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Journal of Computer Science and Technology 2010, Vol. 25 Issue (1) :131-153    DOI:
Special Issue on Computational Challenges from Modern Molecular Biology Current Issue | Archive | Adv Search << Previous Articles | Next Articles >>
Network-Based Predictions and Simulations by Biological State Space Models: Search for Drug Mode of Action
Rui Yamaguchi, Seiya Imoto, and Satoru Miyano
Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan

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Abstract 

Since time-course microarray data are short but contain a large number of genes, most of statistical models should be extended so that they can handle such statistically irregular situations. We introduce biological state space models that are established as suitable computational models for constructing gene networks from microarray gene expression data. This chapter elucidates theory and methodology of our biological state space models together with some representative analyses including discovery of drug mode of action. Through the applications we show the whole strategy of biological state space model analysis involving experimental design of time-course data, model building and analysis of the estimated networks.

Articles by authors
Rui Yamaguchi
Seiya Imoto
Satoru Miyano
Keywordsgene networks    state space models    time-course gene expression data     
Received 2009-09-30;
Cite this article:   
Rui Yamaguchi, Seiya Imoto, and Satoru Miyano.Network-Based Predictions and Simulations by Biological State Space Models: Search for Drug Mode of Action[J]  Journal of Computer Science and Technology, 2010,V25(1): 131-153
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