O. Banerjee, L. Ghaoui, and A. Aspremont, Model selection through sparse maximum likelihood estimation for multivariate gaussian or binary data, J. Machine Learning Research, vol.9, pp.485-516, 2008.

R. Castelo and A. Roverato, A robust procedure for gaussian graphical model search from microarray data with p larger than n, Journal of Machine Learning Research, vol.7, pp.2621-2650, 2006.

J. J. Daudin, F. Picard, and S. Robin, A mixture model for random graphs, INRIA, 2006.
URL : https://hal.archives-ouvertes.fr/inria-00070186

A. Dobra, C. Hans, B. Jones, J. R. Nevins, G. Yao et al., Sparse graphical models for exploring gene expression data, Journal of Multivariate Analysis, vol.90, pp.196-212, 2004.

M. Drton and M. Perlman, Multiple testing and error control in gaussian graphical model selection, Statistical Sciences, 2007.

B. Efron, T. Hastie, I. Johnston, and R. Tibshirani, Least angle regression, The Annals of Statistics, vol.32, pp.407-451, 2004.

J. Friedman, T. Hastie, and R. Tibshirani, Sparse inverse covariance estimation with the lasso, 2007.

C. Giraud, Estimation of gaussian graphs by model selection, Electronic Journal of Statistics, vol.2, pp.542-563, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00178275

J. Huang, N. Liu, M. Pourahmadi, and L. Liu, Covariance matrix selection abd estimation via penalised normal likelihood, Biometrika, vol.93, issue.1, pp.85-98, 2006.

D. Husmeier, Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic bayesian networks, Bioinformatics, vol.19, pp.2271-2282, 2003.

M. Kalisch and P. Bühlmann, Estimating high-dimensional directed acyclic graphs with the pc-algorithm, Journal of Machine Learning Research, vol.8, pp.613-636, 2007.

H. Kishino and P. J. Waddell, Correspondence analysis of genes and tissue types and finding genetic links from microarray data, 2000.

, Genome Informatics, vol.11, pp.83-95

D. Malouche and S. Sevestre, Estimating high dimensional faithful gaussian graphical models : upc-algorithm, 2007.

N. Meinshausen and P. Bühlmann, High dimensional graphs and variable selection with the Lasso, Annals of Statistics, vol.34, issue.3, pp.1436-1462, 2006.

S. Okamoto, Y. Yamanishi, S. Ehira, S. Kawashima, K. Tonomura et al., Prediction of nitrogen metabolism-related genes in anabaena by kernel-based network analysis, Proteomics, vol.7, issue.6, pp.900-909, 2007.

K. Sachs, O. Perez, D. Lauffenburger, D. A. Nolan, and G. P. , Causal protein-signaling networks derived from multiparameter single-cell data, Science, vol.308, pp.523-529, 2005.

J. Schäfer and K. Strimmer, An empirical bayes approach to inferring large-scale gene association nerworks, Bioinformatics, vol.21, issue.6, pp.754-764, 2005.

J. Schäfer and K. Strimmer, A shrinkage approach to largescale covariance matrix estimation and implications for functional genomics, Statistical Applications in Genetics and Molecular Biology, vol.4, pp.1-32, 2005.

P. Spirtes, C. Glymour, and R. Scheines, Causation, Prediction and Search, 2000.

A. Werhli and D. Husmeier, Reconstructing gene regulatory networks with bayesian networks by combining expression data with multiple sources of prior knowledge, Statistical Applications in Genetics and Molecular Biology, p.6, 2007.

A. Wille and P. Bühlmann, Low-order conditional independence graphs for inferring genetic networks, Statistical Applications in Genetics and Molecular Biology, vol.5, pp.1-34, 2006.

W. Wu and Y. Ye, Exploring gene causal interactions using an enhanced constraint-based method, Pattern Recognition, vol.39, pp.2439-2449, 2006.

S. Yellaboina, K. Goyal, and S. Mande, Inferring genomewide functional linkages in e-coli by combining improved genome context methods: Comparison with high-throughput experimental data, Genome Research, vol.17, issue.4, pp.527-535, 2007.

M. Yuan and Y. Lin, Model selection and estimation in the gaussian graphical model, Biometrika, pp.19-35, 2007.