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Nonhomogeneous Dynamic Bayesian Networks in Systems Biology

  • Sophie Lèbre
  • Frank Dondelinger
  • Dirk HusmeierEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 802)

Abstract

Dynamic Bayesian networks (DBNs) have received increasing attention from the computational biology community as models of gene regulatory networks. However, conventional DBNs are based on the homogeneous Markov assumption and cannot deal with inhomogeneity and nonstationarity in temporal processes. The present chapter provides a detailed discussion of how the homogeneity assumption can be relaxed. The improved method is evaluated on simulated data, where the network structure is allowed to change with time, and on gene expression time series during morphogenesis in Drosophila melanogaster.

Key words

Dynamic Bayesian networks (DBNs) Changepoint processes Reversible jump Markov chain Monte Carlo (RJMCMC) Morphogenesis Drosophila melanogaster 

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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Sophie Lèbre
    • 1
  • Frank Dondelinger
    • 2
    • 3
  • Dirk Husmeier
    • 2
    Email author
  1. 1.Université de StrasbourgStrasbourgFrance
  2. 2.Biomathematics and Statistics ScotlandScotlandUK
  3. 3.School of Informatics, University of EdinburghEdinburghUK

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