DriverOmicsNet: an integrated graph convolutional network for multi-omics exploration of cancer driver genes.

ABSTRACT: Cancer is a complex and heterogeneous group of diseases driven by genetic mutations and molecular changes. Identifying and characterizing cancer driver gene is crucial for understanding cancer biology and guiding precision oncology. Integrating multi-omics data can reveal the intricate molecular interactions underlying cancer progression and treatment responses. We developed a graph convolutional network (GCN) framework, DriverOmicsNet, that integrates multi-omics data using STRING protein-protein interaction networks and correlation-based weighted gene correlation network analysis (WGCNA). We applied this framework to 15 cancer types, analyzing 5555 tumor samples to predict cancer-related features such as homologous recombination deficiency, cancer stemness, immune clusters, tumor stage,

SUBMITTER: Dai YH 

PROVIDER: S-EPMC12354958 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature