F. Rapaport, A. Zinovyev, M. Dutreix, E. Barillot, and J. Vert, Classification of microarray data using gene networks, BMC Bioinformatics, vol.8, issue.1, p.35, 2007.
DOI : 10.1186/1471-2105-8-35

URL : https://hal.archives-ouvertes.fr/hal-00433577

D. Marbach, J. C. Costello, R. Küffner, N. M. Vega, R. J. Prill et al., Wisdom of crowds for robust gene network inference, Nature Methods, vol.11, issue.8, pp.796-804, 2012.
DOI : 10.1093/nar/gkm815

S. A. Thomas and Y. Jin, Reconstructing biological gene regulatory networks: where optimization meets big data, Evolutionary Intelligence, vol.28, issue.1, pp.29-47, 2013.
DOI : 10.1007/s12065-013-0098-7

X. Zhang, K. Liu, Z. Liu, B. Duval, J. Richer et al., NARROMI: a noise and redundancy reduction technique improves accuracy of gene regulatory network inference, Bioinformatics, vol.29, issue.1, pp.106-113, 2013.
DOI : 10.1093/bioinformatics/bts619

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

P. E. Meyer, K. Kontos, F. Lafitte, and G. Bontempi, Information-Theoretic Inference of Large Transcriptional Regulatory Networks, EURASIP Journal on Bioinformatics and Systems Biology, vol.6, issue.1, pp.1-9, 2007.
DOI : 10.1162/089976698300017197

J. J. Faith, B. Hayete, J. T. 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, pp.54-66, 2007.
DOI : 10.1371/journal.pbio.0050008.sd001

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.
DOI : 10.2202/1544-6115.1175

C. Charbonnier, J. Chiquet, and C. Ambroise, Weighted-LASSO for Structured Network Inference from Time Course Data, Statistical Applications in Genetics and Molecular Biology, vol.9, issue.1, 2010.
DOI : 10.2202/1544-6115.1519

G. Krouk, P. Mirowski, Y. Lecun, D. E. Shasha, and G. M. Coruzzi, Predictive network modeling of the high-resolution dynamic plant transcriptome in response to nitrate, Genome Biology, vol.11, issue.12, p.123, 2010.
DOI : 10.1186/gb-2010-11-12-r123

URL : https://hal.archives-ouvertes.fr/hal-00553899

S. Feizi, D. Marbach, M. Médard, and M. Kellis, Network deconvolution as a general method to distinguish direct dependencies in networks, Nat. Biotechnol, issue.8, pp.31-726, 2013.

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, pp.1-10, 2010.
DOI : 10.1371/journal.pone.0012776.s003

V. A. Huynh-thu and G. Sanguinetti, Combining tree-based and dynamical systems for the inference of gene regulatory networks, Bioinformatics, vol.31, issue.10, pp.1614-1622, 2015.
DOI : 10.1093/bioinformatics/btu863

M. Hecker, S. Lambeck, S. Toepfer, E. Van-someren, and R. Guthke, Gene regulatory network inference: Data integration in dynamic models???A review, Biosystems, vol.96, issue.1, pp.86-103, 2009.
DOI : 10.1016/j.biosystems.2008.12.004

V. Kolmogorov and C. Rother, Minimizing Nonsubmodular Functions with Graph Cuts-A Review, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, issue.7, pp.1274-1279, 2007.
DOI : 10.1109/TPAMI.2007.1031

J. R. Parikh, Y. Xia, and J. A. Marto, Multi-Edge Gene Set Networks Reveal Novel Insights into Global Relationships between Biological Themes, PLoS ONE, vol.7, issue.9, pp.1-15, 2012.
DOI : 10.1371/journal.pone.0045211.s009

M. Sugiyama, C. Azencott, D. Grimm, Y. Kawahara, and K. M. Borgwardt, Multi-Task Feature Selection on Multiple Networks via Maximum Flows, Proc. SIAM Int. Conf. Data Mining, pp.199-207, 2014.
DOI : 10.1137/1.9781611973440.23

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.
DOI : 10.1093/bioinformatics/btn637

URL : https://hal.archives-ouvertes.fr/hal-00592218

C. Espinosa-soto and A. Wagner, Specialization Can Drive the Evolution of Modularity, PLoS Computational Biology, vol.103, issue.3, p.1000719, 2010.
DOI : 10.1371/journal.pcbi.1000719.s008

J. Canny, A computational approach to edge detection, IEEE Trans. Pattern Anal. Mach. Intell, vol.8, issue.6, pp.679-698, 1986.

J. Ollion, J. Cochennec, F. Loll, C. Escude, and T. Boudier, TANGO: a generic tool for high-throughput 3D image analysis for studying nuclear organization, Bioinformatics, vol.29, issue.14, pp.1840-1841, 2013.
DOI : 10.1093/bioinformatics/btt276

P. Huber, Robust Statistical Procedures, Society for Industrial and Applied Mathematics, 1996.
DOI : 10.1137/1.9781611970036

L. R. Ford, . Jr, and D. R. Fulkerson, Maximal flow through a network, Journal canadien de math??matiques, vol.8, issue.0, pp.399-404, 1956.
DOI : 10.4153/CJM-1956-045-5

V. Kolmogorov and R. Zabih, What energy functions can be minimized via graph cuts?, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.2, pp.147-159, 2004.
DOI : 10.1109/TPAMI.2004.1262177

Y. Boykov and V. Kolmogorov, An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.9, pp.1124-1137, 2004.
DOI : 10.1109/TPAMI.2004.60

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

H. Salgado, S. Gama-castro, M. Peralta-gil, E. Díaz-peredo, F. Sánchez-solano et al., RegulonDB (version 5.0): Escherichia coli K-12 transcriptional regulatory network, operon organization, and growth conditions, Nucleic Acids Research, vol.34, issue.90001, pp.394-397, 2006.
DOI : 10.1093/nar/gkj156

A. Franceschini, D. Szklarczyk, S. Frankild, M. Kuhn, M. Simonovic et al., STRING v9.1: protein-protein interaction networks, with increased coverage and integration, Nucleic Acids Research, vol.41, issue.D1, pp.41-808, 2013.
DOI : 10.1093/nar/gks1094

I. M. Keseler, A. Mackie, M. Peralta-gil, A. Santos-zavaleta, S. Gama-castro et al., EcoCyc: fusing model organism databases with systems biology, Nucleic Acids Research, vol.41, issue.D1, pp.41-605, 2013.
DOI : 10.1093/nar/gks1027

B. Abu-jamous, R. Fa, D. J. Roberts, and A. K. Nandi, Paradigm of Tunable Clustering Using Binarization of Consensus Partition Matrices (Bi-CoPaM) for Gene Discovery, PLoS ONE, vol.10, issue.2, 2013.
DOI : 10.1371/journal.pone.0056432.s001