Ziyang Tang

MVCBench evaluates drug molecular and gene representation methods for predicting drug-induced multimodal cell phenotypes using transcriptomic and imaging data.

SpaIM improves spatial transcriptomics by imputing missing gene expressions using scRNA-seq data, outperforming 12 existing methods across 53 datasets.

xSiGra is a graph-based AI model that identifies interpretable features in spatial single-cell data, showing that cell activity is influenced by neighboring cells and revealing endothelial-tumor interactions.

SiGra integrates spatial images and transcriptomics data to enhance spatial domain detection and improve characterization of cellular heterogeneity and communication in tissues.

SpaRx identifies spatial cellular heterogeneity in drug responses across tumor lesions and reveals mechanisms of drug resistance through a graph-based domain adaptation model.