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Bioinformatics Advance Access originally published online on August 24, 2007
Bioinformatics 2007 23(18):2407-2414; doi:10.1093/bioinformatics/btm352
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© The Author 2007. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

LICORN: learning cooperative regulation networks from gene expression data

Mohamed Elati 1,2,*,{dagger}, Pierre Neuvial 3, Monique Bolotin-Fukuhara 4, Emmanuel Barillot 3, François Radvanyi 2 and Céline Rouveirol 1,{dagger}

1LRI, CNRS UMR 8623, bât 490, Université Paris Sud, 91405 F-Orsay, 2Institut Curie, CNRS UMR 144, 26 rue d'Ulm, 75248 F-Paris, 3Institut Curie, Service de Bioinformatique, 26 rue d’Ulm, 75248 F-Paris and 4IGM, CNRS UMR 8621, bât 400/409, Université Paris-Sud, 91405 F-Orsay, France

*To whom correspondence should be addressed.


  Abstract

Motivation: One of the most challenging tasks in the post-genomic era is the reconstruction of transcriptional regulation networks. The goal is to identify, for each gene expressed in a particular cellular context, the regulators affecting its transcription, and the co-ordination of several regulators in specific types of regulation. DNA microarrays can be used to investigate relationships between regulators and their target genes, through simultaneous observations of their RNA levels.

Results: We propose a data mining system for inferring transcriptional regulation relationships from RNA expression values. This system is particularly suitable for the detection of cooperative transcriptional regulation. We model regulatory relationships as labelled two-layer gene regulatory networks, and describe a method for the efficient learning of these bipartite networks from discretized expression data sets. We also evaluate the statistical significance of such inferred networks and validate our methods on two public yeast expression data sets.

Availability: http://www.lri.fr/~elati/licorn.html

Contact: mohamed.elati{at}curie.fr

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Martin Bishop

{dagger}Present address: LIPN, CNRS UMR 7030 Institut Galilée - Université Paris-Nord F-93430 Villetaneuse, France.

Received on April 27, 2007; revised on June 27, 2007; accepted on June 29, 2007

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