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Inferring the structure of gene regulatory networks (GRN) from geneexpression data has many applications, from the elucidation of complexbiological processes to the identification of potential drug targets. It ishowever a notoriously difficult problem, for which the many existing methodsreach limited accuracy. In this paper, we formulate GRN inference as a sparseregression problem and investigate the performance of a popular featureselection method, least angle regression (LARS) combined with stabilityselection. We introduce a novel, robust and accurate scoring technique forstability selection, which improves the performance of feature selection withLARS. The resulting method, which we call TIGRESS (Trustful Inference of GeneREgulation using Stability Selection), was ranked among the top methods in theDREAM5 gene network reconstruction challenge. We investigate in depth theinfluence of the various parameters of the method and show that a fineparameter tuning can lead to significant improvements and state-of-the-artperformance for GRN inference. TIGRESS reaches state-of-the-art performance onbenchmark data. This study confirms the potential of feature selectiontechniques for GRN inference. Code and data are available onhttp://cbio.ensmp.fr/~ahaury. Running TIGRESS online is possible onGenePattern: http://www.broadinstitute.org/cancer/software/genepattern/.

TIGRESS: Trustful Inference of Gene REgulation using Stability Selection