C. Giallourakis, C. Henson, M. Reich, X. Xie, and V. Mootha, DISEASE GENE DISCOVERY THROUGH INTEGRATIVE GENOMICS, Annual Review of Genomics and Human Genetics, vol.6, issue.1, pp.381-406, 2005.
DOI : 10.1146/annurev.genom.6.080604.162234

C. Perez-iratxeta, P. Bork, and M. Andrade, Association of genes to genetically inherited diseases using data mining, Nature Genetics, vol.31, issue.3, pp.316-319, 2002.
DOI : 10.1038/ng895

F. Turner, D. Clutterbuck, and C. Semple, POCUS: mining genomic sequence annotation to predict disease genes, Genome Biology, vol.4, issue.11, p.75, 2003.
DOI : 10.1186/gb-2003-4-11-r75

N. Tiffin, J. Kelso, A. Powell, H. Pan, V. Bajic et al., Integration of text- and data-mining using ontologies successfully selects disease gene candidates, Nucleic Acids Research, vol.33, issue.5, pp.1544-1552, 2005.
DOI : 10.1093/nar/gki296

J. Freudenberg and P. Propping, A similarity-based method for genome-wide prediction of disease-relevant human genes, Bioinformatics, vol.18, issue.Suppl 2, pp.110-115, 2002.
DOI : 10.1093/bioinformatics/18.suppl_2.S110

S. Aerts, D. Lambrechts, S. Maity, V. Loo, P. Coessens et al., Gene prioritization through genomic data fusion, Nature Biotechnology, vol.352, issue.5, pp.537-544, 2006.
DOI : 10.1038/nbt1203

T. De-bie, L. Tranchevent, L. Van-oeffelen, and Y. Moreau, Kernel-based data fusion for gene prioritization, Bioinformatics, vol.23, issue.13, pp.125-132, 2007.
DOI : 10.1093/bioinformatics/btm187

B. Linghu, E. Snitkin, Z. Hu, Y. Xia, and C. Delisi, Genome-wide prioritization of disease genes and identification of disease-disease associations from an integrated human functional linkage network, Genome Biology, vol.10, issue.9, p.91, 2009.
DOI : 10.1186/gb-2009-10-9-r91

T. Hwang and R. Kuang, A Heterogeneous Label Propagation Algorithm for Disease Gene Discovery, Proceedings of the SIAM International Conference on Data Mining, SDM 2010, pp.583-594, 2010.
DOI : 10.1137/1.9781611972801.51

S. Yu, T. Falck, A. Daemen, L. Tranchevent, Y. Suykens et al., L2-norm multiple kernel learning and its application to biomedical data fusion, BMC Bioinformatics, vol.11, issue.1, p.309, 2010.
DOI : 10.1186/1471-2105-11-309

U. Ala, R. Piro, E. Grassi, C. Damasco, L. Silengo et al., Prediction of Human Disease Genes by Human-Mouse Conserved Coexpression Analysis, PLoS Computational Biology, vol.135, issue.3, p.1000043, 2008.
DOI : 10.1371/journal.pcbi.1000043.s002

X. Wu, R. Jiang, M. Zhang, and S. Li, Network-based global inference of human disease genes, Molecular Systems Biology, vol.27, p.189, 2008.
DOI : 10.1038/ng2110

S. Köhler, S. Bauer, D. Horn, and P. Robinson, Walking the Interactome for Prioritization of Candidate Disease Genes, The American Journal of Human Genetics, vol.82, issue.4, pp.949-958, 2008.
DOI : 10.1016/j.ajhg.2008.02.013

O. Vanunu, O. Magger, E. Ruppin, T. Shlomi, and R. Sharan, Associating Genes and Protein Complexes with Disease via Network Propagation, PLoS Computational Biology, vol.34, issue.1, p.1000641, 2010.
DOI : 10.1371/journal.pcbi.1000641.s012

L. Tranchevent, F. Capdevila, D. Nitsch, D. Moor, B. et al., A guide to web tools to prioritize candidate genes, Briefings in Bioinformatics, vol.12, issue.1, pp.22-32, 2011.
DOI : 10.1093/bib/bbq007

B. Liu, W. Lee, P. Yu, and X. Li, Partially Supervised Classification of Text Documents, ICML '02: Proceedings of the Nineteenth International Conference on Machine Learning, pp.387-394

F. Denis, R. Gilleron, and F. Letouzey, Learning from positive and unlabeled examples, Theoretical Computer Science, vol.348, issue.1, pp.70-83, 2005.
DOI : 10.1016/j.tcs.2005.09.007
URL : https://hal.archives-ouvertes.fr/inria-00538887

F. Mordelet and J. Vert, A bagging SVM to learn from positive and unlabeled examples, Pattern Recognition Letters, vol.37, 2010.
DOI : 10.1016/j.patrec.2013.06.010
URL : https://hal.archives-ouvertes.fr/hal-01101852

T. Evgeniou, C. Micchelli, and M. Pontil, Learning multiple tasks with kernel methods, J Mach Learn Res, vol.6, pp.615-637, 2005.

L. Jacob and J. Vert, Efficient peptide-MHC-I binding prediction for alleles with few known binders, Bioinformatics, vol.24, issue.3, pp.358-366, 2008.
DOI : 10.1093/bioinformatics/btm611
URL : https://hal.archives-ouvertes.fr/hal-00433574

L. Jacob and J. Vert, Protein-ligand interaction prediction: an improved chemogenomics approach, Bioinformatics, vol.24, issue.19, pp.2149-2156, 2008.
DOI : 10.1093/bioinformatics/btn409
URL : https://hal.archives-ouvertes.fr/hal-00433572

P. Pavlidis, J. Weston, J. Cai, and W. Noble, Learning Gene Functional Classifications from Multiple Data Types, Journal of Computational Biology, vol.9, issue.2, pp.401-411, 2002.
DOI : 10.1089/10665270252935539

B. Schölkopf, K. Tsuda, and J. Vert, Kernel Methods in Computational Biology, 2004.

G. Lanckriet, D. Bie, T. Cristianini, N. Jordan, M. Noble et al., A statistical framework for genomic data fusion, Bioinformatics, vol.20, issue.16, pp.2626-2635, 2004.
DOI : 10.1093/bioinformatics/bth294

V. Mckusick, Mendelian Inheritance in Man and Its Online Version, OMIM, The American Journal of Human Genetics, vol.80, issue.4, pp.588-604, 2007.
DOI : 10.1086/514346

B. Brancotte, A. Biton, I. Bernard-pierrot, F. Radvanyi, F. Reyal et al., Gene List significance at-a-glance with GeneValorization, Bioinformatics, vol.27, issue.8, pp.1187-1189, 2011.
DOI : 10.1093/bioinformatics/btr073
URL : https://hal.archives-ouvertes.fr/inria-00627865

B. Calvo, N. López-bigas, S. Furney, P. Larrañaga, and J. Lozano, A partially supervised classification approach to dominant and recessive human disease gene prediction, Computer Methods and Programs in Biomedicine, vol.85, issue.3, pp.229-237, 2007.
DOI : 10.1016/j.cmpb.2006.12.003

B. Schölkopf and A. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond Cambridge, 2002.

C. Chang and C. Lin, LIBSVM, ACM Transactions on Intelligent Systems and Technology, vol.2, issue.3, 2001.
DOI : 10.1145/1961189.1961199

Y. Yamanishi, J. Vert, and M. Kanehisa, Protein network inference from multiple genomic data: a supervised approach, Bioinformatics, vol.20, issue.Suppl 1, pp.363-370, 2004.
DOI : 10.1093/bioinformatics/bth910
URL : https://hal.archives-ouvertes.fr/hal-00433586

K. Bleakley, G. Biau, and J. Vert, Supervised reconstruction of biological networks with local models, Bioinformatics, vol.23, issue.13, pp.57-65, 2007.
DOI : 10.1093/bioinformatics/btm204
URL : https://hal.archives-ouvertes.fr/hal-00130277

G. Lanckriet, N. Cristianini, P. Bartlett, E. Ghaoui, L. Jordan et al., Learning the kernel matrix with semidefinite programming, J Mach Learn Res, vol.5, pp.27-72, 2004.

N. López-bigas and C. Ouzounis, Genome-wide identification of genes likely to be involved in human genetic disease, Nucleic Acids Research, vol.32, issue.10, pp.3108-3114, 2004.
DOI : 10.1093/nar/gkh605

E. Adie, R. Adams, K. Evans, D. Porteous, and B. Pickard, Speeding disease gene discovery by sequence based candidate prioritization, BMC Bioinformatics, vol.6, issue.1, p.55, 2005.
DOI : 10.1186/1471-2105-6-55

K. Lage, E. Karlberg, Z. Størling, P. Olason, A. Pedersen et al., A human phenome-interactome network of protein complexes implicated in genetic disorders, Nature Biotechnology, vol.33, issue.3, pp.309-316, 2007.
DOI : 10.1038/nbt1295

M. Van-driel, J. Bruggeman, G. Vriend, H. Brunner, and J. Leunissen, A text-mining analysis of the human phenome, European Journal of Human Genetics, vol.68, issue.5, pp.535-542, 2006.
DOI : 10.1038/sj.ejhg.5201585

B. Schölkopf, J. Platt, J. Shawe-taylor, A. Smola, and R. Williamson, Estimating the Support of a High-Dimensional Distribution, Neural Computation, vol.6, issue.1, pp.1443-1471, 2001.
DOI : 10.1214/aos/1069362732

C. Son, S. Bilke, S. Davis, B. Greer, J. Wei et al., Database of mRNA gene expression profiles of multiple human organs, Genome Research, vol.15, issue.3, pp.443-450, 2005.
DOI : 10.1101/gr.3124505

A. Su, M. Cooke, K. Ching, Y. Hakak, J. Walker et al., Large-scale analysis of the human and mouse transcriptomes, Proceedings of the National Academy of Sciences, vol.99, issue.7, pp.994465-4470, 2002.
DOI : 10.1073/pnas.012025199
URL : https://hal.archives-ouvertes.fr/hal-00311329

R. Kondor and J. Lafferty, Diffusion kernels on graphs and other discrete input, Proceedings of the Nineteenth International Conference on Machine Learning, pp.315-322, 2002.