GMHAN: A Heterogeneous Graph Attention Framework for Prioritizing Coding and Non-coding Driver Genes (opens in new tab)

AbstractMotivationCancer, a disease of high complexity. Identifying cancer driver genes is fundamental for elucidating oncogenesis and promoting precision medicine. Currently, most approaches mainly focus on homogeneous gene networks and single-omics data, thereby mainly identifying coding driver genes while ignoring non-coding driver genes.ResultThus, we introduced GMHAN, a novel framework based on HAN. Firstly, we integrated the three types of omics data of genes and PPI network topology fe...