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Exploring hierarchical and overlapping modular structure in the yeast protein interaction network
BACKGROUND: Developing effective strategies to reveal modular structures in protein interaction networks is crucial for better understanding of molecular mechanisms of underlying biological processes. In this paper, we propose a new density-based algorithm (ADHOC) for clustering vertices of a protei...
Autores principales: | , , |
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2010
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3005933/ https://www.ncbi.nlm.nih.gov/pubmed/21143800 http://dx.doi.org/10.1186/1471-2164-11-S4-S17 |
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author | Liu, Changning Li, Jing Zhao, Yi |
author_facet | Liu, Changning Li, Jing Zhao, Yi |
author_sort | Liu, Changning |
collection | PubMed |
description | BACKGROUND: Developing effective strategies to reveal modular structures in protein interaction networks is crucial for better understanding of molecular mechanisms of underlying biological processes. In this paper, we propose a new density-based algorithm (ADHOC) for clustering vertices of a protein interaction network using a novel subgraph density measurement. RESULTS: By statistically evaluating several independent criteria, we found that ADHOC could significantly improve the outcome as compared with five previously reported density-dependent methods. We further applied ADHOC to investigate the hierarchical and overlapping modular structure in the yeast PPI network. Our method could effectively detect both protein modules and the overlaps between them, and thus greatly promote the precise prediction of protein functions. Moreover, by further assaying the intermodule layer of the yeast PPI network, we classified hubs into two types, module hubs and inter-module hubs. Each type presents distinct characteristics both in network topology and biological functions, which could conduce to the better understanding of relationship between network architecture and biological implications. CONCLUSIONS: Our proposed algorithm based on the novel subgraph density measurement makes it possible to more precisely detect hierarchical and overlapping modular structures in protein interaction networks. In addition, our method also shows a strong robustness against the noise in network, which is quite critical for analyzing such a high noise network. |
format | Text |
id | pubmed-3005933 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-30059332010-12-22 Exploring hierarchical and overlapping modular structure in the yeast protein interaction network Liu, Changning Li, Jing Zhao, Yi BMC Genomics Proceedings BACKGROUND: Developing effective strategies to reveal modular structures in protein interaction networks is crucial for better understanding of molecular mechanisms of underlying biological processes. In this paper, we propose a new density-based algorithm (ADHOC) for clustering vertices of a protein interaction network using a novel subgraph density measurement. RESULTS: By statistically evaluating several independent criteria, we found that ADHOC could significantly improve the outcome as compared with five previously reported density-dependent methods. We further applied ADHOC to investigate the hierarchical and overlapping modular structure in the yeast PPI network. Our method could effectively detect both protein modules and the overlaps between them, and thus greatly promote the precise prediction of protein functions. Moreover, by further assaying the intermodule layer of the yeast PPI network, we classified hubs into two types, module hubs and inter-module hubs. Each type presents distinct characteristics both in network topology and biological functions, which could conduce to the better understanding of relationship between network architecture and biological implications. CONCLUSIONS: Our proposed algorithm based on the novel subgraph density measurement makes it possible to more precisely detect hierarchical and overlapping modular structures in protein interaction networks. In addition, our method also shows a strong robustness against the noise in network, which is quite critical for analyzing such a high noise network. BioMed Central 2010-12-02 /pmc/articles/PMC3005933/ /pubmed/21143800 http://dx.doi.org/10.1186/1471-2164-11-S4-S17 Text en Copyright ©2010 Liu 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. |
spellingShingle | Proceedings Liu, Changning Li, Jing Zhao, Yi Exploring hierarchical and overlapping modular structure in the yeast protein interaction network |
title | Exploring hierarchical and overlapping modular structure in the yeast protein interaction network |
title_full | Exploring hierarchical and overlapping modular structure in the yeast protein interaction network |
title_fullStr | Exploring hierarchical and overlapping modular structure in the yeast protein interaction network |
title_full_unstemmed | Exploring hierarchical and overlapping modular structure in the yeast protein interaction network |
title_short | Exploring hierarchical and overlapping modular structure in the yeast protein interaction network |
title_sort | exploring hierarchical and overlapping modular structure in the yeast protein interaction network |
topic | Proceedings |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3005933/ https://www.ncbi.nlm.nih.gov/pubmed/21143800 http://dx.doi.org/10.1186/1471-2164-11-S4-S17 |
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