Chiquet Julien, Smith Alexander, Grasseau Gilles, Matias Catherine, Ambroise Christophe
UMR CNRS 8071 Statistique et Génome, 523, place des Terrasses, F-91000 Evry, France.
Bioinformatics. 2009 Feb 1;25(3):417-8. doi: 10.1093/bioinformatics/btn637. Epub 2008 Dec 10.
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.
Under the GNU General Public Licence at http://cran.r-project.org/web/packages/simone/
R包SIMoNe（模块化网络的统计推断）能够基于微阵列实验的偏相关系数推断基因调控网络。该算法使用高斯图形模型（以下简称GGM）对基因表达数据进行建模，在稀疏且可能高维的情况下估计浓度矩阵的非零元素。其独特之处在于，它通过对浓度矩阵进行自适应惩罚来寻找潜在的模块化结构，以驱动推断过程。
根据GNU通用公共许可证，可在http://cran.r-project.org/web/packages/simone/获取。
