A. Arkin, P. Shen, and R. J. , A Test Case of Correlation Metric Construction of a Reaction Pathway from Measurements, Science, vol.277, issue.5330, pp.1275-1279, 1997.
DOI : 10.1126/science.277.5330.1275

S. Liang, S. Fuhrman, and R. Somogyi, REVEAL, a general reverse engineering algorithm for inference of genetic network architectures, Pac Symp Biocomput, vol.3, pp.18-29, 1998.

T. Chen, H. He, and G. Church, MODELING GENE EXPRESSION WITH DIFFERENTIAL EQUATIONS, Biocomputing '99, pp.29-40, 1999.
DOI : 10.1142/9789814447300_0004

T. Akutsu, S. Miyano, and S. Kuhara, Algorithms for Identifying Boolean Networks and Related Biological Networks Based on Matrix Multiplication and Fingerprint Function, Journal of Computational Biology, vol.7, issue.3-4, pp.331-343, 2000.
DOI : 10.1089/106652700750050817

M. Yeung, J. Tegnér, and J. Collins, Reverse engineering gene networks using singular value decomposition and robust regression, Proceedings of the National Academy of Sciences, vol.99, issue.9, pp.6163-6168, 2002.
DOI : 10.1073/pnas.092576199

J. Tegner, M. Yeung, J. Hasty, and J. Collins, Reverse engineering gene networks: Integrating genetic perturbations with dynamical modeling, Proceedings of the National Academy of Sciences, vol.100, issue.10, pp.5944-5949, 2003.
DOI : 10.1073/pnas.0933416100

T. Gardner, D. Bernardo, D. Lorenz, and J. Collins, Inferring Genetic Networks and Identifying Compound Mode of Action via Expression Profiling, Science, vol.301, issue.5629, pp.102-105, 2003.
DOI : 10.1126/science.1081900

K. Chen, T. Wang, H. Tseng, C. Huang, and C. Kao, A stochastic differential equation model for quantifying transcriptional regulatory network in Saccharomyces cerevisiae, Bioinformatics, vol.21, issue.12, pp.2883-2890, 2005.
DOI : 10.1093/bioinformatics/bti415

D. Bernardo, M. Thompson, T. Gardner, S. Chobot, E. Eastwood et al., Chemogenomic profiling on a genome-wide scale using reverse-engineered gene networks, Nature Biotechnology, vol.176, issue.3
DOI : 10.1126/science.1088697

M. Bansal, D. Gatta, G. Bernardo, and D. , Inference of gene regulatory networks and compound mode of action from time course gene expression profiles, Bioinformatics, vol.22, issue.7, pp.815-822, 2006.
DOI : 10.1093/bioinformatics/btl003

P. Zoppoli, S. Morganella, and M. Ceccarelli, TimeDelay-ARACNE: Reverse engineering of gene networks from time-course data by an information theoretic approach, BMC Bioinformatics, vol.11, issue.1, pp.1752-0509145, 2010.
DOI : 10.1186/1471-2105-11-154

A. Butte, P. Tamayo, D. Slonim, T. Golub, and I. Kohane, Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks, Proceedings of the National Academy of Sciences, vol.97, issue.22, pp.12182-12186, 2000.
DOI : 10.1073/pnas.220392197

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, pp.1471-2105, 2006.
DOI : 10.1186/1471-2105-7-S1-S7

J. Faith, B. Hayete, J. Thaden, I. Mogno, J. Wierzbowski et al., 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

J. Rice, Y. Tu, and G. Stolovitzky, Reconstructing biological networks using conditional correlation analysis, Bioinformatics, vol.21, issue.6, pp.765-773, 2005.
DOI : 10.1093/bioinformatics/bti064

N. Friedman, M. Linial, I. Nachman, and D. Pe-'er, Using Bayesian Networks to Analyze Expression Data, Journal of Computational Biology, vol.7, issue.3-4, pp.601-620, 2000.
DOI : 10.1089/106652700750050961

A. Hartemink, D. Gifford, T. Jaakkola, Y. R. Altman, R. Dunker et al., USING GRAPHICAL MODELS AND GENOMIC EXPRESSION DATA TO STATISTICALLY VALIDATE MODELS OF GENETIC REGULATORY NETWORKS, Biocomputing 2001, pp.422-433, 2002.
DOI : 10.1142/9789814447362_0042

B. Perrin, L. Ralaivola, A. Mazurie, S. Bottani, and J. Mallet, Alche Buc F: Gene networks inference using dynamic Bayesian networks, Bioinformatics, vol.19, issue.2, pp.138-148, 2003.

N. Friedman, Inferring Cellular Networks Using Probabilistic Graphical Models, Science, vol.303, issue.5659, p.799, 2004.
DOI : 10.1126/science.1094068

V. 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

F. Markowetz and R. Spang, Inferring cellular networks ??? a review, BMC Bioinformatics, vol.8, issue.Suppl 6, 2007.
DOI : 10.1186/1471-2105-8-S6-S5

D. Marbach, R. 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, vol.107, issue.14, pp.6286-6291, 2010.
DOI : 10.1073/pnas.0913357107

N. Meinshausen, High-dimensional graphs and variable selection with the Lasso, The Annals of Statistics, vol.34, issue.3, pp.1436-1462, 2006.
DOI : 10.1214/009053606000000281

B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, Least angle regression, Ann. Stat, vol.32, issue.2, pp.407-499, 2004.

F. Bach, Bolasso, Proceedings of the 25th international conference on Machine learning, ICML '08, pp.33-40
DOI : 10.1145/1390156.1390161
URL : https://hal.archives-ouvertes.fr/hal-00271289

N. Meinshausen, Stability selection, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.7, issue.4, pp.417-473, 2010.
DOI : 10.1111/j.1467-9868.2010.00740.x

R. Tibshirani, Regression shrinkage and selection via the lasso, J R Stat Soc Ser B, vol.58, pp.267-288, 1996.

D. Marbach, J. Costello, K. ¨. Uffner, R. Vega, N. Prill et al., Wisdom of crowds for robust gene network inference, Nature Methods, vol.11, issue.8, pp.796-804
DOI : 10.1093/nar/gkm815

L. Breiman, Random forests, Machine Learning, vol.45, issue.1, pp.5-321010933404324, 2001.
DOI : 10.1023/A:1010933404324

S. Weisberg, Applied linear regression, 1981.
DOI : 10.1002/0471704091

T. Hastie, R. Tibshirani, and J. Friedman, The elements of statistical learning: data mining, inference, and prediction, 2001.

J. Mairal, F. Bach, J. Ponce, and G. Sapiro, Online Learning for Matrix Factorization and Sparse Coding, J Mach Learn Res, vol.11, pp.19-60, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00408716

T. Schaffter, D. Marbach, and D. Floreano, GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods, Bioinformatics, vol.27, issue.16, pp.2263-2270, 2011.
DOI : 10.1093/bioinformatics/btr373

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, 2008.
DOI : 10.1089/cmb.2008.09TT

J. Faith, M. Driscoll, V. Fusaro, E. Cosgrove, B. Hayete et al., Many Microbe Microarrays Database: uniformly normalized Affymetrix compendia with structured experimental metadata, Database issue):D866?D870, 2008.
DOI : 10.1093/nar/gkm815

S. Gama-castro, H. Salgado, M. Peralta-gil, A. Santos-zavaleta, L. Muñizmu?muñiz-rascado et al., RegulonDB version 7.0: transcriptional regulation of Escherichia coli K-12 integrated within genetic sensory response units (Gensor Units), 1):D98?D105. R: Inferring gene regulatory networks by ANOVA, pp.281376-1382, 2011.
DOI : 10.1093/nar/gkq1110

F. Mordelet and J. Vert, SIRENE: supervised inference of regulatory networks, Bioinformatics, vol.24, issue.16, 2008.
DOI : 10.1093/bioinformatics/btn273
URL : https://hal.archives-ouvertes.fr/hal-00259119

. Haury, Cite this article as TIGRESS: Trustful Inference of Gene REgulation using Stability Selection, BMC Systems Biology, p.145, 2012.