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4 changed files with 352 additions and 12 deletions
@@ -2,9 +2,9 @@
title: "Awesome Computational Biology [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)"
task: ""
lineage_type: import
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/12d87583/README.md
upstream_sha: 12d87583
imported_at: 2026-06-26
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/478be843/README.md
upstream_sha: 478be843
imported_at: 2026-07-17
prompt_class: catalogue
upstream_changes: accepted
author: upstream
@@ -265,6 +265,8 @@ Browse and search the resources via the [GitHub Pages UI](https://inoue0426.gith
- [Tabula Sapiens](https://tabula-sapiens-portal.ds.czbiohub.org/) — Comprehensive human single-cell atlas of ~500K cells from 24 organs and tissues across multiple donors.
- [TAPE (Tasks Assessing Protein Embeddings)](https://github.com/songlab-cal/tape) — Benchmark suite of five biologically meaningful semi-supervised learning tasks for evaluating protein representations.
- [The Cancer Genome Atlas (TCGA)](https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga) — Comprehensive multi-omics (genomics, transcriptomics, proteomics, methylation) dataset for 33 cancer types across ~11,000 patients.
- [TCGA virtual spatial transcriptomics atlas](https://huggingface.co/datasets/ratschlab/TCGA_virtual_spatial_transcriptomics_atlas) — DeepSpot-M predicted transcriptome-wide ST for TCGA H&E (FF + FFPE; 28,664 slides / 32 cancer types; gated). Paper: [DeepSpot-M](https://www.medrxiv.org/content/10.64898/2026.06.19.26356060v1).
- [HEST Xenium virtual spatial transcriptomics](https://huggingface.co/datasets/ratschlab/HEST_Xenium_virtual_spatial_transcriptomics) — DeepSpot-M predicted transcriptome-wide ST for 59 HEST-1k 10x Xenium samples (~13.3M cells) (gated). Paper: [DeepSpot-M](https://www.medrxiv.org/content/10.64898/2026.06.19.26356060v1).
- [Therapeutics Data Commons (TDC)](https://tdcommons.ai/) — Unified benchmark suite covering ADMET, drug-target interaction, drug response, and more.
- [Tox21](https://tripod.nih.gov/tox21/challenge/) — 12,707 compounds tested in 12 nuclear receptor and stress-response pathway biochemical assays for toxicity prediction.
- [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.
@@ -320,6 +322,7 @@ Browse and search the resources via the [GitHub Pages UI](https://inoue0426.gith
- [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.
- [SeqBench](https://seqbench.com/) — Web-based molecular biology sequence workbench for primer design, cloning simulation (Gibson, Golden Gate, restriction digest), CRISPR guide RNA design, and sequence analysis, with a public REST API, OpenAPI 3.1 spec, and MCP server.
---
@@ -412,6 +415,10 @@ Browse and search the resources via the [GitHub Pages UI](https://inoue0426.gith
- [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.
- [DeepSpot](https://github.com/ratschlab/DeepSpot) — Deep learning model predicting spatial transcriptomics from H&E images at spot and single-cell resolution.
- [DeepSpot2Cell](https://github.com/ratschlab/DeepSpot2Cell) — Predicts virtual single-cell spatial transcriptomics from H&E using spot-level supervision (NeurIPS 2025 Imageomics).
- [DeepSpot-M](https://github.com/ratschlab/DeepSpotM) — Multimodal foundation model for transcriptome-wide virtual spatial transcriptomics from histology.
- [AESTETIK](https://github.com/ratschlab/aestetik) — Autoencoder for spatial transcriptomics representation learning using topology and histology image knowledge.
##### Multi-Omics Foundation Models
@@ -2,9 +2,9 @@
title: "Resources"
task: ""
lineage_type: import
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/12d87583/data/resources.json
upstream_sha: 12d87583
imported_at: 2026-06-26
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/478be843/data/resources.json
upstream_sha: 478be843
imported_at: 2026-07-17
prompt_class: catalogue
upstream_changes: accepted
author: upstream
@@ -292,6 +292,20 @@ validated: false
"organism": [],
"api": false
},
{
"id": "hest_xenium_virtual_spatial_transcriptomics",
"name": "HEST Xenium virtual spatial transcriptomics",
"type": "benchmark",
"url": "https://huggingface.co/datasets/ratschlab/HEST_Xenium_virtual_spatial_transcriptomics",
"description": "DeepSpot-M predicted transcriptome-wide ST for 59 HEST-1k 10x Xenium samples (~13.3M cells) (gated). Paper: [DeepSpot-M](https://www.medrxiv.org/content/10.64898/2026.06.19.26356060v1).",
"tags": [
"benchmarks-and-datasets"
],
"tasks": [],
"modalities": [],
"organism": [],
"api": false
},
{
"id": "jump_cell_painting_datasets",
"name": "JUMP Cell Painting Datasets",
@@ -530,6 +544,20 @@ validated: false
"organism": [],
"api": false
},
{
"id": "tcga_virtual_spatial_transcriptomics_atlas",
"name": "TCGA virtual spatial transcriptomics atlas",
"type": "benchmark",
"url": "https://huggingface.co/datasets/ratschlab/TCGA_virtual_spatial_transcriptomics_atlas",
"description": "DeepSpot-M predicted transcriptome-wide ST for TCGA H&E (FF + FFPE; 28,664 slides / 32 cancer types; gated). Paper: [DeepSpot-M](https://www.medrxiv.org/content/10.64898/2026.06.19.26356060v1).",
"tags": [
"benchmarks-and-datasets"
],
"tasks": [],
"modalities": [],
"organism": [],
"api": false
},
{
"id": "the_cancer_genome_atlas_tcga",
"name": "The Cancer Genome Atlas (TCGA)",
@@ -2095,6 +2123,27 @@ validated: false
"organism": [],
"api": false
},
{
"id": "aestetik",
"name": "AESTETIK",
"type": "model",
"url": "https://github.com/ratschlab/aestetik",
"description": "Autoencoder for spatial transcriptomics representation learning using topology and histology image knowledge.",
"tags": [
"foundation-models",
"single-cell-foundation-models",
"spatial-foundation-models"
],
"tasks": [
"Foundation Model"
],
"modalities": [
"Single Cell",
"Spatial Transcriptomics"
],
"organism": [],
"api": false
},
{
"id": "ai4chem_chemllm_7b_chat",
"name": "AI4Chem/ChemLLM-7B-Chat",
@@ -2667,6 +2716,69 @@ validated: false
"organism": [],
"api": false
},
{
"id": "deepspot",
"name": "DeepSpot",
"type": "model",
"url": "https://github.com/ratschlab/DeepSpot",
"description": "Deep learning model predicting spatial transcriptomics from H&E images at spot and single-cell resolution.",
"tags": [
"foundation-models",
"single-cell-foundation-models",
"spatial-foundation-models"
],
"tasks": [
"Foundation Model"
],
"modalities": [
"Single Cell",
"Spatial Transcriptomics"
],
"organism": [],
"api": false
},
{
"id": "deepspot_m",
"name": "DeepSpot-M",
"type": "model",
"url": "https://github.com/ratschlab/DeepSpotM",
"description": "Multimodal foundation model for transcriptome-wide virtual spatial transcriptomics from histology.",
"tags": [
"foundation-models",
"single-cell-foundation-models",
"spatial-foundation-models"
],
"tasks": [
"Foundation Model"
],
"modalities": [
"Single Cell",
"Spatial Transcriptomics"
],
"organism": [],
"api": false
},
{
"id": "deepspot2cell",
"name": "DeepSpot2Cell",
"type": "model",
"url": "https://github.com/ratschlab/DeepSpot2Cell",
"description": "Predicts virtual single-cell spatial transcriptomics from H&E using spot-level supervision (NeurIPS 2025 Imageomics).",
"tags": [
"foundation-models",
"single-cell-foundation-models",
"spatial-foundation-models"
],
"tasks": [
"Foundation Model"
],
"modalities": [
"Single Cell",
"Spatial Transcriptomics"
],
"organism": [],
"api": false
},
{
"id": "dgdrp",
"name": "DGDRP",
@@ -4913,6 +5025,22 @@ validated: false
"organism": [],
"api": false
},
{
"id": "seqbench",
"name": "SeqBench",
"type": "toolkit",
"url": "https://seqbench.com/",
"description": "Web-based molecular biology sequence workbench for primer design, cloning simulation (Gibson, Golden Gate, restriction digest), CRISPR guide RNA design, and sequence analysis, with a public REST API, OpenAPI 3.1 spec, and MCP server.",
"tags": [
"preprocessing-tools"
],
"tasks": [
"Preprocessing"
],
"modalities": [],
"organism": [],
"api": false
},
{
"id": "seurat",
"name": "Seurat",
@@ -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/12d87583/data/resources.yml
upstream_sha: 12d87583
imported_at: 2026-06-26
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/478be843/data/resources.yml
upstream_sha: 478be843
imported_at: 2026-07-17
prompt_class: catalogue
upstream_changes: accepted
author: upstream
@@ -248,6 +248,17 @@ resources:
organism: []
api: false
- id: hest_xenium_virtual_spatial_transcriptomics
name: "HEST Xenium virtual spatial transcriptomics"
type: benchmark
url: https://huggingface.co/datasets/ratschlab/HEST_Xenium_virtual_spatial_transcriptomics
description: "DeepSpot-M predicted transcriptome-wide ST for 59 HEST-1k 10x Xenium samples (~13.3M cells) (gated). Paper: [DeepSpot-M](https://www.medrxiv.org/content/10.64898/2026.06.19.26356060v1)."
tags: [benchmarks-and-datasets]
tasks: []
modalities: []
organism: []
api: false
- id: jump_cell_painting_datasets
name: "JUMP Cell Painting Datasets"
type: benchmark
@@ -435,6 +446,17 @@ resources:
organism: []
api: false
- id: tcga_virtual_spatial_transcriptomics_atlas
name: "TCGA virtual spatial transcriptomics atlas"
type: benchmark
url: https://huggingface.co/datasets/ratschlab/TCGA_virtual_spatial_transcriptomics_atlas
description: "DeepSpot-M predicted transcriptome-wide ST for TCGA H&E (FF + FFPE; 28,664 slides / 32 cancer types; gated). Paper: [DeepSpot-M](https://www.medrxiv.org/content/10.64898/2026.06.19.26356060v1)."
tags: [benchmarks-and-datasets]
tasks: []
modalities: []
organism: []
api: false
- id: the_cancer_genome_atlas_tcga
name: "The Cancer Genome Atlas (TCGA)"
type: benchmark
@@ -1491,6 +1513,17 @@ resources:
organism: []
api: false
- id: aestetik
name: "AESTETIK"
type: model
url: https://github.com/ratschlab/aestetik
description: "Autoencoder for spatial transcriptomics representation learning using topology and histology image knowledge."
tags: [foundation-models, single-cell-foundation-models, spatial-foundation-models]
tasks: [Foundation Model]
modalities: [Single Cell, Spatial Transcriptomics]
organism: []
api: false
- id: ai4chem_chemllm_7b_chat
name: "AI4Chem/ChemLLM-7B-Chat"
type: model
@@ -1810,6 +1843,39 @@ resources:
organism: []
api: false
- id: deepspot
name: "DeepSpot"
type: model
url: https://github.com/ratschlab/DeepSpot
description: "Deep learning model predicting spatial transcriptomics from H&E images at spot and single-cell resolution."
tags: [foundation-models, single-cell-foundation-models, spatial-foundation-models]
tasks: [Foundation Model]
modalities: [Single Cell, Spatial Transcriptomics]
organism: []
api: false
- id: deepspot_m
name: "DeepSpot-M"
type: model
url: https://github.com/ratschlab/DeepSpotM
description: "Multimodal foundation model for transcriptome-wide virtual spatial transcriptomics from histology."
tags: [foundation-models, single-cell-foundation-models, spatial-foundation-models]
tasks: [Foundation Model]
modalities: [Single Cell, Spatial Transcriptomics]
organism: []
api: false
- id: deepspot2cell
name: "DeepSpot2Cell"
type: model
url: https://github.com/ratschlab/DeepSpot2Cell
description: "Predicts virtual single-cell spatial transcriptomics from H&E using spot-level supervision (NeurIPS 2025 Imageomics)."
tags: [foundation-models, single-cell-foundation-models, spatial-foundation-models]
tasks: [Foundation Model]
modalities: [Single Cell, Spatial Transcriptomics]
organism: []
api: false
- id: dgdrp
name: "DGDRP"
type: model
@@ -3097,6 +3163,17 @@ resources:
organism: []
api: false
- id: seqbench
name: "SeqBench"
type: toolkit
url: https://seqbench.com/
description: "Web-based molecular biology sequence workbench for primer design, cloning simulation (Gibson, Golden Gate, restriction digest), CRISPR guide RNA design, and sequence analysis, with a public REST API, OpenAPI 3.1 spec, and MCP server."
tags: [preprocessing-tools]
tasks: [Preprocessing]
modalities: []
organism: []
api: false
- id: seurat
name: "Seurat"
type: toolkit
@@ -2,9 +2,9 @@
title: "Resources"
task: ""
lineage_type: import
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/12d87583/docs/data/resources.json
upstream_sha: 12d87583
imported_at: 2026-06-26
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/478be843/docs/data/resources.json
upstream_sha: 478be843
imported_at: 2026-07-17
prompt_class: catalogue
upstream_changes: accepted
author: upstream
@@ -292,6 +292,20 @@ validated: false
"organism": [],
"api": false
},
{
"id": "hest_xenium_virtual_spatial_transcriptomics",
"name": "HEST Xenium virtual spatial transcriptomics",
"type": "benchmark",
"url": "https://huggingface.co/datasets/ratschlab/HEST_Xenium_virtual_spatial_transcriptomics",
"description": "DeepSpot-M predicted transcriptome-wide ST for 59 HEST-1k 10x Xenium samples (~13.3M cells) (gated). Paper: [DeepSpot-M](https://www.medrxiv.org/content/10.64898/2026.06.19.26356060v1).",
"tags": [
"benchmarks-and-datasets"
],
"tasks": [],
"modalities": [],
"organism": [],
"api": false
},
{
"id": "jump_cell_painting_datasets",
"name": "JUMP Cell Painting Datasets",
@@ -530,6 +544,20 @@ validated: false
"organism": [],
"api": false
},
{
"id": "tcga_virtual_spatial_transcriptomics_atlas",
"name": "TCGA virtual spatial transcriptomics atlas",
"type": "benchmark",
"url": "https://huggingface.co/datasets/ratschlab/TCGA_virtual_spatial_transcriptomics_atlas",
"description": "DeepSpot-M predicted transcriptome-wide ST for TCGA H&E (FF + FFPE; 28,664 slides / 32 cancer types; gated). Paper: [DeepSpot-M](https://www.medrxiv.org/content/10.64898/2026.06.19.26356060v1).",
"tags": [
"benchmarks-and-datasets"
],
"tasks": [],
"modalities": [],
"organism": [],
"api": false
},
{
"id": "the_cancer_genome_atlas_tcga",
"name": "The Cancer Genome Atlas (TCGA)",
@@ -2095,6 +2123,27 @@ validated: false
"organism": [],
"api": false
},
{
"id": "aestetik",
"name": "AESTETIK",
"type": "model",
"url": "https://github.com/ratschlab/aestetik",
"description": "Autoencoder for spatial transcriptomics representation learning using topology and histology image knowledge.",
"tags": [
"foundation-models",
"single-cell-foundation-models",
"spatial-foundation-models"
],
"tasks": [
"Foundation Model"
],
"modalities": [
"Single Cell",
"Spatial Transcriptomics"
],
"organism": [],
"api": false
},
{
"id": "ai4chem_chemllm_7b_chat",
"name": "AI4Chem/ChemLLM-7B-Chat",
@@ -2667,6 +2716,69 @@ validated: false
"organism": [],
"api": false
},
{
"id": "deepspot",
"name": "DeepSpot",
"type": "model",
"url": "https://github.com/ratschlab/DeepSpot",
"description": "Deep learning model predicting spatial transcriptomics from H&E images at spot and single-cell resolution.",
"tags": [
"foundation-models",
"single-cell-foundation-models",
"spatial-foundation-models"
],
"tasks": [
"Foundation Model"
],
"modalities": [
"Single Cell",
"Spatial Transcriptomics"
],
"organism": [],
"api": false
},
{
"id": "deepspot_m",
"name": "DeepSpot-M",
"type": "model",
"url": "https://github.com/ratschlab/DeepSpotM",
"description": "Multimodal foundation model for transcriptome-wide virtual spatial transcriptomics from histology.",
"tags": [
"foundation-models",
"single-cell-foundation-models",
"spatial-foundation-models"
],
"tasks": [
"Foundation Model"
],
"modalities": [
"Single Cell",
"Spatial Transcriptomics"
],
"organism": [],
"api": false
},
{
"id": "deepspot2cell",
"name": "DeepSpot2Cell",
"type": "model",
"url": "https://github.com/ratschlab/DeepSpot2Cell",
"description": "Predicts virtual single-cell spatial transcriptomics from H&E using spot-level supervision (NeurIPS 2025 Imageomics).",
"tags": [
"foundation-models",
"single-cell-foundation-models",
"spatial-foundation-models"
],
"tasks": [
"Foundation Model"
],
"modalities": [
"Single Cell",
"Spatial Transcriptomics"
],
"organism": [],
"api": false
},
{
"id": "dgdrp",
"name": "DGDRP",
@@ -4913,6 +5025,22 @@ validated: false
"organism": [],
"api": false
},
{
"id": "seqbench",
"name": "SeqBench",
"type": "toolkit",
"url": "https://seqbench.com/",
"description": "Web-based molecular biology sequence workbench for primer design, cloning simulation (Gibson, Golden Gate, restriction digest), CRISPR guide RNA design, and sequence analysis, with a public REST API, OpenAPI 3.1 spec, and MCP server.",
"tags": [
"preprocessing-tools"
],
"tasks": [
"Preprocessing"
],
"modalities": [],
"organism": [],
"api": false
},
{
"id": "seurat",
"name": "Seurat",