[upstream-sync] docs/data/resources.json from inoue0426/awesome-computational-biology@0cf037ab [catalogue]

Reviewed-on: #49

upstream/ai-boost-awesome-ai-for-science/catalogue/README.md

@@ -2,9 +2,9 @@
 title: "Readme"
 task: ""
 lineage_type: import
-upstream_source: https://github.com/ai-boost/awesome-ai-for-science/blob/03ff6200/README.md
-upstream_sha: 03ff6200
-imported_at: 2026-07-10
+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
@@ -214,6 +214,7 @@ validated: false
 - [open-science](https://github.com/ai4s-research/open-science) - Local-first, open-source AI workbench for scientists — an open alternative to Claude Science (by ai4s-research, maintainers of this list; TypeScript, MIT, 2026)
 - [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)
 
 ### 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)
@@ -249,6 +250,7 @@ validated: false
 - [FunSearch (DeepMind, Nature 2023)](https://github.com/google-deepmind/funsearch) - First system to make novel, verifiable scientific discoveries by pairing LLMs with evolutionary search, solving open problems in combinatorics (cap set problem) and discovering faster matrix multiplication algorithms
 - [OpenEvolve](https://github.com/algorithmicsuperintelligence/openevolve) - Open-source implementation of AlphaEvolve's evolutionary coding agent paradigm, enabling LLMs to autonomously discover and optimize algorithms through iterative evolution, matching the approach behind DeepMind's breakthrough matrix multiplication discovery (6.2K+ stars, 2025)
 - [SkyDiscover](https://github.com/skydiscover-ai/skydiscover) - Modular framework for AI-driven scientific and algorithmic discovery, providing a unified interface for implementing, running, and fairly comparing discovery algorithms across 200+ optimization tasks; introduces AdaEvolve and EvoX adaptive/evolutionary algorithms and natively supports OpenEvolve, GEPA, and Harbor-format benchmarks (skydiscover-ai, 568+ stars, Apache 2.0, 2026)
+- [EvoMaster (SJTU SAI, arXiv 2026)](https://github.com/sjtu-sai-agents/EvoMaster) - Foundational auto-research agent framework for agentic science at scale, providing modular agent construction, run-level self-evolution, and multiple SciMaster domain agents (ML-Master, X-Master, Browse-Master); outperforms general-purpose agents across authoritative benchmarks including the OpenAI Frontier Science Benchmark (206+ stars, Apache 2.0, 2026)
 - [Virtual Lab (Stanford Zou Group, Nature 2025)](https://github.com/zou-group/virtual-lab) - AI-human collaborative research platform where a human researcher works with a team of LLM agents via team and individual meetings to perform scientific research; demonstrated by designing new SARS-CoV-2 nanobodies with wet-lab validation
 - [The AI Scientist (SakanaAI)](https://github.com/SakanaAI/AI-Scientist) - First fully autonomous open-ended scientific discovery system with official implementation: hypothesis→experiment→writing→review simulation (13.8K+ stars, 2024)
 - [The AI Scientist v2 (SakanaAI)](https://github.com/SakanaAI/AI-Scientist-v2) - Official implementation of the second-generation fully autonomous scientific discovery system, extending the original with agentic tree search and reduced template dependency to achieve workshop-level accepted papers (6.7K+ stars, 2025)
@@ -287,6 +289,7 @@ validated: false
 - [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)
 - [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)
 
 ### 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
@@ -474,6 +477,7 @@ validated: false
 - [ModelAngelo](https://github.com/3dem/model-angelo) - Automatic atomic model building program for cryo-EM maps using deep learning, enabling rapid de novo protein structure determination from electron density with high accuracy (3DEM/EMBL, 169+ stars)
 - [AlphaFold](https://github.com/google-deepmind/alphafold) - Protein structure prediction
 - [AlphaFold3](https://github.com/google-deepmind/alphafold3) - AlphaFold 3 inference pipeline for unified biomolecular structure prediction of proteins, nucleic acids, small molecules, ions, and post-translational modifications (Google DeepMind, Nature 2024)
+- [AlphaFold Server](https://alphafoldserver.com/) - Free, easy-to-use web platform by Google DeepMind and Isomorphic Labs for running AlphaFold 3 predictions of biomolecular structures and interactions, enabling researchers without local infrastructure to model proteins, nucleic acids, small molecules, ions, and post-translational modifications through a searchable proteome interface (2024)
 - [AlphaProteo](https://github.com/google-deepmind/alphaproteo) - Deep learning system for de novo design of high-affinity protein binders, achieving strong binding across diverse target classes including challenging intracellular proteins with significantly higher success rates than traditional wet-lab screening methods (Google DeepMind, Nature 2024)
 - [AlphaPulldown](https://github.com/KosinskiLab/AlphaPulldown) - Automated pipeline for proteome-scale protein-protein interaction screening with AlphaFold-Multimer and AlphaFold 3, supporting flexible inputs (UniProt IDs, FASTA, residue regions, multimers, AF3 JSON features) and integrated downstream analysis for hit prioritization (Kosinski Lab, EMBL, Nature Protocols 2024, 317+ stars, GPL-3.0)
 - [RareFold](https://github.com/PatrickBryant1/RareFold) - Structure prediction and design of proteins with noncanonical amino acids, enabling AI-powered modeling of synthetic biology constructs and expanded genetic code systems (133+ stars, 2025)
@@ -519,6 +523,7 @@ validated: false
 - [Chroma](https://github.com/generatebio/chroma) - Generative model for programmable protein design using diffusion modeling, equivariant graph neural networks, and conditional random fields to efficiently sample diverse all-atom structures; supports conditional generation via composable conditioners for substructure, symmetry, shape, and neural-network predictions; validated crystallographically (Generate Biomedicines, Nature 2023)
 - [EvoDiff](https://github.com/microsoft/evodiff) - Discrete diffusion framework for generative protein sequence design over evolutionary-scale databases, supporting unconditional generation, evolutionary-guided conditional design, motif scaffolding, and intrinsically disordered region generation through order-agnostic autoregressive diffusion, enabling sequence-only protein design without structural priors (Microsoft Research, Nature Communications 2024)
 - [DISCO](https://github.com/DISCO-design/DISCO) - General multimodal protein design framework enabling DNA-encoding of chemistry for programmable enzyme design and diverse protein generation through diffusion-based generative modeling (190+ stars, Apache 2.0, 2026)
+- [SwitchCraft](https://github.com/bjing2016/switchcraft) - Programmatic framework for designing state-switching proteins via backpropagation through compositional design constraints parameterized by structure prediction models; enables de novo design of allosteric regulators and fluorescent biosensors for arbitrary small-molecule analytes (79+ stars, MIT License, ICML 2026)
 - [RFdiffusion3](https://github.com/RosettaCommons/RFdiffusion) - Latest RFdiffusion for protein structure design with 10× speedup and atom-level precision (December 2025)
 - [RFantibody](https://github.com/RosettaCommons/RFantibody) - Structure-based de novo antibody design pipeline built on RFdiffusion for computational generation of target-specific antibodies (RosettaCommons, 2025)
 - [IgGM](https://github.com/TencentAI4S/IgGM) - Generative foundation model for functional antibody and nanobody design, supporting de novo generation, affinity maturation, inverse design, structure prediction, and humanization (Tencent AI4S, ICLR 2025)
@@ -598,8 +603,10 @@ validated: false
 - [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)
 - [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)
 - [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)
+- [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)
 - [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)
 - [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)
+- [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)
 
 #### Neuroscience & Behavioral Analysis
 - [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)
@@ -645,6 +652,7 @@ validated: false
 - [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)
 - [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)
 - [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)
+- [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)
 - [MedAgents](https://github.com/gersteinlab/MedAgents) - Multi-disciplinary collaboration framework for zero-shot medical reasoning using role-playing LLM agents (ACL 2024)
 - [MedAgentGym](https://github.com/wshi83/MedAgentGym) - Scalable agentic training environment for code-centric reasoning in biomedical data science
 - [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)
@@ -789,6 +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)
+- [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)
 - [MinervaAI](https://github.com/google-research/minerva) - Mathematical reasoning
 - [PaLM-2](https://ai.google/discover/palm2) - Scientific reasoning capabilities
 

upstream/inoue0426-awesome-computational-biology/catalogue/README.md

@@ -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/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
 

upstream/inoue0426-awesome-computational-biology/catalogue/cspell.json

@@ -2,9 +2,9 @@
 title: "Cspell"
 task: ""
 lineage_type: import
-upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/12d87583/cspell.json
-upstream_sha: 12d87583
-imported_at: 2026-06-26
+upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/0cf037ab/cspell.json
+upstream_sha: 0cf037ab
+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/**"

upstream/inoue0426-awesome-computational-biology/catalogue/data/resources.json

@@ -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/0cf037ab/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",

upstream/inoue0426-awesome-computational-biology/catalogue/data/resources.yml

@@ -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
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+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

upstream/inoue0426-awesome-computational-biology/catalogue/docs/data/resources.json

@@ -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",