Repository Index

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

In-memory nucleotide sequence k-mer counting, filtering, graph traversal and more

• dna
• k-mer
• python
• bloom-filter

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

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

Schema and generated objects for biolink data model and upper ontology

• biolink
• monarchinitiative
• gene-ontology
• datamodel

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

OpenBioLink is a resource and evaluation framework for evaluating link prediction models on heterogeneous biomedical graph data.

Learning cell communication from spatial graphs of cells

• tensorflow
• gnn
• glm
• cvae

TypeDB Bio: Biomedical Knowledge Graph

• bioinformatics
• knowledge-graph
• knowledge-base
• drug-discovery

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

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

Adaptive Graph Attention Auto-encoder for Spatial Domain Identification of Spatial Transcriptomics

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.