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Network-Based Predictions and Simulations by Biological State Space Models: Search for Drug Mode of Action

Network-Based Predictions and Simulations by Biological State Space Models: Search for Drug Mode of Action
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摘要 Since time-course microarrav 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. Since time-course microarrav 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.
作者 Rui Yamaguchi Seiya Imoto Satoru Miyano
机构地区 Human Genome Center
出处 《Journal of Computer Science & Technology》 SCIE EI CSCD 2010年第1期131-153,共23页 计算机科学技术学报（英文版）
关键词 gene networks state space models time-course gene expression data gene networks, state space models, time-course gene expression data
分类号 TP391.9 [自动化与计算机技术—计算机应用技术]
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Journal of Computer Science & Technology
2010年 第1期

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