Title: Analysis of microarray data to infer transcription regulation in the yeast cell cycle

Authors: Akther Shermin, Mehmet A. Orgun

Addresses: Intelligent Systems Lab, Faculty of Science, Department of Computing, Macquarie University, North Ryde, NSW 2109, Australia. ' Faculty of Science, Department of Computing, Macquarie University, North Ryde, NSW 2109, Australia

Abstract: The experimental microarray data has the potential application in determining the underlying mechanisms of transcription regulation in a living cell. The inference of this regulation circuitry with computational methods suffers from two major challenges: the low accuracy of inferring true positive connections and the excessive computation time. In this paper, we show that models based on Dynamic Bayesian Networks which exploit the biological features of gene expression are more computationally efficient and topologically accurate compared to the other existing models. Using two experimental microarray datasets of the yeast cell cycle, we also evaluate how successfully the available models can address the current challenges with the increasing size of the datasets.

Keywords: gene regulation networks; DBN; dynamic Bayesian networks; microarray data; gene expression; yeast cell cycle; TFs; transcription factors; transcription regulation.

DOI: 10.1504/IJFIPM.2010.033247

International Journal of Functional Informatics and Personalised Medicine, 2010 Vol.3 No.1, pp.73 - 88

Published online: 14 May 2010 *

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