GATOmics: A Novel Multi-Omics Graph Attention Network Model for Cancer Driver Gene Detection

Abstract

Identifying cancer driver genes remains challenging due to the complexity of gene interactions in cancer genomics. Existing methods often face difficulties in integrating multidimensional biological data, which limits their ability to capture diverse gene relationships. GATOmics, a novel multi-omics framework, addresses this by integrating four networks, protein-protein interactions, tissue co-expression, pathway co-occurrence, and gene semantic similarity. Utilizing a graph attention network to enhance feature extraction. By employing self-attention mechanisms and convolutional modules, GATOmics captures long-range gene interactions, improving prediction accuracy. The use of semi-supervised learning enables the model to leverage both labeled and unlabeled data, enhancing generalization across different cancer types. Evaluations on pan-cancer datasets demonstrate that GATOmics consistently outperformed state-of-the-art methods, achieving higher AUC and AUPRC scores, highlighting its potential for broader applications in cancer genomics research. The code and data for GATOmics are available at https://github.com/ggkong/GATOmics