Shuang Ge

The paper decodes pharmacodynamic intratumoral heterogeneity by linking single-cell transcriptomes with drug-response profiles across 24 cancer types, identifying regulatory mechanisms driving differential drug responses.

scEvolver enables continual cell-type annotation across expanding single-cell atlases, revealing cellular state transitions in disease contexts.

This paper introduces Tx2Mol, a framework that translates gene-expression signatures into candidate molecules, outperforming baselines in chemical plausibility and phenotypic preservation.

This paper proposes a framework that transfers knowledge from multi-modal genomic data to gene expression-only models to improve drug response prediction.

This paper develops a framework for predicting single-cell drug responses using pharmacogenomic embeddings and few-shot transfer learning, showing improved accuracy and generalization.