A. Barabási and R. Albert, Emergence of Scaling in Random Networks, Science, vol.286, issue.5439, pp.509-512, 1999.

T. Barrett, O. Tugba, . Suzek, B. Dennis, S. E. Troup et al., NCBI GEO: mining millions of expression profiles--database and tools, Nucleic Acids Research, vol.33, issue.Database issue, pp.D562-D566, 2004.

I. Bartha, J. Di-iulio, J. C. Venter, and A. Telenti, Human gene essentiality, Nature Reviews Genetics, vol.19, issue.1, pp.51-62, 2017.

O. Basha, S. Tirman, A. Eluk, and E. Yeger-lotem, ResponseNet2.0: revealing signaling and regulatory pathways connecting your proteins and genes?now with human data, Nucleic Acids Research, vol.41, issue.W1, pp.W198-W203, 2013.

D. Beisser, G. W. Klau, T. Dandekar, T. Muller, and M. T. Dittrich, BioNet: an R-Package for the functional analysis of biological networks, Bioinformatics, vol.26, issue.8, pp.1129-1130, 2010.

N. Berndt, J. Eckstein, N. Heucke, R. Gajowski, M. Stockmann et al., Characterization of Lipid and Lipid Droplet Metabolism in Human HCC, Cells, vol.8, issue.5, p.512, 2019.

I. Boomgaarden, C. Vock, M. Klapper, and F. Döring, Comparative Analyses of Disease Risk Genes Belonging to the Acyl-CoA Synthetase Medium-Chain (ACSM) Family in Human Liver and Cell Lines, Biochemical Genetics, vol.47, issue.9-10, pp.739-748, 2009.

P. Chen, F. Wang, J. Feng, R. Zhou, Y. Chang et al., Co-expression network analysis identified six hub genes in association with metastasis risk and prognosis in hepatocellular carcinoma, Oncotarget, vol.8, issue.30, pp.48948-48958, 2017.

W. Chen, J. Liu, and S. He, Prior knowledge guided active modules identification: an integrated multi-objective approach, BMC Systems Biology, vol.11, issue.S2, pp.1-12, 2017.

Y. Derek, A. Chiang, Y. Villanueva, J. Hoshida, P. Peix et al., Focal gains of VEGFA and molecular classification of hepatocellular carcinoma, Cancer research, vol.68, pp.6779-6788, 2008.

J. Alex, F. Cornish, and . Markowetz, SANTA: quantifying the functional content of molecular networks, PLoS computational biology, vol.10, p.1003808, 2014.

A. D. Kenneth, W. M. Jong, and . Spears, A formal analysis of the role of multi-point crossover in genetic algorithms, Annals of Mathematics and Artificial Intelligence, vol.5, pp.1-26, 1992.

K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, vol.6, issue.2, pp.182-197, 2002.

M. T. Dittrich, G. W. Klau, A. Rosenwald, T. Dandekar, and T. Muller, Identifying functional modules in protein-protein interaction networks: an integrated exact approach, Bioinformatics, vol.24, issue.13, pp.i223-i231, 2008.

G. Hajk, J. Drost, and . Paszkowski, Biomartr: genomic data retrieval with R, Bioinformatics, vol.33, pp.1216-1217, 2017.

Z. Fang, J. Martin, and Z. Wang, Statistical methods for identifying differentially expressed genes in RNA-Seq experiments, Cell & Bioscience, vol.2, issue.1, p.26, 2012.

M. Lesley, G. E. Forrester, D. J. Neal, . Judah, J. Michael et al., Evidence for involvement of multiple forms of cytochrome P-450 in aflatoxin B1 metabolism in human liver, Proceedings of the National Academy of Sciences, vol.87, pp.8306-8310, 1990.

J. Susan-dina-ghiassian, A. Menche, and . Barabási, A DIseAse MOdule Detection (DIAMOnD) Algorithm Derived from a Systematic Analysis of Connectivity Patterns of Disease Proteins in the Human Interactome, PLoS Computational Biology, vol.11, pp.1-21, 2015.

E. Glaab, A. Baudot, N. Krasnogor, R. Schneider, and A. Valencia, EnrichNet: network-based gene set enrichment analysis, Bioinformatics, vol.28, issue.18, pp.i451-i457, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01266782

D. Hao-he, J. Lin, Y. Zhang, H. Wang, and . Deng, Comparison of statistical methods for subnetwork detection in the integration of gene expression and protein interaction network, BMC Bioinformatics, vol.18, p.149, 2017.

Y. Hoshida, S. M. Nijman, M. A. Kobayashi, J. A. Chan, J. Y. Brunet et al., Integrative Transcriptome Analysis Reveals Common Molecular Subclasses of Human Hepatocellular Carcinoma, Cancer Research, vol.69, issue.18, pp.7385-7392, 2009.

T. Ideker, O. Ozier, B. Schwikowski, and A. F. Siegel, Discovering regulatory and signalling circuits in molecular interaction networks, Bioinformatics, vol.18, issue.Suppl 1, pp.S233-S240, 2002.

P. Jiang, H. Wang, W. Li, C. Zang, B. Li et al., Network analysis of gene essentiality in functional genomics experiments, Genome Biology, vol.16, issue.1, p.239, 2015.

D. Li, Z. Pan, G. Hu, Z. Zhu, and S. He, Active module identification in intracellular networks using a memetic algorithm with a new binary decoding scheme, BMC Genomics, vol.18, issue.S2, p.209, 2017.

Y. Lu, P. Liu, P. Xiao, and H. Deng, Hotelling's T2 multivariate profiling for detecting differential expression in microarrays, Bioinformatics, vol.21, issue.14, pp.3105-3113, 2005.

H. Ma, E. E. Schadt, L. M. Kaplan, and H. Zhao, COSINE: COndition-SpecIfic sub-NEtwork identification using a global optimization method, Bioinformatics, vol.27, issue.9, pp.1290-1298, 2011.

M. A. Mahdavi and Y. Lin, False positive reduction in protein-protein interaction predictions using gene ontology annotations, BMC Bioinformatics, vol.8, issue.1, 2007.

C. A. Miller, S. H. Settle, E. P. Sulman, K. D. Aldape, and A. Milosavljevic, Discovering functional modules by identifying recurrent and mutually exclusive mutational patterns in tumors, BMC Medical Genomics, vol.4, issue.1, p.34, 2011.

E. Mosca and L. Milanesi, Network-based analysis of omics with multi-objective optimization, Molecular BioSystems, vol.9, issue.12, p.2971, 2013.

H. Nguyen, S. Shrestha, D. Tran, A. Shafi, S. Draghici et al., A Comprehensive Survey of Tools and Software for Active Subnetwork Identification, Frontiers in Genetics, vol.10, p.155, 2019.

I. Nikolayeva, O. Guitart-pla, and B. Schwikowski, Network module identification?A widespread theoretical bias and best practices, Methods, vol.132, pp.19-25, 2018.
URL : https://hal.archives-ouvertes.fr/pasteur-02965314

D. Petrochilos, A. Shojaie, J. Gennari, and N. Abernethy, Using random walks to identify cancer-associated modules in expression data, BioData Mining, vol.6, issue.1, p.17, 2013.

F. Rapaport, A. Zinovyev, M. Dutreix, E. Barillot, and J. P. Vert, Classification of microarray data using gene networks, BMC Bioinformatics, vol.8, issue.1, pp.1-15, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00433577

M. A. Reyna, M. D. Leiserson, and B. J. Raphael, Hierarchical HotNet: identifying hierarchies of altered subnetworks, Bioinformatics, vol.34, issue.17, pp.i972-i980, 2018.

M. E. Ritchie, B. Phipson, D. Wu, Y. Hu, C. W. Law et al., limma powers differential expression analyses for RNA-sequencing and microarray studies, Nucleic Acids Research, vol.43, issue.7, pp.e47-e47, 2015.

S. Robinson, J. Nevalainen, G. Pinna, A. Campalans, J. P. Radicella et al., Incorporating interaction networks into the determination of functionally related hit genes in genomic experiments with Markov random fields, Bioinformatics, vol.33, issue.14, pp.i170-i179, 2017.
URL : https://hal.archives-ouvertes.fr/hal-02101154

S. Roessler, H. Jia, A. Budhu, M. Forgues, Q. Ye et al., A Unique Metastasis Gene Signature Enables Prediction of Tumor Relapse in Early-Stage Hepatocellular Carcinoma Patients, Cancer Research, vol.70, issue.24, pp.10202-10212, 2010.

E. Segal, N. Friedman, D. Koller, and A. Regev, A module map showing conditional activity of expression modules in cancer, Nature Genetics, vol.36, issue.10, pp.1090-1098, 2004.

A. Subramanian, P. Tamayo, V. K. Mootha, S. Mukherjee, B. L. Ebert et al., Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles, Proceedings of the National Academy of Sciences, vol.102, issue.43, pp.15545-15550, 2005.

A. Tanay, R. Sharan, M. Kupiec, and R. Shamir, Revealing modularity and organization in the yeast molecular network by integrated analysis of highly heterogeneous genomewide data, Proceedings of the National Academy of Sciences, vol.101, issue.9, pp.2981-2986, 2004.

N. Tuncbag, S. J. Gosline, A. Kedaigle, A. R. Soltis, A. Gitter et al., Network-Based Interpretation of Diverse High-Throughput Datasets through the Omics Integrator Software Package, PLOS Computational Biology, vol.12, issue.4, p.e1004879, 2016.

P. Jean, M. Vert, and . Kanehisa, Extracting active pathways from gene expression data, Bioinformatics, vol.19, pp.238-244, 2003.

L. J. Christian-von-mering, B. Jensen, . Snel, D. Sean, M. Hooper et al., STRING: known and predicted protein-protein associations, integrated and transferred across organisms, Nucleic acids research, vol.33, pp.433-437, 2005.

B. Yates, B. Braschi, K. A. Gray, R. L. Seal, S. Tweedie et al., Genenames.org: the HGNC and VGNC resources in 2017, Nucleic Acids Research, vol.45, issue.D1, pp.D619-D625, 2016.