Title: State-space approach with the maximum likelihood principle to identify the system generating time-course gene expression data of yeast

Authors: Rui Yamaguchi, Tomoyuki Higuchi

Addresses: The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan. ' The Institute of Statistical Mathematics, 4-6-7 Minami-Azabu, Minato-ku, Tokyo, 106-8569, Japan

Abstract: We use linear Gaussian state-space models to analyse time-course gene expression data of yeast. They are modelled to be generated from hidden state variables in a system. To identify the system, we estimate parameters of the model by EM algorithm and determine the dimension of the state variable by BIC.

Keywords: state-space models; time-course data; EM algorithm; BIC; bioinformatics; data mining; cell cycle; gene networks; gene expression data; yeast; parameter estimation; expectation-maximization algorithm; Bayesian information criterion; cDNA microarrays.

DOI: 10.1504/IJDMB.2006.009922

International Journal of Data Mining and Bioinformatics, 2006 Vol.1 No.1, pp.77 - 87

Published online: 02 Jun 2006 *

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