LICORN: learning cooperative regulation networks from gene expression data.
BACKGROUND
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.


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


BACKGROUND
Supplementary data are available at Bioinformatics online.
Elati
Mohamed
M
LRI, CNRS UMR 8623, bât 490, Université Paris Sud, 91405 F-Orsay, France. mohamed.elati@curie.fr
Neuvial
Pierre
P
Bolotin-Fukuhara
Monique
M
Barillot
Emmanuel
E
Radvanyi
François
F
Rouveirol
Céline
C
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D030562:Databases, Protein; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D016247:Information Storage and Retrieval / Q000379:methods*; D008954:Models, Biological; D020543:Proteome / Q000235:genetics / Q000378:metabolism*; D012313:RNA / Q000378:metabolism; D015398:Signal Transduction / Q000502:physiology*
D020543:Proteome; D012313:RNA