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
Affiliations:
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
Venue:
International Journal of Data Mining and Bioinformatics
Year:
2006

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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.