  Inferring the structure of gene regulatory networks (GRN) from gene
expression data has many applications, from the elucidation of complex
biological processes to the identification of potential drug targets. It is
however a notoriously difficult problem, for which the many existing methods
reach limited accuracy. In this paper, we formulate GRN inference as a sparse
regression problem and investigate the performance of a popular feature
selection method, least angle regression (LARS) combined with stability
selection. We introduce a novel, robust and accurate scoring technique for
stability selection, which improves the performance of feature selection with
LARS. The resulting method, which we call TIGRESS (Trustful Inference of Gene
REgulation using Stability Selection), was ranked among the top methods in the
DREAM5 gene network reconstruction challenge. We investigate in depth the
influence of the various parameters of the method and show that a fine
parameter tuning can lead to significant improvements and state-of-the-art
performance for GRN inference. TIGRESS reaches state-of-the-art performance on
benchmark data. This study confirms the potential of feature selection
techniques for GRN inference. Code and data are available on
http://cbio.ensmp.fr/~ahaury. Running TIGRESS online is possible on
GenePattern: http://www.broadinstitute.org/cancer/software/genepattern/.
