R. Agrawal, T. Mielinski, and A. Swami, Mining association rules between sets of items in large databasesMining association rules between sets of items in large databases, ACM SIGMOD Conference, 1993.

S. R. Barber, M. J. Davies, K. Khunti, and L. J. Gray, Risk assessment tools for detecting those with pre-diabetes: A systematic review, Diabetes Research and Clinical Practice, vol.105, issue.1, pp.1-13, 2014.
DOI : 10.1016/j.diabres.2014.03.007

C. Baumgartner, M. Osl, M. Netzer, and D. Baumgartner, Bioinformatic-driven search for metabolic biomarkers in disease, Journal of Clinical Bioinformatics, vol.1, issue.1, pp.2-10, 2011.
DOI : 10.1186/2043-9113-1-2

G. Biau, Analysis of a random forests model, J. Mach. Learn. Res, vol.13, pp.1063-1095, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00476545

J. Boccard, J. L. Veuthey, R. , and S. , Knowledge discovery in metabolomics: An overview of MS data handling, Journal of Separation Science, vol.1184, issue.3, pp.290-304, 2010.
DOI : 10.1002/jssc.200900609

A. Boulesteix, A. Bender, J. L. Bermejo, C. Strobl, and L. Breiman, Random forest Gini importance favours SNPs with large minor allele frequency: impact, sources and recommendations, Briefings in Bioinformatics, vol.13, issue.3, pp.292-304, 1023.
DOI : 10.1093/bib/bbr053
URL : http://bib.oxfordjournals.org/cgi/content/short/13/3/292

G. C. Cawley, T. , and N. L. , On over-fitting in model selection and subsequent selection bias in performance evaluation, J. Mach. Learn. Res, vol.11, pp.2079-2107, 2010.

T. Chen, Y. Cao, Y. Zhang, J. Liu, Y. Bao et al., Random Forest in Clinical Metabolomics for Phenotypic Discrimination and Biomarker Selection, Evidence-Based Complementary and Alternative Medicine, vol.121, issue.24, p.298183, 2013.
DOI : 10.1021/pr9004162

H. Cho, S. B. Kim, M. K. Jeong, Y. Park, N. Gletsu et al., Discovery of metabolite features for the modelling and analysis of high-resolution NMR spectra, International Journal of Data Mining and Bioinformatics, vol.2, issue.2, pp.176-192, 2008.
DOI : 10.1504/IJDMB.2008.019097

C. Cortes, V. Vapnik, R. Uriarte, and S. A. De-andrés, Support-vector networks Gene selection and classification of microarray data using random forest, Mach. Learn. BMC Bioinformatics, vol.20, issue.7, pp.273-2973, 1995.

A. B. Drabovich, M. P. Pavlou, I. Bartruch, E. P. Diamandis, T. D. Issaq et al., Mass spectrometry metabolomic data handling for biomarker discovery in " Proteomic and Metabolomic Approaches to Biomarker Discovery, pp.17-37, 2013.

Y. Fan, T. B. Murphy, J. C. Byrne, L. Brennan, J. M. Fitzpatrick et al., Applying Random Forests To Identify Biomarker Panels in Serum 2D-DIGE Data for the Detection and Staging of Prostate Cancer, Journal of Proteome Research, vol.10, issue.3, pp.1361-1373, 1021.
DOI : 10.1021/pr1011069

O. Fiehn, J. Kopka, P. Dormann, T. Altmann, R. N. Trethewey et al., Metabolite profiling for plant functional genomics, Nature Biotechnology, vol.18, issue.11, pp.1157-1161, 1038.
DOI : 10.1038/81137

Y. Freund and R. E. Schapire, A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting, Journal of Computer and System Sciences, vol.55, issue.1, pp.119-139, 1997.
DOI : 10.1006/jcss.1997.1504

A. Frickenschmidt, H. Frohlich, D. Bullinger, A. Zell, S. Laufer et al., Metabonomics in cancer diagnosis: mass spectrometry-based profiling of urinary nucleosides from breast cancer patients, Biomarkers, vol.1084, issue.4, pp.435-449, 1080.
DOI : 10.1080/13547500410001668379

B. Ganter and R. Wille, Formal Concept Analysis -Mathematical Foundations, 1999.

F. Giacomoni, G. Le-corguille, M. Monsoor, M. Landi, P. Pericard et al., Workflow4Metabolomics: a collaborative research infrastructure for computational metabolomics, Bioinformatics, vol.31, issue.9, pp.1493-1495, 2015.
DOI : 10.1093/bioinformatics/btu813
URL : https://hal.archives-ouvertes.fr/hal-01123263

P. Giudici and S. Figini, A Review of: ???Applied Data Mining ??? Statistical Methods for Business and Industry???, IIE Transactions, vol.38, issue.12, 2009.
DOI : 10.1080/07408170600582880

M. Goldberg, A. Leclerc, and M. Zins, Cohort Profile Update: The GAZEL Cohort Study, International Journal of Epidemiology, vol.44, issue.1, pp.77-77, 2015.
DOI : 10.1093/ije/dyu224

P. Gromski, H. Muhamadali, D. Ellis, Y. Xu, E. Correa et al., A tutorial review: Metabolomics and partial least squares-discriminant analysis ??? a marriage of convenience or a shotgun wedding, Analytica Chimica Acta, vol.879, pp.10-23, 2015.
DOI : 10.1016/j.aca.2015.02.012

P. Gromski, Y. Xu, E. Correa, D. Ellis, M. Turner et al., A comparative investigation of modern feature selection and classification approaches for the analysis of mass spectrometry data, Analytica Chimica Acta, vol.829, 2014.
DOI : 10.1016/j.aca.2014.03.039

Y. Guo and R. Balasubramanian, Comparative Evaluation of Classifiers in the Presence of Statistical Interactions between Features in High Dimensional Data Settings, The International Journal of Biostatistics, vol.8, issue.1, pp.1373-1405, 1373.
DOI : 10.1515/1557-4679.1373

I. Guyon and A. Elisseeff, An introduction to variable and feature selection, J. Mach. Learn. Res, vol.3, pp.1157-1182, 2003.

I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, Gene selection for cancer classification using support vector machines, Machine Learning, vol.46, issue.1/3, pp.389-422, 2002.
DOI : 10.1023/A:1012487302797

A. Hapfelmeier, T. Hothorn, K. Ulm, and C. Strobl, A new variable importance measure for random forests with missing data, Statistics and Computing, vol.5, issue.6, pp.21-34, 2014.
DOI : 10.1007/s11222-012-9349-1

L. Hermes and J. M. Buhmann, Feature selection for support vector machines, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, pp.712-715, 2000.
DOI : 10.1109/ICPR.2000.906174

T. K. Ho, The random subspace method for constructing decision forests, IEEE Trans. Pattern Anal. Mach. Intell, vol.2034, pp.832-844, 1998.

H. Issaq, Q. Van, T. Waybright, G. Muschik, and T. Veenstra, Analytical and statistical approaches to metabolomics research, Journal of Separation Science, vol.9, issue.13, pp.2183-2199, 2009.
DOI : 10.1002/jssc.200900152

R. Kohavi, A study of cross-validation and bootstrap for accuracy estimation and model selection, Proceedings of the 14th International Joint Conference on Artificial Intelligence, pp.1137-1143, 1995.

N. T. Lal, O. Chapelle, J. Weston, and A. Elisseeff, Embedded methods Available online at Classification and Regression by randomForest, Feature Extraction: Foundations and Applications, pp.137-165, 2002.

H. Liu and H. Motoda, Feature Selection for Knowledge Discovery and Data Mining, 1998.
DOI : 10.1007/978-1-4615-5689-3

H. Liu, Y. , and L. , Toward integrating feature selection algorithms for classification and clustering, IEEE Trans. Knowl Data Eng, vol.17, pp.491-502, 2005.

M. Mamas, W. B. Dunn, L. Neyses, and R. Goodacre, The role of metabolites and metabolomics in clinically applicable biomarkers of disease, Archives of Toxicology, vol.81, issue.4, pp.5-17, 2011.
DOI : 10.1007/s00204-010-0609-6

Y. Mao, X. Zhou, S. Wang, and Y. Cheng, Urinary nucleosides based potential biomarker selection by support vector machine for bladder cancer recognition, Analytica Chimica Acta, vol.598, issue.1, 2007.
DOI : 10.1016/j.aca.2007.07.038

B. H. Menze, B. M. Kelm, R. Masuch, U. Himmelreich, P. Bachert et al., A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data, BMC Bioinformatics, vol.10, issue.1, pp.213-223, 2009.
DOI : 10.1186/1471-2105-10-213

J. K. Nicholson, J. C. Lindon, and E. Holmes, 'Metabonomics': understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data, Xenobiotica, vol.13, issue.11, pp.1181-1189, 1080.
DOI : 10.1002/elps.1150191118

A. D. Patterson, J. A. Bonzo, F. Li, K. W. Krausz, G. S. Eichler et al., Metabolomics Reveals Attenuation of the SLC6A20 Kidney Transporter in Nonhuman Primate and Mouse Models of Type 2 Diabetes Mellitus, Journal of Biological Chemistry, vol.286, issue.22, pp.19511-19522, 2011.
DOI : 10.1074/jbc.M111.221739

H. Pereira, J. F. Martin, C. Joly, J. L. Sebedio, and E. Pujos-guillot, Development and validation of a UPLC/MS method for a nutritional metabolomic study of human plasma, Metabolomics, vol.81, issue.2, pp.207-218, 2010.
DOI : 10.1007/s11306-009-0188-9

R. Ramautar, R. Berger, J. Van-der-greef, and T. Hankemeier, Human metabolomics: strategies to understand biology, Current Opinion in Chemical Biology, vol.17, issue.5, pp.841-846, 2013.
DOI : 10.1016/j.cbpa.2013.06.015

X. Robin, N. Turck, A. Hainard, N. Tiberti, F. Lisacek et al., pROC: an open-source package for R and S+ to analyze and compare ROC curves, BMC Bioinformatics, vol.12, issue.1, pp.77-87, 2011.
DOI : 10.1007/s00134-009-1641-y
URL : http://doi.org/10.1186/1471-2105-12-77

E. Saccenti, H. C. Hoefsloot, A. K. Smilde, J. A. Westerhuis, and M. Hendriks, Reflections on univariate and multivariate analysis of metabolomics data, Metabolomics, vol.15, issue.2, pp.361-374, 2014.
DOI : 10.1007/s11306-013-0598-6

Y. Saeys, I. Inza, and P. Larraaga, A review of feature selection techniques in bioinformatics, Bioinformatics, vol.23, issue.19, pp.2507-2517, 2007.
DOI : 10.1093/bioinformatics/btm344

I. M. Scott, W. Lin, M. Liakata, J. E. Wood, C. P. Vermeer et al., Merits of random forests emerge in evaluation of chemometric classifiers by external validation, Analytica Chimica Acta, vol.801, pp.22-33, 2013.
DOI : 10.1016/j.aca.2013.09.027

R. Tautenhahn, C. Bottcher, and S. Neumann, Highly sensitive feature detection for high resolution LC/MS, BMC Bioinformatics, vol.9, issue.1, pp.504-514, 2008.
DOI : 10.1186/1471-2105-9-504
URL : http://doi.org/10.1186/1471-2105-9-504

F. M. Van-der-kloet, I. Bobeldijk, E. R. Verheij, and R. H. Jellema, Analytical Error Reduction Using Single Point Calibration for Accurate and Precise Metabolomic Phenotyping, Journal of Proteome Research, vol.8, issue.11, pp.5132-5141, 1021.
DOI : 10.1021/pr900499r

V. N. Vapnik, Statistical Learning Theory, 1998.

H. Wang, T. M. Khoshgoftaar, W. , and R. , Measuring Stability of Feature Selection Techniques on Real-World Software Datasets, Information Reuse and Integration in Academia And Industry, pp.113-132, 2013.
DOI : 10.1007/978-3-7091-1538-1_6

J. Weston, S. Mukherjee, O. Chapelle, M. Pontil, T. Poggio et al., Feature Selection for SVMs, Advances in Neural Information Processing Systems 13 (NIPS), 2001.

I. H. Witten and E. Frank, Data mining, ACM SIGMOD Record, vol.31, issue.1, 2000.
DOI : 10.1145/507338.507355

B. Xi, H. Gu, H. Baniasadi, and D. Raftery, Statistical Analysis and Modeling of Mass Spectrometry-Based Metabolomics Data, Methods Mol. Biol. Metabolomics, vol.1198, issue.9, pp.333-353, 2013.
DOI : 10.1007/978-1-4939-1258-2_22

S. A. Yevtushenko, System of data analysis 'Concept Explorer, Proceedings of the 7th National Conference on Artificial Intelligence (Russia), pp.127-134, 2000.