title: TIGRESS: Trustful Inference of Gene REgulation using Stability Selection.
abstract: BACKGROUND
  Inferring the structure of gene regulatory networks (GRN) from a collection of gene expression data has many potential 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.
  
  
  RESULTS
  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, for that purpose. 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 (for Trustful Inference of Gene REgulation with Stability Selection), was ranked among the top GRN inference methods in the DREAM5 gene network inference challenge. In particular, TIGRESS was evaluated to be the best linear regression-based method in the 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, in both directed and undirected settings.
  
  
  CONCLUSIONS
  TIGRESS reaches state-of-the-art performance on benchmark data, including both in silico and in vivo (E. coli and S. cerevisiae) networks. This study confirms the potential of feature selection techniques for GRN inference. Code and data are available on http://cbio.ensmp.fr/tigress. Moreover, TIGRESS can be run online through the GenePattern platform (GP-DREAM, http://dream.broadinstitute.org).
authors.affiliation: Centre for Computational Biology, Mines ParisTech, Fontainebleau, F-77300 France.
authors.forename: Anne-Claire
authors.initials: AC
authors.lastname: Haury
authors.forename: Fantine
authors.initials: F
authors.lastname: Mordelet
authors.forename: Paola
authors.initials: P
authors.lastname: Vera-Licona
authors.forename: Jean-Philippe
authors.initials: JP
authors.lastname: Vert
mesh_terms: D004926:Escherichia coli / Q000235:genetics / Q000378:metabolism; D053263:Gene Regulatory Networks; D012044:Regression Analysis; D049490:Systems Biology / Q000379:methods*; D014157:Transcription Factors / Q000378:metabolism; D059467:Transcriptome
chemical_list: D014157:Transcription Factors