C. Ambroise, J. Chiquet, M. , and C. , Inferring sparse Gaussian graphical models with latent structure, Electronic Journal of Statistics, vol.3, issue.0, pp.205-238, 2009.
DOI : 10.1214/08-EJS314
URL : https://hal.archives-ouvertes.fr/hal-00592201

E. Arias-castro, E. Candes, and Y. Plan, Global testing under sparse alternatives: ANOVA, multiple comparisons and the higher criticism, The Annals of Statistics, vol.39, issue.5, pp.2533-2556, 2011.
DOI : 10.1214/11-AOS910SUPP

Z. Bai and H. Saranadasa, Effect of high dimension: by an example of a two sample problem, Statistica Sinica, vol.6, pp.311-329, 1996.

Y. Baraud, C. Giraud, and S. Huet, Estimator selection in the Gaussian setting, Annales de l'Institut Henri Poincar??, Probabilit??s et Statistiques, vol.50, issue.3, 2010.
DOI : 10.1214/13-AIHP539
URL : https://hal.archives-ouvertes.fr/hal-00502156

Y. Baraud, S. Huet, L. , and B. , Adaptive tests of linear hypotheses by model selection, Annals of Statistics, vol.31, pp.225-251, 2003.

P. Bickel, Y. Ritov, and A. Tsybakov, Simultaneous analysis of Lasso and Dantzig selector, The Annals of Statistics, vol.37, issue.4, pp.1705-1732, 2009.
DOI : 10.1214/08-AOS620
URL : https://hal.archives-ouvertes.fr/hal-00401585

P. Bühlmann, Statistical significance in high-dimensional linear models, Bernoulli, vol.19, issue.4, 2012.
DOI : 10.3150/12-BEJSP11

T. Cai, W. Liu, and Y. Xia, Two-Sample Covariance Matrix Testing and Support Recovery in High-Dimensional and Sparse Settings, Journal of the American Statistical Association, vol.359, issue.501, 2011.
DOI : 10.1198/jasa.2011.tm10560

E. Candes and Y. Plan, Near-ideal model selection by ??? 1 minimization, The Annals of Statistics, vol.37, issue.5A, pp.2145-2177, 2007.
DOI : 10.1214/08-AOS653

S. Chen and Y. Qin, A two-sample test for high-dimensional data with applications to gene-set testing, The Annals of Statistics, vol.38, issue.2, pp.808-835, 2010.
DOI : 10.1214/09-AOS716

J. Chiquet, Y. Grandvalet, and C. Ambroise, Inferring multiple graphical structures, Statistics and Computing, vol.94, issue.1, pp.537-553, 2011.
DOI : 10.1007/s11222-010-9191-2
URL : https://hal.archives-ouvertes.fr/hal-00660169

S. Chung, H. Suzuki, T. Miyamoto, N. Takamatsu, A. Tatsuguchi et al., Development of an orally-administrative MELK-targeting inhibitor that suppresses the growth of various types of human cancer, Oncotarget, vol.3, issue.12, pp.1629-1669, 2012.
DOI : 10.18632/oncotarget.790

K. R. Davidson and S. J. Szarek, Local Operator Theory, Random Matrices and Banach Spaces, Handbook of the geometry of Banach spaces, pp.317-366, 2001.
DOI : 10.1016/S1874-5849(01)80010-3

D. Donoho and J. Jin, Higher criticism for detecting sparse heterogeneous mixture, Annals of Statistics, vol.32, pp.962-994, 2004.

B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, Least angle regression, With discussion, and a rejoinder by the authors. MR2060166, pp.407-49962116, 2004.

J. Friedman, T. Hastie, and R. Tibshirani, Sparse inverse covariance estimation with the graphical lasso, Biostatistics, vol.9, issue.3, pp.432-441, 2008.
DOI : 10.1093/biostatistics/kxm045

C. Giraud, S. Huet, and N. Verzelen, Graph Selection with GGMselect, Statistical Applications in Genetics and Molecular Biology, vol.11, issue.3, 2009.
DOI : 10.1515/1544-6115.1625
URL : https://hal.archives-ouvertes.fr/hal-00401550

C. Giraud, S. Huet, and N. Verzelen, Supplement to 'Highdimensional regression with unknown variance, 2012.

J. Heidel, J. Liu, Y. Yen, B. Zhou, B. Heale et al., Potent siRNA Inhibitors of Ribonucleotide Reductase Subunit RRM2 Reduce Cell Proliferation In vitro and In vivo, Clinical Cancer Research, vol.13, issue.7, 2007.
DOI : 10.1158/1078-0432.CCR-06-2218

Y. Ingster, A. Tsybakov, and N. Verzelen, Detection boundary in sparse regression, Electronic Journal of Statistics, vol.4, issue.0, pp.1476-1526, 2010.
DOI : 10.1214/10-EJS589
URL : https://hal.archives-ouvertes.fr/hal-00516259

A. Javanmard and A. Montanari, Confidence Intervals and Hypothesis Testing for High-Dimensional Regression Available from http, 2013.

M. Jeanmougin, M. Guedj, and C. Ambroise, Defining a robust biological prior from pathway analysis to drive network inference, pp.97-110, 2011.

B. Laurent and P. Massart, Adaptive estimation of a quadratic functional of a density by model selection, ESAIM: Probability and Statistics, vol.9, issue.5, pp.1302-1338, 2000.
DOI : 10.1051/ps:2005001

S. L. Lauritzen, Graphical Models, 1996.

J. Li and S. X. Chen, Two sample tests for high-dimensional covariance matrices, The Annals of Statistics, vol.40, issue.2, pp.908-940, 2012.
DOI : 10.1214/12-AOS993
URL : http://arxiv.org/abs/1206.0917

Y. Lin, C. Chen, C. Cheng, Y. , and R. , Domain and Functional Analysis of a Novel Breast Tumor Suppressor Protein, SCUBE2, Journal of Biological Chemistry, vol.286, issue.30, pp.27039-27086, 2011.
DOI : 10.1074/jbc.M111.244418

R. Lockhart, J. Taylor, R. J. Tibshirani, and R. Tibshirani, A significance test for the lasso, The Annals of Statistics, vol.42, issue.2, 2013.
DOI : 10.1214/14-AOS1175REJ

M. Lopes, L. Jacob, and M. Wainwright, A more powerful two-sample test in high dimensions using random projection, NIPS, 2011.

N. Meinshausen, Assumption-free confidence intervals for groups of variables in sparse high-dimensional regression, 2013.

N. Meinshausen, P. N. Bühlmann, L. Meier, and P. Bühlmann, High-dimensional graphs and variable selection with the Lasso P-values for highdimensional regression, Annals of Statistics Journal of the American Statistical Association, vol.34, issue.104, pp.1436-1462, 2006.

N. Meinshausen and B. Yu, Lasso-type recovery of sparse representations for high-dimensional data, The Annals of Statistics, vol.37, issue.1, pp.246-270, 2009.
DOI : 10.1214/07-AOS582

R. Natowicz, R. Incitti, E. Horta, B. Charles, P. Guinot et al., Prediction of the outcome of preoperative chemotherapy in breast cancer using DNA probes that provide information on both complete and incomplete responses, BMC Bioinformatics, vol.9, issue.1, 2008.
DOI : 10.1186/1471-2105-9-149

G. Raskutti, M. Wainwright, Y. , and B. , Restricted eigenvalue properties for correlated Gaussian designs, Journal of Machine Learning Research, vol.11, pp.2241-2259, 2010.

R. Rouzier, C. Perou, F. Symmans, N. Ibrahim, M. Cristofanilli et al., Breast Cancer Molecular Subtypes Respond Differently to Preoperative Chemotherapy, Clinical Cancer Research, vol.11, issue.16, 2005.
DOI : 10.1158/1078-0432.CCR-04-2421

R. Rouzier, R. Rajan, P. Wagner, K. Hess, D. Gold et al., Microtubule-associated protein tau: A marker of paclitaxel sensitivity in breast cancer, Proceedings of the National Academy of Sciences, pp.8315-8320, 2005.
DOI : 10.1073/pnas.0408974102

M. Srivastava and M. Du, A test for the mean vector with fewer observations than the dimension, Journal of Multivariate Analysis, vol.99, issue.3, pp.386-402, 2008.
DOI : 10.1016/j.jmva.2006.11.002

N. Städler and S. Mukherjee, Two-sample testing in high-dimensional models, 2012.

N. Städler and S. Mukherjee, Network-based multivariate gene-set testing, 2013.

T. Sun, C. Zhang, and S. De-geer, Scaled sparse linear regression On the conditions used to prove oracle results for the Lasso, Electronic Journal of Statistics, vol.3, pp.1360-1392, 2009.

N. Verzelen, Minimax risks for sparse regressions: Ultra-high dimensional phenomenons, Electronic Journal of Statistics, vol.6, issue.0, pp.38-90, 2012.
DOI : 10.1214/12-EJS666SUPP
URL : https://hal.archives-ouvertes.fr/hal-00508339

N. Verzelen and F. Villers, Tests for Gaussian graphical models, Computational Statistics & Data Analysis, vol.53, issue.5, pp.1894-1905, 2009.
DOI : 10.1016/j.csda.2008.09.022
URL : https://hal.archives-ouvertes.fr/hal-00193268

N. Verzelen and F. Villers, Goodness-of-fit tests for high-dimensional Gaussian linear models, The Annals of Statistics, vol.38, issue.2, pp.704-752, 2010.
DOI : 10.1214/08-AOS629
URL : https://hal.archives-ouvertes.fr/inria-00186919

M. Wainwright, Sharp thresholds for high-dimensional and noisy sparsity recovery using 1 -constrained quadratic programming (lasso), IEEE Transactions on Information Theory, vol.55, 2009.

L. Wasserman and K. Roeder, High-dimensional variable selection, The Annals of Statistics, vol.37, issue.5A, pp.2178-2201, 2009.
DOI : 10.1214/08-AOS646

C. Zhang and S. Zhang, Confidence intervals for low-dimensional parameters with high-dimensional data, 2011.