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

Marco Grzegorczyk*, Dirk Husmeier

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationPATTERN RECOGNITION IN BIOINFORMATICS, PROCEEDINGS
Editors Kadirkamanathan, G Sanguinetti, M Girolami, M Niranjan, J Noirel
Place of PublicationBERLIN
PublisherSpringer-Verlag GmbH
Pages113-124
Number of pages12
ISBN (Print)978-3-642-04030-6
DOIs
Publication statusPublished - 2009
Event4th International Conference Pattern Recognition in Bioinformatics - Sheffield, United Kingdom
Duration: 7 Sep 20099 Sep 2009

Publication series

NameLECTURE NOTES IN BIOINFORMATICS
PublisherSPRINGER-VERLAG BERLIN
Volume5780
ISSN (Print)0302-9743

Conference

Conference4th International Conference Pattern Recognition in Bioinformatics
Country/TerritoryUnited Kingdom
Period7/09/099/09/09

Keywords

  • ALLOCATION SAMPLER
  • MODEL

Fingerprint

Dive into the research topics of 'Avoiding Spurious Feedback Loops in the Reconstruction of Gene Regulatory Networks with Dynamic Bayesian Networks'. Together they form a unique fingerprint.

Cite this