[upstream-sync] README.md from ai-boost/awesome-ai-for-science@0eab9ce2 [catalogue]

2026-07-07 08:16:03 +00:00
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 title: "Readme"
 task: ""
 lineage_type: import
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-upstream_sha: e0df96d7
-imported_at: 2026-07-06
+upstream_source: https://github.com/ai-boost/awesome-ai-for-science/blob/0eab9ce2/README.md
+upstream_sha: 0eab9ce2
+imported_at: 2026-07-07
 prompt_class: catalogue
 upstream_changes: accepted
 author: upstream
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 - [AlphaMissense](https://github.com/google-deepmind/alphamissense) - Google DeepMind's AlphaFold-derived classifier for proteome-wide missense variant effect prediction, providing pathogenicity scores for all ~71M possible human missense variants and classifying 89% with 90% precision; pre-computed predictions are integrated into Ensembl VEP and UCSC Genome Browser to support clinical variant interpretation (Science 2023)
 - [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)
 - [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)

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 - [PLIP (Nature Medicine 2023)](https://github.com/PathologyFoundation/plip) - First vision-and-language foundation model for pathology AI, fine-tuned from CLIP on 249K image-caption pairs, enabling open-ended visual-semantic search and zero-shot diagnosis across histopathology (Pathology Foundation, 376+ stars)
 - [TITAN (Nature Medicine 2024)](https://github.com/mahmoodlab/TITAN) - Multimodal whole-slide pathology foundation model jointly pretrained on H&E histology and diagnostic text reports, enabling zero-shot cancer subtyping, biomarker prediction, and multimodal reasoning across diverse cancer types (Mahmood Lab, 341+ stars)
 - [Virchow (Nature Medicine 2024)](https://huggingface.co/paige-ai/Virchow) - Self-supervised pathology foundation model (ViT-Huge, 632M parameters) pretrained via DINOv2 on 1.5M whole-slide images from Memorial Sloan Kettering across 17 cancer types, with Virchow2 follow-up scaling to 3.1M slides and mixed magnifications, achieving SOTA on biomarker prediction, mutation classification, and rare cancer detection (Paige AI & MSK)
+- [H-Optimus (Bioptimus, Nature Medicine 2025)](https://huggingface.co/bioptimus/H-optimus-0) - Open-weights pathology foundation model family (H-Optimus-0: 1.1B-parameter ViT pretrained via DINOv2 on 500M+ diagnostic image tiles; H-Optimus-1 follow-up) for whole-slide image analysis, achieving strong zero-shot and fine-tuned transfer across biomarker prediction, cancer subtyping, and mutation classification (Bioptimus, Apache 2.0)
 - [TRIDENT (2025)](https://github.com/mahmoodlab/TRIDENT) - Toolkit for large-scale whole-slide image processing supporting 22+ patch encoders (UNI, CONCH, Virchow, H-Optimus-0, etc.), slide encoders (TITAN, GigaPath, PRISM, CHIEF, Madeleine, Feather), tissue segmentation, and multi-GPU inference with end-to-end pipeline and smart resume for standardized deployment of computational pathology foundation models (Mahmood Lab, Harvard Medical School, 553+ stars)
 - [Feather (Mahmood Lab, ICML 2025 Spotlight)](https://github.com/mahmoodlab/MIL-Lab) - Lightweight supervised slide foundation model with 0.9M parameters pretrained on 24K whole-slide images for pan-cancer morphological classification, achieving competitive performance with much larger self-supervised models (TITAN, GigaPath) while enabling finetuning on consumer-grade GPUs; includes standardized MIL implementations and benchmarking across 15+ classification tasks (Mahmood Lab, Harvard Medical School, 153+ stars)
 - [PathChat (Nature Medicine 2024)](https://github.com/MahmoodLab/PathChat) - Multimodal generative AI assistant for computational pathology enabling interactive visual-language conversations over histopathology images for diagnostic reasoning, case discussion, and education, built on a Mistral-7B backbone with domain-specific fine-tuning (Mahmood Lab, Harvard Medical School, 1.2K+ stars)