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Dynamic identifying protein functional modules based on adaptive density modularity in protein-protein interaction networks
BACKGROUND: The identification of protein functional modules would be a great aid in furthering our knowledge of the principles of cellular organization. Most existing algorithms for identifying protein functional modules have a common defect -- once a protein node is assigned to a functional module...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4705501/ https://www.ncbi.nlm.nih.gov/pubmed/26330105 http://dx.doi.org/10.1186/1471-2105-16-S12-S5 |
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author | Shen, Xianjun Yi, Li Yi, Yang Yang, Jincai He, Tingting Hu, Xiaohua |
author_facet | Shen, Xianjun Yi, Li Yi, Yang Yang, Jincai He, Tingting Hu, Xiaohua |
author_sort | Shen, Xianjun |
collection | PubMed |
description | BACKGROUND: The identification of protein functional modules would be a great aid in furthering our knowledge of the principles of cellular organization. Most existing algorithms for identifying protein functional modules have a common defect -- once a protein node is assigned to a functional module, there is no chance to move the protein to the other functional modules during the follow-up processes, which lead the erroneous partitioning occurred at previous step to accumulate till to the end. RESULTS: In this paper, we design a new algorithm ADM (Adaptive Density Modularity) to detect protein functional modules based on adaptive density modularity. In ADM algorithm, according to the comparison between external closely associated degree and internal closely associated degree, the partitioning of a protein-protein interaction network into functional modules always evolves quickly to increase the density modularity of the network. The integration of density modularity into the new algorithm not only overcomes the drawback mentioned above, but also contributes to identifying protein functional modules more effectively. CONCLUSIONS: The experimental result reveals that the performance of ADM algorithm is superior to many state-of-the-art protein functional modules detection techniques in aspect of the accuracy of prediction. Moreover, the identified protein functional modules are statistically significant in terms of "Biological Process" annotated in Gene Ontology, which provides substantial support for revealing the principles of cellular organization. |
format | Online Article Text |
id | pubmed-4705501 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-47055012016-01-20 Dynamic identifying protein functional modules based on adaptive density modularity in protein-protein interaction networks Shen, Xianjun Yi, Li Yi, Yang Yang, Jincai He, Tingting Hu, Xiaohua BMC Bioinformatics Research BACKGROUND: The identification of protein functional modules would be a great aid in furthering our knowledge of the principles of cellular organization. Most existing algorithms for identifying protein functional modules have a common defect -- once a protein node is assigned to a functional module, there is no chance to move the protein to the other functional modules during the follow-up processes, which lead the erroneous partitioning occurred at previous step to accumulate till to the end. RESULTS: In this paper, we design a new algorithm ADM (Adaptive Density Modularity) to detect protein functional modules based on adaptive density modularity. In ADM algorithm, according to the comparison between external closely associated degree and internal closely associated degree, the partitioning of a protein-protein interaction network into functional modules always evolves quickly to increase the density modularity of the network. The integration of density modularity into the new algorithm not only overcomes the drawback mentioned above, but also contributes to identifying protein functional modules more effectively. CONCLUSIONS: The experimental result reveals that the performance of ADM algorithm is superior to many state-of-the-art protein functional modules detection techniques in aspect of the accuracy of prediction. Moreover, the identified protein functional modules are statistically significant in terms of "Biological Process" annotated in Gene Ontology, which provides substantial support for revealing the principles of cellular organization. BioMed Central 2015-08-25 /pmc/articles/PMC4705501/ /pubmed/26330105 http://dx.doi.org/10.1186/1471-2105-16-S12-S5 Text en Copyright © 2015 Shen et al.; http://creativecommons.org/licenses/by/4.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), 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 Shen, Xianjun Yi, Li Yi, Yang Yang, Jincai He, Tingting Hu, Xiaohua Dynamic identifying protein functional modules based on adaptive density modularity in protein-protein interaction networks |
title | Dynamic identifying protein functional modules based on adaptive density modularity in protein-protein interaction networks |
title_full | Dynamic identifying protein functional modules based on adaptive density modularity in protein-protein interaction networks |
title_fullStr | Dynamic identifying protein functional modules based on adaptive density modularity in protein-protein interaction networks |
title_full_unstemmed | Dynamic identifying protein functional modules based on adaptive density modularity in protein-protein interaction networks |
title_short | Dynamic identifying protein functional modules based on adaptive density modularity in protein-protein interaction networks |
title_sort | dynamic identifying protein functional modules based on adaptive density modularity in protein-protein interaction networks |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4705501/ https://www.ncbi.nlm.nih.gov/pubmed/26330105 http://dx.doi.org/10.1186/1471-2105-16-S12-S5 |
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