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cellot

Learning Single-Cell Perturbation Responses using Neural Optimal Transport

clip

CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image

dmol-book

Deep learning for molecules and materials book

dnabert

DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome

evo

DNA foundation modeling from molecular to genome scale

generative-ai-for-beginners

18 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/

nicheformer

Repository for Nicheformer: a foundation model for single-cell and spatial omics

perception

Utilizing single-cell omics from patients tumor to predict response and resistance.

plip

Pathology Language and Image Pre-Training (PLIP) is the first vision and language foundation model for Pathology AI (Nature Medicine). PLIP is a large-scale pre-trained model that can be used to extract visual and language features from pathology images and text description. The model is a fine-tuned version of the original CLIP model.

progen

Official release of the ProGen models

protgpt2

scgen

Single cell perturbation prediction

scgpt

shap

A game theoretic approach to explain the output of any machine learning model.

splicebert

Primary RNA sequence model

trialpathfinder

tripath

Analysis of 3D pathology samples using weakly supervised AI - Cell

uni

Towards a general-purpose foundation model for computational pathology - Nature Medicine

xai-for-bioinformatics

Explainable AI for Bioinformatics