Multi-information Fusion Graph Convolutional Network for cancer driver gene identification

Abstract

Cancer is generally thought to be caused by the accumulation of mutations in driver genes. The identification of cancer driver genes is crucial for cancer research, diagnosis and treatment. Despite existing methods, challenges remain in comprehensively learning of the attributes and intricate interactions of genetic data. We propose a novel Multi-information Fusion Graph Convolutional Network (MF-GCN) for cancer driver gene identification, based on multi-omics pan-cancer data and Gene Regulatory Network (GRN) data. Directed topological and attribute graph networks learn gene interactions and self-attribute information, while a common graph network captures consistency between topology and attributes. An attention mechanism adaptively fuses these information with importance weights to identify cancer driver genes. Experimental results showed that MF-GCN can effectively identify cancer driver genes across three GRN datasets, with AUROC and AUPRC improvements of 2.66% and 2.69%, respectively, compared with the state-of-the-art approaches.