Repository Index

Open source code for AlphaFold 2.

AlphaFold 3 inference pipeline.

Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology

• drug-discovery
• quantum-chemistry
• deep-learning
• biology

This package contains deep learning models and related scripts for RoseTTAFold

Deep probabilistic analysis of single-cell and spatial omics data

• scrna-seq
• variational-bayes
• variational-autoencoder
• cite-seq

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

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

Foundation Models for Genomics & Transcriptomics

• genomics
• deep-learning
• transformer
• dna

Python package for graph neural networks in chemistry and biology

• deep-learning
• graph-neural-networks
• dgl
• cheminformatics

DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome

• kmer
• dnabert-model
• kmer-format
• gpu

Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.

• deep-learning
• protein-sequences
• protein-structure
• semi-supervised-learning

Graph-linked unified embedding for single-cell multi-omics data integration

• bioinformatics
• deep-learning
• single-cell
• single-cell-multiomics

Sequential regulatory activity predictions with deep convolutional neural networks.

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

Reference mapping for single-cell genomics

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

RNA-seq prediction with deep convolutional neural networks.

Pan-Cancer Integrative Histology-Genomic Analysis via Multimodal Deep Learning - Cancer Cell

• histology
• wsi-images
• pan-cancer-analysis
• genomics

TransformerCPI: Improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments(BIOINFORMATICS 2020) https://doi.org/10.1093/bioinformatics/btaa524

• self-attention
• compound-protein-interaction
• transformercpi
• structure-free-virtual-sreening

A tool for cell instance aware segmentation in densely packed 3D volumetric images

• segmentation
• deep-learning
• bioinformatics
• neural-network

NeoDTI: Neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions

• computational-biology
• bioinformatics
• graph-convolution
• deep-learning

PyTorch implementation of the MIDAS algorithm for single-cell multimodal data integration (Nature Biotechnology 2024).

• deep-generative-model
• deep-learning
• single-cell-data-integration
• single-cell-multiomics

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

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

Deciphering cell-cell communication from spatially resolved transcriptomic data at single-cell resolution with subgraph-based attentional graph neural network

3D shape analysis using deep learning

• cancer-biology
• cell-biology
• deep-learning
• geometric-deep-learning