S. Leavitt and D. Stetten-jr, Deciphering the Genetic Code, 2004.

H. Pearson, What is a gene?, Nature, vol.309, issue.7092, pp.398-401, 2006.
DOI : 10.1126/science.1115901

K. A. Baggerly, K. R. Coombes, and J. S. Morris, An introduction to highthroughput bioinformatics data, 2006.
DOI : 10.1017/cbo9780511584589.002

V. A. Huynh-thu, Machine learning-based feature ranking: Statistical interpretation and gene network inference, 2012.

T. Hastie, R. Tibshirani, J. Friedman, and J. Franklin, The elements of statistical learning: data mining, inference and prediction. The Mathematical Intelligencer, pp.83-85, 2005.

R. Bellman, Adaptive control processes: a guided tour, 1961.
DOI : 10.1515/9781400874668

K. Muller, S. Mika, G. Ratsch, K. Tsuda, and B. Scholkopf, An introduction to kernel-based learning algorithms, IEEE Transactions on Neural Networks, vol.12, issue.2, pp.181-201, 2001.
DOI : 10.1109/72.914517

N. Aronszajn, Theory of reproducing kernels, Transactions of the American Mathematical Society, vol.68, issue.3, pp.337-404, 1950.
DOI : 10.1090/S0002-9947-1950-0051437-7

B. Schölkopf, R. Herbrich, and A. J. Smola, A Generalized Representer Theorem, Computational learning theory, pp.416-426, 2001.
DOI : 10.1007/3-540-44581-1_27

A. Aizerman, M. Emmanuel, L. Braverman, and . Rozoner, Theoretical foundations of the potential function method in pattern recognition learning Automation and remote control [11] C. Spearman. The proof and measurement of association between two things, The American journal of psychology, vol.25, issue.151, pp.821-83772, 1904.

M. G. Kendall, A NEW MEASURE OF RANK CORRELATION, Biometrika, vol.30, issue.1-2, pp.81-93, 1938.
DOI : 10.1093/biomet/30.1-2.81

L. A. Goodman and W. H. , Measures of association for cross classifications*, Journal of the American Statistical Association, vol.49, issue.268, pp.732-764, 1954.
DOI : 10.2307/2281536

A. S. Butte and I. S. Kohane, Relevance Networks: A First Step Toward Finding Genetic Regulatory Networks Within Microarray Data, 2003.
DOI : 10.1007/0-387-21679-0_19

R. Albert and A. Barabási, Statistical mechanics of complex networks, Reviews of Modern Physics, vol.86, issue.1, p.47, 2002.
DOI : 10.1103/PhysRevLett.86.5835

I. Farkas, H. Jeong, T. Vicsek, A. Barabási, and Z. N. Oltvai, The topology of the transcription regulatory network in the yeast, saccharomyces cerevisiae. Physica A: Statistical Mechanics and its Applications, pp.318601-612, 2003.

A. Barabási and Z. N. Oltvai, Network biology: understanding the cell's functional organization, Nature Reviews Genetics, vol.184, issue.2, pp.101-113, 2004.
DOI : 10.1128/JB.184.1.152-164.2002

R. Albert, Scale-free networks in cell biology, Journal of Cell Science, vol.118, issue.21, pp.4947-4957, 2005.
DOI : 10.1242/jcs.02714

M. P. Stumpf, C. Wiuf, and R. M. May, Subnets of scale-free networks are not scale-free: Sampling properties of networks, Proceedings of the National Academy of Sciences, vol.40, issue.4, pp.4221-4224, 2005.
DOI : 10.1002/pmic.200300636
URL : http://www.pnas.org/content/102/12/4221.full.pdf

R. Khanin and E. Wit, How Scale-Free Are Biological Networks, Journal of Computational Biology, vol.13, issue.3, pp.810-818, 2006.
DOI : 10.1089/cmb.2006.13.810
URL : http://dp.univr.it/~laudanna/Systems Biology/Publications/Topological analysis/How scale-free are biological networks .pdf

G. Chen, P. Larsen, E. Almasri, and Y. Dai, Rank-based edge reconstruction for scale-free genetic regulatory networks, BMC Bioinformatics, vol.9, issue.1, p.75, 2008.
DOI : 10.1186/1471-2105-9-75
URL : https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/1471-2105-9-75?site=bmcbioinformatics.biomedcentral.com

J. Cohen, Eta-Squared and Partial Eta-Squared in Fixed Factor Anova Designs, Educational and Psychological Measurement, vol.33, issue.1, 1973.
DOI : 10.1177/001316447003000409

R. Küffner, T. Petri, P. Tavakkolkhah, L. Windhager, and R. Zimmer, Inferring gene regulatory networks by ANOVA, Bioinformatics, vol.10, issue.Suppl. 6, pp.1376-1382, 2012.
DOI : 10.1186/1471-2105-10-122

A. S. Butte and I. S. Kohane, MUTUAL INFORMATION RELEVANCE NETWORKS: FUNCTIONAL GENOMIC CLUSTERING USING PAIRWISE ENTROPY MEASUREMENTS, Biocomputing 2000, pp.418-429, 2000.
DOI : 10.1142/9789814447331_0040

A. A. Margolin, I. Nemenman, K. Basso, C. Wiggins, G. Stolovitzky et al., ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context, BMC Bioinformatics, vol.7, issue.Suppl 1, 2006.
DOI : 10.1186/1471-2105-7-S1-S7

J. J. Faith, B. Hayete, and J. T. Thaden, Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles, PLoS Biology, vol.280, issue.1, p.8, 2007.
DOI : 10.1371/journal.pbio.0050008.sd001

R. Opgen-rhein and K. Strimmer, Accurate Ranking of Differentially Expressed Genes by a Distribution-Free Shrinkage Approach, Statistical Applications in Genetics and Molecular Biology, vol.6, issue.1, 2007.
DOI : 10.2202/1544-6115.1252

C. Stein, Inadmissibility of the usual estimator for the mean of a multivariate normal distribution, Proceedings of the Third Berkeley symposium on mathematical statistics and probability, pp.197-206, 1956.

J. Whittaker, Graphical models in applied multivariate statistics, 2009.

A. P. Dempster, Covariance Selection, Biometrics, vol.28, issue.1, pp.157-175, 1972.
DOI : 10.2307/2528966

D. Kollar and N. Friedman, Probabilistic graphical models: principles and techniques, 2009.

I. M. Johnstone, B. W. Silverman, A. Wille, P. Zimmermann, E. Vranová et al., Needles and straw in haystacks: Empirical bayes estimates of possibly sparse sequences Sparse graphical gaussian modeling of the isoprenoid gene network in arabidopsis thaliana, The Annals of Statistics Genome Biol, vol.32, issue.511, pp.1594-1649, 2004.

Y. Benjamini and Y. Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society. Series B (Methodological), pp.289-300, 1995.

H. Li and J. Gui, Gradient directed regularization for sparse Gaussian concentration graphs, with applications to inference of genetic networks, Biostatistics, vol.7, issue.2, pp.302-317, 2006.
DOI : 10.1186/gb-2004-5-11-r92
URL : https://academic.oup.com/biostatistics/article-pdf/7/2/302/657770/kxj008.pdf

A. Pinna, N. Soranzo, and A. De-la-fuente, From Knockouts to Networks: Establishing Direct Cause-Effect Relationships through Graph Analysis, PLoS ONE, vol.5, issue.10, p.2010
DOI : 10.1371/journal.pone.0012912.t007
URL : https://doi.org/10.1371/journal.pone.0012912

R. Tibshirani, Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society. Series B (Methodological), pp.267-288, 1996.
DOI : 10.1111/j.1467-9868.2011.00771.x

M. Gustafsson, M. Hornquist, and A. Lombardi, Constructing and Analyzing a Large-Scale Gene-to-Gene Regulatory Network-Lasso-Constrained Inference and Biological Validation, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.2, issue.3, pp.254-261, 2005.
DOI : 10.1109/TCBB.2005.35

A. Shojaie, S. Basu, and G. Michailidis, Adaptive Thresholding for Reconstructing Regulatory Networks from Time-Course Gene Expression Data, Statistics in Biosciences, vol.24, issue.1, pp.66-83, 2012.
DOI : 10.1109/MSP.2007.273053
URL : http://www.biostat.washington.edu/~ashojaie/papers/ThrshLasso4GGC.pdf

D. Marbach, J. C. Costello, and R. Küffner, Wisdom of crowds for robust gene network inference, Nature Methods, vol.11, issue.8, 2012.
DOI : 10.1093/nar/gkm815
URL : http://europepmc.org/articles/pmc3512113?pdf=render

M. Yuan and Y. Lin, Model selection and estimation in regression with grouped variables, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.58, issue.1, pp.49-67, 2005.
DOI : 10.1198/016214502753479356
URL : http://www2.isye.gatech.edu/~myuan/papers/glasso.final.pdf

J. Chiquet, Y. Grandvalet, and C. Charbonnier, Sparsity with sign-coherent groups of variables via the cooperative-Lasso, The Annals of Applied Statistics, vol.6, issue.2, pp.795-830, 2012.
DOI : 10.1214/11-AOAS520
URL : https://hal.archives-ouvertes.fr/hal-00707281

Y. Wang, T. Joshi, X. Zhang, D. Xu, and L. Chen, Inferring gene regulatory networks from multiple microarray datasets, Bioinformatics, vol.13, issue.19, pp.222413-2420, 2006.
DOI : 10.1101/gr.1198103
URL : https://academic.oup.com/bioinformatics/article-pdf/22/19/2413/18529447/btl396.pdf

A. Haury, F. Mordelet, P. Vera-licona, and J. Vert, TIGRESS: Trustful Inference of Gene REgulation using Stability Selection, BMC Systems Biology, vol.6, issue.1, pp.145-2012
DOI : 10.1093/bioinformatics/btn273
URL : https://hal.archives-ouvertes.fr/hal-00694218

N. Meinshausen and P. Bühlmann, Stability selection (with discussion), Journal of the Royal Statistical Society:Series B, issue.72, pp.417-473, 2010.
DOI : 10.1111/j.1467-9868.2010.00740.x
URL : http://onlinelibrary.wiley.com/doi/10.1111/j.1467-9868.2010.00740.x/pdf

B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, Least angle regression. The Annals of statistics, pp.407-499, 2004.

T. Akutsu, S. Miyano, and S. Kuhara, IDENTIFICATION OF GENETIC NETWORKS FROM A SMALL NUMBER OF GENE EXPRESSION PATTERNS UNDER THE BOOLEAN NETWORK MODEL, Biocomputing '99, pp.17-28, 1999.
DOI : 10.1142/9789814447300_0003

S. Liang, S. Fuhrman, and R. Somogyi, Reveal, a general reverse engineering algorithm for inference of genetic network architectures, Pacific symposium on biocomputing, 1998.

I. Shmulevich and W. Zhang, Binary analysis and optimization-based normalization of gene expression data, Bioinformatics, vol.18, issue.4, pp.555-565, 2002.
DOI : 10.1093/bioinformatics/18.4.555
URL : https://academic.oup.com/bioinformatics/article-pdf/18/4/555/669285/180555.pdf

I. Tabus, R. Jorma, and J. Astola, Normalized Maximum Likelihood Models for Boolean Regression with Application to Prediction and Classification in Genomics, 2006.
DOI : 10.1007/0-387-26288-1_12

S. Huang, Gene expression profiling, genetic networks, and cellular states: an integrating concept for tumorigenesis and drug discovery, Journal of Molecular Medicine, vol.77, issue.6, pp.469-480, 1007.
DOI : 10.1007/s001099900023

S. A. Kauffman, Metabolic stability and epigenesis in randomly constructed genetic nets, Journal of Theoretical Biology, vol.22, issue.3, pp.437-467, 1969.
DOI : 10.1016/0022-5193(69)90015-0

D. M. Chickering, D. Geiger, and D. Heckerman, Learning bayesian networks: search methods and experimental results, Proceedings of Fifth Conference on Artificial Intelligence and Statistics Learning bayesian networks is np-complete. In Learning from data, pp.569-595, 1995.

N. Friedman, M. Linial, I. Nachman, and D. Pe-'er, Using bayesian networks to analyze expression data, Journal of computational biology, vol.7, pp.3-4601, 2000.
DOI : 10.1145/332306.332355
URL : http://www.ls.huji.ac.il/~michall/papers/Friedman-JBC-paper.pdf

N. Friedman, I. Nachman, and D. Peér, Learning bayesian network structure from massive datasets: the sparse candidate algorithm, Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence, UAI'99, pp.206-215, 1999.

A. J. Hartemink, D. K. Gifford, T. Jaakkola, and R. A. Young, USING GRAPHICAL MODELS AND GENOMIC EXPRESSION DATA TO STATISTICALLY VALIDATE MODELS OF GENETIC REGULATORY NETWORKS, Biocomputing 2001, p.266, 2001.
DOI : 10.1142/9789814447362_0042

T. S. Jaakkola and M. I. Jordan, Variational probabilistic inference and the qmr-dt network, Journal of Artificial Intelligence Research, vol.10, pp.291-322, 1999.

H. Attias, Inferring parameters and structure of latent variable models by variational bayes, Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence, pp.21-30, 1999.

D. Husmeier, Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks, Bioinformatics, vol.19, issue.17, pp.2271-2282, 2003.
DOI : 10.1093/bioinformatics/btg313
URL : https://academic.oup.com/bioinformatics/article-pdf/19/17/2271/538681/btg313.pdf

M. Zou and S. D. Conzen, A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data, Bioinformatics, vol.19, issue.8, pp.71-79, 2005.
DOI : 10.1016/S0168-9525(03)00175-6
URL : https://academic.oup.com/bioinformatics/article-pdf/21/1/71/561279/bth463.pdf

S. Imoto, K. Sunyong, T. Goto, S. Aburatani, K. Tashiro et al., Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network, Bioinformatics Conference, pp.219-227, 2002.
DOI : 10.1109/csb.2002.1039344
URL : http://bonsai.ims.u-tokyo.ac.jp/~imoto/imoto_csb2002.pdf

S. Kim, S. Imoto, and S. Miyano, Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data, Biosystems, vol.75, issue.1-3, pp.57-65, 2004.
DOI : 10.1016/j.biosystems.2004.03.004
URL : http://bonsai.ims.u-tokyo.ac.jp/~imoto/kim_cmsb2003.pdf

E. R. Morrissey, M. A. Juárez, K. J. Denby, and N. J. Burroughs, Inferring the time-invariant topology of a nonlinear sparse gene regulatory network using fully Bayesian spline autoregression, Biostatistics, vol.21, issue.1, pp.682-694, 2011.
DOI : 10.1093/bioinformatics/bth463
URL : https://academic.oup.com/biostatistics/article-pdf/12/4/682/17732579/kxr009.pdf

B. Perrin, L. Ralaivola, A. Mazurie, S. Bottani, J. Mallet et al., Gene networks inference using dynamic Bayesian networks, Bioinformatics, vol.19, issue.Suppl 2, pp.138-148, 2003.
DOI : 10.1093/bioinformatics/btg1071
URL : https://hal.archives-ouvertes.fr/hal-01176902

C. Rangel, J. Angus, Z. Ghahramani, M. Lioumi, E. Sotheran et al., Modeling T-cell activation using gene expression profiling and state-space models, Bioinformatics, vol.20, issue.9, pp.1361-1372, 2004.
DOI : 10.1093/bioinformatics/bth093
URL : https://academic.oup.com/bioinformatics/article-pdf/20/9/1361/488962/bth093.pdf

Z. Wang, X. Liu, Y. Liu, J. Liang, and V. Vinciotti, An Extended Kalman Filtering Approach to Modeling Nonlinear Dynamic Gene Regulatory Networks via Short Gene Expression Time Series, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.6, issue.3, pp.410-419, 2009.
DOI : 10.1109/TCBB.2009.5

L. Qian, H. Wang, and E. R. Dougherty, Inference of Noisy Nonlinear Differential Equation Models for Gene Regulatory Networks Using Genetic Programming and Kalman Filtering, IEEE Transactions on Signal Processing, vol.56, issue.7, pp.3327-3339, 2008.
DOI : 10.1109/TSP.2008.919638

A. Noor, E. Serpedin, M. Nounou, and H. Nounou, Inferring Gene Regulatory Networks via Nonlinear State-Space Models and Exploiting Sparsity, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.9, issue.4, pp.1203-1211, 2012.
DOI : 10.1109/TCBB.2012.32

R. Thomas, S. Mehrotra, E. T. Papoutsakis, and V. Hatzimanikatis, A model-based optimization framework for the inference on gene regulatory networks from DNA array data, Bioinformatics, vol.20, issue.17, pp.3221-3235, 2004.
DOI : 10.1093/bioinformatics/bth389
URL : https://academic.oup.com/bioinformatics/article-pdf/20/17/3221/458490/bth389.pdf

N. Noman and H. Iba, Inference of gene regulatory networks using ssystem and differential evolution [73] S.I. Ao and V. Palade. Ensemble of elman neural networks and support vector machines for reverse engineering of gene regulatory networks, Proceedings of the 2005 conference on Genetic and evolutionary computation, pp.439-4461718, 2005.
DOI : 10.1145/1068009.1068079

M. Grimaldi, R. Visintainer, and G. Jurman, RegnANN: Reverse Engineering Gene Networks Using Artificial Neural Networks, PLoS ONE, vol.6, issue.S6, p.28646, 2011.
DOI : 10.1371/journal.pone.0028646.t006
URL : https://doi.org/10.1371/journal.pone.0028646

T. Aijö and H. Lähdesmäki, Learning gene regulatory networks from gene expression measurements using non-parametric molecular kinetics, Bioinformatics, issue.22, pp.252937-2944, 2009.

R. Bonneau, D. J. Reiss, P. Shannon, M. Facciotti, L. Hood et al., The inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo, Genome Biology, vol.7, issue.5, p.36, 2006.
DOI : 10.1186/gb-2006-7-5-r36

N. Lim, Y. Enbabao?-glu, G. Michailidis, and F. Dalché-buc, OKVAR-Boost: a novel boosting algorithm to infer nonlinear dynamics and interactions in gene regulatory networks, Bioinformatics, vol.20, issue.11, pp.291416-1423, 2013.
DOI : 10.1093/bioinformatics/bth448
URL : https://hal.archives-ouvertes.fr/hal-00819024

V. A. Huynh-thu, A. Irrthum, L. Wehenkel, and P. Geurts, Inferring Regulatory Networks from Expression Data Using Tree-Based Methods, PLoS ONE, vol.6, issue.9, 2010.
DOI : 10.1371/journal.pone.0012776.s003
URL : https://doi.org/10.1371/journal.pone.0012776

F. Emmert-streib, G. V. Glazko, G. Altay, R. De-matos, and . Simoes, Statistical Inference and Reverse Engineering of Gene Regulatory Networks from Observational Expression Data, Frontiers in Genetics, vol.3, 2012.
DOI : 10.3389/fgene.2012.00008
URL : https://www.frontiersin.org/articles/10.3389/fgene.2012.00008/pdf

V. Narendra, N. Lytkin, C. F. Aliferis, and A. Statnikov, A comprehensive assessment of methods for de-novo reverse-engineering of genome-scale regulatory networks, Genomics, vol.97, issue.1, pp.7-18, 2011.
DOI : 10.1016/j.ygeno.2010.10.003

G. Stolovitzky, A. Monroe, and . Califano, Dialogue on Reverse-Engineering Assessment and Methods: The DREAM of High-Throughput Pathway Inference, Annals of the New York Academy of Sciences, vol.1115, issue.2, pp.1-22, 2007.
DOI : 10.1038/ng881

D. Marbach, R. J. Prill, T. Schaffter, C. Mattiussi, D. Floreano et al., Revealing strengths and weaknesses of methods for gene network inference, Proceedings of the National Academy of Sciences USA, pp.6286-6291, 2010.
DOI : 10.1073/pnas.1230759100
URL : http://www.pnas.org/content/107/14/6286.full.pdf

R. Charnigo, M. Francoeur, P. Kenkel, M. Pinar-mengüç, B. Hall et al., Estimating quantitative features of nanoparticles using multiple derivatives of scattering profiles, Journal of Quantitative Spectroscopy and Radiative Transfer, vol.112, issue.8, pp.1369-1382, 2011.
DOI : 10.1016/j.jqsrt.2011.01.019

M. Francoeur, P. G. Venkata, and M. P. Mengüç, Sensitivity analysis for characterization of gold nanoparticles and agglomerates via surface plasmon scattering patterns, Journal of Quantitative Spectroscopy and Radiative Transfer, vol.106, issue.1-3, pp.44-55, 2007.
DOI : 10.1016/j.jqsrt.2007.01.048

R. Charnigo, Estimating Multiple Derivatives Simultaneously: What Is Optimal?, Journal of Biometrics & Biostatistics, vol.02, issue.02, 2011.
DOI : 10.4172/2155-6180.1000102e

J. O. Ramsay and B. W. Silverman, Functional data analysis, 1997.

J. M. Steppe and K. W. Bauer-jr, Feature saliency measures, Computers & Mathematics with Applications, vol.33, issue.8, pp.109-126, 1997.
DOI : 10.1016/S0898-1221(97)00059-X
URL : https://doi.org/10.1016/s0898-1221(97)00059-x

P. Leray and P. Gallinari, FEATURE SELECTION WITH NEURAL NETWORKS, Behaviormetrika, vol.26, issue.1, pp.145-166, 1999.
DOI : 10.2333/bhmk.26.145
URL : https://www.jstage.jst.go.jp/article/bhmk1974/26/1/26_1_145/_pdf

K. De-brabanter, J. D. Brabanter, B. De, and . Moor, Nonparametric derivative estimation, Proc. of the 23rd Benelux Conference on Artificial Intelligence, pp.75-81, 2011.

R. Charnigo, B. Hall, and C. Srinivasan, Criterion for Derivative Estimation, Technometrics, vol.53, issue.3, pp.238-253, 2011.
DOI : 10.1198/TECH.2011.09147

R. Khemchandani and S. Chandra, Regularized least squares support vector regression for the simultaneous learning of a function and its derivatives, Information Sciences, vol.178, issue.17, pp.3402-3414, 2008.

V. Rondonotti, J. S. Marron, and C. Park, SiZer for time series: A new approach to the analysis of trends, Electronic Journal of Statistics, vol.1, issue.0, pp.268-289, 2007.
DOI : 10.1214/07-EJS006
URL : http://doi.org/10.1214/07-ejs006

R. Charnigo, C. Srinivasan, S. Mosci, L. Rosasco, S. Villa et al., Self-consistent estimation of mean response functions and their derivatives Nonparametric sparsity and regularization, Canadian Journal of Statistics Journal of Machine Learning Research, vol.3994, issue.14, pp.280-2991665, 2011.
DOI : 10.1002/cjs.10104

C. A. Micchelli, Y. Xu, and H. Zhang, Universal kernels, The Journal of Machine Learning Research, vol.7, pp.2651-2667, 2006.

A. Caponnetto and E. Vito, Optimal Rates for the Regularized Least-Squares Algorithm, Foundations of Computational Mathematics, vol.7, issue.3, pp.331-368, 2007.
DOI : 10.1007/s10208-006-0196-8
URL : http://publications.csail.mit.edu/tmp/MIT-CSAIL-TR-2005-027.pdf

G. Blanchard and N. Krämer, Kernel partial least squares is universally consistent. arXiv preprint, 2009.

J. S. Simonoff, Smoothing methods in statistics, 1996.
DOI : 10.1007/978-1-4612-4026-6

E. Giné and A. Guillou, Rates of strong uniform consistency for multivariate kernel density estimators, Annales de l'Institut Henri Poincare (B) Probability and Statistics, pp.907-921, 2002.
DOI : 10.1016/S0246-0203(02)01128-7

L. Breiman, Random forests, Machine Learning, vol.45, issue.1, pp.5-32, 2001.
DOI : 10.1023/A:1010933404324

A. Haury, Feature selection for gene expression data: molecular signatures for breast cancer outcome prediction and gene regulatory network inference. phd thesis, ecole nationale des mines et des ponts, 2012.

R. A. Fisher, Design of Experiments, BMJ, vol.1, issue.3923, 1935.
DOI : 10.1136/bmj.1.3923.554-a

E. J. Pitman, Significance tests which may be applied to samples from any populations. Supplement to the, Journal of the Royal Statistical Society, vol.4, issue.1, pp.119-130, 1937.
DOI : 10.2307/2984124

R. Kohavi and G. H. John, Wrappers for feature subset selection, Artificial Intelligence, vol.97, issue.1-2, pp.273-324, 1997.
DOI : 10.1016/S0004-3702(97)00043-X
URL : https://doi.org/10.1016/s0004-3702(97)00043-x

Y. Lecun, J. S. Denker, S. A. Solla, R. E. Howard, and L. D. , Optimal brain damage, NIPs, pp.598-605, 1989.

I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, Gene selection for cancer classification using support vector machines, Machine Learning, vol.46, issue.1/3, pp.389-422, 2002.
DOI : 10.1023/A:1012487302797

R. J. Prill, D. Marbach, J. Saez-rodriguez, P. K. Sorger, L. G. Alexopoulos et al., Towards a Rigorous Assessment of Systems Biology Models: The DREAM3 Challenges, PLoS ONE, vol.5, issue.2, p.9202, 2010.
DOI : 10.1371/journal.pone.0009202.t010

D. Marbach, T. Schaffter, C. Mattiussi, and D. Floreano, Gene Networks for Performance Assessment of Reverse Engineering Methods, Journal of Computational Biology, vol.16, issue.2, pp.229-239, 2009.
DOI : 10.1089/cmb.2008.09TT

T. Schaffter, D. Marbach, and D. Floreano, GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods, Bioinformatics, vol.20, issue.Suppl. 2, pp.2263-2270, 2011.
DOI : 10.1093/bioinformatics/bth448
URL : https://academic.oup.com/bioinformatics/article-pdf/27/16/2263/16900458/btr373.pdf

S. L-`-ebre, Inferring dynamic genetic networks with low order independencies . Statistical applications in genetics and molecular biology, pp.1-38, 2009.

J. Surowiecki, The wisdom of crowds: Why the many are smarter than the few and how collective wisdom shapes business. Economies, Societies and Nations, 2004.

G. Forman, An extensive empirical study of feature selection metrics for text classification. The Journal of machine learning research, pp.1289-1305, 2003.

D. Tuia, G. Camps-valls, G. Matasci, and M. Kanevski, Learning relevant image features with multiple-kernel classification. Geoscience and Remote Sensing, IEEE Transactions on, issue.10, pp.483780-3791, 2010.
DOI : 10.1109/tgrs.2010.2049496
URL : https://serval.unil.ch/resource/serval:BIB_2289989BB4C6.P001/REF.pdf

C. Sotiriou, P. Wirapati, S. Loi, A. Harris, S. Fox et al., Gene Expression Profiling in Breast Cancer: Understanding the Molecular Basis of Histologic Grade To Improve Prognosis, JNCI: Journal of the National Cancer Institute, vol.98, issue.4, pp.262-272, 2006.
DOI : 10.1016/S0140-6736(05)70911-9

L. Ein-dor, I. Kela, G. Getz, D. Givol, and E. Domany, Outcome signature genes in breast cancer: is there a unique set?, Bioinformatics, vol.178, issue.2, pp.171-178, 2005.
DOI : 10.1677/joe.0.1780177

G. H. John, R. Kohavi, and K. Pfleger, Irrelevant features and the subset selection problem [117] I. Guyon and A. Elisseeff. An introduction to variable and feature selection, ICML, pp.121-1291157, 1994.
DOI : 10.1016/b978-1-55860-335-6.50023-4

C. M. Bishop, Neural networks for pattern recognition, 1995.

M. Zaffalon and M. Hutter, Robust feature selection by mutual information distributions, Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence, pp.577-584, 2002.

L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification and regression trees belmont, 1984.

G. R. Lanckriet, M. Deng, N. Cristianini, M. I. Jordan, and W. S. Noble, KERNEL-BASED DATA FUSION AND ITS APPLICATION TO PROTEIN FUNCTION PREDICTION IN YEAST, Biocomputing 2004, p.2, 2004.
DOI : 10.1142/9789812704856_0029

S. Sonnenburg, G. Rätsch, and C. Schäfer, Learning Interpretable SVMs for Biological Sequence Classification, Research in Computational Molecular Biology, pp.389-407, 2005.
DOI : 10.1007/11415770_30

B. Guo, S. Gunn, R. Damper, and J. D. Nelson, Customizing Kernel Functions for SVM-Based Hyperspectral Image Classification, IEEE Transactions on Image Processing, vol.17, issue.4, pp.622-629, 2008.
DOI : 10.1109/TIP.2008.918955
URL : https://eprints.soton.ac.uk/265043/1/04471822.pdf

R. Takashima, T. Takiguchi, and Y. Ariki, Feature selection based on Multiple Kernel Learning for single-channel sound source localization using the acoustic transfer function, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.2696-2699, 2011.
DOI : 10.1109/ICASSP.2011.5947041

M. Gonen and E. Alpaydyn, Multiple kernel learning algorithms, JMLR, vol.12, pp.2211-2268, 2011.

S. R. Gunn and J. S. Kandola, Structural modelling with sparse kernels, Machine Learning, vol.48, issue.1/3, pp.137-163, 2002.
DOI : 10.1023/A:1013903804720

D. G. Luenberger, Linear and nonlinear programming, 2003.
DOI : 10.1007/978-3-319-18842-3

T. F. Coleman and Y. Li, A Reflective Newton Method for Minimizing a Quadratic Function Subject to Bounds on Some of the Variables, SIAM Journal on Optimization, vol.6, issue.4, pp.1040-1058, 1996.
DOI : 10.1137/S1052623494240456

W. Duch, Filter Methods, Feature Extraction, pp.89-117, 2006.
DOI : 10.1007/978-3-540-35488-8_4

N. Sánchez-maroño, A. Alonso-betanzos, and M. Tombilla-sanromán, Filter Methods for Feature Selection ??? A Comparative Study, Intelligent Data Engineering and Automated Learning-IDEAL 2007, pp.178-187, 2007.
DOI : 10.1007/978-3-540-77226-2_19

N. Cristianini, . Shawe-taylor, A. Elisseeff, and . Kandola, On kernel target alignment Advances in neural information processing systems, p.367, 2002.

J. Kandola, J. Shawe-taylor, and N. Cristianini, On the extensions of kernel alignment, NeuroCOLT technical report, 2002.

M. Ramona, G. Richard, and B. David, Multiclass Feature Selection With Kernel Gram-Matrix-Based Criteria, IEEE Transactions on Neural Networks and Learning Systems, vol.23, issue.10, 2012.
DOI : 10.1109/TNNLS.2012.2201748
URL : http://perso.telecom-paristech.fr/~grichard/Publications/TNNLS-2012-P-0123.pdf

K. Kira and L. A. , A Practical Approach to Feature Selection, Proceedings of the ninth international workshop on Machine learning, pp.249-256, 1992.
DOI : 10.1016/B978-1-55860-247-2.50037-1

I. Kononenko, Estimating attributes: Analysis and extensions of RELIEF, Machine Learning: ECML-94, pp.171-182, 1994.
DOI : 10.1007/3-540-57868-4_57
URL : https://link.springer.com/content/pdf/10.1007%2F3-540-57868-4_57.pdf

M. Robnik, -. Sikonja, and I. Kononenko, An adaptation of relief for attribute estimation in regression [138] M. Robnik-? Sikonja and I. Kononenko. Theoretical and empirical analysis of relieff and rrelieff, Machine Learning: Proceedings of the Fourteenth International Conference (ICML97), pp.296-30423, 1997.

J. Weston, S. Mukherjee, O. Chapelle, M. Pontil, T. Poggio et al., Feature selection for svms Adaptive scaling for feature selection in svms, NIPS Advances in neural information processing systems, pp.668-674, 2000.

G. I. Allen, Automatic Feature Selection via Weighted Kernels and Regularization, Journal of Computational and Graphical Statistics, vol.67, issue.2, pp.284-299, 2013.
DOI : 10.1111/j.1467-9868.2005.00503.x

K. Duan, S. S. Keerthi, and A. N. Poo, Evaluation of simple performance measures for tuning SVM hyperparameters, Neurocomputing, vol.51, pp.41-59, 2003.
DOI : 10.1016/S0925-2312(02)00601-X
URL : http://www.svms.org/parameters/DuanKeerthiPoo2003.pdf

L. Breiman, Heuristics of instability and stabilization in model selection. The annals of statistics, pp.2350-2383, 1996.
DOI : 10.1214/aos/1032181158
URL : https://doi.org/10.1214/aos/1032181158

A. Ben-hur, A. Elisseeff, and I. Guyon, A stability based method for discovering structure in clustered data, Biocomputing 2002, pp.6-17, 2001.
DOI : 10.1142/9789812799623_0002

D. N. Politis and H. White, Automatic Block-Length Selection for the Dependent Bootstrap, Econometric Reviews, vol.37, issue.1, pp.53-70, 2004.
DOI : 10.1111/1468-0262.00152
URL : http://1cj3301.ucsd.edu/hwcv-093.pdf

A. Patton, D. N. Politis, and H. White, Correction to ???Automatic Block-Length Selection for the Dependent Bootstrap??? by D. Politis and H. White, Econometric Reviews, vol.28, issue.4, pp.372-375, 2009.
DOI : 10.1081/ETC-120028836

G. Schwarz, Estimating the dimension of a model. The annals of statistics, pp.461-464, 1978.
DOI : 10.1214/aos/1176344136
URL : http://doi.org/10.1214/aos/1176344136

H. Akaike, A new look at the statistical model identification. Automatic Control, IEEE Transactions on, vol.19, issue.6, pp.716-723, 1974.
DOI : 10.1007/978-1-4612-1694-0_16

J. J. Faith, M. E. Driscoll, V. A. Fusaro, E. J. Cosgrove, B. Hayete et al., Many Microbe Microarrays Database: uniformly normalized Affymetrix compendia with structured experimental metadata, Nucleic Acids Research, vol.34, issue.suppl_1, pp.36-866, 2008.
DOI : 10.1093/nar/gkj156
URL : https://academic.oup.com/nar/article-pdf/36/suppl_1/D866/7629518/gkm815.pdf

H. Salgado, M. Peralta-gil, and S. Gama-castro, Regulondb v8. 0: omics data sets, evolutionary conservation, regulatory phrases, cross-validated gold standards and more, Nucleic acids research, issue.D1, pp.41-203, 2013.