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

Introduction

Identification of cancer driver genes plays an important role in precision oncology research, which is helpful to understand cancer initiation and progression. However, most existing computational methods mainly used the protein–protein interaction networks (PPIs), 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 identification. Here, based on the multi-omics pan-cancer data (i.e., gene expression, mutation, copy number variation, and DNA methylation), we proposed 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 process. The results on three gene regulatory networks show that DGMP outperforms other existing state-of-the-art methods. The ablation experimental results on the DawnNet 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 genes. DGMP can not only identify 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 genes.

Publications

  1. DGMP: Identifying Cancer Driver Genes by Jointing DGCN and MLP from Multi-omics Genomic Data
    Shao-Wu Zhang, Jing-Yu Xu, Tong Zhang, - Genomics, Proteomics & Bioinformatics

Credits

  1. Shao-WuZhang zhangsw@nwpu.edu.cn
    Investigator

    School of Automation, Northwestern Polytechnical University, China

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Summary
AccessionBT007338
Tool TypePipeline & Protocol
CategoryDriver mutation prioritization
PlatformsLinux/Unix, MAC OS X, Windows
TechnologiesPython3
User InterfaceTerminal Command Line
Input DataBAM
Latest Release1.0 (January 3, 2023)
Download Count482
Country/RegionChina
Submitted ByTong Zhang
Fundings

This work was supported in part by the National Natural Science Foundation of China (Grant Nos. 62173271 and 61873202 to Shao-Wu Zhang)