. Aerts, Gene prioritization through genomic data fusion, Nature Biotechnology, vol.352, issue.5, pp.537-544, 2006.
DOI : 10.1038/nbt1203

P. L. Bartlett and A. Tewari, Sparseness vs estimating conditional probabilities: Some asymptotic results, J. Mach. Learn. Res, vol.8, issue.207, pp.775-790
DOI : 10.1007/978-3-540-27819-1_39

L. Breiman, Bagging predictors, Machine Learning, vol.10, issue.2, pp.123-140, 1996.
DOI : 10.1007/BF00058655

L. Breiman, Random forests, Machine Learning, vol.45, issue.1, pp.5-32, 2001.
DOI : 10.1023/A:1010933404324

. Chapelle, Semi-Supervised Learning, 2006.
DOI : 10.7551/mitpress/9780262033589.001.0001

D. Bie, Kernel-based data fusion for gene prioritization, Bioinformatics, vol.23, issue.13, pp.125-132, 2007.
DOI : 10.1093/bioinformatics/btm187

. Denis, 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

C. Elkan and K. Noto, Learning classifiers from only positive and unlabeled data, Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD 08, pp.213-220, 2008.
DOI : 10.1145/1401890.1401920
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.140.9201

. Faith, Many Microbe Microarrays Database: uniformly normalized Affymetrix compendia with structured experimental metadata, Nucleic Acids Research, vol.36, issue.Database, pp.866-870, 2008.
DOI : 10.1093/nar/gkm815

. Hastie, The elements of statistical learning: data mining, inference, and prediction, 2001.

T. Joachims, Transductive inference for text classification using support vector machines, ICML '99: Proceedings of the Sixteenth International Conference on Machine Learning, pp.200-209, 1999.

T. Joachims, A probabilistic analysis of the rocchio algorithm with TFIDF for text categorization, ICML '97: Proceedings of the Fourteenth International Conference on Machine Learning, pp.143-151, 1997.

W. S. Lee and B. Liu, Learning with positive and unlabeled examples using weighted logistic regression, Machine Learning, Proceedings of the Twentieth International Conference (ICML 2003, pp.448-455, 2003.

X. Li and B. Liu, Learning to classify texts using positive and unlabeled data, IJCAI'03: Proceedings of the 18th international joint conference on Artificial intelligence, pp.587-592, 2003.

. Liu, Partially supervised classification of text documents, ICML '02: Proceedings of the Nineteenth International Conference on Machine Learning, pp.387-394, 2002.

. Liu, Building text classifiers using positive and unlabeled examples, Third IEEE International Conference on Data Mining, pp.179-186, 2003.
DOI : 10.1109/ICDM.2003.1250918
URL : http://array.bioengr.uic.edu/~yangdai/pub/liub_classifiers.pdf

L. M. Manevitz and M. Yousef, One-class SVMs for document classification, J. Mach. Learn. Res, vol.2, pp.139-154, 2001.
DOI : 10.1016/j.neucom.2006.05.013

F. Mordelet and J. Vert, SIRENE: supervised inference of regulatory networks, Bioinformatics, vol.24, issue.16, pp.76-82, 2008.
DOI : 10.1093/bioinformatics/btn273
URL : https://hal.archives-ouvertes.fr/hal-00259119

K. Pelckmans and J. A. Suykens, Transductively learning from positive examples only, Proc. of the European Symposium on Artificial Neural Networks, 2009.

. Salgado, 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
URL : http://doi.org/10.1093/nar/27.1.59

. Schölkopf, Estimating the Support of a High-Dimensional Distribution, Neural Computation, vol.6, issue.1, pp.1443-1471, 2001.
DOI : 10.1214/aos/1069362732

C. Scott and G. Blanchard, Novelty detection: Unlabeled data definitely help, Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS) 2009, pp.464-471, 2009.

. Shah, SVM-HUSTLE--an iterative semi-supervised machine learning approach for pairwise protein remote homology detection, Bioinformatics, vol.24, issue.6, pp.783-790, 2008.
DOI : 10.1093/bioinformatics/btn028

. Sriphaew, Cool blog classification from positive and unlabeled examples Advances in Knowledge Discovery and Data Mining, pp.62-73, 2009.

I. Steinwart, Sparseness of Support Vector Machines, J. Mach. Learn. Res, vol.4, pp.1071-1105, 2003.

R. Vert and J. Vert, Consistency and convergence rates of one-class SVMs and related algorithms, J. Mach. Learn. Res, vol.7, pp.817-854, 2006.

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

. Zhang, Maximum margin clustering made practical, Proceedings of the 24th international conference on Machine learning, ICML '07, pp.583-596, 2009.
DOI : 10.1145/1273496.1273637