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This article is part of the supplement: Selected Proceedings of Machine Learning in Systems Biology: MLSB 2007

Open Access Proceedings

Identification of functional modules based on transcriptional regulation structure

Etienne Birmelé1*, Mohamed Elati2*, Céline Rouveirol2 and Christophe Ambroise1*

Author Affiliations

1 Laboratoire Statistique et Génome, UMR CNRS 8071, INRA 1152, Tour Evry 2, F-91000 Evry, France

2 LIPN – UMR 7030 CNRS – Université Paris 13, 99 Av. J.B. Clément, F-93430 Villetaneuse, France

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BMC Proceedings 2008, 2(Suppl 4):S4  doi:

Published: 17 December 2008

Abstract

Background

Identifying gene functional modules is an important step towards elucidating gene functions at a global scale. Clustering algorithms mostly rely on co-expression of genes, that is group together genes having similar expression profiles.

Results

We propose to cluster genes by co-regulation rather than by co-expression. We therefore present an inference algorithm for detecting co-regulated groups from gene expression data and introduce a method to cluster genes given that inferred regulatory structure. Finally, we propose to validate the clustering through a score based on the GO enrichment of the obtained groups of genes.

Conclusion

We evaluate the methods on the stress response of S. Cerevisiae data and obtain better scores than clustering obtained directly from gene expression.