Identifying Cancer Driver Genes Based on Vision Transformer and Hierarchical Feature Fusion Module

Cancer is a global public health problem that poses a huge threat to human life and health. The identification of cancer driver genes helps to discover the intrinsic mechanisms of cancer occurrence and development. Existing deep learning-based methods for identifying cancer driver genes are mainly biased towards local structure reconstruction, limiting the global learning. In order to increase the identification capacity of cancer driver genes, in this study, a new cancer driver genes prediction method VGDriver using Vision Transformer(ViT) and hierarchical feature fusion module is proposed. Its steps are as follows: firstly, the gene expression, copy number aberrations, and single nucleotide variations data are used to construct a gene property feature matrix; secondly, a new hierarchical feature fusion module is designed, which can perform a two-pipeline fusion of multi-omics features to obtain a multi-source feature matrix by co-designing the graph convolutional networks block and the multilayer perceptron block; finally, the multi-source feature matrix is input into ViT to train and perform the task of predicting cancer driver genes. The results of comparative experiments on five different protein-protein interaction datasets for pan-cancer and eight single cancer types show that the VGDriver method is better compared to seven representative methods in the evaluation metrics AUC and AUPR.