D. P. Bartel, MicroRNAs, Cell, vol.116, issue.2, pp.281-297, 2004.
DOI : 10.1016/S0092-8674(04)00045-5

URL : https://hal.archives-ouvertes.fr/hal-00369966

Q. Jiang, Prioritization of disease microRNAs through a human phenome-microRNAome network, BMC Systems Biology, vol.4, issue.Suppl 1, p.2, 2010.
DOI : 10.1186/1752-0509-4-S1-S2

Q. Jiang, Y. Hao, G. Wang, T. Zhang, and Y. Wang, Weighted Network-Based Inference of Human MicroRNA-Disease Associations, 2010 Fifth International Conference on Frontier of Computer Science and Technology, pp.431-435, 2010.
DOI : 10.1109/FCST.2010.18

X. Li, Prioritizing human cancer microRNAs based on genes' functional consistency between microRNA and cancer, Nucleic Acids Research, vol.39, issue.22, 2011.
DOI : 10.1093/nar/gkr770

H. Shi, Walking the interactome to identify human miRNA-disease associations through the functional link between miRNA targets and disease genes, BMC Systems Biology, vol.7, issue.1, p.101, 2013.
DOI : 10.1126/science.1140481

W. Ritchie, S. Flamant, and J. E. Rasko, Predicting microRNA targets and functions: traps for the unwary, Nature Methods, vol.6, issue.6, pp.397-398, 2009.
DOI : 10.1038/ng1536

S. Bandyopadhyay, R. Mitra, U. Maulik, and M. Zhang, Development of the human cancer microRNA network, Silence, vol.1, issue.1, p.6, 2010.
DOI : 10.1186/1758-907X-1-6

M. Lu, An Analysis of Human MicroRNA and Disease Associations, PLoS ONE, vol.31, issue.10, pp.1-5, 2008.
DOI : 10.1371/journal.pone.0003420.s005

H. Chen and Z. Zhang, Prediction of Associations between OMIM Diseases and MicroRNAs by Random Walk on OMIM Disease Similarity Network, The Scientific World Journal, vol.7, issue.6, p.2013, 2013.
DOI : 10.1186/1471-2164-11-S4-S5

X. Chen, M. Liu, and G. Yan, RWRMDA: predicting novel human microRNA???disease associations, Molecular BioSystems, vol.94, issue.Suppl 4, p.2792, 2012.
DOI : 10.1039/c2mb25180a

X. Chen and G. Yan, Semi-supervised learning for potential human microRNA-disease associations inference, Scientific Reports, vol.7, p.5501, 2014.
DOI : 10.1038/srep05501

P. Xuan, Prediction of microRNAs Associated with Human Diseases Based on Weighted k Most Similar Neighbors, PLoS ONE, vol.16, issue.22, 2013.
DOI : 10.1371/journal.pone.0070204.s006

P. Xuan, Prediction of potential disease-associated microRNAs based on random walk, Bioinformatics, vol.31, issue.11, pp.1805-1815, 2015.
DOI : 10.1093/bioinformatics/btv039

D. Wang, J. Wang, M. Lu, F. Song, and Q. Cui, Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases, Bioinformatics, vol.26, issue.13, pp.1644-1650, 2010.
DOI : 10.1093/bioinformatics/btq241

T. Zhou, J. Ren, M. Medo, and Y. C. Zhang, Bipartite network projection and personal recommendation, Physical Review E, vol.76, issue.4, pp.1-7, 2007.
DOI : 10.1103/PhysRevE.76.046115

J. Li, Computational prediction of microRNA networks incorporating environmental toxicity and disease etiology, Scientific Reports, vol.13, p.5576, 2014.
DOI : 10.1038/srep05576

Q. Jiang, G. Wang, S. Jin, Y. Li, and Y. Wang, Predicting human microRNA-disease associations based on support vector machine, 2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp.282-293, 2013.
DOI : 10.1109/BIBM.2010.5706611

D. P. Bartel, MicroRNAs: Target Recognition and Regulatory Functions, Cell, vol.136, issue.2, pp.215-233, 2009.
DOI : 10.1016/j.cell.2009.01.002

S. Baskerville and D. P. Bartel, Microarray profiling of microRNAs reveals frequent coexpression with neighboring miRNAs and host genes, RNA, vol.11, issue.3, pp.241-247, 2005.
DOI : 10.1261/rna.7240905

G. Salton, A. Wong, and C. S. Yang, A vector space model for automatic indexing, Communications of the ACM, vol.18, issue.11, pp.613-620, 1975.
DOI : 10.1145/361219.361220

P. D. Turney and P. Pantel, From Frequency to Meaning : Vector Space Models of Semantics, Journal of Artificial Intelligence Research, vol.37, pp.141-188, 2010.

Z. Harris, Distributional structure, pp.146-162, 1954.

S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, and R. Harshman, Indexing by latent semantic analysis, Journal of the American Society for Information Science, vol.41, issue.6, pp.391-407, 1990.
DOI : 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9

T. Lasko, J. G. Bhagwat, K. H. Zou, and L. Ohno-machado, The use of receiver operating characteristic curves in biomedical informatics, Journal of Biomedical Informatics, vol.38, issue.5, pp.404-415, 2005.
DOI : 10.1016/j.jbi.2005.02.008

Y. Li, HMDD v2.0: a database for experimentally supported human microRNA and disease associations, Nucleic Acids Research, vol.42, issue.D1, pp.1070-1074, 2014.
DOI : 10.1093/nar/gkt1023

Y. Dai, Comprehensive analysis of microRNA expression patterns in renal biopsies of lupus nephritis patients, Rheumatology International, vol.38, issue.suppl, pp.749-754, 2009.
DOI : 10.1007/s00296-008-0758-6

Z. Lu, High-throughput sequencing of MicroRNAs in adenovirus type 3 infected human laryngeal epithelial cells, Journal of Biomedicine and Biotechnology, p.2010, 2010.

I. P. Pogribny, Alterations of microRNAs and their targets are associated with acquired resistance of MCF-7 breast cancer cells to cisplatin, International Journal of Cancer, vol.35, issue.8, pp.1785-1794, 2010.
DOI : 10.1002/ijc.25191

O. Giricz, Hsa-miR-375 is differentially expressed during breast lobular neoplasia and promotes loss of mammary acinar polarity, The Journal of Pathology, vol.34, issue.Suppl 2, pp.108-119, 2012.
DOI : 10.1002/path.2978

M. G. Schrauder, Circulating Micro-RNAs as Potential Blood-Based Markers for Early Stage Breast Cancer Detection, PLoS ONE, vol.6, issue.1, 2012.
DOI : 10.1371/journal.pone.0029770.s002

E. Van-schooneveld, Expression profiling of cancerous and normal breast tissues identifies microRNAs that are differentially expressed in serum from patients with (metastatic) breast cancer and healthy volunteers, Breast Cancer Research, vol.5, issue.1, p.34, 2012.
DOI : 10.1371/journal.pone.0013515

J. Jarry, D. Schadendorf, C. Greenwood, . Spatz, and L. C. Van-kempen, The validity of circulating microRNAs in oncology: Five years of challenges and contradictions, Molecular Oncology, vol.118, issue.2, pp.819-829, 2014.
DOI : 10.5732/cjc.012.10271

N. Nishida-aoki and T. Ochiya, Interactions between cancer cells and normal cells via miRNAs in extracellular vesicles, Cellular and Molecular Life Sciences, vol.10, issue.17, pp.1849-1861, 2015.
DOI : 10.1007/s00018-014-1811-0

W. Su, M. S. Aloi, and G. Garden, MicroRNAs mediating CNS inflammation: Small regulators with powerful potential, Brain, Behavior, and Immunity, vol.52, 2015.
DOI : 10.1016/j.bbi.2015.07.003

M. Tsuchiya, Differential Regulation of Inflammation by Inflammatory Mediators in Cystic Fibrosis Lung Epithelial Cells, Journal of Interferon & Cytokine Research, vol.33, issue.3, pp.121-130, 2013.
DOI : 10.1089/jir.2012.0074

H. Zhang, L. Liu, J. Hu, and L. Song, MicroRNA Regulatory Network Revealing the Mechanism of Inflammation in Atrial Fibrillation, Medical Science Monitor, vol.21, pp.3505-3513, 2015.
DOI : 10.12659/MSM.895982

B. C. Peck, MicroRNAs Classify Different Disease Behavior Phenotypes of Crohn??s Disease and May Have Prognostic Utility, Inflammatory Bowel Diseases, vol.21, issue.9, pp.2178-87, 2015.
DOI : 10.1097/MIB.0000000000000478

S. D. Hsu, miRTarBase: a database curates experimentally validated microRNA-target interactions, Nucleic Acids Research, vol.39, issue.Database, pp.163-169, 2011.
DOI : 10.1093/nar/gkq1107

S. Griffiths-jones, R. J. Grocock, S. Van-dongen, A. Bateman, and A. J. Enright, miRBase: microRNA sequences, targets and gene nomenclature, Nucleic Acids Research, vol.34, issue.90001, pp.140-144, 2006.
DOI : 10.1093/nar/gkj112

D. Lin, An Information-Theoretic Definition of Similarity, 15th International Conference of Machine Learning, pp.296-304, 1998.

S. Harispe, S. Ranwez, S. Janaqi, and J. Montmain, The semantic measures library and toolkit: fast computation of semantic similarity and relatedness using biomedical ontologies, Bioinformatics, vol.30, issue.5, pp.740-742, 2014.
DOI : 10.1093/bioinformatics/btt581

URL : https://hal.archives-ouvertes.fr/hal-01059329

R. Bhajun, A statistically inferred microRNA network identifies breast cancer target miR-940 as an actin cytoskeleton regulator, Scientific Reports, vol.15, p.8336, 2015.
DOI : 10.1038/srep08336

URL : https://hal.archives-ouvertes.fr/hal-01152543

D. Le, Network-based ranking methods for prediction of novel disease associated microRNAs, Computational Biology and Chemistry, vol.58, pp.139-148, 2015.
DOI : 10.1016/j.compbiolchem.2015.07.003

M. Li, Weighted networks of scientific communication: The measurement and topological role of weight. Physica A: Statistical Mechanics and its Applications 350, pp.643-656, 2005.