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iOPTICS-GSO for identifying protein complexes from dynamic PPI networks

BACKGROUND: Identifying protein complexes plays an important role for understanding cellular organization and functional mechanisms. As plenty of evidences have indicated that dense sub-networks in dynamic protein-protein interaction network (DPIN) usually correspond to protein complexes, identifyin...

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Autores principales: Lei, Xiujuan, Li, Huan, Zhang, Aidong, Wu, Fang-Xiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5751787/
https://www.ncbi.nlm.nih.gov/pubmed/29297344
http://dx.doi.org/10.1186/s12920-017-0314-x
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author Lei, Xiujuan
Li, Huan
Zhang, Aidong
Wu, Fang-Xiang
author_facet Lei, Xiujuan
Li, Huan
Zhang, Aidong
Wu, Fang-Xiang
author_sort Lei, Xiujuan
collection PubMed
description BACKGROUND: Identifying protein complexes plays an important role for understanding cellular organization and functional mechanisms. As plenty of evidences have indicated that dense sub-networks in dynamic protein-protein interaction network (DPIN) usually correspond to protein complexes, identifying protein complexes is formulated as density-based clustering. METHODS: In this paper, a new approach named iOPTICS-GSO is developed, which is the improved Ordering Points to Identify the Clustering Structure (OPTICS) algorithm with Glowworm swarm optimization algorithm (GSO) to optimize the parameters in OPTICS when finding dense sub-networks. In our iOPTICS-GSO, the concept of core node is redefined and the Euclidean distance in OPTICS is replaced with the improved similarity between the nodes in the PPI network according to their interaction strength, and dense sub-networks are considered as protein complexes. RESULTS: The experiment results have shown that our iOPTICS-GSO outperforms of algorithms such as DBSCAN, CFinder, MCODE, CMC, COACH, ClusterOne MCL and OPTICS_PSO in terms of f-measure and p-value on four DPINs, which are from the DIP, Krogan, MIPS and Gavin datasets. In addition, our predicted protein complexes have a small p-value and thus are highly likely to be true protein complexes. CONCLUSION: The proposed iOPTICS-GSO gains optimal clustering results by adopting GSO algorithm to optimize the parameters in OPTICS, and the result on four datasets shows superior performance. What’s more, the results provided clues for biologists to verify and find new protein complexes.
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spelling pubmed-57517872018-01-05 iOPTICS-GSO for identifying protein complexes from dynamic PPI networks Lei, Xiujuan Li, Huan Zhang, Aidong Wu, Fang-Xiang BMC Med Genomics Research BACKGROUND: Identifying protein complexes plays an important role for understanding cellular organization and functional mechanisms. As plenty of evidences have indicated that dense sub-networks in dynamic protein-protein interaction network (DPIN) usually correspond to protein complexes, identifying protein complexes is formulated as density-based clustering. METHODS: In this paper, a new approach named iOPTICS-GSO is developed, which is the improved Ordering Points to Identify the Clustering Structure (OPTICS) algorithm with Glowworm swarm optimization algorithm (GSO) to optimize the parameters in OPTICS when finding dense sub-networks. In our iOPTICS-GSO, the concept of core node is redefined and the Euclidean distance in OPTICS is replaced with the improved similarity between the nodes in the PPI network according to their interaction strength, and dense sub-networks are considered as protein complexes. RESULTS: The experiment results have shown that our iOPTICS-GSO outperforms of algorithms such as DBSCAN, CFinder, MCODE, CMC, COACH, ClusterOne MCL and OPTICS_PSO in terms of f-measure and p-value on four DPINs, which are from the DIP, Krogan, MIPS and Gavin datasets. In addition, our predicted protein complexes have a small p-value and thus are highly likely to be true protein complexes. CONCLUSION: The proposed iOPTICS-GSO gains optimal clustering results by adopting GSO algorithm to optimize the parameters in OPTICS, and the result on four datasets shows superior performance. What’s more, the results provided clues for biologists to verify and find new protein complexes. BioMed Central 2017-12-28 /pmc/articles/PMC5751787/ /pubmed/29297344 http://dx.doi.org/10.1186/s12920-017-0314-x Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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
Lei, Xiujuan
Li, Huan
Zhang, Aidong
Wu, Fang-Xiang
iOPTICS-GSO for identifying protein complexes from dynamic PPI networks
title iOPTICS-GSO for identifying protein complexes from dynamic PPI networks
title_full iOPTICS-GSO for identifying protein complexes from dynamic PPI networks
title_fullStr iOPTICS-GSO for identifying protein complexes from dynamic PPI networks
title_full_unstemmed iOPTICS-GSO for identifying protein complexes from dynamic PPI networks
title_short iOPTICS-GSO for identifying protein complexes from dynamic PPI networks
title_sort ioptics-gso for identifying protein complexes from dynamic ppi networks
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5751787/
https://www.ncbi.nlm.nih.gov/pubmed/29297344
http://dx.doi.org/10.1186/s12920-017-0314-x
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