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Protein complexes detection based on node local properties and gene expression in PPI weighted networks

BACKGROUND: Identifying protein complexes from protein–protein interaction (PPI) networks is a crucial task, and many related algorithms have been developed. Most algorithms usually employ direct neighbors of nodes and ignore resource allocation and second-order neighbors. The effective use of such...

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Autores principales: Yu, Yang, Kong, Dezhou
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8734347/
https://www.ncbi.nlm.nih.gov/pubmed/34991441
http://dx.doi.org/10.1186/s12859-021-04543-4
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author Yu, Yang
Kong, Dezhou
author_facet Yu, Yang
Kong, Dezhou
author_sort Yu, Yang
collection PubMed
description BACKGROUND: Identifying protein complexes from protein–protein interaction (PPI) networks is a crucial task, and many related algorithms have been developed. Most algorithms usually employ direct neighbors of nodes and ignore resource allocation and second-order neighbors. The effective use of such information is crucial to protein complex detection. RESULT: Based on this observation, we propose a new way by combining node resource allocation and gene expression information to weight protein network (NRAGE-WPN), in which protein complexes are detected based on core-attachment and second-order neighbors. CONCLUSIONS: Through comparison with eleven methods in Yeast and Human PPI network, the experimental results demonstrate that this algorithm not only performs better than other methods on 75% in terms of f-measure+, but also can achieve an ideal overall performance in terms of a composite score consisting of five performance measures. This identification method is simple and can accurately identify more complexes. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-021-04543-4.
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spelling pubmed-87343472022-01-07 Protein complexes detection based on node local properties and gene expression in PPI weighted networks Yu, Yang Kong, Dezhou BMC Bioinformatics Research Article BACKGROUND: Identifying protein complexes from protein–protein interaction (PPI) networks is a crucial task, and many related algorithms have been developed. Most algorithms usually employ direct neighbors of nodes and ignore resource allocation and second-order neighbors. The effective use of such information is crucial to protein complex detection. RESULT: Based on this observation, we propose a new way by combining node resource allocation and gene expression information to weight protein network (NRAGE-WPN), in which protein complexes are detected based on core-attachment and second-order neighbors. CONCLUSIONS: Through comparison with eleven methods in Yeast and Human PPI network, the experimental results demonstrate that this algorithm not only performs better than other methods on 75% in terms of f-measure+, but also can achieve an ideal overall performance in terms of a composite score consisting of five performance measures. This identification method is simple and can accurately identify more complexes. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-021-04543-4. BioMed Central 2022-01-06 /pmc/articles/PMC8734347/ /pubmed/34991441 http://dx.doi.org/10.1186/s12859-021-04543-4 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Yu, Yang
Kong, Dezhou
Protein complexes detection based on node local properties and gene expression in PPI weighted networks
title Protein complexes detection based on node local properties and gene expression in PPI weighted networks
title_full Protein complexes detection based on node local properties and gene expression in PPI weighted networks
title_fullStr Protein complexes detection based on node local properties and gene expression in PPI weighted networks
title_full_unstemmed Protein complexes detection based on node local properties and gene expression in PPI weighted networks
title_short Protein complexes detection based on node local properties and gene expression in PPI weighted networks
title_sort protein complexes detection based on node local properties and gene expression in ppi weighted networks
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8734347/
https://www.ncbi.nlm.nih.gov/pubmed/34991441
http://dx.doi.org/10.1186/s12859-021-04543-4
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