Improved inference of gene regulatory networks through integrated Bayesian clustering and dynamic modeling of time-course expression data.

Inferring gene regulatory networks from expression data is difficult, but it is common and often useful. Most network problems are under-determined--there are more parameters than data points--and therefore data or parameter set reduction is often necessary. Correlation between variables in the mode...

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Autor principal:	Brian Godsey
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9429b2263584432a9127019557ae603b
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id	oai:doaj.org-article:9429b2263584432a9127019557ae603b
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spelling	oai:doaj.org-article:9429b2263584432a9127019557ae603b2021-11-18T09:03:23ZImproved inference of gene regulatory networks through integrated Bayesian clustering and dynamic modeling of time-course expression data.1932-620310.1371/journal.pone.0068358https://doaj.org/article/9429b2263584432a9127019557ae603b2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23935862/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Inferring gene regulatory networks from expression data is difficult, but it is common and often useful. Most network problems are under-determined--there are more parameters than data points--and therefore data or parameter set reduction is often necessary. Correlation between variables in the model also contributes to confound network coefficient inference. In this paper, we present an algorithm that uses integrated, probabilistic clustering to ease the problems of under-determination and correlated variables within a fully Bayesian framework. Specifically, ours is a dynamic Bayesian network with integrated Gaussian mixture clustering, which we fit using variational Bayesian methods. We show, using public, simulated time-course data sets from the DREAM4 Challenge, that our algorithm outperforms non-clustering methods in many cases (7 out of 25) with fewer samples, rarely underperforming (1 out of 25), and often selects a non-clustering model if it better describes the data. Source code (GNU Octave) for BAyesian Clustering Over Networks (BACON) and sample data are available at: http://code.google.com/p/bacon-for-genetic-networks.Brian GodseyPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 7, p e68358 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Brian Godsey Improved inference of gene regulatory networks through integrated Bayesian clustering and dynamic modeling of time-course expression data.
description	Inferring gene regulatory networks from expression data is difficult, but it is common and often useful. Most network problems are under-determined--there are more parameters than data points--and therefore data or parameter set reduction is often necessary. Correlation between variables in the model also contributes to confound network coefficient inference. In this paper, we present an algorithm that uses integrated, probabilistic clustering to ease the problems of under-determination and correlated variables within a fully Bayesian framework. Specifically, ours is a dynamic Bayesian network with integrated Gaussian mixture clustering, which we fit using variational Bayesian methods. We show, using public, simulated time-course data sets from the DREAM4 Challenge, that our algorithm outperforms non-clustering methods in many cases (7 out of 25) with fewer samples, rarely underperforming (1 out of 25), and often selects a non-clustering model if it better describes the data. Source code (GNU Octave) for BAyesian Clustering Over Networks (BACON) and sample data are available at: http://code.google.com/p/bacon-for-genetic-networks.
format	article
author	Brian Godsey
author_facet	Brian Godsey
author_sort	Brian Godsey
title	Improved inference of gene regulatory networks through integrated Bayesian clustering and dynamic modeling of time-course expression data.
title_short	Improved inference of gene regulatory networks through integrated Bayesian clustering and dynamic modeling of time-course expression data.
title_full	Improved inference of gene regulatory networks through integrated Bayesian clustering and dynamic modeling of time-course expression data.
title_fullStr	Improved inference of gene regulatory networks through integrated Bayesian clustering and dynamic modeling of time-course expression data.
title_full_unstemmed	Improved inference of gene regulatory networks through integrated Bayesian clustering and dynamic modeling of time-course expression data.
title_sort	improved inference of gene regulatory networks through integrated bayesian clustering and dynamic modeling of time-course expression data.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/9429b2263584432a9127019557ae603b
work_keys_str_mv	AT briangodsey improvedinferenceofgeneregulatorynetworksthroughintegratedbayesianclusteringanddynamicmodelingoftimecourseexpressiondata
_version_	1718420978014355456

Improved inference of gene regulatory networks through integrated Bayesian clustering and dynamic modeling of time-course expression data.

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