Shao‐Wu Zhang

CaMeRe predicts patient-specific clinical responses to drug combinations by integrating causal representation learning and meta-learning, showing robust performance across multiple datasets and cancer types.

MTGCL identifies cancer driver genes using multi-task graph contrastive learning, improving performance by integrating multi-omics data and unlabeled node information.

The paper defines a TAD separation landscape to address inconsistencies in TAD identification across methods and identifies Consensus Topological Associating Domains.

DGMP identifies cancer driver genes using a combination of DGCN and MLP on multi-omics data, outperforming existing methods by leveraging directed GRN structure and gene features.

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.

This paper introduces Deep-m6A, a deep learning model for predicting m6A sites and identifies 709 functionally significant m6A-regulated genes and nine subnetworks associated with cancer and other diseases.