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Reconstructing Transcriptional Regulatory Networks Using Three-Way Mutual Information and Bayesian Networks

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Computational Biology of Transcription Factor Binding

Part of the book series: Methods in Molecular Biology ((MIMB,volume 674))

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Abstract

Probabilistic methods such as mutual information and Bayesian networks have become a major category of tools for the reconstruction of regulatory relationships from quantitative biological data. In this chapter, we describe the theoretic framework and the implementation for learning gene regulatory networks using high-order mutual information via the MI3 method (Luo et al. (2008) BMC Bioinformatics 9, 467; Luo (2008) Gene regulatory network reconstruction and pathway inference from high throughput gene expression data. PhD thesis). We also cover the closely related Bayesian network method in detail.

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Author information

Authors and Affiliations

  1. Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA

    Weijun Luo

  2. Departments of Chemical and Biomedical Engineering and Bioinformatics Program, University of Michigan, Ann Arbor, MI, USA

    Peter J. Woolf

Authors
  1. Weijun Luo
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  2. Peter J. Woolf
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Corresponding author

Correspondence to Weijun Luo .

Editor information

Editors and Affiliations

  1. , Department of Statistics, University of Nebraska-Lincoln, Vine Street 1901, Lincoln, 68588-0665, Nebraska, USA

    Istvan Ladunga

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Luo, W., Woolf, P.J. (2010). Reconstructing Transcriptional Regulatory Networks Using Three-Way Mutual Information and Bayesian Networks. In: Ladunga, I. (eds) Computational Biology of Transcription Factor Binding. Methods in Molecular Biology, vol 674. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-60761-854-6_23

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  • DOI: https://doi.org/10.1007/978-1-60761-854-6_23

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  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-60761-853-9

  • Online ISBN: 978-1-60761-854-6

  • eBook Packages: Springer Protocols

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