High performance, easy-to-use, and scalable package for learning large-scale knowledge graph embeddings.

TxGNN: Zero-shot prediction of therapeutic use with geometric deep learning and clinician centered design

single-cell latent diffusion model

Repo for code and other materials related to our "Deep Mendelian Randomization" project.

Open Targets python framework for post-GWAS analysis

Tissue-Gene Fine-Mapping

Python module for genetic analyses

An accurate and efficient colocalization method accounting for multiple causal signals

TenK10K causal inference manuscript

pytorch dataloaders for single-cell perturbation data

Comprehensive suite for evaluating perturbation prediction models

scooby: Modeling multi-modal genomic profiles from DNA sequence at single-cell resolution.

Tools to work with GWAS-VCF summary statistics files

GWAS genetics Fine-mapping method

PolyFun (POLYgenic FUNctionally-informed fine-mapping)

Pytorch implementation of the Borzoi model from Calico, and Flashzoi, a 3x faster Borzoi enhancement.

This API provides programmatic access to the AlphaGenome model developed by Google DeepMind.

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

cz-benchmarks is a package for standardized evaluation and comparison of machine learning models for biological applications.

Machine learning methods for DNA sequence analysis.

scDataset: Scalable Data Loading for Deep Learning on Large-Scale Single-Cell Omics

A repository for reproducing experiments from the TxPert paper

RNA-seq prediction with deep convolutional neural networks.

Modeling complex perturbations with CellFlow