[upstream-sync] README.md from ai-boost/awesome-ai-for-science@d57c2d50 [catalogue]

2026-09-01 05:15:18 +00:00
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 title: "Readme"
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-imported_at: 2026-09-04
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 - [snntorch](https://github.com/jeshraghian/snntorch) - Deep learning with spiking neural networks in Python, providing gradient-based training of SNNs via PyTorch autodifferentiation for brain-inspired computing and neuromorphic research, with online learning capabilities and extensive tutorials (1.9K+ stars, actively maintained)
 - [nilearn](https://github.com/nilearn/nilearn) - Machine learning and statistical learning for neuroimaging in Python, providing easy-to-use tools for fMRI and MRI analysis including decoding, connectivity estimation, and parcellation with seamless scikit-learn integration (INRIA Parietal team, 1.4K+ stars)
 - [BrainIAC (Nature Neuroscience 2026)](https://github.com/AIM-KannLab/BrainIAC) - Self-supervised vision foundation model for generalized structural brain MRI analysis, pretrained on ~49,000 scans from diverse datasets and generalizing across brain age prediction, dementia/MCI classification, IDH mutation detection, glioma survival prediction, time-to-stroke estimation, MR sequence classification, and brain tumor segmentation; outperforms task-specific models especially with limited training data (Mass General Brigham & Harvard Medical School, 129+ stars)
- [Zapbench (Google Research, 2025)](https://github.com/google-research/zapbench) - The Zebrafish Activity Prediction Benchmark for forecasting cellular-resolution neural activity throughout an entire vertebrate brain, combining light-sheet microscopy calcium-imaging data, forecasting tasks, and evaluation tools to advance whole-brain neural dynamics modeling (77+ stars, Apache 2.0)

 #### Computational Pathology & Digital Pathology
 - [UNI (Nature Medicine 2024)](https://github.com/mahmoodlab/UNI) - General-purpose pathology foundation model pretrained on 100K+ diagnostic whole-slide images across 20 major tissue types, achieving state-of-the-art transfer learning across 30+ clinical tasks and serving as a universal feature extractor for digital pathology (Mahmood Lab, 722+ stars)
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 ### Domain-Specific Models
 - [ESM](https://github.com/facebookresearch/esm) - Protein language models
 - [BioNeMo Framework](https://github.com/NVIDIA/bionemo-framework) - NVIDIA's open-source platform for building and adapting biological AI models at scale, bundling ESM-2, Geneformer, MolMIM and DNA embedding models with recipes for single-GPU to multi-node training (2025)
- [BioNeMo Recipes (NVIDIA)](https://github.com/NVIDIA-BioNeMo/bionemo-recipes) - TransformerEngine-accelerated checkpoints and training recipes for scaling biological foundation models (ESM-2, AMPLIFY, Geneformer, CodonFM) from single-GPU prototyping to multi-node FSDP training with FP8/MXFP8/NVFP4 precision, compatible with PyTorch, HF Accelerate, and PyTorch Lightning, plus sparse-autoencoder interpretability tools for biological foundation models (851+ stars, 2026)
 - [IBM FM4M](https://github.com/IBM/materials) - IBM's open foundation model family for materials and chemistry, covering SMILES, SELFIES, molecular graphs, 3D atom positions, and electron density grids, with a unified toolkit for representation learning and downstream prediction/generation (Apache 2.0, 2024-2025)
 - [ChemGPT](https://huggingface.co/ncfrey/ChemGPT-1.2B) - Chemistry-focused language model
 - [BioGPT](https://github.com/microsoft/BioGPT) - Biomedical text generation