Journal Article

SIMoNe: Statistical Inference for MOdular NEtworks

Julien Chiquet, Alexander Smith, Gilles Grasseau, Catherine Matias and Christophe Ambroise

in Bioinformatics

Volume 25, issue 3, pages 417-418
Published in print February 2009 | ISSN: 1367-4803
Published online December 2008 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btn637
SIMoNe: Statistical Inference for MOdular NEtworks

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Summary: 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.

Availability: Under the GNU General Public Licence at http://cran.r-project.org/web/packages/simone/

Contact: julien.chiquet@genopole.cnrs.fr

Journal Article.  1158 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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