Repository Index

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

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

Reference mapping for single-cell genomics

• single-cell
• deep-learning
• rna-seq-analysis
• data-integration

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)

Wrapper for RDKit's RunReactants to improve stereochemistry handling

A python Two Source Energy Balance model for estimation of evapotranspiration with remote sensing data

• source-energy-balance
• canopy
• soil
• radiometric-temperatures

🏃 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

Optimising chemical reactions using machine learning

• chemistry
• machine-learning
• optimization
• bayesian-optimization

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

Detecting cancers on mobile phones

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

The repository host gives no description.

UV-first benchmark for protein-ligand docking with reproducible annotation, pocket similarity, and HPC workflows.

• benchmark
• bioinformatics
• cheminformatics
• computational-chemistry

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