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Computational Drug Target Pathway Discovery: A Bayesian Network Approach

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Handbook of Statistical Bioinformatics

Part of the book series: Springer Handbooks of Computational Statistics ((SHCS))

Abstract

Genome-wide transcriptome data together with statistical analysis enable us to reverse-engineer gene networks that can be a kind of views useful for understanding dynamic behaviour of biological elements in cells. In this chapter, we elucidate statistical models for estimating gene networks based on two types of microarray gene expression data, gene knock-down and time-course. In our modeling, nonparametric regression model is combined with Bayesian networks to capture nonlinear relationships between genes and a derived Bayesian information criterion with efficient structure learning algorithm selects network structure. Some efficient algorithms for structure learning of Bayesian networks, which is known as an NP-hard problem for optimal solutions, are also introduced. To demonstrate the statistical gene network analysis shown in this chapter, we estimate gene networks based on microarray data of human endothelial cell treated with an anti-hyperlipidaemia drug fenofibrate. Based on the constructed gene networks, we illustrate computational strategies for discovering drug target genes and pathways.

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Acknowledgements

The authors wish to thank Satoru Kuhara, Kousuke Tashiro, Masao Nagasaki, Yukiko Nakanishi, Atsushi Doi, Yuki Tomiyasu, Kaori Yasuda, Cristin Print, D. Stephen Charnock-Jones, Sally Humphreys, Ben Dunmore, Deborah Sanders, Christopher J. Savoie for their continuous efforts on our HUVEC study. Computation time was provided by the Super Computer System, Human Genome Center, Institute of Medical Science, University of Tokyo.

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Authors and Affiliations

  1. Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan

    Seiya Imoto, Yoshinori Tamada & Satoru Miyano

  2. Cell Innovator Inc., Fukuoka, Japan

    Hiromitsu Araki

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  1. Seiya Imoto
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  2. Yoshinori Tamada
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  3. Hiromitsu Araki
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  4. Satoru Miyano
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Correspondence to Satoru Miyano .

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  1. , Institute of Statistics, National Chiao Tung University, Ta Hsueh Road 1001, Hsinchu, 30050, Taiwan, Taiwan R.O.C.

    Henry Horng-Shing Lu

  2. , Department of Empirical Inference, MPI for Intelligent Systems, Spemannstraße 38, Tübingen, 72076, Germany

    Bernhard Schölkopf

  3. School of Medicine, Dept. Epidemiology & Public Health, Yale University, College Street 60, New Haven, 06520, Connecticut, USA

    Hongyu Zhao

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Imoto, S., Tamada, Y., Araki, H., Miyano, S. (2011). Computational Drug Target Pathway Discovery: A Bayesian Network Approach. In: Lu, HS., Schölkopf, B., Zhao, H. (eds) Handbook of Statistical Bioinformatics. Springer Handbooks of Computational Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16345-6_24

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