[Upstream sync] inoue0426/awesome-computational-biology (github) — 17 added, 0 modified #2
Vendored
+28
@@ -0,0 +1,28 @@
|
||||
---
|
||||
title: "Pull Request Template"
|
||||
task: ""
|
||||
lineage_type: import
|
||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/12d87583/.github/PULL_REQUEST_TEMPLATE.md
|
||||
upstream_sha: 12d87583
|
||||
imported_at: 2026-06-26
|
||||
prompt_class: catalogue
|
||||
upstream_changes: accepted
|
||||
author: upstream
|
||||
validated: false
|
||||
---
|
||||
|
||||
## Description
|
||||
|
||||
<!-- Please describe what you are adding or changing, and why it is awesome. -->
|
||||
|
||||
## Checklist
|
||||
|
||||
- [ ] I have searched previous suggestions and this is not a duplicate.
|
||||
- [ ] I have added only one link per pull request.
|
||||
- [ ] The link follows the format: `[name](https://example.com/)` - A short description ends with a period.
|
||||
- [ ] Descriptions are concise.
|
||||
- [ ] Alphabetical ordering is maintained where applicable.
|
||||
- [ ] If a new section was added, the section description and title are included, and the title is added to the Index.
|
||||
- [ ] Spelling and grammar have been checked.
|
||||
- [ ] There is no trailing whitespace.
|
||||
- [ ] The pull request title follows the format: `Add user/repo - Short repo description`
|
||||
Vendored
+46
@@ -0,0 +1,46 @@
|
||||
---
|
||||
title: "Docs Check"
|
||||
task: ""
|
||||
lineage_type: import
|
||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/12d87583/.github/workflows/docs-check.yml
|
||||
upstream_sha: 12d87583
|
||||
imported_at: 2026-06-26
|
||||
prompt_class: unknown
|
||||
upstream_changes: accepted
|
||||
author: upstream
|
||||
validated: false
|
||||
---
|
||||
|
||||
name: Docs / lint
|
||||
|
||||
on:
|
||||
push:
|
||||
paths:
|
||||
- '**/*.md'
|
||||
pull_request:
|
||||
paths:
|
||||
- '**/*.md'
|
||||
|
||||
permissions:
|
||||
contents: read
|
||||
|
||||
jobs:
|
||||
docs-check:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- name: Checkout
|
||||
uses: actions/checkout@v4
|
||||
|
||||
- name: Setup Node.js
|
||||
uses: actions/setup-node@v4
|
||||
with:
|
||||
node-version: 'lts/*'
|
||||
|
||||
- name: Install tooling
|
||||
run: npm install -g [email protected] [email protected] --no-fund
|
||||
|
||||
- name: Run markdownlint
|
||||
run: markdownlint '**/*.md' --ignore node_modules
|
||||
|
||||
- name: Run cspell (spellcheck)
|
||||
run: cspell "**/*.md" --no-summary --no-progress
|
||||
Vendored
+55
@@ -0,0 +1,55 @@
|
||||
---
|
||||
title: "Generate Artifacts"
|
||||
task: ""
|
||||
lineage_type: import
|
||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/12d87583/.github/workflows/generate-artifacts.yml
|
||||
upstream_sha: 12d87583
|
||||
imported_at: 2026-06-26
|
||||
prompt_class: unknown
|
||||
upstream_changes: accepted
|
||||
author: upstream
|
||||
validated: false
|
||||
---
|
||||
|
||||
name: Generate data artifacts
|
||||
|
||||
on:
|
||||
push:
|
||||
paths:
|
||||
- 'data/resources.yml'
|
||||
pull_request:
|
||||
paths:
|
||||
- 'data/resources.yml'
|
||||
workflow_dispatch:
|
||||
|
||||
jobs:
|
||||
generate:
|
||||
runs-on: ubuntu-latest
|
||||
permissions:
|
||||
contents: write
|
||||
|
||||
steps:
|
||||
- name: Checkout
|
||||
uses: actions/checkout@v4
|
||||
with:
|
||||
ref: ${{ github.head_ref || github.ref_name }}
|
||||
|
||||
- name: Set up Python
|
||||
uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: '3.11'
|
||||
|
||||
- name: Install dependencies
|
||||
run: pip install pyyaml
|
||||
|
||||
- name: Generate JSON and CSV
|
||||
run: python scripts/generate_artifacts.py
|
||||
|
||||
- name: Commit artifacts (push events only)
|
||||
if: github.event_name == 'push' || github.event_name == 'workflow_dispatch'
|
||||
run: |
|
||||
git config user.name "github-actions[bot]"
|
||||
git config user.email "github-actions[bot]@users.noreply.github.com"
|
||||
git add data/resources.json data/resources.csv docs/data/resources.json
|
||||
git diff --cached --quiet || git commit -m "chore: regenerate resources.json and resources.csv"
|
||||
git push
|
||||
Vendored
+42
@@ -0,0 +1,42 @@
|
||||
---
|
||||
title: "Link Check"
|
||||
task: ""
|
||||
lineage_type: import
|
||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/12d87583/.github/workflows/link-check.yml
|
||||
upstream_sha: 12d87583
|
||||
imported_at: 2026-06-26
|
||||
prompt_class: unknown
|
||||
upstream_changes: accepted
|
||||
author: upstream
|
||||
validated: false
|
||||
---
|
||||
|
||||
name: Link Check
|
||||
|
||||
on:
|
||||
schedule:
|
||||
- cron: '0 5 * * 1' # 毎週月曜 05:00 UTC
|
||||
workflow_dispatch: # 手動実行も可能
|
||||
|
||||
permissions:
|
||||
contents: read
|
||||
|
||||
jobs:
|
||||
link-check:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- name: Checkout
|
||||
uses: actions/checkout@v4
|
||||
|
||||
- name: Setup Node.js
|
||||
uses: actions/setup-node@v4
|
||||
with:
|
||||
node-version: 'lts/*'
|
||||
|
||||
- name: Install markdown-link-check
|
||||
run: npm install -g [email protected] --no-fund
|
||||
|
||||
- name: Run markdown-link-check
|
||||
run: |
|
||||
find . -name '*.md' -not -path './node_modules/*' -print0 | \
|
||||
xargs -0 -I{} markdown-link-check {} -q -c .markdown-link-check.json
|
||||
Vendored
+56
@@ -0,0 +1,56 @@
|
||||
---
|
||||
title: "Pr Quality Checks"
|
||||
task: ""
|
||||
lineage_type: import
|
||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/12d87583/.github/workflows/pr-quality-checks.yml
|
||||
upstream_sha: 12d87583
|
||||
imported_at: 2026-06-26
|
||||
prompt_class: unknown
|
||||
upstream_changes: accepted
|
||||
author: upstream
|
||||
validated: false
|
||||
---
|
||||
|
||||
name: PR Quality Checks
|
||||
|
||||
on:
|
||||
pull_request:
|
||||
paths:
|
||||
- README.md
|
||||
- data/resources.yml
|
||||
- data/resources.json
|
||||
- data/resources.csv
|
||||
- docs/data/resources.json
|
||||
- scripts/*.py
|
||||
- scripts/**/*.py
|
||||
|
||||
permissions:
|
||||
contents: read
|
||||
|
||||
jobs:
|
||||
resources-consistency:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- name: Checkout
|
||||
uses: actions/checkout@v4
|
||||
|
||||
- name: Setup uv
|
||||
uses: astral-sh/setup-uv@v3
|
||||
|
||||
- name: Sync resources from README
|
||||
run: uv run --with pyyaml python scripts/sync_resources_from_readme.py
|
||||
|
||||
- name: Build resource artifacts
|
||||
run: uv run --with pyyaml python scripts/build_resources.py
|
||||
|
||||
- name: Verify generated artifacts are committed
|
||||
run: |
|
||||
if git diff --quiet; then
|
||||
echo "Resources are in sync."
|
||||
exit 0
|
||||
fi
|
||||
echo "Generated files are out of date. Run:"
|
||||
echo " uv run --with pyyaml python scripts/sync_resources_from_readme.py"
|
||||
echo " uv run --with pyyaml python scripts/build_resources.py"
|
||||
git status --short
|
||||
exit 1
|
||||
Vendored
+73
@@ -0,0 +1,73 @@
|
||||
---
|
||||
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
|
||||
prompt_class: unknown
|
||||
upstream_changes: accepted
|
||||
author: upstream
|
||||
validated: false
|
||||
---
|
||||
|
||||
name: Sync Resources
|
||||
|
||||
on:
|
||||
push:
|
||||
branches:
|
||||
- main
|
||||
paths:
|
||||
- README.md
|
||||
- data/resources.yml
|
||||
- scripts/*.py
|
||||
- scripts/**/*.py
|
||||
|
||||
permissions:
|
||||
contents: write
|
||||
|
||||
jobs:
|
||||
sync:
|
||||
if: github.actor != 'github-actions[bot]'
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- name: Checkout
|
||||
uses: actions/checkout@v4
|
||||
with:
|
||||
fetch-depth: 0
|
||||
|
||||
- name: Setup uv
|
||||
uses: astral-sh/setup-uv@v3
|
||||
|
||||
- name: Detect Changed Files
|
||||
id: changes
|
||||
run: |
|
||||
BEFORE="${{ github.event.before }}"
|
||||
AFTER="${{ github.sha }}"
|
||||
if [ -z "$BEFORE" ] || [ "$BEFORE" = "0000000000000000000000000000000000000000" ]; then
|
||||
CHANGED=$(git diff --name-only HEAD~1..HEAD)
|
||||
else
|
||||
CHANGED=$(git diff --name-only "$BEFORE" "$AFTER")
|
||||
fi
|
||||
echo "changed<<EOF" >> "$GITHUB_OUTPUT"
|
||||
echo "$CHANGED" >> "$GITHUB_OUTPUT"
|
||||
echo "EOF" >> "$GITHUB_OUTPUT"
|
||||
|
||||
- name: Sync From README
|
||||
if: contains(steps.changes.outputs.changed, 'README.md')
|
||||
run: uv run python scripts/sync_resources_from_readme.py
|
||||
|
||||
- name: Build Artifacts
|
||||
run: uv run --with pyyaml python scripts/build_resources.py
|
||||
|
||||
- name: Commit Updates
|
||||
run: |
|
||||
if git diff --quiet; then
|
||||
echo "No changes to commit."
|
||||
exit 0
|
||||
fi
|
||||
git config user.name "github-actions[bot]"
|
||||
git config user.email "41898282+github-actions[bot]@users.noreply.github.com"
|
||||
git add data/resources.yml data/resources.json data/resources.csv docs/data/resources.json
|
||||
git commit -m "chore: sync resources"
|
||||
git push
|
||||
Vendored
+58
@@ -0,0 +1,58 @@
|
||||
---
|
||||
title: "Update Overview"
|
||||
task: ""
|
||||
lineage_type: import
|
||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/12d87583/.github/workflows/update-overview.yml
|
||||
upstream_sha: 12d87583
|
||||
imported_at: 2026-06-26
|
||||
prompt_class: unknown
|
||||
upstream_changes: accepted
|
||||
author: upstream
|
||||
validated: false
|
||||
---
|
||||
|
||||
name: Update Overview Figure
|
||||
|
||||
on:
|
||||
push:
|
||||
branches:
|
||||
- main
|
||||
paths:
|
||||
- docs/data/resources.json
|
||||
schedule:
|
||||
# Every Monday at 03:00 UTC
|
||||
- cron: '0 3 * * 1'
|
||||
workflow_dispatch:
|
||||
|
||||
permissions:
|
||||
contents: write
|
||||
|
||||
jobs:
|
||||
update-overview:
|
||||
runs-on: ubuntu-latest
|
||||
|
||||
steps:
|
||||
- name: Checkout
|
||||
uses: actions/checkout@v4
|
||||
with:
|
||||
# Use the default GITHUB_TOKEN so commits don't re-trigger this workflow
|
||||
token: ${{ secrets.GITHUB_TOKEN }}
|
||||
|
||||
- name: Set up Python
|
||||
uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: '3.11'
|
||||
|
||||
- name: Install dependencies
|
||||
run: pip install matplotlib
|
||||
|
||||
- name: Regenerate overview figure
|
||||
run: python scripts/generate_overview.py
|
||||
|
||||
- name: Commit updated overview
|
||||
run: |
|
||||
git config user.name "github-actions[bot]"
|
||||
git config user.email "41898282+github-actions[bot]@users.noreply.github.com"
|
||||
git add docs/overview.png
|
||||
git diff --cached --quiet || git commit -m "chore: regenerate overview figure"
|
||||
git push
|
||||
@@ -0,0 +1,16 @@
|
||||
---
|
||||
title: ".Markdown Link Check"
|
||||
task: ""
|
||||
lineage_type: import
|
||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/12d87583/.markdown-link-check.json
|
||||
upstream_sha: 12d87583
|
||||
imported_at: 2026-06-26
|
||||
prompt_class: catalogue
|
||||
upstream_changes: accepted
|
||||
author: upstream
|
||||
validated: false
|
||||
---
|
||||
|
||||
{
|
||||
"aliveStatusCodes": [200, 206, 301, 302, 307, 308, 403]
|
||||
}
|
||||
@@ -0,0 +1,533 @@
|
||||
---
|
||||
title: "Awesome Computational Biology [](https://awesome.re)"
|
||||
task: ""
|
||||
lineage_type: import
|
||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/12d87583/README.md
|
||||
upstream_sha: 12d87583
|
||||
imported_at: 2026-06-26
|
||||
prompt_class: catalogue
|
||||
upstream_changes: accepted
|
||||
author: upstream
|
||||
validated: false
|
||||
---
|
||||
|
||||
# Awesome Computational Biology [](https://awesome.re)
|
||||
|
||||
A curated collection of databases, software, and papers related to computational biology.
|
||||
|
||||
> Computational biology involves the development and application of data-analytical and theoretical methods, mathematical modelling and computational simulation techniques to the study of biological, ecological, behavioural, and social systems. — [Wikipedia](https://en.wikipedia.org/wiki/Computational_biology)
|
||||
|
||||
---
|
||||
|
||||
## Overview
|
||||
|
||||
[](https://inoue0426.github.io/awesome-computational-biology/overview.html)
|
||||
|
||||
> Interactive version: [Resource Overview page](https://inoue0426.github.io/awesome-computational-biology/overview.html)
|
||||
> Regenerate the figure: `python scripts/generate_overview.py`
|
||||
|
||||
---
|
||||
|
||||
## GitHub Pages UI
|
||||
|
||||
Browse and search the resources via the [GitHub Pages UI](https://inoue0426.github.io/awesome-computational-biology/).
|
||||
|
||||
- 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.
|
||||
|
||||
---
|
||||
|
||||
## Table of Contents
|
||||
|
||||
- [Awesome Computational Biology](#awesome-computational-biology-)
|
||||
- [Table of Contents](#table-of-contents)
|
||||
- [Overview](#overview)
|
||||
- [GitHub Pages UI](#github-pages-ui)
|
||||
- [Citation](#citation)
|
||||
- [Curation Criteria (Strict)](#curation-criteria-strict)
|
||||
- [Update & Link Rot Policy](#update--link-rot-policy)
|
||||
- [Data Schema & Contribution Workflow](#data-schema--contribution-workflow)
|
||||
- [Databases](#databases)
|
||||
- [scRNA](#scrna)
|
||||
- [Compound](#compound)
|
||||
- [Pathway](#pathway)
|
||||
- [Mass Spectra](#mass-spectra)
|
||||
- [Protein](#protein)
|
||||
- [Genome](#genome)
|
||||
- [Disease](#disease)
|
||||
- [Interaction](#interaction)
|
||||
- [Drug-Gene Interaction](#drug-gene-interaction)
|
||||
- [Drug (Cell Line) Response](#drug-cell-line-response)
|
||||
- [Chemical-Protein Interaction](#chemical-protein-interaction)
|
||||
- [Protein-Protein Interaction](#protein-protein-interaction)
|
||||
- [Knowledge Graph](#knowledge-graph)
|
||||
- [Gene Regulatory Network](#gene-regulatory-network)
|
||||
- [Clinical Trial](#clinical-trial)
|
||||
- [Benchmarks & Datasets](#benchmarks--datasets)
|
||||
- [API](#api)
|
||||
- [Preprocessing Tools](#preprocessing-tools)
|
||||
- [Machine Learning Tasks and Models](#machine-learning-tasks-and-models)
|
||||
- [Drug Discovery](#drug-discovery)
|
||||
- [Drug Response Prediction](#drug-response-prediction)
|
||||
- [Drug Repurposing](#drug-repurposing)
|
||||
- [Drug Target Interaction](#drug-target-interaction)
|
||||
- [Compound-Protein Interaction](#compound-protein-interaction)
|
||||
- [Molecular Generation](#molecular-generation)
|
||||
- [LLM for Biology](#llm-for-biology)
|
||||
- [Foundation Models](#foundation-models)
|
||||
- [Single-cell Foundation Models](#single-cell-foundation-models)
|
||||
- [Transcriptomics Foundation Models](#transcriptomics-foundation-models)
|
||||
- [Spatial Foundation Models](#spatial-foundation-models)
|
||||
- [Multi-Omics Foundation Models](#multi-omics-foundation-models)
|
||||
- [Domain Alignment](#domain-alignment)
|
||||
- [Compound Foundation Models](#compound-foundation-models)
|
||||
- [Compound Embedding](#compound-embedding)
|
||||
- [Protein Foundation Models](#protein-foundation-models)
|
||||
- [Pre-trained Embedding](#pre-trained-embedding)
|
||||
- [Protein Structure Prediction and Design](#protein-structure-prediction-and-design)
|
||||
- [Multi-Modal Foundation Models](#multi-modal-foundation-models)
|
||||
- [Genomics Foundation Models](#genomics-foundation-models)
|
||||
|
||||
---
|
||||
|
||||
## Databases
|
||||
|
||||
### scRNA
|
||||
|
||||
- [CZ CELLxGENE](https://cellxgene.cziscience.com/) — Single-cell dataset repository and interactive explorer from the Chan Zuckerberg Initiative.
|
||||
- [Gene Expression Omnibus](https://www.ncbi.nlm.nih.gov/geo/) — Public functional genomics database.
|
||||
- [Human Cell Atlas](https://www.humancellatlas.org/) — Open global atlas of all cells in the human body.
|
||||
- [Single Cell PORTAL](https://singlecell.broadinstitute.org/single_cell) — Public database for single-cell RNA.
|
||||
- [Single Cell Expression Atlas](https://www.ebi.ac.uk/gxa/sc/home) — Public database for single-cell RNA.
|
||||
|
||||
### Compound
|
||||
|
||||
- [PubChem](https://pubchem.ncbi.nlm.nih.gov/) — One of the largest chemical databases (compounds, genes, and proteins).
|
||||
- [ChEBI](https://www.ebi.ac.uk/chebi/) — Database focused on small chemical compounds.
|
||||
- [ChEMBL](https://www.ebi.ac.uk/chembl/) — Bioactive molecules with drug-like properties.
|
||||
- [ChemSpider](http://www.chemspider.com/) — Chemical structure database.
|
||||
- [DrugTargetCommons](https://drugtargetcommons.fimm.fi/) — Community platform for curating and integrating experimental bioactivity data across drugs and targets.
|
||||
- [HMDB (Human Metabolome Database)](https://hmdb.ca/) — Comprehensive database of small molecule metabolites found in the human body.
|
||||
- [KEGG COMPOUND](https://www.genome.jp/kegg/compound/) — Collection of small molecules and biopolymers.
|
||||
- [LIPID MAPS](https://www.lipidmaps.org/databases/lmsd/overview) — Database of lipids.
|
||||
- [Rhea](https://www.rhea-db.org/) — Database of chemical reactions.
|
||||
- [DrugCentral](http://drugcentral.org/) — Online drug compendium with drug mode of action and indication information.
|
||||
- [Drug Repurposing Hub](https://repo-hub.broadinstitute.org/repurposing#download-data) — Collections of drug repurposing data (drug, MoA, target, etc).
|
||||
- [Therapeutic Target Database](https://idrblab.net/ttd/full-data-download) — Drug-target, target-disease, and drug-disease datasets.
|
||||
- [ZINC ligand discovery database](https://zinc.docking.org/) — Free database of commercially-available compounds for virtual screening.
|
||||
|
||||
### Pathway
|
||||
|
||||
- [PathwayCommons](https://www.pathwaycommons.org/) — Database of pathways and interactions.
|
||||
- [KEGG PATHWAY](https://www.genome.jp/kegg/pathway.html) — Collection of pathway maps.
|
||||
- [WikiPathways](https://wikipathways.org/) — Database of biological pathways.
|
||||
- [Reactome](https://reactome.org/) — Expert-curated, peer-reviewed pathway database with detailed reaction mechanisms.
|
||||
- [BioCyc](https://biocyc.org/) — Collection of pathway/genome databases across thousands of organisms.
|
||||
- [OmniPath](https://omnipathdb.org/) — Comprehensive resource integrating protein interactions, signaling pathways, gene regulatory networks, and miRNA targets from over 100 databases.
|
||||
- [SIGNOR 2.0](https://signor.uniroma2.it/) — Database of causal signaling interactions and pathways, with signed and directed relationships between proteins.
|
||||
- [MSigDB (Molecular Signatures Database)](https://www.gsea-msigdb.org/gsea/msigdb) — Curated gene sets derived from pathways and biological processes.
|
||||
|
||||
### Mass Spectra
|
||||
|
||||
- [MassBank](http://www.massbank.jp/) — Open source databases and tools for mass spectrometry reference spectra.
|
||||
- [MoNA MassBank of North America](https://mona.fiehnlab.ucdavis.edu/) — Meta-database of metabolite mass spectra, metadata, and associated compounds.
|
||||
|
||||
### Protein
|
||||
|
||||
- [THE HUMAN PROTEIN ATLAS](https://www.proteinatlas.org/) — Comprehensive human protein database (cells, tissues, organs).
|
||||
- [PROTEIN DATA BANK (PDB)](https://www.rcsb.org/) — 3D structures of proteins, nucleic acids, complexes.
|
||||
- [UniProt](https://www.uniprot.org/) — Functional information on proteins.
|
||||
- [AlphaFold Protein Structure Database](https://alphafold.ebi.ac.uk/api-docs) — 3D protein structure predictions.
|
||||
- [RCSB Protein Data Bank](https://www.rcsb.org/) — Repository for structural data of biological molecules.
|
||||
- [Critical Assessment of Structure Prediction (CASP)](https://predictioncenter.org/) — Assessing methods for protein structure prediction.
|
||||
- [Uniclust](https://uniclust.mmseqs.com/) — Clustered protein sequence databases.
|
||||
- [UniRef](https://www.uniprot.org/uniref/) — Non-redundant sequence database clustering UniProtKB entries at multiple sequence identity thresholds.
|
||||
- [CATH database](https://www.cathdb.info/) — Hierarchical classification of protein domain structures.
|
||||
- [SAbDab](https://opig.stats.ox.ac.uk/webapps/sabdab-sabpred/sabdab) — Structural Antibody Database containing all antibody structures in the PDB.
|
||||
- [OADB (Observed Antibody Space Database)](http://opig.stats.ox.ac.uk/webapps/oas/) — Database of antibody sequences from immune repertoire sequencing.
|
||||
- [InterPro](https://www.ebi.ac.uk/interpro/) — Protein families, domains, and functional sites database integrating 14 member databases including Pfam and PROSITE.
|
||||
- [Pfam](https://www.ebi.ac.uk/interpro/entry/pfam/) — Database of protein families described by multiple sequence alignments and hidden Markov models.
|
||||
- [NeXtProt](https://www.nextprot.org/) — Expert knowledge base on human proteins with deep functional annotation, complementary to UniProt.
|
||||
|
||||
### Genome
|
||||
|
||||
- [ENCODE](https://www.encodeproject.org/) — Encyclopedia of DNA Elements; regulatory and functional genomic elements across the genome.
|
||||
- [Ensembl](https://www.ensembl.org/) — Genome browser and annotation database for vertebrate and other eukaryotic genomes.
|
||||
- [Human Genome Resources at NCBI](https://www.ncbi.nlm.nih.gov/projects/genome/guide/human/index.shtml) — Database for genomics, proteomics, transcriptomics, and systems biology.
|
||||
- [GenBank](https://www.ncbi.nlm.nih.gov/genbank/) — NCBI's database of genetic sequences.
|
||||
- [UCSC Genome Browser](https://genome.ucsc.edu/) — UCSC's genome browser.
|
||||
- [cBioPortal](https://www.cbioportal.org/) — Cancer genomics database; aggregating many patient datasets.
|
||||
- [10x Genomics Dataset](https://www.10xgenomics.com/resources/datasets) — Collection of single-cell datasets.
|
||||
- [The Genotype-Tissue Expression (GTEx)](https://gtexportal.org/home/) — Human gene expression and regulation resource.
|
||||
- [Dependency Map (DepMap)](https://depmap.org/portal/) — CRISPR-Cas9 screens in cancer cell lines.
|
||||
- [Catalogue Of Somatic Mutations In Cancer (COSMIC)](https://cancer.sanger.ac.uk/cosmic) — Resource on somatic mutations in cancers.
|
||||
- [MGnify](https://www.ebi.ac.uk/metagenomics/) — Resource for metagenomic and metatranscriptomic data.
|
||||
- [JASPAR](http://jaspar.genereg.net/) — Database of transcription factor binding profiles.
|
||||
- [gnomAD](https://gnomad.broadinstitute.org/) — Genome Aggregation Database; genetic variation from large-scale sequencing projects.
|
||||
- [Rfam](https://rfam.org/) — Database of RNA families with sequence alignments and consensus structures.
|
||||
- [ROADMAP Epigenomics](http://www.roadmapepigenomics.org/) — Reference epigenome maps for 111 primary human cell types and tissues, including histone modifications, chromatin accessibility, and DNA methylation.
|
||||
- [FANTOM5](https://fantom.gsc.riken.jp/5/) — Functional annotation of mammalian genome; comprehensive atlas of active enhancers, promoters, and transcription start sites across human and mouse cell types.
|
||||
|
||||
### Disease
|
||||
|
||||
- [KEGG DRUG](https://www.genome.jp/kegg/drug/) — Comprehensive, approved drug information.
|
||||
- [DrugBank](https://go.drugbank.com/) — Database of drugs and targets (University of Alberta).
|
||||
- [DisGeNET](https://www.disgenet.org/) — Database of gene-disease associations integrating expert-curated and GWAS data.
|
||||
- [OMIM (Online Mendelian Inheritance in Man)](https://www.omim.org/) — Comprehensive database of human genes and genetic disorders.
|
||||
- [Open Targets Platform](https://platform.opentargets.org/) — Systematic target identification and prioritization platform integrating genetics, genomics, and drug data for drug discovery.
|
||||
- [Human Phenotype Ontology (HPO)](https://hpo.jax.org/) — Standardized vocabulary of phenotypic abnormalities in human disease, linking genes, variants, and clinical features.
|
||||
- [DISEASES](https://diseases.jensenlab.org/) — Gene–disease association database integrating evidence from text mining, curated databases, and experimental data.
|
||||
|
||||
### Interaction
|
||||
|
||||
#### Drug-Gene Interaction
|
||||
|
||||
- [DGIdb](https://www.dgidb.org/) — Drug-gene interactions and the druggable genome.
|
||||
- [Comparative Toxicogenomics Database](http://ctdbase.org/) — Chemical-gene interactions, chemical-disease and gene-disease associations, chemical-phenotype associations.
|
||||
- [SNAP](https://snap.stanford.edu/biodata/datasets/10002/10002-ChG-Miner.html) — Dataset of drug-gene interactions.
|
||||
|
||||
#### Drug (Cell Line) Response
|
||||
|
||||
- [NCI60](https://dtp.cancer.gov/discovery_development/nci-60/) — Focuses on 60 cancer cell lines and many drugs.
|
||||
- [Genomics of Drug Sensitivity in Cancer (GDSC)](https://www.cancerrxgene.org/) — Drug sensitivity for ~1000 human cancer cell lines and hundreds of compounds.
|
||||
- [Cancer Cell Line Encyclopedia](https://sites.broadinstitute.org/ccle/) — Database of ~1000 cancer cell lines.
|
||||
- [CellMiner Cross Database (CellMinerCDB)](https://discover.nci.nih.gov/cellminercdb/) — Integrates multiple cancer cell line databases.
|
||||
|
||||
#### Chemical-Protein Interaction
|
||||
|
||||
- [STITCH](http://stitch.embl.de/) — Chemical-protein interactions.
|
||||
- [BindingDB](https://www.bindingdb.org/rwd/bind/index.jsp) — Compounds and target database.
|
||||
- [Davis kinase inhibitors DB](http://staff.cs.utu.fi/~aijrinas/dti/) — Experimental kinase inhibitor binding affinity dataset for protein–ligand interaction research.
|
||||
- [Kinase Inhibitor Bioactivity Data (KIBA)](https://janeliascicomp.github.io/KIBA/) — Integrated bioactivity scores for kinase inhibitors combining Ki, Kd, and IC50 measurements.
|
||||
- [PDBBind](https://www.pdbbind-plus.org.cn/) — Binding affinity data for biomolecular complexes.
|
||||
|
||||
#### Protein-Protein Interaction
|
||||
|
||||
- [STRING](https://string-db.org/) — PPI networks for multiple organisms.
|
||||
- [BioGRID](https://thebiogrid.org/) — Protein, genetic, and chemical interactions.
|
||||
- [HIPPIE](http://cbdm-01.zdv.uni-mainz.de/~mschaefer/hippie/) — Human protein-protein interaction database.
|
||||
- [IntAct](https://www.ebi.ac.uk/intact/home) — Open-source molecular interaction database and analysis system from EMBL-EBI.
|
||||
|
||||
#### Knowledge Graph
|
||||
|
||||
- [Drug Mechanism Database (DrugMechDB)](https://github.com/SuLab/DrugMechDB/tree/2.0.1) — Mechanisms of action from drug to disease.
|
||||
- [DRKG](https://github.com/gnn4dr/DRKG) — Large-scale biological knowledge graph for drug discovery.
|
||||
- [Hetionet](https://github.com/hetio/hetionet) — Heterogeneous network integrating genes, diseases, drugs, pathways, and more.
|
||||
- [PrimeKG](https://github.com/mims-harvard/PrimeKG) — Multi-modal precision medicine knowledge graph integrating clinical, genetic, and drug data.
|
||||
|
||||
#### Gene Regulatory Network
|
||||
|
||||
- [TRRUST v2](https://www.grnpedia.org/trrust/) — Manually curated database of human and mouse transcriptional regulatory interactions between transcription factors and their target genes, expanded with literature-derived evidence.
|
||||
- [RegNetwork](http://www.regnetworkweb.org/) — Database of gene regulatory networks covering transcription factor–target gene and miRNA–gene interaction data across multiple species.
|
||||
- [miRBase](https://www.mirbase.org/) — Reference repository for microRNA gene annotations, sequences, and experimentally validated targets.
|
||||
|
||||
### Clinical Trial
|
||||
|
||||
- [ClinicalTrials.gov](https://clinicaltrials.gov/) — Privately and publicly funded clinical studies.
|
||||
- [ICD10](https://icd.who.int/browse10/2019/en) — International Classification of Diseases, 10th revision.
|
||||
- [EU Drug Regulating Authorities Clinical Trials DB (EudraCT)](https://eudract.ema.europa.eu/) — European clinical trial database.
|
||||
- [MIMIC-IV](https://mimic.mit.edu/) — Freely accessible critical care database.
|
||||
|
||||
---
|
||||
|
||||
## 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.
|
||||
- [BEAT AML](https://biodev.github.io/BeatAML2/) — Functional ex vivo drug sensitivity measurements paired with genomics for acute myeloid leukemia.
|
||||
- [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.
|
||||
- [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.
|
||||
- [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.
|
||||
|
||||
---
|
||||
|
||||
## API
|
||||
|
||||
- [PubMed E-utilities (esearch/efetch)](https://www.nlm.nih.gov/dataguide/edirect/esearch.html) — APIs for searching and retrieving biomedical literature from PubMed.
|
||||
- [NCBI E-utilities](https://www.ncbi.nlm.nih.gov/books/NBK25501/) — Unified APIs for accessing NCBI databases (Gene, GEO, SRA, PubChem, etc).
|
||||
- [UniProt REST API](https://www.uniprot.org/help/api) — Programmatic access to protein sequence and functional annotation data.
|
||||
- [Ensembl REST API](https://rest.ensembl.org/) — API for genomic annotations, variants, genes, and comparative genomics.
|
||||
- [KEGG REST API](https://www.kegg.jp/kegg/rest/keggapi.html) — API for accessing KEGG pathways, compounds, genes, and reactions.
|
||||
- [ChEMBL Web Services](https://www.ebi.ac.uk/chembl/ws) — REST API for bioactive molecules, targets, and bioassays.
|
||||
- [Open Targets Platform API](https://platform.opentargets.org/api) — API for target–disease associations integrating genetics, genomics, and drug data.
|
||||
- [ClinicalTrials.gov API](https://clinicaltrials.gov/api/gui) — API for querying clinical trial metadata and results.
|
||||
|
||||
---
|
||||
|
||||
## Preprocessing Tools
|
||||
|
||||
- [Chemistry Development Kit](https://github.com/cdk/cdk) — Cheminformatics software & machine learning tools.
|
||||
- [Biopython](https://biopython.org/) — Collection of Python tools for biological computation including sequence analysis, structure parsing, and database access.
|
||||
- [FlashDeconv](https://github.com/cafferychen777/flashdeconv) — High-performance spatial transcriptomics deconvolution (~1M spots in ~3 min).
|
||||
- [RDKit](https://github.com/rdkit/rdkit) — Cheminformatics software & machine learning toolkit.
|
||||
- [DeepChem](https://github.com/deepchem/deepchem) — Deep learning library for drug discovery, quantum chemistry, and materials science.
|
||||
- [ChatSpatial](https://github.com/cafferychen777/ChatSpatial) — MCP server for spatial transcriptomics analysis via natural language.
|
||||
- [Scanpy](https://scanpy.readthedocs.io/en/stable/) — Python library for scRNA-seq analysis.
|
||||
- [Seurat](https://satijalab.org/seurat/) — R library for scRNA-seq analysis.
|
||||
- [scvi-tools](https://scvi-tools.org/) — Probabilistic models for single-cell omics data analysis.
|
||||
- [CellTypist](https://github.com/Teichlab/celltypist) — Automated cell type annotation for scRNA-seq.
|
||||
- [Squidpy](https://squidpy.readthedocs.io/) — Python library for spatial single-cell analysis.
|
||||
- [GROMACS](https://www.gromacs.org/) — Molecular dynamics simulation package for biochemical molecules.
|
||||
- [MDAnalysis](https://www.mdanalysis.org/) — Python library for analyzing and altering molecular dynamics simulation trajectories.
|
||||
- [OpenMM](https://openmm.org/) — High-performance toolkit for molecular simulation and GPU-accelerated MD.
|
||||
- [scVelo](https://github.com/theislab/scvelo) — RNA velocity estimation for single-cell transcriptomics, inferring the direction and speed of cell differentiation.
|
||||
- [STAR](https://github.com/alexdobin/STAR) — Ultrafast universal RNA-seq aligner with support for spliced alignment and single-cell quantification via STARsolo.
|
||||
- [kallisto](https://pachterlab.github.io/kallisto/) — Near-optimal RNA-seq quantification using pseudoalignment for fast transcript abundance estimation.
|
||||
- [Harmony](https://github.com/immunogenomics/harmony) — Fast and scalable integration of single-cell data across datasets, conditions, technologies, and species.
|
||||
- [Monocle3](https://cole-trapnell-lab.github.io/monocle3/) — Single-cell trajectory analysis tool for learning developmental trajectories and ordering cells in pseudotime.
|
||||
- [CellChat](https://github.com/sqjin/CellChat) — Inference and analysis of cell-cell communication ligand-receptor networks from single-cell transcriptomics data.
|
||||
- [SCENIC](https://github.com/aertslab/SCENIC) — Single-cell regulatory network inference and clustering linking transcription factors to co-expressed gene modules.
|
||||
- [DoubletFinder](https://github.com/chris-mcginnis-ucsf/DoubletFinder) — Machine learning approach for detecting multiplet (doublet) artifacts in single-cell RNA-seq data.
|
||||
- [Numbat](https://github.com/kharchenkolab/numbat) — Haplotype-aware copy number variation inference from single-cell RNA-seq using hidden Markov models.
|
||||
- [CaSpER](https://github.com/akdess/CaSpER) — CNV identification and visualization by integrative analysis of single-cell or bulk RNA-seq data.
|
||||
- [CellCharter](https://github.com/CSOgroup/cellcharter) — Identification and characterization of spatial cell niches from spatial transcriptomics using VAEs and Gaussian mixture models.
|
||||
- [STAGATE](https://github.com/RucDongLab/STAGATE) — Adaptive graph attention auto-encoder for spatial domain identification in spatial transcriptomics.
|
||||
- [NCEM](https://github.com/theislab/ncem) — GNN-based model for learning intercellular communication from spatial graphs of cells.
|
||||
- [DeepTalk](https://github.com/JiangBioLab/DeepTalk) — Graph attention network for deciphering cell-cell communication from spatial transcriptomics data.
|
||||
- [COMMOT](https://github.com/zcang/COMMOT) — Optimal transport-based framework for screening cell-cell communication in spatial transcriptomics.
|
||||
- [TIGON](https://github.com/yutongo/TIGON) — Neural optimal transport method for reconstructing growth and dynamic trajectories from single-cell transcriptomics.
|
||||
- [LINGER](https://github.com/Durenlab/LINGER) — Neural network for gene regulatory network inference from single-cell multiome (RNA+ATAC-seq) data with bulk data pretraining.
|
||||
- [sciPENN](https://github.com/jlakkis/sciPENN) — RNN-based method for simultaneous protein expression prediction, uncertainty estimation, and cell-type label transfer from CITE-seq and scRNA-seq data.
|
||||
- [MOGONET](https://github.com/txWang/MOGONET) — Multi-omics graph convolutional network framework for patient classification and biomarker identification.
|
||||
- [AutoZyme](https://github.com/ElliotXie/autozyme) — Autonomous agentic framework that speeds up bioinformatics software (e.g. Scanpy, Seurat) on CPUs while preserving the original results.
|
||||
|
||||
---
|
||||
|
||||
## Machine Learning Tasks and Models
|
||||
|
||||
### Drug Discovery
|
||||
|
||||
#### Drug Response Prediction
|
||||
|
||||
- [drGAT](https://github.com/inoue0426/drGAT) — Attention-based model for drug response prediction with gene explainability.
|
||||
- [MOFGCN](https://github.com/weiba/MOFGCN/tree/main) — GCN + heterogeneous network.
|
||||
- [DeepDSC](https://ieeexplore-ieee-org.ezp2.lib.umn.edu/stamp/stamp.jsp?tp=&arnumber=8723620&tag=1) — Autoencoder + fully connected NN.
|
||||
- [DGDRP](https://github.com/minwoopak/heteronet) — Multi-view embedding neural network.
|
||||
- [DeepAEG](https://github.com/zhejiangzhuque/DeepAEG) — GNN embedding + attention mechanism.
|
||||
- [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.
|
||||
- [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.
|
||||
|
||||
#### Drug Repurposing
|
||||
|
||||
- [DeepPurpose](https://github.com/kexinhuang12345/DeepPurpose) — Deep learning library for drug repurposing.
|
||||
- [TranSiGen](https://github.com/myzhengSIMM/TranSiGen) — Dual-VAE architecture for ligand-based virtual screening, drug response prediction, and drug repurposing using chemical-induced transcriptional profiles.
|
||||
|
||||
#### Drug Target Interaction
|
||||
|
||||
- [NeoDTI](https://github.com/FangpingWan/NeoDTI) — Library for drug-target interaction prediction.
|
||||
- [DTINet](https://github.com/luoyunan/DTINet) — Network-based framework integrating heterogeneous biological data for DTI prediction.
|
||||
- [DeepDTA](https://github.com/hkmztrk/DeepDTA) — Deep learning model using CNNs on protein sequences and drug SMILES.
|
||||
- [GraphDTA](https://github.com/thinng/GraphDTA) — Graph neural network–based DTI prediction using molecular graphs.
|
||||
- [MolTrans](https://github.com/kexinhuang12345/MolTrans) — Transformer-based DTI model leveraging molecular substructures.
|
||||
- [DrugBAN](https://github.com/peizhenbai/DrugBAN) — Bilinear attention network for interpretable DTI prediction.
|
||||
|
||||
#### Compound-Protein Interaction
|
||||
|
||||
- [MCPINN](https://github.com/mhlee0903/multi_channels_PINN) — Drug discovery via compound-protein interaction and machine learning.
|
||||
- [TransformerCPI](https://github.com/lifanchen-simm/transformerCPI) — CPI prediction using Transformer.
|
||||
|
||||
#### Molecular Generation
|
||||
|
||||
- [REINVENT](https://github.com/MolecularAI/Reinvent) — Reinforcement learning for de novo drug design.
|
||||
- [MolGPT](https://github.com/devalab/molgpt) — Transformer-based model for molecular generation.
|
||||
- [Molecular Transformer](https://github.com/pschwllr/MolecularTransformer) — Sequence-to-sequence model for retrosynthesis prediction.
|
||||
- [Matcha](https://github.com/LigandPro/Matcha) — Multi-stage Riemannian flow matching model for physically valid molecular docking with scoring, pose filtering, and benchmarks.
|
||||
- [TargetDiff](https://github.com/guanjq/targetdiff) — 3D equivariant diffusion model for structure-based drug design.
|
||||
- [DiffDock](https://github.com/gcorso/DiffDock) — Diffusion generative model for molecular docking, predicting the binding pose of small molecules to protein targets.
|
||||
- [JTVAE](https://github.com/wengong-jin/icml18-jtnn) — Junction tree variational autoencoder for molecular graph generation that guarantees chemical validity via a hierarchical tree decomposition.
|
||||
- [DiffSBDD](https://github.com/arneschneuing/DiffSBDD) — Equivariant diffusion model for structure-based drug design that generates molecules and binding conformations for protein targets.
|
||||
- [ReLeaSE](https://github.com/isayev/ReLeaSE) — Deep reinforcement learning framework for de novo drug design combining a generative and predictive model.
|
||||
- [PaccMannRL](https://github.com/PaccMann/paccmann_generator) — Reinforcement learning-based generative model for de novo hit-like anticancer molecule design from transcriptomic data.
|
||||
|
||||
### LLM for Biology
|
||||
|
||||
- [AI4Chem/ChemLLM-7B-Chat](https://huggingface.co/AI4Chem/ChemLLM-7B-Chat) — LLM for chemical & molecular science.
|
||||
- [BioGPT](https://github.com/microsoft/BioGPT) — LLM for biomedical text generation.
|
||||
- [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.
|
||||
|
||||
### Foundation Models
|
||||
|
||||
#### Single-cell Foundation Models
|
||||
|
||||
##### Transcriptomics Foundation Models
|
||||
|
||||
- [scFoundation](https://github.com/biomap-research/scFoundation) — Large-scale foundation model for single-cell gene expression, enabling multiple downstream tasks.
|
||||
- [scGPT](https://github.com/bowang-lab/scGPT) — Transformer-based foundation model pretrained on millions of single-cell profiles.
|
||||
- [Geneformer](https://huggingface.co/ctheodoris/Geneformer) — Context-aware, attention-based deep learning model pretrained on a large corpus of single-cell transcriptomes.
|
||||
- [BulkFormer](https://github.com/KangBoming/BulkFormer) — Foundation model for bulk RNA-seq data; learns general transcriptomic representations.
|
||||
- [scBERT](https://github.com/TencentAILabHealthcare/scBERT) — BERT-based foundation model pretrained on large-scale scRNA-seq data for cell type annotation.
|
||||
- [CellPLM](https://github.com/OmicsML/CellPLM) — Cell pre-trained language model with inter-cell transformer architecture for diverse single-cell analysis tasks.
|
||||
- [UCE](https://github.com/snap-stanford/UCE) — Universal Cell Embeddings: zero-shot single-cell embedding model trained on 36M cells across species, tissues, and assays without fine-tuning.
|
||||
- [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.
|
||||
|
||||
##### Spatial Foundation Models
|
||||
|
||||
- [GigaPath](https://github.com/prov-gigapath/prov-gigapath) — Slide-level digital pathology foundation model pretrained on 1.3 billion pathology image tokens from whole-slide images.
|
||||
- [UNI](https://github.com/mahmoodlab/UNI) — General-purpose self-supervised pathology foundation model trained on 100K+ whole-slide images for diverse computational pathology tasks.
|
||||
- [CONCH](https://github.com/mahmoodlab/CONCH) — Vision-language foundation model for computational pathology trained with contrastive captioning on pathology image–text pairs.
|
||||
- [Phikon](https://huggingface.co/owkin/phikon) — ViT-based pathology foundation model pretrained with iBOT self-supervision on TCGA whole-slide images.
|
||||
- [Nicheformer](https://github.com/theislab/nicheformer) — Foundation model for single-cell and spatial omics using a transformer architecture with positional embeddings to encode spatial cell information.
|
||||
- [scGPT-spatial](https://github.com/bowang-lab/scGPT-spatial) — Extension of scGPT for spatial transcriptomics with continual pretraining and a mixture-of-experts decoder for spatial gene expression analysis.
|
||||
|
||||
##### 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.
|
||||
- [totalVI](https://github.com/scverse/scvi-tools) — Probabilistic framework for joint analysis of paired scRNA-seq and protein (CITE-seq) data enabling multi-modal cell state representation across single-cell datasets.
|
||||
- [MultiVI](https://github.com/scverse/scvi-tools) — Multi-modal variational autoencoder for integrating paired and unpaired single-cell RNA-seq and ATAC-seq measurements into a unified latent space.
|
||||
- [MIRA](https://github.com/cistrome/MIRA) — Probabilistic multimodal topic model jointly modeling single-cell transcriptomics and chromatin accessibility for regulatory network inference.
|
||||
- [GLUE](https://github.com/gao-lab/GLUE) — Graph-Linked Unified Embedding framework for unpaired single-cell multi-omics data integration across RNA, ATAC, methylation, and protein modalities.
|
||||
- [BABEL](https://github.com/wukevin/babel) — Cross-modality translation model enabling prediction between scRNA-seq and scATAC-seq profiles without requiring paired single-cell measurements.
|
||||
- [Multigrate](https://github.com/theislab/multigrate) — Asymmetric multi-omics variational autoencoder for integrating single-cell data across RNA, ATAC, and protein modalities with missing-modality support.
|
||||
- [MOFA+](https://github.com/bioFAM/MOFA2) — Multi-Omics Factor Analysis framework identifying shared axes of variation across bulk and single-cell datasets including RNA, ATAC, proteomics, methylation, and copy number.
|
||||
- [GeneCompass](https://github.com/xCompass-AI/GeneCompass) — Large-scale foundation model integrating DNA regulatory sequences and single-cell transcriptomics from 120M+ cells across multiple species for gene regulation prediction.
|
||||
- [UnitedNet](https://github.com/LiuLab-Bioelectronics-Harvard/UnitedNet) — Interpretable multi-task deep neural network for single-cell multi-omics integration spanning transcriptomics, chromatin accessibility, and proteomics.
|
||||
- [SpatialGlue](https://github.com/zhanglabtools/SpatialGlue) — Graph attention network for spatial multi-omics integration jointly embedding spatial transcriptomics with chromatin accessibility or proteomics.
|
||||
- [MIDAS](https://github.com/labomics/midas) — Mosaic integration and differential accessibility model for single-cell multi-omics data that handles arbitrary missing-modality combinations across transcriptomics, chromatin accessibility, and proteomics.
|
||||
- [Concerto](https://github.com/melobio/Concerto-reproducibility) — Contrastive self-supervised learning framework for single-cell multimodal data integration, batch correction, and reference-query mapping.
|
||||
- [scButterfly](https://github.com/BioX-NKU/scButterfly) — Dual-aligned variational autoencoder for single-cell cross-modality translation between paired and unpaired multiomics data.
|
||||
- [JAMIE](https://github.com/Oafish1/JAMIE) — Joint variational autoencoder for multimodal single-cell data imputation and embedding.
|
||||
- [scPair](https://github.com/quon-titative-biology/scPair) — Bidirectional feedforward network for single-cell multimodal analysis with cross-modality prediction leveraging single-cell atlases.
|
||||
|
||||
##### Domain Alignment
|
||||
|
||||
- [scArches](https://github.com/theislab/scarches) — Transfer learning framework for mapping new single-cell datasets onto pre-trained reference atlases across batches, conditions, and modalities.
|
||||
- [TOSICA](https://github.com/JackieHanlaopo/TOSICA) — Transformer-based framework for one-stop interpretable cell-type annotation supporting cross-dataset and cross-species transfer.
|
||||
|
||||
#### Compound Foundation Models
|
||||
|
||||
##### Compound Embedding
|
||||
|
||||
- [ChemBERTa-2](https://github.com/seyonechithrananda/bert-loves-chemistry) — RoBERTa-based molecular language model pretrained on SMILES for small-molecule representation learning.
|
||||
- [GROVER](https://github.com/tencent-ailab/grover) — Self-supervised graph transformer for large-scale molecular representation learning from unlabeled compounds.
|
||||
- [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.
|
||||
|
||||
#### Protein Foundation Models
|
||||
|
||||
##### Pre-trained Embedding
|
||||
|
||||
- [Evolutionary Scale Modeling (ESM)](https://github.com/facebookresearch/esm) — Protein embeddings.
|
||||
- [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.
|
||||
|
||||
##### 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.
|
||||
- [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.
|
||||
- [RFdiffusion](https://github.com/RosettaCommons/RFdiffusion) — Generative model for protein backbone design using diffusion.
|
||||
- [ProteinMPNN](https://github.com/dauparas/ProteinMPNN) — Deep learning model for protein sequence design given backbone structure.
|
||||
- [OmegaFold](https://github.com/HeliXonProtein/OmegaFold) — High-resolution de novo protein structure prediction from sequence.
|
||||
- [RoseTTAFold](https://github.com/RosettaCommons/RoseTTAFold) — Three-track neural network for protein structure prediction.
|
||||
- [OpenFold](https://github.com/aqlaboratory/openfold) — Trainable, memory-efficient open-source reproduction of AlphaFold2 enabling custom protein structure prediction workflows.
|
||||
- [SaProt](https://github.com/westlake-reup/SaProt) — Structure-aware protein language model using structure-aware tokens that encode both sequence and backbone geometry for improved function prediction.
|
||||
- [EvoDiff](https://github.com/microsoft/evodiff) — Discrete diffusion framework for protein sequence generation trained on evolutionary-scale data, supporting unconditional generation, disordered region design, and functional motif scaffolding. [ [paper-2023](https://www.biorxiv.org/content/10.1101/2023.09.11.556673v1) ]
|
||||
|
||||
#### Multi-Modal Foundation Models
|
||||
|
||||
- [CHIEF](https://github.com/hms-dbmi/CHIEF) — Clinical Histopathology Imaging Evaluation Foundation model integrating histology images and clinical context for pan-cancer analysis.
|
||||
- [BiomedCLIP](https://huggingface.co/microsoft/BiomedCLIP-PubMedBERT_256-vit_g_14) — CLIP-based vision-language foundation model for biomedical images and text trained on PubMed figure–caption pairs.
|
||||
- [PORPOISE](https://github.com/mahmoodlab/PORPOISE) — Pan-cancer integrative histology-genomic analysis framework using multimodal deep learning for patient stratification.
|
||||
- [PathomicFusion](https://github.com/mahmoodlab/PathomicFusion) — Integrated framework fusing histopathology and genomic features via CNN, GNN, and attention gating for cancer diagnosis and prognosis.
|
||||
- [Virchow](https://huggingface.co/paige-ai/Virchow) — Million-slide digital pathology foundation model using a vision transformer and self-supervised distillation for tile-level pathology image representation.
|
||||
- [TOAD](https://github.com/mahmoodlab/TOAD) — Tumor Origin Assessment via Deep-learning; weakly-supervised multi-task model predicting cancer primary origin from H&E whole-slide images.
|
||||
- [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.
|
||||
|
||||
#### Genomics Foundation Models
|
||||
|
||||
- [Nucleotide Transformer](https://github.com/instadeepai/nucleotide-transformer) — Foundation model for genomic sequences across multiple species.
|
||||
- [DNABERT](https://github.com/jerryji1993/DNABERT) — Pre-trained bidirectional encoder for DNA sequence analysis.
|
||||
- [DNABERT-2](https://github.com/Zhihan1996/DNABERT_2) — Improved genome foundation model with efficient tokenization.
|
||||
- [Enformer](https://github.com/deepmind/deepmind-research/tree/master/enformer) — Transformer model predicting gene expression from DNA sequence.
|
||||
- [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).
|
||||
- [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.
|
||||
- [Sei](https://github.com/FunctionLab/sei-framework) — Sequence-to-function framework learning a genome-wide regulatory activity code from DNA sequences for variant effect prediction.
|
||||
- [GPN (Genomic Pre-trained Network)](https://github.com/songlab-cal/gpn) — Masked language model for DNA sequences enabling zero-shot variant effect prediction without requiring functional annotations.
|
||||
|
||||
---
|
||||
|
||||
## Citation
|
||||
|
||||
If you use this list in papers, slides, or documentation, please cite this repository via [`CITATION.cff`](./CITATION.cff) (also available through GitHub's **Cite this repository** button).
|
||||
|
||||
## Curation Criteria (Strict)
|
||||
|
||||
To keep quality high, additions should meet all of the following:
|
||||
|
||||
- The resource is trustworthy and relevant to computational biology.
|
||||
- The primary link points to an official source (official docs, organization site, maintained repository, or official dataset page).
|
||||
- The resource has evidence of technical substance: ideally a peer-reviewed paper; at minimum a preprint or official technical documentation.
|
||||
- The description is factual and concise (no marketing copy).
|
||||
- Duplicate or near-duplicate entries should be avoided.
|
||||
|
||||
We generally do **not** accept entries that are only promotional pages, personal opinion posts, or generic blog posts without technical references.
|
||||
|
||||
## Update & Link Rot Policy
|
||||
|
||||
- Link validity is monitored by the [Link Check workflow](./.github/workflows/link-check.yml).
|
||||
- If a link repeatedly fails, maintainers may replace it with an official mirror/canonical URL or remove the entry until a stable URL is available.
|
||||
- Contributions fixing broken links are welcome and encouraged.
|
||||
|
||||
## Data Schema & Contribution Workflow
|
||||
|
||||
- Data schema reference: [`docs/data/SCHEMA.md`](./docs/data/SCHEMA.md).
|
||||
- Source-of-truth workflow:
|
||||
1. Edit/add resources in `README.md`.
|
||||
2. Regenerate machine-readable artifacts:
|
||||
- `python scripts/sync_resources_from_readme.py`
|
||||
- `python scripts/build_resources.py`
|
||||
3. Commit updated data files (`data/resources.yml`, `data/resources.json`, `data/resources.csv`, `docs/data/resources.json`) with your README change.
|
||||
- Contribution guide: [`contributing.md`](./contributing.md).
|
||||
@@ -0,0 +1,87 @@
|
||||
---
|
||||
title: "Contributor Covenant Code of Conduct"
|
||||
task: ""
|
||||
lineage_type: import
|
||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/12d87583/code-of-conduct.md
|
||||
upstream_sha: 12d87583
|
||||
imported_at: 2026-06-26
|
||||
prompt_class: unknown
|
||||
upstream_changes: accepted
|
||||
author: upstream
|
||||
validated: false
|
||||
---
|
||||
|
||||
# Contributor Covenant Code of Conduct
|
||||
|
||||
## Our Pledge
|
||||
|
||||
In the interest of fostering an open and welcoming environment, we as
|
||||
contributors and maintainers pledge to making participation in our project and
|
||||
our community a harassment-free experience for everyone, regardless of age, body
|
||||
size, disability, ethnicity, gender identity and expression, level of experience,
|
||||
nationality, personal appearance, race, religion, or sexual identity and
|
||||
orientation.
|
||||
|
||||
## Our Standards
|
||||
|
||||
Examples of behavior that contributes to creating a positive environment
|
||||
include:
|
||||
|
||||
* Using welcoming and inclusive language
|
||||
* Being respectful of differing viewpoints and experiences
|
||||
* Gracefully accepting constructive criticism
|
||||
* Focusing on what is best for the community
|
||||
* Showing empathy towards other community members
|
||||
|
||||
Examples of unacceptable behavior by participants include:
|
||||
|
||||
* The use of sexualized language or imagery and unwelcome sexual attention or
|
||||
advances
|
||||
* Trolling, insulting/derogatory comments, and personal or political attacks
|
||||
* Public or private harassment
|
||||
* Publishing others' private information, such as a physical or electronic
|
||||
address, without explicit permission
|
||||
* Other conduct which could reasonably be considered inappropriate in a
|
||||
professional setting
|
||||
|
||||
## Our Responsibilities
|
||||
|
||||
Project maintainers are responsible for clarifying the standards of acceptable
|
||||
behavior and are expected to take appropriate and fair corrective action in
|
||||
response to any instances of unacceptable behavior.
|
||||
|
||||
Project maintainers have the right and responsibility to remove, edit, or
|
||||
reject comments, commits, code, wiki edits, issues, and other contributions
|
||||
that are not aligned to this Code of Conduct, or to ban temporarily or
|
||||
permanently any contributor for other behaviors that they deem inappropriate,
|
||||
threatening, offensive, or harmful.
|
||||
|
||||
## Scope
|
||||
|
||||
This Code of Conduct applies both within project spaces and in public spaces
|
||||
when an individual is representing the project or its community. Examples of
|
||||
representing a project or community include using an official project e-mail
|
||||
address, posting via an official social media account, or acting as an appointed
|
||||
representative at an online or offline event. Representation of a project may be
|
||||
further defined and clarified by project maintainers.
|
||||
|
||||
## Enforcement
|
||||
|
||||
Instances of abusive, harassing, or otherwise unacceptable behavior may be
|
||||
reported by contacting the project team at inoue019@umn.edu. All
|
||||
complaints will be reviewed and investigated and will result in a response that
|
||||
is deemed necessary and appropriate to the circumstances. The project team is
|
||||
obligated to maintain confidentiality with regard to the reporter of an incident.
|
||||
Further details of specific enforcement policies may be posted separately.
|
||||
|
||||
Project maintainers who do not follow or enforce the Code of Conduct in good
|
||||
faith may face temporary or permanent repercussions as determined by other
|
||||
members of the project's leadership.
|
||||
|
||||
## Attribution
|
||||
|
||||
This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
|
||||
available at [http://contributor-covenant.org/version/1/4][version]
|
||||
|
||||
[homepage]: http://contributor-covenant.org
|
||||
[version]: http://contributor-covenant.org/version/1/4/
|
||||
@@ -0,0 +1,77 @@
|
||||
---
|
||||
title: "Contribution Guidelines"
|
||||
task: ""
|
||||
lineage_type: import
|
||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/12d87583/contributing.md
|
||||
upstream_sha: 12d87583
|
||||
imported_at: 2026-06-26
|
||||
prompt_class: catalogue
|
||||
upstream_changes: accepted
|
||||
author: upstream
|
||||
validated: false
|
||||
---
|
||||
|
||||
# Contribution Guidelines
|
||||
|
||||
Contributions are welcome!
|
||||
|
||||
Please note that this project is released with a
|
||||
[Contributor Code of Conduct](code-of-conduct.md). By participating in this
|
||||
project you agree to abide by its terms.
|
||||
|
||||
## Pull Requests
|
||||
|
||||
- Search previous suggestions before making a new one, as yours may be a duplicate.
|
||||
- Add one link per pull request.
|
||||
- Prefer official and trustworthy sources (official docs, organization pages, maintained repositories, or official dataset pages).
|
||||
- Include supporting technical evidence for new resources:
|
||||
- Ideally a peer-reviewed publication.
|
||||
- At minimum, a preprint or official technical documentation.
|
||||
- Avoid submissions that are primarily promotional pages, generic blog posts, or opinion-only writeups.
|
||||
- Add the link:
|
||||
- `[name](http://example.com/)` - A short description ends with a period.
|
||||
- Keep descriptions concise.
|
||||
- Maintain alphabetical ordering where applicable.
|
||||
- Add a section if needed.
|
||||
- Add the section description.
|
||||
- Add the section title to the [Index](https://github.com/inoue0426/awesome-computational-biology#Contents).
|
||||
- Check your spelling and grammar.
|
||||
- Remove any trailing whitespace.
|
||||
- Send a pull request with the reason why the addition is awesome.
|
||||
- Use the following format for your pull request title:
|
||||
- Add user/repo - Short repo description
|
||||
|
||||
## Data Workflow (README and JSON)
|
||||
|
||||
- The curated source list is maintained in `README.md`.
|
||||
- Machine-readable files are generated from README:
|
||||
- `python scripts/sync_resources_from_readme.py`
|
||||
- `python scripts/build_resources.py`
|
||||
- For resource additions/edits, include updated generated files in the same PR:
|
||||
- `data/resources.yml`
|
||||
- `data/resources.json`
|
||||
- `data/resources.csv`
|
||||
- `docs/data/resources.json`
|
||||
- Field definitions and naming rules are documented in [`docs/data/SCHEMA.md`](docs/data/SCHEMA.md).
|
||||
|
||||
## GitHub Pages UI
|
||||
|
||||
- The UI reads `docs/data/resources.json`.
|
||||
- Search and filters are driven by these fields:
|
||||
- Search: `name`, `description`, `tasks`, `modalities`, `tags`
|
||||
- Filters: `type`, `tasks`, `modalities`
|
||||
|
||||
## Updates to Existing Links or Sections
|
||||
|
||||
- Improvements to the existing sections are welcome.
|
||||
- If you think a listed link is not awesome, feel free to submit an issue or pull request to begin the discussion.
|
||||
- Broken links are checked by CI; if you find one, please submit a fix to the canonical URL (or remove the entry if no stable canonical URL exists).
|
||||
|
||||
## Updating your PR
|
||||
|
||||
A lot of times, making a PR adhere to the standards above can be difficult.
|
||||
If the maintainers notice anything that we'd like changed, we'll ask you to
|
||||
edit your PR before we merge it. There's no need to open a new PR, just edit
|
||||
the existing one. If you're not sure how to do that,
|
||||
[here is a guide](https://github.com/RichardLitt/knowledge/blob/master/github/amending-a-commit-guide.md)
|
||||
on the different ways you can update your PR so that we can merge it.
|
||||
@@ -0,0 +1,165 @@
|
||||
---
|
||||
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
|
||||
prompt_class: unknown
|
||||
upstream_changes: accepted
|
||||
author: upstream
|
||||
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",
|
||||
"Mendelian",
|
||||
"DisGeNET",
|
||||
"GWAS",
|
||||
"IntAct",
|
||||
"Biopython",
|
||||
"MDAnalysis",
|
||||
"trajectories",
|
||||
"Geneformer",
|
||||
"equivariant",
|
||||
"HyenaDNA",
|
||||
"Hyena",
|
||||
"Caduceus",
|
||||
"Mamba",
|
||||
"retrosynthesis",
|
||||
"TargetDiff",
|
||||
"Chai",
|
||||
"Zuckerberg",
|
||||
"HMDB",
|
||||
"CTRP",
|
||||
"ADMET",
|
||||
"Omics",
|
||||
"omics",
|
||||
"omic",
|
||||
"OADB",
|
||||
"Gnify",
|
||||
"gnom",
|
||||
"Rfam",
|
||||
"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",
|
||||
"JTVAE",
|
||||
"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",
|
||||
"MOGONET",
|
||||
"convolutional",
|
||||
"DRUML",
|
||||
"SBDD",
|
||||
"Pacc",
|
||||
"multiomics",
|
||||
"Pathomic",
|
||||
"PLIP"
|
||||
],
|
||||
"ignorePaths": [
|
||||
"node_modules/**"
|
||||
]
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,81 @@
|
||||
---
|
||||
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
|
||||
prompt_class: catalogue
|
||||
upstream_changes: accepted
|
||||
author: upstream
|
||||
validated: false
|
||||
---
|
||||
|
||||
# Resource Data Schema (`docs/data/resources.json`)
|
||||
|
||||
This document describes the JSON schema used by the GitHub Pages UI.
|
||||
|
||||
## Source of truth and generation flow
|
||||
|
||||
- **Canonical list source:** `README.md` (curated resource bullets)
|
||||
- Generated from README to YAML: `scripts/sync_resources_from_readme.py` → `data/resources.yml`
|
||||
- Built artifacts from YAML: `scripts/build_resources.py` → `data/resources.json`, `data/resources.csv`, and `docs/data/resources.json`
|
||||
|
||||
When contributing new resources, update `README.md` first, then regenerate artifacts.
|
||||
|
||||
## Top-level structure
|
||||
|
||||
- `resources.json` is a JSON array.
|
||||
- Each array item is one resource object.
|
||||
|
||||
## Fields
|
||||
|
||||
### Required fields
|
||||
|
||||
| Field | Type | Notes |
|
||||
|---|---|---|
|
||||
| `id` | string | Unique slug. Use lowercase `snake_case`, stable over time. |
|
||||
| `name` | string | Display name shown in README/UI. |
|
||||
| `type` | string | Resource category. Current values: `api`, `benchmark`, `database`, `model`, `toolkit`. |
|
||||
| `url` | string | Canonical landing page URL. |
|
||||
| `description` | string | One-line, factual summary. |
|
||||
|
||||
### Optional fields
|
||||
|
||||
| Field | Type | Notes |
|
||||
|---|---|---|
|
||||
| `tags` | array of strings | Free-form tags. |
|
||||
| `tasks` | array of strings | Task labels used by Task filter. |
|
||||
| `modalities` | array of strings | Data modality labels used by Modality filter. |
|
||||
| `organism` | array of strings | Organism labels. |
|
||||
| `license` | string | SPDX identifier preferred when known. |
|
||||
| `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`. |
|
||||
|
||||
## Naming and consistency guidance
|
||||
|
||||
- `id` must be globally unique across all resources.
|
||||
- Prefer concise, stable IDs (e.g., `open_targets_platform`, `alphafold3`).
|
||||
- Keep `name` aligned with official project/database naming.
|
||||
- Use short, objective descriptions (avoid marketing language).
|
||||
|
||||
## Example object
|
||||
|
||||
```json
|
||||
{
|
||||
"id": "open_targets_platform",
|
||||
"name": "Open Targets Platform",
|
||||
"type": "database",
|
||||
"url": "https://platform.opentargets.org/",
|
||||
"description": "Target identification platform integrating genetics, genomics, and drug evidence.",
|
||||
"tags": ["disease", "drug-discovery"],
|
||||
"tasks": ["target-identification"],
|
||||
"modalities": ["genomics"],
|
||||
"organism": ["human"],
|
||||
"license": "CC-BY-4.0",
|
||||
"api": true,
|
||||
"paper": "https://doi.org/10.1093/nar/gkac1045",
|
||||
"updated": "2026-01-15"
|
||||
}
|
||||
```
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,15 @@
|
||||
---
|
||||
title: "Requirements"
|
||||
task: ""
|
||||
lineage_type: import
|
||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/12d87583/scripts/requirements.txt
|
||||
upstream_sha: 12d87583
|
||||
imported_at: 2026-06-26
|
||||
prompt_class: catalogue
|
||||
upstream_changes: accepted
|
||||
author: upstream
|
||||
validated: false
|
||||
---
|
||||
|
||||
PyYAML>=6.0
|
||||
matplotlib>=3.7
|
||||
Reference in New Issue
Block a user