Systematic review of prediction of cancer driver genes with the application of graph neural networks

Graph Neural Networks (GNNs) have emerged as a potential tool in cancer genomics research due to their ability to capture the structural information and interactions between genes in a network, enabling the prediction of cancer driver genes. This systematic literature review assesses the capabilities and challenges of GNNs in predicting cancer driver genes by accumulating findings from relevant papers and research. This systematic literature review focuses on the effectiveness of GNN-based algorithms related to cancer such as cancer gene identification, cancer progress dissection, prediction, and driver mutation identification. Moreover, this paper highlights the requirement to improve omics data integration, formulating personalized medicine models, and strengthening the interpretability of GNNs for clinical purposes. In general, the utilization of GNNs in clinical practice has a significant potential to lead to improved diagnostics and treatment procedures.