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

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...

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Autores principales:	Yan Wang, Qiong Chen, Lili Yang, Sen Yang, Kai He, Xuping Xie
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	protein-protein interaction network overlapping structure clustering coefficient community detection central edge Genetics QH426-470
Acceso en línea:	https://doaj.org/article/20ce862d36fe4753af3a9dbac77c0540
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spelling	oai:doaj.org-article:20ce862d36fe4753af3a9dbac77c05402021-12-01T09:01:58ZOverlapping Structures Detection in Protein-Protein Interaction Networks Using Community Detection Algorithm Based on Neighbor Clustering Coefficient1664-802110.3389/fgene.2021.689515https://doaj.org/article/20ce862d36fe4753af3a9dbac77c05402021-06-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fgene.2021.689515/fullhttps://doaj.org/toc/1664-8021With 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.Yan WangYan WangQiong ChenLili YangLili YangSen YangKai HeXuping XieFrontiers Media S.A.articleprotein-protein interaction networkoverlapping structureclustering coefficientcommunity detectioncentral edgeGeneticsQH426-470ENFrontiers in Genetics, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	protein-protein interaction network overlapping structure clustering coefficient community detection central edge Genetics QH426-470
spellingShingle	protein-protein interaction network overlapping structure clustering coefficient community detection central edge Genetics QH426-470 Yan Wang Yan Wang Qiong Chen Lili Yang Lili Yang Sen Yang Kai He Xuping Xie Overlapping Structures Detection in Protein-Protein Interaction Networks Using Community Detection Algorithm Based on Neighbor Clustering Coefficient
description	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.
format	article
author	Yan Wang Yan Wang Qiong Chen Lili Yang Lili Yang Sen Yang Kai He Xuping Xie
author_facet	Yan Wang Yan Wang Qiong Chen Lili Yang Lili Yang Sen Yang Kai He Xuping Xie
author_sort	Yan Wang
title	Overlapping Structures Detection in Protein-Protein Interaction Networks Using Community Detection Algorithm Based on Neighbor Clustering Coefficient
title_short	Overlapping Structures Detection in Protein-Protein Interaction Networks Using Community Detection Algorithm Based on Neighbor Clustering Coefficient
title_full	Overlapping Structures Detection in Protein-Protein Interaction Networks Using Community Detection Algorithm Based on Neighbor Clustering Coefficient
title_fullStr	Overlapping Structures Detection in Protein-Protein Interaction Networks Using Community Detection Algorithm Based on Neighbor Clustering Coefficient
title_full_unstemmed	Overlapping Structures Detection in Protein-Protein Interaction Networks Using Community Detection Algorithm Based on Neighbor Clustering Coefficient
title_sort	overlapping structures detection in protein-protein interaction networks using community detection algorithm based on neighbor clustering coefficient
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/20ce862d36fe4753af3a9dbac77c0540
work_keys_str_mv	AT yanwang overlappingstructuresdetectioninproteinproteininteractionnetworksusingcommunitydetectionalgorithmbasedonneighborclusteringcoefficient AT yanwang overlappingstructuresdetectioninproteinproteininteractionnetworksusingcommunitydetectionalgorithmbasedonneighborclusteringcoefficient AT qiongchen overlappingstructuresdetectioninproteinproteininteractionnetworksusingcommunitydetectionalgorithmbasedonneighborclusteringcoefficient AT liliyang overlappingstructuresdetectioninproteinproteininteractionnetworksusingcommunitydetectionalgorithmbasedonneighborclusteringcoefficient AT liliyang overlappingstructuresdetectioninproteinproteininteractionnetworksusingcommunitydetectionalgorithmbasedonneighborclusteringcoefficient AT senyang overlappingstructuresdetectioninproteinproteininteractionnetworksusingcommunitydetectionalgorithmbasedonneighborclusteringcoefficient AT kaihe overlappingstructuresdetectioninproteinproteininteractionnetworksusingcommunitydetectionalgorithmbasedonneighborclusteringcoefficient AT xupingxie overlappingstructuresdetectioninproteinproteininteractionnetworksusingcommunitydetectionalgorithmbasedonneighborclusteringcoefficient
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Overlapping Structures Detection in Protein-Protein Interaction Networks Using Community Detection Algorithm Based on Neighbor Clustering Coefficient

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