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SIMoNe: Statistical Inference for MOdular NEtworks.

Authors :
Julien Chiquet
Alexander Smith
Gilles Grasseau
Catherine Matias
Christophe Ambroise
Source :
Bioinformatics; Feb2009, Vol. 25 Issue 3, p417-417, 1p
Publication Year :
2009

Abstract

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 [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
13674803
Volume :
25
Issue :
3
Database :
Complementary Index
Journal :
Bioinformatics
Publication Type :
Academic Journal
Accession number :
36355766
Full Text :
https://doi.org/10.1093/bioinformatics/btn637