Identifying Cancer Driver Genes Using a Neural Network Framework with Cross-attention Mechanism

Abstract

Identifying cancer driver genes can accelerate the discovery of drug targets and the development of cancer therapies. Recent research methods improve the accuracy of identifying cancer driver genes by using deep learning framework. However, due to ignore the connection among learned features, they usually have weak feature representations that limits further improvement in the accuracy of identifying cancer driver genes. In this work, we propose a graph neural network framework combining graph convolutional network, Transformer with crossattention, and multi-layer perceptron classifier, called GTCM, to improve the accuracy of identifying cancer driver genes. Specifically, GTCM firstly uses graph convolutional network to learn gene feature representations from three different gene association networks. Secondly, to enhance the feature representations of cancer driver genes, GTCM adopts Transformer with crossattention to dynamically learn the connections between different feature sets. Finally, GTCM predicts cancer driver genes using multi-layer perceptron classifier. Ablation experiments prove that Transformer with cross-attention effectively improves the feature representations learned from graph convolutional network and further improves the identification rate. Compared with existing representative methods, GTCM exhibits excellent performance in terms of area under the receiver operating characteristic curves and area under precision-recall curves. The source codes and data are available at https://github.com/MuWang17/GTCM.