title: SIMoNe: Statistical Inference for MOdular NEtworks.
abstract: CONCLUSIONS
  The R package SIMoNe (Statistical Inference for MOdular NEtworks) enables inference of gene-regulatory networks based on partial correlation coefficients from microarray experiments. Modelling gene expression data with a Gaussian graphical model (hereafter GGM), the algorithm estimates non-zero entries of the concentration matrix, in a sparse and possibly high-dimensional setting. Its originality lies in the fact that it searches for a latent modular structure to drive the inference procedure through adaptive penalization of the concentration matrix.
  
  
  BACKGROUND
  Under the GNU General Public Licence at http://cran.r-project.org/web/packages/simone/
authors.affiliation: UMR CNRS 8071 Statistique et Génome, 523, place des Terrasses, F-91000 Evry, France. julien.chiquet@genopole.cnrs.fr
authors.forename: Julien
authors.initials: J
authors.lastname: Chiquet
authors.forename: Alexander
authors.initials: A
authors.lastname: Smith
authors.forename: Gilles
authors.initials: G
authors.lastname: Grasseau
authors.forename: Catherine
authors.initials: C
authors.lastname: Matias
authors.forename: Christophe
authors.initials: C
authors.lastname: Ambroise
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D030541:Databases, Genetic; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D012984:Software