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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
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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. |
format | Online Article Text |
id | pubmed-5102470 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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