A Novel Cancer Driver Genes Identification Method Based on Self-Supervised Dual Masked Graph Autoencoder

Uncovering genes that drive cancer is fundamental to elucidating the mechanisms underlying cancer development and to advancing cancer research. Recent years have witnessed the emergence of cancer driver gene identification from multi-omics data as a key research area, facilitated by the rapid progress of high-throughput molecular technologies. Although numerous algorithms have been proposed for cancer driver gene discovery, the precise identification of these genes continues to pose a challenge owing to the lack of labeled data. This study presents SDMGAE, a Self-supervised Dual Masked Graph AutoEncoder-based method for cancer driver gene identification. This framework integrates two components: a self-supervised graph learning module and a driver gene prediction module. During the self-supervised graph learning phase, nodes and edges of protein-protein interaction (PPI) networks are masked separately to consider both node and structural information. Subsequently, the graph autoencoder is employed to reconstruct the PPI network without using labelled data. In the driver gene prediction stage, we employ the pre-trained graph neural network encoder to obtain the embeddings, which are then processed through the logistic regression to generate prediction outcomes. To evaluate the effectiveness of SDMGAE, we performed benchmarking experiments across 10 distinct types of cancer data. Experimental outcomes reveal that SDMGAE exhibits improved performance in cancer driver gene detection compared with state-of-the-art methods.