O. Banerjee, L. El-ghaoui, and A. Aspremont, Model selection through sparse maximum likelihood estimation for multivariate Gaussian or binary data, J. Mach. Learn. Res, vol.9, p.2417243, 2008.

C. Biernacki, G. Celeux, and G. Govaert, Assessing a mixture model for clustering with the integrated completed likelihood, IEEE Trans. Pattern Anal. Mach. Intell, vol.22, issue.7, pp.719-725, 2000.

R. Castelo and A. Roverato, A robust procedure for Gaussian graphical model search from microarray data with p larger than n, J. Mach. Learn. Res, vol.7, p.2274453, 2006.

S. S. Chen, D. L. Donoho, and M. A. Saunders, Atomic decomposition by basis pursuit, SIAM Rev, vol.43, issue.1, p.1854649, 2001.

J. Chiquet, A. Smith, G. Grasseau, C. Matias, and C. Ambroise, Simone: Statistical inference for modular networks, Bioinformatics, vol.25, issue.3, pp.417-418, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00592218

J. Daudin, F. Picard, R. , and S. , A mixture model for random graphs, Stat. Comput, vol.18, issue.2, pp.173-183, 2008.
URL : https://hal.archives-ouvertes.fr/hal-01197587

A. P. Dempster, Covariance selection, Biometrics, Special Multivariate Issue, vol.28, pp.157-175, 1972.

A. P. Dempster, N. M. Laird, and D. B. Rubin, Maximum likelihood from incomplete data via the EM algorithm, J. Roy. Statist. Soc. Ser. B, vol.39, issue.1, p.501537, 1977.

A. Dobra, C. Hans, B. Jones, J. R. Nevins, G. Yao et al., Sparse graphical models for exploring gene expression data, J. Multivariate Anal, vol.90, issue.1, p.2064941, 2004.

D. L. Donoho and I. M. Johnstone, Adapting to unknown smoothness via wavelet shrinkage, J. Amer. Statist. Assoc, vol.90, issue.432, p.1379464, 1995.

M. Drton and M. D. Perlman, Multiple testing and error control in Gaussian graphical model selection, Statist. Sci, vol.22, p.2416818, 2007.

M. Drton and M. D. Perlman, A SINful approach to Gaussian graphical model selection, J. Statist. Plann. Inference, vol.138, issue.4, p.2416875, 2008.

B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, Least angle regression, Ann. Statist, vol.32, issue.2, p.2060166, 2004.

O. Frank and F. Harary, Cluster inference by using transitivity indices in empirical graphs, J. Amer. Statist. Assoc, vol.77, issue.380, p.686407, 1982.

J. Friedman, T. Hastie, H. Höfling, and R. Tibshirani, Pathwise coordinate optimization, Ann. Appl. Stat, vol.1, issue.2, p.2415737, 2007.

J. Friedman, T. Hastie, and R. Tibshirani, Sparse inverse covariance estimation with the graphical lasso, Biostatistics, vol.9, issue.3, pp.432-441, 2008.

W. J. Fu, Penalized regressions: the bridge versus the lasso, J. Comput. Graph. Statist, vol.7, issue.3, p.1646710, 1998.

K. R. Hess, K. Anderson, W. F. Symmans, V. Valero, N. Ibrahim et al., Pharmacogenomic predictor of sensitivity to preoperative chemotherapy with paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide in breast cancer, Journal of Clinical Oncology, vol.24, issue.26, pp.4236-4244, 2006.

J. Ihmels, G. Friedlander, S. Bergmann, O. Sarig, Y. Ziv et al., Revealing modular organization in the yeast transcriptional network, Nature Genetics, pp.370-377, 2002.

T. Jaakkola, Advanced mean field methods: theory and practice, Neural Information Processing Series, p.1863214, 2001.

B. Jones, C. Carvalho, A. Dobra, C. Hans, C. Carter et al., Experiments in stochastic computation for high-dimensional graphical models, Statist. Sci, vol.20, issue.4, p.2210226, 2005.

S. L. Lauritzen, Graphical models, Oxford Statistical Science Series, vol.17, p.1419991, 1996.

M. Mariadassou and S. Robin, Uncovering latent structure in valued graphs: a variational approach, Statistics for Systems Biology, 2007.
URL : https://hal.archives-ouvertes.fr/hal-01197514

N. Meinshausen and P. Bühlmann, High-dimensional graphs and variable selection with the lasso, Ann. Statist, vol.34, issue.3, p.2278363, 2006.

R. Natowicz, R. Incitti, E. G. Horta, B. Charles, P. Guinot et al., Prediction of the outcome of a preoperative chemotherapy in breast cancer using dna probes that provide information on both complete and incomplete response, BMC Bioinformatics, vol.9, issue.149, 2008.

A. Y. Ng, M. Jordan, and Y. Weiss, On spectral clustering: Analysis and an algorithm, NIPS 14, 2002.

K. Nowicki and T. A. Snijders, Estimation and prediction for stochastic blockstructures, J. Amer. Statist. Assoc, vol.96, issue.455, p.1947255, 2001.

M. R. Osborne, B. Presnell, and B. A. Turlach, On the LASSO and its dual, J. Comput. Graph. Statist, vol.9, issue.2, p.1822089, 2000.

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

T. A. Snijders and K. Nowicki, Estimation and prediction for stochastic blockmodels for graphs with latent block structure, J. Classification, vol.14, issue.1, p.1449742, 1997.

C. Tallberg, A Bayesian approach to modeling stochastic blockstructures with covariates, Journal of Mathematical Sociology, vol.29, issue.1, p.1379242, 1996.

P. Tseng, Convergence of a block coordinate descent method for nondifferentiable minimization, J. Optim. Theory Appl, vol.109, issue.3, p.1835069, 2001.

A. Wille and P. Bühlmann, Low-order conditional independence graphs for inferring genetic networks, Statistical Applications in Genetics and Molecular Biology, vol.5, issue.1, p.2221304, 2006.

T. T. Wu and K. Lange, Coordinate descent algorithms for lasso penalized regression, Ann. Appl. Stat, vol.2, issue.1, pp.224-244, 2008.

M. Yuan and Y. Lin, Model selection and estimation in the Gaussian graphical model, Biometrika, vol.94, issue.1, p.2367824, 2007.

H. Zanghi, C. Ambroise, and V. Miele, Fast online graph clustering via Erdös Rényi mixture, Pattern Recognition, vol.41, issue.12, pp.3592-3599, 2008.

H. Zou, The adaptive lasso and its oracle properties, J. Amer. Statist. Assoc, vol.101, issue.476, p.2279469, 2006.