biostudies-literatureLi XScience and Technology Development Fund of MacaoGuangdong Basic and Applied Basic Research FoundationNational Natural Science Foundation of ChinaState Key Laboratory for Animal Disease Control and Prevention FoundationMacau Young Scholars ProgramNational Key Research and Development Program of Chinabbae691https://www.ebi.ac.uk/biostudies/studies/S-EPMC11697181biostudies-literatureUnknown26(1)The identification of cancer driver genes is crucial for understanding the complex processes involved in cancer development, progression, and therapeutic strategies. Multi-omics data and biological networks provided by numerous databases enable the application of graph deep learning techniques that incorporate network structures into the deep learning framework. However, most existing methods do not account for the heterophily in the biological networks, which hinders the improvement of model performance. Meanwhile, feature confusion often arises in models based on graph neural networks in such graphs. To address this, we propose a Simplified Graph neural network for identifying Cancer Driver genes in heterophilic networks (SGCD), which comprises primarily two components: a graph convolutiBriefings in bioinformaticsTowards simplified graph neural networks for identifying cancer driver genes in heterophilic networks.PMC1169718162202383AM20240270002/2024/RIA12022YFD1801200SKLADCPKFKT2024072024A1515012602Li XLi JGu JShang XXu JfalseTowards simplified graph neural networks for identifying cancer driver genes in heterophilic networks.The identification of cancer driver genes is crucial for understanding the complex processes involved in cancer development, progression, and therapeutic strategies. Multi-omics data and biological networks provided by numerous databases enable the application of graph deep learning techniques that incorporate network structures into the deep learning framework. However, most existing methods do not account for the heterophily in the biological networks, which hinders the improvement of model performance. Meanwhile, feature confusion often arises in models based on graph neural networks in such graphs. To address this, we propose a Simplified Graph neural network for identifying Cancer Driver genes in heterophilic networks (SGCD), which comprises primarily two components: a graph convoluti2024-01-01T00:00:00Z2024 Nov2025-04-04T12:02:33.059Z2025-04-04T12:02:33.059ZS-EPMC116971813975164510.1093/bib/bbae691