Models

Single cell perturbation prediction

The Compositional Perturbation Autoencoder (CPA) is a deep generative framework to learn effects of perturbations at the single-cell level.

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…

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

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

State Embedding model checkpoints are meant to be used with the Arc State