Tao Zeng

The paper develops SCTP, a deep-learning framework that merges bulk and single-cell data with spatial transcriptomics to better understand tumor microenvironments and identify early-warning biomarkers in colorectal cancer.

This paper introduces CPGD, a novel algorithm for identifying personalized driver genes and combinational drugs for individual cancer patients, showing improved performance over existing methods.

This paper introduces a nonlinear network control model to identify personalized driver genes in cancer by analyzing patient-specific molecular networks from TCGA.