DGMP: Identifying Cancer Driver Genes by Jointing DGCN and MLP from Multi-omics Genomic Data

作     者：Shao-Wu Zhang Jing-Yu Xu Tong Zhang Shao-Wu Zhang;Jing-Yu Xu;Tong Zhang

作者机构：MOE Key Laboratory of Information Fusion TechnologySchool of AutomationNorthwestern Polytechnical UniversityXi’an 710072China 

出 版 物：《Genomics, Proteomics & Bioinformatics》 (基因组蛋白质组与生物信息学报（英文版）)

年 卷 期：2022年第20卷第5期

页      面：928-938页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0711[理学-系统科学] 1002[医学-临床医学] 07[理学] 08[工学] 100214[医学-肿瘤学] 10[医学] 

基　　金：supported in part by the National Natural Science Foundation of China(Grant Nos.62173271 and 61873202 to SWZ) 

主　　题：Driver gene Directed graph convolutional network Multilayer perceptron Gene regulatory network Multi-omics data 

摘      要：Identification of cancer driver genes plays an important role in precision oncology research,which is helpful to understand cancer initiation and ***,most existing computational methods mainly used the protein–protein interaction(PPI)networks,or treated the directed gene regulatory networks(GRNs)as the undirected gene–gene association networks to identify the cancer driver genes,which will lose the unique structure regulatory information in the directed GRNs,and then affect the outcome of the cancer driver gene ***,based on the multi-omics pan-cancer data(i.e.,gene expression,mutation,copy number variation,and DNA methylation),we propose a novel method(called DGMP)to identify cancer driver genes by jointing directed graph convolutional network(DGCN)and multilayer perceptron(MLP).DGMP learns the multi-omics features of genes as well as the topological structure features in GRN with the DGCN model and uses MLP to weigh more on gene features for mitigating the bias toward the graph topological features in the DGCN learning *** results on three GRNs show that DGMP outperforms other existing state-of-the-art *** ablation experimental results on the Dawn Net network indicate that introducing MLP into DGCN can offset the performance degradation of DGCN,and jointing MLP and DGCN can effectively improve the performance of identifying cancer driver *** can identify not only the highly mutated cancer driver genes but also the driver genes harboring other kinds of alterations(e.g.,differential expression and aberrant DNA methylation)or genes involved in GRNs with other cancer *** source code of DGMP can be freely downloaded from https://***/NWPU-903PR/DGMP.