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Efficient and accurate identification of protein complexes from protein-protein interaction networks based on the clustering coefficient
Identification of protein complexes from protein-protein interaction (PPI) networks is a key problem in PPI mining, solved by parameter-dependent approaches that suffer from small recall rates. Here we introduce GCC-v, a family of efficient, parameter-free algorithms to accurately predict protein co...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Research Network of Computational and Structural Biotechnology
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8479235/ https://www.ncbi.nlm.nih.gov/pubmed/34630943 http://dx.doi.org/10.1016/j.csbj.2021.09.014 |
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author | Omranian, Sara Angeleska, Angela Nikoloski, Zoran |
author_facet | Omranian, Sara Angeleska, Angela Nikoloski, Zoran |
author_sort | Omranian, Sara |
collection | PubMed |
description | Identification of protein complexes from protein-protein interaction (PPI) networks is a key problem in PPI mining, solved by parameter-dependent approaches that suffer from small recall rates. Here we introduce GCC-v, a family of efficient, parameter-free algorithms to accurately predict protein complexes using the (weighted) clustering coefficient of proteins in PPI networks. Through comparative analyses with gold standards and PPI networks from Escherichia coli, Saccharomyces cerevisiae, and Homo sapiens, we demonstrate that GCC-v outperforms twelve state-of-the-art approaches for identification of protein complexes with respect to twelve performance measures in at least 85.71% of scenarios. We also show that GCC-v results in the exact recovery of ∼35% of protein complexes in a pan-plant PPI network and discover 144 new protein complexes in Arabidopsis thaliana, with high support from GO semantic similarity. Our results indicate that findings from GCC-v are robust to network perturbations, which has direct implications to assess the impact of the PPI network quality on the predicted protein complexes. |
format | Online Article Text |
id | pubmed-8479235 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Research Network of Computational and Structural Biotechnology |
record_format | MEDLINE/PubMed |
spelling | pubmed-84792352021-10-07 Efficient and accurate identification of protein complexes from protein-protein interaction networks based on the clustering coefficient Omranian, Sara Angeleska, Angela Nikoloski, Zoran Comput Struct Biotechnol J Research Article Identification of protein complexes from protein-protein interaction (PPI) networks is a key problem in PPI mining, solved by parameter-dependent approaches that suffer from small recall rates. Here we introduce GCC-v, a family of efficient, parameter-free algorithms to accurately predict protein complexes using the (weighted) clustering coefficient of proteins in PPI networks. Through comparative analyses with gold standards and PPI networks from Escherichia coli, Saccharomyces cerevisiae, and Homo sapiens, we demonstrate that GCC-v outperforms twelve state-of-the-art approaches for identification of protein complexes with respect to twelve performance measures in at least 85.71% of scenarios. We also show that GCC-v results in the exact recovery of ∼35% of protein complexes in a pan-plant PPI network and discover 144 new protein complexes in Arabidopsis thaliana, with high support from GO semantic similarity. Our results indicate that findings from GCC-v are robust to network perturbations, which has direct implications to assess the impact of the PPI network quality on the predicted protein complexes. Research Network of Computational and Structural Biotechnology 2021-09-20 /pmc/articles/PMC8479235/ /pubmed/34630943 http://dx.doi.org/10.1016/j.csbj.2021.09.014 Text en © 2021 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Research Article Omranian, Sara Angeleska, Angela Nikoloski, Zoran Efficient and accurate identification of protein complexes from protein-protein interaction networks based on the clustering coefficient |
title | Efficient and accurate identification of protein complexes from protein-protein interaction networks based on the clustering coefficient |
title_full | Efficient and accurate identification of protein complexes from protein-protein interaction networks based on the clustering coefficient |
title_fullStr | Efficient and accurate identification of protein complexes from protein-protein interaction networks based on the clustering coefficient |
title_full_unstemmed | Efficient and accurate identification of protein complexes from protein-protein interaction networks based on the clustering coefficient |
title_short | Efficient and accurate identification of protein complexes from protein-protein interaction networks based on the clustering coefficient |
title_sort | efficient and accurate identification of protein complexes from protein-protein interaction networks based on the clustering coefficient |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8479235/ https://www.ncbi.nlm.nih.gov/pubmed/34630943 http://dx.doi.org/10.1016/j.csbj.2021.09.014 |
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