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Detecting overlapping protein complexes in PPI networks based on robustness

BACKGROUND: Recently, large data sets of protein-protein interactions (PPI) which can be modeled as PPI networks are generated through high-throughput methods. And locally dense regions in PPI networks are very likely to be protein complexes. Since protein complexes play a key role in many biologica...

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Autores principales: Wang, Shuliang, Wu, Fang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3908676/
https://www.ncbi.nlm.nih.gov/pubmed/24565162
http://dx.doi.org/10.1186/1477-5956-11-S1-S18
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author Wang, Shuliang
Wu, Fang
author_facet Wang, Shuliang
Wu, Fang
author_sort Wang, Shuliang
collection PubMed
description BACKGROUND: Recently, large data sets of protein-protein interactions (PPI) which can be modeled as PPI networks are generated through high-throughput methods. And locally dense regions in PPI networks are very likely to be protein complexes. Since protein complexes play a key role in many biological processes, detecting protein complexes in PPI networks is one of important tasks in post-genomic era. However, PPI networks are often incomplete and noisy, which builds barriers to mining protein complexes. RESULTS: We propose a new and effective algorithm based on robustness to detect overlapping clusters as protein complexes in PPI networks. And in order to improve the accuracy of resulting clusters, our algorithm tries to reduce bad effects brought by noise in PPI networks. And in our algorithm, each new cluster begins from a seed and is expanded through adding qualified nodes from the cluster's neighbourhood nodes. Besides, in our algorithm, a new distance measurement method between a cluster K and a node in the neighbours of K is proposed as well. The performance of our algorithm is evaluated by applying it on two PPI networks which are Gavin network and Database of Interacting Proteins (DIP). The results show that our algorithm is better than Markov clustering algorithm (MCL), Clique Percolation method (CPM) and core-attachment based method (CoAch) in terms of F-measure, co-localization and Gene Ontology (GO) semantic similarity. CONCLUSIONS: Our algorithm detects locally dense regions or clusters as protein complexes. The results show that protein complexes generated by our algorithm have better quality than those generated by some previous classic methods. Therefore, our algorithm is effective and useful.
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spelling pubmed-39086762014-02-13 Detecting overlapping protein complexes in PPI networks based on robustness Wang, Shuliang Wu, Fang Proteome Sci Research BACKGROUND: Recently, large data sets of protein-protein interactions (PPI) which can be modeled as PPI networks are generated through high-throughput methods. And locally dense regions in PPI networks are very likely to be protein complexes. Since protein complexes play a key role in many biological processes, detecting protein complexes in PPI networks is one of important tasks in post-genomic era. However, PPI networks are often incomplete and noisy, which builds barriers to mining protein complexes. RESULTS: We propose a new and effective algorithm based on robustness to detect overlapping clusters as protein complexes in PPI networks. And in order to improve the accuracy of resulting clusters, our algorithm tries to reduce bad effects brought by noise in PPI networks. And in our algorithm, each new cluster begins from a seed and is expanded through adding qualified nodes from the cluster's neighbourhood nodes. Besides, in our algorithm, a new distance measurement method between a cluster K and a node in the neighbours of K is proposed as well. The performance of our algorithm is evaluated by applying it on two PPI networks which are Gavin network and Database of Interacting Proteins (DIP). The results show that our algorithm is better than Markov clustering algorithm (MCL), Clique Percolation method (CPM) and core-attachment based method (CoAch) in terms of F-measure, co-localization and Gene Ontology (GO) semantic similarity. CONCLUSIONS: Our algorithm detects locally dense regions or clusters as protein complexes. The results show that protein complexes generated by our algorithm have better quality than those generated by some previous classic methods. Therefore, our algorithm is effective and useful. BioMed Central 2013-11-07 /pmc/articles/PMC3908676/ /pubmed/24565162 http://dx.doi.org/10.1186/1477-5956-11-S1-S18 Text en Copyright © 2013 Wang and Wu et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Wang, Shuliang
Wu, Fang
Detecting overlapping protein complexes in PPI networks based on robustness
title Detecting overlapping protein complexes in PPI networks based on robustness
title_full Detecting overlapping protein complexes in PPI networks based on robustness
title_fullStr Detecting overlapping protein complexes in PPI networks based on robustness
title_full_unstemmed Detecting overlapping protein complexes in PPI networks based on robustness
title_short Detecting overlapping protein complexes in PPI networks based on robustness
title_sort detecting overlapping protein complexes in ppi networks based on robustness
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3908676/
https://www.ncbi.nlm.nih.gov/pubmed/24565162
http://dx.doi.org/10.1186/1477-5956-11-S1-S18
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