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

@@ -2,9 +2,9 @@
 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
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/4ed7cfef/.github/workflows/ai4bio-schema-check.yml
+upstream_sha: 4ed7cfef
+imported_at: 2026-10-26
 prompt_class: unknown
 upstream_changes: accepted
 author: upstream
@@ -50,6 +50,7 @@ jobs:
       - name: Build enriched artifacts
         run: uv run --with pyyaml python scripts/build_resources_v2.py
       - name: Verify enriched artifacts are committed
+        if: github.event_name == 'pull_request'
         run: |
           if git diff --quiet; then
             echo "AI4Bio artifacts are in sync."

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/c6f07d90/README.md
-upstream_sha: c6f07d90
-imported_at: 2026-08-31
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/4ed7cfef/README.md
+upstream_sha: 4ed7cfef
+imported_at: 2026-10-26
 prompt_class: catalogue
 upstream_changes: accepted
 author: upstream
@@ -32,6 +32,22 @@ A curated collection of databases, software, and papers related to computational
 
 Browse and search the resources via the [GitHub Pages UI](https://inoue0426.github.io/awesome-computational-biology/).
 
+For treatment-response work, the [Dataset Explorer](https://inoue0426.github.io/awesome-computational-biology/dataset-explorer.html) compares curated cell-line, patient, and PDX datasets by year, sample type, perturbation metadata, matched pre/post availability, SMILES coverage, and clinical outcomes.
+
+The [Method Explorer](https://inoue0426.github.io/awesome-computational-biology/method-explorer.html) compares drug-response and perturbation methods by task, molecular/context representation, unseen-drug support, dose/time conditioning, and patient transfer.
+
+The [Foundation Model Explorer](https://inoue0426.github.io/awesome-computational-biology/foundation-explorer.html) compares foundation models by fine-grained modality, parameter count, pretraining scale, species, zero-shot support, weights/code availability, perturbation support, and spatial support.
+
+The [Agent Explorer](https://inoue0426.github.io/awesome-computational-biology/agent-explorer.html) compares agentic AI systems by scientific domain, single- vs multi-agent architecture, tool/code execution, literature and web retrieval, omics and wet-lab support, autonomy, and human-in-the-loop design. The Agent Explorer table is sortable and supports domain, architecture, omics, code-execution, and year filters.
+
+All explorer tables support sortable columns and focused filters for quick comparison, including a Recent (≥2025) toggle.
+
+The Pages home screen also summarizes total resources, profiled datasets, methods, foundation models, agents, and the most common foundation-model modalities.
+
+Chemical and genetic perturbation datasets can be filtered separately in the Dataset Explorer. Genetic screens can also be filtered by CRISPRi, CRISPRa, knockout, enhancer-targeting, combinatorial, or mixed perturbation modes, including mixed-resource collections.
+
+The explorer also includes the eight perturbation datasets used in the Bison unseen-compound benchmark.
+
 - Search matches `name`, `description`, `tasks`, `modalities`, and `tags`.
 - The **Task**, **Modality**, and **Type** filters map directly to `tasks`, `modalities`, and `type` in `docs/data/resources.json`.
 - Clicking badges on cards applies the corresponding filter.
@@ -236,44 +252,82 @@ Browse and search the resources via the [GitHub Pages UI](https://inoue0426.gith
 
 ## Benchmarks & Datasets
 
-- [1000 Genomes Project](https://www.internationalgenome.org/) — Reference panel of human genetic variation from 2,504 individuals across 26 populations.
-- [BACE](https://www.kaggle.com/datasets/gokturkkoch/bace) — Binary classification and regression dataset for β-secretase 1 (BACE-1) inhibitor binding affinity.
+### Drug Response & Perturbation
+
 - [BEAT AML](https://biodev.github.io/BeatAML2/) — Functional ex vivo drug sensitivity measurements paired with genomics for acute myeloid leukemia.
+- [Cancer Therapeutics Response Portal (CTRP)](https://portals.broadinstitute.org/ctrp/) — Drug sensitivity profiles across ~900 cancer cell lines for >400 compounds.
+- [Chem-PerturBridge](https://github.com/theislab/Chem-PerturBridge) — Harmonized compendium and processing pipelines for small-molecule perturbation transcriptomics across heterogeneous assays and gene panels.
+- [GSE191127 Breast Cancer Pre/Post Chemotherapy](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE191127) — Bulk RNA-seq and genomics from matched pre- and post-neoadjuvant chemotherapy breast tumors with treatment outcomes.
+- [Genomics of Drug Sensitivity in Cancer (GDSC)](https://www.cancerrxgene.org/) — Drug sensitivity for ~1000 human cancer cell lines and hundreds of compounds.
+- [JUMP Cell Painting Datasets](https://github.com/jump-cellpainting/datasets) — Consortium-scale cell imaging perturbation datasets (chemical and genetic) for phenotypic profiling and drug discovery research.
+- [LINCS L1000](https://lincsproject.org/LINCS/tools/workflows/find-the-best-place-to-obtain-the-lincs-l1000-data) — Gene expression profiles (978 landmark genes) for >20,000 chemical and genetic perturbations across cell lines.
+- [LINCS L1000 Phase 1](https://github.com/theislab/Chem-PerturBridge) — Phase-1 L1000 benchmark view used by Bison with 692,787 profiles, 9,233 molecules, 70 contexts, and 978 landmark genes.
+- [LINCS L1000 Phase 2](https://github.com/theislab/Chem-PerturBridge) — Phase-2 L1000 benchmark view used by Bison with 333,263 profiles, 1,760 molecules, 30 contexts, and 978 landmark genes.
+- [MIX-Seq](https://www.nature.com/articles/s41467-020-17440-w) — Multiplexed single-cell transcriptional profiling of chemical and genetic perturbation responses across pools of cancer cell lines.
+- [NCI60](https://dtp.cancer.gov/discovery_development/nci-60/) — Drug sensitivity benchmark across 60 diverse human cancer cell lines.
+- [Novartis Perturbation Dataset](https://github.com/theislab/Chem-PerturBridge) — Large chemical perturbation transcriptomic screen used in Chem-PerturBridge; the Bison benchmark view contains 46,748 profiles and 3,770 molecules.
+- [OP3](https://github.com/theislab/Chem-PerturBridge) — Chemical perturbation dataset used by Chem-PerturBridge and the Bison unseen-compound benchmark; the Bison benchmark view contains 1,813 profiles, 138 molecules, and 4 cellular contexts.
+- [PDX-Atlas](https://muralportal.pdxatlas.org/) — Portal integrating clinical, genomic, transcriptomic, and drug-response data from patient-derived xenograft models.
+- [PharmGKB](https://www.pharmgkb.org/) — Curated pharmacogenomics dataset linking genetic variants to drug response phenotypes across thousands of drugs.
+- [PRISM](https://depmap.org/portal/prism/) — Cancer drug sensitivity profiling of >4,500 drugs across >900 cancer cell lines using pooled-cell-line barcoding.
+- [sci-Plex](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE139944) — Single-cell chemical perturbation screen of ~650,000 transcriptomes across three cancer cell lines, 188 compounds, and four doses.
+- [scPerturb](https://github.com/sanderlab/scPerturb) — Curated and continuously updated single-cell perturbation data resource spanning CRISPR and drug perturbation studies.
+- [Tahoe-100M](https://huggingface.co/datasets/tahoebio/Tahoe-100M) — Giga-scale single-cell perturbation atlas with >100 million profiles from 50 cancer cell lines exposed to ~1,100 small molecules.
+- [VCPI-0001](https://github.com/theislab/Chem-PerturBridge) — Bulk chemical perturbation dataset used in Chem-PerturBridge; the Bison benchmark view contains 27,517 profiles and 2,272 molecules in one cellular context.
+- [VCPI-0002](https://github.com/theislab/Chem-PerturBridge) — Bulk chemical perturbation dataset used in Chem-PerturBridge; the Bison benchmark view contains 18,139 profiles and 1,488 molecules in one cellular context.
+
+### Genetic Perturbation & Perturb-seq
+
+- [Dixit et al. 2016 Perturb-seq](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE90063) — Pooled CRISPR knockout with single-cell RNA-seq across K562 and dendritic-cell screens, including stimulated conditions and combinatorial perturbations.
+- [Adamson et al. 2016 Perturb-seq](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE90546) — CRISPRi Perturb-seq in K562 cells for systematic dissection of the unfolded protein response.
+- [Datlinger et al. 2017 CROP-seq](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE92872) — Pooled CRISPR knockout screen with single-cell transcriptomic readout in Jurkat cells under T-cell receptor stimulation.
+- [Norman et al. 2019 CRISPRa Perturb-seq](https://figshare.com/articles/dataset/Norman_et_al_2019_Perturb-seq/27766323) — Large-scale CRISPR activation Perturb-seq in K562 cells spanning single-gene and combinatorial perturbations.
+- [Gasperini et al. 2019 CRISPRi enhancer screen](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE120861) — High-MOI single-cell CRISPRi screen in K562 cells targeting thousands of candidate enhancers to map enhancer-gene regulation.
+- [Replogle et al. 2022 K562 Genome-wide Perturb-seq](https://plus.figshare.com/articles/dataset/_Mapping_information-rich_genotype-phenotype_landscapes_with_genome-scale_Perturb-seq_Replogle_et_al_2022_processed_Perturb-seq_datasets/20029387) — Genome-scale CRISPRi Perturb-seq targeting nearly all expressed genes in K562 cells.
+- [Replogle et al. 2022 K562 Essential Perturb-seq](https://plus.figshare.com/articles/dataset/_Mapping_information-rich_genotype-phenotype_landscapes_with_genome-scale_Perturb-seq_Replogle_et_al_2022_processed_Perturb-seq_datasets/20029387) — CRISPRi Perturb-seq focused on essential genes in K562 cells.
+- [Replogle et al. 2022 RPE1 Essential Perturb-seq](https://plus.figshare.com/articles/dataset/_Mapping_information-rich_genotype-phenotype_landscapes_with_genome-scale_Perturb-seq_Replogle_et_al_2022_processed_Perturb-seq_datasets/20029387) — CRISPRi Perturb-seq focused on essential genes in RPE1 cells.
+- [Frangieh et al. 2021 Perturb-CITE-seq](https://singlecell.broadinstitute.org/single_cell/study/SCP1064/multi-modal-pooled-perturb-cite-seq-screens-in-patient-models-define-novel-mechanisms-of-cancer-immune-evasion) — Multimodal CRISPR knockout screen with RNA and surface-protein readouts in patient-derived melanoma models under immune-related conditions.
+
+### Single-Cell & Spatial
+
+- [HEST Xenium virtual spatial transcriptomics](https://huggingface.co/datasets/ratschlab/HEST_Xenium_virtual_spatial_transcriptomics) — DeepSpot-M predicted transcriptome-wide ST for 59 HEST-1k 10x Xenium samples (~13.3M cells) (gated). Paper: [DeepSpot-M](https://www.medrxiv.org/content/10.64898/2026.06.19.26356060v1).
+- [scIB (Single-cell Integration Benchmarks)](https://github.com/theislab/scib) — Comprehensive benchmarking framework for single-cell data integration methods.
+- [Tabula Muris](https://tabula-muris.ds.czbiohub.org/) — Comprehensive single-cell atlas of 20 mouse organs and tissues, enabling cross-tissue and cross-species comparisons.
+- [Tabula Sapiens](https://tabula-sapiens-portal.ds.czbiohub.org/) — Comprehensive human single-cell atlas of ~500K cells from 24 organs and tissues across multiple donors.
+- [TCGA virtual spatial transcriptomics atlas](https://huggingface.co/datasets/ratschlab/TCGA_virtual_spatial_transcriptomics_atlas) — DeepSpot-M predicted transcriptome-wide ST for TCGA H&E (FF + FFPE; 28,664 slides / 32 cancer types; gated). Paper: [DeepSpot-M](https://www.medrxiv.org/content/10.64898/2026.06.19.26356060v1).
+
+### Molecular, Protein & Drug Discovery
+
+- [BACE](https://www.kaggle.com/datasets/gokturkkoch/bace) — Binary classification and regression dataset for β-secretase 1 (BACE-1) inhibitor binding affinity.
 - [Bento](https://github.com/LigandPro/Bento) — Protein-ligand docking benchmark covering rigid, flexible, de novo, blind, induced-fit, and covalent docking tasks.
 - [BindingDB Curated Sets](https://www.bindingdb.org/rwd/bind/chemsearch/marvin/SDFdownload.jsp?all_download=yes) — Curated binding affinity datasets for protein–ligand interaction benchmarking.
-- [Cancer Therapeutics Response Portal (CTRP)](https://portals.broadinstitute.org/ctrp/) — Drug sensitivity profiles across ~900 cancer cell lines for >400 compounds.
 - [ClinTox](https://tdcommons.ai/single_pred_tasks/tox/#clintox) — Clinical toxicity dataset contrasting FDA-approved drugs with those that failed clinical trials due to toxicity.
-- [CPTAC (Clinical Proteomic Tumor Analysis Consortium)](https://proteomics.cancer.gov/programs/cptac) — Multi-omic proteogenomic datasets for multiple cancer types linking proteomics with genomics.
 - [CrossDocked2020](https://arxiv.org/abs/2001.01037) — Large-scale dataset for structure-based virtual screening.
 - [DUD-E (Directory of Useful Decoys, Enhanced)](http://dude.docking.org/) — Structure-based virtual screening benchmark with active ligands and challenging decoy sets across diverse protein targets.
 - [FLIP (Fitness Landscape Inference for Proteins)](https://github.com/J-SNACKKB/FLIP) — Benchmark collection of protein fitness landscape datasets for evaluating protein ML models.
-- [Genomics of Drug Sensitivity in Cancer (GDSC)](https://www.cancerrxgene.org/) — Drug sensitivity for ~1000 human cancer cell lines and hundreds of compounds.
 - [GuacaMol](https://github.com/BenevolentAI/guacamol) — Benchmark suite for generative molecular design models.
-- [JUMP Cell Painting Datasets](https://github.com/jump-cellpainting/datasets) — Consortium-scale cell imaging perturbation datasets (chemical and genetic) for phenotypic profiling and drug discovery research.
-- [LINCS L1000](https://lincsproject.org/LINCS/tools/workflows/find-the-best-place-to-obtain-the-lincs-l1000-data) — Gene expression profiles (978 landmark genes) for >20,000 chemical and genetic perturbations across cell lines.
 - [MoleculeNet](http://moleculenet.ai/) — Benchmark datasets for molecular machine learning.
 - [MOSES](https://github.com/molecularsets/moses) — Benchmarking platform for molecular generation models.
-- [NCI60](https://dtp.cancer.gov/discovery_development/nci-60/) — Drug sensitivity benchmark across 60 diverse human cancer cell lines.
-- [OGB (Open Graph Benchmark)](https://ogb.stanford.edu/) — Large-scale graph ML benchmark suite including biological datasets such as ogbl-ppa (protein-protein associations) and ogbg-molhiv.
-- [OpenBioLink](https://github.com/OpenBioLink/OpenBioLink) — Benchmark datasets for biological knowledge graph completion.
-- [PharmGKB](https://www.pharmgkb.org/) — Curated pharmacogenomics dataset linking genetic variants to drug response phenotypes across thousands of drugs.
 - [PK-DB](https://pk-db.com/) — Open database of experimental pharmacokinetics (PK) and ADME data from clinical and preclinical studies.
-- [PRISM](https://depmap.org/portal/prism/) — Cancer drug sensitivity profiling of >4,500 drugs across >900 cancer cell lines using pooled-cell-line barcoding.
 - [ProteinGym](https://github.com/OATML-Markslab/ProteinGym) — Large-scale benchmark of deep mutational scanning assays for evaluating protein fitness landscape models.
 - [QM9](https://figshare.com/collections/Quantum_chemistry_structures_and_properties_of_134_kilo_molecules/978904) — Quantum chemistry properties for 134K stable small organic molecules computed at DFT level.
-- [scIB (Single-cell Integration Benchmarks)](https://github.com/theislab/scib) — Comprehensive benchmarking framework for single-cell data integration methods.
-- [scPerturb](https://github.com/sanderlab/scPerturb) — Curated and continuously updated single-cell perturbation data resource spanning CRISPR and drug perturbation studies.
 - [SIDER (Side Effect Resource)](http://sideeffects.embl.de/) — Database of 1,430 approved drugs with their recorded adverse drug reactions across 27 system-organ classes.
-- [Tabula Muris](https://tabula-muris.ds.czbiohub.org/) — Comprehensive single-cell atlas of 20 mouse organs and tissues, enabling cross-tissue and cross-species comparisons.
-- [Tabula Sapiens](https://tabula-sapiens-portal.ds.czbiohub.org/) — Comprehensive human single-cell atlas of ~500K cells from 24 organs and tissues across multiple donors.
 - [TAPE (Tasks Assessing Protein Embeddings)](https://github.com/songlab-cal/tape) — Benchmark suite of five biologically meaningful semi-supervised learning tasks for evaluating protein representations.
-- [The Cancer Genome Atlas (TCGA)](https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga) — Comprehensive multi-omics (genomics, transcriptomics, proteomics, methylation) dataset for 33 cancer types across ~11,000 patients.
-- [TCGA virtual spatial transcriptomics atlas](https://huggingface.co/datasets/ratschlab/TCGA_virtual_spatial_transcriptomics_atlas) — DeepSpot-M predicted transcriptome-wide ST for TCGA H&E (FF + FFPE; 28,664 slides / 32 cancer types; gated). Paper: [DeepSpot-M](https://www.medrxiv.org/content/10.64898/2026.06.19.26356060v1).
-- [HEST Xenium virtual spatial transcriptomics](https://huggingface.co/datasets/ratschlab/HEST_Xenium_virtual_spatial_transcriptomics) — DeepSpot-M predicted transcriptome-wide ST for 59 HEST-1k 10x Xenium samples (~13.3M cells) (gated). Paper: [DeepSpot-M](https://www.medrxiv.org/content/10.64898/2026.06.19.26356060v1).
 - [Therapeutics Data Commons (TDC)](https://tdcommons.ai/) — Unified benchmark suite covering ADMET, drug-target interaction, drug response, and more.
 - [Tox21](https://tripod.nih.gov/tox21/challenge/) — 12,707 compounds tested in 12 nuclear receptor and stress-response pathway biochemical assays for toxicity prediction.
+
+### Genomics, Cancer & Biomedical Cohorts
+
+- [1000 Genomes Project](https://www.internationalgenome.org/) — Reference panel of human genetic variation from 2,504 individuals across 26 populations.
+- [CPTAC (Clinical Proteomic Tumor Analysis Consortium)](https://proteomics.cancer.gov/programs/cptac) — Multi-omic proteogenomic datasets for multiple cancer types linking proteomics with genomics.
+- [The Cancer Genome Atlas (TCGA)](https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga) — Comprehensive multi-omics (genomics, transcriptomics, proteomics, methylation) dataset for 33 cancer types across ~11,000 patients.
 - [UK Biobank](https://www.ukbiobank.ac.uk/) — Large-scale biomedical database of ~500K participants with genetic, imaging, and health data for population genetics and disease studies.
 
+### General Biological ML & Knowledge Graph Benchmarks
+
+- [OGB (Open Graph Benchmark)](https://ogb.stanford.edu/) — Large-scale graph ML benchmark suite including biological datasets such as ogbl-ppa (protein-protein associations) and ogbg-molhiv.
+- [OpenBioLink](https://github.com/OpenBioLink/OpenBioLink) — Benchmark datasets for biological knowledge graph completion.
+
 ---
 
 ## API
@@ -344,6 +398,11 @@ Browse and search the resources via the [GitHub Pages UI](https://inoue0426.gith
 - [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.
 - [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.
+- [PASO](https://github.com/queryang/PASO) — Pathway-aware multi-omics drug response model combining pathway-difference features, multi-scale convolutions, Transformer encoding, and drug SMILES.
+- [DTLCDR](https://doi.org/10.1016/j.jpha.2025.101315) — Target-based multimodal framework for preclinical cancer drug response prediction and transfer to clinical response, with explicit unseen-drug generalization.
+- [THERAPI](https://github.com/Sunginyoung/THERAPI) — Cell-line-to-patient transfer framework that aligns tumor transcriptomes with cancer cell lines and integrates perturbation and gene-level representations for patient drug response prediction.
+- [EXPRESSO](https://doi.org/10.1158/0008-5472.CAN-25-5220) — Supervised treatment-response framework using pretreatment tumor transcriptomics, drug targets, and context-specific biomarkers across multiple cancer types and therapies.
+- [PerturbRx](https://arxiv.org/abs/2608.21349) — Treatment-conditioned representation learning framework that transfers drug-induced latent transitions learned from single-cell perturbation data to patient-level cancer treatment-response prediction.
 
 #### Drug Perturbation
 
@@ -352,6 +411,16 @@ Browse and search the resources via the [GitHub Pages UI](https://inoue0426.gith
 - [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.
 - [PRNet](https://github.com/Perturbation-Response-Prediction/PRnet) — Deep generative model for predicting transcriptional responses to novel chemical perturbations for drug discovery.
+- [Bison](https://arxiv.org/abs/2609.32467) — Cross-dataset model for globally unseen-compound response prediction using a shared gene representation, discrete diffusion models, and matched drug-contrast supervision.
+- [biolord](https://github.com/nitzanlab/biolord) — Deep generative model that disentangles known and unknown attributes for conditional generation of single-cell states.
+- [PerturbNet](https://github.com/welch-lab/PerturbNet) — Conditional generative model for predicting distributions of single-cell states under unseen chemical and genetic perturbations.
+- [CellFlow](https://github.com/theislab/CellFlow) — Conditional flow-matching framework for modeling and predicting cellular phenotypes under chemical, genetic, and other perturbations.
+- [Prophet](https://github.com/theislab/prophet) — Transformer model for predicting cellular phenotypes under unseen chemical or genetic perturbations across heterogeneous assays and contexts.
+- [PrePR-CT](https://github.com/reem12345/Cell-Type-Specific-Graphs) — Graph-based model using cell-type-specific co-expression networks as inductive priors for small-data chemical perturbation response prediction.
+- [XPert](https://github.com/GSanShui/XPert) — Knowledge-informed dual-branch Transformer for drug-induced transcriptional perturbation prediction across dose, time, and cellular context.
+- [State](https://github.com/ArcInstitute/state) — Transition model for predicting cellular perturbation responses across diverse contexts and sets of cells.
+- [TxPert](https://github.com/valence-labs/TxPert) — Knowledge-graph-informed latent-transfer model for transcriptomic perturbation prediction across unseen single perturbations, combinations, and cross-context settings.
+- [LPM](https://github.com/perturblib/perturblib) — Large perturbation model that jointly learns heterogeneous perturbation experiments by disentangling perturbation, readout, and context representations.
 
 #### Drug Repurposing
 
@@ -396,11 +465,45 @@ Browse and search the resources via the [GitHub Pages UI](https://inoue0426.gith
 - [GeneGPT](https://github.com/ncbi/GeneGPT) — LLM for biomedical information, integrated with various APIs.
 - [GenePT](https://github.com/yiqunchen/GenePT) — Foundation LLM for single-cell data.
 - [scPRINT](https://github.com/cantinilab/scPRINT) — Pretrained on 50M cells for scRNA-seq denoising & zero imputation.
-- [ClawBio](https://github.com/ClawBio/ClawBio) — Bioinformatics-native AI agent skill library with local-first pharmacogenomics, ancestry PCA, semantic similarity, nutrigenomics, and metagenomics skills.
 - [BioMedLM](https://huggingface.co/stanford-crfm/BioMedLM) — 2.7B parameter GPT-2-style language model trained exclusively on biomedical literature from PubMed for biomedical question answering and text generation.
 - [MolT5](https://github.com/blender-nlp/MolT5) — Language model for molecular tasks bridging text and SMILES, enabling molecule captioning and text-driven molecule generation.
 - [ChatDrug](https://github.com/chao1224/ChatDrug) — LLM-based conversational pipeline for drug discovery, using natural language prompts for iterative drug editing and optimization.
-- [CASSIA](https://github.com/ElliotXie/CASSIA) — Multi-agent LLM for reference-free, interpretable cell-type annotation of single-cell RNA-seq data, with dedicated annotation, validation, scoring, and reporting agents.
+
+### Agentic AI for Biology
+
+#### General Biomedical Agents
+
+- [Biomni](https://github.com/snap-stanford/Biomni) — General-purpose biomedical AI agent integrating planning, code execution, specialized tools, databases, and software across diverse biomedical research tasks.
+- [ToolUniverse](https://github.com/mims-harvard/ToolUniverse) — Unified scientific tool ecosystem for building AI scientists that can discover, select, and execute biomedical tools and databases.
+- [ClawBio](https://github.com/ClawBio/ClawBio) — Bioinformatics-native AI agent skill library with local-first pharmacogenomics, ancestry PCA, semantic similarity, nutrigenomics, and metagenomics skills.
+- [BioMedAgent](https://github.com/BOBQWERA/BioMedAgent) — Self-evolving multi-agent framework for autonomous biomedical data analysis with tool discovery, workflow planning, code generation, execution, correction, and cross-omics analysis.
+- [BioMaster](https://github.com/ai4nucleome/BioMaster) — Multi-agent system for automated and auditable bioinformatics workflows spanning RNA-seq, ChIP-seq, single-cell, spatial omics, Hi-C, long reads, metagenomics, and proteomics.
+- [BRAD](https://github.com/Jpickard1/BRAD) — Retrieval-augmented bioinformatics assistant integrating scientific literature, databases, external tools, and executable workflows.
+
+#### Therapeutics & Drug Discovery Agents
+
+- [TxAgent](https://github.com/mims-harvard/TxAgent) — Therapeutic reasoning agent using multi-step reasoning and a large scientific tool universe for drug interactions, contraindications, and personalized treatment analysis.
+- [Medea](https://github.com/mims-harvard/Medea) — Multi-agent therapeutic discovery system combining research planning, biological data analysis, literature reasoning, and multi-LLM deliberation across single-cell, cell-line, and patient contexts.
+- [DrugAgent](https://github.com/inoue0426/DrugAgent) — Multi-agent biomedical evidence synthesis framework for computational drug discovery with reliability-aware aggregation.
+
+#### Bioinformatics & Omics Agents
+
+- [CASSIA](https://github.com/ElliotXie/CASSIA) — Multi-agent LLM framework for reference-free and interpretable single-cell cell-type annotation with dedicated annotation, validation, scoring, and reporting agents.
+- [STELLA](https://github.com/zaixizhang/STELLA) — Self-evolving biomedical research agent that expands its tool repertoire and supports literature reasoning, computational analysis, and laboratory-oriented scientific workflows.
+- [AutoBA](https://github.com/JoshuaChou2018/AutoBA) — Automated multi-omics analysis agent that plans, executes, and repairs bioinformatics workflows from natural-language objectives.
+- [GenoMAS](https://github.com/Liu-Hy/GenoMAS) — Multi-agent framework for code-driven gene-expression analysis with planning, execution, debugging, backtracking, and GEO/TCGA-based scientific discovery.
+- [BIA](https://github.com/biagent-dev/bia) — Bioinformatics agent for GEO search, sample metadata extraction, count-matrix processing, and pipeline extraction from papers.
+- [BioAgents](https://github.com/microsoft/bioinformagus) — Multi-agent bioinformatics assistant using specialized language models and retrieval for genomics workflow development and troubleshooting.
+- [Genomi](https://github.com/exon-research/genomi) — Local-first genomics agent runtime that indexes personal variants, queries evidence, and generates evidence-grounded reports while keeping raw genome data on-device.
+
+#### Multi-Agent Scientific Labs
+
+- [Virtual Lab](https://github.com/zou-group/virtual-lab) — Human–AI collaborative research environment in which an LLM principal investigator coordinates specialized scientist agents for scientific discovery.
+- [Agent Laboratory](https://github.com/SamuelSchmidgall/AgentLaboratory) — End-to-end multi-agent research workflow for literature review, experimentation, implementation, analysis, and report generation.
+
+#### Paper & Workflow Agents
+
+- [Paper2Agent](https://github.com/jmiao24/Paper2Agent) — Multi-agent system that transforms research papers and associated code into interactive, testable scientific agents and MCP tools.
 
 ### Foundation Models
 
@@ -418,6 +521,8 @@ Browse and search the resources via the [GitHub Pages UI](https://inoue0426.gith
 - [GEARS](https://github.com/snap-stanford/GEARS) — Graph-based model for predicting transcriptional responses to single and combinatorial genetic perturbations using biological priors.
 - [SATURN](https://github.com/snap-stanford/SATURN) — Transformer-based model integrating gene expression and protein sequences via a protein language model to learn unified multi-species cell embeddings.
 - [CancerFoundation](https://github.com/BoevaLab/CancerFoundation) — Single-cell RNA-seq foundation model trained exclusively on a curated dataset of malignant cells to learn cancer-specific embeddings.
+- [CellFM](https://github.com/biomed-AI/CellFM) — 800M-parameter single-cell foundation model pretrained on transcriptomics from 100 million human cells for annotation, integration, gene-function, and perturbation tasks.
+- [scPRINT-2](https://github.com/cantinilab/scPRINT-2) — Next-generation single-cell foundation model pretrained on 350M+ cells across 22K+ datasets and 16 species for embeddings, denoising, annotation, gene-network inference, and cross-species transfer.
 
 ##### Spatial Foundation Models
 
@@ -432,6 +537,16 @@ Browse and search the resources via the [GitHub Pages UI](https://inoue0426.gith
 - [DeepSpot-M](https://github.com/ratschlab/DeepSpotM) — Multimodal foundation model for transcriptome-wide virtual spatial transcriptomics from histology.
 - [AESTETIK](https://github.com/ratschlab/aestetik) — Autoencoder for spatial transcriptomics representation learning using topology and histology image knowledge.
 
+##### Pathology Foundation Models
+
+- [TITAN](https://github.com/mahmoodlab/TITAN) — Multimodal whole-slide pathology foundation model that combines image and language supervision for slide-level representation and zero-shot analysis.
+- [Virchow2](https://huggingface.co/paige-ai/Virchow2) — 632M-parameter pathology vision transformer pretrained on 3.1M whole-slide images with mixed-magnification self-supervision.
+- [H-Optimus-0](https://huggingface.co/bioptimus/H-optimus-0) — 1.1B-parameter histopathology foundation model trained with self-supervised learning on a large multi-center slide corpus.
+- [H-Optimus-1](https://huggingface.co/bioptimus/H-optimus-1) — 1.1B-parameter pathology foundation model trained on billions of histology images from more than one million slides and 800K+ patients.
+- [UNI2-h](https://huggingface.co/MahmoodLab/UNI2-h) — Billion-parameter histopathology vision foundation model for tile-level feature extraction and downstream computational pathology tasks.
+- [Phikon-v2](https://huggingface.co/owkin/phikon-v2) — Updated pathology foundation model for general-purpose histology feature extraction and transfer learning.
+- [GenBio-PathFM](https://github.com/genbio-ai/genbio-pathfm) — 1.1B-parameter histopathology foundation model trained on public data using morphology-aware curation and dual-stage JEPA+DINO learning.
+
 ##### Multi-Omics Foundation Models
 
 - [scMulan](https://github.com/SuperBianC/scMulan) — Single-cell multi-omic language model pretrained on ~10M cells spanning transcriptomics, epigenomics, and proteomics for cross-omics transfer tasks.
@@ -465,6 +580,8 @@ Browse and search the resources via the [GitHub Pages UI](https://inoue0426.gith
 - [Mol2Vec](https://github.com/samoturk/mol2vec) — Unsupervised molecular embedding method inspired by Word2Vec for learning vector representations of chemical substructures.
 - [MolFormer](https://github.com/IBM/molformer) — Linear attention transformer pretrained on millions of SMILES strings for efficient molecular embeddings.
 - [Uni-Mol](https://github.com/deepmodeling/Uni-Mol) — 3D molecular pretraining framework for universal representation learning on molecules and protein pockets.
+- [Uni-Mol2](https://github.com/deepmodeling/Uni-Mol/tree/main/unimol2) — Scaled molecular pretraining model using atomic, graph, and 3D geometry features, with models up to 1.1B parameters pretrained on 800M conformations.
+- [ChemFM](https://github.com/TheLuoFengLab/ChemFM) — 1B/3B-parameter chemical language model pretrained on 178M molecules for molecular representation, property prediction, generation, and synthesis tasks.
 
 #### Protein Foundation Models
 
@@ -474,11 +591,14 @@ Browse and search the resources via the [GitHub Pages UI](https://inoue0426.gith
 - [ProtTrans](https://github.com/agemagician/ProtTrans) — Suite of protein language models (ProtBERT, ProtT5, ProtXLNet) trained on billions of protein sequences from UniRef and BFD.
 - [ProGen2](https://github.com/salesforce/progen) — Protein language model trained on diverse protein families for sequence generation and fitness prediction.
 - [Ankh](https://github.com/agemagician/Ankh) — Efficient protein language model optimized for downstream prediction tasks including secondary structure, localization, and function annotation.
+- [ESM Cambrian (ESM C)](https://github.com/Biohub/esm) — Protein representation foundation-model family designed as an efficient next-generation successor to ESM2, spanning 300M to multi-billion-parameter models.
 
 ##### Protein Structure Prediction and Design
 
 - [AlphaFold3](https://github.com/google-deepmind/alphafold3) — Predicts structures of proteins, nucleic acids, small molecules, and their complexes.
 - [Boltz-1](https://github.com/jwohlwend/boltz) — Open-source all-atom biomolecular structure prediction model for proteins, nucleic acids, small molecules, and their complexes achieving AlphaFold3-level accuracy.
+- [Boltz-2](https://github.com/jwohlwend/boltz) — Biomolecular foundation model jointly predicting complex structures and binding affinities for protein–ligand interaction modeling and virtual screening.
+- [Protenix](https://github.com/bytedance/Protenix) — Trainable biomolecular structure-prediction framework for proteins, nucleic acids, ligands, and complexes with open training and inference pipelines.
 - [Chai-1](https://github.com/chaidiscovery/chai-lab) — Unified molecular structure prediction model covering proteins, nucleic acids, small molecules, and complexes.
 - [ESM3](https://github.com/evolutionaryscale/esm) — Multimodal protein language model that jointly reasons over sequence, structure, and function for generative protein design and engineering.
 - [ESMFold](https://github.com/facebookresearch/esm) — Fast protein structure prediction using language model embeddings.
@@ -501,6 +621,36 @@ Browse and search the resources via the [GitHub Pages UI](https://inoue0426.gith
 - [PLIP](https://github.com/PathologyFoundation/plip) — Vision-language foundation model for pathology trained with contrastive learning on pathology image–text pairs for image classification and text-to-image retrieval.
 - [MUSK](https://github.com/lilab-stanford/MUSK) — Vision-language foundation model for precision oncology analyzing multimodal paired text and pathology image data for biomarker prediction and retrieval.
 
+#### Cancer Genome Foundation Models
+
+- [TESSERA](https://github.com/JW-Sidhom-Lab/tessera) — Cancer-genome foundation model jointly pretrained on somatic SNVs and copy-number alterations from TCGA using masked reconstruction and cross-modal contrastive learning.
+- [MutationProjector](https://doi.org/10.1101/2025.09.08.674723) — Pan-cancer genotype foundation model trained on mutations and copy-number alterations from >30K tumors for clinical representation learning.
+
+#### Single-Cell Epigenomics Foundation Models
+
+- [EpiAgent](https://github.com/xy-chen16/EpiAgent) — scATAC-seq foundation model pretrained on ~5M cells and >35B tokens for representation learning, annotation, imputation, perturbation prediction, and in-silico cCRE knockout.
+- [SCARF](https://doi.org/10.1101/2025.04.07.647689) — Single-cell RNA+ATAC foundation model pretrained on >2.7M cells for multimodal representation, matching, cross-omics translation, and few-shot annotation.
+- [scDNAm-GPT](https://github.com/ChaoqiLiang/scDNAm-GPM) — Foundation model for single-cell whole-genome bisulfite sequencing with whole-genome context modeling at single-CpG resolution.
+- [EpiFoundation](https://doi.org/10.1101/2025.02.05.636688) — Foundation model for scATAC-seq using peak-to-gene aligned pretraining for cell representation, annotation, batch correction, and gene-expression prediction.
+- [ChromFound](https://github.com/SAIS-LifeScience/ChromFound) — Genome-aware scATAC-seq foundation model pretrained on 1.97M cells across tissues and disease contexts for zero-shot cell representations, annotation, and cross-omics prediction.
+- [Atacformer](https://doi.org/10.1101/2025.11.03.685753) — Transformer foundation model for scATAC-seq that learns embeddings of cis-regulatory elements for clustering, annotation, and reference mapping.
+- [CLM-X](https://doi.org/10.64898/2026.02.17.704943) — Multi-way Transformer foundation model jointly handling RNA-only, ATAC-only, and paired RNA–ATAC single-cell inputs for integration, translation, annotation, and perturbation prediction.
+
+#### Other Omics Foundation Models
+
+- [CAPTAIN](https://doi.org/10.1038/s41467-026-72882-y) — Multimodal foundation model pretrained on co-assayed single-cell RNA and protein for joint representation learning and cross-modal downstream tasks.
+- [HiCFoundation](https://doi.org/10.1038/s41592-026-03097-8) — Hi-C foundation model pretrained on large-scale chromatin-contact maps for 3D-genome analysis, epigenomic prediction, and single-cell Hi-C adaptation.
+- [OmicsFM](https://github.com/CompOmics/OmicsFM) — Modality-agnostic molecular-expression foundation model with matched proteomics, bulk-transcriptomics, and single-cell-transcriptomics checkpoints.
+- [VirTues](https://github.com/bunnelab/virtues) — Spatial-proteomics foundation model learning marker-aware representations across proteins, cells, niches, and tissues from multiplexed imaging.
+- [MethylGPT](https://github.com/albert-ying/MethylGPT) — Transformer foundation model for DNA methylation pretrained on >150K human methylomes across thousands of datasets, with 3M/7M/15M parameter variants.
+- [CpGPT](https://github.com/lucascamillomd/CpGPT) — DNA methylation foundation model pretrained on >150K samples for zero-shot imputation, array conversion, reference mapping, and downstream phenotype prediction.
+- [Casanovo Foundation](https://github.com/Noble-Lab/casanovo-tl) — Tandem mass-spectrometry proteomics foundation model that reuses a pretrained Casanovo spectrum encoder for spectrum quality, chimericity, and post-translational-modification prediction.
+
+#### RNA Foundation Models
+
+- [RNA-FM](https://github.com/ml4bio/RNA-FM) — General-purpose RNA foundation model pretrained on large-scale RNA sequences for structural and functional representation learning.
+- [RiNALMo](https://github.com/lbcb-sci/RiNALMo) — RNA language-model family pretrained on tens of millions of RNA sequences for secondary-structure and functional prediction tasks.
+
 #### Genomics Foundation Models
 
 - [Nucleotide Transformer](https://github.com/instadeepai/nucleotide-transformer) — Foundation model for genomic sequences across multiple species.
@@ -510,6 +660,9 @@ Browse and search the resources via the [GitHub Pages UI](https://inoue0426.gith
 - [Basenji](https://github.com/calico/basenji) — Sequential regulatory activity prediction from DNA sequences.
 - [Caduceus](https://github.com/kuleshov-group/caduceus) — Bidirectional equivariant long-range DNA sequence model based on Mamba.
 - [Evo](https://github.com/evo-design/evo) — Long-context genomic foundation model (up to 1M tokens).
+- [Evo 2](https://github.com/arcinstitute/evo2) — Genome foundation model trained on 9 trillion DNA base pairs across all domains of life with a 1M-token context window and single-nucleotide resolution.
+- [AlphaGenome](https://github.com/google-deepmind/alphagenome) — Long-context DNA model predicting multimodal regulatory outputs including expression, splicing, chromatin features, and contact maps at near base-pair resolution.
+- [modernGENA](https://github.com/AIRI-Institute/GENA_LM) — ModernBERT-style DNA foundation-model family pretrained on hundreds of vertebrate genome assemblies for efficient long-sequence regulatory modeling.
 - [HyenaDNA](https://github.com/HazyResearch/hyena-dna) — Long-range genomic foundation model handling sequences up to 1M tokens with sub-quadratic attention.
 - [Borzoi](https://github.com/calico/borzoi) — Extended successor to Enformer for predicting RNA-seq coverage from long genomic sequence windows (524 kb) with improved resolution.
 - [DeepSEA](http://deepsea.princeton.edu/) — Deep learning framework for predicting chromatin effects of sequence alterations with single-nucleotide sensitivity across thousands of chromatin features.

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/c6f07d90/cspell.json
-upstream_sha: c6f07d90
-imported_at: 2026-08-31
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/4ed7cfef/cspell.json
+upstream_sha: 4ed7cfef
+imported_at: 2026-10-26
 prompt_class: unknown
 upstream_changes: accepted
 author: upstream
@@ -15,166 +15,249 @@ validated: false
   "language": "en",
   "allowCompoundWords": true,
   "words": [
-    "behavioural",
-    "KEGG",
-    "NCBI",
-    "UCSC",
-    "EMBL",
-    "RCSB",
-    "CASP",
-    "Uniclust",
-    "Reactome",
-    "Bioactive",
-    "biopolymers",
-    "proteomics",
-    "transcriptomics",
-    "metagenomic",
-    "metatranscriptomic",
-    "CRISPR",
-    "JASPAR",
-    "druggable",
-    "Toxicogenomics",
-    "GDSC",
-    "biomolecular",
-    "DRKG",
-    "Hetionet",
-    "Eudra",
-    "esearch",
-    "efetch",
-    "Ensembl",
-    "Cheminformatics",
-    "Deconv",
-    "Scanpy",
-    "Squidpy",
-    "explainability",
-    "MOFGCN",
-    "Autoencoder",
-    "DGDRP",
-    "MCPINN",
-    "Pretrained",
-    "pretrained",
-    "denoising",
-    "transcriptomic",
-    "CELLxGENE",
-    "eukaryotic",
-    "metabolites",
-    "OMIM",
-    "OncoKB",
-    "Mendelian",
-    "DisGeNET",
-    "GWAS",
-    "IntAct",
-    "Biopython",
-    "MDAnalysis",
-    "trajectories",
-    "Geneformer",
-    "equivariant",
-    "HyenaDNA",
-    "Hyena",
-    "Caduceus",
-    "Mamba",
-    "retrosynthesis",
-    "TargetDiff",
-    "Chai",
-    "Zuckerberg",
-    "HMDB",
-    "CTRP",
+    "Adamson",
+    "ADME",
     "ADMET",
-    "Omics",
-    "omics",
-    "omic",
-    "OADB",
+    "AESTETIK",
+    "AlphaGenome",
+    "Ankh",
+    "ATAC",
+    "Atacformer",
+    "AutoBA",
+    "Autoencoder",
+    "BACE",
+    "behavioural",
+    "Bento",
+    "Bioactive",
+    "BioAgents",
+    "biolord",
+    "BioMaster",
+    "BioMedAgent",
+    "Biomni",
+    "biomolecular",
+    "biopolymers",
+    "Biopython",
+    "Bison",
+    "Boltz",
+    "bowang",
+    "Caduceus",
+    "Cambrian",
+    "Casanovo",
+    "CASP",
+    "CASSIA",
+    "CellFlow",
+    "CellFM",
+    "CellOT",
+    "CELLxGENE",
+    "Chai",
+    "ChatDrug",
+    "Chem",
+    "ChemFM",
+    "Cheminformatics",
+    "chimericity",
+    "Chrom",
+    "ChromFound",
+    "CITE-seq",
+    "ClawBio",
+    "Clin",
+    "CMonge",
+    "convolutional",
+    "CpGPT",
+    "CPTAC",
+    "CRISPR",
+    "CRISPRa",
+    "CRISPRi",
+    "CROP",
+    "ctheodoris",
+    "CTRP",
+    "Datlinger",
+    "Deconv",
+    "deconvolution",
+    "DeepSEA",
+    "denoising",
+    "DGDRP",
+    "DisGeNET",
+    "Dixit",
+    "DoubletFinder",
+    "DRKG",
+    "druggable",
+    "DRUML",
+    "DTLCDR",
+    "efetch",
+    "EMBL",
+    "Enformer",
+    "Ensembl",
+    "EpiFoundation",
+    "epigenome",
+    "epigenomic",
+    "epigenomics",
+    "equivariant",
+    "esearch",
+    "Eudra",
+    "eukaryotic",
+    "explainability",
+    "EXPRESSO",
+    "FANTOM",
+    "FCFP4",
+    "FFPE",
+    "finetunable",
+    "Frangieh",
+    "Gasperini",
+    "GDSC",
+    "GenBio",
+    "Geneformer",
+    "Geno",
+    "GenoMAS",
+    "Genomi",
+    "Giga",
     "Gnify",
     "gnom",
-    "Rfam",
+    "GPN",
+    "GPT",
     "Guaca",
-    "deconvolution",
-    "scvi",
-    "pharmacogenomics",
-    "nutrigenomics",
-    "Giga",
-    "Phikon",
-    "TCGA",
-    "Mulan",
-    "epigenomics",
-    "methylation",
-    "ATAC",
-    "MOFA",
-    "TOSICA",
-    "Boltz",
-    "MPNN",
-    "Enformer",
-    "Velo",
-    "BACE",
-    "secretase",
-    "Clin",
-    "CPTAC",
-    "Proteomic",
-    "proteogenomic",
-    "LINCS",
-    "ogbl",
-    "ogbg",
-    "SIDER",
-    "Muris",
-    "Pfam",
-    "PROSITE",
-    "epigenome",
-    "TRRUST",
-    "kallisto",
-    "pseudoalignment",
-    "multiplet",
-    "TGSA",
+    "GWAS",
+    "H-Optimus",
+    "Haplotype",
+    "Hetionet",
+    "HiDRA",
+    "HMDB",
+    "Hyena",
+    "HyenaDNA",
+    "Imageomics",
+    "IntAct",
+    "JASPAR",
+    "JEPA",
     "JTVAE",
+    "Jurkat",
+    "kallisto",
+    "KEGG",
+    "KIBA",
+    "L1000",
+    "ligand",
+    "LINCS",
+    "Mamba",
+    "MCPINN",
+    "MDAnalysis",
+    "Medea",
+    "Mendelian",
+    "metabolites",
+    "metagenomic",
+    "metatranscriptomic",
+    "methylation",
+    "MethylGPT",
+    "methylomes",
     "miRBase",
     "miRNA",
-    "ProtTrans",
-    "ProtBERT",
-    "ProGen",
-    "Ankh",
-    "DeepSEA",
-    "RegNetwork",
-    "ROADMAP",
-    "FANTOM",
-    "NeXtProt",
-    "HiDRA",
-    "MolT",
-    "ChatDrug",
-    "DoubletFinder",
-    "pseudotime",
-    "ligand",
-    "SCENIC",
-    "GPN",
-    "Sei",
-    "KIBA",
-    "pharmacokinetics",
-    "ADME",
-    "Haplotype",
-    "NCEM",
-    "multiome",
+    "modernGENA",
+    "MOFA",
+    "MOFGCN",
     "MOGONET",
-    "convolutional",
-    "DRUML",
-    "SBDD",
-    "Pacc",
+    "MolT",
+    "Morgan",
+    "MPNN",
+    "Mulan",
+    "multiome",
     "multiomics",
-    "Pathomic",
-    "PLIP",
-    "Omni",
-    "Bento",
-    "FFPE",
-    "Xenium",
-    "Zyme",
-    "Neur",
-    "Imageomics",
-    "AESTETIK",
-    "CellOT",
-    "CMonge",
-    "bowang",
-    "ctheodoris",
-    "OpenAI",
-    "GPT",
+    "multiplet",
+    "Muris",
+    "MutationProjector",
     "nanobody",
-    "thermostability"
+    "NCBI",
+    "NCEM",
+    "Neur",
+    "NeXtProt",
+    "Novartis",
+    "nutrigenomics",
+    "OADB",
+    "ogbg",
+    "ogbl",
+    "omic",
+    "omics",
+    "Omics",
+    "OmicsFM",
+    "OMIM",
+    "Omni",
+    "OncoKB",
+    "OP3",
+    "OpenAI",
+    "Pacc",
+    "Paper2Agent",
+    "Pathomic",
+    "Perturb",
+    "Perturb-seq",
+    "PerturbNet",
+    "PerturBridge",
+    "PerturbRx",
+    "Pfam",
+    "pharmacogenomics",
+    "pharmacokinetics",
+    "Phikon",
+    "PLIP",
+    "PrePR",
+    "pretrained",
+    "Pretrained",
+    "ProGen",
+    "PROSITE",
+    "ProtBERT",
+    "Protenix",
+    "proteogenomic",
+    "Proteomic",
+    "proteomics",
+    "ProtTrans",
+    "pseudoalignment",
+    "pseudobulk",
+    "pseudotime",
+    "RCSB",
+    "RDKit",
+    "Reactome",
+    "RegNetwork",
+    "Replogle",
+    "retrosynthesis",
+    "Rfam",
+    "RiNALMo",
+    "ROADMAP",
+    "SBDD",
+    "Scanpy",
+    "SCENIC",
+    "SciPlex",
+    "scPRINT",
+    "scrna",
+    "scvi",
+    "secretase",
+    "Sei",
+    "SIDER",
+    "silico",
+    "Squidpy",
+    "State",
+    "STELLA",
+    "TargetDiff",
+    "TCGA",
+    "TGSA",
+    "THERAPI",
+    "thermostability",
+    "TITAN",
+    "ToolUniverse",
+    "TOSICA",
+    "Toxicogenomics",
+    "trajectories",
+    "transcriptomic",
+    "transcriptomics",
+    "TRRUST",
+    "TxAgent",
+    "TxPert",
+    "UCSC",
+    "Uni-Mol2",
+    "UNI2",
+    "Uniclust",
+    "UniMol",
+    "VCPI",
+    "Velo",
+    "Virchow2",
+    "VirTues",
+    "Xenium",
+    "XPert",
+    "Zuckerberg",
+    "Zyme"
   ],
   "ignorePaths": [
     "node_modules/**"

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

@@ -0,0 +1,298 @@
+---
+title: "Agent Explorer v1 metadata."
+task: ""
+lineage_type: import
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/4ed7cfef/data/enrichment.agent-explorer-v1.yml
+upstream_sha: 4ed7cfef
+imported_at: 2026-10-26
+prompt_class: unknown
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# Agent Explorer v1 metadata.
+resources:
+  biomni:
+    agent_profile:
+      year: 2025
+      domains: [general-biomedicine, bioinformatics, therapeutics, omics]
+      architecture: single-agent
+      tool_use: true
+      code_execution: true
+      web_retrieval: true
+      literature: true
+      omics: true
+      wet_lab: limited
+      autonomous_experiment: limited
+      human_in_loop: true
+      open_source: true
+
+  tooluniverse:
+    agent_profile:
+      year: 2025
+      domains: [general-biomedicine, tool-ecosystem, therapeutics, bioinformatics]
+      architecture: agent-ecosystem
+      tool_use: true
+      code_execution: limited
+      web_retrieval: true
+      literature: true
+      omics: true
+      wet_lab: limited
+      autonomous_experiment: limited
+      human_in_loop: true
+      open_source: true
+
+  clawbio:
+    agent_profile:
+      year: 2026
+      domains: [bioinformatics, genomics, pharmacogenomics, omics]
+      architecture: agent-ecosystem
+      tool_use: true
+      code_execution: true
+      web_retrieval: true
+      literature: limited
+      omics: true
+      wet_lab: false
+      autonomous_experiment: false
+      human_in_loop: true
+      open_source: true
+
+  txagent:
+    agent_profile:
+      year: 2025
+      domains: [therapeutics, drug-discovery, precision-medicine]
+      architecture: single-agent
+      tool_use: true
+      code_execution: limited
+      web_retrieval: true
+      literature: true
+      omics: limited
+      wet_lab: false
+      autonomous_experiment: limited
+      human_in_loop: true
+      open_source: true
+
+  medea:
+    agent_profile:
+      year: 2025
+      domains: [therapeutics, drug-discovery, single-cell, omics]
+      architecture: multi-agent
+      tool_use: true
+      code_execution: true
+      web_retrieval: true
+      literature: true
+      omics: true
+      wet_lab: limited
+      autonomous_experiment: limited
+      human_in_loop: true
+      open_source: true
+
+  drugagent:
+    agent_profile:
+      year: 2026
+      domains: [drug-discovery, therapeutics, evidence-synthesis]
+      architecture: multi-agent
+      tool_use: true
+      code_execution: limited
+      web_retrieval: true
+      literature: true
+      omics: limited
+      wet_lab: false
+      autonomous_experiment: limited
+      human_in_loop: true
+      open_source: true
+
+  cassia:
+    agent_profile:
+      year: 2024
+      domains: [single-cell, bioinformatics, omics]
+      architecture: multi-agent
+      tool_use: limited
+      code_execution: limited
+      web_retrieval: limited
+      literature: true
+      omics: true
+      wet_lab: false
+      autonomous_experiment: false
+      human_in_loop: true
+      open_source: true
+
+  stella:
+    agent_profile:
+      year: 2025
+      domains: [general-biomedicine, bioinformatics, scientific-workflow]
+      architecture: single-agent
+      tool_use: true
+      code_execution: true
+      web_retrieval: true
+      literature: true
+      omics: limited
+      wet_lab: limited
+      autonomous_experiment: true
+      human_in_loop: true
+      open_source: true
+
+  virtual_lab:
+    agent_profile:
+      year: 2024
+      domains: [scientific-workflow, protein-design, experimental-design]
+      architecture: multi-agent
+      tool_use: true
+      code_execution: true
+      web_retrieval: limited
+      literature: true
+      omics: limited
+      wet_lab: true
+      autonomous_experiment: limited
+      human_in_loop: true
+      open_source: true
+
+  agent_laboratory:
+    agent_profile:
+      year: 2025
+      domains: [scientific-workflow, research-automation]
+      architecture: multi-agent
+      tool_use: true
+      code_execution: true
+      web_retrieval: true
+      literature: true
+      omics: false
+      wet_lab: false
+      autonomous_experiment: true
+      human_in_loop: true
+      open_source: true
+
+  paper2agent:
+    agent_profile:
+      year: 2025
+      domains: [paper-to-agent, scientific-workflow, tool-generation]
+      architecture: multi-agent
+      tool_use: true
+      code_execution: true
+      web_retrieval: true
+      literature: true
+      omics: limited
+      wet_lab: false
+      autonomous_experiment: limited
+      human_in_loop: true
+      open_source: true
+
+  biomedagent:
+    agent_profile:
+      year: 2026
+      domains: [general-biomedicine, bioinformatics, omics]
+      architecture: multi-agent
+      tool_use: true
+      code_execution: true
+      web_retrieval: limited
+      literature: limited
+      omics: true
+      wet_lab: false
+      autonomous_experiment: true
+      human_in_loop: true
+      open_source: true
+
+  biomaster:
+    agent_profile:
+      year: 2026
+      domains: [bioinformatics, omics, scientific-workflow]
+      architecture: multi-agent
+      tool_use: true
+      code_execution: true
+      web_retrieval: limited
+      literature: limited
+      omics: true
+      wet_lab: false
+      autonomous_experiment: true
+      human_in_loop: true
+      open_source: true
+
+  brad:
+    agent_profile:
+      year: 2025
+      domains: [bioinformatics, literature, scientific-workflow]
+      architecture: single-agent
+      tool_use: true
+      code_execution: true
+      web_retrieval: true
+      literature: true
+      omics: limited
+      wet_lab: false
+      autonomous_experiment: limited
+      human_in_loop: true
+      open_source: true
+
+  autoba:
+    agent_profile:
+      year: 2024
+      domains: [bioinformatics, omics, scientific-workflow]
+      architecture: single-agent
+      tool_use: true
+      code_execution: true
+      web_retrieval: limited
+      literature: limited
+      omics: true
+      wet_lab: false
+      autonomous_experiment: true
+      human_in_loop: true
+      open_source: true
+
+  genomas:
+    agent_profile:
+      year: 2025
+      domains: [bioinformatics, gene-expression, scientific-workflow]
+      architecture: multi-agent
+      tool_use: true
+      code_execution: true
+      web_retrieval: limited
+      literature: true
+      omics: true
+      wet_lab: false
+      autonomous_experiment: true
+      human_in_loop: true
+      open_source: true
+
+  bia:
+    agent_profile:
+      domains: [bioinformatics, genomics, literature]
+      architecture: single-agent
+      tool_use: true
+      code_execution: true
+      web_retrieval: true
+      literature: true
+      omics: true
+      wet_lab: false
+      autonomous_experiment: limited
+      human_in_loop: true
+      open_source: true
+
+  bioagents:
+    agent_profile:
+      year: 2025
+      domains: [bioinformatics, genomics, scientific-workflow]
+      architecture: multi-agent
+      tool_use: true
+      code_execution: limited
+      web_retrieval: limited
+      literature: limited
+      omics: true
+      wet_lab: false
+      autonomous_experiment: limited
+      human_in_loop: true
+      open_source: true
+
+  genomi:
+    agent_profile:
+      year: 2026
+      domains: [genomics, precision-medicine, evidence-synthesis]
+      architecture: agent-ecosystem
+      tool_use: true
+      code_execution: limited
+      web_retrieval: limited
+      literature: true
+      omics: limited
+      wet_lab: false
+      autonomous_experiment: false
+      human_in_loop: true
+      open_source: true

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

@@ -2,9 +2,9 @@
 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
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/4ed7cfef/data/enrichment.benchmark-v2.yml
+upstream_sha: 4ed7cfef
+imported_at: 2026-10-26
 prompt_class: catalogue
 upstream_changes: accepted
 author: upstream
@@ -39,13 +39,55 @@ resources:
     last_checked: 2026-08-08
     metadata_sources:
       - https://lincsproject.org/LINCS/tools/workflows/find-the-best-place-to-obtain-the-lincs-l1000-data
+    dataset_profile:
+      year: 2017
+      readout: l1000
+      gene_panel: "978 landmark genes"
+      time: true
+      view_scope: full-resource
+      species: [human]
+      sample_type: cell-line
+      biological_context: perturbation-screen
+      perturbation_type: [chemical, genetic]
+      genetic_modes: [mixed]
+      paired: false
+      pre_post: false
+      longitudinal: false
+      drug_identity: true
+      dose: true
+      smiles: partial
+      clinical_outcome: false
+      n_samples: "large-scale perturbational profiles"
+      n_cells: null
+      access: open
 
   prism:
     entities: [cell, drug]
     tasks: [drug-response-prediction]
-    last_checked: 2026-08-08
+    last_checked: 2026-10-02
     metadata_sources:
       - https://depmap.org/portal/prism/
+    dataset_profile:
+      year: 2020
+      readout: viability
+      n_compounds: ">4,500"
+      n_contexts: ">900 cell lines"
+      time: false
+      view_scope: full-resource
+      species: [human]
+      sample_type: cell-line
+      biological_context: cancer
+      perturbation_type: [chemical]
+      paired: true
+      pre_post: false
+      longitudinal: false
+      drug_identity: true
+      dose: true
+      smiles: partial
+      clinical_outcome: false
+      n_samples: "PRISM pooled viability screens"
+      n_cells: null
+      access: open
 
   pharmgkb:
     entities: [drug, gene, phenotype, variant]

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

@@ -0,0 +1,125 @@
+---
+title: "Provenance-backed cancer and pharmacogenomics database enrichment batch."
+task: ""
+lineage_type: import
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/4ed7cfef/data/enrichment.cancer-databases-v1.yml
+upstream_sha: 4ed7cfef
+imported_at: 2026-10-26
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# Provenance-backed cancer and pharmacogenomics database enrichment batch.
+resources:
+  cancer_cell_line_encyclopedia:
+    entities: [cell, gene, disease, drug]
+    modalities: [genomics, transcriptomics]
+    documentation: https://sites.broadinstitute.org/ccle/
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://sites.broadinstitute.org/ccle/
+
+  catalogue_of_somatic_mutations_in_cancer_cosmic:
+    entities: [disease, gene, variant]
+    modalities: [genomics]
+    documentation: https://cancer.sanger.ac.uk/cosmic
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://cancer.sanger.ac.uk/cosmic
+
+  cbioportal:
+    entities: [disease, gene, variant]
+    modalities: [genomics, clinical]
+    github: https://github.com/cBioPortal/cbioportal
+    documentation: https://www.cbioportal.org/
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://www.cbioportal.org/
+      - https://github.com/cBioPortal/cbioportal
+
+  dependency_map_depmap:
+    entities: [cell, gene, disease, drug]
+    modalities: [genomics]
+    documentation: https://depmap.org/portal/
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://depmap.org/portal/
+
+  dgidb:
+    entities: [drug, gene]
+    modalities: [knowledge-graph]
+    documentation: https://www.dgidb.org/
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://www.dgidb.org/
+
+  drug_mechanism_database_drugmechdb:
+    entities: [drug, disease, gene, pathway]
+    modalities: [knowledge-graph]
+    github: https://github.com/SuLab/DrugMechDB
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://github.com/SuLab/DrugMechDB
+
+  drug_repurposing_hub:
+    entities: [drug, protein, disease]
+    modalities: [chemical-structure]
+    documentation: https://repo-hub.broadinstitute.org/repurposing
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://repo-hub.broadinstitute.org/repurposing
+
+  drugcentral:
+    entities: [drug, protein, disease]
+    modalities: [chemical-structure]
+    documentation: https://drugcentral.org/
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://drugcentral.org/
+
+  drugtargetcommons:
+    entities: [drug, protein]
+    modalities: [chemical-structure]
+    documentation: https://drugtargetcommons.fimm.fi/
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://drugtargetcommons.fimm.fi/
+
+  comparative_toxicogenomics_database:
+    entities: [compound, gene, disease]
+    modalities: [knowledge-graph]
+    documentation: https://ctdbase.org/
+    last_checked: 2026-08-08
+    metadata_sources:
+      - https://ctdbase.org/
+
+  the_cancer_genome_atlas_tcga:
+    entities: [disease, gene]
+    modalities: [clinical, genomics]
+    last_checked: 2026-10-02
+    metadata_sources:
+      - https://www.cancer.gov/ccg/research/genome-sequencing/tcga
+    dataset_profile:
+      year: 2018
+      readout: multi-omics
+      n_profiles: ">11,000 patients"
+      n_contexts: 33
+      gene_panel: "transcriptome-wide plus multi-omics"
+      time: false
+      view_scope: full-resource
+      species: [human]
+      sample_type: patient
+      biological_context: cancer
+      perturbation_type: []
+      paired: false
+      pre_post: false
+      longitudinal: false
+      drug_identity: limited
+      dose: false
+      smiles: false
+      clinical_outcome: true
+      n_samples: ">11,000 patients across 33 cancer types"
+      n_cells: null
+      access: mixed

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

@@ -0,0 +1,785 @@
+---
+title: "Foundation Model Explorer v1 metadata."
+task: ""
+lineage_type: import
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/4ed7cfef/data/enrichment.foundation-explorer-v1.yml
+upstream_sha: 4ed7cfef
+imported_at: 2026-10-26
+prompt_class: unknown
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# Foundation Model Explorer v1 metadata.
+# Fine-grained modalities intentionally distinguish assay/data types for navigation.
+resources:
+  scfoundation:
+    foundation_profile:
+      year: 2024
+      modalities: [scrna]
+      params: "100M"
+      params_millions: 100
+      pretraining_scale: ">50M human single-cell transcriptomes"
+      species: [human]
+      zero_shot: limited
+      finetunable: true
+      weights: true
+      code: true
+      perturbation: limited
+      spatial: false
+
+  scgpt:
+    foundation_profile:
+      year: 2024
+      modalities: [scrna, multi-omics]
+      pretraining_scale: ">33M single-cell profiles"
+      species: [human]
+      zero_shot: limited
+      finetunable: true
+      weights: true
+      code: true
+      perturbation: true
+      spatial: limited
+
+  geneformer:
+    foundation_profile:
+      year: 2024
+      modalities: [scrna]
+      params: "10M / 104M / 316M"
+      params_millions: 316
+      pretraining_scale: "~104M human single-cell transcriptomes in V2; original corpus ~30M"
+      species: [human]
+      zero_shot: true
+      finetunable: true
+      weights: true
+      code: true
+      perturbation: true
+      spatial: false
+
+  bulkformer:
+    foundation_profile:
+      year: 2025
+      modalities: [bulk-rna]
+      pretraining_scale: "bulk RNA-seq compendium"
+      species: [human]
+      zero_shot: limited
+      finetunable: true
+      weights: true
+      code: true
+      perturbation: false
+      spatial: false
+
+  scbert:
+    foundation_profile:
+      year: 2022
+      modalities: [scrna]
+      pretraining_scale: "large-scale scRNA-seq corpus"
+      species: [human]
+      zero_shot: limited
+      finetunable: true
+      weights: true
+      code: true
+      perturbation: false
+      spatial: false
+
+  cellplm:
+    foundation_profile:
+      year: 2023
+      modalities: [scrna]
+      pretraining_scale: "multi-dataset single-cell pretraining"
+      species: [human]
+      zero_shot: limited
+      finetunable: true
+      weights: true
+      code: true
+      perturbation: limited
+      spatial: false
+
+  uce:
+    foundation_profile:
+      year: 2026
+      modalities: [scrna]
+      params: ">650M"
+      params_millions: 650
+      pretraining_scale: ">36M cells across >300 datasets"
+      species: [multi-species]
+      zero_shot: true
+      finetunable: limited
+      weights: true
+      code: true
+      perturbation: false
+      spatial: false
+
+  saturn:
+    foundation_profile:
+      year: 2024
+      modalities: [scrna, protein]
+      pretraining_scale: "cross-species single-cell atlases with protein-language-model embeddings"
+      species: [multi-species]
+      zero_shot: limited
+      finetunable: true
+      weights: true
+      code: true
+      perturbation: false
+      spatial: false
+
+  cancerfoundation:
+    foundation_profile:
+      year: 2025
+      modalities: [scrna]
+      pretraining_scale: "curated malignant-cell single-cell corpus"
+      species: [human]
+      zero_shot: limited
+      finetunable: true
+      weights: true
+      code: true
+      perturbation: limited
+      spatial: false
+
+  cellfm:
+    foundation_profile:
+      year: 2025
+      modalities: [scrna]
+      params: "800M"
+      params_millions: 800
+      pretraining_scale: "100M human cells"
+      species: [human]
+      zero_shot: limited
+      finetunable: true
+      weights: true
+      code: true
+      perturbation: true
+      spatial: false
+
+  scprint:
+    foundation_profile:
+      year: 2025
+      modalities: [scrna]
+      pretraining_scale: "large multi-dataset single-cell corpus"
+      species: [human]
+      zero_shot: limited
+      finetunable: true
+      weights: true
+      code: true
+      perturbation: limited
+      spatial: false
+
+  scprint_2:
+    foundation_profile:
+      year: 2026
+      modalities: [scrna]
+      pretraining_scale: "350M+ cells; 22K+ datasets; 16 species"
+      species: [multi-species]
+      zero_shot: true
+      finetunable: true
+      weights: true
+      code: true
+      perturbation: limited
+      spatial: false
+
+  nicheformer:
+    foundation_profile:
+      year: 2025
+      modalities: [scrna, spatial]
+      params: "49.3M"
+      params_millions: 49.3
+      pretraining_scale: "SpatialCorpus-110M"
+      species: [human, mouse]
+      zero_shot: true
+      finetunable: true
+      weights: true
+      code: true
+      perturbation: false
+      spatial: true
+
+  scgpt_spatial:
+    foundation_profile:
+      year: 2025
+      modalities: [scrna, spatial, multi-omics]
+      pretraining_scale: "continued pretraining from scGPT with spatial transcriptomics"
+      species: [human, mouse]
+      zero_shot: limited
+      finetunable: true
+      weights: true
+      code: true
+      perturbation: false
+      spatial: true
+
+  scmulan:
+    foundation_profile:
+      year: 2024
+      modalities: [scrna, atac, proteomics, multi-omics]
+      pretraining_scale: "~10M cells across transcriptomics, epigenomics, and proteomics"
+      species: [human, mouse]
+      zero_shot: limited
+      finetunable: true
+      weights: true
+      code: true
+      perturbation: false
+      spatial: false
+
+  genecompass:
+    foundation_profile:
+      year: 2024
+      modalities: [scrna, dna, multi-omics]
+      pretraining_scale: "120M+ cells across multiple species"
+      species: [multi-species]
+      zero_shot: limited
+      finetunable: true
+      weights: true
+      code: true
+      perturbation: limited
+      spatial: false
+
+  titan:
+    foundation_profile:
+      year: 2025
+      modalities: [pathology]
+      pretraining_scale: "whole-slide pathology with multimodal image-language supervision"
+      species: [human]
+      zero_shot: true
+      finetunable: true
+      weights: true
+      code: true
+      perturbation: false
+      spatial: false
+
+  virchow2:
+    foundation_profile:
+      year: 2024
+      modalities: [pathology]
+      params: "632M"
+      params_millions: 632
+      pretraining_scale: "3.1M whole-slide images"
+      species: [human]
+      zero_shot: limited
+      finetunable: true
+      weights: true
+      code: limited
+      perturbation: false
+      spatial: false
+
+  h_optimus_0:
+    foundation_profile:
+      year: 2024
+      modalities: [pathology]
+      params: "1.1B"
+      params_millions: 1100
+      pretraining_scale: "large multi-center histopathology corpus"
+      species: [human]
+      zero_shot: limited
+      finetunable: true
+      weights: true
+      code: limited
+      perturbation: false
+      spatial: false
+
+  h_optimus_1:
+    foundation_profile:
+      year: 2025
+      modalities: [pathology]
+      params: "1.1B"
+      params_millions: 1100
+      pretraining_scale: ">1M slides; >800K patients"
+      species: [human]
+      zero_shot: limited
+      finetunable: true
+      weights: true
+      code: limited
+      perturbation: false
+      spatial: false
+
+  uni2_h:
+    foundation_profile:
+      year: 2025
+      modalities: [pathology]
+      params: "~1B"
+      params_millions: 1000
+      pretraining_scale: "large-scale histopathology pretraining"
+      species: [human]
+      zero_shot: limited
+      finetunable: true
+      weights: true
+      code: limited
+      perturbation: false
+      spatial: false
+
+  phikon_v2:
+    foundation_profile:
+      year: 2025
+      modalities: [pathology]
+      pretraining_scale: "large-scale histopathology pretraining"
+      species: [human]
+      zero_shot: limited
+      finetunable: true
+      weights: true
+      code: limited
+      perturbation: false
+      spatial: false
+
+  genbio_pathfm:
+    foundation_profile:
+      year: 2026
+      modalities: [pathology]
+      params: "1.1B"
+      params_millions: 1100
+      pretraining_scale: "public histopathology corpus with morphology-aware curation"
+      species: [human]
+      zero_shot: limited
+      finetunable: true
+      weights: true
+      code: true
+      perturbation: false
+      spatial: false
+
+  uni_mol:
+    foundation_profile:
+      year: 2023
+      modalities: [chemical, protein]
+      pretraining_scale: "large-scale 3D molecular and pocket pretraining"
+      species: [multi-species]
+      zero_shot: limited
+      finetunable: true
+      weights: true
+      code: true
+      perturbation: false
+      spatial: false
+
+  uni_mol2:
+    foundation_profile:
+      year: 2024
+      modalities: [chemical]
+      params: "up to 1.1B"
+      params_millions: 1100
+      pretraining_scale: "800M molecular conformations"
+      species: []
+      zero_shot: limited
+      finetunable: true
+      weights: true
+      code: true
+      perturbation: false
+      spatial: false
+
+  chemfm:
+    foundation_profile:
+      year: 2024
+      modalities: [chemical]
+      params: "1B / 3B"
+      params_millions: 3000
+      pretraining_scale: "178M molecules"
+      species: []
+      zero_shot: limited
+      finetunable: true
+      weights: true
+      code: true
+      perturbation: false
+      spatial: false
+
+  esm_cambrian_esm_c:
+    foundation_profile:
+      year: 2024
+      modalities: [protein]
+      params: "300M / 600M / 6B"
+      params_millions: 6000
+      pretraining_scale: "evolutionary-scale protein sequence corpus"
+      species: [multi-species]
+      zero_shot: true
+      finetunable: true
+      weights: limited
+      code: true
+      perturbation: false
+      spatial: false
+
+  esm3:
+    foundation_profile:
+      year: 2024
+      modalities: [protein]
+      params: "1.4B / 7B / 98B"
+      params_millions: 98000
+      pretraining_scale: "multimodal protein sequence, structure, and function corpus"
+      species: [multi-species]
+      zero_shot: true
+      finetunable: limited
+      weights: limited
+      code: true
+      perturbation: false
+      spatial: false
+
+  boltz_2:
+    foundation_profile:
+      year: 2025
+      modalities: [protein, chemical]
+      pretraining_scale: "biomolecular complex structure and affinity data"
+      species: [multi-species]
+      zero_shot: true
+      finetunable: limited
+      weights: true
+      code: true
+      perturbation: false
+      spatial: false
+
+  protenix:
+    foundation_profile:
+      year: 2025
+      modalities: [protein, chemical]
+      pretraining_scale: "biomolecular structure pretraining"
+      species: [multi-species]
+      zero_shot: true
+      finetunable: true
+      weights: true
+      code: true
+      perturbation: false
+      spatial: false
+
+  rna_fm:
+    foundation_profile:
+      year: 2022
+      modalities: [rna]
+      params: "99M"
+      params_millions: 99
+      pretraining_scale: "23.7M non-coding RNA sequences"
+      species: [multi-species]
+      zero_shot: true
+      finetunable: true
+      weights: true
+      code: true
+      perturbation: false
+      spatial: false
+
+  rinalmo:
+    foundation_profile:
+      year: 2025
+      modalities: [rna]
+      params: "33.5M / 148M / 650M"
+      params_millions: 650
+      pretraining_scale: "36M non-coding RNA sequences"
+      species: [multi-species]
+      zero_shot: true
+      finetunable: true
+      weights: true
+      code: true
+      perturbation: false
+      spatial: false
+
+  evo_2:
+    foundation_profile:
+      year: 2026
+      modalities: [dna]
+      params: "1B / 7B / 40B"
+      params_millions: 40000
+      pretraining_scale: "9T DNA base pairs"
+      species: [multi-species]
+      zero_shot: true
+      finetunable: true
+      weights: true
+      code: true
+      perturbation: false
+      spatial: false
+
+  alphagenome:
+    foundation_profile:
+      year: 2025
+      modalities: [dna]
+      pretraining_scale: "long-context regulatory genomics corpus"
+      species: [human, mouse]
+      zero_shot: true
+      finetunable: unknown
+      weights: no
+      code: limited
+      perturbation: false
+      spatial: false
+
+  moderngena:
+    foundation_profile:
+      year: 2026
+      modalities: [dna]
+      pretraining_scale: "hundreds of vertebrate genome assemblies"
+      species: [multi-species]
+      zero_shot: limited
+      finetunable: true
+      weights: true
+      code: true
+      perturbation: false
+      spatial: false
+
+  nucleotide_transformer:
+    foundation_profile:
+      year: 2025
+      modalities: [dna]
+      params: "50M – 2.5B"
+      params_millions: 2500
+      pretraining_scale: "3,202 human genomes and up to 850 species"
+      species: [multi-species]
+      zero_shot: true
+      finetunable: true
+      weights: true
+      code: true
+      perturbation: false
+      spatial: false
+
+  dnabert_2:
+    foundation_profile:
+      year: 2023
+      modalities: [dna]
+      params: "117M"
+      params_millions: 117
+      pretraining_scale: "multi-species GenBank genome corpus"
+      species: [multi-species]
+      zero_shot: limited
+      finetunable: true
+      weights: true
+      code: true
+      perturbation: false
+      spatial: false
+
+  caduceus:
+    foundation_profile:
+      year: 2024
+      modalities: [dna]
+      pretraining_scale: "long-range genomic sequence pretraining"
+      species: [human]
+      zero_shot: limited
+      finetunable: true
+      weights: true
+      code: true
+      perturbation: false
+      spatial: false
+
+  hyenadna:
+    foundation_profile:
+      year: 2023
+      modalities: [dna]
+      pretraining_scale: "long-context human genome sequence"
+      species: [human]
+      zero_shot: limited
+      finetunable: true
+      weights: true
+      code: true
+      perturbation: false
+      spatial: false
+
+  gpn_genomic_pre_trained_network:
+    foundation_profile:
+      year: 2023
+      modalities: [dna]
+      pretraining_scale: "genomic sequence pretraining for variant-effect prediction"
+      species: [multi-species]
+      zero_shot: true
+      finetunable: true
+      weights: true
+      code: true
+      perturbation: false
+      spatial: false
+
+  methylgpt:
+    foundation_profile:
+      year: 2024
+      modalities: [methylation]
+      params: "3M / 7M / 15M"
+      params_millions: 15
+      pretraining_scale: "154,063 human methylomes after QC; 5,281 datasets; 49,156 CpG sites"
+      species: [human]
+      zero_shot: limited
+      finetunable: true
+      weights: true
+      code: true
+      perturbation: false
+      spatial: false
+
+  cpgpt:
+    foundation_profile:
+      year: 2025
+      modalities: [methylation]
+      pretraining_scale: "150K+ methylation samples across >2,000 datasets"
+      species: [human]
+      zero_shot: true
+      finetunable: true
+      weights: true
+      code: true
+      perturbation: false
+      spatial: false
+
+  mutationprojector:
+    foundation_profile:
+      year: 2026
+      modalities: [mutation, copy-number]
+      pretraining_scale: ">30K tumors with somatic genomic alterations including copy-number changes"
+      species: [human]
+      zero_shot: limited
+      finetunable: unknown
+      weights: unknown
+      code: unknown
+      perturbation: false
+      spatial: false
+
+  casanovo_foundation:
+    foundation_profile:
+      year: 2025
+      modalities: [proteomics]
+      pretraining_scale: "30M labeled tandem mass spectra"
+      species: [human]
+      zero_shot: limited
+      finetunable: true
+      weights: true
+      code: true
+      perturbation: false
+      spatial: false
+
+  tessera:
+    foundation_profile:
+      year: 2026
+      modalities: [mutation, copy-number]
+      pretraining_scale: "TCGA pan-cancer somatic SNVs and copy-number segments"
+      species: [human]
+      zero_shot: limited
+      finetunable: true
+      weights: true
+      code: true
+      perturbation: false
+      spatial: false
+
+  epiagent:
+    foundation_profile:
+      year: 2025
+      modalities: [atac]
+      pretraining_scale: ">5.4M scATAC-seq cells across 37 tissues or cell lines"
+      species: [human]
+      zero_shot: limited
+      finetunable: true
+      weights: true
+      code: true
+      perturbation: true
+      spatial: false
+
+  scarf:
+    foundation_profile:
+      year: 2025
+      modalities: [scrna, atac, multi-omics]
+      pretraining_scale: ">2.7M paired and unpaired single cells across tissues and species"
+      species: [multi-species]
+      zero_shot: limited
+      finetunable: true
+      weights: true
+      code: true
+      perturbation: limited
+      spatial: false
+
+  scdnam_gpt:
+    foundation_profile:
+      year: 2025
+      modalities: [methylation]
+      pretraining_scale: ">1M single-cell methylomes from human and mouse tissues"
+      species: [human, mouse]
+      zero_shot: true
+      finetunable: true
+      weights: true
+      code: true
+      perturbation: false
+      spatial: false
+
+  epifoundation:
+    foundation_profile:
+      year: 2025
+      modalities: [atac]
+      pretraining_scale: "large-scale scATAC-seq pretraining with peak-to-gene alignment"
+      species: [human]
+      zero_shot: limited
+      finetunable: true
+      weights: limited
+      code: limited
+      perturbation: false
+      spatial: false
+
+  chromfound:
+    foundation_profile:
+      year: 2025
+      modalities: [atac]
+      pretraining_scale: "1.97M scATAC-seq cells; 30 tissues; 6 disease contexts"
+      species: [human]
+      zero_shot: true
+      finetunable: true
+      weights: true
+      code: true
+      perturbation: false
+      spatial: false
+
+  atacformer:
+    foundation_profile:
+      year: 2025
+      modalities: [atac]
+      pretraining_scale: "large atlas of scATAC-seq experiments"
+      species: [human]
+      zero_shot: limited
+      finetunable: true
+      weights: unknown
+      code: unknown
+      perturbation: false
+      spatial: false
+
+  clm_x:
+    foundation_profile:
+      year: 2026
+      modalities: [scrna, atac, multi-omics]
+      pretraining_scale: "million-scale RNA-only, ATAC-only, and paired RNA-ATAC data"
+      species: [human]
+      zero_shot: limited
+      finetunable: true
+      weights: unknown
+      code: unknown
+      perturbation: true
+      spatial: false
+
+  captain:
+    foundation_profile:
+      year: 2026
+      modalities: [scrna, proteomics, multi-omics]
+      pretraining_scale: "co-assayed single-cell RNA and protein data"
+      species: [human]
+      zero_shot: limited
+      finetunable: true
+      weights: unknown
+      code: unknown
+      perturbation: false
+      spatial: false
+
+  hicfoundation:
+    foundation_profile:
+      year: 2026
+      modalities: [hic]
+      pretraining_scale: "hundreds of Hi-C assays; 118M contact-matrix submatrices"
+      species: [multi-species]
+      zero_shot: limited
+      finetunable: true
+      weights: unknown
+      code: unknown
+      perturbation: false
+      spatial: false
+
+  omicsfm:
+    foundation_profile:
+      year: 2026
+      modalities: [proteomics, bulk-rna, scrna]
+      params: "4.9M"
+      params_millions: 4.9
+      pretraining_scale: "48,837 proteomics profiles; 680,216 bulk RNA profiles; 4.55M single cells"
+      species: [human]
+      zero_shot: limited
+      finetunable: true
+      weights: true
+      code: true
+      perturbation: false
+      spatial: false
+
+  virtues:
+    foundation_profile:
+      year: 2026
+      modalities: [proteomics, spatial]
+      pretraining_scale: "31-32 harmonized spatial-proteomics datasets across technologies"
+      species: [human]
+      zero_shot: true
+      finetunable: true
+      weights: true
+      code: true
+      perturbation: false
+      spatial: true

upstream/inoue0426-awesome-computational-biology/catalogue/data/enrichment.foundation-models-v3.yml

@@ -0,0 +1,232 @@
+---
+title: "Provenance-backed foundation model enrichment batch 3."
+task: ""
+lineage_type: import
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/4ed7cfef/data/enrichment.foundation-models-v3.yml
+upstream_sha: 4ed7cfef
+imported_at: 2026-10-26
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# Provenance-backed foundation model enrichment batch 3.
+resources:
+  cellfm:
+    entities: [cell, gene]
+    methods: [self-supervised-learning, transformer]
+    modalities: [single-cell-rna-seq, transcriptomics]
+    tasks: [cell-type-annotation, foundation-model-pretraining, perturbation-prediction, representation-learning]
+    year: 2025
+    github: https://github.com/biomed-AI/CellFM
+    paper: https://doi.org/10.1038/s41467-025-59926-5
+    last_checked: 2026-10-02
+    metadata_sources:
+      - https://github.com/biomed-AI/CellFM
+      - https://doi.org/10.1038/s41467-025-59926-5
+
+  scprint_2:
+    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: 2026
+    github: https://github.com/cantinilab/scPRINT-2
+    last_checked: 2026-10-02
+    metadata_sources:
+      - https://github.com/cantinilab/scPRINT-2
+
+  titan:
+    entities: [disease, tissue]
+    methods: [contrastive-learning, self-supervised-learning, transformer]
+    modalities: [histopathology, imaging]
+    tasks: [classification, foundation-model-pretraining, representation-learning]
+    year: 2025
+    github: https://github.com/mahmoodlab/TITAN
+    last_checked: 2026-10-02
+    metadata_sources:
+      - https://github.com/mahmoodlab/TITAN
+
+  virchow2:
+    entities: [disease, tissue]
+    methods: [self-supervised-learning, transformer]
+    modalities: [histopathology, imaging]
+    tasks: [classification, foundation-model-pretraining, representation-learning]
+    year: 2024
+    documentation: https://huggingface.co/paige-ai/Virchow2
+    last_checked: 2026-10-02
+    metadata_sources:
+      - https://huggingface.co/paige-ai/Virchow2
+
+  h_optimus_0:
+    entities: [disease, tissue]
+    methods: [self-supervised-learning, transformer]
+    modalities: [histopathology, imaging]
+    tasks: [classification, foundation-model-pretraining, representation-learning]
+    year: 2024
+    documentation: https://huggingface.co/bioptimus/H-optimus-0
+    last_checked: 2026-10-02
+    metadata_sources:
+      - https://huggingface.co/bioptimus/H-optimus-0
+
+  h_optimus_1:
+    entities: [disease, tissue]
+    methods: [self-supervised-learning, transformer]
+    modalities: [histopathology, imaging]
+    tasks: [classification, foundation-model-pretraining, representation-learning]
+    year: 2025
+    documentation: https://huggingface.co/bioptimus/H-optimus-1
+    last_checked: 2026-10-02
+    metadata_sources:
+      - https://huggingface.co/bioptimus/H-optimus-1
+
+  uni2_h:
+    entities: [disease, tissue]
+    methods: [self-supervised-learning, transformer]
+    modalities: [histopathology, imaging]
+    tasks: [classification, foundation-model-pretraining, representation-learning]
+    year: 2025
+    documentation: https://huggingface.co/MahmoodLab/UNI2-h
+    last_checked: 2026-10-02
+    metadata_sources:
+      - https://huggingface.co/MahmoodLab/UNI2-h
+
+  phikon_v2:
+    entities: [disease, tissue]
+    methods: [self-supervised-learning, transformer]
+    modalities: [histopathology, imaging]
+    tasks: [classification, foundation-model-pretraining, representation-learning]
+    year: 2025
+    documentation: https://huggingface.co/owkin/phikon-v2
+    last_checked: 2026-10-02
+    metadata_sources:
+      - https://huggingface.co/owkin/phikon-v2
+
+  genbio_pathfm:
+    entities: [disease, tissue]
+    methods: [self-supervised-learning, transformer]
+    modalities: [histopathology, imaging]
+    tasks: [classification, foundation-model-pretraining, representation-learning]
+    year: 2026
+    github: https://github.com/genbio-ai/genbio-pathfm
+    paper: https://www.biorxiv.org/content/10.64898/2026.03.17.712534v1
+    last_checked: 2026-10-02
+    metadata_sources:
+      - https://github.com/genbio-ai/genbio-pathfm
+      - https://www.biorxiv.org/content/10.64898/2026.03.17.712534v1
+
+  uni_mol2:
+    entities: [molecule]
+    methods: [self-supervised-learning, transformer]
+    modalities: [chemical-structure, molecular-structure]
+    tasks: [foundation-model-pretraining, representation-learning]
+    year: 2024
+    github: https://github.com/deepmodeling/Uni-Mol
+    last_checked: 2026-10-02
+    metadata_sources:
+      - https://github.com/deepmodeling/Uni-Mol
+
+  chemfm:
+    entities: [molecule]
+    methods: [generative-model, language-model, transformer]
+    modalities: [chemical-structure]
+    tasks: [foundation-model-pretraining, molecular-generation, representation-learning]
+    year: 2024
+    github: https://github.com/TheLuoFengLab/ChemFM
+    last_checked: 2026-10-02
+    metadata_sources:
+      - https://github.com/TheLuoFengLab/ChemFM
+
+  esm_cambrian_esm_c:
+    entities: [protein]
+    methods: [language-model, self-supervised-learning, transformer]
+    modalities: [protein-sequence]
+    tasks: [foundation-model-pretraining, protein-function-prediction, representation-learning]
+    year: 2024
+    github: https://github.com/Biohub/esm
+    last_checked: 2026-10-02
+    metadata_sources:
+      - https://github.com/Biohub/esm
+      - https://www.evolutionaryscale.ai/blog/esm-cambrian
+
+  boltz_2:
+    entities: [molecule, protein, protein-complex]
+    methods: [diffusion, generative-model, transformer]
+    modalities: [molecular-structure, protein-sequence]
+    tasks: [drug-target-interaction, structure-prediction, virtual-screening]
+    year: 2025
+    github: https://github.com/jwohlwend/boltz
+    paper: https://doi.org/10.1101/2025.06.14.659707
+    last_checked: 2026-10-02
+    metadata_sources:
+      - https://github.com/jwohlwend/boltz
+      - https://doi.org/10.1101/2025.06.14.659707
+
+  protenix:
+    entities: [molecule, protein, protein-complex]
+    methods: [diffusion, generative-model, transformer]
+    modalities: [molecular-structure, protein-sequence]
+    tasks: [drug-target-interaction, structure-prediction]
+    year: 2025
+    github: https://github.com/bytedance/Protenix
+    last_checked: 2026-10-02
+    metadata_sources:
+      - https://github.com/bytedance/Protenix
+
+  rna_fm:
+    entities: [gene, transcript]
+    methods: [language-model, self-supervised-learning, transformer]
+    modalities: [rna-sequence]
+    tasks: [foundation-model-pretraining, representation-learning, structure-prediction]
+    year: 2022
+    github: https://github.com/ml4bio/RNA-FM
+    last_checked: 2026-10-02
+    metadata_sources:
+      - https://github.com/ml4bio/RNA-FM
+
+  rinalmo:
+    entities: [gene, transcript]
+    methods: [language-model, self-supervised-learning, transformer]
+    modalities: [rna-sequence]
+    tasks: [foundation-model-pretraining, representation-learning, structure-prediction]
+    year: 2024
+    github: https://github.com/lbcb-sci/RiNALMo
+    last_checked: 2026-10-02
+    metadata_sources:
+      - https://github.com/lbcb-sci/RiNALMo
+
+  evo_2:
+    entities: [gene, genome, organism]
+    methods: [generative-model, language-model]
+    modalities: [dna-sequence, genomics]
+    tasks: [foundation-model-pretraining, representation-learning]
+    year: 2026
+    github: https://github.com/arcinstitute/evo2
+    paper: https://doi.org/10.1038/s41586-026-10176-5
+    last_checked: 2026-10-02
+    metadata_sources:
+      - https://github.com/arcinstitute/evo2
+      - https://doi.org/10.1038/s41586-026-10176-5
+
+  alphagenome:
+    entities: [gene, genome, regulatory-element, variant]
+    methods: [supervised-learning, transformer]
+    modalities: [dna-sequence, epigenomics, genomics]
+    tasks: [regression, representation-learning]
+    year: 2025
+    github: https://github.com/google-deepmind/alphagenome
+    last_checked: 2026-10-02
+    metadata_sources:
+      - https://github.com/google-deepmind/alphagenome
+
+  moderngena:
+    entities: [gene, genome, regulatory-element]
+    methods: [language-model, self-supervised-learning, transformer]
+    modalities: [dna-sequence, genomics]
+    tasks: [classification, foundation-model-pretraining, regression, representation-learning]
+    year: 2026
+    github: https://github.com/AIRI-Institute/GENA_LM
+    last_checked: 2026-10-02
+    metadata_sources:
+      - https://github.com/AIRI-Institute/GENA_LM

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

@@ -0,0 +1,292 @@
+---
+title: "Genetic perturbation Dataset Explorer metadata; artifact sync verified."
+task: ""
+lineage_type: import
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/4ed7cfef/data/enrichment.genetic-perturbation-v1.yml
+upstream_sha: 4ed7cfef
+imported_at: 2026-10-26
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# Genetic perturbation Dataset Explorer metadata; artifact sync verified.
+# Curated genetic perturbation datasets for Dataset Explorer.
+resources:
+  dixit_et_al_2016_perturb_seq:
+    paper: https://doi.org/10.1016/j.cell.2016.11.038
+    last_checked: 2026-10-02
+    metadata_sources:
+      - https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE90063
+      - https://doi.org/10.1016/j.cell.2016.11.038
+    dataset_profile:
+      year: 2016
+      view_scope: full-study
+      species: [human, mouse]
+      sample_type: cell-line+primary
+      biological_context: immune-and-cell-line
+      perturbation_type: [genetic]
+      genetic_modes: [crispr-ko, combinatorial]
+      readout: single-cell-rna
+      n_profiles: "~200,000 cells"
+      n_perturbations: "pooled CRISPR-KO across six screens"
+      n_contexts: 6
+      gene_panel: transcriptome-wide
+      paired: true
+      pre_post: false
+      longitudinal: false
+      drug_identity: false
+      dose: false
+      time: true
+      smiles: false
+      clinical_outcome: false
+      n_samples: "six pooled screens across K562 and dendritic-cell conditions"
+      n_cells: "~200,000"
+      access: open
+
+  adamson_et_al_2016_perturb_seq:
+    paper: https://doi.org/10.1016/j.cell.2016.11.048
+    last_checked: 2026-10-02
+    metadata_sources:
+      - https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE90546
+    dataset_profile:
+      year: 2016
+      view_scope: full-study
+      species: [human]
+      sample_type: cell-line
+      biological_context: unfolded-protein-response
+      perturbation_type: [genetic]
+      genetic_modes: [crispri, combinatorial]
+      readout: single-cell-rna
+      n_profiles: "~86,000 cells"
+      n_perturbations: "~100 UPR hits plus combinatorial CRISPRi perturbations"
+      n_contexts: 3
+      gene_panel: transcriptome-wide
+      paired: true
+      pre_post: false
+      longitudinal: false
+      drug_identity: false
+      dose: false
+      time: false
+      smiles: false
+      clinical_outcome: false
+      n_samples: "three pooled CRISPRi Perturb-seq experiments in K562"
+      n_cells: "~86,000"
+      access: open
+
+  datlinger_et_al_2017_crop_seq:
+    paper: https://doi.org/10.1038/nmeth.4177
+    last_checked: 2026-10-02
+    metadata_sources:
+      - https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE92872
+      - https://doi.org/10.1038/nmeth.4177
+    dataset_profile:
+      year: 2017
+      view_scope: full-study
+      species: [human]
+      sample_type: cell-line
+      biological_context: t-cell-receptor-signaling
+      perturbation_type: [genetic]
+      genetic_modes: [crispr-ko]
+      readout: single-cell-rna
+      n_profiles: 5905
+      n_perturbations: "TCR regulators and transcription factors"
+      n_contexts: 2
+      gene_panel: transcriptome-wide
+      paired: true
+      pre_post: false
+      longitudinal: false
+      drug_identity: false
+      dose: false
+      time: false
+      smiles: false
+      clinical_outcome: false
+      n_samples: "stimulated and unstimulated Jurkat CROP-seq"
+      n_cells: 5905
+      access: open
+
+  norman_et_al_2019_crispra_perturb_seq:
+    paper: https://doi.org/10.1126/science.aax4438
+    last_checked: 2026-10-02
+    metadata_sources:
+      - https://figshare.com/articles/dataset/Norman_et_al_2019_Perturb-seq/27766323
+      - https://doi.org/10.1126/science.aax4438
+    dataset_profile:
+      year: 2019
+      view_scope: full-study
+      species: [human]
+      sample_type: cell-line
+      biological_context: genetic-interactions
+      perturbation_type: [genetic]
+      genetic_modes: [crispra, combinatorial]
+      readout: single-cell-rna
+      n_profiles: "~110,000 cells"
+      n_perturbations: 287
+      n_contexts: 1
+      gene_panel: transcriptome-wide
+      paired: true
+      pre_post: false
+      longitudinal: false
+      drug_identity: false
+      dose: false
+      time: false
+      smiles: false
+      clinical_outcome: false
+      n_samples: "single-gene and pairwise CRISPRa perturbations in K562"
+      n_cells: "~110,000"
+      access: open
+
+  gasperini_et_al_2019_crispri_enhancer_screen:
+    paper: https://doi.org/10.1016/j.cell.2018.11.029
+    last_checked: 2026-10-02
+    metadata_sources:
+      - https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE120861
+    dataset_profile:
+      year: 2019
+      view_scope: full-study
+      species: [human]
+      sample_type: cell-line
+      biological_context: enhancer-gene-regulation
+      perturbation_type: [genetic]
+      genetic_modes: [crispri, enhancer-targeting]
+      readout: single-cell-rna
+      n_profiles: 254974
+      n_perturbations: 5920
+      n_contexts: 3
+      gene_panel: transcriptome-wide
+      paired: true
+      pre_post: false
+      longitudinal: false
+      drug_identity: false
+      dose: false
+      time: false
+      smiles: false
+      clinical_outcome: false
+      n_samples: "three CRISPRi screens in K562, including an at-scale enhancer screen"
+      n_cells: 254974
+      access: open
+
+  replogle_et_al_2022_k562_genome_wide_perturb_seq:
+    paper: https://doi.org/10.1016/j.cell.2022.05.013
+    last_checked: 2026-10-02
+    metadata_sources:
+      - https://plus.figshare.com/articles/dataset/_Mapping_information-rich_genotype-phenotype_landscapes_with_genome-scale_Perturb-seq_Replogle_et_al_2022_processed_Perturb-seq_datasets/20029387
+      - https://gwps.wi.mit.edu/
+    dataset_profile:
+      year: 2022
+      view_scope: full-study
+      species: [human]
+      sample_type: cell-line
+      biological_context: genome-wide-functional-genomics
+      perturbation_type: [genetic]
+      genetic_modes: [crispri]
+      readout: single-cell-rna
+      n_profiles: "genome-scale; mean ~183 cells per detected perturbation"
+      n_perturbations: "9,866 target genes; 11,258 detected sgRNA-pair perturbations"
+      n_contexts: 1
+      gene_panel: transcriptome-wide
+      paired: true
+      pre_post: false
+      longitudinal: false
+      drug_identity: false
+      dose: false
+      time: true
+      smiles: false
+      clinical_outcome: false
+      n_samples: "K562 CRISPRi genome-wide screen sampled at day 8"
+      n_cells: "mean ~183 cells per detected perturbation"
+      access: open
+
+  replogle_et_al_2022_k562_essential_perturb_seq:
+    paper: https://doi.org/10.1016/j.cell.2022.05.013
+    last_checked: 2026-10-02
+    metadata_sources:
+      - https://plus.figshare.com/articles/dataset/_Mapping_information-rich_genotype-phenotype_landscapes_with_genome-scale_Perturb-seq_Replogle_et_al_2022_processed_Perturb-seq_datasets/20029387
+      - https://gwps.wi.mit.edu/
+    dataset_profile:
+      year: 2022
+      view_scope: full-study
+      species: [human]
+      sample_type: cell-line
+      biological_context: essential-gene-screen
+      perturbation_type: [genetic]
+      genetic_modes: [crispri]
+      readout: single-cell-rna
+      n_profiles: 310385
+      n_perturbations: 2057
+      n_contexts: 1
+      gene_panel: transcriptome-wide
+      paired: true
+      pre_post: false
+      longitudinal: false
+      drug_identity: false
+      dose: false
+      time: true
+      smiles: false
+      clinical_outcome: false
+      n_samples: "K562 CRISPRi essential-gene screen sampled at day 6"
+      n_cells: 310385
+      access: open
+
+  replogle_et_al_2022_rpe1_essential_perturb_seq:
+    paper: https://doi.org/10.1016/j.cell.2022.05.013
+    last_checked: 2026-10-02
+    metadata_sources:
+      - https://plus.figshare.com/articles/dataset/_Mapping_information-rich_genotype-phenotype_landscapes_with_genome-scale_Perturb-seq_Replogle_et_al_2022_processed_Perturb-seq_datasets/20029387
+      - https://gwps.wi.mit.edu/
+    dataset_profile:
+      year: 2022
+      view_scope: full-study
+      species: [human]
+      sample_type: cell-line
+      biological_context: essential-gene-screen
+      perturbation_type: [genetic]
+      genetic_modes: [crispri]
+      readout: single-cell-rna
+      n_profiles: 247914
+      n_perturbations: 2393
+      n_contexts: 1
+      gene_panel: transcriptome-wide
+      paired: true
+      pre_post: false
+      longitudinal: false
+      drug_identity: false
+      dose: false
+      time: true
+      smiles: false
+      clinical_outcome: false
+      n_samples: "RPE1 CRISPRi essential-gene screen sampled at day 7"
+      n_cells: 247914
+      access: open
+
+  frangieh_et_al_2021_perturb_cite_seq:
+    paper: https://doi.org/10.1038/s41588-021-00779-1
+    last_checked: 2026-10-02
+    metadata_sources:
+      - https://singlecell.broadinstitute.org/single_cell/study/SCP1064/multi-modal-pooled-perturb-cite-seq-screens-in-patient-models-define-novel-mechanisms-of-cancer-immune-evasion
+      - https://doi.org/10.1038/s41588-021-00779-1
+    dataset_profile:
+      year: 2021
+      view_scope: full-study
+      species: [human]
+      sample_type: patient-derived-cell-line
+      biological_context: melanoma-immunotherapy
+      perturbation_type: [genetic]
+      genetic_modes: [crispr-ko]
+      readout: cite-seq
+      n_profiles: ">200,000 cells"
+      n_perturbations: 248
+      n_contexts: 3
+      gene_panel: "transcriptome + surface proteins"
+      paired: true
+      pre_post: false
+      longitudinal: false
+      drug_identity: false
+      dose: false
+      time: false
+      smiles: false
+      clinical_outcome: false
+      n_samples: "patient-derived melanoma under control, IFN-gamma, and TIL co-culture conditions"
+      n_cells: ">200,000"
+      access: mixed

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

@@ -0,0 +1,285 @@
+---
+title: "Method Explorer v1; changes to this file trigger artifact regeneration."
+task: ""
+lineage_type: import
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/4ed7cfef/data/enrichment.method-explorer-v1.yml
+upstream_sha: 4ed7cfef
+imported_at: 2026-10-26
+prompt_class: unknown
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# Method Explorer v1; changes to this file trigger artifact regeneration.
+# Structured comparison metadata for Method Explorer v1.
+resources:
+  bison:
+    method_profile:
+      task_family: drug-perturbation
+      year: 2026
+      input: bulk/pseudobulk transcriptomics
+      drug_representation: Morgan fingerprints + chemical tokens
+      context_representation: control-derived context features + embeddings
+      output: transcriptomic response
+      unseen_drug: yes
+      unseen_context: limited
+      dose: true
+      time: true
+      patient_transfer: false
+      code: false
+
+  txpert:
+    method_profile:
+      task_family: drug-perturbation
+      year: 2026
+      input: transcriptomics + biological knowledge
+      drug_representation: knowledge-graph-informed perturbation representation
+      context_representation: cellular context representation
+      output: transcriptomic response
+      unseen_drug: yes
+      unseen_context: yes
+      dose: false
+      time: false
+      patient_transfer: false
+      code: true
+
+  lpm:
+    method_profile:
+      task_family: drug-perturbation
+      year: 2025
+      input: heterogeneous perturbation experiments
+      drug_representation: learned perturbation representation
+      context_representation: learned context representation
+      output: molecular state
+      unseen_drug: yes
+      unseen_context: yes
+      dose: limited
+      time: limited
+      patient_transfer: false
+      code: true
+
+  state:
+    method_profile:
+      task_family: drug-perturbation
+      year: 2026
+      input: sets of cellular expression profiles
+      drug_representation: perturbation embedding
+      context_representation: cellular state
+      output: transcriptomic response
+      unseen_drug: limited
+      unseen_context: yes
+      dose: limited
+      time: limited
+      patient_transfer: false
+      code: true
+
+  chemcpa:
+    method_profile:
+      task_family: drug-perturbation
+      year: 2022
+      input: single-cell expression + covariates
+      drug_representation: RDKit2D descriptors
+      context_representation: cell covariates + basal state
+      output: transcriptomic response
+      unseen_drug: yes
+      unseen_context: limited
+      dose: true
+      time: limited
+      patient_transfer: false
+      code: true
+
+  cmonge:
+    method_profile:
+      task_family: drug-perturbation
+      year: 2026
+      input: source/target expression distributions
+      drug_representation: RDKit2D descriptors
+      context_representation: matched control distribution
+      output: transported expression distribution
+      unseen_drug: yes
+      unseen_context: yes
+      dose: true
+      time: limited
+      patient_transfer: false
+      code: true
+
+  prnet:
+    method_profile:
+      task_family: drug-perturbation
+      year: 2024
+      input: basal expression + perturbation
+      drug_representation: FCFP4 fingerprint
+      context_representation: matched basal expression
+      output: transcriptomic response
+      unseen_drug: yes
+      unseen_context: limited
+      dose: true
+      time: limited
+      patient_transfer: false
+      code: true
+
+  biolord:
+    method_profile:
+      task_family: drug-perturbation
+      year: 2024
+      input: expression + known attributes
+      drug_representation: chemical descriptors
+      context_representation: unknown latent attributes
+      output: transcriptomic response
+      unseen_drug: yes
+      unseen_context: yes
+      dose: true
+      time: true
+      patient_transfer: false
+      code: true
+
+  perturbnet:
+    method_profile:
+      task_family: drug-perturbation
+      year: 2025
+      input: single-cell expression distributions
+      drug_representation: molecular representation
+      context_representation: conditional latent state
+      output: cell-state distribution
+      unseen_drug: yes
+      unseen_context: limited
+      dose: limited
+      time: limited
+      patient_transfer: false
+      code: true
+
+  cellflow:
+    method_profile:
+      task_family: drug-perturbation
+      year: 2025
+      input: control/treated expression distributions
+      drug_representation: molecular features
+      context_representation: matched control distribution
+      output: transported cell-state distribution
+      unseen_drug: yes
+      unseen_context: yes
+      dose: true
+      time: true
+      patient_transfer: false
+      code: true
+
+  prophet:
+    method_profile:
+      task_family: drug-perturbation
+      year: 2024
+      input: control expression + perturbation
+      drug_representation: learned chemical representation
+      context_representation: control-derived context
+      output: gene-expression response
+      unseen_drug: yes
+      unseen_context: yes
+      dose: true
+      time: true
+      patient_transfer: false
+      code: true
+
+  prepr_ct:
+    method_profile:
+      task_family: drug-perturbation
+      year: 2026
+      input: aggregate expression + context graph
+      drug_representation: RDKit descriptors
+      context_representation: context-specific gene graph
+      output: transcriptomic response
+      unseen_drug: yes
+      unseen_context: limited
+      dose: true
+      time: true
+      patient_transfer: false
+      code: true
+
+  xpert:
+    method_profile:
+      task_family: drug-perturbation
+      year: 2026
+      input: transcriptomics + heterogeneous graph
+      drug_representation: Uni-Mol + knowledge graph
+      context_representation: basal expression + gene embeddings
+      output: transcriptomic response
+      unseen_drug: yes
+      unseen_context: yes
+      dose: true
+      time: true
+      patient_transfer: false
+      code: true
+
+  paso:
+    method_profile:
+      task_family: drug-response-prediction
+      year: 2025
+      input: pathway-aware multi-omics
+      drug_representation: SMILES-derived representation
+      context_representation: pathway-difference representation
+      output: cell-line drug response
+      unseen_drug: limited
+      unseen_context: limited
+      dose: false
+      time: false
+      patient_transfer: false
+      code: true
+
+  dtlcdr:
+    method_profile:
+      task_family: drug-response-prediction
+      year: 2025
+      input: multimodal tumor/cell-line features + targets
+      drug_representation: target-aware drug representation
+      context_representation: biological sample representation
+      output: preclinical/clinical response
+      unseen_drug: yes
+      unseen_context: limited
+      dose: false
+      time: false
+      patient_transfer: true
+      code: false
+
+  therapi:
+    method_profile:
+      task_family: drug-response-prediction
+      year: 2026
+      input: tumor transcriptomics + cell-line knowledge
+      drug_representation: perturbation/gene-level representation
+      context_representation: aligned tumor/cell-line representation
+      output: patient drug response
+      unseen_drug: limited
+      unseen_context: yes
+      dose: false
+      time: false
+      patient_transfer: true
+      code: true
+
+  expresso:
+    method_profile:
+      task_family: drug-response-prediction
+      year: 2026
+      input: pretreatment tumor transcriptomics
+      drug_representation: drug targets + biomarkers
+      context_representation: tumor transcriptomic context
+      output: patient treatment response
+      unseen_drug: limited
+      unseen_context: yes
+      dose: false
+      time: false
+      patient_transfer: true
+      code: false
+
+  perturbrx:
+    method_profile:
+      task_family: drug-response-prediction
+      year: 2026
+      input: pretreatment tumor RNA-seq + perturbation pretraining
+      drug_representation: drug embedding + treatment-conditioned transition
+      context_representation: patient latent state
+      output: patient treatment response
+      unseen_drug: limited
+      unseen_context: yes
+      dose: true
+      time: false
+      patient_transfer: true
+      code: false

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

@@ -0,0 +1,321 @@
+---
+title: "Includes Bison benchmark coverage metadata."
+task: ""
+lineage_type: import
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/4ed7cfef/data/enrichment.perturbation-datasets-v1.yml
+upstream_sha: 4ed7cfef
+imported_at: 2026-10-26
+prompt_class: catalogue
+upstream_changes: accepted
+author: upstream
+validated: false
+---
+
+# Includes Bison benchmark coverage metadata.
+# Curated perturbation, patient, and PDX dataset profiles for Dataset Explorer.
+resources:
+  tahoe_100m:
+    dataset_profile:
+      year: 2025
+      view_scope: bison-benchmark-view
+      readout: single-cell-rna
+      n_profiles: 67018
+      n_compounds: 376
+      n_contexts: 50
+      gene_panel: "20,573 full / 2,000 HVG (Bison benchmark view)"
+      time: true
+      species: [human]
+      sample_type: cell-line
+      biological_context: cancer
+      perturbation_type: [chemical]
+      paired: false
+      pre_post: false
+      longitudinal: false
+      drug_identity: true
+      dose: true
+      smiles: partial
+      clinical_outcome: false
+      n_samples: ">100 million profiles; 50 cancer cell lines; ~1,100 compounds"
+      n_cells: ">100 million"
+      access: open
+    last_checked: 2026-10-02
+    metadata_sources:
+      - https://huggingface.co/datasets/tahoebio/Tahoe-100M
+
+  sci_plex:
+    dataset_profile:
+      year: 2020
+      view_scope: bison-benchmark-view
+      readout: pseudobulk-rna
+      n_profiles: 4974
+      n_compounds: 186
+      n_contexts: 3
+      gene_panel: "20,251 full / 2,000 HVG (Bison benchmark view)"
+      time: true
+      species: [human, mouse]
+      sample_type: cell-line
+      biological_context: perturbation-screen
+      perturbation_type: [chemical]
+      paired: false
+      pre_post: false
+      longitudinal: false
+      drug_identity: true
+      dose: true
+      smiles: partial
+      clinical_outcome: false
+      n_samples: "~5,000 conditions; 188 compounds across 3 human cancer cell lines"
+      n_cells: 649340
+      access: open
+    last_checked: 2026-10-02
+    metadata_sources:
+      - https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE139944
+      - https://pubmed.ncbi.nlm.nih.gov/31806696/
+
+  mix_seq:
+    dataset_profile:
+      year: 2020
+      view_scope: full-resource
+      readout: single-cell-rna
+      n_compounds: 13
+      n_contexts: "100+ cell lines"
+      gene_panel: "transcriptome-wide"
+      time: true
+      species: [human]
+      sample_type: cell-line
+      biological_context: cancer
+      perturbation_type: [chemical, genetic]
+      genetic_modes: [mixed]
+      paired: false
+      pre_post: false
+      longitudinal: true
+      drug_identity: true
+      dose: true
+      smiles: partial
+      clinical_outcome: false
+      n_samples: "pools of up to 100+ cancer cell lines; 13-drug demonstration"
+      n_cells: "single-cell RNA-seq"
+      access: open
+    last_checked: 2026-10-02
+    metadata_sources:
+      - https://www.nature.com/articles/s41467-020-17440-w
+
+  pdx_atlas:
+    dataset_profile:
+      year: 2026
+      view_scope: current-portal
+      readout: multi-omics
+      n_profiles: ">50 PDXs"
+      n_contexts: "30 patients"
+      gene_panel: "multi-omics"
+      time: false
+      species: [human, mouse]
+      sample_type: pdx
+      biological_context: cancer
+      perturbation_type: [chemical]
+      paired: true
+      pre_post: false
+      longitudinal: false
+      drug_identity: true
+      dose: false
+      smiles: partial
+      clinical_outcome: false
+      n_samples: ">50 PDXs from 30 prostate cancer patients in the current portal"
+      n_cells: null
+      access: open
+    last_checked: 2026-10-02
+    metadata_sources:
+      - https://muralportal.pdxatlas.org/
+
+  gse191127_breast_cancer_pre_post_chemotherapy:
+    dataset_profile:
+      year: 2022
+      view_scope: study-cohort
+      readout: bulk-rna
+      n_profiles: "317 pretreatment; 22 matched pre/post deeply profiled"
+      n_contexts: 1
+      gene_panel: "transcriptome-wide"
+      time: true
+      species: [human]
+      sample_type: patient
+      biological_context: breast-cancer
+      perturbation_type: [chemical]
+      paired: true
+      pre_post: true
+      longitudinal: true
+      drug_identity: limited
+      dose: false
+      smiles: partial
+      clinical_outcome: true
+      n_samples: "317 pretreatment biopsies; 22 matched pre/post tumors with deep profiling"
+      n_cells: null
+      access: open
+    last_checked: 2026-10-02
+    metadata_sources:
+      - https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE191127
+
+
+  op3:
+    dataset_profile:
+      year: 2024
+      view_scope: bison-benchmark-view
+      readout: bulk-rna
+      n_profiles: 1813
+      n_compounds: 138
+      n_contexts: 4
+      gene_panel: "5,198 full / 2,000 HVG"
+      time: true
+      species: [human]
+      sample_type: cell-line
+      biological_context: perturbation-screen
+      perturbation_type: [chemical]
+      paired: false
+      pre_post: false
+      longitudinal: false
+      drug_identity: true
+      dose: true
+      smiles: true
+      clinical_outcome: false
+      n_samples: "1,813 profiles; 138 molecules; 4 contexts"
+      n_cells: null
+      access: open
+    last_checked: 2026-10-02
+    metadata_sources:
+      - https://github.com/theislab/Chem-PerturBridge
+
+  vcpi_0001:
+    dataset_profile:
+      view_scope: bison-benchmark-view
+      readout: bulk-rna
+      n_profiles: 27517
+      n_compounds: 2272
+      n_contexts: 1
+      gene_panel: "20,629 full / 2,000 HVG"
+      time: true
+      species: [human]
+      sample_type: cell-line
+      biological_context: perturbation-screen
+      perturbation_type: [chemical]
+      paired: false
+      pre_post: false
+      longitudinal: false
+      drug_identity: true
+      dose: true
+      smiles: true
+      clinical_outcome: false
+      n_samples: "27,517 profiles; 2,272 molecules; 1 context"
+      n_cells: null
+      access: open
+    last_checked: 2026-10-02
+    metadata_sources:
+      - https://github.com/theislab/Chem-PerturBridge
+
+  vcpi_0002:
+    dataset_profile:
+      view_scope: bison-benchmark-view
+      readout: bulk-rna
+      n_profiles: 18139
+      n_compounds: 1488
+      n_contexts: 1
+      gene_panel: "20,629 full / 2,000 HVG"
+      time: true
+      species: [human]
+      sample_type: cell-line
+      biological_context: perturbation-screen
+      perturbation_type: [chemical]
+      paired: false
+      pre_post: false
+      longitudinal: false
+      drug_identity: true
+      dose: true
+      smiles: true
+      clinical_outcome: false
+      n_samples: "18,139 profiles; 1,488 molecules; 1 context"
+      n_cells: null
+      access: open
+    last_checked: 2026-10-02
+    metadata_sources:
+      - https://github.com/theislab/Chem-PerturBridge
+
+  novartis_perturbation_dataset:
+    dataset_profile:
+      year: 2025
+      view_scope: bison-benchmark-view
+      readout: bulk-rna
+      n_profiles: 46748
+      n_compounds: 3770
+      n_contexts: 1
+      gene_panel: "20,038 full / 2,000 HVG"
+      time: true
+      species: [human]
+      sample_type: cell-line
+      biological_context: perturbation-screen
+      perturbation_type: [chemical]
+      paired: false
+      pre_post: false
+      longitudinal: false
+      drug_identity: true
+      dose: true
+      smiles: true
+      clinical_outcome: false
+      n_samples: "46,748 profiles; 3,770 molecules; 1 context"
+      n_cells: null
+      access: open
+    last_checked: 2026-10-02
+    metadata_sources:
+      - https://github.com/theislab/Chem-PerturBridge
+
+  lincs_l1000_phase_1:
+    dataset_profile:
+      year: 2017
+      view_scope: bison-benchmark-view
+      readout: l1000
+      n_profiles: 692787
+      n_compounds: 9233
+      n_contexts: 70
+      gene_panel: "978 landmark genes"
+      time: true
+      species: [human]
+      sample_type: cell-line
+      biological_context: perturbation-screen
+      perturbation_type: [chemical]
+      paired: false
+      pre_post: false
+      longitudinal: false
+      drug_identity: true
+      dose: true
+      smiles: true
+      clinical_outcome: false
+      n_samples: "692,787 profiles; 9,233 molecules; 70 contexts; 978 landmark genes"
+      n_cells: null
+      access: open
+    last_checked: 2026-10-02
+    metadata_sources:
+      - https://github.com/theislab/Chem-PerturBridge
+
+  lincs_l1000_phase_2:
+    dataset_profile:
+      year: 2017
+      view_scope: bison-benchmark-view
+      readout: l1000
+      n_profiles: 333263
+      n_compounds: 1760
+      n_contexts: 30
+      gene_panel: "978 landmark genes"
+      time: true
+      species: [human]
+      sample_type: cell-line
+      biological_context: perturbation-screen
+      perturbation_type: [chemical]
+      paired: false
+      pre_post: false
+      longitudinal: false
+      drug_identity: true
+      dose: true
+      smiles: true
+      clinical_outcome: false
+      n_samples: "333,263 profiles; 1,760 molecules; 30 contexts; 978 landmark genes"
+      n_cells: null
+      access: open
+    last_checked: 2026-10-02
+    metadata_sources:
+      - https://github.com/theislab/Chem-PerturBridge

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

@@ -2,9 +2,9 @@
 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
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/4ed7cfef/data/enrichment.pharmacogenomics-v1.yml
+upstream_sha: 4ed7cfef
+imported_at: 2026-10-26
 prompt_class: catalogue
 upstream_changes: accepted
 author: upstream
@@ -20,6 +20,25 @@ resources:
     last_checked: 2026-08-08
     metadata_sources:
       - https://biodev.github.io/BeatAML2/
+    dataset_profile:
+      year: 2018
+      readout: ex-vivo-viability
+      time: false
+      view_scope: full-resource
+      species: [human]
+      sample_type: patient
+      biological_context: cancer
+      perturbation_type: [chemical]
+      paired: true
+      pre_post: false
+      longitudinal: false
+      drug_identity: true
+      dose: true
+      smiles: partial
+      clinical_outcome: true
+      n_samples: "primary AML patient samples"
+      n_cells: null
+      access: open
 
   cancer_therapeutics_response_portal_ctrp:
     entities: [cell, drug]
@@ -27,6 +46,27 @@ resources:
     last_checked: 2026-08-08
     metadata_sources:
       - https://portals.broadinstitute.org/ctrp/
+    dataset_profile:
+      year: 2013
+      readout: viability
+      n_compounds: ">400"
+      n_contexts: "~900 cell lines"
+      time: false
+      view_scope: full-resource
+      species: [human]
+      sample_type: cell-line
+      biological_context: cancer
+      perturbation_type: [chemical]
+      paired: true
+      pre_post: false
+      longitudinal: false
+      drug_identity: true
+      dose: true
+      smiles: partial
+      clinical_outcome: false
+      n_samples: "~900 cancer cell lines"
+      n_cells: null
+      access: open
 
   bindingdb_curated_sets:
     entities: [molecule, protein]
@@ -71,9 +111,30 @@ resources:
     modalities: [single-cell-rna-seq]
     tasks: [perturbation-prediction]
     github: https://github.com/sanderlab/scPerturb
-    last_checked: 2026-08-08
+    last_checked: 2026-10-02
     metadata_sources:
       - https://github.com/sanderlab/scPerturb
+    dataset_profile:
+      year: 2024
+      readout: single-cell-rna
+      gene_panel: "varies by source study"
+      time: true
+      view_scope: harmonized-collection
+      species: [human, mouse]
+      sample_type: cell-line
+      biological_context: perturbation-collection
+      perturbation_type: [chemical, genetic]
+      genetic_modes: [mixed]
+      paired: false
+      pre_post: false
+      longitudinal: false
+      drug_identity: true
+      dose: true
+      smiles: partial
+      clinical_outcome: false
+      n_samples: "scPerturb integrated perturbation datasets"
+      n_cells: "multi-study single-cell compendium"
+      access: open
 
   genomics_of_drug_sensitivity_in_cancer_gdsc:
     entities: [cell, drug, gene]
@@ -82,6 +143,26 @@ resources:
     last_checked: 2026-08-08
     metadata_sources:
       - https://www.cancerrxgene.org/
+    dataset_profile:
+      year: 2012
+      readout: viability
+      n_contexts: "~1,000 cell lines"
+      time: false
+      view_scope: full-resource
+      species: [human]
+      sample_type: cell-line
+      biological_context: cancer
+      perturbation_type: [chemical]
+      paired: true
+      pre_post: false
+      longitudinal: false
+      drug_identity: true
+      dose: true
+      smiles: partial
+      clinical_outcome: false
+      n_samples: "~1,000 cancer cell lines"
+      n_cells: null
+      access: open
 
   cellminer_cross_database_cellminercdb:
     entities: [cell, drug, gene]

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

@@ -2,9 +2,9 @@
 title: "Resource Data Schema (`docs/data/resources.json`)"
 task: ""
 lineage_type: import
-upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/12d87583/docs/data/SCHEMA.md
-upstream_sha: 12d87583
-imported_at: 2026-06-26
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/4ed7cfef/docs/data/SCHEMA.md
+upstream_sha: 4ed7cfef
+imported_at: 2026-10-26
 prompt_class: catalogue
 upstream_changes: accepted
 author: upstream
@@ -52,6 +52,10 @@ When contributing new resources, update `README.md` first, then regenerate artif
 | `api` | boolean | Whether programmatic API access is available. Defaults to `false`. |
 | `paper` | string | DOI or URL to preprint/peer-reviewed publication. |
 | `updated` | string | Last-known update date, recommended `YYYY-MM-DD`. |
+| `dataset_profile` | object | Optional Dataset Explorer metadata for structured comparison of datasets/benchmarks. |
+| `method_profile` | object | Optional Method Explorer metadata for structured comparison of response and perturbation methods. |
+| `foundation_profile` | object | Optional Foundation Model Explorer metadata for modality, model scale, size, and availability. |
+| `agent_profile` | object | Optional Agent Explorer metadata for agent architecture, capabilities, and scientific workflow support. |
 
 ## Naming and consistency guidance
 
@@ -79,3 +83,101 @@ When contributing new resources, update `README.md` first, then regenerate artif
   "updated": "2026-01-15"
 }
 ```
+
+## Dataset Explorer profile
+
+Resources that participate in the Dataset Explorer may define a `dataset_profile` object in a modular `data/enrichment.*.yml` file. This keeps README-derived identity fields separate from deeper dataset metadata.
+
+Supported v1 fields:
+
+| Field | Type | Meaning |
+|---|---|---|
+| `year` | integer | Representative primary publication or public release year. |
+| `species` | array of strings | Species represented in the dataset. |
+| `sample_type` | string | Primary unit, e.g. `patient` or `cell-line`. |
+| `biological_context` | string | High-level context such as `cancer` or `perturbation-screen`. |
+| `perturbation_type` | array of strings | Chemical, genetic, or other interventions. |
+| `genetic_modes` | array of strings | Genetic perturbation mechanisms, e.g. `crispri`, `crispra`, `crispr-ko`, `enhancer-targeting`, `combinatorial`, or `mixed`. |
+| `paired` | boolean | Whether molecular/sample identity can be linked to the measured response or perturbation condition. |
+| `pre_post` | boolean | Whether matched pre-treatment and post-treatment samples are available. |
+| `longitudinal` | boolean | Whether repeated measurements over time are available as a core design feature. |
+| `drug_identity` | boolean or string | Whether treatment identity is available; strings such as `limited` may encode partial availability. |
+| `dose` | boolean | Whether dose/concentration metadata are available. |
+| `smiles` | boolean or `partial`/`limited` | Whether compound structures are directly available or can only be partially mapped. |
+| `clinical_outcome` | boolean | Whether patient-level clinical outcomes are available. |
+| `n_samples` | string | Human-readable scale summary. |
+| `n_cells` | integer, string, or null | Cell count for single-cell datasets when meaningful. |
+| `n_profiles` | integer, string, or null | Number of profiles in the curated comparison view. |
+| `n_perturbations` | integer, string, or null | Number of perturbation targets/conditions, especially useful for genetic screens. |
+| `n_compounds` | integer, string, or null | Number of compounds/molecules in the curated comparison view. |
+| `n_contexts` | integer, string, or null | Number of cellular or experimental contexts. |
+| `readout` | string | Primary readout, e.g. `bulk-rna`, `pseudobulk-rna`, `single-cell-rna`, or `viability`. |
+| `gene_panel` | string | Gene/readout panel summary, e.g. `20,251 full / 2,000 HVG` or `978 landmark genes`. |
+| `time` | boolean | Whether treatment time metadata are available. |
+| `view_scope` | string | Scope of the displayed scale fields, e.g. `full-resource`, `study-cohort`, or `bison-benchmark-view`. |
+| `access` | string | Dataset-level access summary. |
+
+The v1 profiles are colocated with their existing modular `data/enrichment.*.yml` owners and rendered at `docs/dataset-explorer.html`.
+
+## Method Explorer profile
+
+Methods shown in the Method Explorer may define a `method_profile` object in a modular enrichment fragment.
+
+| Field | Type | Meaning |
+|---|---|---|
+| `task_family` | string | High-level task, currently `drug-response-prediction` or `drug-perturbation`. |
+| `year` | integer | Publication/release year used for navigation. |
+| `input` | string | Main biological/model input. |
+| `drug_representation` | string | Chemical or treatment representation. |
+| `context_representation` | string | Cellular, patient, or experimental context representation. |
+| `output` | string | Main prediction target. |
+| `unseen_drug` | boolean/string | Support for unseen-drug generalization; `limited` denotes partial or task-dependent support. |
+| `unseen_context` | boolean/string | Support for new cellular/patient contexts. |
+| `dose` | boolean/string | Whether dose is explicitly represented. |
+| `time` | boolean/string | Whether treatment time is explicitly represented. |
+| `patient_transfer` | boolean/string | Whether the method explicitly transfers to or predicts patient response. |
+| `code` | boolean/string | Whether an implementation is linked from the curated resource entry. |
+
+The Method Explorer is rendered at `docs/method-explorer.html`.
+
+## Foundation Model Explorer profile
+
+Foundation models shown in the Foundation Model Explorer may define a `foundation_profile` object in a modular enrichment fragment.
+
+| Field | Type | Meaning |
+|---|---|---|
+| `year` | integer | Publication or release year used for navigation. |
+| `modalities` | array of strings | Fine-grained modalities such as `scrna`, `bulk-rna`, `proteomics`, `mutation`, `methylation`, `atac`, `spatial`, `pathology`, `dna`, `rna`, `chemical`, or `protein`. |
+| `params` | string or null | Human-readable parameter count or family range, e.g. `100M` or `1B / 7B / 40B`. |
+| `params_millions` | number | Largest representative parameter count in millions, used only for sorting. |
+| `pretraining_scale` | string or null | Human-readable summary of the pretraining corpus size. |
+| `species` | array of strings | Species represented in pretraining or primary use. |
+| `zero_shot` | boolean/string | Whether zero-shot use is explicitly supported; `limited` denotes task-dependent support. |
+| `finetunable` | boolean/string | Whether fine-tuning or parameter-efficient adaptation is supported. |
+| `weights` | boolean/string | Whether pretrained weights are publicly available. |
+| `code` | boolean/string | Whether implementation code is publicly available. |
+| `perturbation` | boolean/string | Whether perturbation prediction or in-silico perturbation is a supported use case. |
+| `spatial` | boolean/string | Whether spatial data are directly modeled or supported. |
+
+Unknown or unverified values should be omitted rather than inferred. The Foundation Model Explorer is rendered at `docs/foundation-explorer.html`.
+
+## Agent Explorer profile
+
+Agentic AI systems shown in the Agent Explorer may define an `agent_profile` object in a modular enrichment fragment.
+
+| Field | Type | Meaning |
+|---|---|---|
+| `year` | integer | Initial public release or publication year. |
+| `domains` | array of strings | Main scientific domains such as `general-biomedicine`, `therapeutics`, `bioinformatics`, `single-cell`, `scientific-workflow`, or `paper-to-agent`. |
+| `architecture` | string | `single-agent`, `multi-agent`, or `agent-ecosystem`. |
+| `tool_use` | boolean/string | Whether external scientific tools/APIs are used. |
+| `code_execution` | boolean/string | Whether the system executes code as part of its workflow. |
+| `web_retrieval` | boolean/string | Whether web or online database retrieval is supported. |
+| `literature` | boolean/string | Whether literature search/reasoning is explicitly supported. |
+| `omics` | boolean/string | Whether omics analysis is a first-class supported workflow. |
+| `wet_lab` | boolean/string | Whether wet-lab planning, experimental design, or laboratory-facing workflows are supported. |
+| `autonomous_experiment` | boolean/string | Whether the system can carry out iterative computational/experimental research loops. |
+| `human_in_loop` | boolean/string | Whether human collaboration or approval is an explicit part of the design. |
+| `open_source` | boolean/string | Whether implementation code is publicly available. |
+
+Capability fields may use `limited` or `unknown` when support is workflow-dependent or not clearly documented. The Agent Explorer is rendered at `docs/agent-explorer.html`.