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Affiliation of Author(s):山东大学

Teaching and Research Group:高性能中心

Journal:Briefings in Functional Genomics

Place of Publication:英国

Funded by:国家自然科学基金

Key Words:Differential Expression Genes，Protein Interaction Networks， Dysregulated Modules

Abstract:In the human genome, abnormal gene expression usually causes gene mutations, leading to abnormal gene networks, and then triggering certain complex diseases. At the bio-molecular level, gene sets composed of differential expression genes usually lead to dysregulation of the biological pathways that they regulate. Therefore, differential expression genes are usually used as biomarkers for early diagnosis of disease in clinical practice. Detection of cancer-causing dysregulated modules provides a new perspective to study the mechanism of cancer. However, it has become a big challenge in cancer research how to accurately and effectively detect dysregulated modules that promote diseases in massive data. In this study, we propose a dysregulated module detection method (Netkmeans) based on a network model, which integrates differential expression genes and human protein interaction networks. This method integrates the characteristics of high mutual exclusivity, high coverage and complex network topology among genes widely existed in the genome. Firstly, the study constructs an undirected-weighted gene interaction network based on the above three characteristics. Secondly, the study constructs a comprehensive evaluation function to select the number of clusters scientifically and effectively. Finally, the K-means clustering method is applied to perform cluster analysis and obtain the optimal dysregulated modules. Compared with the differential expression genes detected by IBA and CCEN methods, the results of the Netkmeans have higher statistical significance and biological relevance. Besides, compared with the dysregulated modules detected by MCODE, CFinder and ClusterONE, the results of Netkmeans have higher accuracy, precision and F-measure values. The experimental results show that the multiple dysregulated modules detected by Netkmeans play an important role in the generation, development and progression of cancer, and thus they play a vital role in the precise diagnosis, treatment and new drug development for cancer patients.

All the Authors:Biting Liang,Yingfu Wu,Hongming Zhang,Quanzhong Liu

First Author:Zhongli Chen

Indexed by:Journal paper

Correspondence Author:Hao Wu*

Document Code:10.1093/bfgp/elac010

Discipline:Engineering

First-Level Discipline:Software Engineering

Document Type:J

Volume:4

Issue:23

Translation or Not:no

Date of Publication:2022-04-01

Included Journals:SCI