Avoiding spurious feedback loops in the reconstruction of gene regulatory networks with dynamic bayesian networks

Grzegorczyk, M. and Husmeier, D. (2009) Avoiding spurious feedback loops in the reconstruction of gene regulatory networks with dynamic bayesian networks. Lecture Notes in Computer Science, 5780, pp. 113-124. (doi:10.1007/978-3-642-04031-3_11)

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Publisher's URL: http://dx.doi.org/10.1007/978-3-642-04031-3_11

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.

Item Type:Articles
Additional Information:Pattern Recognition in Bioinformatics
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Husmeier, Professor Dirk
Authors: Grzegorczyk, M., and Husmeier, D.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Lecture Notes in Computer Science
Publisher:Springer
ISSN:0302-9743
Copyright Holders:Copyright © 2009 Springer Verlag
First Published:First published in Lecture Notes in Computer Science 5780:113-124
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher
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