Repository Index

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.