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Inferring genetic networks from gene expression data is one of the mostchallenging work in the post-genomic era, partly due to the vast space ofpossible networks and the relatively small amount of data available. In thisfield, Gaussian Graphical Model (GGM) provides a convenient framework for thediscovery of biological networks. In this paper, we propose an originalapproach for inferring gene regulation networks using a robust biological prioron their structure in order to limit the set of candidate networks.  Pathways, that represent biological knowledge on the regulatory networks,will be used as an informative prior knowledge to drive Network Inference. Thisapproach is based on the selection of a relevant set of genes, called the"molecular signature", associated with a condition of interest (for instance,the genes involved in disease development). In this context, differentialexpression analysis is a well established strategy. However outcome signaturesare often not consistent and show little overlap between studies. Thus, we willdedicate the first part of our work to the improvement of the standard processof biomarker identification to guarantee the robustness and reproducibility ofthe molecular signature.  Our approach enables to compare the networks inferred between two conditionsof interest (for instance case and control networks) and help along thebiological interpretation of results. Thus it allows to identify differentialregulations that occur in these conditions. We illustrate the proposed approachby applying our method to a study of breast cancer's response to treatment.

Defining a robust biological prior from Pathway Analysis to drive Network Inference