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Protein Complexes Prediction Method Based on Core—Attachment Structure and Functional Annotations

Recent advances in high-throughput laboratory techniques captured large-scale protein–protein interaction (PPI) data, making it possible to create a detailed map of protein interaction networks, and thus enable us to detect protein complexes from these PPI networks. However, most of the current stat...

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Detalles Bibliográficos
Autores principales: Li, Bo, Liao, Bo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5618559/
https://www.ncbi.nlm.nih.gov/pubmed/28878201
http://dx.doi.org/10.3390/ijms18091910
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author Li, Bo
Liao, Bo
author_facet Li, Bo
Liao, Bo
author_sort Li, Bo
collection PubMed
description Recent advances in high-throughput laboratory techniques captured large-scale protein–protein interaction (PPI) data, making it possible to create a detailed map of protein interaction networks, and thus enable us to detect protein complexes from these PPI networks. However, most of the current state-of-the-art studies still have some problems, for instance, incapability of identifying overlapping clusters, without considering the inherent organization within protein complexes, and overlooking the biological meaning of complexes. Therefore, we present a novel overlapping protein complexes prediction method based on core–attachment structure and function annotations (CFOCM), which performs in two stages: first, it detects protein complex cores with the maximum value of our defined cluster closeness function, in which the proteins are also closely related to at least one common function. Then it appends attach proteins into these detected cores to form the returned complexes. For performance evaluation, CFOCM and six classical methods have been used to identify protein complexes on three different yeast PPI networks, and three sets of real complexes including the Munich Information Center for Protein Sequences (MIPS), the Saccharomyces Genome Database (SGD) and the Catalogues of Yeast protein Complexes (CYC2008) are selected as benchmark sets, and the results show that CFOCM is indeed effective and robust for achieving the highest F-measure values in all tests.
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spelling pubmed-56185592017-09-30 Protein Complexes Prediction Method Based on Core—Attachment Structure and Functional Annotations Li, Bo Liao, Bo Int J Mol Sci Article Recent advances in high-throughput laboratory techniques captured large-scale protein–protein interaction (PPI) data, making it possible to create a detailed map of protein interaction networks, and thus enable us to detect protein complexes from these PPI networks. However, most of the current state-of-the-art studies still have some problems, for instance, incapability of identifying overlapping clusters, without considering the inherent organization within protein complexes, and overlooking the biological meaning of complexes. Therefore, we present a novel overlapping protein complexes prediction method based on core–attachment structure and function annotations (CFOCM), which performs in two stages: first, it detects protein complex cores with the maximum value of our defined cluster closeness function, in which the proteins are also closely related to at least one common function. Then it appends attach proteins into these detected cores to form the returned complexes. For performance evaluation, CFOCM and six classical methods have been used to identify protein complexes on three different yeast PPI networks, and three sets of real complexes including the Munich Information Center for Protein Sequences (MIPS), the Saccharomyces Genome Database (SGD) and the Catalogues of Yeast protein Complexes (CYC2008) are selected as benchmark sets, and the results show that CFOCM is indeed effective and robust for achieving the highest F-measure values in all tests. MDPI 2017-09-06 /pmc/articles/PMC5618559/ /pubmed/28878201 http://dx.doi.org/10.3390/ijms18091910 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Li, Bo
Liao, Bo
Protein Complexes Prediction Method Based on Core—Attachment Structure and Functional Annotations
title Protein Complexes Prediction Method Based on Core—Attachment Structure and Functional Annotations
title_full Protein Complexes Prediction Method Based on Core—Attachment Structure and Functional Annotations
title_fullStr Protein Complexes Prediction Method Based on Core—Attachment Structure and Functional Annotations
title_full_unstemmed Protein Complexes Prediction Method Based on Core—Attachment Structure and Functional Annotations
title_short Protein Complexes Prediction Method Based on Core—Attachment Structure and Functional Annotations
title_sort protein complexes prediction method based on core—attachment structure and functional annotations
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5618559/
https://www.ncbi.nlm.nih.gov/pubmed/28878201
http://dx.doi.org/10.3390/ijms18091910
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