title: State-space approach with the maximum likelihood principle to identify the system generating time-course gene expression data of yeast.
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.
authors.affiliation: Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan. ruiy@ims.u-tokyo.ac.jp
authors.forename: Rui
authors.initials: R
authors.lastname: Yamaguchi
authors.forename: Tomoyuki
authors.initials: T
authors.lastname: Higuchi
mesh_terms: D000465:Algorithms; D001330:Electronic Data Processing / Q000379:methods; D015966:Gene Expression Regulation, Fungal / Q000502:physiology*; D012441:Saccharomyces cerevisiae / Q000502:physiology*