A data mining system for inferring transcriptional regulation relationships from RNA expression values. LICORN 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.
LRI, CNRS UMR 8623, Université Paris Sud, Orsay, France; Institut Curie, CNRS UMR 144, Paris, France; Institut Curie, Service de Bioinformatique, Paris, France; IGM, CNRS UMR 8621, Université Paris-Sud, Orsay, France
LICORN funding source(s)
This work was supported by the CNRS, the Institut Curie, the Plan Pluri-Formation Bioinformatique et Génomique and the IFR Génome.