A Deep Learning Framework for Identifying Cancer Driver Genes Based on Transformer and Graph Convolutional Network

Abstract

Correct identification of cancer driver genes plays a significant role in cancer research. The advancement of graph neural network (GNN) research has led to the emergence of many high-performance cancer driver gene prediction methods. However, GNN-based methods frequently overlook the importance of capturing global information. Additionally, as GNN layers increase, the feature representation of genes begins to become overly smooth. These problems hinder the effectiveness of GNN-based identification methods. In this study, we introduce TGCN, a method integrating Transformer and graph convolutional network (GCN), aiming to address these issues and improve cancer driver gene identification. First, we composed multivariate feature matrices of genes from multi-omics data and multi-dimensional gene association networks. Second, we constructed a Transformer module to enrich gene feature representations. Finally, we utilized Chebyshev GCN to yield the identification results. The experimental results demonstrate that TGCN outperforms representative methods in identifying driver genes for both pan-cancer and single-type cancers.