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Identifying cancer genes (CGs) associated with the initiation and progression of cancer is critical for characterization of molecular-level mechanism in cancer research. In recent years, the growing availability of high-throughput molecular data and advancements in deep learning technologies has enabled the modelling of complex interactions and topological information within genomic data. Nevertheless, because of the limited labelled data, pinpointing CGs from a multitude of potential mutations remains an exceptionally challenging task. To address this, we propose a novel deep learning framework, termed self-supervised masked graph learning (SMG), which comprises SMG reconstruction (pretext task) and task-specific fine-tuning (downstream task). In the pretext task, the nodes of multi-omic featured protein\u2013protein interaction (PPI) networks are randomly substituted with a defined mask token. The PPI networks are then reconstructed using the graph neural network (GNN)-based autoencoder, which explores the node correlations in a self-prediction manner. In the downstream tasks, the pre-trained GNN encoder embeds the input networks into feature graphs, whereas a task-specific layer proceeds with the final prediction. To assess the performance of the proposed SMG method, benchmarking experiments are performed on three node-level tasks (identification of CGs, essential genes and healthy driver genes) and one graph-level task (identification of disease subnetwork) across eight PPI networks. Benchmarking experiments and performance comparison with existing state-of-the-art methods demonstrate the superiority of SMG on multi-omic feature engineering.<\/jats:p>","DOI":"10.1093\/bib\/bbad406","type":"journal-article","created":{"date-parts":[[2023,11,11]],"date-time":"2023-11-11T18:21:52Z","timestamp":1699726912000},"source":"Crossref","is-referenced-by-count":14,"title":["SMG: self-supervised masked graph learning for cancer gene identification"],"prefix":"10.1093","volume":"24","author":[{"given":"Yan","family":"Cui","sequence":"first","affiliation":[{"name":"Bioinformatics Center , Institute for Chemical Research, , Kyoto 611-0011 , Japan"},{"name":"Kyoto University , Institute for Chemical Research, , Kyoto 611-0011 , Japan"}]},{"given":"Zhikang","family":"Wang","sequence":"additional","affiliation":[{"name":"Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University , Melbourne, VIC 3800 , Australia"}]},{"given":"Xiaoyu","family":"Wang","sequence":"additional","affiliation":[{"name":"Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University , Melbourne, VIC 3800 , Australia"}]},{"given":"Yiwen","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Public Health and Preventive Medicine, Monash University , Melbourne, VIC 3004 , Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1792-0121","authenticated-orcid":false,"given":"Ying","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology , 200 Xiaolingwei, Nanjing, 210094 , China"}]},{"given":"Tong","family":"Pan","sequence":"additional","affiliation":[{"name":"Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University , Melbourne, VIC 3800 , 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