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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2017
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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. |
format | Online Article Text |
id | pubmed-5618559 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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