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dffd9a2f99 |
@@ -2,9 +2,9 @@
|
||||
title: "Readme"
|
||||
task: ""
|
||||
lineage_type: import
|
||||
upstream_source: https://github.com/ai-boost/awesome-ai-for-science/blob/024f42bf/README.md
|
||||
upstream_sha: 024f42bf
|
||||
imported_at: 2026-08-09
|
||||
upstream_source: https://github.com/ai-boost/awesome-ai-for-science/blob/67d8e68d/README.md
|
||||
upstream_sha: 67d8e68d
|
||||
imported_at: 2026-08-12
|
||||
prompt_class: catalogue
|
||||
upstream_changes: accepted
|
||||
author: upstream
|
||||
@@ -638,6 +638,7 @@ validated: false
|
||||
- [SpikeInterface](https://github.com/SpikeInterface/spikeinterface) - Unified Python framework for extracellular electrophysiology, standardizing interfaces to 10+ ML-based spike sorting algorithms including Kilosort for reproducible neural spike sorting workflows (792+ stars, actively maintained)
|
||||
- [CaImAn (Flatiron Institute)](https://github.com/flatironinstitute/CaImAn) - Computational toolbox for large scale Calcium Imaging Analysis, including movie handling, motion correction, source extraction, spike deconvolution and result visualization, using machine learning for automated neuron detection and activity inference in two-photon and one-photon calcium imaging data (723+ stars, actively maintained)
|
||||
- [TRIBE v2](https://github.com/facebookresearch/tribev2) - Meta FAIR's foundation model of vision, audition, and language for in-silico neuroscience, predicting fMRI brain responses to naturalistic multimodal stimuli (video, audio, text) through unified Transformer architecture mapped to the cortical surface (2026)
|
||||
- [Brain2Qwerty (Meta FAIR, Nature Neuroscience 2026)](https://github.com/facebookresearch/brain2qwerty) - Non-invasive decoding of typed sentences from MEG and EEG brain recordings using a convolutional encoder, transformer, and character-level language model; official code for the Nature Neuroscience paper and Meta blog post on brain-AI communication (Meta FAIR, 894+ stars, CC BY-NC 4.0, 2026)
|
||||
- [braindecode](https://github.com/braindecode/braindecode) - Deep learning software to decode EEG, ECG or MEG signals, providing standardized neural network models, preprocessing pipelines, and evaluation workflows for brain-computer interfaces and cognitive neuroscience research (1.2K+ stars, BSD 3-Clause, actively maintained)
|
||||
- [snntorch](https://github.com/jeshraghian/snntorch) - Deep learning with spiking neural networks in Python, providing gradient-based training of SNNs via PyTorch autodifferentiation for brain-inspired computing and neuromorphic research, with online learning capabilities and extensive tutorials (1.9K+ stars, actively maintained)
|
||||
- [nilearn](https://github.com/nilearn/nilearn) - Machine learning and statistical learning for neuroimaging in Python, providing easy-to-use tools for fMRI and MRI analysis including decoding, connectivity estimation, and parcellation with seamless scikit-learn integration (INRIA Parietal team, 1.4K+ stars)
|
||||
@@ -741,6 +742,7 @@ validated: false
|
||||
- [TORAX](https://github.com/google-deepmind/torax) - Differentiable tokamak core transport simulator for fusion energy research, coupling PDE solvers with JAX auto-differentiation and neural-network surrogates for fast forward modelling, pulse-design, and trajectory optimization (Google DeepMind, Apache 2.0)
|
||||
- [DiffPhysDrone (Nature Machine Intelligence 2025)](https://github.com/HenryHuYu/DiffPhysDrone) - First real quadrotor robot trained end-to-end with differentiable physics for vision-based agile flight, bridging simulation-based learning and real-world deployment with physics-informed neural network controllers (558+ stars)
|
||||
- [Walrus (arXiv 2025)](https://github.com/PolymathicAI/walrus) - Cross-domain foundation model for continuum dynamics trained on 19 physical scenarios spanning 63 variables, featuring adaptive compute via stride modulation and patch jittering for long-run stability (Polymathic AI, 293+ stars, MIT License)
|
||||
- [GeoPT (ICML 2026)](https://github.com/Physics-Scaling/GeoPT) - Unified pre-trained model for general physics simulation via lifted geometric pre-training, augmenting static geometry with synthetic dynamics to enable dynamics-aware self-supervision without physics labels; improves industrial-fidelity benchmarks spanning fluid mechanics and solid mechanics while reducing labeled data requirements by 20–60% (Physics-Scaling, 224+ stars)
|
||||
|
||||
#### Astronomy & Astrophysics
|
||||
- [AstroCLIP](https://github.com/PolymathicAI/AstroCLIP) - Cross-modal self-supervised foundation model for galaxies by Polymathic AI, jointly embedding multi-band galaxy imaging and optical spectra into a shared latent space to enable zero/few-shot redshift estimation, galaxy property prediction, morphology classification, and cross-modal similarity search (MNRAS Letters 2024)
|
||||
@@ -785,6 +787,7 @@ validated: false
|
||||
- [SkySensePlusPlus](https://github.com/kang-wu/SkySensePlusPlus) - Semantic-enhanced multi-modal remote sensing foundation model for Earth observation (Nature Machine Intelligence 2025), enabling universal interpretation across diverse satellite imagery modalities with open-source weights and benchmarks
|
||||
- [TESSERA (CVPR 2026)](https://github.com/ucam-eo/tessera) - University of Cambridge's foundation model for time-series satellite imagery, enabling efficient extraction of temporal patterns from Earth observation for land classification, canopy height prediction, and other remote sensing tasks
|
||||
- [TerraMind (IBM & ESA, 2025)](https://github.com/IBM/terramind) - First any-to-any generative foundation model for Earth Observation, enabling unified multimodal understanding and generation across diverse satellite sensors and geospatial tasks through a single architecture (258+ stars)
|
||||
- [GeoAgent (opengeos, 2026)](https://github.com/opengeos/GeoAgent) - Shared multimodal AI agent layer for geospatial Python packages (leafmap, geoai, geemap, STAC, NASA Earthdata) and QGIS, exposing geospatial tools to LLMs with structured metadata, confirmation hooks, and support for OpenAI, Anthropic, Google Gemini, Ollama, and more; includes the OpenGeoAgent QGIS plugin (456+ stars, MIT License)
|
||||
- [Awesome Remote Sensing Foundation Models](https://github.com/Jack-bo1220/Awesome-Remote-Sensing-Foundation-Models) - Curated collection of papers, datasets, benchmarks, code, and pre-trained weights for Remote Sensing Foundation Models (RSFMs), tracking the rapidly evolving landscape of vision, vision-language, generative, and agent-based geospatial AI (1.9K+ stars, 2024-2026)
|
||||
|
||||
### 🌾 Agriculture & Ecology
|
||||
|
||||
upstream/inoue0426-awesome-computational-biology/catalogue/.github/workflows/ai4bio-schema-check.yml
Vendored
+61
@@ -0,0 +1,61 @@
|
||||
---
|
||||
title: "Ai4Bio Schema Check"
|
||||
task: ""
|
||||
lineage_type: import
|
||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/.github/workflows/ai4bio-schema-check.yml
|
||||
upstream_sha: 7a064bf0
|
||||
imported_at: 2026-08-08
|
||||
prompt_class: unknown
|
||||
upstream_changes: accepted
|
||||
author: upstream
|
||||
validated: false
|
||||
---
|
||||
|
||||
name: AI4Bio Schema Check
|
||||
|
||||
on:
|
||||
pull_request:
|
||||
paths:
|
||||
- data/resources.yml
|
||||
- data/enrichment.yml
|
||||
- 'data/enrichment.*.yml'
|
||||
- data/vocabulary.yml
|
||||
- docs/data/resource.schema.json
|
||||
- scripts/enrichment_fragments.py
|
||||
- scripts/validate_resources.py
|
||||
- scripts/build_resources_v2.py
|
||||
push:
|
||||
branches: [main]
|
||||
paths:
|
||||
- data/resources.yml
|
||||
- data/enrichment.yml
|
||||
- 'data/enrichment.*.yml'
|
||||
- data/vocabulary.yml
|
||||
- docs/data/resource.schema.json
|
||||
- scripts/enrichment_fragments.py
|
||||
- scripts/validate_resources.py
|
||||
- scripts/build_resources_v2.py
|
||||
|
||||
permissions:
|
||||
contents: read
|
||||
|
||||
jobs:
|
||||
validate:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
- uses: astral-sh/setup-uv@v3
|
||||
- name: Validate schema and enrichment
|
||||
run: uv run --with pyyaml python scripts/validate_resources.py
|
||||
- name: Build enriched artifacts
|
||||
run: uv run --with pyyaml python scripts/build_resources_v2.py
|
||||
- name: Verify enriched artifacts are committed
|
||||
run: |
|
||||
if git diff --quiet; then
|
||||
echo "AI4Bio artifacts are in sync."
|
||||
exit 0
|
||||
fi
|
||||
echo "Generated AI4Bio artifacts are out of date. Run:"
|
||||
echo " uv run --with pyyaml python scripts/build_resources_v2.py"
|
||||
git status --short
|
||||
exit 1
|
||||
Vendored
+43
@@ -0,0 +1,43 @@
|
||||
---
|
||||
title: "Landscape Ui Check"
|
||||
task: ""
|
||||
lineage_type: import
|
||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/.github/workflows/landscape-ui-check.yml
|
||||
upstream_sha: 7a064bf0
|
||||
imported_at: 2026-08-08
|
||||
prompt_class: unknown
|
||||
upstream_changes: accepted
|
||||
author: upstream
|
||||
validated: false
|
||||
---
|
||||
|
||||
name: Landscape UI Check
|
||||
|
||||
on:
|
||||
pull_request:
|
||||
paths:
|
||||
- docs/landscape.html
|
||||
- docs/landscape.css
|
||||
- docs/landscape.js
|
||||
- .github/workflows/landscape-ui-check.yml
|
||||
push:
|
||||
branches: [main]
|
||||
paths:
|
||||
- docs/landscape.html
|
||||
- docs/landscape.css
|
||||
- docs/landscape.js
|
||||
- .github/workflows/landscape-ui-check.yml
|
||||
|
||||
permissions:
|
||||
contents: read
|
||||
|
||||
jobs:
|
||||
ui-check:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
- uses: actions/setup-node@v4
|
||||
with:
|
||||
node-version: 'lts/*'
|
||||
- name: Check landscape JavaScript syntax
|
||||
run: node --check docs/landscape.js
|
||||
Vendored
+9
-4
@@ -2,9 +2,9 @@
|
||||
title: "Sync Resources"
|
||||
task: ""
|
||||
lineage_type: import
|
||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/12d87583/.github/workflows/sync_resources.yml
|
||||
upstream_sha: 12d87583
|
||||
imported_at: 2026-06-26
|
||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/.github/workflows/sync_resources.yml
|
||||
upstream_sha: 7a064bf0
|
||||
imported_at: 2026-08-08
|
||||
prompt_class: unknown
|
||||
upstream_changes: accepted
|
||||
author: upstream
|
||||
@@ -20,6 +20,8 @@ on:
|
||||
paths:
|
||||
- README.md
|
||||
- data/resources.yml
|
||||
- data/enrichment.yml
|
||||
- 'data/enrichment.*.yml'
|
||||
- scripts/*.py
|
||||
- scripts/**/*.py
|
||||
|
||||
@@ -55,7 +57,10 @@ jobs:
|
||||
|
||||
- name: Sync From README
|
||||
if: contains(steps.changes.outputs.changed, 'README.md')
|
||||
run: uv run python scripts/sync_resources_from_readme.py
|
||||
run: uv run --with pyyaml python scripts/sync_resources_from_readme.py
|
||||
|
||||
- name: Validate Resource Schema
|
||||
run: uv run --with pyyaml python scripts/validate_resources.py
|
||||
|
||||
- name: Build Artifacts
|
||||
run: uv run --with pyyaml python scripts/build_resources.py
|
||||
|
||||
@@ -2,9 +2,9 @@
|
||||
title: "Awesome Computational Biology [](https://awesome.re)"
|
||||
task: ""
|
||||
lineage_type: import
|
||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/478be843/README.md
|
||||
upstream_sha: 478be843
|
||||
imported_at: 2026-07-17
|
||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/README.md
|
||||
upstream_sha: 7a064bf0
|
||||
imported_at: 2026-08-08
|
||||
prompt_class: catalogue
|
||||
upstream_changes: accepted
|
||||
author: upstream
|
||||
@@ -70,6 +70,7 @@ Browse and search the resources via the [GitHub Pages UI](https://inoue0426.gith
|
||||
- [Machine Learning Tasks and Models](#machine-learning-tasks-and-models)
|
||||
- [Drug Discovery](#drug-discovery)
|
||||
- [Drug Response Prediction](#drug-response-prediction)
|
||||
- [Drug Perturbation](#drug-perturbation)
|
||||
- [Drug Repurposing](#drug-repurposing)
|
||||
- [Drug Target Interaction](#drug-target-interaction)
|
||||
- [Compound-Protein Interaction](#compound-protein-interaction)
|
||||
@@ -340,10 +341,15 @@ Browse and search the resources via the [GitHub Pages UI](https://inoue0426.gith
|
||||
- [RECOVER](https://github.com/RECOVERcoalition/Recover) — Machine learning framework for predicting synergistic drug combination responses across cell lines.
|
||||
- [TGSA](https://github.com/violet-sto/TGSA) — Tumor gene set and attention-based model leveraging biological pathway knowledge for drug response prediction.
|
||||
- [HiDRA](https://github.com/bsml320/HiDRA) — Hierarchical network model incorporating gene and pathway-level information for cancer drug response prediction.
|
||||
- [PRNet](https://github.com/Perturbation-Response-Prediction/PRnet) — Deep generative model for predicting transcriptional responses to novel chemical perturbations for drug discovery.
|
||||
- [DRUML](https://github.com/CutillasLab/DRUMLR) — Ensemble machine learning framework combining standard ML with deep learning to systematically rank anti-cancer drugs from proteomics and RNA-seq data.
|
||||
|
||||
#### Drug Perturbation
|
||||
|
||||
- [CellOT](https://github.com/bunnech/cellot) — Neural optimal transport framework for predicting single-cell responses to drug and genetic perturbations.
|
||||
- [CMonge](https://github.com/AI4SCR/conditional-monge-gap) — Conditional optimal transport model for generalizable single-cell perturbation response prediction across drugs and doses.
|
||||
- [chemCPA](https://github.com/theislab/chemCPA) — Compositional perturbation autoencoder for predicting single-cell transcriptional responses to unseen drug perturbations and dose combinations.
|
||||
- [cycleCDR](https://github.com/hliulab/cycleCDR) — Interpretable cycle-consistency framework for modeling cellular responses to drug perturbations.
|
||||
- [DRUML](https://github.com/CutillasLab/DRUMLR) — Ensemble machine learning framework combining standard ML with deep learning to systematically rank anti-cancer drugs from proteomics and RNA-seq data.
|
||||
- [PRNet](https://github.com/Perturbation-Response-Prediction/PRnet) — Deep generative model for predicting transcriptional responses to novel chemical perturbations for drug discovery.
|
||||
|
||||
#### Drug Repurposing
|
||||
|
||||
|
||||
@@ -2,9 +2,9 @@
|
||||
title: "Cspell"
|
||||
task: ""
|
||||
lineage_type: import
|
||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/12d87583/cspell.json
|
||||
upstream_sha: 12d87583
|
||||
imported_at: 2026-06-26
|
||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/cspell.json
|
||||
upstream_sha: 7a064bf0
|
||||
imported_at: 2026-08-08
|
||||
prompt_class: unknown
|
||||
upstream_changes: accepted
|
||||
author: upstream
|
||||
@@ -157,7 +157,21 @@ validated: false
|
||||
"Pacc",
|
||||
"multiomics",
|
||||
"Pathomic",
|
||||
"PLIP"
|
||||
"PLIP",
|
||||
"Omni",
|
||||
"Bento",
|
||||
"FFPE",
|
||||
"Xenium",
|
||||
"Zyme",
|
||||
"Neur",
|
||||
"Imageomics",
|
||||
"AESTETIK",
|
||||
"CellOT",
|
||||
"CMonge",
|
||||
"bowang",
|
||||
"ctheodoris",
|
||||
"OpenAI",
|
||||
"GPT"
|
||||
],
|
||||
"ignorePaths": [
|
||||
"node_modules/**"
|
||||
|
||||
+56
@@ -0,0 +1,56 @@
|
||||
---
|
||||
title: "Provenance-backed single-cell and biomedical benchmark enrichment batch."
|
||||
task: ""
|
||||
lineage_type: import
|
||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/data/enrichment.benchmark-v2.yml
|
||||
upstream_sha: 7a064bf0
|
||||
imported_at: 2026-08-08
|
||||
prompt_class: catalogue
|
||||
upstream_changes: accepted
|
||||
author: upstream
|
||||
validated: false
|
||||
---
|
||||
|
||||
# Provenance-backed single-cell and biomedical benchmark enrichment batch.
|
||||
resources:
|
||||
scmulan:
|
||||
entities: [cell, gene]
|
||||
methods: [language-model, transformer]
|
||||
modalities: [epigenomics, multi-omics, proteomics, single-cell-rna-seq, transcriptomics]
|
||||
tasks: [foundation-model-pretraining, representation-learning]
|
||||
github: https://github.com/SuperBianC/scMulan
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://github.com/SuperBianC/scMulan
|
||||
|
||||
proteingym:
|
||||
entities: [protein]
|
||||
modalities: [protein-sequence]
|
||||
tasks: [regression]
|
||||
github: https://github.com/OATML-Markslab/ProteinGym
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://github.com/OATML-Markslab/ProteinGym
|
||||
|
||||
lincs_l1000:
|
||||
entities: [cell, compound, gene]
|
||||
modalities: [transcriptomics]
|
||||
tasks: [perturbation-prediction]
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://lincsproject.org/LINCS/tools/workflows/find-the-best-place-to-obtain-the-lincs-l1000-data
|
||||
|
||||
prism:
|
||||
entities: [cell, drug]
|
||||
tasks: [drug-response-prediction]
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://depmap.org/portal/prism/
|
||||
|
||||
pharmgkb:
|
||||
entities: [drug, gene, phenotype, variant]
|
||||
modalities: [clinical, genomics]
|
||||
tasks: [drug-response-prediction]
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://www.pharmgkb.org/
|
||||
+91
@@ -0,0 +1,91 @@
|
||||
---
|
||||
title: "Provenance-backed database and API enrichment batch."
|
||||
task: ""
|
||||
lineage_type: import
|
||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/data/enrichment.database-api-v1.yml
|
||||
upstream_sha: 7a064bf0
|
||||
imported_at: 2026-08-08
|
||||
prompt_class: catalogue
|
||||
upstream_changes: accepted
|
||||
author: upstream
|
||||
validated: false
|
||||
---
|
||||
|
||||
# Provenance-backed database and API enrichment batch.
|
||||
resources:
|
||||
chembl_web_services:
|
||||
entities: [molecule, protein]
|
||||
modalities: [chemical-structure]
|
||||
documentation: https://www.ebi.ac.uk/chembl/api/data/docs
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://www.ebi.ac.uk/chembl/api/data/docs
|
||||
|
||||
clinicaltrials_gov_api:
|
||||
entities: [disease, drug]
|
||||
modalities: [clinical]
|
||||
documentation: https://clinicaltrials.gov/data-api/api
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://clinicaltrials.gov/data-api/api
|
||||
|
||||
ensembl_rest_api:
|
||||
entities: [gene, genome, transcript, variant]
|
||||
modalities: [genomics]
|
||||
documentation: https://rest.ensembl.org/
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://rest.ensembl.org/
|
||||
|
||||
kegg_rest_api:
|
||||
entities: [compound, gene, pathway]
|
||||
documentation: https://www.kegg.jp/kegg/rest/keggapi.html
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://www.kegg.jp/kegg/rest/keggapi.html
|
||||
|
||||
ncbi_e_utilities:
|
||||
entities: [gene, genome, protein, transcript, variant]
|
||||
modalities: [genomics, transcriptomics]
|
||||
documentation: https://www.ncbi.nlm.nih.gov/books/NBK25501/
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://www.ncbi.nlm.nih.gov/books/NBK25501/
|
||||
|
||||
open_targets_platform_api:
|
||||
entities: [disease, drug, gene, variant]
|
||||
modalities: [genomics, knowledge-graph]
|
||||
documentation: https://platform.opentargets.org/api
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://platform.opentargets.org/api
|
||||
|
||||
pubmed_e_utilities_esearch_efetch:
|
||||
documentation: https://www.ncbi.nlm.nih.gov/books/NBK25501/
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://www.ncbi.nlm.nih.gov/books/NBK25501/
|
||||
|
||||
uniprot_rest_api:
|
||||
entities: [protein]
|
||||
modalities: [protein-sequence, proteomics]
|
||||
documentation: https://www.uniprot.org/help/api
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://www.uniprot.org/help/api
|
||||
|
||||
drugbank:
|
||||
entities: [disease, drug, protein]
|
||||
modalities: [chemical-structure]
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://go.drugbank.com/
|
||||
|
||||
string:
|
||||
entities: [protein]
|
||||
modalities: [knowledge-graph, proteomics]
|
||||
documentation: https://string-db.org/help/api/
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://string-db.org/
|
||||
- https://string-db.org/help/api/
|
||||
+73
@@ -0,0 +1,73 @@
|
||||
---
|
||||
title: "Provenance-backed foundation model enrichment batch 2."
|
||||
task: ""
|
||||
lineage_type: import
|
||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/data/enrichment.foundation-models-v2.yml
|
||||
upstream_sha: 7a064bf0
|
||||
imported_at: 2026-08-08
|
||||
prompt_class: catalogue
|
||||
upstream_changes: accepted
|
||||
author: upstream
|
||||
validated: false
|
||||
---
|
||||
|
||||
# Provenance-backed foundation model enrichment batch 2.
|
||||
resources:
|
||||
nicheformer:
|
||||
entities: [cell, gene, tissue]
|
||||
methods: [self-supervised-learning, transformer]
|
||||
modalities: [single-cell-rna-seq, spatial-transcriptomics, transcriptomics]
|
||||
tasks: [foundation-model-pretraining, representation-learning]
|
||||
year: 2024
|
||||
github: https://github.com/theislab/nicheformer
|
||||
paper: https://doi.org/10.1101/2024.04.15.589472
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://github.com/theislab/nicheformer
|
||||
- https://doi.org/10.1101/2024.04.15.589472
|
||||
|
||||
genept:
|
||||
entities: [cell, gene]
|
||||
methods: [language-model]
|
||||
modalities: [single-cell-rna-seq, transcriptomics]
|
||||
tasks: [batch-correction, classification, representation-learning]
|
||||
year: 2023
|
||||
github: https://github.com/yiqunchen/GenePT
|
||||
paper: https://www.biorxiv.org/content/10.1101/2023.10.16.562533v2
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://github.com/yiqunchen/GenePT
|
||||
- https://www.biorxiv.org/content/10.1101/2023.10.16.562533v2
|
||||
|
||||
scgpt_spatial:
|
||||
entities: [cell, gene, tissue]
|
||||
methods: [generative-model, self-supervised-learning, transformer]
|
||||
modalities: [multi-omics, single-cell-rna-seq, spatial-transcriptomics]
|
||||
tasks: [foundation-model-pretraining, imputation, representation-learning]
|
||||
year: 2025
|
||||
github: https://github.com/bowang-lab/scGPT-spatial
|
||||
paper: https://www.biorxiv.org/content/10.1101/2025.02.05.636714v1
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://github.com/bowang-lab/scGPT-spatial
|
||||
- https://www.biorxiv.org/content/10.1101/2025.02.05.636714v1
|
||||
|
||||
scprint:
|
||||
entities: [cell, gene]
|
||||
methods: [self-supervised-learning, transformer]
|
||||
modalities: [single-cell-rna-seq, transcriptomics]
|
||||
tasks:
|
||||
- batch-correction
|
||||
- cell-type-annotation
|
||||
- foundation-model-pretraining
|
||||
- gene-regulatory-network-inference
|
||||
- imputation
|
||||
- representation-learning
|
||||
year: 2025
|
||||
github: https://github.com/cantinilab/scPRINT
|
||||
documentation: https://www.jkobject.com/scPRINT/
|
||||
paper: https://www.nature.com/articles/s41467-025-58699-1
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://github.com/cantinilab/scPRINT
|
||||
- https://www.nature.com/articles/s41467-025-58699-1
|
||||
+62
@@ -0,0 +1,62 @@
|
||||
---
|
||||
title: "Provenance-backed molecular model and benchmark enrichment batch."
|
||||
task: ""
|
||||
lineage_type: import
|
||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/data/enrichment.molecular-v2.yml
|
||||
upstream_sha: 7a064bf0
|
||||
imported_at: 2026-08-08
|
||||
prompt_class: catalogue
|
||||
upstream_changes: accepted
|
||||
author: upstream
|
||||
validated: false
|
||||
---
|
||||
|
||||
# Provenance-backed molecular model and benchmark enrichment batch.
|
||||
resources:
|
||||
chemberta_2:
|
||||
entities: [molecule]
|
||||
methods: [language-model, self-supervised-learning, transformer]
|
||||
modalities: [chemical-structure]
|
||||
tasks: [representation-learning]
|
||||
github: https://github.com/seyonechithrananda/bert-loves-chemistry
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://github.com/seyonechithrananda/bert-loves-chemistry
|
||||
|
||||
molformer:
|
||||
entities: [molecule]
|
||||
methods: [language-model, self-supervised-learning, transformer]
|
||||
modalities: [chemical-structure]
|
||||
tasks: [representation-learning]
|
||||
github: https://github.com/IBM/molformer
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://github.com/IBM/molformer
|
||||
|
||||
grover:
|
||||
entities: [molecule]
|
||||
methods: [graph-neural-network, self-supervised-learning, transformer]
|
||||
modalities: [chemical-structure]
|
||||
tasks: [representation-learning]
|
||||
github: https://github.com/tencent-ailab/grover
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://github.com/tencent-ailab/grover
|
||||
|
||||
moleculenet:
|
||||
entities: [molecule]
|
||||
modalities: [chemical-structure]
|
||||
tasks: [classification, regression]
|
||||
github: https://github.com/deepchem/moleculenet
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://github.com/deepchem/moleculenet
|
||||
|
||||
guacamol:
|
||||
entities: [molecule]
|
||||
modalities: [chemical-structure]
|
||||
tasks: [molecular-generation]
|
||||
github: https://github.com/BenevolentAI/guacamol
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://github.com/BenevolentAI/guacamol
|
||||
+92
@@ -0,0 +1,92 @@
|
||||
---
|
||||
title: "Provenance-backed drug-response and pharmacogenomics enrichment batch."
|
||||
task: ""
|
||||
lineage_type: import
|
||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/data/enrichment.pharmacogenomics-v1.yml
|
||||
upstream_sha: 7a064bf0
|
||||
imported_at: 2026-08-08
|
||||
prompt_class: catalogue
|
||||
upstream_changes: accepted
|
||||
author: upstream
|
||||
validated: false
|
||||
---
|
||||
|
||||
# Provenance-backed drug-response and pharmacogenomics enrichment batch.
|
||||
resources:
|
||||
beat_aml:
|
||||
entities: [cell, disease, drug, gene]
|
||||
modalities: [genomics]
|
||||
tasks: [drug-response-prediction]
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://biodev.github.io/BeatAML2/
|
||||
|
||||
cancer_therapeutics_response_portal_ctrp:
|
||||
entities: [cell, drug]
|
||||
tasks: [drug-response-prediction]
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://portals.broadinstitute.org/ctrp/
|
||||
|
||||
bindingdb_curated_sets:
|
||||
entities: [molecule, protein]
|
||||
modalities: [chemical-structure]
|
||||
tasks: [drug-target-interaction]
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://www.bindingdb.org/
|
||||
|
||||
bace:
|
||||
entities: [molecule, protein]
|
||||
modalities: [chemical-structure]
|
||||
tasks: [classification, regression]
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://www.kaggle.com/datasets/gokturkkoch/bace
|
||||
|
||||
clintox:
|
||||
entities: [drug]
|
||||
modalities: [clinical]
|
||||
tasks: [classification]
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://tdcommons.ai/single_pred_tasks/tox/#clintox
|
||||
|
||||
sider_side_effect_resource:
|
||||
entities: [drug, phenotype]
|
||||
modalities: [clinical]
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- http://sideeffects.embl.de/
|
||||
|
||||
pk_db:
|
||||
entities: [drug]
|
||||
modalities: [clinical]
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://pk-db.com/
|
||||
|
||||
scperturb:
|
||||
entities: [cell, drug, gene]
|
||||
modalities: [single-cell-rna-seq]
|
||||
tasks: [perturbation-prediction]
|
||||
github: https://github.com/sanderlab/scPerturb
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://github.com/sanderlab/scPerturb
|
||||
|
||||
genomics_of_drug_sensitivity_in_cancer_gdsc:
|
||||
entities: [cell, drug, gene]
|
||||
modalities: [genomics]
|
||||
tasks: [drug-response-prediction]
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://www.cancerrxgene.org/
|
||||
|
||||
cellminer_cross_database_cellminercdb:
|
||||
entities: [cell, drug, gene]
|
||||
modalities: [genomics]
|
||||
tasks: [drug-response-prediction]
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://discover.nci.nih.gov/cellminercdb/
|
||||
+62
@@ -0,0 +1,62 @@
|
||||
---
|
||||
title: "Provenance-backed protein and drug-discovery enrichment batch."
|
||||
task: ""
|
||||
lineage_type: import
|
||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/data/enrichment.protein-drug-v1.yml
|
||||
upstream_sha: 7a064bf0
|
||||
imported_at: 2026-08-08
|
||||
prompt_class: catalogue
|
||||
upstream_changes: accepted
|
||||
author: upstream
|
||||
validated: false
|
||||
---
|
||||
|
||||
# Provenance-backed protein and drug-discovery enrichment batch.
|
||||
resources:
|
||||
esmfold:
|
||||
entities: [protein]
|
||||
methods: [language-model, transformer]
|
||||
modalities: [molecular-structure, protein-sequence]
|
||||
tasks: [representation-learning, structure-prediction]
|
||||
year: 2023
|
||||
github: https://github.com/facebookresearch/esm
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://github.com/facebookresearch/esm
|
||||
|
||||
proteinmpnn:
|
||||
entities: [protein]
|
||||
methods: [graph-neural-network, message-passing-neural-network]
|
||||
modalities: [molecular-structure, protein-sequence]
|
||||
tasks: [protein-sequence-design]
|
||||
year: 2022
|
||||
github: https://github.com/dauparas/ProteinMPNN
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://github.com/dauparas/ProteinMPNN
|
||||
|
||||
diffdock:
|
||||
entities: [molecule, protein]
|
||||
methods: [diffusion, geometric-deep-learning]
|
||||
modalities: [molecular-structure]
|
||||
tasks: [docking]
|
||||
year: 2023
|
||||
github: https://github.com/gcorso/DiffDock
|
||||
paper: https://openreview.net/forum?id=kKF8_K-mBbS
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://github.com/gcorso/DiffDock
|
||||
- https://openreview.net/forum?id=kKF8_K-mBbS
|
||||
|
||||
uni_mol:
|
||||
entities: [molecule, protein]
|
||||
methods: [self-supervised-learning, transformer]
|
||||
modalities: [chemical-structure, molecular-structure]
|
||||
tasks: [docking, representation-learning]
|
||||
year: 2023
|
||||
github: https://github.com/deepmodeling/Uni-Mol
|
||||
paper: https://openreview.net/forum?id=6K2RM6wVqKu
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://github.com/deepmodeling/Uni-Mol
|
||||
- https://openreview.net/forum?id=6K2RM6wVqKu
|
||||
+64
@@ -0,0 +1,64 @@
|
||||
---
|
||||
title: "Provenance-backed protein model enrichment batch."
|
||||
task: ""
|
||||
lineage_type: import
|
||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/data/enrichment.protein-v2.yml
|
||||
upstream_sha: 7a064bf0
|
||||
imported_at: 2026-08-08
|
||||
prompt_class: catalogue
|
||||
upstream_changes: accepted
|
||||
author: upstream
|
||||
validated: false
|
||||
---
|
||||
|
||||
# Provenance-backed protein model enrichment batch.
|
||||
resources:
|
||||
esm3:
|
||||
entities: [protein]
|
||||
methods: [generative-model, language-model, transformer]
|
||||
modalities: [molecular-structure, protein-sequence]
|
||||
tasks: [protein-sequence-design, representation-learning]
|
||||
github: https://github.com/evolutionaryscale/esm
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://github.com/evolutionaryscale/esm
|
||||
|
||||
evolutionary_scale_modeling_esm:
|
||||
entities: [protein]
|
||||
methods: [language-model, self-supervised-learning, transformer]
|
||||
modalities: [protein-sequence]
|
||||
tasks: [representation-learning]
|
||||
github: https://github.com/facebookresearch/esm
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://github.com/facebookresearch/esm
|
||||
|
||||
prottrans:
|
||||
entities: [protein]
|
||||
methods: [language-model, self-supervised-learning, transformer]
|
||||
modalities: [protein-sequence]
|
||||
tasks: [representation-learning]
|
||||
github: https://github.com/agemagician/ProtTrans
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://github.com/agemagician/ProtTrans
|
||||
|
||||
progen2:
|
||||
entities: [protein]
|
||||
methods: [generative-model, language-model, transformer]
|
||||
modalities: [protein-sequence]
|
||||
tasks: [protein-sequence-design, representation-learning]
|
||||
github: https://github.com/salesforce/progen
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://github.com/salesforce/progen
|
||||
|
||||
alphafold3:
|
||||
entities: [molecule, protein, protein-complex]
|
||||
methods: [diffusion]
|
||||
modalities: [molecular-structure, protein-sequence]
|
||||
tasks: [structure-prediction]
|
||||
github: https://github.com/google-deepmind/alphafold3
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://github.com/google-deepmind/alphafold3
|
||||
+110
@@ -0,0 +1,110 @@
|
||||
---
|
||||
title: "Provenance-backed spatial transcriptomics and imaging enrichment batch."
|
||||
task: ""
|
||||
lineage_type: import
|
||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/data/enrichment.spatial-imaging-v1.yml
|
||||
upstream_sha: 7a064bf0
|
||||
imported_at: 2026-08-08
|
||||
prompt_class: catalogue
|
||||
upstream_changes: accepted
|
||||
author: upstream
|
||||
validated: false
|
||||
---
|
||||
|
||||
# Provenance-backed spatial transcriptomics and imaging enrichment batch.
|
||||
resources:
|
||||
aestetik:
|
||||
entities: [cell, gene, tissue]
|
||||
methods: [autoencoder]
|
||||
modalities: [histopathology, spatial-transcriptomics]
|
||||
tasks: [representation-learning]
|
||||
github: https://github.com/ratschlab/aestetik
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://github.com/ratschlab/aestetik
|
||||
|
||||
conch:
|
||||
entities: [tissue]
|
||||
methods: [contrastive-learning, transformer]
|
||||
modalities: [histopathology, imaging]
|
||||
tasks: [foundation-model-pretraining, representation-learning]
|
||||
github: https://github.com/mahmoodlab/CONCH
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://github.com/mahmoodlab/CONCH
|
||||
|
||||
deepspot:
|
||||
entities: [gene, tissue]
|
||||
modalities: [histopathology, spatial-transcriptomics]
|
||||
tasks: [regression]
|
||||
github: https://github.com/ratschlab/DeepSpot
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://github.com/ratschlab/DeepSpot
|
||||
|
||||
deepspot_m:
|
||||
entities: [gene, tissue]
|
||||
modalities: [histopathology, spatial-transcriptomics, transcriptomics]
|
||||
tasks: [foundation-model-pretraining, regression]
|
||||
github: https://github.com/ratschlab/DeepSpotM
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://github.com/ratschlab/DeepSpotM
|
||||
|
||||
deepspot2cell:
|
||||
entities: [cell, gene, tissue]
|
||||
modalities: [histopathology, spatial-transcriptomics]
|
||||
tasks: [regression]
|
||||
github: https://github.com/ratschlab/DeepSpot2Cell
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://github.com/ratschlab/DeepSpot2Cell
|
||||
|
||||
gigapath:
|
||||
entities: [tissue]
|
||||
methods: [self-supervised-learning, transformer]
|
||||
modalities: [histopathology, imaging]
|
||||
tasks: [foundation-model-pretraining, representation-learning]
|
||||
github: https://github.com/prov-gigapath/prov-gigapath
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://github.com/prov-gigapath/prov-gigapath
|
||||
|
||||
phikon:
|
||||
entities: [tissue]
|
||||
methods: [self-supervised-learning, transformer]
|
||||
modalities: [histopathology, imaging]
|
||||
tasks: [foundation-model-pretraining, representation-learning]
|
||||
documentation: https://huggingface.co/owkin/phikon
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://huggingface.co/owkin/phikon
|
||||
|
||||
plip:
|
||||
entities: [tissue]
|
||||
methods: [contrastive-learning]
|
||||
modalities: [histopathology, imaging]
|
||||
tasks: [classification, representation-learning]
|
||||
github: https://github.com/PathologyFoundation/plip
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://github.com/PathologyFoundation/plip
|
||||
|
||||
uni:
|
||||
entities: [tissue]
|
||||
methods: [self-supervised-learning, transformer]
|
||||
modalities: [histopathology, imaging]
|
||||
tasks: [foundation-model-pretraining, representation-learning]
|
||||
github: https://github.com/mahmoodlab/UNI
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://github.com/mahmoodlab/UNI
|
||||
|
||||
hest_xenium_virtual_spatial_transcriptomics:
|
||||
entities: [cell, gene, tissue]
|
||||
modalities: [histopathology, spatial-transcriptomics, transcriptomics]
|
||||
tasks: [regression]
|
||||
documentation: https://huggingface.co/datasets/ratschlab/HEST_Xenium_virtual_spatial_transcriptomics
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://huggingface.co/datasets/ratschlab/HEST_Xenium_virtual_spatial_transcriptomics
|
||||
@@ -0,0 +1,128 @@
|
||||
---
|
||||
title: "AI4Bio landscape enrichment overlay"
|
||||
task: ""
|
||||
lineage_type: import
|
||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/data/enrichment.yml
|
||||
upstream_sha: 7a064bf0
|
||||
imported_at: 2026-08-08
|
||||
prompt_class: catalogue
|
||||
upstream_changes: accepted
|
||||
author: upstream
|
||||
validated: false
|
||||
---
|
||||
|
||||
# AI4Bio landscape enrichment overlay
|
||||
#
|
||||
# README.md remains the canonical source for resource membership and basic fields.
|
||||
# Add richer, independently curated metadata here, keyed by the stable resource id.
|
||||
# scripts/build_resources.py merges these fields into generated JSON/CSV artifacts.
|
||||
#
|
||||
# Enrichment values should be source-verifiable. Controlled vocabulary fields are
|
||||
# validated against data/vocabulary.yml.
|
||||
|
||||
resources:
|
||||
scgpt:
|
||||
entities: [cell, gene]
|
||||
methods: [generative-model, self-supervised-learning, transformer]
|
||||
modalities: [multi-omics, single-cell-rna-seq, transcriptomics]
|
||||
tasks:
|
||||
- cell-type-annotation
|
||||
- foundation-model-pretraining
|
||||
- gene-regulatory-network-inference
|
||||
- perturbation-prediction
|
||||
- representation-learning
|
||||
year: 2024
|
||||
github: https://github.com/bowang-lab/scGPT
|
||||
documentation: https://scgpt.readthedocs.io/en/latest/
|
||||
paper: https://www.nature.com/articles/s41592-024-02201-0
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://github.com/bowang-lab/scGPT
|
||||
- https://www.nature.com/articles/s41592-024-02201-0
|
||||
|
||||
geneformer:
|
||||
entities: [cell, gene]
|
||||
methods: [self-supervised-learning, transformer]
|
||||
modalities: [single-cell-rna-seq, transcriptomics]
|
||||
tasks:
|
||||
- classification
|
||||
- foundation-model-pretraining
|
||||
- perturbation-prediction
|
||||
- representation-learning
|
||||
year: 2023
|
||||
documentation: https://geneformer.readthedocs.io/
|
||||
paper: https://www.nature.com/articles/s41586-023-06139-9
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://huggingface.co/ctheodoris/Geneformer
|
||||
- https://www.nature.com/articles/s41586-023-06139-9
|
||||
|
||||
scfoundation:
|
||||
entities: [cell, gene]
|
||||
methods: [self-supervised-learning, transformer]
|
||||
modalities: [single-cell-rna-seq, transcriptomics]
|
||||
tasks:
|
||||
- cell-type-annotation
|
||||
- drug-response-prediction
|
||||
- foundation-model-pretraining
|
||||
- perturbation-prediction
|
||||
- representation-learning
|
||||
year: 2024
|
||||
github: https://github.com/biomap-research/scFoundation
|
||||
paper: https://www.nature.com/articles/s41592-024-02305-7
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://github.com/biomap-research/scFoundation
|
||||
- https://www.nature.com/articles/s41592-024-02305-7
|
||||
|
||||
genecompass:
|
||||
entities: [cell, gene]
|
||||
methods: [self-supervised-learning, transformer]
|
||||
modalities: [single-cell-rna-seq, transcriptomics]
|
||||
tasks: [foundation-model-pretraining, representation-learning]
|
||||
year: 2024
|
||||
github: https://github.com/xCompass-AI/GeneCompass
|
||||
paper: https://www.nature.com/articles/s41422-024-01034-y
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://github.com/xCompass-AI/GeneCompass
|
||||
- https://www.nature.com/articles/s41422-024-01034-y
|
||||
|
||||
uce:
|
||||
entities: [cell]
|
||||
methods: [self-supervised-learning]
|
||||
modalities: [single-cell-rna-seq, transcriptomics]
|
||||
tasks: [foundation-model-pretraining, representation-learning]
|
||||
year: 2026
|
||||
github: https://github.com/snap-stanford/UCE
|
||||
paper: https://www.nature.com/articles/s41586-026-10689-z
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://github.com/snap-stanford/UCE
|
||||
- https://www.nature.com/articles/s41586-026-10689-z
|
||||
|
||||
cellplm:
|
||||
entities: [cell, gene]
|
||||
methods: [self-supervised-learning, transformer]
|
||||
modalities: [single-cell-rna-seq, transcriptomics]
|
||||
tasks: [foundation-model-pretraining, representation-learning]
|
||||
year: 2023
|
||||
github: https://github.com/OmicsML/CellPLM
|
||||
paper: https://www.biorxiv.org/content/10.1101/2023.10.03.560734v1
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://github.com/OmicsML/CellPLM
|
||||
- https://www.biorxiv.org/content/10.1101/2023.10.03.560734v1
|
||||
|
||||
scbert:
|
||||
entities: [cell, gene]
|
||||
methods: [language-model, self-supervised-learning, transformer]
|
||||
modalities: [single-cell-rna-seq, transcriptomics]
|
||||
tasks: [cell-type-annotation, classification, foundation-model-pretraining]
|
||||
year: 2022
|
||||
github: https://github.com/TencentAILabHealthcare/scBERT
|
||||
paper: https://www.nature.com/articles/s42256-022-00534-z
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://github.com/TencentAILabHealthcare/scBERT
|
||||
- https://www.nature.com/articles/s42256-022-00534-z
|
||||
File diff suppressed because it is too large
Load Diff
@@ -2,9 +2,9 @@
|
||||
title: "Awesome Computational Biology - machine-readable resource list"
|
||||
task: ""
|
||||
lineage_type: import
|
||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/478be843/data/resources.yml
|
||||
upstream_sha: 478be843
|
||||
imported_at: 2026-07-17
|
||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/data/resources.yml
|
||||
upstream_sha: 7a064bf0
|
||||
imported_at: 2026-08-08
|
||||
prompt_class: catalogue
|
||||
upstream_changes: accepted
|
||||
author: upstream
|
||||
@@ -1678,6 +1678,17 @@ resources:
|
||||
organism: []
|
||||
api: false
|
||||
|
||||
- id: cellot
|
||||
name: "CellOT"
|
||||
type: model
|
||||
url: https://github.com/bunnech/cellot
|
||||
description: "Neural optimal transport framework for predicting single-cell responses to drug and genetic perturbations."
|
||||
tags: [drug-discovery, drug-perturbation]
|
||||
tasks: [Drug Discovery, Drug Perturbation]
|
||||
modalities: [Small Molecule]
|
||||
organism: []
|
||||
api: false
|
||||
|
||||
- id: cellplm
|
||||
name: "CellPLM"
|
||||
type: model
|
||||
@@ -1727,8 +1738,8 @@ resources:
|
||||
type: model
|
||||
url: https://github.com/theislab/chemCPA
|
||||
description: "Compositional perturbation autoencoder for predicting single-cell transcriptional responses to unseen drug perturbations and dose combinations."
|
||||
tags: [drug-discovery, drug-response-prediction]
|
||||
tasks: [Drug Discovery, Drug Response Prediction]
|
||||
tags: [drug-discovery, drug-perturbation]
|
||||
tasks: [Drug Discovery, Drug Perturbation]
|
||||
modalities: [Small Molecule]
|
||||
organism: []
|
||||
api: false
|
||||
@@ -1755,6 +1766,17 @@ resources:
|
||||
organism: []
|
||||
api: false
|
||||
|
||||
- id: cmonge
|
||||
name: "CMonge"
|
||||
type: model
|
||||
url: https://github.com/AI4SCR/conditional-monge-gap
|
||||
description: "Conditional optimal transport model for generalizable single-cell perturbation response prediction across drugs and doses."
|
||||
tags: [drug-discovery, drug-perturbation]
|
||||
tasks: [Drug Discovery, Drug Perturbation]
|
||||
modalities: [Small Molecule]
|
||||
organism: []
|
||||
api: false
|
||||
|
||||
- id: concerto
|
||||
name: "Concerto"
|
||||
type: model
|
||||
@@ -1782,8 +1804,8 @@ resources:
|
||||
type: model
|
||||
url: https://github.com/hliulab/cycleCDR
|
||||
description: "Interpretable cycle-consistency framework for modeling cellular responses to drug perturbations."
|
||||
tags: [drug-discovery, drug-response-prediction]
|
||||
tasks: [Drug Discovery, Drug Response Prediction]
|
||||
tags: [drug-discovery, drug-perturbation]
|
||||
tasks: [Drug Discovery, Drug Perturbation]
|
||||
modalities: [Small Molecule]
|
||||
organism: []
|
||||
api: false
|
||||
@@ -2475,8 +2497,8 @@ resources:
|
||||
type: model
|
||||
url: https://github.com/Perturbation-Response-Prediction/PRnet
|
||||
description: "Deep generative model for predicting transcriptional responses to novel chemical perturbations for drug discovery."
|
||||
tags: [drug-discovery, drug-response-prediction]
|
||||
tasks: [Drug Discovery, Drug Response Prediction]
|
||||
tags: [drug-discovery, drug-perturbation]
|
||||
tasks: [Drug Discovery, Drug Perturbation]
|
||||
modalities: [Small Molecule]
|
||||
organism: []
|
||||
api: false
|
||||
|
||||
@@ -0,0 +1,105 @@
|
||||
---
|
||||
title: "Canonical vocabulary for new AI4Bio enrichment metadata."
|
||||
task: ""
|
||||
lineage_type: import
|
||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/data/vocabulary.yml
|
||||
upstream_sha: 7a064bf0
|
||||
imported_at: 2026-08-08
|
||||
prompt_class: unknown
|
||||
upstream_changes: accepted
|
||||
author: upstream
|
||||
validated: false
|
||||
---
|
||||
|
||||
# Canonical vocabulary for new AI4Bio enrichment metadata.
|
||||
#
|
||||
# These values are enforced only for fields explicitly added through
|
||||
# data/enrichment.yml. README-derived legacy values remain backward compatible.
|
||||
# Canonical terms use lowercase kebab-case.
|
||||
|
||||
version: 1
|
||||
|
||||
controlled_fields:
|
||||
entities:
|
||||
- cell
|
||||
- compound
|
||||
- disease
|
||||
- drug
|
||||
- gene
|
||||
- genome
|
||||
- molecule
|
||||
- organism
|
||||
- pathway
|
||||
- phenotype
|
||||
- protein
|
||||
- protein-complex
|
||||
- regulatory-element
|
||||
- tissue
|
||||
- transcript
|
||||
- variant
|
||||
|
||||
methods:
|
||||
- autoencoder
|
||||
- contrastive-learning
|
||||
- convolutional-neural-network
|
||||
- diffusion
|
||||
- generative-model
|
||||
- geometric-deep-learning
|
||||
- graph-neural-network
|
||||
- knowledge-graph
|
||||
- language-model
|
||||
- message-passing-neural-network
|
||||
- multi-agent-system
|
||||
- optimal-transport
|
||||
- recurrent-neural-network
|
||||
- reinforcement-learning
|
||||
- retrieval-augmented-generation
|
||||
- self-supervised-learning
|
||||
- state-space-model
|
||||
- supervised-learning
|
||||
- transformer
|
||||
- unsupervised-learning
|
||||
- variational-autoencoder
|
||||
|
||||
modalities:
|
||||
- cell-painting
|
||||
- chemical-structure
|
||||
- clinical
|
||||
- dna-sequence
|
||||
- electronic-health-record
|
||||
- epigenomics
|
||||
- genomics
|
||||
- histopathology
|
||||
- imaging
|
||||
- knowledge-graph
|
||||
- metabolomics
|
||||
- molecular-structure
|
||||
- multi-omics
|
||||
- protein-sequence
|
||||
- proteomics
|
||||
- rna-sequence
|
||||
- single-cell-rna-seq
|
||||
- spatial-transcriptomics
|
||||
- transcriptomics
|
||||
|
||||
tasks:
|
||||
- batch-correction
|
||||
- cell-type-annotation
|
||||
- classification
|
||||
- dimensionality-reduction
|
||||
- docking
|
||||
- drug-response-prediction
|
||||
- drug-target-interaction
|
||||
- foundation-model-pretraining
|
||||
- gene-regulatory-network-inference
|
||||
- imputation
|
||||
- link-prediction
|
||||
- molecular-generation
|
||||
- perturbation-prediction
|
||||
- protein-function-prediction
|
||||
- protein-sequence-design
|
||||
- regression
|
||||
- representation-learning
|
||||
- structure-prediction
|
||||
- trajectory-inference
|
||||
- virtual-screening
|
||||
@@ -0,0 +1,73 @@
|
||||
---
|
||||
title: "AI4Bio Landscape Database"
|
||||
task: ""
|
||||
lineage_type: import
|
||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/docs/AI4BIO_LANDSCAPE.md
|
||||
upstream_sha: 7a064bf0
|
||||
imported_at: 2026-08-08
|
||||
prompt_class: catalogue
|
||||
upstream_changes: accepted
|
||||
author: upstream
|
||||
validated: false
|
||||
---
|
||||
|
||||
# AI4Bio Landscape Database
|
||||
|
||||
The landscape view treats the existing computational biology registry as a multidimensional database rather than a single hierarchical list.
|
||||
|
||||
## Design goals
|
||||
|
||||
- Keep the current curated resource records and generation pipeline intact.
|
||||
- Expose orthogonal facets so one resource can be explored by resource type, biological/ML task, data modality, organism, and domain tag.
|
||||
- Make the landscape useful without introducing a server or build-time dependency.
|
||||
- Keep the data model extensible for richer AI4Bio metadata over time.
|
||||
|
||||
## Current facet model
|
||||
|
||||
The landscape UI derives the following dimensions from `docs/data/resources.json`:
|
||||
|
||||
| Dimension | Source field | Example values |
|
||||
|---|---|---|
|
||||
| Resource type | `type` | `database`, `benchmark`, `model`, `toolkit`, `api` |
|
||||
| Task | `tasks` | `drug-response-prediction`, `cell-type-annotation`, `molecular-generation` |
|
||||
| Modality | `modalities` | `transcriptomics`, `spatial-transcriptomics`, `protein-sequence` |
|
||||
| Organism | `organism` | `human`, `mouse`, `multi-species` |
|
||||
| Domain/tag | `tags` | `drug-discovery`, `single-cell`, `foundation-model` |
|
||||
|
||||
These are deliberately treated as separate axes. A model can therefore be, for example, a `model` that performs `perturbation-prediction` on `single-cell-rna-seq` data for `human` and carry tags such as `drug-discovery` and `foundation-model`.
|
||||
|
||||
## Recommended schema evolution
|
||||
|
||||
The current schema is compatible with a richer landscape database. New fields should be added incrementally and only when they can be curated consistently.
|
||||
|
||||
Suggested fields:
|
||||
|
||||
| Field | Type | Purpose |
|
||||
|---|---|---|
|
||||
| `entities` | array of strings | Biological entities such as `gene`, `protein`, `compound`, `cell`, `disease` |
|
||||
| `methods` | array of strings | Method families such as `transformer`, `gnn`, `diffusion`, `optimal-transport` |
|
||||
| `organizations` | array of strings | Primary organizations responsible for the resource |
|
||||
| `year` | integer | Initial public release/publication year |
|
||||
| `github` | string | Source repository when distinct from the canonical landing page |
|
||||
| `documentation` | string | Documentation URL |
|
||||
| `maintenance_status` | string | Curated status such as `active`, `maintenance`, `archived`, `unknown` |
|
||||
| `last_checked` | string | Date the metadata/link was last manually or automatically checked |
|
||||
|
||||
Avoid adding dynamic popularity metrics such as GitHub stars directly to canonical records unless a reproducible refresh pipeline is introduced. Such values become stale quickly and should be stored as generated metadata rather than curated facts.
|
||||
|
||||
## Canonical-source policy
|
||||
|
||||
At present, `README.md` is the canonical curated list, with generated YAML/JSON/CSV artifacts. The landscape page intentionally consumes `docs/data/resources.json` without changing that policy.
|
||||
|
||||
A future migration may make `data/resources.yml` the canonical source once all README-only categorization semantics can be represented explicitly in structured fields. That migration should be a separate change because it changes contribution workflow and source-of-truth semantics.
|
||||
|
||||
## Landscape page
|
||||
|
||||
Open `docs/landscape.html` through GitHub Pages. It provides:
|
||||
|
||||
- full-text search across names, descriptions, tasks, modalities, organisms, and tags;
|
||||
- filters for type, task, modality, organism, and tag;
|
||||
- summary counts for resources and major dimensions;
|
||||
- frequency bars recalculated for the current filtered result set;
|
||||
- direct resource and paper links;
|
||||
- client-side rendering with no additional dependencies.
|
||||
+72
@@ -0,0 +1,72 @@
|
||||
---
|
||||
title: "Foundation Model Enrichment"
|
||||
task: ""
|
||||
lineage_type: import
|
||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/docs/FOUNDATION_MODEL_ENRICHMENT.md
|
||||
upstream_sha: 7a064bf0
|
||||
imported_at: 2026-08-08
|
||||
prompt_class: catalogue
|
||||
upstream_changes: accepted
|
||||
author: upstream
|
||||
validated: false
|
||||
---
|
||||
|
||||
# Foundation Model Enrichment
|
||||
|
||||
This document tracks the first curated metadata-enrichment pass for AI4Bio foundation models.
|
||||
|
||||
## Scope
|
||||
|
||||
The initial pass focuses on representative single-cell and transcriptomics foundation models already present in the resource registry, beginning with:
|
||||
|
||||
- scGPT
|
||||
- Geneformer
|
||||
|
||||
The scope may be expanded incrementally once the curation rules below are validated in practice.
|
||||
|
||||
## Curation rules
|
||||
|
||||
Metadata must be supported by at least one primary or official source:
|
||||
|
||||
- official project repository or model card;
|
||||
- official documentation;
|
||||
- primary peer-reviewed publication or preprint.
|
||||
|
||||
Unknown or ambiguous metadata is omitted rather than inferred.
|
||||
|
||||
For each resource, curate fields where evidence is available:
|
||||
|
||||
- `entities`
|
||||
- `methods`
|
||||
- `organizations`
|
||||
- `year`
|
||||
- `github`
|
||||
- `documentation`
|
||||
- `maintenance_status`
|
||||
- `last_checked`
|
||||
- `metadata_sources`
|
||||
|
||||
`maintenance_status` should only be marked `active` when there is direct evidence of ongoing maintenance, such as a recent official release or repository activity. Otherwise use `unknown` or omit the field.
|
||||
|
||||
## Initial evidence targets
|
||||
|
||||
### scGPT
|
||||
|
||||
Primary evidence should include the official `bowang-lab/scGPT` repository and the Nature Methods publication.
|
||||
|
||||
### Geneformer
|
||||
|
||||
Primary evidence should include the official `ctheodoris/Geneformer` model repository/model card and the primary Nature publication.
|
||||
|
||||
## Completion criteria
|
||||
|
||||
A resource is considered enriched when:
|
||||
|
||||
1. all added metadata is supported by `metadata_sources`;
|
||||
2. no unsupported organization, method, year, or maintenance claim is introduced;
|
||||
3. generated JSON/CSV artifacts are regenerated and committed;
|
||||
4. schema validation and resource-consistency CI checks pass.
|
||||
|
||||
## Provenance
|
||||
|
||||
This enrichment pass is being prepared with assistance from OpenAI GPT-5.6 Sol. Final metadata is intended to remain source-verifiable and reviewable through the recorded provenance URLs.
|
||||
@@ -0,0 +1,21 @@
|
||||
---
|
||||
title: "AI4Bio data files"
|
||||
task: ""
|
||||
lineage_type: import
|
||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/docs/data/README.md
|
||||
upstream_sha: 7a064bf0
|
||||
imported_at: 2026-08-08
|
||||
prompt_class: catalogue
|
||||
upstream_changes: accepted
|
||||
author: upstream
|
||||
validated: false
|
||||
---
|
||||
|
||||
# AI4Bio data files
|
||||
|
||||
- `resources.json`: generated merged resource registry consumed by GitHub Pages.
|
||||
- `resource.schema.json`: JSON Schema 2020-12 contract for one resource object.
|
||||
- `SCHEMA.md`: original schema notes.
|
||||
- `SCHEMA_V2.md`: richer AI4Bio landscape schema and enrichment workflow.
|
||||
|
||||
The enriched build path is `scripts/build_resources_v2.py`, which combines `data/resources.yml` with `data/enrichment.yml` and runs `scripts/validate_resources.py` before writing artifacts.
|
||||
@@ -0,0 +1,132 @@
|
||||
---
|
||||
title: "AI4Bio Resource Schema v2"
|
||||
task: ""
|
||||
lineage_type: import
|
||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/docs/data/SCHEMA_V2.md
|
||||
upstream_sha: 7a064bf0
|
||||
imported_at: 2026-08-08
|
||||
prompt_class: catalogue
|
||||
upstream_changes: accepted
|
||||
author: upstream
|
||||
validated: false
|
||||
---
|
||||
|
||||
# AI4Bio Resource Schema v2
|
||||
|
||||
This document defines the richer landscape metadata layered on top of the curated Awesome Computational Biology list.
|
||||
|
||||
## Source model
|
||||
|
||||
The repository intentionally separates **membership/basic metadata** from **landscape enrichment**:
|
||||
|
||||
1. `README.md` is the canonical curated resource list.
|
||||
2. `scripts/sync_resources_from_readme.py` derives `data/resources.yml` from README headings and bullets.
|
||||
3. `data/enrichment.yml` stores richer metadata keyed by stable resource `id`.
|
||||
4. `data/vocabulary.yml` defines canonical terms for controlled enrichment dimensions.
|
||||
5. `scripts/build_resources.py` merges base records and enrichment, validates them, and writes `data/resources.json`, `data/resources.csv`, and `docs/data/resources.json`.
|
||||
|
||||
This separation prevents hand-curated AI4Bio metadata from being erased by README synchronization.
|
||||
|
||||
## Core identity fields
|
||||
|
||||
These fields are required and may not be overridden by `data/enrichment.yml`:
|
||||
|
||||
| Field | Type | Meaning |
|
||||
|---|---|---|
|
||||
| `id` | string | Stable lowercase `snake_case` identifier |
|
||||
| `name` | string | Official display name |
|
||||
| `type` | enum | `api`, `benchmark`, `database`, `model`, `resource`, or `toolkit` |
|
||||
| `url` | URL | Canonical landing page |
|
||||
| `description` | string | Short factual description |
|
||||
|
||||
## Landscape dimensions
|
||||
|
||||
| Field | Type | Meaning |
|
||||
|---|---|---|
|
||||
| `tasks` | string[] | Biological or ML tasks performed |
|
||||
| `modalities` | string[] | Input/output data modalities |
|
||||
| `organism` | string[] | Covered organisms or species groups |
|
||||
| `entities` | string[] | Biological entities: gene, protein, compound, cell, disease, etc. |
|
||||
| `methods` | string[] | Method families: transformer, GNN, diffusion, optimal transport, etc. |
|
||||
| `tags` | string[] | Broad domain and curation labels |
|
||||
| `organizations` | string[] | Organizations maintaining or primarily responsible for the resource |
|
||||
|
||||
These dimensions are deliberately orthogonal. Do not encode a task as a modality or a biological entity as a resource type.
|
||||
|
||||
## Controlled vocabulary
|
||||
|
||||
New values added through `data/enrichment.yml` for `entities`, `methods`, `modalities`, and `tasks` must use canonical terms from `data/vocabulary.yml`.
|
||||
|
||||
Canonical terms use lowercase kebab-case, for example:
|
||||
|
||||
```yaml
|
||||
entities: [cell, gene]
|
||||
methods: [transformer, self-supervised-learning]
|
||||
modalities: [single-cell-rna-seq, transcriptomics]
|
||||
tasks: [foundation-model-pretraining, cell-type-annotation]
|
||||
```
|
||||
|
||||
This rule is intentionally applied only to enrichment metadata. Existing README-derived values remain valid for backward compatibility and can be migrated separately without blocking routine resource updates.
|
||||
|
||||
When a required concept is missing, add a reusable canonical term to `data/vocabulary.yml` instead of inventing a one-off spelling in an enrichment record. `tags`, `organism`, and `organizations` remain free-form because their vocabularies are broader or context dependent.
|
||||
|
||||
## Provenance and lifecycle fields
|
||||
|
||||
| Field | Type | Meaning |
|
||||
|---|---|---|
|
||||
| `year` | integer | Initial public release or primary publication year |
|
||||
| `github` | URL | Source repository when available |
|
||||
| `documentation` | URL | Documentation landing page |
|
||||
| `paper` | URL | Primary publication or preprint |
|
||||
| `license` | string | SPDX identifier preferred |
|
||||
| `api` | boolean | Programmatic API availability |
|
||||
| `access` | enum | `open`, `registration`, `restricted`, `commercial`, `unknown` |
|
||||
| `maintenance_status` | enum | `active`, `maintenance`, `archived`, `unknown` |
|
||||
| `updated` | date | Last-known upstream update date |
|
||||
| `last_checked` | date | Date this repository verified the metadata |
|
||||
| `metadata_sources` | URL[] | Sources supporting enriched metadata |
|
||||
|
||||
`last_checked` is a curation timestamp, not an upstream release date. `updated` should only be populated when an upstream update date is known.
|
||||
|
||||
## Enrichment rules
|
||||
|
||||
`data/enrichment.yml` is a mapping keyed by resource id:
|
||||
|
||||
```yaml
|
||||
resources:
|
||||
example_resource:
|
||||
entities: [gene, disease]
|
||||
methods: [transformer]
|
||||
organizations: [Example Lab]
|
||||
year: 2025
|
||||
github: https://github.com/example/project
|
||||
documentation: https://example.org/docs
|
||||
maintenance_status: active
|
||||
access: open
|
||||
last_checked: 2026-08-08
|
||||
metadata_sources:
|
||||
- https://example.org/about
|
||||
```
|
||||
|
||||
Enrichment cannot override `id`, `name`, `type`, `url`, or `description`. A referenced id must already exist in `data/resources.yml`.
|
||||
|
||||
## Validation contract
|
||||
|
||||
`python scripts/validate_resources.py` checks:
|
||||
|
||||
- required fields and field types;
|
||||
- stable id format and id uniqueness;
|
||||
- allowed enum values;
|
||||
- HTTP(S) URL shape;
|
||||
- ISO `YYYY-MM-DD` dates;
|
||||
- list uniqueness and non-empty values;
|
||||
- enrichment references and forbidden identity overrides;
|
||||
- controlled enrichment terms against `data/vocabulary.yml`;
|
||||
- vocabulary uniqueness and lowercase kebab-case normalization;
|
||||
- unknown field names.
|
||||
|
||||
The machine-readable resource counterpart is `docs/data/resource.schema.json` (JSON Schema 2020-12). Controlled vocabulary enforcement is performed at the enrichment layer because legacy README-derived values intentionally remain backward compatible.
|
||||
|
||||
## Curation guidance
|
||||
|
||||
Prefer verified metadata over exhaustive metadata. Unknown fields should be omitted rather than guessed. For facts likely to change, include `last_checked` and at least one `metadata_sources` URL. Dynamic popularity metrics such as GitHub stars should remain generated telemetry rather than canonical curated fields.
|
||||
+53
@@ -0,0 +1,53 @@
|
||||
---
|
||||
title: "Resource.Schema"
|
||||
task: ""
|
||||
lineage_type: import
|
||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/docs/data/resource.schema.json
|
||||
upstream_sha: 7a064bf0
|
||||
imported_at: 2026-08-08
|
||||
prompt_class: catalogue
|
||||
upstream_changes: accepted
|
||||
author: upstream
|
||||
validated: false
|
||||
---
|
||||
|
||||
{
|
||||
"$schema": "https://json-schema.org/draft/2020-12/schema",
|
||||
"$id": "https://inoue0426.github.io/awesome-computational-biology/data/resource.schema.json",
|
||||
"title": "AI4Bio Resource",
|
||||
"type": "object",
|
||||
"required": ["id", "name", "type", "url", "description"],
|
||||
"additionalProperties": false,
|
||||
"properties": {
|
||||
"id": {"type": "string", "pattern": "^[a-z0-9]+(?:_[a-z0-9]+)*$"},
|
||||
"name": {"type": "string", "minLength": 1},
|
||||
"type": {"enum": ["api", "benchmark", "database", "model", "resource", "toolkit"]},
|
||||
"url": {"type": "string", "format": "uri", "pattern": "^https?://"},
|
||||
"description": {"type": "string", "minLength": 1},
|
||||
"tags": {"$ref": "#/$defs/stringArray"},
|
||||
"tasks": {"$ref": "#/$defs/stringArray"},
|
||||
"modalities": {"$ref": "#/$defs/stringArray"},
|
||||
"organism": {"$ref": "#/$defs/stringArray"},
|
||||
"entities": {"$ref": "#/$defs/stringArray"},
|
||||
"methods": {"$ref": "#/$defs/stringArray"},
|
||||
"organizations": {"$ref": "#/$defs/stringArray"},
|
||||
"metadata_sources": {"type": "array", "items": {"type": "string", "format": "uri", "pattern": "^https?://"}, "uniqueItems": true},
|
||||
"license": {"type": "string"},
|
||||
"api": {"type": "boolean"},
|
||||
"paper": {"type": "string", "format": "uri", "pattern": "^https?://"},
|
||||
"github": {"type": "string", "format": "uri", "pattern": "^https://github\\.com/"},
|
||||
"documentation": {"type": "string", "format": "uri", "pattern": "^https?://"},
|
||||
"year": {"type": "integer", "minimum": 1900, "maximum": 2100},
|
||||
"maintenance_status": {"enum": ["active", "maintenance", "archived", "unknown"]},
|
||||
"access": {"enum": ["open", "registration", "restricted", "commercial", "unknown"]},
|
||||
"updated": {"type": "string", "format": "date"},
|
||||
"last_checked": {"type": "string", "format": "date"}
|
||||
},
|
||||
"$defs": {
|
||||
"stringArray": {
|
||||
"type": "array",
|
||||
"items": {"type": "string", "minLength": 1},
|
||||
"uniqueItems": true
|
||||
}
|
||||
}
|
||||
}
|
||||
+1053
-198
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Load Diff
Reference in New Issue
Block a user