Models

AI Toolkit for Healthcare Imaging

This is a convenient code wrapper to run Lundberg lab SubCell model in inference with your own images

a generalist algorithm for cellular segmentation with human-in-the-loop capabilities

Implementation of DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking

Message Passing Neural Networks for Molecule Property Prediction

Official Repository for the Uni-Mol Series Methods

Single cell perturbation prediction

The Compositional Perturbation Autoencoder (CPA) is a deep generative framework to learn effects of perturbations at the single-cell level. CPA performs OOD predictions of unseen combinations of drugs, learns interpretable embeddings, estimates dose-response curves, and provides uncertainty estimates.

GEARS is a geometric deep learning model that predicts outcomes of novel multi-gene perturbations

State is a machine learning model that predicts cellular perturbation response across diverse contexts

Deep probabilistic analysis of single-cell and spatial omics data

A unifying representation of single cell expression profiles that quantifies similarity between expression states and generalizes to represent new studies without additional training.

UCE is a zero-shot foundation model for single-cell gene expression data

Source code repository mirrored from GitHub (biomap-research/scFoundation).

Source code repository mirrored from GitHub (bowang-lab/scGPT).

A deep learning-based tool to identify splice variants

Code for running RFdiffusion

Source code repository mirrored from GitHub (dauparas/LigandMPNN).

Code for the ProteinMPNN paper

Trainable, memory-efficient, and GPU-friendly PyTorch reproduction of AlphaFold 2