生物信息学2025，Vol.23Issue(2)：111-121,11.DOI:10.12113/202308001
HDGICN:一种基于图卷积网络的癌症驱动基因识别方法
HDGICN:A graph convolutional network based method for identifying cancer driver genes
摘要
Abstract
In recent years,extensive research efforts on cancer has accumulated a massive amount of multi-omics data,providing opportunities for efficient identification of cancer driver genes.In this study,we propose a novel Heterophilic Deep Graph Information Maximization Convolutional Network(HDGICN)model for cancer driver gene identification.HDGICN integrates graph information maximization and Personalized PageRank algorithm to enhance gene node features in heterophilic molecular networks.Subsequently,a hierarchical mixture of graph convolutions with dual residual structures is employed to learn gene features on heterophilic molecular networks.Finally,cancer driver genes are identified based on prediction scores.Experimental results on three heterophilic molecular networks demonstrate that HDGICN outperforms traditional methods in both Area Under Receiver Operating Characteristic(AUROC)and Area Under the Precision-Recall Curve(AUPRC).Further ablation experiments validate the method's effectiveness in improving predictive performance.HDGICN proves effective in identifying cancer driver genes on heterophilic molecular networks,offering valuable support for precision cancer treatment and biomarker discovery.关键词
癌症驱动基因/图信息最大化/异亲网络/图卷积网络Key words
Cancer driver genes/Graph information maximization/Heterophilic network/Graph convolutional network分类
计算机与自动化引用本文复制引用
谢兵,苏波,刘宁..HDGICN:一种基于图卷积网络的癌症驱动基因识别方法[J].生物信息学,2025,23(2):111-121,11.基金项目
西南科技大学博士基金资助项目(No.19zx7142). （No.19zx7142）
