A density-based approach for detecting complexes in weighted PPI networks by semantic similarity

Protein complex detection in PPI networks plays an important role in analyzing biological processes. A new algorithm-DBGPWN-is proposed for predicting complexes in PPI networks. Firstly, a method based on gene ontology is used to measure semantic similarities between interacted proteins, and the sim...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhou, HongFang, Liu, Jie, Li, JunHuai, Duan, WenCong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5507511/
https://www.ncbi.nlm.nih.gov/pubmed/28704455
http://dx.doi.org/10.1371/journal.pone.0180570
_version_ 1783249749890039808
author Zhou, HongFang
Liu, Jie
Li, JunHuai
Duan, WenCong
author_facet Zhou, HongFang
Liu, Jie
Li, JunHuai
Duan, WenCong
author_sort Zhou, HongFang
collection PubMed
description Protein complex detection in PPI networks plays an important role in analyzing biological processes. A new algorithm-DBGPWN-is proposed for predicting complexes in PPI networks. Firstly, a method based on gene ontology is used to measure semantic similarities between interacted proteins, and the similarity values are used as their weights. Then, a density-based graph partitioning algorithm is developed to find clusters in the weighted PPI networks, and the identified ones are considered to be dense and similar. Experimental results demonstrate that our approach achieves good performance as compared with such algorithms as MCL, CMC, MCODE, RNSC, CORE, ClusterOne and FGN.
format Online
Article
Text
id pubmed-5507511
institution National Center for Biotechnology Information
language English
publishDate 2017
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-55075112017-07-25 A density-based approach for detecting complexes in weighted PPI networks by semantic similarity Zhou, HongFang Liu, Jie Li, JunHuai Duan, WenCong PLoS One Research Article Protein complex detection in PPI networks plays an important role in analyzing biological processes. A new algorithm-DBGPWN-is proposed for predicting complexes in PPI networks. Firstly, a method based on gene ontology is used to measure semantic similarities between interacted proteins, and the similarity values are used as their weights. Then, a density-based graph partitioning algorithm is developed to find clusters in the weighted PPI networks, and the identified ones are considered to be dense and similar. Experimental results demonstrate that our approach achieves good performance as compared with such algorithms as MCL, CMC, MCODE, RNSC, CORE, ClusterOne and FGN. Public Library of Science 2017-07-12 /pmc/articles/PMC5507511/ /pubmed/28704455 http://dx.doi.org/10.1371/journal.pone.0180570 Text en © 2017 Zhou et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Zhou, HongFang
Liu, Jie
Li, JunHuai
Duan, WenCong
A density-based approach for detecting complexes in weighted PPI networks by semantic similarity
title A density-based approach for detecting complexes in weighted PPI networks by semantic similarity
title_full A density-based approach for detecting complexes in weighted PPI networks by semantic similarity
title_fullStr A density-based approach for detecting complexes in weighted PPI networks by semantic similarity
title_full_unstemmed A density-based approach for detecting complexes in weighted PPI networks by semantic similarity
title_short A density-based approach for detecting complexes in weighted PPI networks by semantic similarity
title_sort density-based approach for detecting complexes in weighted ppi networks by semantic similarity
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5507511/
https://www.ncbi.nlm.nih.gov/pubmed/28704455
http://dx.doi.org/10.1371/journal.pone.0180570
work_keys_str_mv AT zhouhongfang adensitybasedapproachfordetectingcomplexesinweightedppinetworksbysemanticsimilarity
AT liujie adensitybasedapproachfordetectingcomplexesinweightedppinetworksbysemanticsimilarity
AT lijunhuai adensitybasedapproachfordetectingcomplexesinweightedppinetworksbysemanticsimilarity
AT duanwencong adensitybasedapproachfordetectingcomplexesinweightedppinetworksbysemanticsimilarity
AT zhouhongfang densitybasedapproachfordetectingcomplexesinweightedppinetworksbysemanticsimilarity
AT liujie densitybasedapproachfordetectingcomplexesinweightedppinetworksbysemanticsimilarity
AT lijunhuai densitybasedapproachfordetectingcomplexesinweightedppinetworksbysemanticsimilarity
AT duanwencong densitybasedapproachfordetectingcomplexesinweightedppinetworksbysemanticsimilarity

Ejemplares similares