S. Klamt, U. Haus, and F. Theis, Hypergraphs and Cellular Networks, PLoS Computational Biology, vol.9, issue.5, 2009.
DOI : 10.1371/journal.pcbi.1000385.g002
URL : http://doi.org/10.1371/journal.pcbi.1000385

S. Croset, J. Rupp, and M. Romacker, Flexible data integration and curation using a graph-based approach, Bioinformatics, vol.32, issue.6, pp.918-925, 2016.
DOI : 10.1093/bioinformatics/btv644

V. Bonnici, F. Busato, G. Micale, N. Bombieri, A. Pulvirenti et al., APPAGATO: an APproximate PArallel and stochastic GrAph querying TOol for biological networks, Bioinformatics, vol.32, issue.14, pp.2159-2166, 2016.
DOI : 10.1093/bioinformatics/btw223

J. Chen, A. O. Hero, and I. Rajapakse, Spectral identification of topological domains, Bioinformatics, vol.32, issue.14, pp.2151-2158, 2016.
DOI : 10.1093/bioinformatics/btw221

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

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

Z. Kurt, N. Aydin, and G. Altay, A comprehensive comparison of association estimators for gene network inference algorithms, Bioinformatics, vol.30, issue.15, pp.2142-2149, 2014.
DOI : 10.1093/bioinformatics/btu182

Z. Liu, Reverse Engineering of Genome-wide Gene Regulatory Networks from Gene Expression Data, Current Genomics, vol.16, issue.1, pp.3-22, 2015.
DOI : 10.2174/1389202915666141110210634

A. J. Butte, I. S. Kohane, and T. E. , MUTUAL INFORMATION RELEVANCE NETWORKS: FUNCTIONAL GENOMIC CLUSTERING USING PAIRWISE ENTROPY MEASUREMENTS, Biocomputing 2000
DOI : 10.1142/9789814447331_0040

J. J. Faith, B. Hayete, J. T. Thaden, I. Mogno, J. Wierzbowski et al., Largescale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles, PLoS Biol, vol.5, issue.1, pp.54-66, 2007.

P. E. Meyer, F. Lafitte, and G. Bontempi, minet: A R/Bioconductor Package for Inferring Large Transcriptional Networks Using Mutual Information, BMC Bioinformatics, vol.9, issue.1, p.461, 2008.
DOI : 10.1186/1471-2105-9-461

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

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, 2002.
DOI : 10.1093/bioinformatics/bts619

N. Friedman, Inferring Cellular Networks Using Probabilistic Graphical Models, Science, vol.303, issue.5659, pp.799-805, 2004.
DOI : 10.1126/science.1094068

H. Lähdesmäki, S. Hautaniemi, I. Shmulevich, and O. Yli-harja, Relationships between probabilistic Boolean networks and dynamic Bayesian networks as models of gene regulatory networks, Signal Processing, vol.86, issue.4, pp.814-834, 2006.
DOI : 10.1016/j.sigpro.2005.06.008

P. Li, C. Zhang, E. J. Perkins, P. Gong, and Y. Deng, Comparison of probabilistic Boolean network and dynamic Bayesian network approaches for inferring gene regulatory networks, BMC Bioinformatics, vol.8, issue.Suppl 7, pp.131471-2105, 2007.
DOI : 10.1186/1471-2105-8-S7-S13

J. Chiquet, A. Smith, C. Grasseau, C. Matias, and . 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

A. Wiesel, Y. C. Eldar, A. O. Hero, and I. , Covariance Estimation in Decomposable Gaussian Graphical Models, IEEE Transactions on Signal Processing, vol.58, issue.3, pp.1482-1492, 2009.
DOI : 10.1109/TSP.2009.2037350

Z. Liu, H. Wu, J. Zhu, and H. Miao, Systematic identification of transcriptional and post-transcriptional regulations in human respiratory epithelial cells during influenza A virus infection, BMC Bioinformatics, vol.15, issue.1, p.336, 2014.
DOI : 10.1186/1471-2105-15-336

Z. Liu, C. Wu, H. Miao, and H. Wu, RegNetwork: an integrated database of transcriptional and posttranscriptional regulatory networks in human and mouse, Database, vol.2015, pp.95-095, 2015.

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, p.12776, 2010.
DOI : 10.1371/journal.pone.0012776.s003

A. Pirayre, C. Couprie, F. Bidard, L. Duval, and J. Pesquet, BRANE Cut: biologically-related a priori network enhancement with graph cuts for gene regulatory network inference, BMC Bioinformatics, vol.16, issue.1, p.369, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01330611

P. Langfelder and S. Horvath, WGCNA: an R package for weighted correlation network analysis, BMC Bioinformatics, vol.9, issue.1, pp.5591471-2105, 2008.
DOI : 10.1186/1471-2105-9-559

M. E. Newman, Communities, modules and large-scale structure in networks, Nature Physics, vol.9, issue.1, pp.25-31, 2012.
DOI : 10.1038/nature06830

E. Segal, M. Shapira, A. Regev, D. Pe-'er, D. Botstein et al., Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data, Nature Genetics, vol.291, issue.2, pp.166-176, 2003.
DOI : 10.1038/35057062

A. Joshi, R. De-smet, K. Marchal, Y. Van-de-peer, and T. Michoel, Module networks revisited: computational assessment and prioritization of model predictions, Bioinformatics, vol.25, issue.4, pp.490-496, 2009.
DOI : 10.1093/bioinformatics/btn658

S. Roy, S. Lagree, Z. Hou, J. A. Thomson, R. Stewart et al., Integrated Module and Gene-Specific Regulatory Inference Implicates Upstream Signaling Networks, PLoS Computational Biology, vol.122, issue.3???4, p.1003252, 2013.
DOI : 10.1371/journal.pcbi.1003252.s010
URL : http://doi.org/10.1371/journal.pcbi.1003252

A. Pirayre, C. Couprie, L. Duval, and J. Pesquet, Graph inference enhancement with clustering: Application to Gene Regulatory Network reconstruction, 2015 23rd European Signal Processing Conference (EUSIPCO)
DOI : 10.1109/EUSIPCO.2015.7362816

L. Grady, Random Walks for Image Segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, issue.11, pp.1768-1783233, 2006.
DOI : 10.1109/TPAMI.2006.233
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.375.3389

D. Marbach, T. Schaffter, C. Mattiussi, and D. Floreano, Generating realistic in silico gene networks for performance assessment of reverse engineering methods, J

. Comput, T. Biol, D. Schaffter, D. Marbach, and . Floreano, Available: http://dx.doi.org/10 GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods, Bioinformatics, vol.16, issue.27 16, pp.229-239, 2009.

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.808-815, 2013.
DOI : 10.1093/nar/gks1094

C. Von-mering, L. J. Jensen, B. Snel, S. D. Hooper, M. Krupp et al., STRING: known and predicted protein-protein associations, integrated and transferred across organisms, Nucleic Acids Research, vol.33, issue.Database issue, pp.433-437, 2005.
DOI : 10.1093/nar/gki005

D. Pelleg and A. Moore, X-means: Extending K-means with efficient estimation of the number of clusters, Proc. Int, pp.727-734, 2000.

H. Steinhaus, Sur la division des corps matériels en parties, Bull. Acad. Polon. Sci. Cl. III ?, vol.IV, issue.12, pp.801-804, 1956.

J. Macqueen, Some methods for classification and analysis of multivariate observations, Proc. Fifth Berkeley Symp, pp.281-297, 1967.

M. Wang, N. Jiang, T. Jia, L. Leach, J. Cockram et al., Genome-wide association mapping of agronomic and morphologic traits in highly structured populations of barley cultivars

A. Halleran, S. Clamons, and M. Saha, Transcriptomic Characterization of an Infection of Mycobacterium smegmatis by the Cluster A4 Mycobacteriophage Kampy, PLOS ONE, vol.15, issue.2, p.141100, 2015.
DOI : 10.1371/journal.pone.0141100.s001

S. Gama-castro, H. Salgado, A. Santos-zavaleta, D. Ledezma-tejeida, L. Muñiz-rascado et al., RegulonDB version 9.0: high-level integration of gene regulation, coexpression, motif clustering and beyond, Nucleic Acids Research, vol.44, issue.D1, pp.133-143, 2016.
DOI : 10.1093/nar/gkv1156
URL : https://hal.archives-ouvertes.fr/hal-01460125