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An ensemble framework for identifying essential proteins

BACKGROUND: Many centrality measures have been proposed to mine and characterize the correlations between network topological properties and protein essentiality. However, most of them show limited prediction accuracy, and the number of common predicted essential proteins by different methods is ver...

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Autores principales: Zhang, Xue, Xiao, Wangxin, Acencio, Marcio Luis, Lemke, Ney, Wang, Xujing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4997703/
https://www.ncbi.nlm.nih.gov/pubmed/27557880
http://dx.doi.org/10.1186/s12859-016-1166-7
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author Zhang, Xue
Xiao, Wangxin
Acencio, Marcio Luis
Lemke, Ney
Wang, Xujing
author_facet Zhang, Xue
Xiao, Wangxin
Acencio, Marcio Luis
Lemke, Ney
Wang, Xujing
author_sort Zhang, Xue
collection PubMed
description BACKGROUND: Many centrality measures have been proposed to mine and characterize the correlations between network topological properties and protein essentiality. However, most of them show limited prediction accuracy, and the number of common predicted essential proteins by different methods is very small. RESULTS: In this paper, an ensemble framework is proposed which integrates gene expression data and protein-protein interaction networks (PINs). It aims to improve the prediction accuracy of basic centrality measures. The idea behind this ensemble framework is that different protein-protein interactions (PPIs) may show different contributions to protein essentiality. Five standard centrality measures (degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, and subgraph centrality) are integrated into the ensemble framework respectively. We evaluated the performance of the proposed ensemble framework using yeast PINs and gene expression data. The results show that it can considerably improve the prediction accuracy of the five centrality measures individually. It can also remarkably increase the number of common predicted essential proteins among those predicted by each centrality measure individually and enable each centrality measure to find more low-degree essential proteins. CONCLUSIONS: This paper demonstrates that it is valuable to differentiate the contributions of different PPIs for identifying essential proteins based on network topological characteristics. The proposed ensemble framework is a successful paradigm to this end. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-1166-7) contains supplementary material, which is available to authorized users.
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spelling pubmed-49977032016-08-31 An ensemble framework for identifying essential proteins Zhang, Xue Xiao, Wangxin Acencio, Marcio Luis Lemke, Ney Wang, Xujing BMC Bioinformatics Methodology Article BACKGROUND: Many centrality measures have been proposed to mine and characterize the correlations between network topological properties and protein essentiality. However, most of them show limited prediction accuracy, and the number of common predicted essential proteins by different methods is very small. RESULTS: In this paper, an ensemble framework is proposed which integrates gene expression data and protein-protein interaction networks (PINs). It aims to improve the prediction accuracy of basic centrality measures. The idea behind this ensemble framework is that different protein-protein interactions (PPIs) may show different contributions to protein essentiality. Five standard centrality measures (degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, and subgraph centrality) are integrated into the ensemble framework respectively. We evaluated the performance of the proposed ensemble framework using yeast PINs and gene expression data. The results show that it can considerably improve the prediction accuracy of the five centrality measures individually. It can also remarkably increase the number of common predicted essential proteins among those predicted by each centrality measure individually and enable each centrality measure to find more low-degree essential proteins. CONCLUSIONS: This paper demonstrates that it is valuable to differentiate the contributions of different PPIs for identifying essential proteins based on network topological characteristics. The proposed ensemble framework is a successful paradigm to this end. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-1166-7) contains supplementary material, which is available to authorized users. BioMed Central 2016-08-25 /pmc/articles/PMC4997703/ /pubmed/27557880 http://dx.doi.org/10.1186/s12859-016-1166-7 Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Methodology Article
Zhang, Xue
Xiao, Wangxin
Acencio, Marcio Luis
Lemke, Ney
Wang, Xujing
An ensemble framework for identifying essential proteins
title An ensemble framework for identifying essential proteins
title_full An ensemble framework for identifying essential proteins
title_fullStr An ensemble framework for identifying essential proteins
title_full_unstemmed An ensemble framework for identifying essential proteins
title_short An ensemble framework for identifying essential proteins
title_sort ensemble framework for identifying essential proteins
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4997703/
https://www.ncbi.nlm.nih.gov/pubmed/27557880
http://dx.doi.org/10.1186/s12859-016-1166-7
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