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An Improved Memetic Algorithm for Detecting Protein Complexes in Protein Interaction Networks
Identifying the protein complexes in protein-protein interaction (PPI) networks is essential for understanding cellular organization and biological processes. To address the high false positive/negative rates of PPI networks and detect protein complexes with multiple topological structures, we devel...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8712950/ https://www.ncbi.nlm.nih.gov/pubmed/34970305 http://dx.doi.org/10.3389/fgene.2021.794354 |
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author | Wang, Rongquan Ma, Huimin Wang, Caixia |
author_facet | Wang, Rongquan Ma, Huimin Wang, Caixia |
author_sort | Wang, Rongquan |
collection | PubMed |
description | Identifying the protein complexes in protein-protein interaction (PPI) networks is essential for understanding cellular organization and biological processes. To address the high false positive/negative rates of PPI networks and detect protein complexes with multiple topological structures, we developed a novel improved memetic algorithm (IMA). IMA first combines the topological and biological properties to obtain a weighted PPI network with reduced noise. Next, it integrates various clustering results to construct the initial populations. Furthermore, a fitness function is designed based on the five topological properties of the protein complexes. Finally, we describe the rest of our IMA method, which primarily consists of four steps: selection operator, recombination operator, local optimization strategy, and updating the population operator. In particular, IMA is a combination of genetic algorithm and a local optimization strategy, which has a strong global search ability, and searches for local optimal solutions effectively. The experimental results demonstrate that IMA performs much better than the base methods and existing state-of-the-art techniques. The source code and datasets of the IMA can be found at https://github.com/RongquanWang/IMA. |
format | Online Article Text |
id | pubmed-8712950 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-87129502021-12-29 An Improved Memetic Algorithm for Detecting Protein Complexes in Protein Interaction Networks Wang, Rongquan Ma, Huimin Wang, Caixia Front Genet Genetics Identifying the protein complexes in protein-protein interaction (PPI) networks is essential for understanding cellular organization and biological processes. To address the high false positive/negative rates of PPI networks and detect protein complexes with multiple topological structures, we developed a novel improved memetic algorithm (IMA). IMA first combines the topological and biological properties to obtain a weighted PPI network with reduced noise. Next, it integrates various clustering results to construct the initial populations. Furthermore, a fitness function is designed based on the five topological properties of the protein complexes. Finally, we describe the rest of our IMA method, which primarily consists of four steps: selection operator, recombination operator, local optimization strategy, and updating the population operator. In particular, IMA is a combination of genetic algorithm and a local optimization strategy, which has a strong global search ability, and searches for local optimal solutions effectively. The experimental results demonstrate that IMA performs much better than the base methods and existing state-of-the-art techniques. The source code and datasets of the IMA can be found at https://github.com/RongquanWang/IMA. Frontiers Media S.A. 2021-12-14 /pmc/articles/PMC8712950/ /pubmed/34970305 http://dx.doi.org/10.3389/fgene.2021.794354 Text en Copyright © 2021 Wang, Ma and Wang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Wang, Rongquan Ma, Huimin Wang, Caixia An Improved Memetic Algorithm for Detecting Protein Complexes in Protein Interaction Networks |
title | An Improved Memetic Algorithm for Detecting Protein Complexes in Protein Interaction Networks |
title_full | An Improved Memetic Algorithm for Detecting Protein Complexes in Protein Interaction Networks |
title_fullStr | An Improved Memetic Algorithm for Detecting Protein Complexes in Protein Interaction Networks |
title_full_unstemmed | An Improved Memetic Algorithm for Detecting Protein Complexes in Protein Interaction Networks |
title_short | An Improved Memetic Algorithm for Detecting Protein Complexes in Protein Interaction Networks |
title_sort | improved memetic algorithm for detecting protein complexes in protein interaction networks |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8712950/ https://www.ncbi.nlm.nih.gov/pubmed/34970305 http://dx.doi.org/10.3389/fgene.2021.794354 |
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