Article Abstract

	Title:	State-space approach with the maximum likelihood principle to identify the system generating time-course gene expression data of yeast
	Author:	Rui Yamaguchi, Tomoyuki Higuchi
	Address:	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
	Journal:	International Journal of Data Mining and Bioinformatics 2006 - Vol. 1, No.1  pp. 77 - 87
	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
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