[upstream-sync] README.md from ai-boost/awesome-ai-for-science@d18ecc3a [catalogue]

Reviewed-on: #67

upstream/ai-boost-awesome-ai-for-science/catalogue/README.md

@@ -2,9 +2,9 @@
 title: "Readme"
 task: ""
 lineage_type: import
-upstream_source: https://github.com/ai-boost/awesome-ai-for-science/blob/024f42bf/README.md
-upstream_sha: 024f42bf
-imported_at: 2026-08-09
+upstream_source: https://github.com/ai-boost/awesome-ai-for-science/blob/d18ecc3a/README.md
+upstream_sha: d18ecc3a
+imported_at: 2026-08-18
 prompt_class: catalogue
 upstream_changes: accepted
 author: upstream
@@ -63,6 +63,7 @@ validated: false
   - [🌍 Earth & Climate Science](#-earth--climate-science)
   - [🌾 Agriculture & Ecology](#-agriculture--ecology)
   - [🧠 Social Sciences](#-social-sciences)
+  - [🏗 Engineering & Built Environment](#-engineering--built-environment)
 - [🤖 Foundation Models for Science](#-foundation-models-for-science)
 - [📈 Datasets & Benchmarks](#-datasets--benchmarks)
 - [💻 Computing Frameworks](#-computing-frameworks)
@@ -328,12 +329,14 @@ validated: false
 ### Domain-Specific Research Agents
 - [Aletheia](https://arxiv.org/abs/2602.10177) - Google DeepMind's autonomous mathematics research agent powered by Gemini Deep Think, autonomously solving 4 open problems from 700 Erdős conjectures and generating complete research papers without human intervention (February 2026)
 - [AlphaProof Nexus (Google DeepMind, arXiv 2026)](https://github.com/google-deepmind/alphaproof-nexus-results) - LLM-driven formal proof search system that pairs large language models with Lean verification to solve open mathematics problems; autonomously resolved 9 of 353 Erdős problems and 44 of 492 OEIS conjectures, with proofs and natural-language prose released for combinatorics, optimization, graph theory, algebraic geometry, and quantum optics collaborations (282+ stars, Apache 2.0)
+- [Ten Proofs (OpenAI, 2026)](https://github.com/openai/ten-proofs) - Lean 4 formalizations of ten major advances in mathematics and theoretical computer science, including improved sphere-packing bounds, non-sofic groups, a counterexample to Connes's rigidity conjecture, and quantum parallel repetition; released with the OpenAI paper and reasoning walkthroughs (57+ stars, Apache 2.0)
 - [AlphaGeometry](https://github.com/google-deepmind/alphageometry) - DeepMind's Olympiad-level geometry theorem prover combining neural language model with symbolic deduction engine, AlphaGeometry2 solves 84% of IMO geometry problems (42/50) at gold-medalist level (Nature 2024)
 - [Goedel-Prover-V2](https://github.com/Goedel-LM/Goedel-Prover-V2) - Strongest open-source automated theorem prover in Lean 4, 8B model matches DeepSeek-Prover-V2-671B at 84.6% MiniF2F, 32B model achieves 90.4% with self-correction, using scaffolded data synthesis and verifier-guided proof refinement (Princeton, 2025)
 - [DeepSeek-Prover-V2](https://github.com/deepseek-ai/DeepSeek-Prover-V2) - DeepSeek's open-source large language model for formal theorem proving in Lean 4, integrating informal and formal mathematical reasoning through recursive subgoal decomposition and reinforcement learning powered by DeepSeek-V3, with open weights and ProverBench evaluation (2025)
 - [LeanDojo](https://github.com/lean-dojo/LeanDojo) - Open-source toolkit and benchmark for learning-based theorem proving in Lean, providing programmatic Lean interaction, a 98K+ theorem dataset extracted from 217 Lean projects, and ReProver—the first retrieval-augmented LLM-based theorem prover for Lean—with reproducible training pipelines underpinning much subsequent Lean prover research (Caltech & NVIDIA, NeurIPS 2023 Outstanding Paper, Datasets & Benchmarks)
 - [Lean Copilot](https://github.com/lean-dojo/LeanCopilot) - LLMs as copilots for theorem proving in Lean 4, exposing native tactics (`suggest_tactics`, `search_proof`, `select_premises`) that embed language model inference and premise retrieval directly inside the Lean proof environment, supporting local CTranslate2/CUDA inference as well as remote model APIs for interactive and automated proof search (Caltech & NVIDIA, NeurIPS 2024, 1.2K+ stars)
 - [MathCode](https://github.com/math-ai-org/mathcode) - Terminal AI coding assistant with a built-in math formalization engine that converts plain-language math problems into Lean 4 theorems and attempts formal proofs; bundles a local Lean toolchain and WebUI for interactive mathematical reasoning (math-ai-org, 582+ stars, 2026)
+- [TorchLean (lean-dojo, 2026)](https://github.com/lean-dojo/TorchLean) - First unified Lean 4 framework for neural-network specification, execution, and verification; tensor shapes are part of the types, models are executable Lean programs, and the same definitions can be used by training code, graph transformations, certificate checkers, and proofs with CPU/CUDA backends (123+ stars, MIT License)
 - [Get Physics Done (PSI)](https://github.com/psi-oss/get-physics-done) - First open-source agentic AI physicist turning research questions into structured workflows with rigorous verification and multi-step analytical work for long-horizon physics projects; integrates with Claude Code, Codex, Gemini CLI, and OpenCode (804+ stars, Apache 2.0, 2026)
 - [Foam-Agent (NeurIPS 2025)](https://github.com/csml-rpi/Foam-Agent) - End-to-end composable multi-agent framework for automating OpenFOAM-based CFD simulations from natural language prompts, managing meshing, case setup, execution, error correction, and post-processing; achieves 100% success rate on 110 FoamBench tasks with Claude Opus 4.6 through Architect-Input Writer-Runner-Reviewer agent collaboration with RAG-enhanced generation and MCP tool integration (RPI CSML, 242+ stars, MIT License)
 - [Zephyrus (ICLR 2026)](https://github.com/Rose-STL-Lab/Zephyrus) - First agentic framework for weather science, pairing an LLM with ZephyrusWorld (a code-execution environment exposing WeatherBench 2 data, geolocation, forecasting, simulation, and climatology tools) and ZephyrusBench (2,230 Q&A pairs across 49 weather-science tasks); outperforms text-only baselines by up to 44.2 percentage points (UC San Diego Rose-STL-Lab, 99+ stars, MIT License, 2026)
@@ -638,6 +641,7 @@ validated: false
 - [SpikeInterface](https://github.com/SpikeInterface/spikeinterface) - Unified Python framework for extracellular electrophysiology, standardizing interfaces to 10+ ML-based spike sorting algorithms including Kilosort for reproducible neural spike sorting workflows (792+ stars, actively maintained)
 - [CaImAn (Flatiron Institute)](https://github.com/flatironinstitute/CaImAn) - Computational toolbox for large scale Calcium Imaging Analysis, including movie handling, motion correction, source extraction, spike deconvolution and result visualization, using machine learning for automated neuron detection and activity inference in two-photon and one-photon calcium imaging data (723+ stars, actively maintained)
 - [TRIBE v2](https://github.com/facebookresearch/tribev2) - Meta FAIR's foundation model of vision, audition, and language for in-silico neuroscience, predicting fMRI brain responses to naturalistic multimodal stimuli (video, audio, text) through unified Transformer architecture mapped to the cortical surface (2026)
+- [Brain2Qwerty (Meta FAIR, Nature Neuroscience 2026)](https://github.com/facebookresearch/brain2qwerty) - Non-invasive decoding of typed sentences from MEG and EEG brain recordings using a convolutional encoder, transformer, and character-level language model; official code for the Nature Neuroscience paper and Meta blog post on brain-AI communication (Meta FAIR, 894+ stars, CC BY-NC 4.0, 2026)
 - [braindecode](https://github.com/braindecode/braindecode) - Deep learning software to decode EEG, ECG or MEG signals, providing standardized neural network models, preprocessing pipelines, and evaluation workflows for brain-computer interfaces and cognitive neuroscience research (1.2K+ stars, BSD 3-Clause, actively maintained)
 - [snntorch](https://github.com/jeshraghian/snntorch) - Deep learning with spiking neural networks in Python, providing gradient-based training of SNNs via PyTorch autodifferentiation for brain-inspired computing and neuromorphic research, with online learning capabilities and extensive tutorials (1.9K+ stars, actively maintained)
 - [nilearn](https://github.com/nilearn/nilearn) - Machine learning and statistical learning for neuroimaging in Python, providing easy-to-use tools for fMRI and MRI analysis including decoding, connectivity estimation, and parcellation with seamless scikit-learn integration (INRIA Parietal team, 1.4K+ stars)
@@ -726,6 +730,7 @@ validated: false
 
 #### Lab Automation & Robotics
 - [PyLabRobot](https://github.com/PyLabRobot/pylabrobot) - Interactive and hardware-agnostic SDK for laboratory automation, enabling programmatic control of liquid handlers, plate readers, and other lab instruments across multiple vendors; foundational infrastructure for self-driving laboratories and AI-driven experimental execution (447+ stars)
+- [RoboChem-Flex](https://github.com/Noel-Research-Group/Robochem_Flex) - Low-cost, modular self-driving laboratory platform democratizing autonomous chemical experimentation with open control software, device CAD/PCB files, and example optimization campaigns (Noël Research Group, University of Amsterdam, Apache 2.0, 2026)
 
 ### 🌌 Physics & Astronomy
 
@@ -741,6 +746,7 @@ validated: false
 - [TORAX](https://github.com/google-deepmind/torax) - Differentiable tokamak core transport simulator for fusion energy research, coupling PDE solvers with JAX auto-differentiation and neural-network surrogates for fast forward modelling, pulse-design, and trajectory optimization (Google DeepMind, Apache 2.0)
 - [DiffPhysDrone (Nature Machine Intelligence 2025)](https://github.com/HenryHuYu/DiffPhysDrone) - First real quadrotor robot trained end-to-end with differentiable physics for vision-based agile flight, bridging simulation-based learning and real-world deployment with physics-informed neural network controllers (558+ stars)
 - [Walrus (arXiv 2025)](https://github.com/PolymathicAI/walrus) - Cross-domain foundation model for continuum dynamics trained on 19 physical scenarios spanning 63 variables, featuring adaptive compute via stride modulation and patch jittering for long-run stability (Polymathic AI, 293+ stars, MIT License)
+- [GeoPT (ICML 2026)](https://github.com/Physics-Scaling/GeoPT) - Unified pre-trained model for general physics simulation via lifted geometric pre-training, augmenting static geometry with synthetic dynamics to enable dynamics-aware self-supervision without physics labels; improves industrial-fidelity benchmarks spanning fluid mechanics and solid mechanics while reducing labeled data requirements by 20–60% (Physics-Scaling, 224+ stars)
 
 #### Astronomy & Astrophysics
 - [AstroCLIP](https://github.com/PolymathicAI/AstroCLIP) - Cross-modal self-supervised foundation model for galaxies by Polymathic AI, jointly embedding multi-band galaxy imaging and optical spectra into a shared latent space to enable zero/few-shot redshift estimation, galaxy property prediction, morphology classification, and cross-modal similarity search (MNRAS Letters 2024)
@@ -785,6 +791,7 @@ validated: false
 - [SkySensePlusPlus](https://github.com/kang-wu/SkySensePlusPlus) - Semantic-enhanced multi-modal remote sensing foundation model for Earth observation (Nature Machine Intelligence 2025), enabling universal interpretation across diverse satellite imagery modalities with open-source weights and benchmarks
 - [TESSERA (CVPR 2026)](https://github.com/ucam-eo/tessera) - University of Cambridge's foundation model for time-series satellite imagery, enabling efficient extraction of temporal patterns from Earth observation for land classification, canopy height prediction, and other remote sensing tasks
 - [TerraMind (IBM & ESA, 2025)](https://github.com/IBM/terramind) - First any-to-any generative foundation model for Earth Observation, enabling unified multimodal understanding and generation across diverse satellite sensors and geospatial tasks through a single architecture (258+ stars)
+- [GeoAgent (opengeos, 2026)](https://github.com/opengeos/GeoAgent) - Shared multimodal AI agent layer for geospatial Python packages (leafmap, geoai, geemap, STAC, NASA Earthdata) and QGIS, exposing geospatial tools to LLMs with structured metadata, confirmation hooks, and support for OpenAI, Anthropic, Google Gemini, Ollama, and more; includes the OpenGeoAgent QGIS plugin (456+ stars, MIT License)
 - [Awesome Remote Sensing Foundation Models](https://github.com/Jack-bo1220/Awesome-Remote-Sensing-Foundation-Models) - Curated collection of papers, datasets, benchmarks, code, and pre-trained weights for Remote Sensing Foundation Models (RSFMs), tracking the rapidly evolving landscape of vision, vision-language, generative, and agent-based geospatial AI (1.9K+ stars, 2024-2026)
 
 ### 🌾 Agriculture & Ecology
@@ -813,6 +820,19 @@ validated: false
 
 ---
 
+## 🏗 Engineering & Built Environment
+
+### Structural & Civil Engineering
+- [StructureClaw](https://github.com/structureclaw/structureclaw) - AI-assisted structural engineering workspace for AEC workflows: natural language to structural model, analysis, code-check, and report (171+ stars, MIT License, 2026)
+
+### Architectural Design & BIM
+- [Aedifex](https://github.com/TangSY/aedifex) - Open-source 3D architectural editor with an AI design assistant; build floor plans with walls, doors, windows, and furniture using natural language, with real-time WebGPU-powered previews (TangSY, 59+ stars, MIT License, 2026)
+
+### Electrical & Electronics Engineering
+- [kicad-happy](https://github.com/aklofas/kicad-happy) - AI coding agent skills for KiCad electronics design that turn Claude Code, Codex, Gemini CLI, and other coding agents into full electronics design assistants; parses schematics and PCB layouts, builds power trees, audits connectors/ESD protection, validates passive networks, runs SPICE simulation, sources components from major distributors, and prepares boards for fabrication (aklofas, 974+ stars, MIT License, 2026)
+
+---
+
 ## 🤖 Foundation Models for Science
 
 ### General Science Models
@@ -983,6 +1003,7 @@ This project builds upon and complements several excellent resources:
 
 ### 📊 Paper & Research Collections
 - [Scientific LLM Papers](https://github.com/yuzhimanhua/Awesome-Scientific-Language-Models) - 260+ scientific language models
+- [Awesome Scientific LLM Benchmarks](https://github.com/subinium/Awesome-Scientific-LLM-Benchmarks) - Curated, accuracy-first collection of benchmarks for evaluating LLMs on scientific reasoning and discovery across mathematics, physics, chemistry, materials science, biology, and agentic science (subinium, 29+ stars, MIT License, 2026)
 - [LLM4SR Repository](https://github.com/du-nlp-lab/LLM4SR) - LLM for scientific research survey materials
 - [PINNs Paper Collection](https://github.com/idrl-lab/PINNpapers) - Physics-informed neural networks research
 - [SciML Papers](https://sciml.ai/papers/) - Scientific computing and machine learning papers

upstream/inoue0426-awesome-computational-biology/catalogue/.github/workflows/ai4bio-schema-check.yml

@@ -0,0 +1,61 @@
+---
+title: "Ai4Bio Schema Check"
+task: ""
+lineage_type: import
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/.github/workflows/ai4bio-schema-check.yml
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
+prompt_class: unknown
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+name: AI4Bio Schema Check
+
+on:
+  pull_request:
+    paths:
+      - data/resources.yml
+      - data/enrichment.yml
+      - 'data/enrichment.*.yml'
+      - data/vocabulary.yml
+      - docs/data/resource.schema.json
+      - scripts/enrichment_fragments.py
+      - scripts/validate_resources.py
+      - scripts/build_resources_v2.py
+  push:
+    branches: [main]
+    paths:
+      - data/resources.yml
+      - data/enrichment.yml
+      - 'data/enrichment.*.yml'
+      - data/vocabulary.yml
+      - docs/data/resource.schema.json
+      - scripts/enrichment_fragments.py
+      - scripts/validate_resources.py
+      - scripts/build_resources_v2.py
+
+permissions:
+  contents: read
+
+jobs:
+  validate:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: astral-sh/setup-uv@v3
+      - name: Validate schema and enrichment
+        run: uv run --with pyyaml python scripts/validate_resources.py
+      - name: Build enriched artifacts
+        run: uv run --with pyyaml python scripts/build_resources_v2.py
+      - name: Verify enriched artifacts are committed
+        run: |
+          if git diff --quiet; then
+            echo "AI4Bio artifacts are in sync."
+            exit 0
+          fi
+          echo "Generated AI4Bio artifacts are out of date. Run:"
+          echo "  uv run --with pyyaml python scripts/build_resources_v2.py"
+          git status --short
+          exit 1

upstream/inoue0426-awesome-computational-biology/catalogue/.github/workflows/landscape-ui-check.yml

@@ -0,0 +1,43 @@
+---
+title: "Landscape Ui Check"
+task: ""
+lineage_type: import
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/.github/workflows/landscape-ui-check.yml
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
+prompt_class: unknown
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+name: Landscape UI Check
+
+on:
+  pull_request:
+    paths:
+      - docs/landscape.html
+      - docs/landscape.css
+      - docs/landscape.js
+      - .github/workflows/landscape-ui-check.yml
+  push:
+    branches: [main]
+    paths:
+      - docs/landscape.html
+      - docs/landscape.css
+      - docs/landscape.js
+      - .github/workflows/landscape-ui-check.yml
+
+permissions:
+  contents: read
+
+jobs:
+  ui-check:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-node@v4
+        with:
+          node-version: 'lts/*'
+      - name: Check landscape JavaScript syntax
+        run: node --check docs/landscape.js

upstream/inoue0426-awesome-computational-biology/catalogue/.github/workflows/sync_resources.yml

@@ -2,9 +2,9 @@
 title: "Sync Resources"
 task: ""
 lineage_type: import
-upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/12d87583/.github/workflows/sync_resources.yml
-upstream_sha: 12d87583
-imported_at: 2026-06-26
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/.github/workflows/sync_resources.yml
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
 prompt_class: unknown
 upstream_changes: accepted
 author: upstream
@@ -20,6 +20,8 @@ on:
     paths:
       - README.md
       - data/resources.yml
+      - data/enrichment.yml
+      - 'data/enrichment.*.yml'
       - scripts/*.py
       - scripts/**/*.py
 
@@ -55,7 +57,10 @@ jobs:
 
       - name: Sync From README
         if: contains(steps.changes.outputs.changed, 'README.md')
-        run: uv run python scripts/sync_resources_from_readme.py
+        run: uv run --with pyyaml python scripts/sync_resources_from_readme.py
+
+      - name: Validate Resource Schema
+        run: uv run --with pyyaml python scripts/validate_resources.py
 
       - name: Build Artifacts
         run: uv run --with pyyaml python scripts/build_resources.py

upstream/inoue0426-awesome-computational-biology/catalogue/README.md

@@ -2,9 +2,9 @@
 title: "Awesome Computational Biology [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)"
 task: ""
 lineage_type: import
-upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/478be843/README.md
-upstream_sha: 478be843
-imported_at: 2026-07-17
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/README.md
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
 prompt_class: catalogue
 upstream_changes: accepted
 author: upstream
@@ -70,6 +70,7 @@ Browse and search the resources via the [GitHub Pages UI](https://inoue0426.gith
   - [Machine Learning Tasks and Models](#machine-learning-tasks-and-models)
     - [Drug Discovery](#drug-discovery)
       - [Drug Response Prediction](#drug-response-prediction)
+      - [Drug Perturbation](#drug-perturbation)
       - [Drug Repurposing](#drug-repurposing)
       - [Drug Target Interaction](#drug-target-interaction)
       - [Compound-Protein Interaction](#compound-protein-interaction)
@@ -340,10 +341,15 @@ Browse and search the resources via the [GitHub Pages UI](https://inoue0426.gith
 - [RECOVER](https://github.com/RECOVERcoalition/Recover) — Machine learning framework for predicting synergistic drug combination responses across cell lines.
 - [TGSA](https://github.com/violet-sto/TGSA) — Tumor gene set and attention-based model leveraging biological pathway knowledge for drug response prediction.
 - [HiDRA](https://github.com/bsml320/HiDRA) — Hierarchical network model incorporating gene and pathway-level information for cancer drug response prediction.
-- [PRNet](https://github.com/Perturbation-Response-Prediction/PRnet) — Deep generative model for predicting transcriptional responses to novel chemical perturbations for drug discovery.
+- [DRUML](https://github.com/CutillasLab/DRUMLR) — Ensemble machine learning framework combining standard ML with deep learning to systematically rank anti-cancer drugs from proteomics and RNA-seq data.
+
+#### Drug Perturbation
+
+- [CellOT](https://github.com/bunnech/cellot) — Neural optimal transport framework for predicting single-cell responses to drug and genetic perturbations.
+- [CMonge](https://github.com/AI4SCR/conditional-monge-gap) — Conditional optimal transport model for generalizable single-cell perturbation response prediction across drugs and doses.
 - [chemCPA](https://github.com/theislab/chemCPA) — Compositional perturbation autoencoder for predicting single-cell transcriptional responses to unseen drug perturbations and dose combinations.
 - [cycleCDR](https://github.com/hliulab/cycleCDR) — Interpretable cycle-consistency framework for modeling cellular responses to drug perturbations.
-- [DRUML](https://github.com/CutillasLab/DRUMLR) — Ensemble machine learning framework combining standard ML with deep learning to systematically rank anti-cancer drugs from proteomics and RNA-seq data.
+- [PRNet](https://github.com/Perturbation-Response-Prediction/PRnet) — Deep generative model for predicting transcriptional responses to novel chemical perturbations for drug discovery.
 
 #### Drug Repurposing
 

upstream/inoue0426-awesome-computational-biology/catalogue/cspell.json

@@ -2,9 +2,9 @@
 title: "Cspell"
 task: ""
 lineage_type: import
-upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/12d87583/cspell.json
-upstream_sha: 12d87583
-imported_at: 2026-06-26
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/cspell.json
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
 prompt_class: unknown
 upstream_changes: accepted
 author: upstream
@@ -157,7 +157,21 @@ validated: false
     "Pacc",
     "multiomics",
     "Pathomic",
-    "PLIP"
+    "PLIP",
+    "Omni",
+    "Bento",
+    "FFPE",
+    "Xenium",
+    "Zyme",
+    "Neur",
+    "Imageomics",
+    "AESTETIK",
+    "CellOT",
+    "CMonge",
+    "bowang",
+    "ctheodoris",
+    "OpenAI",
+    "GPT"
   ],
   "ignorePaths": [
     "node_modules/**"

upstream/inoue0426-awesome-computational-biology/catalogue/data/enrichment.benchmark-v2.yml

@@ -0,0 +1,56 @@
+---
+title: "Provenance-backed single-cell and biomedical benchmark enrichment batch."
+task: ""
+lineage_type: import
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/data/enrichment.benchmark-v2.yml
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# Provenance-backed single-cell and biomedical benchmark enrichment batch.
+resources:
+  scmulan:
+    entities: [cell, gene]
+    methods: [language-model, transformer]
+    modalities: [epigenomics, multi-omics, proteomics, single-cell-rna-seq, transcriptomics]
+    tasks: [foundation-model-pretraining, representation-learning]
+    github: https://github.com/SuperBianC/scMulan
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/SuperBianC/scMulan
+
+  proteingym:
+    entities: [protein]
+    modalities: [protein-sequence]
+    tasks: [regression]
+    github: https://github.com/OATML-Markslab/ProteinGym
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/OATML-Markslab/ProteinGym
+
+  lincs_l1000:
+    entities: [cell, compound, gene]
+    modalities: [transcriptomics]
+    tasks: [perturbation-prediction]
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://lincsproject.org/LINCS/tools/workflows/find-the-best-place-to-obtain-the-lincs-l1000-data
+
+  prism:
+    entities: [cell, drug]
+    tasks: [drug-response-prediction]
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://depmap.org/portal/prism/
+
+  pharmgkb:
+    entities: [drug, gene, phenotype, variant]
+    modalities: [clinical, genomics]
+    tasks: [drug-response-prediction]
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://www.pharmgkb.org/

upstream/inoue0426-awesome-computational-biology/catalogue/data/enrichment.database-api-v1.yml

@@ -0,0 +1,91 @@
+---
+title: "Provenance-backed database and API enrichment batch."
+task: ""
+lineage_type: import
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/data/enrichment.database-api-v1.yml
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# Provenance-backed database and API enrichment batch.
+resources:
+  chembl_web_services:
+    entities: [molecule, protein]
+    modalities: [chemical-structure]
+    documentation: https://www.ebi.ac.uk/chembl/api/data/docs
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://www.ebi.ac.uk/chembl/api/data/docs
+
+  clinicaltrials_gov_api:
+    entities: [disease, drug]
+    modalities: [clinical]
+    documentation: https://clinicaltrials.gov/data-api/api
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://clinicaltrials.gov/data-api/api
+
+  ensembl_rest_api:
+    entities: [gene, genome, transcript, variant]
+    modalities: [genomics]
+    documentation: https://rest.ensembl.org/
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://rest.ensembl.org/
+
+  kegg_rest_api:
+    entities: [compound, gene, pathway]
+    documentation: https://www.kegg.jp/kegg/rest/keggapi.html
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://www.kegg.jp/kegg/rest/keggapi.html
+
+  ncbi_e_utilities:
+    entities: [gene, genome, protein, transcript, variant]
+    modalities: [genomics, transcriptomics]
+    documentation: https://www.ncbi.nlm.nih.gov/books/NBK25501/
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://www.ncbi.nlm.nih.gov/books/NBK25501/
+
+  open_targets_platform_api:
+    entities: [disease, drug, gene, variant]
+    modalities: [genomics, knowledge-graph]
+    documentation: https://platform.opentargets.org/api
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://platform.opentargets.org/api
+
+  pubmed_e_utilities_esearch_efetch:
+    documentation: https://www.ncbi.nlm.nih.gov/books/NBK25501/
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://www.ncbi.nlm.nih.gov/books/NBK25501/
+
+  uniprot_rest_api:
+    entities: [protein]
+    modalities: [protein-sequence, proteomics]
+    documentation: https://www.uniprot.org/help/api
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://www.uniprot.org/help/api
+
+  drugbank:
+    entities: [disease, drug, protein]
+    modalities: [chemical-structure]
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://go.drugbank.com/
+
+  string:
+    entities: [protein]
+    modalities: [knowledge-graph, proteomics]
+    documentation: https://string-db.org/help/api/
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://string-db.org/
+      - https://string-db.org/help/api/

upstream/inoue0426-awesome-computational-biology/catalogue/data/enrichment.foundation-models-v2.yml

@@ -0,0 +1,73 @@
+---
+title: "Provenance-backed foundation model enrichment batch 2."
+task: ""
+lineage_type: import
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/data/enrichment.foundation-models-v2.yml
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# Provenance-backed foundation model enrichment batch 2.
+resources:
+  nicheformer:
+    entities: [cell, gene, tissue]
+    methods: [self-supervised-learning, transformer]
+    modalities: [single-cell-rna-seq, spatial-transcriptomics, transcriptomics]
+    tasks: [foundation-model-pretraining, representation-learning]
+    year: 2024
+    github: https://github.com/theislab/nicheformer
+    paper: https://doi.org/10.1101/2024.04.15.589472
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/theislab/nicheformer
+      - https://doi.org/10.1101/2024.04.15.589472
+
+  genept:
+    entities: [cell, gene]
+    methods: [language-model]
+    modalities: [single-cell-rna-seq, transcriptomics]
+    tasks: [batch-correction, classification, representation-learning]
+    year: 2023
+    github: https://github.com/yiqunchen/GenePT
+    paper: https://www.biorxiv.org/content/10.1101/2023.10.16.562533v2
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/yiqunchen/GenePT
+      - https://www.biorxiv.org/content/10.1101/2023.10.16.562533v2
+
+  scgpt_spatial:
+    entities: [cell, gene, tissue]
+    methods: [generative-model, self-supervised-learning, transformer]
+    modalities: [multi-omics, single-cell-rna-seq, spatial-transcriptomics]
+    tasks: [foundation-model-pretraining, imputation, representation-learning]
+    year: 2025
+    github: https://github.com/bowang-lab/scGPT-spatial
+    paper: https://www.biorxiv.org/content/10.1101/2025.02.05.636714v1
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/bowang-lab/scGPT-spatial
+      - https://www.biorxiv.org/content/10.1101/2025.02.05.636714v1
+
+  scprint:
+    entities: [cell, gene]
+    methods: [self-supervised-learning, transformer]
+    modalities: [single-cell-rna-seq, transcriptomics]
+    tasks:
+      - batch-correction
+      - cell-type-annotation
+      - foundation-model-pretraining
+      - gene-regulatory-network-inference
+      - imputation
+      - representation-learning
+    year: 2025
+    github: https://github.com/cantinilab/scPRINT
+    documentation: https://www.jkobject.com/scPRINT/
+    paper: https://www.nature.com/articles/s41467-025-58699-1
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/cantinilab/scPRINT
+      - https://www.nature.com/articles/s41467-025-58699-1

upstream/inoue0426-awesome-computational-biology/catalogue/data/enrichment.molecular-v2.yml

@@ -0,0 +1,62 @@
+---
+title: "Provenance-backed molecular model and benchmark enrichment batch."
+task: ""
+lineage_type: import
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/data/enrichment.molecular-v2.yml
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# Provenance-backed molecular model and benchmark enrichment batch.
+resources:
+  chemberta_2:
+    entities: [molecule]
+    methods: [language-model, self-supervised-learning, transformer]
+    modalities: [chemical-structure]
+    tasks: [representation-learning]
+    github: https://github.com/seyonechithrananda/bert-loves-chemistry
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/seyonechithrananda/bert-loves-chemistry
+
+  molformer:
+    entities: [molecule]
+    methods: [language-model, self-supervised-learning, transformer]
+    modalities: [chemical-structure]
+    tasks: [representation-learning]
+    github: https://github.com/IBM/molformer
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/IBM/molformer
+
+  grover:
+    entities: [molecule]
+    methods: [graph-neural-network, self-supervised-learning, transformer]
+    modalities: [chemical-structure]
+    tasks: [representation-learning]
+    github: https://github.com/tencent-ailab/grover
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/tencent-ailab/grover
+
+  moleculenet:
+    entities: [molecule]
+    modalities: [chemical-structure]
+    tasks: [classification, regression]
+    github: https://github.com/deepchem/moleculenet
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/deepchem/moleculenet
+
+  guacamol:
+    entities: [molecule]
+    modalities: [chemical-structure]
+    tasks: [molecular-generation]
+    github: https://github.com/BenevolentAI/guacamol
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/BenevolentAI/guacamol

upstream/inoue0426-awesome-computational-biology/catalogue/data/enrichment.pharmacogenomics-v1.yml

@@ -0,0 +1,92 @@
+---
+title: "Provenance-backed drug-response and pharmacogenomics enrichment batch."
+task: ""
+lineage_type: import
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/data/enrichment.pharmacogenomics-v1.yml
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# Provenance-backed drug-response and pharmacogenomics enrichment batch.
+resources:
+  beat_aml:
+    entities: [cell, disease, drug, gene]
+    modalities: [genomics]
+    tasks: [drug-response-prediction]
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://biodev.github.io/BeatAML2/
+
+  cancer_therapeutics_response_portal_ctrp:
+    entities: [cell, drug]
+    tasks: [drug-response-prediction]
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://portals.broadinstitute.org/ctrp/
+
+  bindingdb_curated_sets:
+    entities: [molecule, protein]
+    modalities: [chemical-structure]
+    tasks: [drug-target-interaction]
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://www.bindingdb.org/
+
+  bace:
+    entities: [molecule, protein]
+    modalities: [chemical-structure]
+    tasks: [classification, regression]
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://www.kaggle.com/datasets/gokturkkoch/bace
+
+  clintox:
+    entities: [drug]
+    modalities: [clinical]
+    tasks: [classification]
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://tdcommons.ai/single_pred_tasks/tox/#clintox
+
+  sider_side_effect_resource:
+    entities: [drug, phenotype]
+    modalities: [clinical]
+    last_checked: 2026-08-08
+    metadata_sources:
+      - http://sideeffects.embl.de/
+
+  pk_db:
+    entities: [drug]
+    modalities: [clinical]
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://pk-db.com/
+
+  scperturb:
+    entities: [cell, drug, gene]
+    modalities: [single-cell-rna-seq]
+    tasks: [perturbation-prediction]
+    github: https://github.com/sanderlab/scPerturb
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/sanderlab/scPerturb
+
+  genomics_of_drug_sensitivity_in_cancer_gdsc:
+    entities: [cell, drug, gene]
+    modalities: [genomics]
+    tasks: [drug-response-prediction]
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://www.cancerrxgene.org/
+
+  cellminer_cross_database_cellminercdb:
+    entities: [cell, drug, gene]
+    modalities: [genomics]
+    tasks: [drug-response-prediction]
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://discover.nci.nih.gov/cellminercdb/

upstream/inoue0426-awesome-computational-biology/catalogue/data/enrichment.protein-drug-v1.yml

@@ -0,0 +1,62 @@
+---
+title: "Provenance-backed protein and drug-discovery enrichment batch."
+task: ""
+lineage_type: import
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/data/enrichment.protein-drug-v1.yml
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# Provenance-backed protein and drug-discovery enrichment batch.
+resources:
+  esmfold:
+    entities: [protein]
+    methods: [language-model, transformer]
+    modalities: [molecular-structure, protein-sequence]
+    tasks: [representation-learning, structure-prediction]
+    year: 2023
+    github: https://github.com/facebookresearch/esm
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/facebookresearch/esm
+
+  proteinmpnn:
+    entities: [protein]
+    methods: [graph-neural-network, message-passing-neural-network]
+    modalities: [molecular-structure, protein-sequence]
+    tasks: [protein-sequence-design]
+    year: 2022
+    github: https://github.com/dauparas/ProteinMPNN
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/dauparas/ProteinMPNN
+
+  diffdock:
+    entities: [molecule, protein]
+    methods: [diffusion, geometric-deep-learning]
+    modalities: [molecular-structure]
+    tasks: [docking]
+    year: 2023
+    github: https://github.com/gcorso/DiffDock
+    paper: https://openreview.net/forum?id=kKF8_K-mBbS
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/gcorso/DiffDock
+      - https://openreview.net/forum?id=kKF8_K-mBbS
+
+  uni_mol:
+    entities: [molecule, protein]
+    methods: [self-supervised-learning, transformer]
+    modalities: [chemical-structure, molecular-structure]
+    tasks: [docking, representation-learning]
+    year: 2023
+    github: https://github.com/deepmodeling/Uni-Mol
+    paper: https://openreview.net/forum?id=6K2RM6wVqKu
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/deepmodeling/Uni-Mol
+      - https://openreview.net/forum?id=6K2RM6wVqKu

upstream/inoue0426-awesome-computational-biology/catalogue/data/enrichment.protein-v2.yml

@@ -0,0 +1,64 @@
+---
+title: "Provenance-backed protein model enrichment batch."
+task: ""
+lineage_type: import
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/data/enrichment.protein-v2.yml
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# Provenance-backed protein model enrichment batch.
+resources:
+  esm3:
+    entities: [protein]
+    methods: [generative-model, language-model, transformer]
+    modalities: [molecular-structure, protein-sequence]
+    tasks: [protein-sequence-design, representation-learning]
+    github: https://github.com/evolutionaryscale/esm
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/evolutionaryscale/esm
+
+  evolutionary_scale_modeling_esm:
+    entities: [protein]
+    methods: [language-model, self-supervised-learning, transformer]
+    modalities: [protein-sequence]
+    tasks: [representation-learning]
+    github: https://github.com/facebookresearch/esm
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/facebookresearch/esm
+
+  prottrans:
+    entities: [protein]
+    methods: [language-model, self-supervised-learning, transformer]
+    modalities: [protein-sequence]
+    tasks: [representation-learning]
+    github: https://github.com/agemagician/ProtTrans
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/agemagician/ProtTrans
+
+  progen2:
+    entities: [protein]
+    methods: [generative-model, language-model, transformer]
+    modalities: [protein-sequence]
+    tasks: [protein-sequence-design, representation-learning]
+    github: https://github.com/salesforce/progen
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/salesforce/progen
+
+  alphafold3:
+    entities: [molecule, protein, protein-complex]
+    methods: [diffusion]
+    modalities: [molecular-structure, protein-sequence]
+    tasks: [structure-prediction]
+    github: https://github.com/google-deepmind/alphafold3
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/google-deepmind/alphafold3

upstream/inoue0426-awesome-computational-biology/catalogue/data/enrichment.spatial-imaging-v1.yml

@@ -0,0 +1,110 @@
+---
+title: "Provenance-backed spatial transcriptomics and imaging enrichment batch."
+task: ""
+lineage_type: import
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/data/enrichment.spatial-imaging-v1.yml
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# Provenance-backed spatial transcriptomics and imaging enrichment batch.
+resources:
+  aestetik:
+    entities: [cell, gene, tissue]
+    methods: [autoencoder]
+    modalities: [histopathology, spatial-transcriptomics]
+    tasks: [representation-learning]
+    github: https://github.com/ratschlab/aestetik
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/ratschlab/aestetik
+
+  conch:
+    entities: [tissue]
+    methods: [contrastive-learning, transformer]
+    modalities: [histopathology, imaging]
+    tasks: [foundation-model-pretraining, representation-learning]
+    github: https://github.com/mahmoodlab/CONCH
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/mahmoodlab/CONCH
+
+  deepspot:
+    entities: [gene, tissue]
+    modalities: [histopathology, spatial-transcriptomics]
+    tasks: [regression]
+    github: https://github.com/ratschlab/DeepSpot
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/ratschlab/DeepSpot
+
+  deepspot_m:
+    entities: [gene, tissue]
+    modalities: [histopathology, spatial-transcriptomics, transcriptomics]
+    tasks: [foundation-model-pretraining, regression]
+    github: https://github.com/ratschlab/DeepSpotM
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/ratschlab/DeepSpotM
+
+  deepspot2cell:
+    entities: [cell, gene, tissue]
+    modalities: [histopathology, spatial-transcriptomics]
+    tasks: [regression]
+    github: https://github.com/ratschlab/DeepSpot2Cell
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/ratschlab/DeepSpot2Cell
+
+  gigapath:
+    entities: [tissue]
+    methods: [self-supervised-learning, transformer]
+    modalities: [histopathology, imaging]
+    tasks: [foundation-model-pretraining, representation-learning]
+    github: https://github.com/prov-gigapath/prov-gigapath
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/prov-gigapath/prov-gigapath
+
+  phikon:
+    entities: [tissue]
+    methods: [self-supervised-learning, transformer]
+    modalities: [histopathology, imaging]
+    tasks: [foundation-model-pretraining, representation-learning]
+    documentation: https://huggingface.co/owkin/phikon
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://huggingface.co/owkin/phikon
+
+  plip:
+    entities: [tissue]
+    methods: [contrastive-learning]
+    modalities: [histopathology, imaging]
+    tasks: [classification, representation-learning]
+    github: https://github.com/PathologyFoundation/plip
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/PathologyFoundation/plip
+
+  uni:
+    entities: [tissue]
+    methods: [self-supervised-learning, transformer]
+    modalities: [histopathology, imaging]
+    tasks: [foundation-model-pretraining, representation-learning]
+    github: https://github.com/mahmoodlab/UNI
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/mahmoodlab/UNI
+
+  hest_xenium_virtual_spatial_transcriptomics:
+    entities: [cell, gene, tissue]
+    modalities: [histopathology, spatial-transcriptomics, transcriptomics]
+    tasks: [regression]
+    documentation: https://huggingface.co/datasets/ratschlab/HEST_Xenium_virtual_spatial_transcriptomics
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://huggingface.co/datasets/ratschlab/HEST_Xenium_virtual_spatial_transcriptomics

upstream/inoue0426-awesome-computational-biology/catalogue/data/enrichment.yml

@@ -0,0 +1,128 @@
+---
+title: "AI4Bio landscape enrichment overlay"
+task: ""
+lineage_type: import
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/data/enrichment.yml
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# AI4Bio landscape enrichment overlay
+#
+# README.md remains the canonical source for resource membership and basic fields.
+# Add richer, independently curated metadata here, keyed by the stable resource id.
+# scripts/build_resources.py merges these fields into generated JSON/CSV artifacts.
+#
+# Enrichment values should be source-verifiable. Controlled vocabulary fields are
+# validated against data/vocabulary.yml.
+
+resources:
+  scgpt:
+    entities: [cell, gene]
+    methods: [generative-model, self-supervised-learning, transformer]
+    modalities: [multi-omics, single-cell-rna-seq, transcriptomics]
+    tasks:
+      - cell-type-annotation
+      - foundation-model-pretraining
+      - gene-regulatory-network-inference
+      - perturbation-prediction
+      - representation-learning
+    year: 2024
+    github: https://github.com/bowang-lab/scGPT
+    documentation: https://scgpt.readthedocs.io/en/latest/
+    paper: https://www.nature.com/articles/s41592-024-02201-0
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/bowang-lab/scGPT
+      - https://www.nature.com/articles/s41592-024-02201-0
+
+  geneformer:
+    entities: [cell, gene]
+    methods: [self-supervised-learning, transformer]
+    modalities: [single-cell-rna-seq, transcriptomics]
+    tasks:
+      - classification
+      - foundation-model-pretraining
+      - perturbation-prediction
+      - representation-learning
+    year: 2023
+    documentation: https://geneformer.readthedocs.io/
+    paper: https://www.nature.com/articles/s41586-023-06139-9
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://huggingface.co/ctheodoris/Geneformer
+      - https://www.nature.com/articles/s41586-023-06139-9
+
+  scfoundation:
+    entities: [cell, gene]
+    methods: [self-supervised-learning, transformer]
+    modalities: [single-cell-rna-seq, transcriptomics]
+    tasks:
+      - cell-type-annotation
+      - drug-response-prediction
+      - foundation-model-pretraining
+      - perturbation-prediction
+      - representation-learning
+    year: 2024
+    github: https://github.com/biomap-research/scFoundation
+    paper: https://www.nature.com/articles/s41592-024-02305-7
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/biomap-research/scFoundation
+      - https://www.nature.com/articles/s41592-024-02305-7
+
+  genecompass:
+    entities: [cell, gene]
+    methods: [self-supervised-learning, transformer]
+    modalities: [single-cell-rna-seq, transcriptomics]
+    tasks: [foundation-model-pretraining, representation-learning]
+    year: 2024
+    github: https://github.com/xCompass-AI/GeneCompass
+    paper: https://www.nature.com/articles/s41422-024-01034-y
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/xCompass-AI/GeneCompass
+      - https://www.nature.com/articles/s41422-024-01034-y
+
+  uce:
+    entities: [cell]
+    methods: [self-supervised-learning]
+    modalities: [single-cell-rna-seq, transcriptomics]
+    tasks: [foundation-model-pretraining, representation-learning]
+    year: 2026
+    github: https://github.com/snap-stanford/UCE
+    paper: https://www.nature.com/articles/s41586-026-10689-z
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/snap-stanford/UCE
+      - https://www.nature.com/articles/s41586-026-10689-z
+
+  cellplm:
+    entities: [cell, gene]
+    methods: [self-supervised-learning, transformer]
+    modalities: [single-cell-rna-seq, transcriptomics]
+    tasks: [foundation-model-pretraining, representation-learning]
+    year: 2023
+    github: https://github.com/OmicsML/CellPLM
+    paper: https://www.biorxiv.org/content/10.1101/2023.10.03.560734v1
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/OmicsML/CellPLM
+      - https://www.biorxiv.org/content/10.1101/2023.10.03.560734v1
+
+  scbert:
+    entities: [cell, gene]
+    methods: [language-model, self-supervised-learning, transformer]
+    modalities: [single-cell-rna-seq, transcriptomics]
+    tasks: [cell-type-annotation, classification, foundation-model-pretraining]
+    year: 2022
+    github: https://github.com/TencentAILabHealthcare/scBERT
+    paper: https://www.nature.com/articles/s42256-022-00534-z
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/TencentAILabHealthcare/scBERT
+      - https://www.nature.com/articles/s42256-022-00534-z

upstream/inoue0426-awesome-computational-biology/catalogue/data/resources.yml

@@ -2,9 +2,9 @@
 title: "Awesome Computational Biology - machine-readable resource list"
 task: ""
 lineage_type: import
-upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/478be843/data/resources.yml
-upstream_sha: 478be843
-imported_at: 2026-07-17
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/data/resources.yml
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
 prompt_class: catalogue
 upstream_changes: accepted
 author: upstream
@@ -1678,6 +1678,17 @@ resources:
     organism: []
     api: false
 
+  - id: cellot
+    name: "CellOT"
+    type: model
+    url: https://github.com/bunnech/cellot
+    description: "Neural optimal transport framework for predicting single-cell responses to drug and genetic perturbations."
+    tags: [drug-discovery, drug-perturbation]
+    tasks: [Drug Discovery, Drug Perturbation]
+    modalities: [Small Molecule]
+    organism: []
+    api: false
+
   - id: cellplm
     name: "CellPLM"
     type: model
@@ -1727,8 +1738,8 @@ resources:
     type: model
     url: https://github.com/theislab/chemCPA
     description: "Compositional perturbation autoencoder for predicting single-cell transcriptional responses to unseen drug perturbations and dose combinations."
-    tags: [drug-discovery, drug-response-prediction]
-    tasks: [Drug Discovery, Drug Response Prediction]
+    tags: [drug-discovery, drug-perturbation]
+    tasks: [Drug Discovery, Drug Perturbation]
     modalities: [Small Molecule]
     organism: []
     api: false
@@ -1755,6 +1766,17 @@ resources:
     organism: []
     api: false
 
+  - id: cmonge
+    name: "CMonge"
+    type: model
+    url: https://github.com/AI4SCR/conditional-monge-gap
+    description: "Conditional optimal transport model for generalizable single-cell perturbation response prediction across drugs and doses."
+    tags: [drug-discovery, drug-perturbation]
+    tasks: [Drug Discovery, Drug Perturbation]
+    modalities: [Small Molecule]
+    organism: []
+    api: false
+
   - id: concerto
     name: "Concerto"
     type: model
@@ -1782,8 +1804,8 @@ resources:
     type: model
     url: https://github.com/hliulab/cycleCDR
     description: "Interpretable cycle-consistency framework for modeling cellular responses to drug perturbations."
-    tags: [drug-discovery, drug-response-prediction]
-    tasks: [Drug Discovery, Drug Response Prediction]
+    tags: [drug-discovery, drug-perturbation]
+    tasks: [Drug Discovery, Drug Perturbation]
     modalities: [Small Molecule]
     organism: []
     api: false
@@ -2475,8 +2497,8 @@ resources:
     type: model
     url: https://github.com/Perturbation-Response-Prediction/PRnet
     description: "Deep generative model for predicting transcriptional responses to novel chemical perturbations for drug discovery."
-    tags: [drug-discovery, drug-response-prediction]
-    tasks: [Drug Discovery, Drug Response Prediction]
+    tags: [drug-discovery, drug-perturbation]
+    tasks: [Drug Discovery, Drug Perturbation]
     modalities: [Small Molecule]
     organism: []
     api: false

upstream/inoue0426-awesome-computational-biology/catalogue/data/vocabulary.yml

@@ -0,0 +1,105 @@
+---
+title: "Canonical vocabulary for new AI4Bio enrichment metadata."
+task: ""
+lineage_type: import
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/data/vocabulary.yml
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
+prompt_class: unknown
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# Canonical vocabulary for new AI4Bio enrichment metadata.
+#
+# These values are enforced only for fields explicitly added through
+# data/enrichment.yml. README-derived legacy values remain backward compatible.
+# Canonical terms use lowercase kebab-case.
+
+version: 1
+
+controlled_fields:
+  entities:
+    - cell
+    - compound
+    - disease
+    - drug
+    - gene
+    - genome
+    - molecule
+    - organism
+    - pathway
+    - phenotype
+    - protein
+    - protein-complex
+    - regulatory-element
+    - tissue
+    - transcript
+    - variant
+
+  methods:
+    - autoencoder
+    - contrastive-learning
+    - convolutional-neural-network
+    - diffusion
+    - generative-model
+    - geometric-deep-learning
+    - graph-neural-network
+    - knowledge-graph
+    - language-model
+    - message-passing-neural-network
+    - multi-agent-system
+    - optimal-transport
+    - recurrent-neural-network
+    - reinforcement-learning
+    - retrieval-augmented-generation
+    - self-supervised-learning
+    - state-space-model
+    - supervised-learning
+    - transformer
+    - unsupervised-learning
+    - variational-autoencoder
+
+  modalities:
+    - cell-painting
+    - chemical-structure
+    - clinical
+    - dna-sequence
+    - electronic-health-record
+    - epigenomics
+    - genomics
+    - histopathology
+    - imaging
+    - knowledge-graph
+    - metabolomics
+    - molecular-structure
+    - multi-omics
+    - protein-sequence
+    - proteomics
+    - rna-sequence
+    - single-cell-rna-seq
+    - spatial-transcriptomics
+    - transcriptomics
+
+  tasks:
+    - batch-correction
+    - cell-type-annotation
+    - classification
+    - dimensionality-reduction
+    - docking
+    - drug-response-prediction
+    - drug-target-interaction
+    - foundation-model-pretraining
+    - gene-regulatory-network-inference
+    - imputation
+    - link-prediction
+    - molecular-generation
+    - perturbation-prediction
+    - protein-function-prediction
+    - protein-sequence-design
+    - regression
+    - representation-learning
+    - structure-prediction
+    - trajectory-inference
+    - virtual-screening

upstream/inoue0426-awesome-computational-biology/catalogue/docs/AI4BIO_LANDSCAPE.md

@@ -0,0 +1,73 @@
+---
+title: "AI4Bio Landscape Database"
+task: ""
+lineage_type: import
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/docs/AI4BIO_LANDSCAPE.md
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# AI4Bio Landscape Database
+
+The landscape view treats the existing computational biology registry as a multidimensional database rather than a single hierarchical list.
+
+## Design goals
+
+- Keep the current curated resource records and generation pipeline intact.
+- Expose orthogonal facets so one resource can be explored by resource type, biological/ML task, data modality, organism, and domain tag.
+- Make the landscape useful without introducing a server or build-time dependency.
+- Keep the data model extensible for richer AI4Bio metadata over time.
+
+## Current facet model
+
+The landscape UI derives the following dimensions from `docs/data/resources.json`:
+
+| Dimension | Source field | Example values |
+|---|---|---|
+| Resource type | `type` | `database`, `benchmark`, `model`, `toolkit`, `api` |
+| Task | `tasks` | `drug-response-prediction`, `cell-type-annotation`, `molecular-generation` |
+| Modality | `modalities` | `transcriptomics`, `spatial-transcriptomics`, `protein-sequence` |
+| Organism | `organism` | `human`, `mouse`, `multi-species` |
+| Domain/tag | `tags` | `drug-discovery`, `single-cell`, `foundation-model` |
+
+These are deliberately treated as separate axes. A model can therefore be, for example, a `model` that performs `perturbation-prediction` on `single-cell-rna-seq` data for `human` and carry tags such as `drug-discovery` and `foundation-model`.
+
+## Recommended schema evolution
+
+The current schema is compatible with a richer landscape database. New fields should be added incrementally and only when they can be curated consistently.
+
+Suggested fields:
+
+| Field | Type | Purpose |
+|---|---|---|
+| `entities` | array of strings | Biological entities such as `gene`, `protein`, `compound`, `cell`, `disease` |
+| `methods` | array of strings | Method families such as `transformer`, `gnn`, `diffusion`, `optimal-transport` |
+| `organizations` | array of strings | Primary organizations responsible for the resource |
+| `year` | integer | Initial public release/publication year |
+| `github` | string | Source repository when distinct from the canonical landing page |
+| `documentation` | string | Documentation URL |
+| `maintenance_status` | string | Curated status such as `active`, `maintenance`, `archived`, `unknown` |
+| `last_checked` | string | Date the metadata/link was last manually or automatically checked |
+
+Avoid adding dynamic popularity metrics such as GitHub stars directly to canonical records unless a reproducible refresh pipeline is introduced. Such values become stale quickly and should be stored as generated metadata rather than curated facts.
+
+## Canonical-source policy
+
+At present, `README.md` is the canonical curated list, with generated YAML/JSON/CSV artifacts. The landscape page intentionally consumes `docs/data/resources.json` without changing that policy.
+
+A future migration may make `data/resources.yml` the canonical source once all README-only categorization semantics can be represented explicitly in structured fields. That migration should be a separate change because it changes contribution workflow and source-of-truth semantics.
+
+## Landscape page
+
+Open `docs/landscape.html` through GitHub Pages. It provides:
+
+- full-text search across names, descriptions, tasks, modalities, organisms, and tags;
+- filters for type, task, modality, organism, and tag;
+- summary counts for resources and major dimensions;
+- frequency bars recalculated for the current filtered result set;
+- direct resource and paper links;
+- client-side rendering with no additional dependencies.

upstream/inoue0426-awesome-computational-biology/catalogue/docs/FOUNDATION_MODEL_ENRICHMENT.md

@@ -0,0 +1,72 @@
+---
+title: "Foundation Model Enrichment"
+task: ""
+lineage_type: import
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/docs/FOUNDATION_MODEL_ENRICHMENT.md
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# Foundation Model Enrichment
+
+This document tracks the first curated metadata-enrichment pass for AI4Bio foundation models.
+
+## Scope
+
+The initial pass focuses on representative single-cell and transcriptomics foundation models already present in the resource registry, beginning with:
+
+- scGPT
+- Geneformer
+
+The scope may be expanded incrementally once the curation rules below are validated in practice.
+
+## Curation rules
+
+Metadata must be supported by at least one primary or official source:
+
+- official project repository or model card;
+- official documentation;
+- primary peer-reviewed publication or preprint.
+
+Unknown or ambiguous metadata is omitted rather than inferred.
+
+For each resource, curate fields where evidence is available:
+
+- `entities`
+- `methods`
+- `organizations`
+- `year`
+- `github`
+- `documentation`
+- `maintenance_status`
+- `last_checked`
+- `metadata_sources`
+
+`maintenance_status` should only be marked `active` when there is direct evidence of ongoing maintenance, such as a recent official release or repository activity. Otherwise use `unknown` or omit the field.
+
+## Initial evidence targets
+
+### scGPT
+
+Primary evidence should include the official `bowang-lab/scGPT` repository and the Nature Methods publication.
+
+### Geneformer
+
+Primary evidence should include the official `ctheodoris/Geneformer` model repository/model card and the primary Nature publication.
+
+## Completion criteria
+
+A resource is considered enriched when:
+
+1. all added metadata is supported by `metadata_sources`;
+2. no unsupported organization, method, year, or maintenance claim is introduced;
+3. generated JSON/CSV artifacts are regenerated and committed;
+4. schema validation and resource-consistency CI checks pass.
+
+## Provenance
+
+This enrichment pass is being prepared with assistance from OpenAI GPT-5.6 Sol. Final metadata is intended to remain source-verifiable and reviewable through the recorded provenance URLs.

upstream/inoue0426-awesome-computational-biology/catalogue/docs/data/README.md

@@ -0,0 +1,21 @@
+---
+title: "AI4Bio data files"
+task: ""
+lineage_type: import
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/docs/data/README.md
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# AI4Bio data files
+
+- `resources.json`: generated merged resource registry consumed by GitHub Pages.
+- `resource.schema.json`: JSON Schema 2020-12 contract for one resource object.
+- `SCHEMA.md`: original schema notes.
+- `SCHEMA_V2.md`: richer AI4Bio landscape schema and enrichment workflow.
+
+The enriched build path is `scripts/build_resources_v2.py`, which combines `data/resources.yml` with `data/enrichment.yml` and runs `scripts/validate_resources.py` before writing artifacts.

upstream/inoue0426-awesome-computational-biology/catalogue/docs/data/SCHEMA_V2.md

@@ -0,0 +1,132 @@
+---
+title: "AI4Bio Resource Schema v2"
+task: ""
+lineage_type: import
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/docs/data/SCHEMA_V2.md
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# AI4Bio Resource Schema v2
+
+This document defines the richer landscape metadata layered on top of the curated Awesome Computational Biology list.
+
+## Source model
+
+The repository intentionally separates **membership/basic metadata** from **landscape enrichment**:
+
+1. `README.md` is the canonical curated resource list.
+2. `scripts/sync_resources_from_readme.py` derives `data/resources.yml` from README headings and bullets.
+3. `data/enrichment.yml` stores richer metadata keyed by stable resource `id`.
+4. `data/vocabulary.yml` defines canonical terms for controlled enrichment dimensions.
+5. `scripts/build_resources.py` merges base records and enrichment, validates them, and writes `data/resources.json`, `data/resources.csv`, and `docs/data/resources.json`.
+
+This separation prevents hand-curated AI4Bio metadata from being erased by README synchronization.
+
+## Core identity fields
+
+These fields are required and may not be overridden by `data/enrichment.yml`:
+
+| Field | Type | Meaning |
+|---|---|---|
+| `id` | string | Stable lowercase `snake_case` identifier |
+| `name` | string | Official display name |
+| `type` | enum | `api`, `benchmark`, `database`, `model`, `resource`, or `toolkit` |
+| `url` | URL | Canonical landing page |
+| `description` | string | Short factual description |
+
+## Landscape dimensions
+
+| Field | Type | Meaning |
+|---|---|---|
+| `tasks` | string[] | Biological or ML tasks performed |
+| `modalities` | string[] | Input/output data modalities |
+| `organism` | string[] | Covered organisms or species groups |
+| `entities` | string[] | Biological entities: gene, protein, compound, cell, disease, etc. |
+| `methods` | string[] | Method families: transformer, GNN, diffusion, optimal transport, etc. |
+| `tags` | string[] | Broad domain and curation labels |
+| `organizations` | string[] | Organizations maintaining or primarily responsible for the resource |
+
+These dimensions are deliberately orthogonal. Do not encode a task as a modality or a biological entity as a resource type.
+
+## Controlled vocabulary
+
+New values added through `data/enrichment.yml` for `entities`, `methods`, `modalities`, and `tasks` must use canonical terms from `data/vocabulary.yml`.
+
+Canonical terms use lowercase kebab-case, for example:
+
+```yaml
+entities: [cell, gene]
+methods: [transformer, self-supervised-learning]
+modalities: [single-cell-rna-seq, transcriptomics]
+tasks: [foundation-model-pretraining, cell-type-annotation]
+```
+
+This rule is intentionally applied only to enrichment metadata. Existing README-derived values remain valid for backward compatibility and can be migrated separately without blocking routine resource updates.
+
+When a required concept is missing, add a reusable canonical term to `data/vocabulary.yml` instead of inventing a one-off spelling in an enrichment record. `tags`, `organism`, and `organizations` remain free-form because their vocabularies are broader or context dependent.
+
+## Provenance and lifecycle fields
+
+| Field | Type | Meaning |
+|---|---|---|
+| `year` | integer | Initial public release or primary publication year |
+| `github` | URL | Source repository when available |
+| `documentation` | URL | Documentation landing page |
+| `paper` | URL | Primary publication or preprint |
+| `license` | string | SPDX identifier preferred |
+| `api` | boolean | Programmatic API availability |
+| `access` | enum | `open`, `registration`, `restricted`, `commercial`, `unknown` |
+| `maintenance_status` | enum | `active`, `maintenance`, `archived`, `unknown` |
+| `updated` | date | Last-known upstream update date |
+| `last_checked` | date | Date this repository verified the metadata |
+| `metadata_sources` | URL[] | Sources supporting enriched metadata |
+
+`last_checked` is a curation timestamp, not an upstream release date. `updated` should only be populated when an upstream update date is known.
+
+## Enrichment rules
+
+`data/enrichment.yml` is a mapping keyed by resource id:
+
+```yaml
+resources:
+  example_resource:
+    entities: [gene, disease]
+    methods: [transformer]
+    organizations: [Example Lab]
+    year: 2025
+    github: https://github.com/example/project
+    documentation: https://example.org/docs
+    maintenance_status: active
+    access: open
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://example.org/about
+```
+
+Enrichment cannot override `id`, `name`, `type`, `url`, or `description`. A referenced id must already exist in `data/resources.yml`.
+
+## Validation contract
+
+`python scripts/validate_resources.py` checks:
+
+- required fields and field types;
+- stable id format and id uniqueness;
+- allowed enum values;
+- HTTP(S) URL shape;
+- ISO `YYYY-MM-DD` dates;
+- list uniqueness and non-empty values;
+- enrichment references and forbidden identity overrides;
+- controlled enrichment terms against `data/vocabulary.yml`;
+- vocabulary uniqueness and lowercase kebab-case normalization;
+- unknown field names.
+
+The machine-readable resource counterpart is `docs/data/resource.schema.json` (JSON Schema 2020-12). Controlled vocabulary enforcement is performed at the enrichment layer because legacy README-derived values intentionally remain backward compatible.
+
+## Curation guidance
+
+Prefer verified metadata over exhaustive metadata. Unknown fields should be omitted rather than guessed. For facts likely to change, include `last_checked` and at least one `metadata_sources` URL. Dynamic popularity metrics such as GitHub stars should remain generated telemetry rather than canonical curated fields.

upstream/inoue0426-awesome-computational-biology/catalogue/docs/data/resource.schema.json

@@ -0,0 +1,53 @@
+---
+title: "Resource.Schema"
+task: ""
+lineage_type: import
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/docs/data/resource.schema.json
+upstream_sha: 7a064bf0
+imported_at: 2026-08-08
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+{
+  "$schema": "https://json-schema.org/draft/2020-12/schema",
+  "$id": "https://inoue0426.github.io/awesome-computational-biology/data/resource.schema.json",
+  "title": "AI4Bio Resource",
+  "type": "object",
+  "required": ["id", "name", "type", "url", "description"],
+  "additionalProperties": false,
+  "properties": {
+    "id": {"type": "string", "pattern": "^[a-z0-9]+(?:_[a-z0-9]+)*$"},
+    "name": {"type": "string", "minLength": 1},
+    "type": {"enum": ["api", "benchmark", "database", "model", "resource", "toolkit"]},
+    "url": {"type": "string", "format": "uri", "pattern": "^https?://"},
+    "description": {"type": "string", "minLength": 1},
+    "tags": {"$ref": "#/$defs/stringArray"},
+    "tasks": {"$ref": "#/$defs/stringArray"},
+    "modalities": {"$ref": "#/$defs/stringArray"},
+    "organism": {"$ref": "#/$defs/stringArray"},
+    "entities": {"$ref": "#/$defs/stringArray"},
+    "methods": {"$ref": "#/$defs/stringArray"},
+    "organizations": {"$ref": "#/$defs/stringArray"},
+    "metadata_sources": {"type": "array", "items": {"type": "string", "format": "uri", "pattern": "^https?://"}, "uniqueItems": true},
+    "license": {"type": "string"},
+    "api": {"type": "boolean"},
+    "paper": {"type": "string", "format": "uri", "pattern": "^https?://"},
+    "github": {"type": "string", "format": "uri", "pattern": "^https://github\\.com/"},
+    "documentation": {"type": "string", "format": "uri", "pattern": "^https?://"},
+    "year": {"type": "integer", "minimum": 1900, "maximum": 2100},
+    "maintenance_status": {"enum": ["active", "maintenance", "archived", "unknown"]},
+    "access": {"enum": ["open", "registration", "restricted", "commercial", "unknown"]},
+    "updated": {"type": "string", "format": "date"},
+    "last_checked": {"type": "string", "format": "date"}
+  },
+  "$defs": {
+    "stringArray": {
+      "type": "array",
+      "items": {"type": "string", "minLength": 1},
+      "uniqueItems": true
+    }
+  }
+}