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Inferring gene regulatory networks from gene expression data by a dynamic Bayesian network-based model

Enabled by recent advances in bioinformatics, the inference of gene regulatory networks (GRNs) from gene expression data has garnered much interest from researchers. This is due to the need of researchers to understand the dynamic behavior and uncover the vast information lay hidden within the netwo...

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Main Authors:	Chai, L. E., Mohammad, Mohd. Saberi, Deris, Safaai, Chong, C. K., Choon, Y. W., Ibrahim, Zuwairie, Omatu, S.
Format:	Book Section
Published:	Springer 2012
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://eprints.utm.my/id/eprint/33916/ http://dx.doi.org/10.1007/978-3-642-28765-7_45
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id	my.utm.33916
record_format	eprints
spelling	my.utm.339162017-02-02T05:20:07Z http://eprints.utm.my/id/eprint/33916/ Inferring gene regulatory networks from gene expression data by a dynamic Bayesian network-based model Chai, L. E. Mohammad, Mohd. Saberi Deris, Safaai Chong, C. K. Choon, Y. W. Ibrahim, Zuwairie Omatu, S. QA75 Electronic computers. Computer science Enabled by recent advances in bioinformatics, the inference of gene regulatory networks (GRNs) from gene expression data has garnered much interest from researchers. This is due to the need of researchers to understand the dynamic behavior and uncover the vast information lay hidden within the networks. In this regard, dynamic Bayesian network (DBN) is extensively used to infer GRNs due to its ability to handle time-series microarray data and modeling feedback loops. However, the efficiency of DBN in inferring GRNs is often hampered by missing values in expression data, and excessive computation time due to the large search space whereby DBN treats all genes as potential regulators for a target gene. In this paper, we proposed a DBN-based model with missing values imputation to improve inference efficiency, and potential regulators detection which aims to lessen computation time by limiting potential regulators based on expression changes. The performance of the proposed model is assessed by using time-series expression data of yeast cell cycle. The experimental results showed reduced computation time and improved efficiency in detecting gene-gene relationships. Springer 2012 Book Section PeerReviewed Chai, L. E. and Mohammad, Mohd. Saberi and Deris, Safaai and Chong, C. K. and Choon, Y. W. and Ibrahim, Zuwairie and Omatu, S. (2012) Inferring gene regulatory networks from gene expression data by a dynamic Bayesian network-based model. In: Advances in Intelligent and Soft Computing. Springer, Berlin, pp. 379-386. ISBN 978-364228764-0 http://dx.doi.org/10.1007/978-3-642-28765-7_45 DOI:10.1007/978-3-642-28765-7_45
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Chai, L. E. Mohammad, Mohd. Saberi Deris, Safaai Chong, C. K. Choon, Y. W. Ibrahim, Zuwairie Omatu, S. Inferring gene regulatory networks from gene expression data by a dynamic Bayesian network-based model
description	Enabled by recent advances in bioinformatics, the inference of gene regulatory networks (GRNs) from gene expression data has garnered much interest from researchers. This is due to the need of researchers to understand the dynamic behavior and uncover the vast information lay hidden within the networks. In this regard, dynamic Bayesian network (DBN) is extensively used to infer GRNs due to its ability to handle time-series microarray data and modeling feedback loops. However, the efficiency of DBN in inferring GRNs is often hampered by missing values in expression data, and excessive computation time due to the large search space whereby DBN treats all genes as potential regulators for a target gene. In this paper, we proposed a DBN-based model with missing values imputation to improve inference efficiency, and potential regulators detection which aims to lessen computation time by limiting potential regulators based on expression changes. The performance of the proposed model is assessed by using time-series expression data of yeast cell cycle. The experimental results showed reduced computation time and improved efficiency in detecting gene-gene relationships.
format	Book Section
author	Chai, L. E. Mohammad, Mohd. Saberi Deris, Safaai Chong, C. K. Choon, Y. W. Ibrahim, Zuwairie Omatu, S.
author_facet	Chai, L. E. Mohammad, Mohd. Saberi Deris, Safaai Chong, C. K. Choon, Y. W. Ibrahim, Zuwairie Omatu, S.
author_sort	Chai, L. E.
title	Inferring gene regulatory networks from gene expression data by a dynamic Bayesian network-based model
title_short	Inferring gene regulatory networks from gene expression data by a dynamic Bayesian network-based model
title_full	Inferring gene regulatory networks from gene expression data by a dynamic Bayesian network-based model
title_fullStr	Inferring gene regulatory networks from gene expression data by a dynamic Bayesian network-based model
title_full_unstemmed	Inferring gene regulatory networks from gene expression data by a dynamic Bayesian network-based model
title_sort	inferring gene regulatory networks from gene expression data by a dynamic bayesian network-based model
publisher	Springer
publishDate	2012
url	http://eprints.utm.my/id/eprint/33916/ http://dx.doi.org/10.1007/978-3-642-28765-7_45
_version_	1643649468579446784
score	13.211869

Inferring gene regulatory networks from gene expression data by a dynamic Bayesian network-based model

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