Jiawei Zou

scSTAR2 integrates multiomics data to identify disease-specific cellular signatures by linking single-cell features with clinical metadata.

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.

scCODE optimizes differentially expressed gene detection in scRNA-seq data by incorporating gene filtering and evaluating performance without prior knowledge of true results.