Comparative analysis of network algorithms to address modularity with gene expression temporal data
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
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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