Gene Regulatory Network Inferences Using a Maximum-Relevance and Maximum-Significance Strategy

Recovering gene regulatory networks from expression data is a challenging problem in systems biology that provides valuable information on the regulatory mechanisms of cells. A number of algorithms based on computational models are currently used to recover network topology. However, most of these a...

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Detalles Bibliográficos
Autores principales: Liu, Wei, Zhu, Wen, Liao, Bo, Chen, Xiangtao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5102470/
https://www.ncbi.nlm.nih.gov/pubmed/27829000
http://dx.doi.org/10.1371/journal.pone.0166115
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author Liu, Wei
Zhu, Wen
Liao, Bo
Chen, Xiangtao
author_facet Liu, Wei
Zhu, Wen
Liao, Bo
Chen, Xiangtao
author_sort Liu, Wei
collection PubMed
description Recovering gene regulatory networks from expression data is a challenging problem in systems biology that provides valuable information on the regulatory mechanisms of cells. A number of algorithms based on computational models are currently used to recover network topology. However, most of these algorithms have limitations. For example, many models tend to be complicated because of the “large p, small n” problem. In this paper, we propose a novel regulatory network inference method called the maximum-relevance and maximum-significance network (MRMSn) method, which converts the problem of recovering networks into a problem of how to select the regulator genes for each gene. To solve the latter problem, we present an algorithm that is based on information theory and selects the regulator genes for a specific gene by maximizing the relevance and significance. A first-order incremental search algorithm is used to search for regulator genes. Eventually, a strict constraint is adopted to adjust all of the regulatory relationships according to the obtained regulator genes and thus obtain the complete network structure. We performed our method on five different datasets and compared our method to five state-of-the-art methods for network inference based on information theory. The results confirm the effectiveness of our method.
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spelling pubmed-51024702016-11-18 Gene Regulatory Network Inferences Using a Maximum-Relevance and Maximum-Significance Strategy Liu, Wei Zhu, Wen Liao, Bo Chen, Xiangtao PLoS One Research Article Recovering gene regulatory networks from expression data is a challenging problem in systems biology that provides valuable information on the regulatory mechanisms of cells. A number of algorithms based on computational models are currently used to recover network topology. However, most of these algorithms have limitations. For example, many models tend to be complicated because of the “large p, small n” problem. In this paper, we propose a novel regulatory network inference method called the maximum-relevance and maximum-significance network (MRMSn) method, which converts the problem of recovering networks into a problem of how to select the regulator genes for each gene. To solve the latter problem, we present an algorithm that is based on information theory and selects the regulator genes for a specific gene by maximizing the relevance and significance. A first-order incremental search algorithm is used to search for regulator genes. Eventually, a strict constraint is adopted to adjust all of the regulatory relationships according to the obtained regulator genes and thus obtain the complete network structure. We performed our method on five different datasets and compared our method to five state-of-the-art methods for network inference based on information theory. The results confirm the effectiveness of our method. Public Library of Science 2016-11-09 /pmc/articles/PMC5102470/ /pubmed/27829000 http://dx.doi.org/10.1371/journal.pone.0166115 Text en © 2016 Liu et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Liu, Wei
Zhu, Wen
Liao, Bo
Chen, Xiangtao
Gene Regulatory Network Inferences Using a Maximum-Relevance and Maximum-Significance Strategy
title Gene Regulatory Network Inferences Using a Maximum-Relevance and Maximum-Significance Strategy
title_full Gene Regulatory Network Inferences Using a Maximum-Relevance and Maximum-Significance Strategy
title_fullStr Gene Regulatory Network Inferences Using a Maximum-Relevance and Maximum-Significance Strategy
title_full_unstemmed Gene Regulatory Network Inferences Using a Maximum-Relevance and Maximum-Significance Strategy
title_short Gene Regulatory Network Inferences Using a Maximum-Relevance and Maximum-Significance Strategy
title_sort gene regulatory network inferences using a maximum-relevance and maximum-significance strategy
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5102470/
https://www.ncbi.nlm.nih.gov/pubmed/27829000
http://dx.doi.org/10.1371/journal.pone.0166115
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