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Bioinformatics Advance Access published online on August 12, 2004
Bioinformatics, doi:10.1093/bioinformatics/bth463
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received June 4, 2004
Revised August 2, 2004
Accepted August 3, 2004

Article

A new Dynamic Bayesian Network (DBN) approach for identifying gene regulatory networks from time course microarray data

Min Zou 1 Suzanne D. Conzen 2*

1 Department of Medicine, University of Chicago, Chicago, IL 60637, USA
2 Department of Medicine, University of Chicago, Chicago, IL 60637, USA; Committee on Cancer Biology, University of Chicago, Chicago, IL 60637, USA

* To whom correspondence should be addressed. E-mail: sconzen{at}medicine.bsd.uchicago.edu.


  Abstract

Motivation: Signaling pathways are dynamic events that take place over a given period of time. In order to identify these pathways, expression data over time are required. Dynamic Bayesian Network (DBN) is an important approach for predicting gene regulatory networks from time course expression data. However, two fundamental problems greatly reduce the effectiveness of current DBN methods. The first problem is the relatively low accuracy of prediction, and the second is the excessive computational time.

Results: In this paper, we present a DBN-based approach with increased accuracy and reduced computational time compared with existing DBN methods. Unlike previous methods, our approach limits potential regulators to those genes with either earlier or simultaneous expression changes (up- or down-regulation) in relation to their target genes. This allows us to limit the number of potential regulators and consequently reduce the search space. Furthermore, we use the time difference between the initial change in expression of a given regulator gene and its potential target gene to estimate the transcriptional time lag between these two genes. This method of time lag estimation increases the accuracy of predicting gene regulatory networks. Our approach is evaluated using time series expression data measured during the yeast cell cycle. Results demonstrate that this approach can predict regulatory networks with significantly improved accuracy and reduced computational time compared with existing DBN approaches.

Availability: The programs described in this paper can be obtained from the corresponding author upon request.


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