ABC and IFC: Modules Detection Method for PPI Network
Many clustering algorithms are unable to solve the clustering problem of protein-protein interaction (PPI) networks effectively. A novel clustering model which combines the optimization mechanism of artificial bee colony (ABC) with the fuzzy membership matrix is proposed in this paper. The proposed...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4060787/ https://www.ncbi.nlm.nih.gov/pubmed/24991575 http://dx.doi.org/10.1155/2014/968173 |
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author | Lei, Xiujuan Wu, Fang-Xiang Tian, Jianfang Zhao, Jie |
author_facet | Lei, Xiujuan Wu, Fang-Xiang Tian, Jianfang Zhao, Jie |
author_sort | Lei, Xiujuan |
collection | PubMed |
description | Many clustering algorithms are unable to solve the clustering problem of protein-protein interaction (PPI) networks effectively. A novel clustering model which combines the optimization mechanism of artificial bee colony (ABC) with the fuzzy membership matrix is proposed in this paper. The proposed ABC-IFC clustering model contains two parts: searching for the optimum cluster centers using ABC mechanism and forming clusters using intuitionistic fuzzy clustering (IFC) method. Firstly, the cluster centers are set randomly and the initial clustering results are obtained by using fuzzy membership matrix. Then the cluster centers are updated through different functions of bees in ABC algorithm; then the clustering result is obtained through IFC method based on the new optimized cluster center. To illustrate its performance, the ABC-IFC method is compared with the traditional fuzzy C-means clustering and IFC method. The experimental results on MIPS dataset show that the proposed ABC-IFC method not only gets improved in terms of several commonly used evaluation criteria such as precision, recall, and P value, but also obtains a better clustering result. |
format | Online Article Text |
id | pubmed-4060787 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-40607872014-07-02 ABC and IFC: Modules Detection Method for PPI Network Lei, Xiujuan Wu, Fang-Xiang Tian, Jianfang Zhao, Jie Biomed Res Int Research Article Many clustering algorithms are unable to solve the clustering problem of protein-protein interaction (PPI) networks effectively. A novel clustering model which combines the optimization mechanism of artificial bee colony (ABC) with the fuzzy membership matrix is proposed in this paper. The proposed ABC-IFC clustering model contains two parts: searching for the optimum cluster centers using ABC mechanism and forming clusters using intuitionistic fuzzy clustering (IFC) method. Firstly, the cluster centers are set randomly and the initial clustering results are obtained by using fuzzy membership matrix. Then the cluster centers are updated through different functions of bees in ABC algorithm; then the clustering result is obtained through IFC method based on the new optimized cluster center. To illustrate its performance, the ABC-IFC method is compared with the traditional fuzzy C-means clustering and IFC method. The experimental results on MIPS dataset show that the proposed ABC-IFC method not only gets improved in terms of several commonly used evaluation criteria such as precision, recall, and P value, but also obtains a better clustering result. Hindawi Publishing Corporation 2014 2014-06-02 /pmc/articles/PMC4060787/ /pubmed/24991575 http://dx.doi.org/10.1155/2014/968173 Text en Copyright © 2014 Xiujuan Lei et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Lei, Xiujuan Wu, Fang-Xiang Tian, Jianfang Zhao, Jie ABC and IFC: Modules Detection Method for PPI Network |
title | ABC and IFC: Modules Detection Method for PPI Network |
title_full | ABC and IFC: Modules Detection Method for PPI Network |
title_fullStr | ABC and IFC: Modules Detection Method for PPI Network |
title_full_unstemmed | ABC and IFC: Modules Detection Method for PPI Network |
title_short | ABC and IFC: Modules Detection Method for PPI Network |
title_sort | abc and ifc: modules detection method for ppi network |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4060787/ https://www.ncbi.nlm.nih.gov/pubmed/24991575 http://dx.doi.org/10.1155/2014/968173 |
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