Avoiding Spurious Feedback Loops in the Reconstruction of Gene Regulatory Networks with Dynamic Bayesian Networks

  • Marco Grzegorczyk
  • Dirk Husmeier
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5780)

Abstract

Feedback loops and recurrent structures are essential to the regulation and stable control of complex biological systems. The application of dynamic as opposed to static Bayesian networks is promising in that, in principle, these feedback loops can be learned. However, we show that the widely applied BGe score is susceptible to learning spurious feedback loops, which are a consequence of non-linear regulation and autocorrelation in the data. We propose a non-linear generalisation of the BGe model, based on a mixture model, and demonstrate that this approach successfully represses spurious feedback loops.

Keywords

Mixture Model Bayesian Network Marginal Likelihood Dynamic Bayesian Network Correct Edge 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Marco Grzegorczyk
    • 1
  • Dirk Husmeier
    • 2
  1. 1.Department of StatisticsTU Dortmund UniversityDortmundGermany
  2. 2.Biomathematics and Statistics Scotland, JCMBEdinburghUK

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