Identification of cancer-related module in protein–protein interaction network based on gene prioritization

Author:

Wu Jingli12ORCID,Zhang Qi3,Li Gaoshi1

Affiliation:

1. Guangxi Key Lab of Multi-Source Information Mining & Security, Guangxi Normal University, Guilin 541004, P. R. China

2. Yimeng Executive Leadership Academy, Linyi 276000, P. R. China

3. College of Computer Science and Information Engineering, Guangxi Normal University, Guilin 541004, P. R. China

Abstract

With the rapid development of deep sequencing technologies, a large amount of high-throughput data has been available for studying the carcinogenic mechanism at the molecular level. It has been widely accepted that the development and progression of cancer are regulated by modules/pathways rather than individual genes. The investigation of identifying cancer-related active modules has received an extensive attention. In this paper, we put forward an identification method ModFinder by integrating both biological networks and gene expression profiles. More concretely, a gene scoring function is devised by using the regression model with [Formula: see text]-step random walk kernel, and the genes are ranked according to both of their active scores and degrees in the PPI network. Then a greedy algorithm NSEA is introduced to find an active module with high score and strong connectivity. Experiments were performed on both simulated data and real biological one, i.e. breast cancer and cervical cancer. Compared with the previous methods SigMod, LEAN and RegMod, ModFinder shows competitive performance. It can successfully identify a well-connected module that contains a large proportion of cancer-related genes, including some well-known oncogenes or tumor suppressors enriched in cancer-related pathways.

Funder

National Natural Science Foundation of China

Natural Science Foundation of Guangxi Province

Guangxi Science Base and Talent Special Support

Publisher

World Scientific Pub Co Pte Ltd

Subject

Computer Science Applications,Molecular Biology,Biochemistry

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