748, + 5.342663288116455 ], "pca3d": [ - 0.3382532298564911, - -0.02526758797466755, - -0.08871599286794662 + 0.3384822905063629, + -0.024750124663114548, + -0.09382416307926178 ], "tsne3d": [ - 27.480022430419922, - 33.53255844116211, - 10.018085479736328 + 30.232303619384766, + 13.418586730957031, + 4.364329814910889 ], "umap3d": [ - 3.7680046558380127, - 0.09313248842954636, - 2.677285671234131 + 3.895402669906616, + 2.5928215980529785, + 6.420776844024658 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 18, - "label": "Github Tools" + "id": 17, + "label": "GitHub Creation" } ], "_embedding": [ @@ -338284,47 +338481,47 @@ "abstract": "Analyses conducting GWAS across the UKBB diverse superpopulations - ukbb_pan_ancestry/data at master \u00b7 atgu/ukbb_pan_ancestry", "projections": { "pca": [ - 0.09246721863746643, - -0.20455101132392883 + 0.08920371532440186, + -0.2058624029159546 ], "tsne": [ - 11.874197959899902, - -19.84760856628418 + -2.8979854583740234, + -19.978126525878906 ], "umap": [ - -1.545318603515625, - 4.702907085418701 + -1.8668900728225708, + 6.137728214263916 ], "pca3d": [ - 0.09246804565191269, - -0.20453765988349915, - -0.12823286652565002 + 0.08920476585626602, + -0.2058500200510025, + -0.12640728056430817 ], "tsne3d": [ - -38.462852478027344, - 7.864351272583008, - 26.86650276184082 + -10.56394100189209, + -15.444397926330566, + -39.93309020996094 ], "umap3d": [ - 1.9342973232269287, - -0.3721936047077179, - -0.5979940295219421 + 0.5895666480064392, + 1.320214033126831, + 5.179314613342285 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 2, - "label": "AI Drug Discovery" + "id": 7, + "label": "Cell Genes" }, { - "id": 2, - "label": "Genetic Disease" + "id": 27, + "label": "Genetic Variants" } ], "_embedding": [ @@ -338732,47 +338929,47 @@ "abstract": "Analyses conducting GWAS across the UKBB diverse superpopulations - atgu/ukbb_pan_ancestry", "projections": { "pca": [ - 0.10941679775714874, - -0.11515891551971436 + 0.10618381947278976, + -0.11595503985881805 ], "tsne": [ - 11.878360748291016, - -19.865272521972656 + -2.919405221939087, + -19.992708206176758 ], "umap": [ - -1.7664482593536377, - 4.4411444664001465 + -1.8161758184432983, + 6.387691974639893 ], "pca3d": [ - 0.10941830277442932, - -0.11513696610927582, - -0.19290858507156372 + 0.10618546605110168, + -0.11593523621559143, + -0.19269384443759918 ], "tsne3d": [ - -39.107627868652344, - 6.869850158691406, - 22.988725662231445 + -12.6519136428833, + -12.916757583618164, + -39.34375762939453 ], "umap3d": [ - 1.790998935699463, - -0.3052971363067627, - -0.6864137649536133 + 0.45058250427246094, + 1.3918583393096924, + 4.974346160888672 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 2, - "label": "Genetic Disease" + "id": 27, + "label": "Genetic Variants" } ], "_embedding": [ @@ -339180,47 +339377,47 @@ "abstract": "Below you can find details about data release v20, made available November 2025.This list is not exhaustive, and we encourage you to...", "projections": { "pca": [ - 0.28440138697624207, - -0.12145364284515381 + 0.28430256247520447, + -0.12223535776138306 ], "tsne": [ - 21.097421646118164, - -3.707634687423706 + 10.605299949645996, + -13.60943603515625 ], "umap": [ - 2.0320093631744385, - 6.4769768714904785 + 1.5115028619766235, + 5.265289783477783 ], "pca3d": [ - 0.2844018042087555, - -0.12144342064857483, - 0.015331064350903034 + 0.284303218126297, + -0.12222528457641602, + 0.012526828795671463 ], "tsne3d": [ - 7.189323902130127, - 10.176371574401855, - 29.25411605834961 + 17.76137924194336, + -2.5160415172576904, + 18.80974006652832 ], "umap3d": [ - 3.0042660236358643, - -0.12584640085697174, - 1.4436419010162354 + 2.4462239742279053, + 2.2213985919952393, + 6.033829212188721 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 6, + "label": "Data Needs" }, { - "id": 25, - "label": "Python Data" + "id": 10, + "label": "Research Data" } ], "_embedding": [ @@ -339627,47 +339824,47 @@ "title": "Does dxdata still work?", "projections": { "pca": [ - 0.24639706313610077, - -0.08711672574281693 + 0.24864621460437775, + -0.08548247069120407 ], "tsne": [ - 26.33509635925293, - -3.053375244140625 + 15.711956977844238, + -17.976303100585938 ], "umap": [ - 1.8603296279907227, - 7.707857131958008 + 0.925459623336792, + 6.272753715515137 ], "pca3d": [ - 0.24639692902565002, - -0.0871196910738945, - -0.0019995542243123055 + 0.2486461102962494, + -0.08548309653997421, + -0.004065470304340124 ], "tsne3d": [ - 12.13547134399414, - 20.991121292114258, - 43.03043746948242 + 20.494417190551758, + 5.771864891052246, + 33.554534912109375 ], "umap3d": [ - 2.161956548690796, - -0.44162407517433167, - 1.5787569284439087 + 1.8326985836029053, + 2.774104356765747, + 6.16758394241333 ] }, "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { "id": 1, - "label": "Open Agent Tools" + "label": "Bioinformatics Models" }, { - "id": 3, - "label": "Github Data" + "id": 6, + "label": "Data Needs" }, { - "id": 10, - "label": "Medical Data" + "id": 25, + "label": "Data Standards" } ], "_embedding": [ @@ -340073,47 +340270,47 @@ ], "projections": { "pca": [ - 0.10345347970724106, - -0.17721465229988098 + 0.10236158221960068, + -0.17695318162441254 ], "tsne": [ - 18.80050277709961, - -11.800000190734863 + 3.845937728881836, + -11.404855728149414 ], "umap": [ - 0.7425701022148132, - 5.49922513961792 + 0.22217586636543274, + 4.562067031860352 ], "pca3d": [ - 0.10345364362001419, - -0.17722369730472565, - 0.06018337979912758 + 0.10236168652772903, + -0.1769629418849945, + 0.0579398050904274 ], "tsne3d": [ - 17.100950241088867, - -4.308313369750977, - 25.751758575439453 + 30.638059616088867, + -25.170549392700195, + -5.5842485427856445 ], "umap3d": [ - 3.488072156906128, - 1.2453663349151611, - 0.24849577248096466 + 1.9042487144470215, + 2.248748779296875, + 5.290721893310547 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 4, - "label": "Com Area Amp" + "id": 9, + "label": "Drug Discovery" }, { - "id": 0, - "label": "Clinical LLMs" + "id": 23, + "label": "Peer Review" } ], "_embedding": [ @@ -340521,47 +340718,47 @@ "abstract": "OpenAI and Molecule.one show how a near-autonomous AI chemist using GPT-5.4 improved a key drug-making reaction, advancing medicinal chemistry research.", "projections": { "pca": [ - 0.056611496955156326, - 0.09090650826692581 + 0.059644948691129684, + 0.09314712136983871 ], "tsne": [ - 0.6049968600273132, - 13.227931022644043 + -0.7290407419204712, + 1.0593891143798828 ], "umap": [ - 3.888904571533203, - 3.95119309425354 + 3.0988903045654297, + 2.7697503566741943 ], "pca3d": [ - 0.056610822677612305, - 0.09089075028896332, - 0.1361466944217682 + 0.05964416265487671, + 0.09313347935676575, + 0.13401687145233154 ], "tsne3d": [ - 33.71681213378906, - -10.45555591583252, - -4.809688568115234 + 7.611569404602051, + 34.0285530090332, + -10.0868501663208 ], "umap3d": [ - 5.330877304077148, - 1.085376262664795, - 0.7469007968902588 + 3.569822311401367, + 0.425990492105484, + 4.873496055603027 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 0, - "label": "AI Agents" + "id": 8, + "label": "Open AI" }, { - "id": 1, - "label": "AI Agents" + "id": 22, + "label": "AI Gemini" } ], "_embedding": [ @@ -340969,46 +341166,46 @@ ], "projections": { "pca": [ - 0.013380169868469238, - -0.10641214996576309 + 0.016027437523007393, + -0.10428380221128464 ], "tsne": [ - -7.6504645347595215, - -5.937237739562988 + -13.276188850402832, + 2.5729377269744873 ], "umap": [ - -0.22737763822078705, - 6.145659446716309 + -1.7469886541366577, + 3.833071708679199 ], "pca3d": [ - 0.013381111435592175, - -0.1064070612192154, - 0.2707906663417816 + 0.016028346493840218, + -0.10428005456924438, + 0.2705308496952057 ], "tsne3d": [ - 20.25320053100586, - -33.52971649169922, - 3.6356279850006104 + 1.4662295579910278, + -16.334199905395508, + 18.330312728881836 ], "umap3d": [ - 3.8982834815979004, - -0.8092815279960632, - -1.9498109817504883 + 0.7616037726402283, + 0.9281764030456543, + 6.967001438140869 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, + "id": 14, "label": "Clinical Health" } ], @@ -341419,46 +341616,46 @@ "abstract": "High\u2011quality 3D medical imaging data is the foundation of modern radiology AI, but access to it is often constrained by data scarcity, privacy restrictions…", "projections": { "pca": [ - 0.02395639941096306, - -0.08017532527446747 + 0.028565384447574615, + -0.07736953347921371 ], "tsne": [ - -16.594003677368164, - 2.4266791343688965 + -1.9936373233795166, + 8.417614936828613 ], "umap": [ - 0.0568956695497036, - 7.6302666664123535 + -1.0989952087402344, + 2.676743507385254 ], "pca3d": [ - 0.02395571395754814, - -0.08017902076244354, - 0.2223605364561081 + 0.02856459841132164, + -0.07737112790346146, + 0.22794251143932343 ], "tsne3d": [ - 1.1396839618682861, - -14.93339729309082, - 37.980018615722656 + -4.363959312438965, + 1.6125059127807617, + 37.64407730102539 ], "umap3d": [ - 4.219074249267578, - -1.8252453804016113, - -0.8152517080307007 + 1.5604596138000488, + -0.39838770031929016, + 7.0926384925842285 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 11, + "id": 19, "label": "Medical Imaging" } ], @@ -341865,47 +342062,47 @@ ], "projections": { "pca": [ - 0.18169890344142914, - 0.044883016496896744 + 0.18242168426513672, + 0.04569714888930321 ], "tsne": [ - 12.923232078552246, - 1.0844556093215942 + 11.792766571044922, + 0.7854223847389221 ], "umap": [ - 2.0683789253234863, - 5.424462795257568 + 1.2879137992858887, + 4.75049352645874 ], "pca3d": [ - 0.1816992610692978, - 0.04487491399049759, - 0.12382391840219498 + 0.1824217438697815, + 0.04568777605891228, + 0.12002022564411163 ], "tsne3d": [ - 10.293954849243164, - 11.513416290283203, - 3.8563995361328125 + 25.71578598022461, + -8.912006378173828, + 2.794867753982544 ], "umap3d": [ - 3.56897234916687, - 0.7257874608039856, - 1.2671692371368408 + 2.9941911697387695, + 1.7651479244232178, + 5.471313953399658 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 4, - "label": "Com Area Amp" + "id": 6, + "label": "Data Needs" }, { - "id": 26, - "label": "AI Information" + "id": 10, + "label": "Research Data" } ], "_embedding": [ @@ -342313,47 +342510,47 @@ "abstract": "Trust is no longer enough: secure data sharing requires international collaboration across institutions and governments.", "projections": { "pca": [ - 0.007719745393842459, - -0.19115503132343292 + 0.007731201127171516, + -0.18966801464557648 ], "tsne": [ - 10.252638816833496, - -16.676895141601562 + -2.624309778213501, + -14.444897651672363 ], "umap": [ - -1.7908512353897095, - 5.041943073272705 + -1.6105924844741821, + 5.099152088165283 ], "pca3d": [ - 0.00772098358720541, - -0.1911381185054779, - 0.009498200379312038 + 0.0077323890291154385, + -0.1896534413099289, + 0.008821900933980942 ], "tsne3d": [ - -28.222671508789062, - 11.442782402038574, - 8.99893856048584 + 2.675877809524536, + -37.568729400634766, + -0.7179571390151978 ], "umap3d": [ - 2.4882330894470215, - 0.14569996297359467, - -0.5471643209457397 + 1.091378927230835, + 1.499889612197876, + 5.303910732269287 ] }, "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_label": "Clinical AI Data", "cluster_levels": [ { "id": 2, - "label": "Bio Models Gene" + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 9, - "label": "Cancer Biobank" + "id": 1, + "label": "Cancer Data" } ], "_embedding": [ @@ -342761,47 +342958,47 @@ "abstract": "AI will not cure cancer. It will improve outcomes by closing gaps in detection, diagnosis, and treatment.", "projections": { "pca": [ - -0.047648776322603226, - -0.11657841503620148 + -0.04314827919006348, + -0.11363724619150162 ], "tsne": [ - -15.889242172241211, - -13.48300552368164 + -25.204025268554688, + 9.918420791625977 ], "umap": [ - -0.8946251273155212, - 5.443796634674072 + -1.2690101861953735, + 4.498195171356201 ], "pca3d": [ - -0.04764909669756889, - -0.11658880114555359, - 0.2801855206489563 + -0.04314868524670601, + -0.11364495009183884, + 0.2824328541755676 ], "tsne3d": [ - 1.9860682487487793, - -41.684322357177734, - -15.894488334655762 + -27.05733871459961, + 5.551908493041992, + 1.5057321786880493 ], "umap3d": [ - 3.435507297515869, - 0.042949378490448, - -1.336575984954834 + 0.373249351978302, + -0.17416658997535706, + 6.651963233947754 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 2, - "label": "AI Drug Discovery" + "id": 9, + "label": "Drug Discovery" }, { - "id": 28, - "label": "Clinical AI" + "id": 9, + "label": "Biomedical AI" } ], "_embedding": [ @@ -343209,47 +343406,47 @@ "abstract": "Log in with your Hugging Face account, enter the agent harness and model names (optional organisation and email), and upload a TSV file with answers for all benchmark questions. The app validates t...", "projections": { "pca": [ - 0.16228574514389038, - -0.03379453346133232 + 0.16184327006340027, + -0.03349359333515167 ], "tsne": [ - 7.822352409362793, - 15.23333740234375 + 16.94412612915039, + 15.566536903381348 ], "umap": [ - 4.7450947761535645, - 4.597900867462158 + 3.8687186241149902, + 3.25636887550354 ], "pca3d": [ - 0.16228584945201874, - -0.03379044309258461, - -0.023569663986563683 + 0.16184335947036743, + -0.03349060192704201, + -0.02540791779756546 ], "tsne3d": [ - 43.21784973144531, - -15.455244064331055, - 21.07974624633789 + -7.341559886932373, + 32.5329475402832, + 16.58578109741211 ], "umap3d": [ - 5.002163410186768, - 1.5305356979370117, - 2.0142295360565186 + 4.503761291503906, + 2.1656625270843506, + 4.674240589141846 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { "id": 7, - "label": "Open AI" + "label": "Hugging Face Models" } ], "_embedding": [ @@ -343657,47 +343854,47 @@ "abstract": "Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.", "projections": { "pca": [ - 0.14791998267173767, - -0.004284042865037918 + 0.1513882875442505, + -0.0016321561997756362 ], "tsne": [ - 22.306791305541992, - 28.38966941833496 + 32.9625129699707, + 10.625360488891602 ], "umap": [ - 4.969552516937256, - 5.9095563888549805 + 4.40314245223999, + 4.501535415649414 ], "pca3d": [ - 0.14792007207870483, - -0.004282355774194002, - 0.21476154029369354 + 0.15138836205005646, + -0.001629707869142294, + 0.2126752883195877 ], "tsne3d": [ - 58.99969482421875, - 14.85544204711914, - 1.3862029314041138 + 22.150232315063477, + 43.600433349609375, + 3.9923524856567383 ], "umap3d": [ - 5.278626918792725, - 0.732688844203949, - 3.118121862411499 + 5.51391077041626, + 2.274951457977295, + 6.068752765655518 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { - "id": 16, - "label": "Claude Code" + "id": 11, + "label": "Code Tools" } ], "_embedding": [ @@ -344104,47 +344301,47 @@ "title": "AI co-clinician: researching the path toward AI-augmented care \u00e2\u0080\u0094 Google DeepMind", "projections": { "pca": [ - -0.06975548714399338, - 0.12348294258117676 + -0.06319611519575119, + 0.1278204321861267 ], "tsne": [ - -15.477967262268066, - -8.476609230041504 + -21.092357635498047, + 13.487944602966309 ], "umap": [ - -0.3556744456291199, - 5.894564151763916 + -1.221352219581604, + 3.4387667179107666 ], "pca3d": [ - -0.06975532323122025, - 0.12347813695669174, - 0.3594912886619568 + -0.06319595873355865, + 0.12781652808189392, + 0.3587217628955841 ], "tsne3d": [ - 13.528353691101074, - -37.5079231262207, - -1.9017014503479004 + -14.528371810913086, + 12.418088912963867, + 2.8586316108703613 ], "umap3d": [ - 4.343011856079102, - -0.18651345372200012, - -2.0072708129882812 + 0.61860191822052, + 0.1824188083410263, + 7.527430534362793 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 2, - "label": "AI Drug Discovery" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, - "label": "Clinical Health" + "id": 5, + "label": "Health Benchmarks" } ], "_embedding": [ @@ -344553,47 +344750,47 @@ "abstract": "Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.", "projections": { "pca": [ - 0.05320984125137329, - -0.011583173647522926 + 0.05520835891366005, + -0.00927208736538887 ], "tsne": [ - 21.715789794921875, - 28.359840393066406 + 33.50579833984375, + 10.993169784545898 ], "umap": [ - 4.964492321014404, - 5.889089584350586 + 4.390566825866699, + 4.517963409423828 ], "pca3d": [ - 0.053209904581308365, - -0.011577487923204899, - 0.11044193059206009 + 0.05520835518836975, + -0.009266817942261696, + 0.10834693908691406 ], "tsne3d": [ - 58.212215423583984, - 9.631750106811523, - 3.4012043476104736 + 17.87836456298828, + 43.523006439208984, + 6.059539794921875 ], "umap3d": [ - 5.290403366088867, - 0.7788003087043762, - 3.1350455284118652 + 5.5579304695129395, + 2.3055853843688965, + 6.037627220153809 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { - "id": 16, - "label": "Claude Code" + "id": 11, + "label": "Code Tools" } ], "_embedding": [ @@ -345003,47 +345200,47 @@ "abstract": "Org profile for Merck & Co., Inc. on Hugging Face, the AI community building the future.", "projections": { "pca": [ - 0.16879163682460785, - -0.09524177759885788 + 0.16889353096485138, + -0.0945775955915451 ], "tsne": [ - 18.59642791748047, - 8.634103775024414 + 15.826027870178223, + 11.868494033813477 ], "umap": [ - 2.499725818634033, - 5.500606060028076 + 3.929425001144409, + 3.6728241443634033 ], "pca3d": [ - 0.1687915027141571, - -0.09525492787361145, - 0.1302538514137268 + 0.16889354586601257, + -0.09459168463945389, + 0.12842732667922974 ], "tsne3d": [ - 26.302261352539062, - -9.422029495239258, - 18.0756893157959 + 36.20420837402344, + -9.797545433044434, + 13.792765617370605 ], "umap3d": [ - 4.4877610206604, - 1.392377495765686, - 1.8176681995391846 + 3.9279556274414062, + 2.222633123397827, + 4.883393287658691 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, - "label": "AI Agents" + "id": 8, + "label": "Open AI" }, { - "id": 26, - "label": "AI Information" + "id": 7, + "label": "Hugging Face Models" } ], "_embedding": [ @@ -345451,47 +345648,47 @@ "abstract": "We created a marketplace for employees in our San Francisco office, with one big twist. We tasked Claude with buying, selling and negotiating on our colleagues\u2019 behalf.", "projections": { "pca": [ - 0.3220837414264679, - 0.006521186791360378 + 0.3225746750831604, + 0.0066488138400018215 ], "tsne": [ - 24.098268508911133, - 27.89254379272461 + 32.091068267822266, + 9.04493236541748 ], "umap": [ - 4.797499656677246, - 5.956377029418945 + 4.301290988922119, + 4.5285797119140625 ], "pca3d": [ - 0.3220842182636261, - 0.006523523014038801, - 0.04795646667480469 + 0.3225753605365753, + 0.00664973771199584, + 0.04401318356394768 ], "tsne3d": [ - 60.047515869140625, - 21.130281448364258, - 3.172961711883545 + 24.202653884887695, + 40.412925720214844, + 0.1382981687784195 ], "umap3d": [ - 5.184022426605225, - 0.7177090644836426, - 3.021143913269043 + 5.3387980461120605, + 2.2513906955718994, + 6.146905899047852 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { - "id": 16, - "label": "Claude Code" + "id": 11, + "label": "Code Tools" } ], "_embedding": [ @@ -345897,47 +346094,47 @@ ], "projections": { "pca": [ - 0.13793300092220306, - -0.06380227953195572 + 0.1385742872953415, + -0.06377045065164566 ], "tsne": [ - 19.243303298950195, - -12.948538780212402 + 4.960332870483398, + -10.994921684265137 ], "umap": [ - 0.6701997518539429, - 5.467927932739258 + 0.2089529186487198, + 4.550637722015381 ], "pca3d": [ - 0.13793328404426575, - -0.06380950659513474, - 0.1430082470178604 + 0.13857463002204895, + -0.06377765536308289, + 0.14080478250980377 ], "tsne3d": [ - 15.81286907196045, - -3.3104639053344727, - 32.16200256347656 + 29.67420196533203, + -27.591108322143555, + -10.49483871459961 ], "umap3d": [ - 3.505377769470215, - 1.2628898620605469, - 0.12462335079908371 + 1.8690552711486816, + 2.289076566696167, + 5.33530855178833 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 4, - "label": "Com Area Amp" + "id": 9, + "label": "Drug Discovery" }, { - "id": 0, - "label": "Clinical LLMs" + "id": 23, + "label": "Peer Review" } ], "_embedding": [ @@ -346352,47 +346549,47 @@ "doi": "10.1136/bmj.s781", "projections": { "pca": [ - 0.17036160826683044, - -0.26266881823539734 + 0.17114482820034027, + -0.26305413246154785 ], "tsne": [ - 17.789878845214844, - -8.839479446411133 + 5.731909275054932, + -20.145008087158203 ], "umap": [ - -1.6959868669509888, - 5.311821937561035 + -2.1100361347198486, + 5.751383304595947 ], "pca3d": [ - 0.17036131024360657, - -0.26267480850219727, - 0.08015201985836029 + 0.17114463448524475, + -0.26306018233299255, + 0.07933856546878815 ], "tsne3d": [ - -27.2031307220459, - 23.624866485595703, - 39.10089111328125 + 8.963397979736328, + -15.23088264465332, + 35.830223083496094 ], "umap3d": [ - 2.218914031982422, - -0.5081669688224792, - -0.8296928405761719 + 0.7342729568481445, + 1.3459690809249878, + 5.622685432434082 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 1, "label": "Cancer Data" }, { - "id": 9, - "label": "Cancer Biobank" + "id": 1, + "label": "Cancer Data" } ], "_embedding": [ @@ -346798,47 +346995,47 @@ ], "projections": { "pca": [ - 0.2251947522163391, - 0.15178629755973816 + 0.2259392887353897, + 0.151953786611557 ], "tsne": [ - 14.61307144165039, - 16.650007247924805 + 22.179401397705078, + 4.681188583374023 ], "umap": [ - 3.559314250946045, - 5.379245281219482 + 2.6776814460754395, + 4.239092826843262 ], "pca3d": [ - 0.22519464790821075, - 0.15177813172340393, - 0.16100990772247314 + 0.22593936324119568, + 0.1519438624382019, + 0.1548224687576294 ], "tsne3d": [ - 27.188825607299805, - 7.085885524749756, - 12.359414100646973 + 9.21027660369873, + 18.72207260131836, + 6.05546760559082 ], "umap3d": [ - 4.287191867828369, - 0.8789209723472595, - 1.686440110206604 + 3.783923387527466, + 1.9697980880737305, + 5.50989294052124 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 20, - "label": "AI Agents" + "id": 4, + "label": "Open Agent Tools" } ], "_embedding": [ @@ -347244,47 +347441,47 @@ ], "projections": { "pca": [ - 0.11882615834474564, - -0.07443226873874664 + 0.12241581082344055, + -0.07095571607351303 ], "tsne": [ - -2.4711480140686035, - 22.30532455444336 + 2.1686794757843018, + 6.8750691413879395 ], "umap": [ - 0.10417615622282028, - 6.225245475769043 + -0.9669309854507446, + 3.6643595695495605 ], "pca3d": [ - 0.1188260018825531, - -0.07444749027490616, - 0.2887403964996338 + 0.12241558730602264, + -0.07096908241510391, + 0.28851616382598877 ], "tsne3d": [ - 21.22871208190918, - -27.9892520904541, - 12.772961616516113 + -5.260644912719727, + 12.750215530395508, + 3.4387760162353516 ], "umap3d": [ - 4.171290397644043, - -0.583894670009613, - -1.767433524131775 + 1.0923911333084106, + 0.6207237839698792, + 7.587244033813477 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, - "label": "Clinical Health" + "id": 5, + "label": "Health Benchmarks" } ], "_embedding": [ @@ -347692,47 +347889,47 @@ "abstract": "Generate synthetic EHR, genomics, imaging, and other data types from Python. - synthlab/notebooks/Coherent_MultimodalDataset.ipynb at main \u00b7 bschilder/synthlab", "projections": { "pca": [ - 0.12684951722621918, - -0.17333823442459106 + 0.1276707947254181, + -0.17268043756484985 ], "tsne": [ - 31.18990135192871, - 0.8341345191001892 + 26.272687911987305, + -16.950458526611328 ], "umap": [ - 2.0872273445129395, - 7.062680721282959 + 1.8901418447494507, + 6.473989963531494 ], "pca3d": [ - 0.12684929370880127, - -0.17334453761577606, - -0.028689226135611534 + 0.1276705116033554, + -0.17268459498882294, + -0.02597297914326191 ], "tsne3d": [ - 7.7914934158325195, - 46.55423355102539, - 31.834203720092773 + 9.776036262512207, + 16.8060302734375, + 34.777252197265625 ], "umap3d": [ - 3.086362361907959, - -1.1619349718093872, - 1.397510290145874 + 2.3529398441314697, + 2.0886313915252686, + 7.286941051483154 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { "id": 25, - "label": "Python Data" + "label": "Data Standards" } ], "_embedding": [ @@ -348140,47 +348337,47 @@ "abstract": "Generate synthetic EHR, genomics, imaging, and other data types from Python. - synthlab/synthlab/coherent.py at main \u00b7 bschilder/synthlab", "projections": { "pca": [ - 0.08328508585691452, - -0.20397605001926422 + 0.08320175111293793, + -0.20359504222869873 ], "tsne": [ - 31.32220458984375, - 0.49093732237815857 + 26.219619750976562, + -17.370378494262695 ], "umap": [ - 1.9750560522079468, - 7.086119174957275 + 1.8337668180465698, + 6.520239353179932 ], "pca3d": [ - 0.08328481763601303, - -0.20398375391960144, - -0.06469381600618362 + 0.0832013487815857, + -0.20360067486763, + -0.06145964935421944 ], "tsne3d": [ - 3.137036085128784, - 48.80169677734375, - 32.02802276611328 + 7.248600482940674, + 19.755199432373047, + 36.61264419555664 ], "umap3d": [ - 2.998488187789917, - -1.258203387260437, - 1.310935616493225 + 2.2667853832244873, + 2.086290121078491, + 7.290268421173096 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 10, - "label": "Medical Data" + "id": 25, + "label": "Data Standards" } ], "_embedding": [ @@ -348588,47 +348785,47 @@ "abstract": "Generate synthetic EHR, genomics, imaging, and other data types from Python. - bschilder/synthlab", "projections": { "pca": [ - 0.13447259366512299, - -0.14550389349460602 + 0.13429531455039978, + -0.1453249454498291 ], "tsne": [ - 31.6153507232666, - 0.6418623924255371 + 26.448389053344727, + -17.462430953979492 ], "umap": [ - 1.9871203899383545, - 7.130295276641846 + 1.8684093952178955, + 6.50960111618042 ], "pca3d": [ - 0.13447248935699463, - -0.14551493525505066, - -0.0948290154337883 + 0.13429513573646545, + -0.1453339159488678, + -0.09249701350927353 ], "tsne3d": [ - 5.001122951507568, - 48.08208084106445, - 27.10541534423828 + 7.225155353546143, + 19.899381637573242, + 32.158538818359375 ], "umap3d": [ - 3.0790340900421143, - -1.1531704664230347, - 1.4039263725280762 + 2.3021390438079834, + 2.056145668029785, + 7.276614189147949 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 10, - "label": "Medical Data" + "id": 25, + "label": "Data Standards" } ], "_embedding": [ @@ -349034,47 +349231,47 @@ ], "projections": { "pca": [ - -0.08802183717489243, - -0.008318813517689705 + -0.08325804769992828, + -0.0031659486703574657 ], "tsne": [ - 0.4865374267101288, - -0.4115438759326935 + -2.5944325923919678, + -0.5804377198219299 ], "umap": [ - -0.30737441778182983, - 5.192266464233398 + -0.7899796366691589, + 4.629756927490234 ], "pca3d": [ - -0.08802162110805511, - -0.008323107846081257, - 0.23876018822193146 + -0.0832580029964447, + -0.0031695852521806955, + 0.23823510110378265 ], "tsne3d": [ - 26.57677459716797, - -19.5960750579834, - -5.357227802276611 + -5.601303577423096, + -14.80833625793457, + -10.599305152893066 ], "umap3d": [ - 4.080897808074951, - 0.6393399238586426, - -0.9271109104156494 + 2.7202680110931396, + 0.2005452662706375, + 4.933132171630859 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ - { - "id": 0, - "label": "Medical Data AI" - }, { "id": 2, - "label": "AI Drug Discovery" + "label": "Clinical AI Data" }, { - "id": 13, + "id": 9, "label": "Drug Discovery" + }, + { + "id": 9, + "label": "Biomedical AI" } ], "_embedding": [ @@ -349481,47 +349678,47 @@ "title": "Introducing Muse Spark: Scaling Towards Personal Superintelligence", "projections": { "pca": [ - -0.054025206714868546, - 0.2763546407222748 + -0.05042256414890289, + 0.2790594696998596 ], "tsne": [ - -9.70877742767334, - 19.0450439453125 + 8.772948265075684, + 21.345212936401367 ], "umap": [ - 3.028913974761963, - 3.1082499027252197 + 2.0668845176696777, + 2.3576018810272217 ], "pca3d": [ - -0.05402493104338646, - 0.27636685967445374, - 0.13204325735569 + -0.050422362983226776, + 0.27907201647758484, + 0.12407601624727249 ], "tsne3d": [ - 25.73845672607422, - 12.254168510437012, - -22.584659576416016 + 4.851367473602295, + 28.535982131958008, + -30.35236358642578 ], "umap3d": [ - 6.299201011657715, - -0.0894111692905426, - 1.1394652128219604 + 4.573596477508545, + -0.2907634675502777, + 5.923337459564209 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 6, + "label": "Learning Techniques" } ], "_embedding": [ @@ -349929,47 +350126,47 @@ "abstract": "The winners of the MedGemma Impact Challenge demonstrated the potential of Google\u2019s open medical models for solving diverse healthcare challenges.", "projections": { "pca": [ - 0.019986674189567566, - -0.1566709578037262 + 0.022246763110160828, + -0.15485501289367676 ], "tsne": [ - -10.534818649291992, - 5.3836188316345215 + -16.683937072753906, + 16.921215057373047 ], "umap": [ - 0.22001738846302032, - 6.82501220703125 + -0.8639606833457947, + 3.1401572227478027 ], "pca3d": [ - 0.019986750558018684, - -0.15666913986206055, - 0.23760412633419037 + 0.022246863692998886, + -0.15485325455665588, + 0.23838283121585846 ], "tsne3d": [ - 29.901758193969727, - -36.53997802734375, - -4.714696884155273 + -11.87574291229248, + 22.481914520263672, + 11.34327220916748 ], "umap3d": [ - 4.579989433288574, - -0.9325414299964905, - -1.0565953254699707 + 1.6437054872512817, + 0.36239930987358093, + 7.128900051116943 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 6, - "label": "Clinical Health" + "id": 5, + "label": "Health Benchmarks" } ], "_embedding": [ @@ -350377,47 +350574,47 @@ "abstract": "Accelerates Codex growth to power the next generation of Python developer tools", "projections": { "pca": [ - 0.20172135531902313, - 0.10584381222724915 + 0.20185960829257965, + 0.10646678507328033 ], "tsne": [ - 12.995497703552246, - 15.702865600585938 + 19.500185012817383, + 4.851756572723389 ], "umap": [ - 3.774662971496582, - 5.311049461364746 + 2.856498956680298, + 4.075043678283691 ], "pca3d": [ - 0.20171993970870972, - 0.10582359880208969, - -0.0021889458876103163 + 0.20185811817646027, + 0.10644827783107758, + -0.006109270267188549 ], "tsne3d": [ - 25.00834083557129, - 6.986079692840576, - 4.3938093185424805 + 13.0322904586792, + 25.096607208251953, + 7.098643779754639 ], "umap3d": [ - 4.578474998474121, - 0.8456332683563232, - 1.820428729057312 + 3.9829330444335938, + 1.982743501663208, + 5.28228759765625 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 20, - "label": "AI Agents" + "id": 4, + "label": "Open Agent Tools" } ], "_embedding": [ @@ -350824,46 +351021,46 @@ "title": "Google Research at The Check Up: from healthcare innovation to real-world care settings", "projections": { "pca": [ - -0.007345771882683039, - -0.11143345385789871 + -0.0012623327784240246, + -0.1061711460351944 ], "tsne": [ - -14.695786476135254, - -8.245818138122559 + -20.289718627929688, + 13.89301586151123 ], "umap": [ - -0.27247685194015503, - 5.93752908706665 + -1.2453413009643555, + 3.566582202911377 ], "pca3d": [ - -0.0073442053981125355, - -0.11141453683376312, - 0.34224170446395874 + -0.0012607453390955925, + -0.10615449398756027, + 0.34018975496292114 ], "tsne3d": [ - 18.142656326293945, - -35.25817108154297, - -4.80142068862915 + -14.457337379455566, + 13.21299934387207, + -2.1050825119018555 ], "umap3d": [ - 4.384591579437256, - -0.1340954452753067, - -2.016357660293579 + 0.5770174264907837, + 0.23703040182590485, + 7.480020999908447 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, + "id": 14, "label": "Clinical Health" } ], @@ -351272,47 +351469,47 @@ "abstract": "Cerebras is the go-to platform for fast and effortless AI training. Learn more at cerebras.ai.", "projections": { "pca": [ - 0.1571502983570099, - 0.08970841765403748 + 0.16084259748458862, + 0.09182234108448029 ], "tsne": [ - -3.9778289794921875, - 23.690053939819336 + 8.505026817321777, + 36.898681640625 ], "umap": [ - 1.4059138298034668, - 5.95438814163208 + 0.5873655080795288, + 3.6319968700408936 ], "pca3d": [ - 0.15714900195598602, - 0.08969073742628098, - 0.13693085312843323 + 0.160841166973114, + 0.09180828183889389, + 0.1349460333585739 ], "tsne3d": [ - 19.69253921508789, - -23.591623306274414, - -34.00341033935547 + -0.5536500215530396, + 14.878966331481934, + 7.353760719299316 ], "umap3d": [ - 4.675965309143066, - -0.1585233360528946, - -0.5518292784690857 + 2.6049106121063232, + 0.9090508222579956, + 6.500176906585693 ] }, "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_label": "Multimodal Models", "cluster_levels": [ { "id": 0, - "label": "Medical Data AI" + "label": "Multimodal Models" }, { - "id": 4, - "label": "Com Area Amp" + "id": 2, + "label": "Medical Models" }, { - "id": 26, - "label": "AI Information" + "id": 0, + "label": "Multimodal Models" } ], "_embedding": [ @@ -351720,47 +351917,47 @@ "journal": "Nature Medicine", "projections": { "pca": [ - 0.07267183065414429, - -0.11960864812135696 + 0.07564452290534973, + -0.11843904852867126 ], "tsne": [ - -5.953059196472168, - 0.34639304876327515 + -9.92294692993164, + 6.766210556030273 ], "umap": [ - 0.4456680715084076, - 6.432731628417969 + -1.3157039880752563, + 3.9090030193328857 ], "pca3d": [ - 0.072672538459301, - -0.11960555613040924, - 0.2458532452583313 + 0.075645312666893, + -0.11843768507242203, + 0.243020698428154 ], "tsne3d": [ - 12.465291023254395, - -12.138936042785645, - 18.98268699645996 + 9.113149642944336, + -13.70149040222168, + 13.285541534423828 ], "umap3d": [ - 4.336112976074219, - -0.6697453260421753, - -1.655634880065918 + 1.465448021888733, + 0.9542384743690491, + 7.1526360511779785 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 0, - "label": "Clinical LLMs" + "id": 14, + "label": "Clinical Health" } ], "_embedding": [ @@ -352166,47 +352363,47 @@ ], "projections": { "pca": [ - 0.14010584354400635, - -0.2089216262102127 + 0.1398368626832962, + -0.20880168676376343 ], "tsne": [ - 18.92633056640625, - -12.67196273803711 + 4.695653438568115, + -11.32553768157959 ], "umap": [ - 0.6594647169113159, - 5.49002742767334 + 0.24109776318073273, + 4.575752258300781 ], "pca3d": [ - 0.1401067078113556, - -0.20892216265201569, - 0.12091018259525299 + 0.13983780145645142, + -0.20880399644374847, + 0.12025295197963715 ], "tsne3d": [ - 20.825658798217773, - -2.2921855449676514, - 30.0548095703125 + 26.43048095703125, + -27.433347702026367, + -7.3299560546875 ], "umap3d": [ - 3.4903972148895264, - 1.2568035125732422, - 0.16216623783111572 + 1.891221523284912, + 2.312577724456787, + 5.29649019241333 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 4, - "label": "Com Area Amp" + "id": 9, + "label": "Drug Discovery" }, { - "id": 0, - "label": "Clinical LLMs" + "id": 23, + "label": "Peer Review" } ], "_embedding": [ @@ -352614,46 +352811,46 @@ "abstract": "Summary Protege offers multimodal data aggregation and standardization with AI training/evaluation curation aligned to foundation model builders, delivered with enterprise-grade compliance and repeatable, deal-driven programs. Segmed offers multimodal data access through direct connections within its broad healthcare provider network (all 50 U.S. states & international) focused on imaging data with… Read More", "projections": { "pca": [ - 0.03622334823012352, - -0.05924629792571068 + 0.04043455049395561, + -0.05456141009926796 ], "tsne": [ - -18.71685218811035, - -8.902192115783691 + -23.04200553894043, + 15.832927703857422 ], "umap": [ - -0.19745442271232605, - 6.329364776611328 + -1.327012300491333, + 3.3610680103302 ], "pca3d": [ - 0.03622405603528023, - -0.05925077572464943, - 0.27075064182281494 + 0.04043534770607948, + -0.05456514656543732, + 0.2733684480190277 ], "tsne3d": [ - 16.77505111694336, - -48.41975784301758, - 0.4167993366718292 + -22.403688430786133, + 17.343238830566406, + 8.772941589355469 ], "umap3d": [ - 4.389189720153809, - -0.7453965544700623, - -2.0317907333374023 + 0.989856481552124, + 0.40358322858810425, + 7.675260066986084 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, + "id": 14, "label": "Clinical Health" } ], @@ -353061,47 +353258,47 @@ "title": "The Isomorphic Labs Drug Design Engine unlocks a new frontier beyond AlphaFold - Isomorphic Labs", "projections": { "pca": [ - -0.03832748532295227, - -0.0961342453956604 + -0.036212969571352005, + -0.09317991137504578 ], "tsne": [ - 0.7851177453994751, - -14.347902297973633 + -7.4836106300354, + -8.453680992126465 ], "umap": [ - -0.6113244891166687, - 4.513300895690918 + -0.7695417404174805, + 5.550817012786865 ], "pca3d": [ - -0.03832750767469406, - -0.09614232927560806, - 0.04799420014023781 + -0.03621313348412514, + -0.09318726509809494, + 0.04958998039364815 ], "tsne3d": [ - -21.282421112060547, - -15.045544624328613, - 31.37241554260254 + -5.576447010040283, + -30.19512367248535, + -7.730532646179199 ], "umap3d": [ - 3.1022911071777344, - 0.9313728213310242, - -1.2347626686096191 + 1.8102014064788818, + 0.17512835562229156, + 4.485966205596924 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 2, - "label": "AI Drug Discovery" + "id": 9, + "label": "Drug Discovery" }, { - "id": 13, - "label": "Drug Discovery" + "id": 28, + "label": "Spatial Drug" } ], "_embedding": [ @@ -353507,47 +353704,47 @@ ], "projections": { "pca": [ - 0.0935550406575203, - -0.050775207579135895 + 0.09223242849111557, + -0.05115954950451851 ], "tsne": [ - 19.099380493164062, - -13.080458641052246 + 5.123050212860107, + -11.103513717651367 ], "umap": [ - 0.6962952017784119, - 5.498506546020508 + 0.23838220536708832, + 4.551621437072754 ], "pca3d": [ - 0.09355521202087402, - -0.05078683793544769, - 0.029653897508978844 + 0.09223280102014542, + -0.05117190629243851, + 0.028382722288370132 ], "tsne3d": [ - 19.43738555908203, - -2.6027474403381348, - 35.27631378173828 + 25.88736343383789, + -28.17215347290039, + -11.746672630310059 ], "umap3d": [ - 3.5056095123291016, - 1.2357335090637207, - 0.14543834328651428 + 1.8406938314437866, + 2.304960250854492, + 5.352700710296631 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 4, - "label": "Com Area Amp" + "id": 9, + "label": "Drug Discovery" }, { - "id": 0, - "label": "Clinical LLMs" + "id": 23, + "label": "Peer Review" } ], "_embedding": [ @@ -353963,46 +354160,46 @@ "doi": "10.1016/j.annonc.2024.04.010", "projections": { "pca": [ - 0.1234336793422699, - -0.2337481826543808 + 0.12332594394683838, + -0.23339524865150452 ], "tsne": [ - -4.4810285568237305, - -14.523545265197754 + -23.498394012451172, + -2.389352321624756 ], "umap": [ - -2.1701598167419434, - 6.317984104156494 + -2.761284351348877, + 4.983547210693359 ], "pca3d": [ - 0.12343458086252213, - -0.2337377965450287, - 0.03465690463781357 + 0.12332694977521896, + -0.2333850860595703, + 0.037934109568595886 ], "tsne3d": [ - -22.173505783081055, - -48.00611114501953, - 12.92770004272461 + -18.63484764099121, + -27.201169967651367, + 24.738494873046875 ], "umap3d": [ - 2.6852309703826904, - -1.3454742431640625, - -0.896416962146759 + -0.06953956931829453, + 0.26605871319770813, + 6.149256229400635 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 24, + "id": 1, "label": "Cancer Data" } ], @@ -354411,47 +354608,47 @@ "abstract": "How Formation Bio built a human-in-the-loop pipeline to make genetic insights operational at scale", "projections": { "pca": [ - -0.16198459267616272, - -0.04498758539557457 + -0.16192112863063812, + -0.04233575984835625 ], "tsne": [ - 2.843721866607666, - -31.880964279174805 + -11.173775672912598, + -24.325599670410156 ], "umap": [ - -2.1837921142578125, - 3.46359920501709 + -1.5920439958572388, + 7.380454063415527 ], "pca3d": [ - -0.1619839072227478, - -0.044974036514759064, - -0.055254943668842316 + -0.16192036867141724, + -0.04232316091656685, + -0.05589497461915016 ], "tsne3d": [ - -54.42298889160156, - -0.24653589725494385, - 4.17061185836792 + -38.178680419921875, + -9.990479469299316, + -24.58391571044922 ], "umap3d": [ - 1.1697579622268677, - 0.5301163792610168, - -1.1404905319213867 + 0.11609353125095367, + 1.1186224222183228, + 3.948922634124756 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 8, - "label": "Gene Regulation" + "id": 3, + "label": "Genomic Sequences" } ], "_embedding": [ @@ -354861,47 +355058,47 @@ "abstract": "ChatGPT Health is a dedicated experience that securely connects your health data and apps, with privacy protections and a physician-informed design.", "projections": { "pca": [ - 0.13434547185897827, - -0.18917199969291687 + 0.13609090447425842, + -0.187985360622406 ], "tsne": [ - -1.259914517402649, - 3.9601986408233643 + -2.3750555515289307, + -7.927663326263428 ], "umap": [ - 0.29584601521492004, - 5.930076599121094 + -1.102126121520996, + 3.875 ], "pca3d": [ - 0.13434620201587677, - -0.18917430937290192, - 0.1569993942975998 + 0.13609185814857483, + -0.1879901885986328, + 0.15621037781238556 ], "tsne3d": [ - 7.549738883972168, - -11.9310302734375, - 27.61353874206543 + 9.88064956665039, + -21.437740325927734, + 17.912578582763672 ], "umap3d": [ - 4.340540409088135, - -0.6730629205703735, - -1.3575383424758911 + 1.4111384153366089, + 0.8649723529815674, + 6.686861991882324 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 0, - "label": "Clinical LLMs" + "id": 5, + "label": "Health Benchmarks" } ], "_embedding": [ @@ -355309,47 +355506,47 @@ "abstract": "How Formation looks for \u201cdark assets\u201d\u2013 silently deprioritized candidates that may reveal hidden opportunity", "projections": { "pca": [ - -0.04583615064620972, - 0.19220338761806488 + -0.04411325976252556, + 0.19266998767852783 ], "tsne": [ - -22.12213897705078, - 25.670597076416016 + 7.747453212738037, + 25.019758224487305 ], "umap": [ - 2.5944457054138184, - 3.290431022644043 + 1.5401912927627563, + 2.10093355178833 ], "pca3d": [ - -0.045835431665182114, - 0.1922231763601303, - 0.033464498817920685 + -0.04411254823207855, + 0.1926862746477127, + 0.0303904227912426 ], "tsne3d": [ - 9.68605899810791, - -9.695760726928711, - -21.81186294555664 + 27.110734939575195, + 6.696293830871582, + -36.834228515625 ], "umap3d": [ - 6.281884670257568, - -0.29062339663505554, - 0.5352215766906738 + 4.620294570922852, + -0.9164518713951111, + 6.181125164031982 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 18, + "label": "Retrieval Learning" } ], "_embedding": [ @@ -355757,47 +355954,47 @@ "abstract": "Formation Bio collaborates with Sanofi and OpenAI to Introduce Muse, a first of its kind AI tool to accelerate patient recruitment in drug development.", "projections": { "pca": [ - 0.07906824350357056, - -0.017146725207567215 + 0.08003482967615128, + -0.01601051539182663 ], "tsne": [ - 1.2570058107376099, - -2.157228708267212 + -5.760982036590576, + -1.4759875535964966 ], "umap": [ - -0.3024456799030304, - 4.977290630340576 + -0.7065547108650208, + 4.71309757232666 ], "pca3d": [ - 0.07906769216060638, - -0.017160724848508835, - 0.0715731531381607 + 0.08003439009189606, + -0.01602279022336006, + 0.0709352046251297 ], "tsne3d": [ - -5.795895099639893, - -10.522232055664062, - 29.661663055419922 + 6.429745674133301, + -36.23136901855469, + 11.503393173217773 ], "umap3d": [ - 3.8222856521606445, - 0.6781311631202698, - -1.003064751625061 + 2.4610798358917236, + 0.1507767289876938, + 4.938619136810303 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ - { - "id": 0, - "label": "Medical Data AI" - }, { "id": 2, - "label": "AI Drug Discovery" + "label": "Clinical AI Data" }, { - "id": 13, + "id": 9, "label": "Drug Discovery" + }, + { + "id": 9, + "label": "Biomedical AI" } ], "_embedding": [ @@ -356204,47 +356401,47 @@ "title": "Pretraining: The First Scaling Frontier", "projections": { "pca": [ - -0.161767840385437, - 0.2638344168663025 + -0.159683495759964, + 0.26625198125839233 ], "tsne": [ - -9.749470710754395, - 18.506580352783203 + 8.496748924255371, + 20.848159790039062 ], "umap": [ - 2.999492883682251, - 3.0161967277526855 + 2.106333017349243, + 2.32079815864563 ], "pca3d": [ - -0.16176773607730865, - 0.2638494074344635, - 0.017143817618489265 + -0.15968342125415802, + 0.26626667380332947, + 0.015779802575707436 ], "tsne3d": [ - 22.345895767211914, - 11.083465576171875, - -27.062021255493164 + 2.7838361263275146, + 25.552453994750977, + -33.354278564453125 ], "umap3d": [ - 6.304063320159912, - -0.28183117508888245, - 1.2548218965530396 + 4.680447578430176, + -0.3751608431339264, + 6.0171308517456055 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 14, - "label": "Model Training" + "id": 6, + "label": "Learning Techniques" } ], "_embedding": [ @@ -356650,47 +356847,47 @@ ], "projections": { "pca": [ - 0.1534808725118637, - -0.009593411348760128 + 0.15312112867832184, + -0.010212261229753494 ], "tsne": [ - 17.134361267089844, - -12.307687759399414 + 4.580471038818359, + -13.006730079650879 ], "umap": [ - 0.8612366318702698, - 5.3399128913879395 + 0.3912501335144043, + 4.690027713775635 ], "pca3d": [ - 0.15348142385482788, - -0.009581525810062885, - 0.10528327524662018 + 0.15312181413173676, + -0.010202208533883095, + 0.10175411403179169 ], "tsne3d": [ - 16.811988830566406, - 3.889483690261841, - 26.99704360961914 + 23.547964096069336, + -21.832109451293945, + -5.0483717918396 ], "umap3d": [ - 3.4132258892059326, - 1.2587299346923828, - 0.3367170989513397 + 2.0839524269104004, + 2.09122896194458, + 5.173784255981445 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 4, - "label": "Com Area Amp" + "id": 6, + "label": "Data Needs" }, { "id": 23, - "label": "Research Authors" + "label": "Peer Review" } ], "_embedding": [ @@ -357098,47 +357295,47 @@ "abstract": "OpenAI is acquiring Neptune to deepen visibility into model behavior and strengthen the tools researchers use to track experiments and monitor training.", "projections": { "pca": [ - 0.15535010397434235, - 0.14818952977657318 + 0.1565721184015274, + 0.1499084085226059 ], "tsne": [ - 12.130084037780762, - 15.31115436553955 + 18.755836486816406, + 5.129391193389893 ], "umap": [ - 4.008009910583496, - 5.0792317390441895 + 3.0477144718170166, + 3.843604564666748 ], "pca3d": [ - 0.15534919500350952, - 0.14817790687084198, - 0.053651537746191025 + 0.15657125413417816, + 0.14989912509918213, + 0.051026515662670135 ], "tsne3d": [ - 26.889375686645508, - 5.661309242248535, - -1.5200217962265015 + 14.135491371154785, + 28.441574096679688, + 3.1459829807281494 ], "umap3d": [ - 4.721652030944824, - 0.9067660570144653, - 1.7407276630401611 + 4.00492000579834, + 1.7962709665298462, + 5.24123477935791 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 8, - "label": "Open Models" + "id": 4, + "label": "AI Agents" }, { - "id": 20, - "label": "AI Agents" + "id": 4, + "label": "Open Agent Tools" } ], "_embedding": [ @@ -357546,46 +357743,46 @@ "abstract": "Explore groundbreaking academic, peer-reviewed, and research publications about Natural Language Processing (NLP) authored by IMO Health\u2019s leading NLP scientists.", "projections": { "pca": [ - 0.050455495715141296, - -0.13015559315681458 + 0.05152416601777077, + -0.1290803700685501 ], "tsne": [ - -2.8597612380981445, - -1.083763837814331 + -7.822022914886475, + 5.444502830505371 ], "umap": [ - -0.10548385232686996, - 6.256103038787842 + -1.1055142879486084, + 4.076267242431641 ], "pca3d": [ - 0.050455473363399506, - -0.1301637887954712, - 0.22829318046569824 + 0.0515243262052536, + -0.12908786535263062, + 0.22760522365570068 ], "tsne3d": [ - 0.3474392890930176, - -20.448314666748047, - 27.17901039123535 + 7.766674041748047, + -28.820470809936523, + 1.234235405921936 ], "umap3d": [ - 3.9788031578063965, - -1.0877134799957275, - -1.6968896389007568 + 0.9782188534736633, + 1.2229552268981934, + 6.704041957855225 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, + "id": 14, "label": "Clinical Health" } ], @@ -357995,47 +358192,47 @@ "abstract": "AI models like GPT-5 are an increasingly valuable tool for scientists, but many remain unaware of the capabilities of frontier AI. We present a collection of short case studies in which GPT-5 produced new, concrete steps in ongoing research across mathematics, physics, astronomy, computer science, biology, and materials science. In these examples, the authors highlight how AI accelerated their work, and where it fell short; where expert time was saved, and where human input was still key. We document the interactions of the human authors with GPT-5, as guiding examples of fruitful collaboration with AI. Of note, this paper includes four new results in mathematics (carefully verified by the human authors), underscoring how GPT-5 can help human mathematicians settle previously unsolved problems. These contributions are modest in scope but profound in implication, given the rate at which frontier AI is progressing. Contents", "projections": { "pca": [ - -0.03536105528473854, - 0.2229820340871811 + -0.033362068235874176, + 0.22467836737632751 ], "tsne": [ - -0.8363491296768188, - 14.09611988067627 + 0.21480204164981842, + 2.0142860412597656 ], "umap": [ - 3.7572519779205322, - 3.789806842803955 + 2.8695061206817627, + 2.574403762817383 ], "pca3d": [ - -0.035361334681510925, - 0.2229854166507721, - 0.10098740458488464 + -0.03336242958903313, + 0.2246817648410797, + 0.09972167760133743 ], "tsne3d": [ - 28.310630798339844, - -8.818026542663574, - -10.03762149810791 + 4.69577693939209, + 28.018291473388672, + -12.221994400024414 ], "umap3d": [ - 5.721377849578857, - 0.9029802083969116, - 0.9137263894081116 + 3.9853739738464355, + 0.23589099943637848, + 5.098002910614014 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 0, - "label": "AI Agents" + "id": 8, + "label": "Open AI" }, { - "id": 1, - "label": "AI Agents" + "id": 22, + "label": "AI Gemini" } ], "_embedding": [ @@ -358445,47 +358642,47 @@ "abstract": "OpenAI introduces the first research cases showing how GPT-5 accelerates scientific progress across math, physics, biology, and computer science. Explore how AI and researchers collaborate to generate proofs, uncover new insights, and reshape the pace of discovery.", "projections": { "pca": [ - 0.04475225508213043, - 0.04414929077029228 + 0.046512212604284286, + 0.046811070293188095 ], "tsne": [ - 1.751582384109497, - 12.901342391967773 + 0.27987608313560486, + 0.6555638313293457 ], "umap": [ - 4.0767412185668945, - 4.166120529174805 + 3.1515274047851562, + 2.842893362045288 ], "pca3d": [ - 0.04475168138742447, - 0.044140905141830444, - 0.12075547128915787 + 0.04651166871190071, + 0.0468045212328434, + 0.1212620735168457 ], "tsne3d": [ - 27.039033889770508, - -9.791970252990723, - -4.169490814208984 + 2.201201915740967, + 32.450870513916016, + -12.03787899017334 ], "umap3d": [ - 5.326356410980225, - 1.1903815269470215, - 0.9798316955566406 + 3.7361464500427246, + 0.5999806523323059, + 4.868757247924805 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, - "label": "AI Agents" + "id": 8, + "label": "Open AI" }, { - "id": 1, - "label": "AI Agents" + "id": 22, + "label": "AI Gemini" } ], "_embedding": [ @@ -358893,47 +359090,47 @@ "abstract": "A report describing an a highly sophisticated AI-led cyberattack", "projections": { "pca": [ - 0.04210374131798744, - 0.18746444582939148 + 0.046969037503004074, + 0.1904079020023346 ], "tsne": [ - -3.988908290863037, - 17.479761123657227 + 12.511709213256836, + 15.59096622467041 ], "umap": [ - 3.427992343902588, - 4.194256782531738 + 2.537015438079834, + 2.8768253326416016 ], "pca3d": [ - 0.042103398591279984, - 0.18746860325336456, - 0.17048493027687073 + 0.046968474984169006, + 0.19041162729263306, + 0.16577570140361786 ], "tsne3d": [ - 39.09342575073242, - 3.9676811695098877, - -17.300527572631836 + 3.344236135482788, + 18.834142684936523, + -1.134438395500183 ], "umap3d": [ - 5.494928359985352, - 0.5261503458023071, - 1.2690832614898682 + 4.239066123962402, + 0.5041053891181946, + 5.542366981506348 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 1, - "label": "AI Agents" + "id": 22, + "label": "AI Gemini" } ], "_embedding": [ @@ -359341,47 +359538,47 @@ "abstract": "NVIDIA-Digital-Bio has one repository available. Follow their code on GitHub.", "projections": { "pca": [ - 0.27281907200813293, - -0.07464694231748581 + 0.272382527589798, + -0.07408946007490158 ], "tsne": [ - 22.23805809020996, - 10.12817668914795 + 26.76247787475586, + -6.062901020050049 ], "umap": [ - 3.465057849884033, - 6.5533976554870605 + 2.850921392440796, + 5.349537372589111 ], "pca3d": [ - 0.2728191316127777, - -0.07464998960494995, - -0.09682563692331314 + 0.2723828852176666, + -0.07409147173166275, + -0.09855163842439651 ], "tsne3d": [ - 17.067440032958984, - 46.10987091064453, - -2.0707528591156006 + 42.81382751464844, + 5.7981367111206055, + -1.7625410556793213 ], "umap3d": [ - 3.7705695629119873, - -0.18089719116687775, - 2.5179903507232666 + 3.882080554962158, + 2.2708091735839844, + 6.748871803283691 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 18, - "label": "Github Tools" + "id": 17, + "label": "GitHub Creation" } ], "_embedding": [ @@ -359791,47 +359988,47 @@ "abstract": "We\u2019re on a journey to advance and democratize artificial intelligence through open source and open science.", "projections": { "pca": [ - 0.13206923007965088, - 0.17455337941646576 + 0.135940283536911, + 0.17697042226791382 ], "tsne": [ - 6.179032325744629, - 19.924196243286133 + 22.987836837768555, + 13.476550102233887 ], "umap": [ - 5.034140586853027, - 4.843612194061279 + 4.184490203857422, + 2.8630192279815674 ], "pca3d": [ - 0.13206863403320312, - 0.17455318570137024, - 0.21120202541351318 + 0.13593946397304535, + 0.17696844041347504, + 0.2085384726524353 ], "tsne3d": [ - 48.02955627441406, - -4.699000358581543, - 4.241321563720703 + -2.4843132495880127, + 35.661746978759766, + 5.372872352600098 ], "umap3d": [ - 5.482852935791016, - 1.6519460678100586, - 2.0512430667877197 + 4.95108699798584, + 2.1527113914489746, + 4.657943248748779 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { "id": 7, - "label": "Open AI" + "label": "Hugging Face Models" } ], "_embedding": [ @@ -360239,47 +360436,47 @@ "abstract": "Biohub is launching a large-scale scientific initiative that combines frontier AI with frontier biology to solve diseases and accelerate discovery.", "projections": { "pca": [ - 0.11456767469644547, - -0.086517333984375 + 0.11522319912910461, + -0.08497053384780884 ], "tsne": [ - 1.6639314889907837, - 15.70549488067627 + -3.062563896179199, + -4.18534517288208 ], "umap": [ - -0.36398956179618835, - 4.821378707885742 + -0.7235902547836304, + 4.918632507324219 ], "pca3d": [ - 0.11456754058599472, - -0.08652354776859283, - 0.04580514878034592 + 0.1152232214808464, + -0.08497608453035355, + 0.04435349255800247 ], "tsne3d": [ - -16.933191299438477, - -2.27225399017334, - 29.947147369384766 + -1.107155442237854, + -17.719085693359375, + -15.312538146972656 ], "umap3d": [ - 3.844860792160034, - 0.7469287514686584, - -0.8947741985321045 + 2.3958582878112793, + 0.1949939876794815, + 4.838449478149414 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ - { - "id": 0, - "label": "Medical Data AI" - }, { "id": 2, - "label": "AI Drug Discovery" + "label": "Clinical AI Data" }, { - "id": 13, + "id": 9, "label": "Drug Discovery" + }, + { + "id": 9, + "label": "Biomedical AI" } ], "_embedding": [ @@ -360695,47 +360892,47 @@ ], "projections": { "pca": [ - 0.007782728876918554, - 0.055443376302719116 + 0.010553035885095596, + 0.05929509922862053 ], "tsne": [ - -0.9239066243171692, - 19.053075790405273 + 9.383444786071777, + 13.311504364013672 ], "umap": [ - 3.5198354721069336, - 3.9309170246124268 + 2.660529136657715, + 2.9382643699645996 ], "pca3d": [ - 0.007783968932926655, - 0.0554644800722599, - 0.1747199445962906 + 0.010554321110248566, + 0.059315040707588196, + 0.1740105152130127 ], "tsne3d": [ - 41.072349548339844, - -16.826683044433594, - -14.096627235412598 + -6.482421875, + 21.63685417175293, + -6.994165420532227 ], "umap3d": [ - 5.556911945343018, - 0.8134185075759888, - 0.9517307281494141 + 4.31647253036499, + 0.6226943135261536, + 5.252978324890137 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 1, - "label": "AI Agents" + "id": 22, + "label": "AI Gemini" } ], "_embedding": [ @@ -361143,47 +361340,47 @@ "abstract": "Built with over 1,000 NVIDIA Blackwell Ultra GPUs, the AI factory will be essential to supporting modern scientific research using foundation models, as well as physical and agentic AI.", "projections": { "pca": [ - 0.04753521829843521, - -0.036321405321359634 + 0.04897381365299225, + -0.033899422734975815 ], "tsne": [ - 0.6599993109703064, - -3.1533162593841553 + -4.863358020782471, + -1.9345494508743286 ], "umap": [ - -0.3235021233558655, - 4.962503910064697 + -0.7044215798377991, + 4.8187761306762695 ], "pca3d": [ - 0.04753424972295761, - -0.03633887320756912, - 0.10026399046182632 + 0.048972733318805695, + -0.03391259163618088, + 0.10153806954622269 ], "tsne3d": [ - -10.197724342346191, - -14.582499504089355, - 33.62179946899414 + -10.116605758666992, + -19.322132110595703, + -7.442354679107666 ], "umap3d": [ - 3.765392303466797, - 0.7102920413017273, - -1.0169967412948608 + 2.4092440605163574, + 0.17886358499526978, + 4.913106918334961 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ - { - "id": 0, - "label": "Medical Data AI" - }, { "id": 2, - "label": "AI Drug Discovery" + "label": "Clinical AI Data" }, { - "id": 13, + "id": 9, "label": "Drug Discovery" + }, + { + "id": 9, + "label": "Biomedical AI" } ], "_embedding": [ @@ -361591,47 +361788,47 @@ "abstract": "Announcing a dramatic increase in Anthropic's compute resources", "projections": { "pca": [ - 0.13808265328407288, - 0.04599885642528534 + 0.1407071202993393, + 0.04861811175942421 ], "tsne": [ - 11.697870254516602, - 12.98381519317627 + 16.099716186523438, + 6.30454683303833 ], "umap": [ - 2.421640634536743, - 5.957695007324219 + 2.7547414302825928, + 4.483784198760986 ], "pca3d": [ - 0.13808323442935944, - 0.04601774364709854, - 0.08005301654338837 + 0.1407076120376587, + 0.04863513633608818, + 0.074844591319561 ], "tsne3d": [ - 31.281166076660156, - 6.396305084228516, - 37.72918701171875 + 9.15222454071045, + 36.92586135864258, + 23.508028030395508 ], "umap3d": [ - 4.074534893035889, - 0.8291268348693848, - 1.8554139137268066 + 3.8106400966644287, + 2.325286626815796, + 5.517178535461426 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 4, + "label": "AI Agents" }, { - "id": 3, - "label": "SMB Data" + "id": 4, + "label": "Open Agent Tools" } ], "_embedding": [ @@ -362042,47 +362239,47 @@ "abstract": "About the TalkGrace Zheng, co-founder of PerturbAI, and Xin Jin, co-founder of PerturbAI, and Professor of Scripps/HHMI, will show how we\u2019re moving from mapping the brain to predicting how it works. In partnership with OpenAI, they\u2019ve built one of the largest brain datasets ever created, capturing activity across 8 million cells, and are using AI to uncover the rules that drive biology.The result: a new era of biology and medicine that is more predictive, more precise, and far more effective.Zheng and Jin will also demonstrate how AI is transforming science itself, replacing complex, manual workflows with systems that let researchers explore data using everyday language, to help accelerate discovery and open the door to many more contributors.They\u2019ll even introduce the idea of a \u201cdigital brain\u201d, a simulation that could allow scientists to test treatments virtually, understand disease at its root, and bring life-saving therapies to patients faster than ever before.About the SpeakersXin Jin is a neuroscientist and molecular biologist whose work lies at the intersection of in vivo functional genomics and disease biology. She is an Associate Professor in the Department of Neuroscience and the Dorris Neuroscience Center at Scripps Research, and an HHMI Freeman Hrabowski Scholar. Her lab develops scalable in vivo genomic technologies to uncover how genetic programs shape brain circuits across development, homeostasis, and disease. She recently co-founded PerturbAI to help bring this vision into therapeutic discovery.Her work has pioneered in vivo Perturb-seq, a high-throughput approach that combines pooled CRISPR perturbation with single-cell readouts directly in living tissue. By integrating in vivo CRISPR screening with molecular, spatial, and whole-brain imaging approaches, her research aims to define how genes influence cell types, tissue architecture, homeostasis, and neural circuit function.Xin\u2019s contributions have been recognized by honors including the HHMI Freeman Hrabowski Scholar Award, Sloan Research Fellowship, Pew Biomedical Scholar Award, McKnight Scholar Award, Peter Gruss Young Investigator Award from the Max Planck Society, and MIT Technology Review\u2019s Innovators Under 35. Before joining Scripps Research, she was a Junior Fellow in the Harvard Society of Fellows. She received her PhD in Biology from The Rockefeller University and her BS in Chemistry from MIT.Grace Zheng is a genomics and machine learning leader with over 13 years of experience developing breakthrough platforms at the intersection of biology, data, and therapeutics.She is the Co-Founder and CEO of PerturbAI, an AI-native therapeutics company accelerating drug discovery through a systems-level understanding of disease. Her work focuses on integrating in vivo Perturb-seq with AI agents and models to map biological circuits directly inside intact organisms, enabling a new generation of biological models and therapeutics grounded in causal, in vivo biology.Previously, Grace served as Vice President of Computational Biology and Machine Learning at ArsenalBio, where she built and led interdisciplinary teams spanning computational biology, machine learning, and software engineering. She also led strategic collaborations with partners including Genentech and NVIDIA to generate large-scale functional genomics datasets and develop AI models for cell therapy discovery.Earlier in her career, she joined 10x Genomics as one of the company\u2019s first employees, where she helped pioneer and launch several foundational single-cell genomics technologies.Her work centers on building enabling technologies, scaling interdisciplinary teams, and transforming biological data into predictive models that guide the next generation of medicines.Joy Jiao leads the life sciences team at OpenAI. The goal of the team is to accelerate basic research and drug discovery, by operating across the model training stack to improve model capabilities at all levels of biology, from molecules to organisms. At OpenAI, Joy previously worked on model safety, personalization, search, and representation learning. She holds a PhD in Systems Biology from Harvard, where she studied the in-patient evolution of cancer cells during immunotherapy as well as the evolution of antibiotic resistance.", "projections": { "pca": [ - -0.06651267409324646, - -0.015283147804439068 + -0.06765484064817429, + -0.0151555510237813 ], "tsne": [ - 2.48101806640625, - -3.313384771347046 + -3.366053819656372, + -2.8919832706451416 ], "umap": [ - -0.45262372493743896, - 4.7656168937683105 + -0.7068310379981995, + 5.002479553222656 ], "pca3d": [ - -0.06651318818330765, - -0.015302453190088272, - 0.0315978042781353 + -0.06765540689229965, + -0.015173551626503468, + 0.03310154005885124 ], "tsne3d": [ - -13.442948341369629, - -6.0277862548828125, - 35.13800811767578 + -7.230788230895996, + -20.001291275024414, + -14.70749568939209 ], "umap3d": [ - 3.8513646125793457, - 0.8552168607711792, - -0.9306336045265198 + 2.4667887687683105, + 0.15084581077098846, + 4.7397379875183105 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ - { - "id": 0, - "label": "Medical Data AI" - }, { "id": 2, - "label": "AI Drug Discovery" + "label": "Clinical AI Data" }, { - "id": 13, + "id": 9, "label": "Drug Discovery" + }, + { + "id": 9, + "label": "Biomedical AI" } ], "_embedding": [ @@ -362488,47 +362685,47 @@ ], "projections": { "pca": [ - 0.24085256457328796, - -0.002627347595989704 + 0.2397388219833374, + -0.004513174295425415 ], "tsne": [ - 13.746455192565918, - -1.3406039476394653 + 10.125179290771484, + -1.1921405792236328 ], "umap": [ - 1.4199422597885132, - 5.323992729187012 + 0.6033113598823547, + 4.81662654876709 ], "pca3d": [ - 0.24085234105587006, - -0.0026294044218957424, - 0.08627472817897797 + 0.23973865807056427, + -0.004517744295299053, + 0.08009897172451019 ], "tsne3d": [ - 1.4215728044509888, - 21.70035743713379, - -0.8856019973754883 + 25.375520706176758, + -17.873355865478516, + 0.4480624198913574 ], "umap3d": [ - 3.2103872299194336, - 1.0334738492965698, - 0.8459334969520569 + 2.4122512340545654, + 1.8886091709136963, + 5.216924667358398 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 4, - "label": "Com Area Amp" + "id": 6, + "label": "Data Needs" }, { - "id": 23, - "label": "Research Authors" + "id": 10, + "label": "Research Data" } ], "_embedding": [ @@ -362938,47 +363135,47 @@ ], "projections": { "pca": [ - 0.18569287657737732, - 0.15659363567829132 + 0.18744368851184845, + 0.15834012627601624 ], "tsne": [ - 12.897103309631348, - 9.501063346862793 + 17.605648040771484, + -7.877870559692383 ], "umap": [ - 3.1988840103149414, - 5.11154842376709 + 2.2437806129455566, + 4.150514125823975 ], "pca3d": [ - 0.1856924444437027, - 0.15660063922405243, - -0.11847008019685745 + 0.18744312226772308, + 0.15834885835647583, + -0.12303806841373444 ], "tsne3d": [ - 1.0534696578979492, - 47.674713134765625, - 0.3662538230419159 + 32.10963821411133, + 3.8045196533203125, + -2.378046989440918 ], "umap3d": [ - 4.490042686462402, - -0.4271526038646698, - 2.238085985183716 + 4.18009090423584, + 1.5024254322052002, + 6.75934362411499 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 5, - "label": "GPU Inference" + "id": 15, + "label": "PyTorch Models" } ], "_embedding": [ @@ -363386,47 +363583,47 @@ "abstract": "Contribute to standardmodelbio/quickstart development by creating an account on GitHub.", "projections": { "pca": [ - 0.3353129029273987, - -0.06431598961353302 + 0.33240580558776855, + -0.06523915380239487 ], "tsne": [ - 23.53509521484375, - 9.903164863586426 + 25.584238052368164, + -5.537093162536621 ], "umap": [ - 3.417473793029785, - 6.575682163238525 + 2.7394626140594482, + 5.4809722900390625 ], "pca3d": [ - 0.33531397581100464, - -0.06429722160100937, - -0.19639086723327637 + 0.3324071168899536, + -0.06522244215011597, + -0.19913363456726074 ], "tsne3d": [ - 21.74346923828125, - 35.738712310791016, - 13.640833854675293 + 33.107688903808594, + 7.966614723205566, + 12.750341415405273 ], "umap3d": [ - 3.6016809940338135, - -0.02259543351829052, - 2.584005117416382 + 3.572338581085205, + 2.61828875541687, + 6.447582721710205 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 18, - "label": "Github Tools" + "id": 17, + "label": "GitHub Creation" } ], "_embedding": [ @@ -363834,47 +364031,47 @@ "abstract": "Make imaging deps optional so pip install smb-utils works without monai/nibabel/pydicom (~850 MB lighter).\n\nMove monai[all], nibabel, pydicom into [project.optional-dependencies] imaging extra \u2014 install with pip install smb-utils[imaging]\nFix pyproject.toml field ordering \u2014 readme, keywords, classifiers were accidentally inside [project.optional-dependencies] instead of [project]\nFix eager monai import crash \u2014 PermuteImage class inherited from monai.transforms.MapTransform at module load time; moved class definition inside _get_base_transforms() so monai is only required at call time\nGuard imaging imports in __init__.py with try/except ImportError so core EHR functionality (process_ehr_info) is always available\nAdd 14 unit tests covering: core import without imaging deps, imaging modules load without crash, monai-dependent functions raise clear ImportError when called, and non-monai utilities (extract_imaging_info) work end-to-end", "projections": { "pca": [ - 0.23211340606212616, - -0.038531798869371414 + 0.23301836848258972, + -0.0378512404859066 ], "tsne": [ - 22.564525604248047, - 3.6317098140716553 + 20.789615631103516, + -8.130671501159668 ], "umap": [ - 2.908287525177002, - 6.508022308349609 + 2.2885351181030273, + 5.293722629547119 ], "pca3d": [ - 0.23211246728897095, - -0.03853451460599899, - -0.08708188682794571 + 0.23301751911640167, + -0.03785102814435959, + -0.0851466953754425 ], "tsne3d": [ - 6.97706413269043, - 29.886722564697266, - 18.266952514648438 + 21.618053436279297, + 9.805095672607422, + 23.193269729614258 ], "umap3d": [ - 3.6212172508239746, - -0.3370901942253113, - 1.9178704023361206 + 3.375343084335327, + 2.0368337631225586, + 6.594370365142822 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 3, - "label": "SMB Data" + "id": 24, + "label": "SMB Models" } ], "_embedding": [ @@ -364280,47 +364477,47 @@ ], "projections": { "pca": [ - 0.21666499972343445, - -0.11389333754777908 + 0.21759775280952454, + -0.11205966025590897 ], "tsne": [ - 22.031639099121094, - 4.4697265625 + 21.254812240600586, + -6.785316467285156 ], "umap": [ - 2.898036003112793, - 6.286388397216797 + 2.3007938861846924, + 5.223361968994141 ], "pca3d": [ - 0.21666544675827026, - -0.11387985199689865, - -0.041390545666217804 + 0.21759815514087677, + -0.11204488575458527, + -0.03965240716934204 ], "tsne3d": [ - 12.451873779296875, - 27.7706298828125, - 17.884504318237305 + 23.162189483642578, + 7.2513508796691895, + 19.24714469909668 ], "umap3d": [ - 3.5005011558532715, - -0.1641119420528412, - 1.9925730228424072 + 3.3388657569885254, + 2.2027323246002197, + 6.42920446395874 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 3, - "label": "SMB Data" + "id": 24, + "label": "SMB Models" } ], "_embedding": [ @@ -364728,47 +364925,47 @@ ], "projections": { "pca": [ - 0.21792539954185486, - 0.012572738341987133 + 0.21633385121822357, + 0.012192415073513985 ], "tsne": [ - 22.878095626831055, - 4.390876770019531 + 21.332015991210938, + -7.802487850189209 ], "umap": [ - 3.0200448036193848, - 6.274661064147949 + 2.407721519470215, + 5.195971488952637 ], "pca3d": [ - 0.21792520582675934, - 0.012582661584019661, - -0.17108695209026337 + 0.21633371710777283, + 0.012203934602439404, + -0.1724063605070114 ], "tsne3d": [ - 10.473244667053223, - 31.525739669799805, - 22.618122100830078 + 26.474815368652344, + 9.447918891906738, + 22.774818420410156 ], "umap3d": [ - 3.74945068359375, - -0.19668160378932953, - 1.991287112236023 + 3.504716634750366, + 2.0957846641540527, + 6.598894119262695 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 3, - "label": "SMB Data" + "id": 24, + "label": "SMB Models" } ], "_embedding": [ @@ -365176,47 +365373,47 @@ "abstract": "We\u2019re on a journey to advance and democratize artificial intelligence through open source and open science.", "projections": { "pca": [ - 0.02740393579006195, - -0.022053785622119904 + 0.02840067818760872, + -0.020763477310538292 ], "tsne": [ - 16.527681350708008, - 7.492708683013916 + 16.02191734313965, + -2.4619903564453125 ], "umap": [ - 2.5347790718078613, - 6.303153038024902 + 2.121689796447754, + 4.730920314788818 ], "pca3d": [ - 0.02740294300019741, - -0.022064287215471268, - 0.04541509225964546 + 0.028399603441357613, + -0.020771831274032593, + 0.05194864794611931 ], "tsne3d": [ - 6.187039852142334, - 31.963024139404297, - 7.578691005706787 + 10.135261535644531, + 7.7806243896484375, + 22.385150909423828 ], "umap3d": [ - 3.8373959064483643, - -0.26324009895324707, - 1.428579330444336 + 3.213881731033325, + 1.4887374639511108, + 6.475587368011475 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 4, + "label": "AI Agents" }, { - "id": 3, - "label": "SMB Data" + "id": 24, + "label": "SMB Models" } ], "_embedding": [ @@ -365626,47 +365823,47 @@ "abstract": "SMB Model Utils. Contribute to standardmodelbio/smb-utils development by creating an account on GitHub.", "projections": { "pca": [ - 0.242472305893898, - -0.11485331505537033 + 0.2390705794095993, + -0.11641909927129745 ], "tsne": [ - 24.153728485107422, - 8.554183006286621 + 25.840856552124023, + -8.24460220336914 ], "umap": [ - 3.0900564193725586, - 6.893879413604736 + 2.732144355773926, + 5.638264179229736 ], "pca3d": [ - 0.24247272312641144, - -0.11484227329492569, - -0.23955278098583221 + 0.23907101154327393, + -0.11640925705432892, + -0.23906081914901733 ], "tsne3d": [ - 16.597745895385742, - 41.33098220825195, - 9.566340446472168 + 37.28477096557617, + 4.707503795623779, + 17.36217498779297 ], "umap3d": [ - 3.349245071411133, - -0.15436293184757233, - 2.346431016921997 + 3.3347976207733154, + 2.6487438678741455, + 6.58263635635376 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 18, - "label": "Github Tools" + "id": 17, + "label": "GitHub Creation" } ], "_embedding": [ @@ -366072,47 +366269,47 @@ ], "projections": { "pca": [ - 0.30504313111305237, - 0.008685399778187275 + 0.3039531111717224, + 0.008860264904797077 ], "tsne": [ - 22.33430290222168, - 4.944368839263916 + 21.57227325439453, + -6.777791976928711 ], "umap": [ - 2.9566636085510254, - 6.304708957672119 + 2.3458571434020996, + 5.195260047912598 ], "pca3d": [ - 0.3050432801246643, - 0.008702405728399754, - -0.15317291021347046 + 0.3039534091949463, + 0.008878313936293125, + -0.1564393788576126 ], "tsne3d": [ - 15.582965850830078, - 30.361963272094727, - 20.708250045776367 + 27.17833137512207, + 6.601640701293945, + 19.090248107910156 ], "umap3d": [ - 3.50533390045166, - -0.17710162699222565, - 2.032745599746704 + 3.3776960372924805, + 2.2377758026123047, + 6.455982208251953 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 3, - "label": "SMB Data" + "id": 24, + "label": "SMB Models" } ], "_embedding": [ @@ -366518,46 +366715,46 @@ ], "projections": { "pca": [ - 0.0885729193687439, - 0.09064941853284836 + 0.09004302322864532, + 0.09172476828098297 ], "tsne": [ - -17.878503799438477, - 6.04603385925293 + 0.3430824875831604, + 12.6326322555542 ], "umap": [ - 0.11411022394895554, - 7.43783712387085 + -0.8056778907775879, + 2.4506940841674805 ], "pca3d": [ - 0.08857205510139465, - 0.09063760191202164, - -0.03609442338347435 + 0.0900421291589737, + 0.09171562641859055, + -0.03718964383006096 ], "tsne3d": [ - -10.035436630249023, - 1.5515894889831543, - 9.568154335021973 + -10.269085884094238, + -3.306792736053467, + 33.35539627075195 ], "umap3d": [ - 4.113178253173828, - -1.5689127445220947, - -0.305618554353714 + 2.122950315475464, + -0.25518232583999634, + 6.342734336853027 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 11, + "id": 19, "label": "Medical Imaging" } ], @@ -366966,47 +367163,47 @@ "abstract": "Mainly interested in diving into complex Github repos and making AI easier and more accessible to everyone", "projections": { "pca": [ - 0.1766422986984253, - 0.25410428643226624 + 0.17824359238147736, + 0.2554641664028168 ], "tsne": [ - 14.662281036376953, - 18.47460174560547 + 23.75004768371582, + 6.436954498291016 ], "umap": [ - 3.8014843463897705, - 5.217871189117432 + 3.1789870262145996, + 3.9753379821777344 ], "pca3d": [ - 0.17664238810539246, - 0.25410231947898865, - 0.012196405790746212 + 0.17824354767799377, + 0.2554607391357422, + 0.006230165250599384 ], "tsne3d": [ - 32.142478942871094, - 9.007307052612305, - 19.198135375976562 + 9.902283668518066, + 24.756746292114258, + 13.567359924316406 ], "umap3d": [ - 4.434091091156006, - 0.8368343114852905, - 2.129309892654419 + 4.183786392211914, + 2.139288902282715, + 5.416512489318848 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, - "label": "AI Agents" + "id": 8, + "label": "Open AI" }, { - "id": 20, - "label": "AI Agents" + "id": 4, + "label": "Open Agent Tools" } ], "_embedding": [ @@ -367414,47 +367611,47 @@ "abstract": "ML @HuggingFace. Interested in deep learning, NLP. \r\n\r\nContributed 40+ models to HuggingFace Transformers - NielsRogge", "projections": { "pca": [ - 0.08634940534830093, - 0.19033144414424896 + 0.08656332641839981, + 0.190676748752594 ], "tsne": [ - 14.2301607131958, - 18.574491500854492 + 23.62491226196289, + 6.834351539611816 ], "umap": [ - 4.46879243850708, - 4.444184303283691 + 3.604766368865967, + 3.5148444175720215 ], "pca3d": [ - 0.08634871989488602, - 0.1903255581855774, - -0.032461393624544144 + 0.08656265586614609, + 0.19066958129405975, + -0.033416617661714554 ], "tsne3d": [ - 33.557743072509766, - 4.614523887634277, - 19.81325340270996 + 6.364803314208984, + 23.308284759521484, + 13.398143768310547 ], "umap3d": [ - 4.804800987243652, - 1.385298490524292, - 1.9219164848327637 + 4.284776210784912, + 2.0065643787384033, + 4.73254919052124 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { "id": 7, - "label": "Open AI" + "label": "Hugging Face Models" } ], "_embedding": [ @@ -367864,47 +368061,47 @@ "abstract": "Org profile for Qwen on Hugging Face, the AI community building the future.", "projections": { "pca": [ - -0.04373611882328987, - -0.04444834217429161 + -0.04449189081788063, + -0.042957939207553864 ], "tsne": [ - 15.715622901916504, - 6.643522262573242 + 16.19447898864746, + -1.026562213897705 ], "umap": [ - 2.5027272701263428, - 6.125713348388672 + 1.9280097484588623, + 4.636209487915039 ], "pca3d": [ - -0.04373558238148689, - -0.04443584010004997, - -0.02109476923942566 + -0.04449126869440079, + -0.04294667765498161, + -0.015567166730761528 ], "tsne3d": [ - 10.300151824951172, - 29.328594207763672, - 0.9353938102722168 + 7.840728282928467, + 7.351955413818359, + 16.488523483276367 ], "umap3d": [ - 3.968043804168701, - -0.14727729558944702, - 1.2433782815933228 + 3.1635191440582275, + 1.4313393831253052, + 6.241978645324707 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 4, + "label": "AI Agents" }, { - "id": 3, - "label": "SMB Data" + "id": 24, + "label": "SMB Models" } ], "_embedding": [ @@ -368310,47 +368507,47 @@ ], "projections": { "pca": [ - 0.2820461392402649, - -0.006221849471330643 + 0.28039923310279846, + -0.006403835956007242 ], "tsne": [ - 20.6026554107666, - 6.115118026733398 + 22.651453018188477, + -4.995573043823242 ], "umap": [ - 2.827073812484741, - 6.1424431800842285 + 2.1300156116485596, + 5.257676601409912 ], "pca3d": [ - 0.28204643726348877, - -0.006208912469446659, - -0.07403956353664398 + 0.28039953112602234, + -0.006392256356775761, + -0.07687733322381973 ], "tsne3d": [ - 19.81635856628418, - 28.321550369262695, - 12.648455619812012 + 28.410375595092773, + 2.5888376235961914, + 11.810166358947754 ], "umap3d": [ - 3.423382520675659, - 0.20473460853099823, - 1.9456636905670166 + 3.2623696327209473, + 2.3494889736175537, + 6.060847759246826 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 3, - "label": "SMB Data" + "id": 24, + "label": "SMB Models" } ], "_embedding": [ @@ -368757,47 +368954,47 @@ "title": "Gemma \u00e2\u0080\u0094 Google DeepMind", "projections": { "pca": [ - 0.1329670548439026, - 0.16587640345096588 + 0.1345890462398529, + 0.16462105512619019 ], "tsne": [ - -4.377954959869385, - 28.018205642700195 + -15.372126579284668, + 22.035921096801758 ], "umap": [ - 1.7384978532791138, - 4.415177345275879 + 0.8487107157707214, + 3.5165486335754395 ], "pca3d": [ - 0.13296620547771454, - 0.1658668965101242, - 0.08724849671125412 + 0.1345880776643753, + 0.16461142897605896, + 0.0818796381354332 ], "tsne3d": [ - 40.601318359375, - 8.681535720825195, - -39.31415557861328 + -17.687707901000977, + 25.64967918395996, + -0.6911534070968628 ], "umap3d": [ - 4.710072994232178, - -0.011439790949225426, - 0.185198575258255 + 3.2262558937072754, + 0.2493869811296463, + 5.884699821472168 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { "id": 4, - "label": "Com Area Amp" + "label": "AI Agents" }, { - "id": 23, - "label": "Research Authors" + "id": 0, + "label": "Multimodal Models" } ], "_embedding": [ @@ -369205,47 +369402,47 @@ "abstract": "Contribute to standardmodelbio/quickstart development by creating an account on GitHub.", "projections": { "pca": [ - 0.2825374901294708, - -0.045449212193489075 + 0.2811824381351471, + -0.04506843909621239 ], "tsne": [ - 22.405532836914062, - 6.129772186279297 + 22.88532257080078, + -6.304769039154053 ], "umap": [ - 3.025564193725586, - 6.387630939483643 + 2.530599355697632, + 5.311590671539307 ], "pca3d": [ - 0.2825383245944977, - -0.045438673347234726, - -0.12689419090747833 + 0.2811831533908844, + -0.04505816847085953, + -0.12963595986366272 ], "tsne3d": [ - 18.524168014526367, - 33.1870231628418, - 16.649930953979492 + 29.77146339416504, + 7.358767509460449, + 15.072640419006348 ], "umap3d": [ - 3.5915839672088623, - -0.023160895332694054, - 2.2633213996887207 + 3.4222710132598877, + 2.3957595825195312, + 6.410867214202881 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 3, - "label": "SMB Data" + "id": 2, + "label": "Bioinformatics Tools" } ], "_embedding": [ @@ -369653,47 +369850,47 @@ "abstract": "Toolkit for large-scale whole-slide image processing. - GitHub - mahmoodlab/TRIDENT: Toolkit for large-scale whole-slide image processing.", "projections": { "pca": [ - 0.06123547628521919, - -0.09449990093708038 + 0.06105482578277588, + -0.09244725853204727 ], "tsne": [ - 20.781526565551758, - 1.0007269382476807 + 16.840879440307617, + -14.018771171569824 ], "umap": [ - 0.8183826208114624, - 7.264550685882568 + 1.1376328468322754, + 5.3911452293396 ], "pca3d": [ - 0.061234887689352036, - -0.09450719505548477, - -0.10051572322845459 + 0.06105431914329529, + -0.09245320409536362, + -0.09642061591148376 ], "tsne3d": [ - -5.313518047332764, - 32.11396408081055, - 12.537403106689453 + 14.068554878234863, + 13.403940200805664, + 27.268423080444336 ], "umap3d": [ - 3.582348346710205, - -0.9938753247261047, - 0.10534464567899704 + 1.6710293292999268, + -0.02370539866387844, + 5.955729961395264 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 3, + "label": "Open Source Tools" }, { - "id": 9, - "label": "Medical Models" + "id": 6, + "label": "Data Needs" }, { "id": 10, - "label": "Medical Data" + "label": "Research Data" } ], "_embedding": [ @@ -370101,47 +370298,47 @@ ], "projections": { "pca": [ - 0.2625678777694702, - -0.024139955639839172 + 0.2617633044719696, + -0.0233770739287138 ], "tsne": [ - 24.470413208007812, - 4.111852169036865 + 22.711353302001953, + -8.271993637084961 ], "umap": [ - 3.134425163269043, - 6.345738887786865 + 2.5727932453155518, + 5.216470241546631 ], "pca3d": [ - 0.26256781816482544, - -0.024126198142766953, - -0.10672242939472198 + 0.2617633044719696, + -0.02336331456899643, + -0.10791105777025223 ], "tsne3d": [ - 12.151118278503418, - 38.41347885131836, - 23.00649070739746 + 30.679052352905273, + 6.792102336883545, + 26.546844482421875 ], "umap3d": [ - 3.635284900665283, - 0.023714536800980568, - 2.267540693283081 + 3.544489860534668, + 2.24191951751709, + 6.534445285797119 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 3, - "label": "SMB Data" + "id": 24, + "label": "SMB Models" } ], "_embedding": [ @@ -370549,47 +370746,47 @@ ], "projections": { "pca": [ - 0.2352922111749649, - 0.0993676483631134 + 0.23441770672798157, + 0.09900771826505661 ], "tsne": [ - 23.112085342407227, - 17.87265396118164 + 29.29067039489746, + 0.29633569717407227 ], "umap": [ - 3.7786693572998047, - 5.978316783905029 + 2.9980576038360596, + 4.707338809967041 ], "pca3d": [ - 0.2352921962738037, - 0.09936564415693283, - -0.0477512925863266 + 0.2344178408384323, + 0.09900513291358948, + -0.04852469637989998 ], "tsne3d": [ - 26.206064224243164, - 21.930715560913086, - 12.45718765258789 + 22.571575164794922, + 17.27189826965332, + 10.604515075683594 ], "umap3d": [ - 4.43365478515625, - 0.2626200020313263, - 2.4338810443878174 + 4.361074924468994, + 2.0850579738616943, + 6.301985263824463 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 0, - "label": "AI Agents" + "label": "GitHub Tools" }, { - "id": 20, - "label": "AI Agents" + "id": 4, + "label": "Open Agent Tools" } ], "_embedding": [ @@ -370997,47 +371194,47 @@ "abstract": "Code and sample data for the MSK-CHORD manuscript (public version) - clinical-data-mining/msk-chord-figures-public", "projections": { "pca": [ - 0.17028111219406128, - -0.1952309012413025 + 0.17059865593910217, + -0.19513873755931854 ], "tsne": [ - 25.03098487854004, - -10.30108642578125 + -1.8558520078659058, + -10.960090637207031 ], "umap": [ - 2.519011974334717, - 7.221375465393066 + 1.8151371479034424, + 5.922025203704834 ], "pca3d": [ - 0.17028161883354187, - -0.19523237645626068, - -0.08017551898956299 + 0.17059922218322754, + -0.1951402872800827, + -0.07881961762905121 ], "tsne3d": [ - -11.916358947753906, - 2.63289213180542, - 49.25859451293945 + -13.578614234924316, + -13.448134422302246, + 38.234840393066406 ], "umap3d": [ - 2.934446334838867, - -0.9787657260894775, - 0.05036582052707672 + 2.6880640983581543, + 2.129589319229126, + 6.685100555419922 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { "id": 25, - "label": "Python Data" + "label": "Data Standards" } ], "_embedding": [ @@ -371447,47 +371644,47 @@ "abstract": "We\u2019re on a journey to advance and democratize artificial intelligence through open source and open science.", "projections": { "pca": [ - 0.146279975771904, - 0.1816340535879135 + 0.14956602454185486, + 0.18347285687923431 ], "tsne": [ - 6.395748138427734, - 20.550458908081055 + 22.52287483215332, + 12.358211517333984 ], "umap": [ - 5.019581317901611, - 4.825553894042969 + 4.050273895263672, + 2.8829102516174316 ], "pca3d": [ - 0.14627939462661743, - 0.18163920938968658, - 0.18397335708141327 + 0.1495654582977295, + 0.183476522564888, + 0.18104392290115356 ], "tsne3d": [ - 42.484127044677734, - -1.5749704837799072, - 3.411353588104248 + -1.2831066846847534, + 40.35295104980469, + 3.27111554145813 ], "umap3d": [ - 5.588822364807129, - 1.5305185317993164, - 1.934178113937378 + 4.944490909576416, + 1.9939717054367065, + 4.786344528198242 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { "id": 7, - "label": "Open AI" + "label": "Hugging Face Models" } ], "_embedding": [ @@ -371895,47 +372092,47 @@ "abstract": "Learn how to turn text into numbers, unlocking use cases like search, clustering, and more with OpenAI API embeddings.", "projections": { "pca": [ - 0.023736802861094475, - 0.1475382149219513 + 0.024528970941901207, + 0.14863954484462738 ], "tsne": [ - -11.880914688110352, - 10.189082145690918 + -6.78629732131958, + 32.448307037353516 ], "umap": [ - 2.0094337463378906, - 3.4780914783477783 + 0.9959361553192139, + 2.498227119445801 ], "pca3d": [ - 0.023736020550131798, - 0.14751183986663818, - 0.02130313217639923 + 0.02452828735113144, + 0.14861391484737396, + 0.022332027554512024 ], "tsne3d": [ - 4.745519161224365, - 3.3720526695251465, - -10.329154014587402 + 10.889774322509766, + -19.55089569091797, + -26.499103546142578 ], "umap3d": [ - 5.6542277336120605, - -0.4215894043445587, - 0.3031395971775055 + 3.8861801624298096, + -0.4454858601093292, + 6.622642517089844 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 27, - "label": "Embeddings" + "id": 18, + "label": "Retrieval Learning" } ], "_embedding": [ @@ -372343,47 +372540,47 @@ "abstract": "Qwen3-omni is a natively end-to-end, omni-modal LLM developed by the Qwen team at Alibaba Cloud, capable of understanding text, audio, images, and video, as well as generating speech in real time. - QwenLM/Qwen3-Omni", "projections": { "pca": [ - 0.1770099252462387, - 0.023818550631403923 + 0.17629559338092804, + 0.023174835368990898 ], "tsne": [ - 14.208009719848633, - 6.302117824554443 + 16.703645706176758, + 0.5906456708908081 ], "umap": [ - 2.65895414352417, - 5.945033550262451 + 1.918816089630127, + 4.5940842628479 ], "pca3d": [ - 0.1770087033510208, - 0.023809636011719704, - -0.05164545029401779 + 0.1762944757938385, + 0.023167302832007408, + -0.05099425092339516 ], "tsne3d": [ - 14.681963920593262, - 27.91098403930664, - -4.478902339935303 + 5.1871819496154785, + 13.187371253967285, + 15.083757400512695 ], "umap3d": [ - 3.877546548843384, - 0.04019762948155403, - 1.372145414352417 + 3.208251953125, + 1.484692096710205, + 6.115863800048828 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 4, + "label": "AI Agents" }, { - "id": 3, - "label": "SMB Data" + "id": 24, + "label": "SMB Models" } ], "_embedding": [ @@ -372791,47 +372988,47 @@ "abstract": "Fast and memory-efficient exact attention. Contribute to Dao-AILab/flash-attention development by creating an account on GitHub.", "projections": { "pca": [ - 0.1638665348291397, - 0.10140471160411835 + 0.16518166661262512, + 0.10233372449874878 ], "tsne": [ - 10.700578689575195, - 30.19090461730957 + 19.813467025756836, + 26.229610443115234 ], "umap": [ - 2.887907028198242, - 5.085165023803711 + 1.8394297361373901, + 3.499502420425415 ], "pca3d": [ - 0.16386541724205017, - 0.10139158368110657, - -0.04137319326400757 + 0.16518069803714752, + 0.10232307761907578, + -0.04243118688464165 ], "tsne3d": [ - 14.750460624694824, - 35.111961364746094, - -22.37055778503418 + -14.922910690307617, + 28.601694107055664, + -18.69903564453125 ], "umap3d": [ - 4.957607269287109, - -0.3576047122478485, - 1.2625987529754639 + 3.7220723628997803, + 0.8803068399429321, + 6.3948493003845215 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 5, - "label": "GPU Inference" + "id": 15, + "label": "PyTorch Models" } ], "_embedding": [ @@ -373264,47 +373461,47 @@ "openalex_url": "https://openalex.org/W4403350553", "projections": { "pca": [ - -0.1666162610054016, - 0.02986322157084942 + -0.16996270418167114, + 0.029901983216404915 ], "tsne": [ - -1.047741413116455, - -35.01127624511719 + -14.975557327270508, + -27.892505645751953 ], "umap": [ - -1.6702326536178589, - 2.8703603744506836 + -0.8951556086540222, + 7.482374668121338 ], "pca3d": [ - -0.16661646962165833, - 0.029857907444238663, - -0.23734301328659058 + -0.16996291279792786, + 0.029895693063735962, + -0.23507605493068695 ], "tsne3d": [ - -41.21332931518555, - -4.028728485107422, - -16.13556671142578 + -24.4400691986084, + -26.15511131286621, + -17.450769424438477 ], "umap3d": [ - 1.186584711074829, - 1.1181151866912842, - -0.9259634613990784 + 0.5486167669296265, + 0.847161054611206, + 3.555614471435547 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 21, - "label": "Protein RNA" + "id": 12, + "label": "Protein Language" } ], "_embedding": [ @@ -373722,47 +373919,47 @@ "doi": "10.1101/2025.03.31.646349", "projections": { "pca": [ - -0.18328212201595306, - -0.12454300373792648 + -0.18722297251224518, + -0.1245080903172493 ], "tsne": [ - -3.258328437805176, - -29.5626277923584 + -17.953447341918945, + -21.361034393310547 ], "umap": [ - -1.8331751823425293, - 3.433595895767212 + -1.4684747457504272, + 7.233307838439941 ], "pca3d": [ - -0.18328168988227844, - -0.12453893572092056, - -0.22297188639640808 + -0.18722259998321533, + -0.12450561672449112, + -0.21912206709384918 ], "tsne3d": [ - -54.36734390258789, - -16.420013427734375, - 3.809325695037842 + -42.443721771240234, + -15.779240608215332, + -8.320527076721191 ], "umap3d": [ - 1.1596542596817017, - 0.7250566482543945, - -1.4228670597076416 + -0.07299210876226425, + 0.5795093178749084, + 3.6315207481384277 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 8, - "label": "Gene Regulation" + "id": 3, + "label": "Genomic Sequences" } ], "_embedding": [ @@ -374172,47 +374369,47 @@ "journal": "Nature Communications", "projections": { "pca": [ - -0.08443013578653336, - -0.04564865678548813 + -0.08699309825897217, + -0.045904651284217834 ], "tsne": [ - 14.867741584777832, - -24.520837783813477 + -6.900871753692627, + -29.442548751831055 ], "umap": [ - 0.6857487559318542, - 4.509134769439697 + 0.018326018005609512, + 6.158184051513672 ], "pca3d": [ - -0.08443032950162888, - -0.04566871002316475, - -0.0882461667060852 + -0.08699333667755127, + -0.045924242585897446, + -0.08813417702913284 ], "tsne3d": [ - -28.920124053955078, - 21.899465560913086, - -7.90859317779541 + -15.223811149597168, + -34.19979476928711, + -21.750070571899414 ], "umap3d": [ - 2.3218190670013428, - 0.9299752116203308, - 0.2712627053260803 + 1.5400116443634033, + 1.1258964538574219, + 4.378121852874756 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 4, - "label": "Com Area Amp" + "id": 6, + "label": "Data Needs" }, { - "id": 17, - "label": "Literature AI" + "id": 16, + "label": "Lab Research" } ], "_embedding": [ @@ -374622,47 +374819,47 @@ "journal": "Nature Communications", "projections": { "pca": [ - -0.04056080803275108, - 0.0057931761257350445 + -0.04405731335282326, + 0.004338565282523632 ], "tsne": [ - 14.869112968444824, - -24.57809066772461 + -7.04097318649292, + -29.48661231994629 ], "umap": [ - 0.7168866395950317, - 4.524292945861816 + 0.047490596771240234, + 6.266798973083496 ], "pca3d": [ - -0.0405622236430645, - 0.005753665696829557, - -0.1016281247138977 + -0.04405868053436279, + 0.004301407374441624, + -0.10165002942085266 ], "tsne3d": [ - -32.36606979370117, - 20.753297805786133, - -10.500386238098145 + -17.913921356201172, + -34.9809684753418, + -24.150602340698242 ], "umap3d": [ - 2.144392967224121, - 0.9945600628852844, - 0.19891522824764252 + 1.4697853326797485, + 1.1421540975570679, + 4.145045757293701 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 4, - "label": "Com Area Amp" + "id": 6, + "label": "Data Needs" }, { - "id": 17, - "label": "Literature AI" + "id": 16, + "label": "Lab Research" } ], "_embedding": [ @@ -375088,47 +375285,47 @@ "openalex_url": "https://openalex.org/W7118514966", "projections": { "pca": [ - -0.1572706401348114, - -0.004667315632104874 + -0.16265632212162018, + -0.007799910381436348 ], "tsne": [ - 9.124924659729004, - -27.111133575439453 + -6.87655782699585, + -25.307764053344727 ], "umap": [ - -2.590719699859619, - 3.486546039581299 + -1.8734787702560425, + 7.451246738433838 ], "pca3d": [ - -0.15727083384990692, - -0.0046843793243169785, - -0.12503720819950104 + -0.16265654563903809, + -0.00781478825956583, + -0.1255282163619995 ], "tsne3d": [ - -46.52082443237305, - 10.538064002990723, - 1.0092684030532837 + -20.246458053588867, + -19.36304473876953, + -21.66993522644043 ], "umap3d": [ - 0.9873616099357605, - 0.22993901371955872, - -0.8823896050453186 + -0.3469999134540558, + 1.1771907806396484, + 3.9329617023468018 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 22, - "label": "Genetic Variants" + "id": 3, + "label": "Genomic Sequences" } ], "_embedding": [ @@ -375546,47 +375743,47 @@ "year": 2025, "projections": { "pca": [ - -0.018908224999904633, - 0.11812130361795425 + -0.02266199141740799, + 0.11394336074590683 ], "tsne": [ - 13.40158462524414, - -9.447091102600098 + -4.770749092102051, + -28.130220413208008 ], "umap": [ - 1.028576374053955, - 4.85142707824707 + 0.23274533450603485, + 5.801156997680664 ], "pca3d": [ - -0.018909357488155365, - 0.11809170991182327, - -0.06810667365789413 + -0.02266313135623932, + 0.11391547322273254, + -0.06856504082679749 ], "tsne3d": [ - -22.805999755859375, - 28.024532318115234, - -2.5295770168304443 + -14.486071586608887, + -28.457006454467773, + -28.687341690063477 ], "umap3d": [ - 2.430701971054077, - 0.9247296452522278, - 0.458822101354599 + 1.676997184753418, + 1.2566505670547485, + 4.59738826751709 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 4, - "label": "Com Area Amp" + "id": 6, + "label": "Data Needs" }, { - "id": 23, - "label": "Research Authors" + "id": 16, + "label": "Lab Research" } ], "_embedding": [ @@ -376006,47 +376203,47 @@ "doi": "10.52202/079017-1981", "projections": { "pca": [ - -0.08074484765529633, - -0.03109380230307579 + -0.08537422865629196, + -0.0336463563144207 ], "tsne": [ - 12.659712791442871, - -9.60466480255127 + -5.391676425933838, + -27.824033737182617 ], "umap": [ - 0.9858245849609375, - 4.804104328155518 + 0.20962290465831757, + 5.784417152404785 ], "pca3d": [ - -0.08074565976858139, - -0.031113076955080032, - -0.09872539341449738 + -0.08537496626377106, + -0.0336647555232048, + -0.09944283962249756 ], "tsne3d": [ - -23.36121940612793, - 22.83392333984375, - -0.6392077207565308 + -15.072020530700684, + -27.0460205078125, + -24.479354858398438 ], "umap3d": [ - 2.394644021987915, - 0.9184239506721497, - 0.3712225556373596 + 1.6432839632034302, + 1.2707751989364624, + 4.59136962890625 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 4, - "label": "Com Area Amp" + "id": 6, + "label": "Data Needs" }, { - "id": 23, - "label": "Research Authors" + "id": 16, + "label": "Lab Research" } ], "_embedding": [ @@ -376463,47 +376660,47 @@ "doi": "10.1101/2024.03.13.583868", "projections": { "pca": [ - -0.12055002897977829, - -0.039193034172058105 + -0.12538181245326996, + -0.03953620791435242 ], "tsne": [ - -1.3590190410614014, - -31.54736328125 + -15.775235176086426, + -24.368627548217773 ], "umap": [ - -2.0778472423553467, - 3.1101231575012207 + -1.3774852752685547, + 7.546650409698486 ], "pca3d": [ - -0.12055046111345291, - -0.03920828178524971, - -0.29128575325012207 + -0.1253822296857834, + -0.03955073282122612, + -0.288736492395401 ], "tsne3d": [ - -52.2448616027832, - -4.343893051147461, - -3.741870641708374 + -36.38190460205078, + -22.547761917114258, + -17.298959732055664 ], "umap3d": [ - 1.0028090476989746, - 0.7721095085144043, - -1.1258779764175415 + 0.09548196941614151, + 1.0353111028671265, + 3.5834851264953613 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 8, - "label": "Gene Regulation" + "id": 3, + "label": "Genomic Sequences" } ], "_embedding": [ @@ -376911,435 +377108,452 @@ ], "projections": { "pca": [ - 0.17472855746746063, - 0.1106189712882042 + -0.23930411040782928, + -0.040349625051021576 ], "tsne": [ - 13.603960037231445, - -8.11583137512207 + -14.210885047912598, + -20.02249526977539 ], "umap": [ - 1.1038002967834473, - 4.914341926574707 + -1.9640436172485352, + 7.389138221740723 ], "pca3d": [ - 0.17472772300243378, - 0.1105942651629448, - 0.030704429373145103 + -0.23930400609970093, + -0.04035039618611336, + -0.20035193860530853 ], "tsne3d": [ - -16.09866714477539, - 29.078590393066406, - -0.2752794921398163 + -22.600982666015625, + -16.721630096435547, + -17.430156707763672 ], "umap3d": [ - 2.601348400115967, - 0.9611487984657288, - 0.6702223420143127 + -0.39353519678115845, + 1.2248777151107788, + 4.102903366088867 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 4, - "label": "Com Area Amp" + "id": 7, + "label": "Cell Genes" }, { - "id": 23, - "label": "Research Authors" + "id": 3, + "label": "Genomic Sequences" } ], "_embedding": [ - -0.0395, - 0.016, - 0.0119, - -0.0002, - 0.0028, + -0.0606, + -0.0073, + -0.0245, -0.0103, - -0.0644, - 0.0094, - 0.0418, + 0.0116, + 0.0112, + -0.0186, + 0.0109, + 0.0461, + -0.0124, + 0.0021, + -0.0305, + 0.0442, + -0.0069, + 0.019, + -0.0057, + -0.0278, + 0.0164, + -0.0215, + -0.0115, + -0.032, + -0.0157, + 0.0102, + -0.0294, + 0.0178, + -0.0324, + 0.0043, + 0.0005, + -0.0418, + -0.2512, + 0.0206, + 0.039, + 0.0301, + -0.0072, + -0.041, + -0.016, + -0.045, + -0.0462, + -0.0654, + 0.0251, + 0.0435, + 0.0264, + -0.0163, + 0.0104, + 0.0179, + 0.0051, + 0.0008, + -0.0287, + -0.0244, + 0.0036, + -0.0236, + -0.0744, + -0.0535, + 0.0845, + 0.0002, + 0.0153, + 0.0442, + 0.0078, + -0.0143, + -0.0236, + -0.0317, + 0.0453, + -0.1421, + 0.057, + 0.0134, + 0.0319, + -0.0469, + -0.0379, + -0.006, + 0.0596, + -0.0336, + 0.038, + 0.0155, + 0.0151, + 0.0281, + 0.0551, + 0.0376, + -0.0137, + 0.0672, + 0.0603, + 0.0509, + 0.0036, + 0.0125, + -0.056, + 0.0212, + 0.0064, + -0.0062, + -0.0567, + -0.0108, + -0.0339, + -0.0003, + -0.0253, + 0.0302, + 0.0508, + -0.0196, + 0.028, + 0.0555, + -0.0473, + -0.021, + 0.3673, + -0.011, + 0.0281, + -0.0313, + 0.0173, + -0.0074, + -0.0385, + 0.0059, + -0.0018, + 0.0055, + 0.0193, + 0.0096, + 0.0007, + 0.0068, + 0.0068, + -0.0242, + -0.0048, + 0.0734, + 0.0136, + 0.0248, + -0.0005, + 0.0109, + 0.0178, + -0.0019, + 0.0019, + 0.0321, + 0.0249, + -0.0178, + 0.1147, + 0.0429, + -0.0186, + 0.0736, + 0.0275, + -0.0882, + 0.0342, + 0.0156, + -0.025, + 0.0407, + -0.0179, + 0.0095, + 0.0195, + -0.0088, + -0.0289, + -0.0086, -0.0237, - 0.0223, - 0.0077, - 0.0214, - 0.0298, - 0.0775, - 0.0395, - 0.0256, - -0.0325, - -0.0679, - 0.0049, + -0.083, + 0.1266, + -0.0282, + 0.0043, -0.0255, - -0.0398, - 0.0125, - -0.0283, + -0.0468, + -0.0065, + 0.054, + 0.0215, + 0.0292, + 0.025, + 0.0401, + 0.0244, + -0.0511, + -0.0508, + -0.0048, + -0.041, + 0.0133, + -0.0256, + 0.0264, + 0.0257, + -0.069, + -0.0319, + 0.0271, + 0.0425, + -0.0282, + 0.0578, + -0.0272, + -0.0043, + 0.0473, + -0.0478, + -0.001, + -0.0598, + 0.0262, + -0.0328, + 0.0828, + 0.0627, + -0.0353, -0.015, - 0.015, + 0.0197, + -0.0325, + -0.0508, + 0.0093, + 0.0019, + -0.0, + -0.0386, + -0.0243, + 0.0119, + -0.0336, + 0.0126, + -0.0109, + -0.0224, + 0.0266, + 0.0131, + -0.0296, + 0.0027, + -0.0125, + 0.0803, 0.0406, - -0.0706, - -0.0792, - -0.1253, - -0.0217, + 0.0044, + -0.0664, + -0.0145, + -0.0763, + -0.0126, 0.0527, - 0.0227, + 0.0071, + -0.0701, + -0.0326, + -0.0418, + 0.0432, + -0.0337, + 0.0241, + 0.0453, + 0.0409, + 0.0426, + 0.0326, + 0.017, + -0.0149, + -0.0533, + -0.317, + -0.0386, + -0.0033, + 0.0091, + -0.0187, + -0.0255, + 0.0194, + 0.0065, 0.0212, - 0.036, - -0.0034, - -0.0115, - 0.0139, - 0.0096, - -0.0095, - -0.0088, - -0.0477, - 0.0419, - -0.0381, - 0.0449, - -0.0046, - -0.034, - 0.0226, + 0.0238, + -0.0374, + 0.013, + -0.0437, + -0.0062, + -0.0436, + 0.0299, + 0.0081, + 0.0071, + -0.0355, + 0.0435, + 0.0195, + 0.0546, + 0.0513, + -0.1046, + 0.0693, + 0.0154, + 0.1139, -0.0192, - -0.0018, - -0.0422, - -0.01, - -0.0049, - -0.053, - -0.012, - 0.0083, - 0.0454, - 0.0083, - -0.0089, - 0.0572, - 0.0003, - 0.0347, - -0.1251, - 0.0224, - 0.0002, + -0.0076, + -0.0132, + 0.0019, + 0.0251, + 0.0014, + -0.0591, + 0.1083, + -0.0095, 0.0051, - -0.0473, - 0.0034, - 0.0389, - 0.0302, - 0.0505, - 0.0007, - 0.0346, - 0.0185, - 0.0084, - -0.0261, - 0.0163, - -0.015, - -0.007, - 0.0028, - 0.0326, - 0.0163, + -0.0386, + -0.0087, + -0.0472, + 0.0143, + -0.0265, + -0.0166, + -0.0848, + -0.0865, + -0.0035, + -0.0531, + -0.0005, + -0.0795, + 0.0388, + 0.0247, + -0.0163, + 0.0652, + -0.0269, + -0.0051, + -0.0598, + -0.0789, + 0.0609, + -0.0419, + 0.0119, 0.0053, - 0.0009, - -0.0396, - 0.0059, - 0.0435, - 0.0208, - -0.0492, - 0.029, - 0.0314, - 0.0249, - 0.0629, - 0.0452, - -0.0486, - -0.053, - -0.0036, - 0.004, - -0.0133, - 0.4836, - -0.0555, - 0.0312, - 0.0675, - 0.0105, - -0.0544, - 0.0066, - 0.0156, - -0.1152, - -0.045, - -0.0388, - -0.0207, - -0.0177, - 0.035, - -0.0352, - 0.0436, - 0.0773, - 0.1003, - 0.0265, - -0.0294, - -0.0412, - -0.0162, - 0.0351, - -0.0223, - -0.0091, - -0.0103, - -0.0983, + -0.0699, + -0.0375, -0.0106, - 0.0963, - 0.0026, - 0.0427, - 0.0324, - 0.0277, - -0.0609, - 0.0092, - 0.0052, - -0.0379, - -0.0005, - -0.0458, + -0.011, + -0.0409, 0.0145, - 0.0084, - 0.0428, - -0.0174, - -0.0597, - -0.0333, - 0.0076, - 0.0139, - 0.0445, - -0.0199, - -0.0366, - 0.0008, - 0.0248, - 0.0072, - -0.0532, - -0.0132, - 0.0449, - -0.0179, - 0.0883, - -0.0031, - -0.0191, - 0.0359, - 0.0336, - -0.0158, - -0.0097, + -0.02, + 0.0289, + -0.0297, + 0.02, + 0.1055, + -0.0042, + 0.0625, + 0.0144, + 0.0169, + -0.0743, + -0.063, + -0.1008, + -0.0357, + 0.0673, + 0.0296, 0.0809, - -0.0085, - -0.1146, - 0.0098, - 0.0278, - -0.0044, + 0.0523, + 0.009, + 0.0274, + 0.0745, + 0.0326, + 0.0503, + 0.089, -0.0341, - 0.0742, - -0.0206, - -0.0067, - 0.0114, - 0.0299, - 0.0117, - 0.0228, - -0.035, - 0.0232, - 0.0235, - 0.005, - 0.025, - 0.0318, - 0.0548, - -0.0293, - -0.1013, - 0.0189, - -0.0284, - -0.0404, - -0.0038, - -0.0414, - 0.0442, - -0.0127, - 0.056, - -0.0301, - -0.0142, - 0.0301, - -0.0241, + -0.0262, + -0.0014, + -0.072, + 0.0208, + 0.0183, + -0.2616, + 0.0145, -0.0292, - -0.0599, - -0.0535, - 0.0056, - -0.088, - 0.0043, - 0.0351, - -0.0171, - -0.0602, - 0.0341, - 0.09, - 0.0107, - 0.0003, - -0.0064, - 0.0161, - 0.0254, - -0.0344, - -0.0539, - -0.0076, - 0.0242, - -0.0045, - 0.0138, - -0.0207, - -0.0175, - -0.076, - -0.2949, - 0.0136, - 0.0114, - -0.0969, - -0.0001, - -0.0762, - 0.0506, - 0.0198, - 0.1002, - 0.1408, - -0.0287, - -0.017, - -0.0343, - 0.0062, - 0.0209, - 0.0144, - 0.0107, - -0.0063, - 0.0567, - -0.0081, - 0.0426, - 0.0575, - -0.0368, - 0.0014, - -0.0351, - 0.0043, - 0.0844, - 0.1683, - -0.0471, - -0.0197, - 0.0144, - 0.0267, - 0.0624, - -0.1332, - -0.0013, - 0.0485, - 0.0449, - -0.0704, - -0.0265, - -0.0392, - 0.0175, - 0.0438, - 0.0165, - -0.0176, - -0.0869, - -0.0248, - -0.0496, - -0.0353, - 0.0018, - 0.0416, - -0.0288, - 0.0105, - 0.0313, - -0.0151, - -0.0592, - -0.0239, - -0.0677, - -0.0016, - 0.0055, - 0.0264, - -0.028, - -0.0205, - 0.0008, - 0.0533, - -0.0296, - 0.0185, - -0.0102, - 0.0013, - -0.0257, - 0.0034, - -0.0201, - -0.0019, - -0.0474, - 0.0317, - 0.005, - -0.0148, - 0.0205, - -0.0358, - -0.0022, - -0.0295, - 0.0316, - -0.0338, - 0.0644, - -0.0028, - -0.0081, + 0.0514, + 0.036, + 0.0244, + 0.0728, + -0.0883, + -0.0158, + 0.0027, + 0.0404, + 0.0147, + 0.0835, + 0.0627, + 0.0059, + 0.015, + 0.1073, + -0.0092, + 0.0322, + -0.0228, + 0.0545, + 0.0135, + 0.1436, + -0.0038, 0.0374, - 0.0342, - -0.0067, - 0.0612, - 0.0573, - -0.0264, - 0.0438, - -0.0078, - 0.0206, - -0.0218, - -0.0396, - -0.2157, - 0.0099, - 0.004, - 0.0019, - 0.0163, - -0.012, - -0.0351, - -0.0352, - -0.0118, - -0.0075, - 0.0399, - 0.0686, - -0.0197, - 0.0276, - -0.0, - 0.0387, - 0.0139, - 0.0362, - 0.0046, - 0.0384, + -0.0168, + -0.0347, + -0.0452, + 0.028, + -0.0186, + -0.0275, + 0.0229, + 0.086, + -0.003, + 0.0056, + 0.0578, -0.0184, - 0.0285, - 0.1344, - -0.0449, - 0.0653, - 0.0395, - -0.025, - 0.0106, - -0.0663, - 0.0093, - 0.0238, - -0.0505, - 0.0491, - 0.001, - -0.037, - 0.0133, - -0.0094, - 0.0244, - 0.0307, - -0.0141, - 0.0442, - -0.009, - -0.0246, - -0.0105, - 0.0496, - 0.044, - -0.0974, - 0.005, - -0.0284, - 0.0024, - 0.0123, - -0.0137, - 0.0216, - 0.0436, - 0.0179, - 0.0638, - -0.0003, - -0.0268, - -0.0237, - -0.0054, + -0.0138, + -0.0005, + 0.0031, + 0.0413, + -0.015, + -0.0138, + -0.0329, + 0.0456, + -0.0202, + 0.0162, + 0.0015, + -0.0605, + -0.0124, -0.0292, - -0.0671, - -0.0411, - 0.0079, - -0.0065 - ] + 0.017, + 0.0015, + -0.0349, + 0.0393, + 0.0366, + -0.0182, + 0.0036, + 0.0247, + -0.0525, + 0.0075, + -0.0081, + -0.0819, + 0.0686, + -0.0418 + ], + "title": "Fine-tuning sequence-to-expression models on personal genome and transcriptome data", + "authors": [ + "Ruchir Rastogi", + "Aniketh Janardhan Reddy", + "Ryan Chung", + "Nilah M. Ioannidis" + ], + "affiliations": [ + "University of California, Berkeley", + "Chan Zuckerberg Initiative (United States)" + ], + "year": 2024, + "journal": "bioRxiv (Cold Spring Harbor Laboratory)", + "abstract": "BACKGROUND: Genomic sequence-to-expression deep learning models, which are trained to predict gene expression and other molecular phenotypes across the reference genome, have recently been shown to have poor out-of-the-box performance in predicting gene expression variation across individuals based on their personal genome sequences. RESULTS: Here, we explore whether additional training (fine-tuning) on paired personal genome and transcriptome data improves the performance of such sequence-to-expression models. Using Enformer as a representative pre-trained model, we explore various fine-tuning strategies. Our results show that fine-tuning improves expression predictions on held-out individuals, including from held-out populations, for genes seen during fine-tuning, with comparable performance to variant-based linear models commonly used in transcriptome-wide association studies. However, fine-tuning does not improve model generalizability to held-out genes, which contain sequences and variants unseen during fine-tuning. CONCLUSIONS: Including individual-level genetic variation and paired expression data during the training of sequence-to-expression models improves their understanding of seen variants, enabling their application to held-out individuals. However, this strategy does not improve generalizability to unseen genes, highlighting a remaining open challenge in the field.", + "doi": "10.1101/2024.09.23.614632", + "cited_by_count": 15, + "openalex_url": "https://openalex.org/W4402826099" }, { "channel": "area-recruiting", @@ -377359,435 +377573,455 @@ ], "projections": { "pca": [ - 0.1747286319732666, - 0.1106189638376236 + -0.2543034255504608, + -0.06798479706048965 ], "tsne": [ - 13.603777885437012, - -8.11507797241211 + -14.23934268951416, + -21.147289276123047 ], "umap": [ - 1.1539474725723267, - 4.949365615844727 + -1.75095796585083, + 7.254825115203857 ], "pca3d": [ - 0.174727663397789, - 0.11059430241584778, - 0.03070436790585518 + -0.25430288910865784, + -0.06797613948583603, + -0.22508074343204498 ], "tsne3d": [ - -16.01421546936035, - 27.2321720123291, - -4.398801326751709 + -27.23639678955078, + -14.215234756469727, + -16.25181770324707 ], "umap3d": [ - 2.6007373332977295, - 0.9687210321426392, - 0.6791201233863831 + -0.19174303114414215, + 1.067893624305725, + 3.893798589706421 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 4, - "label": "Com Area Amp" + "id": 7, + "label": "Cell Genes" }, { - "id": 23, - "label": "Research Authors" + "id": 3, + "label": "Genomic Sequences" } ], "_embedding": [ - -0.0395, - 0.016, - 0.0119, - -0.0002, - 0.0028, - -0.0103, - -0.0644, - 0.0094, - 0.0418, - -0.0237, - 0.0223, - 0.0077, - 0.0214, - 0.0298, - 0.0775, - 0.0395, - 0.0256, - -0.0325, - -0.0679, - 0.0049, - -0.0255, - -0.0398, - 0.0125, - -0.0283, - -0.015, - 0.015, - 0.0406, - -0.0706, - -0.0792, - -0.1253, - -0.0217, - 0.0527, - 0.0227, - 0.0212, - 0.036, - -0.0034, - -0.0115, - 0.0139, - 0.0096, - -0.0095, - -0.0088, - -0.0477, - 0.0419, - -0.0381, - 0.0449, - -0.0046, - -0.034, - 0.0226, - -0.0192, - -0.0018, - -0.0422, - -0.01, - -0.0049, - -0.053, - -0.012, - 0.0083, - 0.0454, - 0.0083, - -0.0089, - 0.0572, - 0.0003, - 0.0347, - -0.1251, - 0.0224, - 0.0002, - 0.0051, - -0.0473, + -0.0439, + -0.0338, + -0.0544, + -0.0418, + 0.028, + 0.0444, + -0.0211, + 0.0341, + 0.0171, + -0.0404, + 0.0305, + -0.0662, + 0.052, 0.0034, - 0.0389, - 0.0302, - 0.0505, - 0.0007, - 0.0346, - 0.0185, - 0.0084, - -0.0261, - 0.0163, - -0.015, - -0.007, - 0.0028, - 0.0326, - 0.0163, - 0.0053, - 0.0009, - -0.0396, - 0.0059, - 0.0435, - 0.0208, - -0.0492, - 0.029, + 0.0239, + -0.0213, + -0.0938, 0.0314, - 0.0249, - 0.0629, - 0.0452, - -0.0486, - -0.053, - -0.0036, - 0.004, - -0.0133, - 0.4836, - -0.0555, - 0.0312, - 0.0675, - 0.0105, - -0.0544, - 0.0066, - 0.0156, - -0.1152, - -0.045, - -0.0388, - -0.0207, - -0.0177, - 0.035, - -0.0352, - 0.0436, - 0.0773, - 0.1003, - 0.0265, - -0.0294, - -0.0412, - -0.0162, - 0.0351, - -0.0223, - -0.0091, - -0.0103, - -0.0983, - -0.0106, - 0.0963, - 0.0026, - 0.0427, - 0.0324, - 0.0277, - -0.0609, - 0.0092, - 0.0052, - -0.0379, - -0.0005, - -0.0458, - 0.0145, - 0.0084, - 0.0428, - -0.0174, - -0.0597, - -0.0333, - 0.0076, - 0.0139, - 0.0445, - -0.0199, - -0.0366, - 0.0008, - 0.0248, + -0.0152, + 0.0124, + 0.0222, + -0.014, + -0.0187, + -0.0465, + -0.009, + 0.0055, + -0.0319, + 0.0199, + -0.0274, + -0.2397, + 0.0479, + 0.0395, + 0.0263, + 0.0079, + -0.0209, + -0.0482, + -0.0246, + -0.0383, + -0.0503, + 0.0346, 0.0072, - -0.0532, - -0.0132, - 0.0449, - -0.0179, - 0.0883, - -0.0031, - -0.0191, - 0.0359, - 0.0336, - -0.0158, - -0.0097, - 0.0809, - -0.0085, - -0.1146, - 0.0098, - 0.0278, - -0.0044, - -0.0341, - 0.0742, - -0.0206, - -0.0067, - 0.0114, - 0.0299, - 0.0117, - 0.0228, - -0.035, - 0.0232, - 0.0235, - 0.005, + 0.0218, 0.025, - 0.0318, - 0.0548, - -0.0293, - -0.1013, - 0.0189, - -0.0284, - -0.0404, - -0.0038, - -0.0414, - 0.0442, - -0.0127, - 0.056, - -0.0301, - -0.0142, - 0.0301, - -0.0241, - -0.0292, - -0.0599, - -0.0535, - 0.0056, - -0.088, - 0.0043, - 0.0351, - -0.0171, - -0.0602, - 0.0341, - 0.09, - 0.0107, - 0.0003, - -0.0064, - 0.0161, - 0.0254, - -0.0344, - -0.0539, - -0.0076, - 0.0242, - -0.0045, - 0.0138, - -0.0207, - -0.0175, - -0.076, - -0.2949, - 0.0136, - 0.0114, - -0.0969, + 0.0167, + 0.038, + -0.0156, -0.0001, - -0.0762, - 0.0506, - 0.0198, - 0.1002, - 0.1408, - -0.0287, - -0.017, - -0.0343, - 0.0062, - 0.0209, - 0.0144, - 0.0107, - -0.0063, - 0.0567, - -0.0081, + -0.0243, + -0.019, + 0.0007, + -0.0378, + -0.0314, + -0.0303, + 0.0976, + 0.0118, + 0.0395, + 0.0667, + 0.0231, + -0.0508, + 0.0038, + -0.0064, 0.0426, - 0.0575, - -0.0368, - 0.0014, - -0.0351, - 0.0043, - 0.0844, - 0.1683, - -0.0471, - -0.0197, + -0.1407, + 0.063, 0.0144, - 0.0267, - 0.0624, - -0.1332, - -0.0013, - 0.0485, - 0.0449, - -0.0704, - -0.0265, - -0.0392, - 0.0175, - 0.0438, - 0.0165, + 0.0549, + -0.0116, + -0.0283, + 0.0111, + 0.0223, + -0.0274, + 0.0974, + 0.0089, + 0.06, + 0.035, + 0.0533, + 0.0108, + -0.0199, + 0.0587, + 0.0001, + 0.0158, + -0.0062, + -0.014, + -0.0686, + 0.0197, + 0.0243, + -0.0024, + -0.054, + -0.004, + 0.0017, + -0.0011, + -0.0258, + 0.0324, + 0.023, + -0.0409, + 0.0192, + 0.0581, + -0.0393, + 0.001, + 0.3916, + 0.0129, + 0.0211, + -0.0511, + 0.0206, + 0.007, + -0.0193, + -0.0307, + -0.0311, + -0.0048, + -0.0245, + -0.0135, + -0.007, + -0.0009, + -0.0044, + 0.0007, + 0.0338, + 0.0687, + 0.0366, + -0.0106, + -0.0022, + -0.0269, + -0.0267, + 0.0122, + 0.0138, + 0.0407, + 0.0207, + -0.0164, + 0.0972, + 0.0586, + -0.0181, + 0.0584, + 0.0511, + -0.1096, + 0.036, + -0.0043, -0.0176, - -0.0869, - -0.0248, - -0.0496, - -0.0353, - 0.0018, - 0.0416, - -0.0288, - 0.0105, - 0.0313, - -0.0151, - -0.0592, - -0.0239, - -0.0677, - -0.0016, - 0.0055, - 0.0264, - -0.028, - -0.0205, + -0.0043, + -0.0061, + -0.0398, + -0.0023, + -0.0284, + -0.0087, + 0.0166, + -0.0413, + -0.1064, + 0.1098, + 0.0121, + 0.0144, + -0.0253, + -0.0282, 0.0008, - 0.0533, - -0.0296, - 0.0185, - -0.0102, - 0.0013, - -0.0257, - 0.0034, - -0.0201, - -0.0019, - -0.0474, + 0.0445, + 0.0214, + -0.0078, + 0.0237, + 0.0428, + 0.0084, + -0.0278, + -0.0291, + -0.0195, + -0.0594, + -0.002, + -0.0306, + 0.0621, + 0.0017, + -0.0729, + -0.004, + 0.031, + 0.02, + -0.0336, + 0.0681, + -0.0439, + 0.0004, 0.0317, - 0.005, - -0.0148, - 0.0205, - -0.0358, - -0.0022, - -0.0295, - 0.0316, - -0.0338, + -0.0473, + -0.003, + -0.1104, + 0.0121, + -0.0004, + 0.0724, + 0.0565, + -0.0504, + -0.0077, + 0.0525, + -0.0101, + -0.0489, + 0.0127, + -0.0024, + 0.0214, + -0.0356, + -0.0139, + 0.036, + -0.0422, + 0.0121, + -0.0165, + -0.0937, + 0.021, + 0.0422, + -0.0233, + -0.0058, + 0.0115, + 0.0776, + 0.0471, + -0.0231, + -0.0554, + -0.0461, + -0.0469, + -0.0119, + 0.0462, + -0.0144, + -0.0547, + -0.0178, + -0.0312, + 0.0172, + -0.0281, + 0.0338, + 0.0609, + 0.0284, + 0.0705, + 0.0383, + 0.0428, + -0.011, + -0.0682, + -0.2946, + -0.0064, + 0.008, + -0.0452, + -0.0361, + -0.0012, + 0.0198, + 0.0383, + 0.0047, + 0.0049, + -0.0153, + 0.059, + -0.0282, + 0.0121, + -0.0202, + 0.0075, + 0.0185, + -0.0288, + -0.0326, + 0.0011, + 0.0203, + 0.0357, + 0.0497, + -0.0607, + 0.073, + -0.0065, + 0.1174, + -0.0147, + 0.0086, + -0.0158, + 0.0154, + -0.007, + 0.0262, + -0.0344, 0.0644, - -0.0028, - -0.0081, - 0.0374, - 0.0342, - -0.0067, - 0.0612, - 0.0573, + -0.0262, + -0.0032, + -0.0652, + 0.0047, + -0.0251, + 0.0133, -0.0264, - 0.0438, - -0.0078, - 0.0206, - -0.0218, - -0.0396, - -0.2157, - 0.0099, - 0.004, - 0.0019, - 0.0163, - -0.012, - -0.0351, - -0.0352, - -0.0118, - -0.0075, - 0.0399, - 0.0686, - -0.0197, - 0.0276, - -0.0, - 0.0387, - 0.0139, - 0.0362, + -0.0084, + -0.1026, + -0.0649, + -0.0171, + -0.0128, + -0.0025, 0.0046, - 0.0384, - -0.0184, - 0.0285, - 0.1344, - -0.0449, - 0.0653, - 0.0395, - -0.025, - 0.0106, - -0.0663, - 0.0093, - 0.0238, - -0.0505, - 0.0491, - 0.001, - -0.037, - 0.0133, - -0.0094, + 0.0146, + 0.0537, + -0.002, + 0.0608, + -0.0284, + -0.0063, + -0.0596, + -0.0931, + 0.028, + -0.0576, + -0.0123, + 0.0177, + -0.045, + 0.0013, + -0.0147, 0.0244, - 0.0307, + -0.0524, + -0.0279, + -0.0223, + 0.0281, + -0.024, + -0.0072, + 0.0825, + 0.0, + 0.0361, + 0.0268, + 0.0083, + -0.0476, + -0.064, + -0.0947, + -0.0281, + 0.0728, + -0.034, + 0.0674, + 0.0277, + 0.0286, + 0.0007, + 0.041, + 0.0278, + 0.0428, + 0.0349, + -0.0341, + -0.0411, + 0.0306, + -0.0443, + 0.0438, + 0.0012, + -0.2676, + 0.0137, + -0.0274, + 0.0725, -0.0141, - 0.0442, - -0.009, - -0.0246, - -0.0105, - 0.0496, - 0.044, - -0.0974, - 0.005, - -0.0284, - 0.0024, - 0.0123, - -0.0137, - 0.0216, - 0.0436, + 0.027, + 0.0975, + -0.0741, + -0.0103, + -0.0221, + 0.0424, + 0.0009, + 0.1181, + 0.0652, + 0.0164, + 0.0294, + 0.088, + -0.01, + 0.001, + -0.0518, + 0.0504, + 0.0372, + 0.1595, + -0.037, + 0.0373, + 0.0167, + -0.0171, + -0.0577, + 0.0579, + -0.0202, + -0.0243, + -0.0005, + 0.079, + -0.0169, + -0.0132, + 0.0208, + 0.0009, + -0.0153, + 0.0045, + -0.008, + 0.0368, + -0.0376, + -0.0064, + -0.0196, + 0.0566, + -0.0271, 0.0179, - 0.0638, - -0.0003, - -0.0268, - -0.0237, - -0.0054, - -0.0292, - -0.0671, - -0.0411, - 0.0079, - -0.0065 - ] + -0.0448, + -0.019, + 0.0122, + -0.0191, + 0.0314, + 0.0107, + -0.0125, + 0.0006, + 0.0266, + -0.0018, + -0.0391, + -0.0098, + -0.053, + 0.0283, + 0.0058, + -0.0299, + 0.0835, + -0.0155 + ], + "title": "A scalable approach to investigating sequence-to-function predictions from personal genomes", + "authors": [ + "Anna Spiro", + "Xinming Tu", + "Yilun Sheng", + "Alexander Sasse", + "Rezwan Hosseini", + "Maria Chikina", + "Sara Mostafavi" + ], + "affiliations": [ + "University of Washington", + "University of Pittsburgh" + ], + "year": 2025, + "journal": "bioRxiv (Cold Spring Harbor Laboratory)", + "abstract": "Abstract Sequence-to-function (S2F) models hold the promise of evaluating arbitrary DNA sequences, providing a powerful framework for linking genotype to phenotype. Yet, despite strong performance across genomic loci, these models often struggle to capture inter-individual variation in gene expression. To address this, we propose personal genome training \u2014training models to make genotype-specific predictions at a single locus. We introduce SAGE-net, a scalable framework and software package for training and evaluating S2F models using personal genomes. Using SAGE-net, we systematically explore model architectures and training regimes, showing that personal genome training improves gene expression prediction accuracy for held-out individuals. However, performance gains arise primarily from identifying predictive variants, rather than learning a cis -regulatory grammar that generalizes across loci. This lack of generalization persists across a wide range of hyperparameters. In contrast, when applied to DNA methylation (DNAm), personal genome training enables improved generalization to unseen individuals in unseen genomic regions. This suggests that S2F models may more readily capture the sequence-level determinants of inter-individual variation in epigenomic traits. These findings highlight the need for further exploration to unlock the full potential of S2F models in decoding the regulatory grammar of personal genomes. Scalable software and infrastructure development will be critical to this progress.", + "doi": "10.1101/2025.02.21.639494", + "cited_by_count": 4, + "openalex_url": "https://openalex.org/W4407860396" }, { "channel": "area-recruiting", @@ -377818,47 +378052,47 @@ "doi": "10.64898/2026.02.01.702969", "projections": { "pca": [ - -0.22299818694591522, - -0.11551318317651749 + -0.22820240259170532, + -0.11578545719385147 ], "tsne": [ - 0.8004282116889954, - -28.279926300048828 + -13.654495239257812, + -21.001819610595703 ], "umap": [ - -2.2090940475463867, - 3.5670759677886963 + -1.944769263267517, + 7.291738033294678 ], "pca3d": [ - -0.22299763560295105, - -0.11550355702638626, - -0.2209130972623825 + -0.22820183634757996, + -0.11577652394771576, + -0.21912603080272675 ], "tsne3d": [ - -45.449649810791016, - -12.155826568603516, - 4.705127716064453 + -29.98531150817871, + -11.072111129760742, + -14.114062309265137 ], "umap3d": [ - 1.169570803642273, - 0.19176124036312103, - -1.3872625827789307 + -0.2201201468706131, + 1.0755679607391357, + 3.9452412128448486 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 8, - "label": "Gene Regulation" + "id": 3, + "label": "Genomic Sequences" } ], "_embedding": [ @@ -378268,47 +378502,47 @@ "journal": "Nature", "projections": { "pca": [ - 0.051964953541755676, - -0.12504082918167114 + 0.045362502336502075, + -0.12955257296562195 ], "tsne": [ - 12.558257102966309, - -9.409037590026855 + -5.464857578277588, + -27.79188346862793 ], "umap": [ - 1.0293755531311035, - 4.872344017028809 + 0.3410145342350006, + 5.508282661437988 ], "pca3d": [ - 0.051964256912469864, - -0.12506109476089478, - -0.13705259561538696 + 0.04536183178424835, + -0.12957240641117096, + -0.13903222978115082 ], "tsne3d": [ - -19.088926315307617, - 22.612031936645508, - 0.5368539690971375 + -11.918192863464355, + -28.60374641418457, + -23.605419158935547 ], "umap3d": [ - 2.5782527923583984, - 0.8852550983428955, - 0.5137338042259216 + 1.8249082565307617, + 1.3988230228424072, + 4.793177127838135 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 4, - "label": "Com Area Amp" + "id": 6, + "label": "Data Needs" }, { - "id": 23, - "label": "Research Authors" + "id": 16, + "label": "Lab Research" } ], "_embedding": [ @@ -378732,47 +378966,47 @@ "openalex_url": "https://openalex.org/W2800388620", "projections": { "pca": [ - -0.1071544960141182, - -0.04499281197786331 + -0.10877404361963272, + -0.045242488384246826 ], "tsne": [ - 10.478772163391113, - -6.559310436248779 + 5.082179546356201, + -3.5135281085968018 ], "umap": [ - 0.9897146224975586, - 5.111212253570557 + 0.4312863349914551, + 5.519471645355225 ], "pca3d": [ - -0.10715412348508835, - -0.04499518871307373, - -0.09252122044563293 + -0.10877375304698944, + -0.04524609073996544, + -0.08901520073413849 ], "tsne3d": [ - -6.834122657775879, - 22.80004119873047, - 11.617332458496094 + 26.19138526916504, + -16.954002380371094, + 20.348604202270508 ], "umap3d": [ - 2.7591712474823, - 0.1557638794183731, - 0.2872370183467865 + 1.7852946519851685, + 1.0652693510055542, + 5.2072601318359375 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 4, - "label": "Com Area Amp" + "id": 6, + "label": "Data Needs" }, { - "id": 23, - "label": "Research Authors" + "id": 16, + "label": "Lab Research" } ], "_embedding": [ @@ -379182,47 +379416,47 @@ "journal": "Nature Biomedical Engineering", "projections": { "pca": [ - -0.05597035586833954, - -0.09173546731472015 + -0.059343501925468445, + -0.09324964880943298 ], "tsne": [ - 10.48786449432373, - -6.4587225914001465 + 5.195587158203125, + -3.442929267883301 ], "umap": [ - 1.1075289249420166, - 5.004769325256348 + 0.5306021571159363, + 5.478142261505127 ], "pca3d": [ - -0.05597041919827461, - -0.0917443335056305, - -0.09209145605564117 + -0.0593436136841774, + -0.09325844049453735, + -0.09043066948652267 ], "tsne3d": [ - -7.918064594268799, - 18.920578002929688, - 10.611091613769531 + 24.962665557861328, + -17.679492950439453, + 17.226497650146484 ], "umap3d": [ - 2.7852790355682373, - 0.4426608681678772, - 0.49020177125930786 + 1.981150507926941, + 1.296326756477356, + 5.112415790557861 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 4, - "label": "Com Area Amp" + "id": 6, + "label": "Data Needs" }, { - "id": 23, - "label": "Research Authors" + "id": 16, + "label": "Lab Research" } ], "_embedding": [ @@ -379630,47 +379864,47 @@ "journal": "Nature", "projections": { "pca": [ - -0.037775225937366486, - 0.012550166808068752 + -0.04098506271839142, + 0.011555862613022327 ], "tsne": [ - 10.987820625305176, - -27.509490966796875 + -5.040124416351318, + -23.883563995361328 ], "umap": [ - -2.7854111194610596, - 3.731665849685669 + -2.1761372089385986, + 7.428099155426025 ], "pca3d": [ - -0.03777538612484932, - 0.012532882392406464, - -0.10549911856651306 + -0.040985338389873505, + 0.01153985969722271, + -0.10638801008462906 ], "tsne3d": [ - -40.464683532714844, - 15.261433601379395, - 8.205876350402832 + -19.42115592956543, + -16.086894989013672, + -30.549489974975586 ], "umap3d": [ - 0.9624039530754089, - 0.02162914164364338, - -0.6963953971862793 + -0.4888560473918915, + 1.2039507627487183, + 4.080604076385498 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 22, - "label": "Genetic Variants" + "id": 3, + "label": "Genomic Sequences" } ], "_embedding": [ @@ -380078,47 +380312,47 @@ "journal": "Nature", "projections": { "pca": [ - -0.10181177407503128, - 0.014048623852431774 + -0.10493399202823639, + 0.013517234474420547 ], "tsne": [ - 10.817497253417969, - -27.502779006958008 + -5.188604831695557, + -23.984296798706055 ], "umap": [ - -2.7868447303771973, - 3.750338077545166 + -2.1049091815948486, + 7.382434368133545 ], "pca3d": [ - -0.10181166976690292, - 0.01403888501226902, - -0.1105230525135994 + -0.10493394732475281, + 0.013508565723896027, + -0.10943109542131424 ], "tsne3d": [ - -45.1411247253418, - 16.092424392700195, - 7.665524959564209 + -21.82746696472168, + -19.132610321044922, + -31.29996681213379 ], "umap3d": [ - 0.9068864583969116, - -0.01464853435754776, - -0.7814629077911377 + -0.5032787919044495, + 1.171247959136963, + 4.096994400024414 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 22, - "label": "Genetic Variants" + "id": 3, + "label": "Genomic Sequences" } ], "_embedding": [ @@ -380526,47 +380760,47 @@ "journal": "Nature Methods", "projections": { "pca": [ - -0.09837360680103302, - 0.006941553670912981 + -0.10265820473432541, + 0.005765963811427355 ], "tsne": [ - 10.575111389160156, - -27.36954116821289 + -5.369409561157227, + -24.204757690429688 ], "umap": [ - -2.7361252307891846, - 3.52634859085083 + -1.9674677848815918, + 7.5789794921875 ], "pca3d": [ - -0.09837368875741959, - 0.006922048516571522, - -0.17075419425964355 + -0.10265830159187317, + 0.005747039802372456, + -0.16993652284145355 ], "tsne3d": [ - -43.03823471069336, - 15.476579666137695, - 3.331690788269043 + -20.330963134765625, + -19.527807235717773, + -27.588153839111328 ], "umap3d": [ - 0.9622070789337158, - 0.10700853168964386, - -0.6917919516563416 + -0.46434667706489563, + 1.2097408771514893, + 4.00070333480835 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 22, - "label": "Genetic Variants" + "id": 3, + "label": "Genomic Sequences" } ], "_embedding": [ @@ -380974,47 +381208,47 @@ "abstract": "Schema definitions and Python types for Medical Event Data Standard, a standard for medical event data such as EHR and claims data - Medical-Event-Data-Standard/meds", "projections": { "pca": [ - 0.18855299055576324, - -0.30486342310905457 + 0.19000345468521118, + -0.30361735820770264 ], "tsne": [ - 30.429241180419922, - -6.099593162536621 + 13.125161170959473, + -23.68775177001953 ], "umap": [ - 1.5435171127319336, - 7.189014911651611 + 1.4550111293792725, + 6.182186126708984 ], "pca3d": [ - 0.18855269253253937, - -0.30486586689949036, - 0.08029966801404953 + 0.1900031417608261, + -0.30361852049827576, + 0.08381655812263489 ], "tsne3d": [ - 2.4662015438079834, - -1.561034083366394, - 49.4608268737793 + 11.451606750488281, + 3.3620529174804688, + 42.347755432128906 ], "umap3d": [ - 3.1573774814605713, - -1.3644957542419434, - 1.075958013534546 + 1.9379708766937256, + 1.9643843173980713, + 7.353945255279541 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 6, + "label": "Data Needs" }, { - "id": 10, - "label": "Medical Data" + "id": 25, + "label": "Data Standards" } ], "_embedding": [ @@ -381424,47 +381658,47 @@ "abstract": "Setup a unix env to my liking. Contribute to d-laub/dlaub-togo development by creating an account on GitHub.", "projections": { "pca": [ - 0.33091458678245544, - 0.038832489401102066 + 0.3297230899333954, + 0.03832002729177475 ], "tsne": [ - 24.02191734313965, - 16.929136276245117 + 29.623687744140625, + -1.180350422859192 ], "umap": [ - 3.7844817638397217, - 6.183241844177246 + 3.0452990531921387, + 4.843830108642578 ], "pca3d": [ - 0.3309149146080017, - 0.03883224353194237, - -0.12250398844480515 + 0.32972344756126404, + 0.038318704813718796, + -0.1268276423215866 ], "tsne3d": [ - 31.10555076599121, - 26.627229690551758, - 15.251753807067871 + 28.411191940307617, + 17.87494468688965, + 11.433577537536621 ], "umap3d": [ - 4.316090106964111, - 0.09617596119642258, - 2.573787212371826 + 4.2335662841796875, + 2.2765846252441406, + 6.430751323699951 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { - "id": 18, - "label": "Github Tools" + "id": 4, + "label": "Open Agent Tools" } ], "_embedding": [ @@ -381870,47 +382104,47 @@ ], "projections": { "pca": [ - 0.017651477828621864, - 0.07315707206726074 + 0.019106833264231682, + 0.07565159350633621 ], "tsne": [ - 1.5049160718917847, - 25.91362762451172 + 19.99455451965332, + 20.38355255126953 ], "umap": [ - 4.610064506530762, - 4.275351524353027 + 3.7127902507781982, + 3.5231211185455322 ], "pca3d": [ - 0.017651420086622238, - 0.07318150252103806, - -0.05524376407265663 + 0.019106706604361534, + 0.07567564398050308, + -0.054524458944797516 ], "tsne3d": [ - 39.236167907714844, - -27.815929412841797, - 9.909067153930664 + 10.98679256439209, + 46.07585906982422, + -19.692964553833008 ], "umap3d": [ - 4.710852146148682, - 1.7457612752914429, - 1.849334716796875 + 4.261201858520508, + 2.0401594638824463, + 4.495240688323975 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { "id": 7, - "label": "Open AI" + "label": "Hugging Face Models" } ], "_embedding": [ @@ -382316,47 +382550,47 @@ ], "projections": { "pca": [ - -0.04311995580792427, - 0.16228711605072021 + -0.04100409522652626, + 0.1643868088722229 ], "tsne": [ - 1.3916442394256592, - 25.918411254882812 + 19.9039306640625, + 20.447751998901367 ], "umap": [ - 4.57481050491333, - 4.252440452575684 + 3.7016847133636475, + 3.4871273040771484 ], "pca3d": [ - -0.04312063753604889, - 0.16229282319545746, - -0.008449776098132133 + -0.041004810482263565, + 0.16439251601696014, + -0.006603339221328497 ], "tsne3d": [ - 42.38774490356445, - -28.804946899414062, - 7.484921932220459 + 7.6446027755737305, + 46.116844177246094, + -20.122970581054688 ], "umap3d": [ - 4.776005268096924, - 1.666149616241455, - 1.8806169033050537 + 4.322808742523193, + 1.9840669631958008, + 4.511287212371826 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { "id": 7, - "label": "Open AI" + "label": "Hugging Face Models" } ], "_embedding": [ @@ -382764,47 +382998,47 @@ ], "projections": { "pca": [ - 0.17636674642562866, - -0.0030071937944740057 + 0.17600668966770172, + -0.0026318361051380634 ], "tsne": [ - 2.711082696914673, - 25.76241111755371 + 20.980669021606445, + 19.663652420043945 ], "umap": [ - 4.668140888214111, - 4.298928260803223 + 3.7148935794830322, + 3.484558343887329 ], "pca3d": [ - 0.17636637389659882, - -0.002997091505676508, - -0.1524069756269455 + 0.1760062277317047, + -0.002621866762638092, + -0.15441851317882538 ], "tsne3d": [ - 40.120357513427734, - -26.896785736083984, - 15.926928520202637 + 13.043992042541504, + 47.19782257080078, + -15.093552589416504 ], "umap3d": [ - 4.746071815490723, - 1.7451592683792114, - 1.9128700494766235 + 4.266829967498779, + 2.0611469745635986, + 4.480775356292725 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { "id": 7, - "label": "Open AI" + "label": "Hugging Face Models" } ], "_embedding": [ @@ -383210,47 +383444,47 @@ ], "projections": { "pca": [ - 0.07362053543329239, - 0.06486276537179947 + 0.07246857136487961, + 0.06540306657552719 ], "tsne": [ - 15.548016548156738, - 3.434314727783203 + 20.10991859436035, + -1.8348780870437622 ], "umap": [ - 2.427368640899658, - 5.597279071807861 + 1.6093900203704834, + 5.075000286102295 ], "pca3d": [ - 0.07362082600593567, - 0.06487443298101425, - -0.09535816311836243 + 0.07246889919042587, + 0.06541382521390915, + -0.0958319753408432 ], "tsne3d": [ - 4.264671802520752, - 22.78407859802246, - 12.08763599395752 + 21.978973388671875, + 0.4948766529560089, + -1.5397354364395142 ], "umap3d": [ - 3.4011967182159424, - 0.22231875360012054, - 1.2638815641403198 + 3.1138081550598145, + 1.8699309825897217, + 5.743617534637451 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, - "label": "AI Agents" + "id": 6, + "label": "Data Needs" }, { - "id": 26, - "label": "AI Information" + "id": 10, + "label": "Research Data" } ], "_embedding": [ @@ -383658,47 +383892,47 @@ "abstract": "Genome modeling and design across all domains of life - ArcInstitute/evo2", "projections": { "pca": [ - 0.13504594564437866, - -0.10471198707818985 + 0.13330286741256714, + -0.10342000424861908 ], "tsne": [ - 28.218034744262695, - 0.02363445609807968 + 23.568254470825195, + -20.060625076293945 ], "umap": [ - -2.0905914306640625, - 4.218372821807861 + -1.877732515335083, + 6.84891939163208 ], "pca3d": [ - 0.13504692912101746, - -0.10469568520784378, - -0.13308663666248322 + 0.1333039104938507, + -0.10340351611375809, + -0.13253533840179443 ], "tsne3d": [ - -7.98624849319458, - 42.747825622558594, - 29.10414695739746 + 45.21157455444336, + -4.026812553405762, + 4.251317977905273 ], "umap3d": [ - 1.4776899814605713, - -0.26249784231185913, - -0.8474705219268799 + 0.18730908632278442, + 1.575426697731018, + 4.465177059173584 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { - "id": 6, - "label": "Genetic Variants" + "id": 7, + "label": "Cell Genes" }, { - "id": 2, - "label": "Genetic Disease" + "id": 27, + "label": "Genetic Variants" } ], "_embedding": [ @@ -384106,47 +384340,47 @@ "abstract": "We\u2019re on a journey to advance and democratize artificial intelligence through open source and open science.", "projections": { "pca": [ - 0.08742070198059082, - 0.1126670241355896 + 0.08926450461149216, + 0.11481020599603653 ], "tsne": [ - 7.937536716461182, - 19.212947845458984 + 21.16874885559082, + 13.65190601348877 ], "umap": [ - 4.9471755027771, - 4.8336687088012695 + 4.141310214996338, + 3.20587420463562 ], "pca3d": [ - 0.0874207466840744, - 0.11268510669469833, - 0.1281331330537796 + 0.089264415204525, + 0.11482615023851395, + 0.12715917825698853 ], "tsne3d": [ - 39.85732650756836, - -10.73531723022461, - 10.0072603225708 + -0.6505627632141113, + 40.355133056640625, + 9.589221000671387 ], "umap3d": [ - 5.2609076499938965, - 1.6642611026763916, - 1.986562967300415 + 4.7953386306762695, + 2.2108449935913086, + 4.631794452667236 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { "id": 7, - "label": "Open AI" + "label": "Hugging Face Models" } ], "_embedding": [ @@ -384558,47 +384792,47 @@ "doi": "10.1016/s2211-0380(24)48857-1", "projections": { "pca": [ - 0.11837685108184814, - -0.03751836717128754 + 0.11782380938529968, + -0.03853980079293251 ], "tsne": [ - 11.155771255493164, - -3.764038324356079 + 6.597588062286377, + -1.5252716541290283 ], "umap": [ - 1.3456807136535645, - 4.952021598815918 + 0.6321673393249512, + 5.288827896118164 ], "pca3d": [ - 0.11837638914585114, - -0.03753812611103058, - -0.02888091467320919 + 0.11782331019639969, + -0.03856072947382927, + -0.031754881143569946 ], "tsne3d": [ - -3.948526382446289, - 9.62125015258789, - 9.43697452545166 + 18.045345306396484, + -17.544137954711914, + 11.178197860717773 ], "umap3d": [ - 2.969879627227783, - 0.5938988924026489, - 0.6285388469696045 + 2.1427624225616455, + 1.430379033088684, + 5.192646026611328 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 3, + "label": "Open Source Tools" }, { - "id": 4, - "label": "Com Area Amp" + "id": 6, + "label": "Data Needs" }, { - "id": 23, - "label": "Research Authors" + "id": 16, + "label": "Lab Research" } ], "_embedding": [ @@ -385016,47 +385250,47 @@ "doi": "10.64898/2026.03.01.708874", "projections": { "pca": [ - -0.12100125849246979, - -0.013528788462281227 + -0.12374719977378845, + -0.012572725303471088 ], "tsne": [ - 3.2276885509490967, - -36.21881866455078 + -11.74319839477539, + -30.307422637939453 ], "umap": [ - -1.4666625261306763, - 2.4298954010009766 + -0.5461625456809998, + 7.650570869445801 ], "pca3d": [ - -0.12100104987621307, - -0.0135313356295228, - -0.1963219940662384 + -0.12374702095985413, + -0.01257529016584158, + -0.19480302929878235 ], "tsne3d": [ - -43.1746940612793, - 14.7814302444458, - -21.05547523498535 + -35.3191032409668, + -37.183048248291016, + -15.833209991455078 ], "umap3d": [ - 1.4318616390228271, - 1.2961617708206177, - -0.4598419964313507 + 1.0327645540237427, + 0.9972977638244629, + 3.354745626449585 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 21, - "label": "Protein RNA" + "id": 12, + "label": "Protein Language" } ], "_embedding": [ @@ -385464,47 +385698,47 @@ "abstract": "We\u2019re on a journey to advance and democratize artificial intelligence through open source and open science.", "projections": { "pca": [ - 0.08958035707473755, - -0.031249361112713814 + 0.08990377932786942, + -0.029452169314026833 ], "tsne": [ - 17.03352928161621, - 7.002463340759277 + 16.88919448852539, + -1.8738421201705933 ], "umap": [ - 2.6945314407348633, - 6.277459621429443 + 2.266164541244507, + 4.728059768676758 ], "pca3d": [ - 0.08958016335964203, - -0.031253471970558167, - 0.030064327642321587 + 0.08990360051393509, + -0.02945612370967865, + 0.03219878673553467 ], "tsne3d": [ - 12.266657829284668, - 30.337522506713867, - 7.819204807281494 + 12.924757957458496, + 10.843785285949707, + 19.285377502441406 ], "umap3d": [ - 3.763467311859131, - -0.038874972611665726, - 1.5588563680648804 + 3.2495810985565186, + 1.700400948524475, + 6.247479438781738 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 4, + "label": "AI Agents" }, { - "id": 3, - "label": "SMB Data" + "id": 24, + "label": "SMB Models" } ], "_embedding": [ @@ -385911,47 +386145,47 @@ "title": "Prompt guidance | OpenAI API", "projections": { "pca": [ - 0.15177376568317413, - 0.1639842391014099 + 0.15525276958942413, + 0.1675279587507248 ], "tsne": [ - 3.8973851203918457, - 15.329593658447266 + 16.874116897583008, + 9.162521362304688 ], "umap": [ - 4.219324588775635, - 4.2325239181518555 + 3.448861598968506, + 2.8965299129486084 ], "pca3d": [ - 0.1517735868692398, - 0.16398462653160095, - 0.0963960736989975 + 0.15525265038013458, + 0.16752983629703522, + 0.09214258193969727 ], "tsne3d": [ - 28.686067581176758, - -10.908955574035645, - 4.676187515258789 + 2.245561122894287, + 39.287010192871094, + -8.310529708862305 ], "umap3d": [ - 5.42455530166626, - 1.301658272743225, - 1.2396597862243652 + 3.911841630935669, + 0.8102283477783203, + 4.819141387939453 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { - "id": 1, - "label": "AI Agents" + "id": 7, + "label": "Hugging Face Models" } ], "_embedding": [ @@ -386361,47 +386595,47 @@ "abstract": "Generate synthetic EHR, genomics, imaging, and other data types from Python. - synthlab/notebooks/olink_demo.ipynb at main \u00b7 bschilder/synthlab", "projections": { "pca": [ - 0.1452506184577942, - -0.16818936169147491 + 0.14539781212806702, + -0.1671953797340393 ], "tsne": [ - 31.11881446838379, - 0.836833119392395 + 26.21304702758789, + -16.987186431884766 ], "umap": [ - 2.054274797439575, - 7.053647518157959 + 1.9432587623596191, + 6.52923059463501 ], "pca3d": [ - 0.1452505886554718, - -0.1681961864233017, - -0.07925291359424591 + 0.14539772272109985, + -0.1672011762857437, + -0.07665961235761642 ], "tsne3d": [ - 3.731990098953247, - 43.79283905029297, - 30.215282440185547 + 11.012889862060547, + 20.938064575195312, + 34.48124694824219 ], "umap3d": [ - 3.067927837371826, - -1.1743916273117065, - 1.3879661560058594 + 2.3352253437042236, + 2.085601329803467, + 7.285902976989746 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 3, - "label": "Github Data" + "id": 0, + "label": "GitHub Tools" }, { "id": 25, - "label": "Python Data" + "label": "Data Standards" } ], "_embedding": [ @@ -386810,47 +387044,47 @@ "abstract": "Causal models of cellular systems hold the promise to empower broad biological discovery, includ- ing the systematic identification of novel targets for drug discovery. Predicting how genetic and pathway perturbations reshape gene expression across diverse cellular contexts is a prerequisite for building generalizable cellular foundation models. However, current methods typically fail to extrapolate beyond their training distributions because they rely predominantly on observational expression atlases rather than interventional perturbation data. We present X-Atlas/Pisces, the largest genome-wide CRISPRi Perturb-seq compendium to date, comprising 25.6 million perturbed single-cell transcriptomes across 16 biologically diverse contexts, including widely used cell lines, induced pluripotent stem cells (iPSCs), resting and CD3/CD28 activated Jurkat T lymphoma cells, and multi-lineage differentiating iPSCs. Leveraging this resource, we develop X-Cell, a diffusion lan- guage model that predicts perturbation responses by iteratively refining control-to-perturbed state transitions through cross-attention to multi-modal biological priors derived from natural language, protein language models, interaction networks, genetic dependency maps, and morphological pro- files. X-Cell outperforms existing state-of-the-art models by up to five-fold on key metrics such as Pearson \u2206(correlation between predicted and observed perturbation-induced log-fold changes), and demonstrates zero-shot prediction of T cell inactivating perturbations in stimulated Jurkat cells. We scale X-Cell to 4.9 billion parameters (X-Cell-Ultra), the largest causal perturbation model to date. We demonstrate for the first time that perturbation prediction follows power-law scaling with an exponent matching large language models. X-Cell-Ultra demonstrates zero-shot generalization to novel biological contexts, including unseen iPSC-derived melanocyte progenitors and primary human CD4+ T cells from multiple donors, and outperforms all baselines after self-supervised test-time adap- tation. These results demonstrate that coordinated scaling of causal perturbation data and model capacity yields foundation models capable of generalizable perturbation prediction across cellular contexts, with potential applications for improving computational target identification, validation, and context-specific therapeutic prioritization. 1", "projections": { "pca": [ - -0.22732609510421753, - -0.07461941987276077 + -0.22997502982616425, + -0.0735650435090065 ], "tsne": [ - -5.7236127853393555, - -25.48595428466797 + -21.71229362487793, + -22.463756561279297 ], "umap": [ - -0.961839497089386, - 3.64754319190979 + -0.5946674942970276, + 6.6534318923950195 ], "pca3d": [ - -0.2273254692554474, - -0.07461433112621307, - -0.17300236225128174 + -0.22997444868087769, + -0.07356098294258118, + -0.16926690936088562 ], "tsne3d": [ - -46.230308532714844, - -29.583044052124023, - -6.379697799682617 + -20.420108795166016, + -28.728912353515625, + -5.780055999755859 ], "umap3d": [ - 1.9731866121292114, - 1.2561216354370117, - -1.2894803285598755 + 0.7843499183654785, + 0.07432373613119125, + 3.8408286571502686 ] }, - "cluster_id": 2, - "cluster_label": "Bio Models Gene", + "cluster_id": 1, + "cluster_label": "Bioinformatics Models", "cluster_levels": [ { - "id": 2, - "label": "Bio Models Gene" + "id": 1, + "label": "Bioinformatics Models" }, { "id": 7, - "label": "Cell Models" + "label": "Cell Genes" }, { - "id": 19, - "label": "Cell Models" + "id": 20, + "label": "Cell Foundation" } ], "_embedding": [ @@ -387258,47 +387492,47 @@ "abstract": "An overview of Gemini Embedding 2, our first fully multimodal embedding model that maps text, images, video, audio and documents into a single space.", "projections": { "pca": [ - 0.013291962444782257, - 0.11285541206598282 + 0.014688469469547272, + 0.11376804113388062 ], "tsne": [ - -19.824012756347656, - 8.68783187866211 + -6.92043399810791, + 20.781641006469727 ], "umap": [ - 1.5246447324752808, - 3.449814796447754 + 0.44739773869514465, + 2.2178616523742676 ], "pca3d": [ - 0.013291075825691223, - 0.11284582316875458, - -0.012156971730291843 + 0.014687685295939445, + 0.11375939100980759, + -0.009322939440608025 ], "tsne3d": [ - -2.8483290672302246, - 5.926110744476318, - -17.69466781616211 + -4.631319046020508, + 8.637540817260742, + -10.819401741027832 ], "umap3d": [ - 5.617144584655762, - -0.9206865429878235, - 0.04486783593893051 + 3.3045034408569336, + -0.7629795670509338, + 6.369198322296143 ] }, - "cluster_id": 3, - "cluster_label": "AI Training Models", + "cluster_id": 0, + "cluster_label": "Multimodal Models", "cluster_levels": [ { - "id": 3, - "label": "AI Training Models" + "id": 0, + "label": "Multimodal Models" }, { - "id": 5, + "id": 3, "label": "Model Training" }, { - "id": 27, - "label": "Embeddings" + "id": 8, + "label": "JEPA Video Models" } ], "_embedding": [ @@ -387704,47 +387938,47 @@ ], "projections": { "pca": [ - 0.04472237080335617, - -0.12331594526767731 + 0.048933107405900955, + -0.11852126568555832 ], "tsne": [ - -18.9700927734375, - -8.869325637817383 + -23.11322784423828, + 16.049495697021484 ], "umap": [ - -0.04028555750846863, - 6.5550713539123535 + -1.2533797025680542, + 3.246321678161621 ], "pca3d": [ - 0.04472282901406288, - -0.12332489341497421, - 0.2530941963195801 + 0.048933614045381546, + -0.11852951347827911, + 0.2553440034389496 ], "tsne3d": [ - 20.712745666503906, - -48.83898162841797, - 2.513481616973877 + -22.5819149017334, + 18.842884063720703, + 12.144506454467773 ], "umap3d": [ - 4.442698955535889, - -0.9156237244606018, - -1.9934195280075073 + 1.0440768003463745, + 0.37837767601013184, + 7.671409606933594 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, - "label": "Clinical Health" + "id": 5, + "label": "Health Benchmarks" } ], "_embedding": [ @@ -388150,47 +388384,47 @@ ], "projections": { "pca": [ - -0.151515394449234, - 0.08006705343723297 + -0.1433916687965393, + 0.08742475509643555 ], "tsne": [ - -17.463773727416992, - -8.872854232788086 + -22.522869110107422, + 14.572854995727539 ], "umap": [ - -0.34806206822395325, - 6.070719242095947 + -1.2277729511260986, + 3.353616952896118 ], "pca3d": [ - -0.1515149176120758, - 0.08006894588470459, - 0.3606692850589752 + -0.14339132606983185, + 0.0874277800321579, + 0.361278235912323 ], "tsne3d": [ - 14.647006034851074, - -44.72630310058594, - -3.3073606491088867 + -19.876171112060547, + 14.837756156921387, + 5.725200176239014 ], "umap3d": [ - 4.411810398101807, - -0.3915177285671234, - -2.0751049518585205 + 0.7419881820678711, + 0.2888699769973755, + 7.605600357055664 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, - "label": "Medical Models" + "id": 5, + "label": "Clinical Data" }, { - "id": 6, - "label": "Clinical Health" + "id": 5, + "label": "Health Benchmarks" } ], "_embedding": [ @@ -388598,47 +388832,47 @@ "abstract": "AirLLM 70B inference with single 4GB GPU. Contribute to lyogavin/airllm development by creating an account on GitHub.", "projections": { "pca": [ - 0.17612852156162262, - 0.07840796560049057 + 0.17520177364349365, + 0.07764150947332382 ], "tsne": [ - 10.992181777954102, - 8.153387069702148 + 15.600302696228027, + -7.461578369140625 ], "umap": [ - 2.988147258758545, - 4.801650047302246 + 2.0711240768432617, + 4.099747180938721 ], "pca3d": [ - 0.17612765729427338, - 0.07840504497289658, - -0.1297929584980011 + 0.17520086467266083, + 0.07764022797346115, + -0.13056927919387817 ], "tsne3d": [ - 4.499627590179443, - 40.900184631347656, - -3.5831918716430664 + 30.6450138092041, + 0.05919073522090912, + -7.7838664054870605 ], "umap3d": [ - 4.664714813232422, - -0.4919193387031555, - 2.097836971282959 + 4.2970380783081055, + 1.4025620222091675, + 6.704352855682373 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 0, + "id": 4, "label": "AI Agents" }, { - "id": 5, - "label": "GPU Inference" + "id": 15, + "label": "PyTorch Models" } ], "_embedding": [ @@ -389046,47 +389280,47 @@ "abstract": "We\u2019re on a journey to advance and democratize artificial intelligence through open source and open science.", "projections": { "pca": [ - 0.12228322774171829, - 0.09363611787557602 + 0.12684643268585205, + 0.09579251706600189 ], "tsne": [ - 7.818821907043457, - 20.962326049804688 + 21.33974838256836, + 12.697699546813965 ], "umap": [ - 5.184941291809082, - 4.696190357208252 + 4.20255184173584, + 2.971646547317505 ], "pca3d": [ - 0.12228235602378845, - 0.09363441169261932, - 0.26059481501579285 + 0.12684546411037445, + 0.0957900732755661, + 0.25977644324302673 ], "tsne3d": [ - 46.822792053222656, - -9.60153579711914, - 8.357748031616211 + 5.309053897857666, + 38.336246490478516, + 10.315648078918457 ], "umap3d": [ - 5.474649906158447, - 1.7165858745574951, - 1.9754399061203003 + 4.933165550231934, + 2.1296069622039795, + 4.656134605407715 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { "id": 7, - "label": "Open AI" + "label": "Hugging Face Models" } ], "_embedding": [ @@ -389511,46 +389745,46 @@ ], "projections": { "pca": [ - -0.13042587041854858, - -0.050296321511268616 + -0.1283307671546936, + -0.04833468794822693 ], "tsne": [ - -20.872013092041016, - -1.5962698459625244 + -6.776362895965576, + 11.910202980041504 ], "umap": [ - -0.07199324667453766, - 7.884659290313721 + -1.079807162284851, + 2.1966686248779297 ], "pca3d": [ - -0.1304256021976471, - -0.05029746890068054, - 0.08205044269561768 + -0.1283305287361145, + -0.04833598434925079, + 0.08899009972810745 ], "tsne3d": [ - -10.40958309173584, - 2.467320442199707, - -7.062150955200195 + -7.5507707595825195, + 8.154539108276367, + 27.250577926635742 ], "umap3d": [ - 4.557443141937256, - -1.8547253608703613, - -0.5093329548835754 + 1.8976398706436157, + -0.6310032606124878, + 6.786762714385986 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 11, + "id": 19, "label": "Medical Imaging" } ], @@ -389975,46 +390209,46 @@ "doi": "10.1038/s41597-026-07271-7", "projections": { "pca": [ - 0.06339597702026367, - -0.2140454202890396 + 0.06494300812482834, + -0.21290545165538788 ], "tsne": [ - -23.40623664855957, - -2.7461750507354736 + -3.056079149246216, + 11.322959899902344 ], "umap": [ - 0.009248325601220131, - 7.947380065917969 + -1.0423591136932373, + 2.2135584354400635 ], "pca3d": [ - 0.06339550018310547, - -0.2140638828277588, - 0.11843971163034439 + 0.06494257599115372, + -0.21292176842689514, + 0.1231452003121376 ], "tsne3d": [ - -10.271858215332031, - 4.004796504974365, - -0.8008049130439758 + -8.260047912597656, + 8.50468921661377, + 33.0578727722168 ], "umap3d": [ - 4.473992347717285, - -1.9615107774734497, - -0.5683922171592712 + 1.8701406717300415, + -0.7174116969108582, + 6.796195983886719 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 11, + "id": 19, "label": "Medical Imaging" } ], @@ -390423,46 +390657,46 @@ "abstract": "Benchmarking vision-language models on MS-CXR-T temporal chest X-ray tasks - jjanizek/ms-cxr-t-bench", "projections": { "pca": [ - 0.06148366630077362, - 0.012150709517300129 + 0.06149933487176895, + 0.012473572045564651 ], "tsne": [ - 3.1516716480255127, - 4.853250503540039 + 14.936819076538086, + -3.3550262451171875 ], "umap": [ - 0.5654110908508301, - 7.648719310760498 + -0.46266087889671326, + 2.293868064880371 ], "pca3d": [ - 0.061482612043619156, - 0.01214241050183773, - -0.05773777514696121 + 0.061498336493968964, + 0.012467695400118828, + -0.05334175378084183 ], "tsne3d": [ - -0.13390658795833588, - 34.19103240966797, - 6.020781517028809 + 4.697288990020752, + 10.259628295898438, + 24.432559967041016 ], "umap3d": [ - 4.779048919677734, - -1.479251503944397, - -0.05570821836590767 + 2.498028516769409, + -0.3980036973953247, + 6.74275016784668 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 11, + "id": 19, "label": "Medical Imaging" } ], @@ -390871,47 +391105,47 @@ "abstract": "We\u2019re on a journey to advance and democratize artificial intelligence through open source and open science.", "projections": { "pca": [ - 0.0886281356215477, - 0.10769882053136826 + 0.09149173647165298, + 0.11006756126880646 ], "tsne": [ - 8.185554504394531, - 19.272741317749023 + 20.841154098510742, + 13.474665641784668 ], "umap": [ - 5.060279369354248, - 4.715020656585693 + 4.262601375579834, + 3.1097874641418457 ], "pca3d": [ - 0.0886276364326477, - 0.10770803689956665, - 0.1402778923511505 + 0.09149111807346344, + 0.11007508635520935, + 0.13989661633968353 ], "tsne3d": [ - 43.30199432373047, - -8.234379768371582, - 13.483930587768555 + -0.2016497701406479, + 35.754058837890625, + 10.371135711669922 ], "umap3d": [ - 5.3063764572143555, - 1.7099242210388184, - 2.0276737213134766 + 4.751420974731445, + 2.2606537342071533, + 4.606719493865967 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { "id": 7, - "label": "Open AI" + "label": "Hugging Face Models" } ], "_embedding": [ @@ -391337,46 +391571,46 @@ ], "projections": { "pca": [ - -0.1964751034975052, - 0.0014078397070989013 + -0.19543245434761047, + 0.003360801376402378 ], "tsne": [ - -18.937999725341797, - -0.7152189612388611 + -7.673929214477539, + 11.707626342773438 ], "umap": [ - 0.19353953003883362, - 7.6637396812438965 + -0.6217959523200989, + 2.306833505630493 ], "pca3d": [ - -0.1964755356311798, - 0.0013895267620682716, - 0.034243300557136536 + -0.19543293118476868, + 0.00334337679669261, + 0.04240306466817856 ], "tsne3d": [ - -8.00134563446045, - -0.22488188743591309, - -10.95033073425293 + -7.7226409912109375, + 8.460610389709473, + 22.68889808654785 ], "umap3d": [ - 4.763745307922363, - -1.5220956802368164, - -0.39053842425346375 + 2.18043851852417, + -0.49735137820243835, + 6.737499237060547 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 11, + "id": 19, "label": "Medical Imaging" } ], @@ -391784,46 +392018,46 @@ "title": "SynthVision: Building a 110K Synthetic Medical VQA Dataset with Cross-Model Validation", "projections": { "pca": [ - -0.17613492906093597, - -0.027825122699141502 + -0.170930415391922, + -0.02348298579454422 ], "tsne": [ - -17.296306610107422, - 0.1134062260389328 + -7.900540351867676, + 9.691204071044922 ], "umap": [ - 0.03440047800540924, - 7.2570343017578125 + -0.9157165884971619, + 2.701683759689331 ], "pca3d": [ - -0.17613500356674194, - -0.027826549485325813, - 0.16626770794391632 + -0.1709306389093399, + -0.023483265191316605, + 0.17040081322193146 ], "tsne3d": [ - -8.75798511505127, - -10.113818168640137, - -4.917397499084473 + -11.580033302307129, + 14.436827659606934, + 16.394977569580078 ], "umap3d": [ - 4.699331760406494, - -1.2763043642044067, - -0.5444541573524475 + 1.8233000040054321, + -0.10371393710374832, + 6.901892185211182 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 11, + "id": 19, "label": "Medical Imaging" } ], @@ -392234,46 +392468,46 @@ "journal": "Nature", "projections": { "pca": [ - 0.041212212294340134, - -0.1581173837184906 + 0.043732766062021255, + -0.15584851801395416 ], "tsne": [ - -23.513011932373047, - -1.7989071607589722 + -3.593501091003418, + 12.016975402832031 ], "umap": [ - -0.013011837378144264, - 7.9274725914001465 + -0.9794129729270935, + 2.1483280658721924 ], "pca3d": [ - 0.04121210798621178, - -0.15813201665878296, - 0.06936517357826233 + 0.04373269900679588, + -0.15586118400096893, + 0.07458621263504028 ], "tsne3d": [ - -4.634878635406494, - 2.601494073867798, - -2.897411584854126 + -7.261233329772949, + 13.683972358703613, + 32.89458084106445 ], "umap3d": [ - 4.624908924102783, - -1.8967219591140747, - -0.4848388731479645 + 1.9017977714538574, + -0.7260304093360901, + 6.838649749755859 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 9, + "id": 2, "label": "Medical Models" }, { - "id": 11, + "id": 19, "label": "Medical Imaging" } ], @@ -392682,46 +392916,46 @@ "abstract": "Provides access to oncology data, AI tools, analysis support, cohort development, and tech assistance to enhance research and drive innovation.", "projections": { "pca": [ - 0.013600761070847511, - -0.2678956091403961 + 0.016215061768889427, + -0.26574867963790894 ], "tsne": [ - -9.292095184326172, - -16.128175735473633 + -17.52537727355957, + -6.992020130157471 ], "umap": [ - -2.1286773681640625, - 6.444399833679199 + -2.2494630813598633, + 5.107069492340088 ], "pca3d": [ - 0.013600768521428108, - -0.2679038345813751, - 0.1610732227563858 + 0.016215059906244278, + -0.2657546103000641, + 0.16346397995948792 ], "tsne3d": [ - -13.036083221435547, - -26.49618148803711, - -5.031840801239014 + -24.292367935180664, + -12.600957870483398, + 25.592660903930664 ], "umap3d": [ - 2.8398735523223877, - -1.0316835641860962, - -0.4373123049736023 + 0.37286993861198425, + 0.2660504877567291, + 5.923450469970703 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { "id": 1, "label": "Cancer Data" }, { - "id": 24, + "id": 1, "label": "Cancer Data" } ], @@ -393128,47 +393362,47 @@ ], "projections": { "pca": [ - 0.10873828083276749, - 0.09270627796649933 + 0.10741805285215378, + 0.09014607965946198 ], "tsne": [ - 9.374173164367676, - -2.510946035385132 + 7.298180103302002, + 1.1988927125930786 ], "umap": [ - 1.5927997827529907, - 5.004122257232666 + 0.820736825466156, + 4.988891124725342 ], "pca3d": [ - 0.10873766988515854, - 0.09269566088914871, - 0.009252175688743591 + 0.10741754621267319, + 0.09013423323631287, + 0.0038743321783840656 ], "tsne3d": [ - -8.559465408325195, - 19.12649917602539, - -5.105287551879883 + 30.126630783081055, + -24.227127075195312, + 10.842036247253418 ], "umap3d": [ - 3.033299446105957, - 0.8930105566978455, - 0.6963620185852051 + 2.398864984512329, + 1.5307893753051758, + 5.094870567321777 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 4, - "label": "Com Area Amp" + "id": 6, + "label": "Data Needs" }, { - "id": 23, - "label": "Research Authors" + "id": 10, + "label": "Research Data" } ], "_embedding": [ @@ -393574,47 +393808,47 @@ ], "projections": { "pca": [ - 0.33429932594299316, - -0.021263910457491875 + 0.3333030641078949, + -0.02402615174651146 ], "tsne": [ - 8.703276634216309, - 2.0578620433807373 + 9.267483711242676, + 6.031907558441162 ], "umap": [ - 1.4240390062332153, - 5.389296054840088 + 0.7797856330871582, + 4.621971607208252 ], "pca3d": [ - 0.3342972993850708, - -0.021282056346535683, - -0.04073573276400566 + 0.3333011865615845, + -0.02404375933110714, + -0.04524976760149002 ], "tsne3d": [ - 12.050835609436035, - 18.43109893798828, - -8.757539749145508 + 15.243301391601562, + -13.674428939819336, + 1.208260178565979 ], "umap3d": [ - 3.4130139350891113, - 0.8289299011230469, - 0.827972412109375 + 2.6372950077056885, + 1.7185759544372559, + 5.630856990814209 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { - "id": 4, - "label": "Com Area Amp" + "id": 6, + "label": "Data Needs" }, { - "id": 23, - "label": "Research Authors" + "id": 10, + "label": "Research Data" } ], "_embedding": [ @@ -394022,47 +394256,47 @@ "abstract": "We\u2019re partnering with the Gates Foundation to commit $200 million in grant funding, Claude usage credits, and technical support for programs in global health, life sciences, education, and economic mobility over the next four years.", "projections": { "pca": [ - 0.14023646712303162, - -0.09446321427822113 + 0.14075422286987305, + -0.09290138632059097 ], "tsne": [ - 22.23523712158203, - 29.66642951965332 + 34.329715728759766, + 11.84196949005127 ], "umap": [ - 5.030112266540527, - 5.981852054595947 + 4.407198429107666, + 4.507111072540283 ], "pca3d": [ - 0.14023718237876892, - -0.0944480448961258, - 0.0695105791091919 + 0.14075499773025513, + -0.09288876503705978, + 0.0680931955575943 ], "tsne3d": [ - 64.58324432373047, - 11.821383476257324, - 4.407681465148926 + 17.984703063964844, + 46.51482391357422, + 1.3407847881317139 ], "umap3d": [ - 5.275880336761475, - 0.7612078785896301, - 3.145033597946167 + 5.585160732269287, + 2.34196400642395, + 6.0052690505981445 ] }, - "cluster_id": 1, - "cluster_label": "Open Agent Tools", + "cluster_id": 3, + "cluster_label": "Open Source Tools", "cluster_levels": [ { - "id": 1, - "label": "Open Agent Tools" + "id": 3, + "label": "Open Source Tools" }, { "id": 8, - "label": "Open Models" + "label": "Open AI" }, { - "id": 16, - "label": "Claude Code" + "id": 11, + "label": "Code Tools" } ], "_embedding": [ @@ -394468,47 +394702,47 @@ ], "projections": { "pca": [ - 0.1188523918390274, - -0.05901579186320305 + 0.12061358243227005, + -0.05654577538371086 ], "tsne": [ - 20.47593116760254, - -13.531340599060059 + 4.900243759155273, + -9.68040657043457 ], "umap": [ - 0.5249944925308228, - 5.58619499206543 + 0.1021176427602768, + 4.509263038635254 ], "pca3d": [ - 0.11885218322277069, - -0.0590125136077404, - 0.13040225207805634 + 0.12061332911252975, + -0.05654241517186165, + 0.12993347644805908 ], "tsne3d": [ - 19.484956741333008, - -11.05098819732666, - 35.906715393066406 + 28.415132522583008, + -22.05614471435547, + -14.688549041748047 ], "umap3d": [ - 3.605370044708252, - 1.1769520044326782, - -0.0269108098000288 + 1.8178075551986694, + 2.2470827102661133, + 5.400990962982178 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 4, - "label": "Com Area Amp" + "id": 9, + "label": "Drug Discovery" }, { - "id": 0, - "label": "Clinical LLMs" + "id": 23, + "label": "Peer Review" } ], "_embedding": [ @@ -394914,47 +395148,47 @@ ], "projections": { "pca": [ - 0.13444258272647858, - -0.03491274639964104 + 0.13615664839744568, + -0.033833377063274384 ], "tsne": [ - 19.710844039916992, - -13.125998497009277 + 4.8802900314331055, + -10.515661239624023 ], "umap": [ - 0.6046928763389587, - 5.54733419418335 + 0.191236212849617, + 4.54343843460083 ], "pca3d": [ - 0.13444314897060394, - -0.03491402789950371, - 0.12343296408653259 + 0.13615721464157104, + -0.03383539244532585, + 0.12127509713172913 ], "tsne3d": [ - 18.72105598449707, - -7.478860378265381, - 32.16850280761719 + 27.69995880126953, + -23.83965301513672, + -10.7705078125 ], "umap3d": [ - 3.524930715560913, - 1.2525701522827148, - 0.09798187017440796 + 1.8555978536605835, + 2.3261818885803223, + 5.367035388946533 ] }, - "cluster_id": 0, - "cluster_label": "Medical Data AI", + "cluster_id": 2, + "cluster_label": "Clinical AI Data", "cluster_levels": [ { - "id": 0, - "label": "Medical Data AI" + "id": 2, + "label": "Clinical AI Data" }, { - "id": 4, - "label": "Com Area Amp" + "id": 9, + "label": "Drug Discovery" }, { - "id": 0, - "label": "Clinical LLMs" + "id": 23, + "label": "Peer Review" } ], "_embedding": [