K. Giacomini, R. Krauss, D. Roden, M. Eichelbaum, M. Hayden et al., When good drugs go bad, Nature, vol.43, issue.7139, pp.446975-977, 2007.
DOI : 10.1038/446975a

D. Houtsma, H. Guchelaar, and H. Gelderblom, Pharmacogenetics in Oncology: A Promising Field, Current Pharmaceutical Design, vol.16, issue.2, pp.155-163, 2010.
DOI : 10.2174/138161210790112719

S. Mcwhinney, R. Goldberg, and H. Mcleod, Platinum neurotoxicity pharmacogenetics, Molecular Cancer Therapeutics, vol.8, issue.1, pp.10-16, 2009.
DOI : 10.1158/1535-7163.MCT-08-0840

N. Tatonetti, T. Liu, and R. Altman, Predicting drug side-effects by chemical systems biology, Genome Biology, vol.10, issue.9, p.238, 2009.
DOI : 10.1186/gb-2009-10-9-238

J. Scheiber, B. Chen, M. Milik, S. Sukuru, A. Bender et al., Gaining Insight into Off-Target Mediated Effects of Drug Candidates with a Comprehensive Systems Chemical Biology Analysis, Journal of Chemical Information and Modeling, vol.49, issue.2, pp.308-325, 2009.
DOI : 10.1021/ci800344p

S. Whitebread, J. Hamon, D. Bojanic, and L. Urban, Keynote review: in vitro safety pharmacology profiling: an essential tool for successful drug development. Drug DiscoVery Today, pp.1421-1433, 2005.

E. Benfenatia and G. Gini, Computational predictive programs (expert systems) in toxicology, Toxicology, vol.119, issue.3, pp.213-225, 1997.
DOI : 10.1016/S0300-483X(97)03631-7

M. Campillos, M. Kuhn, A. Gavin, L. Jensen, and P. Bork, Drug Target Identification Using Side-Effect Similarity, Science, vol.321, issue.5886, pp.263-269, 2008.
DOI : 10.1126/science.1158140

M. Fukuzaki, M. Seki, H. Kashima, and J. Sese, Side Effect Prediction Using Cooperative Pathways, 2009 IEEE International Conference on Bioinformatics and Biomedicine, pp.142-147, 2009.
DOI : 10.1109/BIBM.2009.26

L. Xie, J. Li, L. Xie, and P. Bourne, Drug Discovery Using Chemical Systems Biology: Identification of the Protein-Ligand Binding Network To Explain the Side Effects of CETP Inhibitors, PLoS Computational Biology, vol.45, issue.5, p.1000387, 2009.
DOI : 10.1371/journal.pcbi.1000387.s012

J. Scheiber, J. Jenkins, S. Sukuru, A. Bender, D. Mikhailov et al., Mapping Adverse Drug Reactions in Chemical Space, Journal of Medicinal Chemistry, vol.52, issue.9, pp.523103-523110, 2009.
DOI : 10.1021/jm801546k

Y. Yamanishi, M. Kotera, M. Kanehisa, and S. Goto, Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework, Bioinformatics, vol.26, issue.12, pp.246-254, 2010.
DOI : 10.1093/bioinformatics/btq176

M. Kuhn, M. Campillos, I. Letunic, L. Jensen, and P. Bork, A side effect resource to capture phenotypic effects of drugs, Molecular Systems Biology, vol.6, p.343, 2010.
DOI : 10.1093/nar/gkm958

B. Chen, D. Wild, and R. Guha, PubChem as a Source of Polypharmacology, Journal of Chemical Information and Modeling, vol.49, issue.9, pp.2044-2055, 2009.
DOI : 10.1021/ci9001876

D. Wishart, C. Knox, A. Guo, S. Shrivastava, M. Hassanali et al., DrugBank: a comprehensive resource for in silico drug discovery and exploration, Nucleic Acids Research, vol.34, issue.90001, pp.668-672, 2006.
DOI : 10.1093/nar/gkj067

M. Gribskov and N. Robinson, Use of receiver operating characteristic (ROC) analysis to evaluate sequence matching, Computers & Chemistry, vol.20, issue.1, pp.25-33, 1996.
DOI : 10.1016/S0097-8485(96)80004-0

M. Kravette, Perilymphatic atrophy of skin. An adverse side effect of intralesional steroid injections, Clin Podiatr Med Surg, vol.3, pp.457-62, 1986.

K. Sander and D. Sander, New insights into transient global amnesia: recent imaging and clinical findings, The Lancet Neurology, vol.4, issue.7, pp.437-444, 2005.
DOI : 10.1016/S1474-4422(05)70121-6

C. Richard and M. Klein, Ventricular arrhythmias in aortic valve disease: Analysis of 102 patients. The American Journal of Cardiology, pp.1079-1083, 1984.

T. Yang, Computational verb decision trees, International Journal of Computational Cognition, vol.4, issue.4, 2006.

S. Kramer, E. Frank, and C. Helma, Fragment generation and support vector machines for inducing SARs, SAR and QSAR in Environmental Research, vol.2, issue.5, pp.509-523, 2002.
DOI : 10.1214/aos/1028144844

L. De-raedt and S. Kramer, The levelwise version space algorithm and its application to molecular fragment finding, International Joint Conference on Artificial Intelligence, pp.853-862, 2001.

M. Kuramochi and K. , Frequent subgraph discovery, Proceedings 2001 IEEE International Conference on Data Mining, p.313, 2001.
DOI : 10.1109/ICDM.2001.989534
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.20.7488

A. Inokuchi, T. Washio, H. Motoda, K. Kumazawa, and N. Arai, Fast and Complete Mining Method for Frequent Graph Patterns, Journal of Japanese Society for Artificial Intelligence, vol.15, issue.6, pp.1052-1063, 2000.

U. Rückert and S. Kramer, Frequent free tree discovery in graph data, Proceedings of the 2004 ACM symposium on Applied computing , SAC '04, pp.564-570, 2004.
DOI : 10.1145/967900.968018

X. Yan and J. Han, gSpan: Graph-Based Substructure Pattern Mining, Proceedings of the 2002 IEEE International Conference on Data Mining ICDM '02, p.721, 2002.

H. Saigo, N. Krämer, and K. Tsuda, Partial least squares regression for graph mining, Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD 08, pp.578-586, 2008.
DOI : 10.1145/1401890.1401961

R. Gozalbes, R. Carbajo, and A. Pineda-lucena, From fragment screening to potent binders: strategies for fragment-to-lead evolution, Mini Reviews in Medicinal Chemistry, vol.9, issue.8, pp.956-961, 2009.

T. Furey, N. Cristianini, N. Duffy, D. Bednarski, M. Schummer et al., Support vector machine classification and validation of cancer tissue samples using microarray expression data, Bioinformatics, vol.16, issue.10, pp.906-914, 2000.
DOI : 10.1093/bioinformatics/16.10.906

H. Hotelling, RELATIONS BETWEEN TWO SETS OF VARIATES, Biometrika, vol.28, issue.3-4, pp.321-377, 1936.
DOI : 10.1093/biomet/28.3-4.321

S. Dudoit, J. Fridlyand, and T. Speed, Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data, Journal of the American Statistical Association, vol.97, issue.457, pp.1151-1160, 2001.
DOI : 10.1198/016214502753479248

R. Tibshirani, T. Hastie, B. Narasimhan, and G. Chu, Class Prediction by Nearest Shrunken Centroids, with Applications to DNA Microarrays, Statistical Science, vol.18, issue.1, pp.104-117, 2003.
DOI : 10.1214/ss/1056397488

D. Witten, R. Tibshirani, and T. Hastie, A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis, Biostatistics, vol.10, issue.3, p.515, 2009.
DOI : 10.1093/biostatistics/kxp008