ICDM-GEHC: identifying cancer driver module based on graph embedding and hierarchical clustering

被引:0
|
作者
Shiyu Deng
Jingli Wu
Gaoshi Li
Jiafei Liu
Yumeng Zhao
机构
[1] Guangxi Normal University,Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education
[2] Guangxi Normal University,Guangxi Key Lab of Multi
[3] Guangxi Normal University,Source Information Mining and Security
来源
Complex & Intelligent Systems | 2024年 / 10卷
关键词
Cancer driver module; Graph embedding; Multi-omics; Hierarchical clustering;
D O I
暂无
中图分类号
学科分类号
摘要
Due to the high heterogeneity of cancers, it is rather essential to explore driver modules with the help of gene mutation data as well as known interactions between genes/proteins. Unfortunately, latent false positive interactions are inevitable in the Protein-Protein Interaction (PPI) network. Hence in the presented method, a new weight evaluation index, based on the gene-microRNA network as well as somatic mutation profile, is introduced for weighting the PPI network first. Subsequently, the vertices in the weighted PPI network are hierarchically clustered by measuring the Mahalanobis distance of their feature vectors, extracted with the graph embedding method Node2vec. Finally, a heuristic process with dropping and extracting is conducted on the gene clusters to produce a group of gene modules. Numerous experiment results demonstrate that the proposed method exhibits superior performance to four cutting-edge identification methods in most cases regarding the capability of recognizing the acknowledged cancer-related genes, generating modules having relatively high coverage and mutual exclusivity, and are significantly enriched for specific types of cancers. The majority of the genes in the identified modules are involved in cancer-related signaling pathways, or have been reported to be carcinogenic in the literature. Furthermore, many cancer related genes detected by the proposed method are actually omitted by the four comparison methods, which has been verified in the experiments.
引用
收藏
页码:3411 / 3427
页数:16
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