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Functional diversity of topological modules in human protein-protein interaction networks
A large-scale molecular interaction network of protein-protein interactions (PPIs) enables the automatic detection of molecular functional modules through a computational approach. However, the functional modules that are typically detected by topological community detection algorithms may be divers...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5701033/ https://www.ncbi.nlm.nih.gov/pubmed/29170401 http://dx.doi.org/10.1038/s41598-017-16270-z |
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author | Liu, Guangming Wang, Huixin Chu, Hongwei Yu, Jian Zhou, Xuezhong |
author_facet | Liu, Guangming Wang, Huixin Chu, Hongwei Yu, Jian Zhou, Xuezhong |
author_sort | Liu, Guangming |
collection | PubMed |
description | A large-scale molecular interaction network of protein-protein interactions (PPIs) enables the automatic detection of molecular functional modules through a computational approach. However, the functional modules that are typically detected by topological community detection algorithms may be diverse in functional homogeneity and are empirically considered to be default functional modules. Thus, a significant challenge that has been described but not elucidated is investigating the relationship between topological modules and functional modules. We systematically investigated this issue by initially using seven widely used community detection algorithms to partition the PPI network into communities. Four homogeneity measures were subsequently implemented to evaluate the functional homogeneity of protein community. We determined that a significant portion of topological modules with heterogeneous functionality exists and should be further investigated; moreover, these findings indicated that topologically based functional module detection approaches must be reconsidered. Furthermore, we found that the functional homogeneity of topological modules is positively correlated with their edge densities, degree of association with diseases and general Gene Ontology (GO) terms. Thus, topologically based module detection approaches should be used with caution in the identification of functional modules with high homogeneity |
format | Online Article Text |
id | pubmed-5701033 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-57010332017-11-30 Functional diversity of topological modules in human protein-protein interaction networks Liu, Guangming Wang, Huixin Chu, Hongwei Yu, Jian Zhou, Xuezhong Sci Rep Article A large-scale molecular interaction network of protein-protein interactions (PPIs) enables the automatic detection of molecular functional modules through a computational approach. However, the functional modules that are typically detected by topological community detection algorithms may be diverse in functional homogeneity and are empirically considered to be default functional modules. Thus, a significant challenge that has been described but not elucidated is investigating the relationship between topological modules and functional modules. We systematically investigated this issue by initially using seven widely used community detection algorithms to partition the PPI network into communities. Four homogeneity measures were subsequently implemented to evaluate the functional homogeneity of protein community. We determined that a significant portion of topological modules with heterogeneous functionality exists and should be further investigated; moreover, these findings indicated that topologically based functional module detection approaches must be reconsidered. Furthermore, we found that the functional homogeneity of topological modules is positively correlated with their edge densities, degree of association with diseases and general Gene Ontology (GO) terms. Thus, topologically based module detection approaches should be used with caution in the identification of functional modules with high homogeneity Nature Publishing Group UK 2017-11-23 /pmc/articles/PMC5701033/ /pubmed/29170401 http://dx.doi.org/10.1038/s41598-017-16270-z Text en © The Author(s) 2017 Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Liu, Guangming Wang, Huixin Chu, Hongwei Yu, Jian Zhou, Xuezhong Functional diversity of topological modules in human protein-protein interaction networks |
title | Functional diversity of topological modules in human protein-protein interaction networks |
title_full | Functional diversity of topological modules in human protein-protein interaction networks |
title_fullStr | Functional diversity of topological modules in human protein-protein interaction networks |
title_full_unstemmed | Functional diversity of topological modules in human protein-protein interaction networks |
title_short | Functional diversity of topological modules in human protein-protein interaction networks |
title_sort | functional diversity of topological modules in human protein-protein interaction networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5701033/ https://www.ncbi.nlm.nih.gov/pubmed/29170401 http://dx.doi.org/10.1038/s41598-017-16270-z |
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