Zexi Chen

ODFormer predicts personalized drug responses in pancreatic cancer by simulating patient-derived organoids using transcriptomic and mutational data, outperforming existing methods and improving clinical outcomes.

This paper introduces CarHE, a method that predicts spatial transcriptomics data from H&E images using contrastive learning, achieving high accuracy and enabling cost-effective analysis.

UniCure predicts personalized cancer therapy response by integrating omics and chemical models, achieving high accuracy in drug response and combination effects across diverse data.

CauFinder identifies and steers cell-state and phenotype transitions by causal disentanglement learning, outperforming existing methods in simulated and real-world scenarios.