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| Author | SHA1 | Date | |
|---|---|---|---|
 promptadmin
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c93915f321 |
@@ -2,9 +2,9 @@
|
||||
title: "Readme"
|
||||
task: ""
|
||||
lineage_type: import
|
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upstream_source: https://github.com/ai-boost/awesome-ai-for-science/blob/709cee28/README.md
|
||||
upstream_sha: 709cee28
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||||
imported_at: 2026-07-15
|
||||
upstream_source: https://github.com/ai-boost/awesome-ai-for-science/blob/7bb407ee/README.md
|
||||
upstream_sha: 7bb407ee
|
||||
imported_at: 2026-07-12
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||||
prompt_class: catalogue
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||||
upstream_changes: accepted
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||||
author: upstream
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||||
@@ -289,7 +289,6 @@ validated: false
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- [Science-Star (USTC AI4Science, 2025)](https://github.com/ustc-ai4science/Science-Star) - Open-source platform for building, extending, and experimenting with scientific agents, providing modular agent construction tools and standardized evaluation pipelines for accelerating autonomous scientific discovery research (748+ stars, MIT License)
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- [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)
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- [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)
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- [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)
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### Evaluation & Benchmarking
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- [ScienceAgentBench (ICLR 2025)](https://github.com/OSU-NLP-Group/ScienceAgentBench) - 102 executable tasks from 44 peer-reviewed papers across 4 disciplines with containerized evaluation
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@@ -603,10 +602,8 @@ validated: false
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- [AlphaGenome](https://github.com/google-deepmind/alphagenome) - Google DeepMind's unified DNA sequence foundation model predicting molecular consequences of genetic variants from single-base resolution up to 1 megabase context, jointly outputting thousands of regulatory tracks (RNA expression, splicing, chromatin accessibility, TF binding, contact maps) for human and mouse genomes via a Python client and non-commercial API (2025)
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- [GPN-Star (Song Lab, UC Berkeley, bioRxiv 2025)](https://github.com/songlab-cal/gpn) - Phylogeny-aware genomic language model trained on whole-genome alignments across multiple evolutionary timescales, predicting functional constraints and variant effects for human, mouse, chicken, fly, worm, and Arabidopsis genomes (344+ stars, MIT License)
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||||
- [GENERanno (bioRxiv 2025)](https://github.com/GenerTeam/GENERanno) - Genomic foundation model for metagenomic and genome annotation, featuring an 8k base-pair context and 500M parameters trained on 386B base pairs of eukaryotic DNA; provides expert models and a unified CLI for prokaryotic/eukaryotic coding-sequence annotation with strong performance on Genomic Benchmarks, Nucleotide Transformer tasks, and custom Gener tasks (GenerTeam, 314+ stars, MIT License)
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- [GENERator (bioRxiv 2026)](https://github.com/GenerTeam/GENERator) - Long-context generative genomic foundation model using 6-mer tokenization for DNA sequence modeling and generation, with v2 model families for prokaryote and eukaryote genomes and pretrained weights available on HuggingFace (GenerTeam, 460+ stars, MIT License, 2025-2026)
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- [DeepVariant](https://github.com/google/deepvariant) - Google DeepMind's deep learning analysis pipeline for calling genetic variants (SNPs and indels) from next-generation DNA sequencing data, achieving human expert-level accuracy and widely adopted in clinical genomics, population genetics, and precision medicine; pre-trained models available for multiple sequencing platforms and organismal genomes (Nature Biotechnology 2018, 3.7K+ stars)
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- [Casanovo](https://github.com/Noble-Lab/casanovo) - Transformer encoder-decoder for de novo peptide sequencing from tandem mass spectrometry, translating MS/MS spectra directly to peptide sequences without reference databases, enabling identification of novel peptides for immunopeptidomics, antibody repertoires, and metaproteomes (Noble Lab UW, Nature Communications 2024)
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- [Dorado](https://github.com/nanoporetech/dorado) - Oxford Nanopore's official deep-learning basecaller for nanopore sequencing, converting raw electrical signals into DNA/RNA sequences with integrated modified-base (methylation) detection and efficient CPU/GPU inference; foundational tool for long-read genomics, epigenetics, and real-time sequencing analysis (nanoporetech, 846+ stars, actively maintained)
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#### Neuroscience & Behavioral Analysis
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- [DeepLabCut](https://github.com/DeepLabCut/DeepLabCut) - Markerless pose estimation of user-defined features with deep learning for all animals including humans, enabling quantitative behavioral analysis in neuroscience and ethology (Nature Neuroscience 2018, 5.6K+ stars)
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@@ -652,7 +649,6 @@ validated: false
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- [UniBiomed (Nature Communications 2026)](https://github.com/Luffy03/UniBiomed) - Universal foundation model for grounded biomedical image interpretation, enabling comprehensive visual understanding, reasoning, and grounding across diverse biomedical imaging modalities with strong zero-shot generalization (55+ stars, Apache 2.0, 2025-2026)
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- [MIRA (NeurIPS 2025)](https://github.com/microsoft/MIRA) - Medical time series foundation model pretrained on 454B time points from heterogeneous clinical corpora spanning ICU physiological signals and hospital EHR, with continuous-time rotary positional encoding, frequency-specialized Mixture-of-Experts, and neural ODE extrapolation for zero-shot forecasting across irregular and multimodal temporal health data (Microsoft, 399+ stars, MIT License)
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- [HealthGPT (ICML 2025 Spotlight)](https://github.com/ZJU4HealthCare/HealthGPT) - Medical large vision-language model unifying comprehension and generation via heterogeneous knowledge adaptation, enabling holistic medical image understanding, visual question answering, and clinical report generation across diverse modalities (ZJU4HealthCare, 1.6K+ stars)
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- [Merlin (Stanford MIMI, Nature 2026)](https://github.com/StanfordMIMI/Merlin) - 3D vision-language model for computed tomography that leverages both structured electronic health records (EHR) and unstructured radiology reports for pretraining, enabling multimodal medical understanding and radiology report generation (447+ stars, MIT License, 2026)
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- [MedAgents](https://github.com/gersteinlab/MedAgents) - Multi-disciplinary collaboration framework for zero-shot medical reasoning using role-playing LLM agents (ACL 2024)
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- [MedAgentGym](https://github.com/wshi83/MedAgentGym) - Scalable agentic training environment for code-centric reasoning in biomedical data science
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- [MedRAX (ICML 2025)](https://github.com/bowang-lab/MedRAX) - First versatile medical reasoning agent for chest X-ray interpretation, dynamically integrating state-of-the-art CXR analysis tools and multimodal LLMs into a unified framework; introduces ChestAgentBench with 2,500 complex medical queries across 7 categories (bowang-lab, 1.1K+ stars)
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@@ -797,7 +793,6 @@ validated: false
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- [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)
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- [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)
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- [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)
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- [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)
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- [MinervaAI](https://github.com/google-research/minerva) - Mathematical reasoning
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- [PaLM-2](https://ai.google/discover/palm2) - Scientific reasoning capabilities
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@@ -2,9 +2,9 @@
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title: "Awesome Computational Biology [](https://awesome.re)"
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task: ""
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lineage_type: import
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upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/0cf037ab/README.md
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||||
upstream_sha: 0cf037ab
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||||
imported_at: 2026-07-16
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||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/12d87583/README.md
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upstream_sha: 12d87583
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imported_at: 2026-06-26
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prompt_class: catalogue
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upstream_changes: accepted
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author: upstream
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@@ -265,8 +265,6 @@ Browse and search the resources via the [GitHub Pages UI](https://inoue0426.gith
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||||
- [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.
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- [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.
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- [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).
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- [Therapeutics Data Commons (TDC)](https://tdcommons.ai/) — Unified benchmark suite covering ADMET, drug-target interaction, drug response, and more.
|
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- [Tox21](https://tripod.nih.gov/tox21/challenge/) — 12,707 compounds tested in 12 nuclear receptor and stress-response pathway biochemical assays for toxicity prediction.
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||||
- [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.
|
||||
@@ -414,10 +412,6 @@ 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.
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- [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.
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- [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.
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- [DeepSpot](https://github.com/ratschlab/DeepSpot) — Deep learning model predicting spatial transcriptomics from H&E images at spot and single-cell resolution.
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- [DeepSpot2Cell](https://github.com/ratschlab/DeepSpot2Cell) — Predicts virtual single-cell spatial transcriptomics from H&E using spot-level supervision (NeurIPS 2025 Imageomics).
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- [DeepSpot-M](https://github.com/ratschlab/DeepSpotM) — Multimodal foundation model for transcriptome-wide virtual spatial transcriptomics from histology.
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- [AESTETIK](https://github.com/ratschlab/aestetik) — Autoencoder for spatial transcriptomics representation learning using topology and histology image knowledge.
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##### Multi-Omics Foundation Models
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@@ -2,9 +2,9 @@
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title: "Cspell"
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||||
task: ""
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||||
lineage_type: import
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||||
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
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||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/12d87583/cspell.json
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upstream_sha: 12d87583
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||||
imported_at: 2026-06-26
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prompt_class: unknown
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upstream_changes: accepted
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||||
author: upstream
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||||
@@ -157,15 +157,7 @@ validated: false
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||||
"Pacc",
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"multiomics",
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"Pathomic",
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||||
"PLIP",
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||||
"Omni",
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||||
"Bento",
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||||
"FFPE",
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||||
"Xenium",
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||||
"Zyme",
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||||
"Neur",
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||||
"Imageomics",
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||||
"AESTETIK"
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||||
"PLIP"
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||||
],
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||||
"ignorePaths": [
|
||||
"node_modules/**"
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||||
|
||||
@@ -2,9 +2,9 @@
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||||
title: "Resources"
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||||
task: ""
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||||
lineage_type: import
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||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/0cf037ab/data/resources.json
|
||||
upstream_sha: 0cf037ab
|
||||
imported_at: 2026-07-16
|
||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/12d87583/data/resources.json
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||||
upstream_sha: 12d87583
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||||
imported_at: 2026-06-26
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||||
prompt_class: catalogue
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||||
upstream_changes: accepted
|
||||
author: upstream
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||||
@@ -292,20 +292,6 @@ validated: false
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||||
"organism": [],
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"api": false
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},
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||||
{
|
||||
"id": "hest_xenium_virtual_spatial_transcriptomics",
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||||
"name": "HEST Xenium virtual spatial transcriptomics",
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||||
"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"
|
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],
|
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"tasks": [],
|
||||
"modalities": [],
|
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"organism": [],
|
||||
"api": false
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},
|
||||
{
|
||||
"id": "jump_cell_painting_datasets",
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||||
"name": "JUMP Cell Painting Datasets",
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||||
@@ -544,20 +530,6 @@ validated: false
|
||||
"organism": [],
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"api": false
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},
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{
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||||
"id": "tcga_virtual_spatial_transcriptomics_atlas",
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||||
"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
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||||
},
|
||||
{
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||||
"id": "the_cancer_genome_atlas_tcga",
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"name": "The Cancer Genome Atlas (TCGA)",
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||||
@@ -2123,27 +2095,6 @@ validated: false
|
||||
"organism": [],
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"api": false
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},
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||||
{
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||||
"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.",
|
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"tags": [
|
||||
"foundation-models",
|
||||
"single-cell-foundation-models",
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"spatial-foundation-models"
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||||
],
|
||||
"tasks": [
|
||||
"Foundation Model"
|
||||
],
|
||||
"modalities": [
|
||||
"Single Cell",
|
||||
"Spatial Transcriptomics"
|
||||
],
|
||||
"organism": [],
|
||||
"api": false
|
||||
},
|
||||
{
|
||||
"id": "ai4chem_chemllm_7b_chat",
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||||
"name": "AI4Chem/ChemLLM-7B-Chat",
|
||||
@@ -2716,69 +2667,6 @@ validated: false
|
||||
"organism": [],
|
||||
"api": false
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||||
},
|
||||
{
|
||||
"id": "deepspot",
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||||
"name": "DeepSpot",
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||||
"type": "model",
|
||||
"url": "https://github.com/ratschlab/DeepSpot",
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||||
"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/0cf037ab/data/resources.yml
|
||||
upstream_sha: 0cf037ab
|
||||
imported_at: 2026-07-16
|
||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/12d87583/data/resources.yml
|
||||
upstream_sha: 12d87583
|
||||
imported_at: 2026-06-26
|
||||
prompt_class: catalogue
|
||||
upstream_changes: accepted
|
||||
author: upstream
|
||||
@@ -248,17 +248,6 @@ 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
|
||||
@@ -446,17 +435,6 @@ 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
|
||||
@@ -1513,17 +1491,6 @@ 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
|
||||
@@ -1843,39 +1810,6 @@ 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
|
||||
|
||||
+3
-115
@@ -2,9 +2,9 @@
|
||||
title: "Resources"
|
||||
task: ""
|
||||
lineage_type: import
|
||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/0cf037ab/docs/data/resources.json
|
||||
upstream_sha: 0cf037ab
|
||||
imported_at: 2026-07-16
|
||||
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/12d87583/docs/data/resources.json
|
||||
upstream_sha: 12d87583
|
||||
imported_at: 2026-06-26
|
||||
prompt_class: catalogue
|
||||
upstream_changes: accepted
|
||||
author: upstream
|
||||
@@ -292,20 +292,6 @@ 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",
|
||||
@@ -544,20 +530,6 @@ 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)",
|
||||
@@ -2123,27 +2095,6 @@ 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",
|
||||
@@ -2716,69 +2667,6 @@ 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