Tools
Cancer Precision Medicine
DiSyn
This is a python implementation of Disentangled Synthesis Transfer Network (DiSyn) which enhances generalizability of drug response prediction by extracting features related and unrelated to drug responses to synthesize new training samples and improve prediction accuracy of label-scarce target domains.
JointSyn
We have proposed JointSyn that utilizes dual-view jointly learning to predict sample-specific effects of drug combination from drug and cell features. JointSyn capture the drug synergy related features from two views. One view is the embedding of drug combination on cancer cell lines, and the other view is the combination of two drugs’ embeddings on cancer cell lines. Finally, the prediction net uses the features learned from the two views to predict the drug synergy of the drug combination on the cell line.
Spatial Omics Analysis
SOAPy
SOAPy is an integrated toolkit that focuses on addressing spatial heterogeneity. SOAPy contains four data preprocessing modules (“Data Import”, “spatial network”, and “Spatial Domain”), three modules for analyzing spatial expression patterns of genes (“Spatial Variation”, “Spatial Tendency”, and “Spatiotemporal Pattern”), two modules for analyzing the spatial architecture of cells (“Spatial Proximity”, “Spatial Composition”), and two module for analyzing Spatial Communication.