Repository Index

Scientific workflow engine designed for simplicity & scalability. Trivially transition between one off use cases to massive scale production environments

• workflow-execution
• workflow
• cloud
• hpc

Precision Medicine Knowledge Graph (PrimeKG)

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

Python package for graph neural networks in chemistry and biology

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

SCENIC is an R package to infer Gene Regulatory Networks and cell types from single-cell RNA-seq data.

Sequential regulatory activity predictions with deep convolutional neural networks.

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

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

RNA-seq prediction with deep convolutional neural networks.

A Network Integration Approach for Drug-Target Interaction Prediction

MOGONET (Multi-Omics Graph cOnvolutional NETworks) is a novel multi-omics data integrative analysis framework for classification tasks in biomedical applications.

PhyloNetworks: a julia package with utilities for phylogenetic networks

🏃 The go-to single-cell Foundation Model

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

Interpretable bilinear attention network with domain adaptation improves drug-target prediction.

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

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

PyTorch implementation of "TGSA: Protein-Protein Association-Based Twin Graph Neural Networks for Drug Response Prediction with Similarity Augmentation"

DetecDiv provides a comprehensive set of tools to analyze time microscopy images using deep learning methods. The software structure is such that data can be processed either at the command line or using a graphical user-interface. Detecdiv classification models include : image classification and regression, semantic segmentation, LSTM networks to analyze data and image timeseries.

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