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Gene regulatory network modeling via global optimization of high-order dynamic Bayesian network

BACKGROUND: Dynamic Bayesian network (DBN) is among the mainstream approaches for modeling various biological networks, including the gene regulatory network (GRN). Most current methods for learning DBN employ either local search such as hill-climbing, or a meta stochastic global optimization framew...

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Detalles Bibliográficos
Autores principales: Xuan, Nguyen, Chetty, Madhu, Coppel, Ross, Wangikar, Pramod P
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3433362/
https://www.ncbi.nlm.nih.gov/pubmed/22694481
http://dx.doi.org/10.1186/1471-2105-13-131
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author Xuan, Nguyen
Chetty, Madhu
Coppel, Ross
Wangikar, Pramod P
author_facet Xuan, Nguyen
Chetty, Madhu
Coppel, Ross
Wangikar, Pramod P
author_sort Xuan, Nguyen
collection PubMed
description BACKGROUND: Dynamic Bayesian network (DBN) is among the mainstream approaches for modeling various biological networks, including the gene regulatory network (GRN). Most current methods for learning DBN employ either local search such as hill-climbing, or a meta stochastic global optimization framework such as genetic algorithm or simulated annealing, which are only able to locate sub-optimal solutions. Further, current DBN applications have essentially been limited to small sized networks. RESULTS: To overcome the above difficulties, we introduce here a deterministic global optimization based DBN approach for reverse engineering genetic networks from time course gene expression data. For such DBN models that consist only of inter time slice arcs, we show that there exists a polynomial time algorithm for learning the globally optimal network structure. The proposed approach, named GlobalMIT(+), employs the recently proposed information theoretic scoring metric named mutual information test (MIT). GlobalMIT(+) is able to learn high-order time delayed genetic interactions, which are common to most biological systems. Evaluation of the approach using both synthetic and real data sets, including a 733 cyanobacterial gene expression data set, shows significantly improved performance over other techniques. CONCLUSIONS: Our studies demonstrate that deterministic global optimization approaches can infer large scale genetic networks.
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spelling pubmed-34333622012-09-06 Gene regulatory network modeling via global optimization of high-order dynamic Bayesian network Xuan, Nguyen Chetty, Madhu Coppel, Ross Wangikar, Pramod P BMC Bioinformatics Research Article BACKGROUND: Dynamic Bayesian network (DBN) is among the mainstream approaches for modeling various biological networks, including the gene regulatory network (GRN). Most current methods for learning DBN employ either local search such as hill-climbing, or a meta stochastic global optimization framework such as genetic algorithm or simulated annealing, which are only able to locate sub-optimal solutions. Further, current DBN applications have essentially been limited to small sized networks. RESULTS: To overcome the above difficulties, we introduce here a deterministic global optimization based DBN approach for reverse engineering genetic networks from time course gene expression data. For such DBN models that consist only of inter time slice arcs, we show that there exists a polynomial time algorithm for learning the globally optimal network structure. The proposed approach, named GlobalMIT(+), employs the recently proposed information theoretic scoring metric named mutual information test (MIT). GlobalMIT(+) is able to learn high-order time delayed genetic interactions, which are common to most biological systems. Evaluation of the approach using both synthetic and real data sets, including a 733 cyanobacterial gene expression data set, shows significantly improved performance over other techniques. CONCLUSIONS: Our studies demonstrate that deterministic global optimization approaches can infer large scale genetic networks. BioMed Central 2012-06-13 /pmc/articles/PMC3433362/ /pubmed/22694481 http://dx.doi.org/10.1186/1471-2105-13-131 Text en Copyright ©2012 Vinh et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Xuan, Nguyen
Chetty, Madhu
Coppel, Ross
Wangikar, Pramod P
Gene regulatory network modeling via global optimization of high-order dynamic Bayesian network
title Gene regulatory network modeling via global optimization of high-order dynamic Bayesian network
title_full Gene regulatory network modeling via global optimization of high-order dynamic Bayesian network
title_fullStr Gene regulatory network modeling via global optimization of high-order dynamic Bayesian network
title_full_unstemmed Gene regulatory network modeling via global optimization of high-order dynamic Bayesian network
title_short Gene regulatory network modeling via global optimization of high-order dynamic Bayesian network
title_sort gene regulatory network modeling via global optimization of high-order dynamic bayesian network
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3433362/
https://www.ncbi.nlm.nih.gov/pubmed/22694481
http://dx.doi.org/10.1186/1471-2105-13-131
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