Repository Index

Code for the ProteinMPNN paper

ProtTrans is providing state of the art pretrained language models for proteins. ProtTrans was trained on thousands of GPUs from Summit and hundreds of Google TPUs using Transformers Models.

A Deep Learning Toolkit for DTI, Drug Property, PPI, DDI, Protein Function Prediction (Bioinformatics)

• drug-repurposing
• deep-learning
• drug-target-interactions
• toolkit

Precision Medicine Knowledge Graph (PrimeKG)

• knowledge-graph
• precision-medicine
• therapeutics
• bioinformatics

Genomic Pretrained Network - GPN, GPN-MSA, PhyloGPN, GPN-Star

• dna
• language-model
• variant-effect-prediction
• genomics

MolTrans: Molecular Interaction Transformer for Drug Target Interaction Prediction (Bioinformatics)

🏃 The go-to single-cell Foundation Model

• batch-correction
• cell-biology
• celltype-annotation
• denoising

Code for "Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution", NeurIPS 2022.

• disentanglement
• drug-discovery
• genomics
• perturbation

A collection of tasks to probe the effectiveness of protein sequence representations in modeling aspects of protein design

• protein
• protein-sequences
• protein-design
• representation-learning

Official repo for CellPLM: Pre-training of Cell Language Model Beyond Single Cells.

PRnet is a flexible and scalable perturbation-conditioned generative model predicting transcriptional responses to unseen complex perturbations at bulk and single-cell levels.

Peax is a tool for interactive visual pattern search and exploration in epigenomic data based on unsupervised representation learning with autoencoders

• epigenomics
• pattern-search
• data-visualization
• interactive-machine-learning

Machine learning for Analysis of Proteomics in Spatial biology - Nature Communications

• cell-annotation
• multiplexed-imaging
• mpif

Repository for paper scMulan: a multitask generative pre-trained language model for single-cell analysis.

• foundation-models
• gpt
• single-cell

High-Dimensional Gene Expression and Morphology Profiles of Cells across 28,000 Genetic and Chemical Perturbations

Deep learning model for single-cell inference of multi-omic profiles from a single input modality.

• single-cell
• scrna-seq
• scatac-seq
• deep-neural-networks

Interpretable drug response prediction with graph neural networks over drugs, cell lines, and genes.