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| Author | SHA1 | Date | |
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@@ -2,9 +2,9 @@
|
||||
title: "Readme"
|
||||
task: ""
|
||||
lineage_type: import
|
||||
upstream_source: https://github.com/ai-boost/awesome-ai-for-science/blob/93d8280c/README.md
|
||||
upstream_sha: 93d8280c
|
||||
imported_at: 2026-07-23
|
||||
upstream_source: https://github.com/ai-boost/awesome-ai-for-science/blob/709cee28/README.md
|
||||
upstream_sha: 709cee28
|
||||
imported_at: 2026-07-15
|
||||
prompt_class: catalogue
|
||||
upstream_changes: accepted
|
||||
author: upstream
|
||||
@@ -144,7 +144,6 @@ validated: false
|
||||
- [Chat2Plot](https://github.com/nyanp/chat2plot) - Secure text-to-visualization through standardized chart specifications
|
||||
- [AutoViz](https://github.com/AutoViML/AutoViz) - Automated data visualization with minimal code
|
||||
- [PlotlyAI](https://plotly.com/ai/) - AI-powered data visualization and dashboard creation
|
||||
- [Flint (Microsoft)](https://github.com/microsoft/flint-chart) - Visualization intermediate language that lets AI agents create expressive, polished charts from simple, human-editable specs, compiling the same input to 30+ chart types across Vega-Lite, ECharts, and Chart.js with an MCP server for agent integration (1.9K+ stars, MIT License, 2026)
|
||||
|
||||
---
|
||||
|
||||
@@ -216,7 +215,6 @@ validated: false
|
||||
- [OpenScience (Synthetic Sciences)](https://github.com/synthetic-sciences/openscience) - Open-source AI workbench for scientific research that automates the full research loop — literature review, hypothesis generation, code writing, experiment execution, database querying, and report writing — with 290+ skills, specialized research agents, and a browser-based workspace (1453+ stars, Apache 2.0, 2026)
|
||||
- [Claude Scholar](https://github.com/Galaxy-Dawn/claude-scholar) - Semi-automated research assistant for academic research and software development, supporting Claude Code, Codex CLI, Kimi Code CLI, and OpenCode across ideation, coding, experiments, writing, and publication (Galaxy-Dawn, 4.5K+ stars, MIT License, 2026)
|
||||
- [K-Dense BYOK](https://github.com/K-Dense-AI/k-dense-byok) - Free, open-source desktop AI research assistant that runs locally and turns natural-language requests into real data analysis, literature search, figure generation, and manuscript review; ships with 149 scientific skills, 326 workflow templates, and 229 databases across genomics, proteomics, drug discovery, and materials science, plus a living lab notebook, 60+ scientific file previews, and LaTeX editing (K-Dense-AI, 908+ stars, MIT License, 2026)
|
||||
- [Academic Research Skills (ARS)](https://github.com/Imbad0202/academic-research-skills) - Comprehensive Claude Code skill suite covering the full academic pipeline from deep research and paper writing to multi-perspective peer review, revision, and finalization; features multi-agent teams, PRISMA systematic review, style calibration, claim-level citation audits, integrity gates, and human-in-the-loop safeguards (38K+ stars, CC BY-NC 4.0, 2026)
|
||||
|
||||
### Literature Management Plugins
|
||||
- [llm-for-zotero](https://github.com/yilewang/llm-for-zotero) - Research agent system deeply integrated with Zotero supporting Agent Mode, skills, multi-model backends (OpenAI-compatible, Claude Code, WebChat, Codex), and MinerU PDF parsing for literature Q&A, summarization, figure inspection, and source comparison (1.3K+ stars, 2026)
|
||||
@@ -292,8 +290,6 @@ validated: false
|
||||
- [SR-Scientist (ICLR 2026)](https://github.com/GAIR-NLP/SR-Scientist) - Scientific equation discovery with agentic AI, elevating LLMs from equation proposers to autonomous scientists that write code, analyze data, implement equations, and optimize based on experimental feedback; outperforms baselines by 6-35% across four science disciplines with robustness to noise and out-of-domain generalization (GAIR-NLP / SJTU, 49+ stars, Apache 2.0)
|
||||
- [ARA (Agent-Native Research Artifact)](https://github.com/ARA-Labs/Agent-Native-Research-Artifact) - Research ecosystem for rigorous and trustworthy AI scientists — a protocol and skill bundle that makes autonomous research verifiable, crystallized, and observable through structured, machine-executable research artifacts and five agent skills for research management, compilation, verification, visualization, and publication (ARA-Labs, 447+ stars, MIT License, 2026)
|
||||
- [Scholar Loop](https://github.com/renee-jia/scholar-loop) - Autonomous multi-agent AI scientist that mirrors a PhD workflow: literature review → grounded hypothesis → real ML experiments → self-critique → write-up; features a deterministic harness with frozen-metric scoring, edit allowlists, and a verified registry to make reward-hacking and hallucination impossible, plus 108 unit tests runnable without API keys or GPUs (461+ stars, MIT License, 2026)
|
||||
- [ResearchStudio (Microsoft)](https://github.com/microsoft/ResearchStudio) - AI co-author covering the entire research lifecycle — from an under-specified research direction to a published paper; includes ResearchStudio-Idea for evidence-grounded research ideation and ResearchStudio-Reel for turning finished papers into posters, narrated videos, blogs, and interactive reels; runs as skills on Claude Code and Codex (1.2K+ stars, MIT License, 2026)
|
||||
- [Principia](https://github.com/pzqpzq/Principia) - Principle-first scientific idea discovery framework that extracts reusable principles from public literature and private research materials, composes them into traceable Idea Cards with prior-art comparisons, and exports validation-ready research packs; emphasizes inspectable scientific objects, risk disclosure, and falsification paths (ICML 2026, 411+ stars, MIT License)
|
||||
|
||||
### Evaluation & Benchmarking
|
||||
- [ScienceAgentBench (ICLR 2025)](https://github.com/OSU-NLP-Group/ScienceAgentBench) - 102 executable tasks from 44 peer-reviewed papers across 4 disciplines with containerized evaluation
|
||||
@@ -307,7 +303,6 @@ validated: false
|
||||
- [SciBench](https://arxiv.org/abs/2307.10635) - College-level scientific problem-solving evaluation across multiple domains
|
||||
- [NewtonBench (ICLR 2026)](https://github.com/HKUST-KnowComp/NewtonBench) - First benchmark evaluating LLMs' ability to rediscover scientific laws through interactive experimentation across 324 tasks in 12 physics domains, featuring memorization-resistant metaphysical shifts of canonical laws (HKUST)
|
||||
- [ResearchClawBench (InternScience, arXiv 2026)](https://github.com/InternScience/ResearchClawBench) - Benchmark evaluating AI agents for end-to-end automated research from re-discovery to new-discovery, with 40 real-science tasks across 10 disciplines, curated datasets from published papers, and expert-curated multimodal rubrics (170+ stars, MIT License)
|
||||
- [Terminal-Bench Science (Harbor Framework, 2026)](https://github.com/harbor-framework/terminal-bench-science) - Benchmark evaluating AI agents on complex real-world scientific workflows in terminal environments across life, physical, earth, and mathematical sciences; featured on model cards for Claude Opus 4.7, GPT-5.5, and Gemini 3.1 Pro (200+ stars, Apache 2.0)
|
||||
|
||||
### Academic Review & Evaluation
|
||||
- [AgentReview](https://agentreview.github.io/) - LLM agents simulating academic peer review ecosystems
|
||||
@@ -334,7 +329,6 @@ validated: false
|
||||
- [Coscientist](https://www.nature.com/articles/s41586-023-06792-1) - Autonomous chemical experiment planning and execution
|
||||
- [SciAgents](https://github.com/lamm-mit/SciAgentsDiscovery) - Bioinspired multi-agent intelligent graph reasoning system that autonomously traverses ontological knowledge graphs to generate, critique, and refine novel research hypotheses, demonstrated on bio-inspired materials discovery with cross-disciplinary connection mining (MIT Lamm Group, 2024)
|
||||
- [TxAgent](https://github.com/mims-harvard/TxAgent) - AI agent for therapeutic reasoning across a universe of tools, achieving 92.1% accuracy in drug reasoning and outperforming GPT-4o by 25.8% (Harvard MIMS, 2025)
|
||||
- [ATHENA-R1 (Harvard MIMS)](https://github.com/mims-harvard/ATHENA) - Reinforcement-learning-trained AI agent for treatment reasoning over a universe of 212 biomedical tools, performing multi-step evidence gathering and spawning parallel reasoning branches to reach evidence-grounded clinical decisions (55+ stars, MIT License, 2026)
|
||||
- [ClawBio](https://github.com/ClawBio/ClawBio) - First bioinformatics-native AI agent skill library enabling local-first, reproducible genomic and population-genetics research workflows built on OpenClaw (871+ stars, MIT License, 2026)
|
||||
|
||||
---
|
||||
@@ -446,7 +440,6 @@ validated: false
|
||||
- [ChemCrow: Augmenting large-language models with chemistry tools](https://arxiv.org/abs/2304.05376) (2023.04) - LLM agents for chemistry research
|
||||
- [Autonomous chemical research with large language models](https://www.nature.com/articles/s41586-023-06792-0) - Automated chemical experimentation
|
||||
- [Coscientist: Autonomously planning and executing scientific experiments](https://www.nature.com/articles/s41586-023-06792-1) - Robotic lab automation
|
||||
- [The AutoResearch Moment: From Experimenter to Research Director](https://www.preprints.org/manuscript/202603.1329) (2026.03) - Position paper on claim governance for autonomous research: proposes a research-director bundle (objective sheet, discovery trace, verification ledger, provenance bundle) for evaluating agent-driven science
|
||||
|
||||
### Recent Advances & Domain Applications
|
||||
- [AlphaFold: Protein Structure Prediction](https://www.nature.com/articles/s41586-021-03819-2)
|
||||
@@ -653,7 +646,6 @@ validated: false
|
||||
- [micro-sam](https://github.com/computational-cell-analytics/micro-sam) - Segment Anything Model for microscopy: interactive and automatic segmentation of light, electron, and fluorescence microscopy images in 2D and 3D, with domain-specific fine-tuning workflows for scientific imaging (1.5K+ stars)
|
||||
- [MedSAM](https://github.com/bowang-lab/MedSAM) - Universal medical image segmentation foundation model trained on 1.57M image-mask pairs across 10 imaging modalities and 30+ cancer types (Nature Communications 2024)
|
||||
- [MedSAM2](https://github.com/bowang-lab/MedSAM2) - Segment Anything in 3D medical images and videos, extending SAM2 to volumetric and temporal medical imaging with state-of-the-art zero-shot segmentation performance across CT, MRI, and surgical video (arXiv 2025)
|
||||
- [Medical SAM3 (AIM Research Lab, arXiv 2026)](https://github.com/AIM-Research-Lab/Medical-SAM3) - Foundation model for universal prompt-driven medical image segmentation extending SAM3 to clinical imaging, supporting 2D public benchmarks and 3D training/evaluation with text and box prompts; pretrained weights available on HuggingFace (189+ stars)
|
||||
- [MedSegX](https://github.com/MedSegX/MedSegX-code) - Generalist foundation model and database for open-world medical image segmentation, enabling universal segmentation of diverse anatomical structures and pathologies with zero-shot generalization to unseen tasks and modalities (Nature Biomedical Engineering 2025)
|
||||
- [VoxTell (MIC-DKFZ, 2025)](https://github.com/MIC-DKFZ/VoxTell) - Free-text promptable universal 3D medical image segmentation foundation model enabling zero-shot segmentation of diverse anatomical structures and pathologies via natural language prompts across CT, MRI, and other volumetric imaging modalities (DKFZ, 195+ stars, Apache 2.0)
|
||||
- [BiomedParse](https://github.com/microsoft/BiomedParse) - Foundation model for joint segmentation, detection, and recognition of biomedical objects across nine imaging modalities, with v2 introducing BoltzFormer architecture for end-to-end 3D inference (Microsoft, Nature Methods 2025)
|
||||
@@ -805,9 +797,7 @@ validated: false
|
||||
- [TimesFM (Google Research)](https://github.com/google-research/timesfm) - Pretrained time series foundation model for long-horizon forecasting across diverse scientific domains including climate variables, biomedical signals, and physical observations; decoder-only Transformer architecture with strong zero-shot generalization (19.8K+ stars, Apache 2.0, 2024-2025)
|
||||
- [Chronos (Amazon Science, NeurIPS 2024)](https://github.com/amazon-science/chronos-forecasting) - Pretrained time series foundation model for zero-shot forecasting across diverse scientific and real-world domains; tokenizes continuous time series into discrete bins to train transformer language models on large-scale corpora, achieving strong zero-shot generalization and competitive performance with task-specific supervised models on climate, energy, and health benchmarks (5.3K+ stars, Apache 2.0, 2024-2026)
|
||||
- [TabPFN (Prior Labs, Nature 2025)](https://github.com/PriorLabs/tabpfn) - Foundation model for tabular data that predicts on unseen real-world tables in a single forward pass, achieving accurate small-data classification and regression without task-specific training; widely applicable to scientific datasets with limited samples (7.4K+ stars, 2022-2026)
|
||||
- [TabFM (Google Research, 2026)](https://github.com/google-research/tabfm) - Scikit-learn compatible tabular foundation model for zero-shot classification and regression on mixed-type tabular datasets via in-context learning; applicable to diverse scientific datasets (1.8K+ stars, Apache 2.0)
|
||||
- [DeepInnovator (HKUDS, arXiv 2026)](https://github.com/HKUDS/DeepInnovator) - Scientific foundation model and AI research copilot for idea generation, cross-disciplinary connection discovery, and hypothesis formation; trained with a decoupled reward-comment RL architecture and achieves GPT-4o-competitive novelty/rationale on STEM and social-science idea-generation benchmarks (270+ stars, MIT License, 2026)
|
||||
- [LOGOS (arXiv 2026)](https://github.com/LOGOS-Hub/LOGOS) - First multi-domain generative foundation model for the natural sciences built on a unified scientific grammar, encoding proteins, antibodies, small molecules, chemical reactions, materials, and their spatial interactions into a shared token vocabulary; enables unified generation, prediction, and design across domains under a purely autoregressive paradigm (134+ stars, Apache 2.0, 2026)
|
||||
- [MinervaAI](https://github.com/google-research/minerva) - Mathematical reasoning
|
||||
- [PaLM-2](https://ai.google/discover/palm2) - Scientific reasoning capabilities
|
||||
|
||||
|
||||
@@ -2,9 +2,9 @@
|
||||
title: "Awesome Computational Biology [](https://awesome.re)"
|
||||
task: ""
|
||||
lineage_type: import
|
||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/12d87583/README.md
|
||||
upstream_sha: 12d87583
|
||||
imported_at: 2026-06-26
|
||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/0cf037ab/README.md
|
||||
upstream_sha: 0cf037ab
|
||||
imported_at: 2026-07-16
|
||||
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.
|
||||
@@ -412,6 +414,10 @@ 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: "Cspell"
|
||||
task: ""
|
||||
lineage_type: import
|
||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/12d87583/cspell.json
|
||||
upstream_sha: 12d87583
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||||
imported_at: 2026-06-26
|
||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/0cf037ab/cspell.json
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||||
upstream_sha: 0cf037ab
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||||
imported_at: 2026-07-16
|
||||
prompt_class: unknown
|
||||
upstream_changes: accepted
|
||||
author: upstream
|
||||
@@ -157,7 +157,15 @@ validated: false
|
||||
"Pacc",
|
||||
"multiomics",
|
||||
"Pathomic",
|
||||
"PLIP"
|
||||
"PLIP",
|
||||
"Omni",
|
||||
"Bento",
|
||||
"FFPE",
|
||||
"Xenium",
|
||||
"Zyme",
|
||||
"Neur",
|
||||
"Imageomics",
|
||||
"AESTETIK"
|
||||
],
|
||||
"ignorePaths": [
|
||||
"node_modules/**"
|
||||
|
||||
@@ -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
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||||
imported_at: 2026-06-26
|
||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/0cf037ab/data/resources.json
|
||||
upstream_sha: 0cf037ab
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||||
imported_at: 2026-07-16
|
||||
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",
|
||||
|
||||
@@ -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/0cf037ab/data/resources.yml
|
||||
upstream_sha: 0cf037ab
|
||||
imported_at: 2026-07-16
|
||||
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
|
||||
|
||||
+115
-3
@@ -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/0cf037ab/docs/data/resources.json
|
||||
upstream_sha: 0cf037ab
|
||||
imported_at: 2026-07-16
|
||||
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",
|
||||
|
||||
Reference in New Issue
Block a user