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Identifying Protein Complexes With Clear Module Structure Using Pairwise Constraints in Protein Interaction Networks
The protein-protein interaction (PPI) networks can be regarded as powerful platforms to elucidate the principle and mechanism of cellular organization. Uncovering protein complexes from PPI networks will lead to a better understanding of the science of biological function in cellular systems. In rec...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8430217/ https://www.ncbi.nlm.nih.gov/pubmed/34512712 http://dx.doi.org/10.3389/fgene.2021.664786 |
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author | Liu, Guangming Liu, Bo Li, Aimin Wang, Xiaofan Yu, Jian Zhou, Xuezhong |
author_facet | Liu, Guangming Liu, Bo Li, Aimin Wang, Xiaofan Yu, Jian Zhou, Xuezhong |
author_sort | Liu, Guangming |
collection | PubMed |
description | The protein-protein interaction (PPI) networks can be regarded as powerful platforms to elucidate the principle and mechanism of cellular organization. Uncovering protein complexes from PPI networks will lead to a better understanding of the science of biological function in cellular systems. In recent decades, numerous computational algorithms have been developed to identify protein complexes. However, the majority of them primarily concern the topological structure of PPI networks and lack of the consideration for the native organized structure among protein complexes. The PPI networks generated by high-throughput technology include a fraction of false protein interactions which make it difficult to identify protein complexes efficiently. To tackle these challenges, we propose a novel semi-supervised protein complex detection model based on non-negative matrix tri-factorization, which not only considers topological structure of a PPI network but also makes full use of available high quality known protein pairs with must-link constraints. We propose non-overlapping (NSSNMTF) and overlapping (OSSNMTF) protein complex detection algorithms to identify the significant protein complexes with clear module structures from PPI networks. In addition, the proposed two protein complex detection algorithms outperform a diverse range of state-of-the-art protein complex identification algorithms on both synthetic networks and human related PPI networks. |
format | Online Article Text |
id | pubmed-8430217 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-84302172021-09-11 Identifying Protein Complexes With Clear Module Structure Using Pairwise Constraints in Protein Interaction Networks Liu, Guangming Liu, Bo Li, Aimin Wang, Xiaofan Yu, Jian Zhou, Xuezhong Front Genet Genetics The protein-protein interaction (PPI) networks can be regarded as powerful platforms to elucidate the principle and mechanism of cellular organization. Uncovering protein complexes from PPI networks will lead to a better understanding of the science of biological function in cellular systems. In recent decades, numerous computational algorithms have been developed to identify protein complexes. However, the majority of them primarily concern the topological structure of PPI networks and lack of the consideration for the native organized structure among protein complexes. The PPI networks generated by high-throughput technology include a fraction of false protein interactions which make it difficult to identify protein complexes efficiently. To tackle these challenges, we propose a novel semi-supervised protein complex detection model based on non-negative matrix tri-factorization, which not only considers topological structure of a PPI network but also makes full use of available high quality known protein pairs with must-link constraints. We propose non-overlapping (NSSNMTF) and overlapping (OSSNMTF) protein complex detection algorithms to identify the significant protein complexes with clear module structures from PPI networks. In addition, the proposed two protein complex detection algorithms outperform a diverse range of state-of-the-art protein complex identification algorithms on both synthetic networks and human related PPI networks. Frontiers Media S.A. 2021-08-27 /pmc/articles/PMC8430217/ /pubmed/34512712 http://dx.doi.org/10.3389/fgene.2021.664786 Text en Copyright © 2021 Liu, Liu, Li, Wang, Yu and Zhou. 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 Liu, Guangming Liu, Bo Li, Aimin Wang, Xiaofan Yu, Jian Zhou, Xuezhong Identifying Protein Complexes With Clear Module Structure Using Pairwise Constraints in Protein Interaction Networks |
title | Identifying Protein Complexes With Clear Module Structure Using Pairwise Constraints in Protein Interaction Networks |
title_full | Identifying Protein Complexes With Clear Module Structure Using Pairwise Constraints in Protein Interaction Networks |
title_fullStr | Identifying Protein Complexes With Clear Module Structure Using Pairwise Constraints in Protein Interaction Networks |
title_full_unstemmed | Identifying Protein Complexes With Clear Module Structure Using Pairwise Constraints in Protein Interaction Networks |
title_short | Identifying Protein Complexes With Clear Module Structure Using Pairwise Constraints in Protein Interaction Networks |
title_sort | identifying protein complexes with clear module structure using pairwise constraints in protein interaction networks |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8430217/ https://www.ncbi.nlm.nih.gov/pubmed/34512712 http://dx.doi.org/10.3389/fgene.2021.664786 |
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