Overlapping Structures Detection in Protein-Protein Interaction Networks Using Community Detection Algorithm Based on Neighbor Clustering Coefficient.

Yan Wang,Xuping Xie,Sen Yang,Kai He,Lili Yang,Qiong Chen

doi:10.3389/fgene.2021.689515

Abstract

With the rapid development of bioinformatics, researchers have applied community detection algorithms to detect functional modules in protein-protein interaction (PPI) networks that can predict the function of unknown proteins at the molecular level and further reveal the regularity of cell activity. Clusters in a PPI network may overlap where a protein is involved in multiple functional modules. To identify overlapping structures in protein functional modules, this paper proposes a novel overlapping community detection algorithm based on the neighboring local clustering coefficient (NLC). The contributions of the NLC algorithm are threefold: (i) Combine the edge-based community detection method with local expansion in seed selection and the local clustering coefficient of neighboring nodes to improve the accuracy of seed selection; (ii) A method of measuring the distance between edges is improved to make the result of community division more accurate; (iii) A community optimization strategy for the excessive overlapping nodes makes the overlapping structure more reasonable. The experimental results on standard networks, Lancichinetti-Fortunato-Radicchi (LFR) benchmark networks and PPI networks show that the NLC algorithm can improve the Extended modularity (EQ) value and Normalized Mutual Information (NMI) value of the community division, which verifies that the algorithm can not only detect reasonable communities but also identify overlapping structures in networks.

Highlights

Due to the rapid development of experimental and computing technology, a large number of protein-protein interaction (PPI) networks have been mined (Chen et al, 2020)
To improve the accuracy of community division, this paper proposes an overlapping community detection algorithm based on the neighbor local clustering coefficient (NLC) to select the central edge
The neighboring local clustering coefficient (NLC) algorithm was compared with the central edge selection (CES), CNS, cluster percolation method (CPM), and LC algorithms, by comparing the EQ, coverage rate (CR) and number of predicted communities (NPC) values in the four PPI networks: M. musculus, H. sapiens, D. melanogaster and R. norvegicus

Summary

Introduction

Due to the rapid development of experimental and computing technology, a large number of PPI networks have been mined (Chen et al, 2020). Biological functions are performed by many functionally related proteins Such clustering proteins are called functional module. A module represents a group of Overlapping Structures in PPI Networks proteins taking part in specific, separable functions such as protein complexes, metabolic pathways or signal transduction systems (Vella et al, 2018). Lots of overlapping structures are shared by the functional modules in PPI networks, indicating some proteins play indispensable roles in different biological processes (Gu et al, 2019). Research on detecting protein functional modules has become one of the most important topics in both life science and computing science since the completion of the Human Genome Project (Ying and Lin, 2020). Detecting overlapping structures in functional modules have good application prospects in protein biological function, disease-causing gene, and drug target prediction

Methods

Results

Conclusion