M. Alam, A. Buzmakov, and A. Napoli, Exploratory Knowledge Discovery over Web of Data, Discrete Applied Mathematics, vol.249, pp.2-17, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01673439

M. Alam, T. N. Le, and A. Napoli, LatViz: A New Practical Tool for Performing Interactive Exploration over Concept Lattices, Proceedings of the Thirteenth International Conference on Concept Lattices and Their Applications (CLA 2016). CEUR Workshop Proceedings 1624, pp.9-20, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01420751

R. Armstrong, S. Slade, and F. Eperjesi, Statistical review -An introduction to analysis of variance (ANOVA) with special reference to data from clinical experiments in optometry, Ophthalmic and Physiological Optics, vol.20, issue.3, pp.235-241, 2000.

H. Bartel and R. Brüggemann, Application of formal concept analysis to structure-activity relationships, Fresenius Journal of Analytical Chemistry, vol.361, issue.1, pp.23-28, 1998.

J. Bartel, J. Krumsiek, and F. J. Theis, Statistical Methods for the Analysis of High-Throughput Metabolomics Data, Computational and Structural Biotechnology Journal, vol.4, p.5, 2013.

A. Berry, A. Gutierrez, M. Huchard, A. Napoli, and A. Sigayret, Hermes: a simple and efficient algorithm for building the AOC-poset of a binary relation, Annals of Mathematics and Artificial Intelligence, vol.72, pp.45-71, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01101144

T. D. Bie, Subjective Interestingness in Exploratory Data Mining, Proceedings of the International Symposium on Advances in Intelligent Data Analysis (XII), vol.8207, pp.19-31, 2013.

V. G. Blinova, D. A. Dobrynin, V. K. Finn, S. O. Kuznetsov, and E. S. Pankratova, Toxicology Analysis by Means of the JSM-method. Bioinformatics, vol.19, issue.10, pp.1201-1207, 2003.

H. Blockeel, Data Mining: From Procedural to Declarative Approaches, New Generation Computing, vol.33, issue.2, pp.115-135, 2015.

L. Breiman, Random Forests, Machine Learning, vol.45, issue.1, pp.5-32, 2001.

A. Buzmakov, S. O. Kuznetsov, and A. Napoli, Scalable Estimates of Stability, 12th International Conference on Formal Concept Analysis (ICFCA 2014), vol.8478, pp.157-172, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01095920

A. Buzmakov, S. O. Kuznetsov, A. Napoli, A. Appice, P. P. Rodrigues et al., Fast Generation of Best Interval Patterns for Nonmonotonic Constraints, Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD), vol.9285, pp.157-172, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01186718

M. Cuperlovic-culf, Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling, Metabolites, vol.8, issue.4, pp.2-16, 2018.

C. Ding and H. Peng, Minimum Redundancy Feature Selection from Microarray Gene Expression Data, Journal of Bioinformatics and Computational Biology, vol.3, issue.2, pp.185-205, 2005.

T. Fawcett, An introduction to ROC analysis, Pattern Recognition Letters, vol.27, issue.8, pp.861-874, 2006.

P. A. Flach, Machine Learning, 2012.

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

S. García, J. Luengo, and F. Herrera, of Intelligent Systems Reference Library, Data Preprocessing in Data Mining, vol.72, 2015.

J. Gebert, S. Motameny, U. Faigle, C. Forst, and R. Schrader, Identifying genes of gene regulatory networks using formal concept analysis, Journal of Computational Biology, vol.2, pp.185-194, 2008.

D. Grissa, B. Comte, E. Pujos-guillot, and A. Napoli, A Hybrid Data Mining Approach for the Identification of Biomarkers in Metabolomic Data, Proceedings of the Thirteenth International Conference on Concept Lattices and Their Applications (CLA-2016), pp.161-174, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01421015

D. Grissa, B. Comte, E. Pujos-guillot, and A. Napoli, A Hybrid Knowledge Discovery Approach for Mining Predictive Biomarkers in Metabolomic Data, Proceedings of ECML-PKDD 2016, pp.572-587, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01421011

D. Grissa, M. Pétéra, M. Brandolini, A. Napoli, B. Comte et al., Feature Selection Methods for Early Predictive Biomarker Discovery Using Untargeted Metabolomic Data, Frontiers in Molecular Biosciences, vol.3, issue.30, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01581591

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.

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, pp.1-8, 2014.

I. Guyon and A. Elisseeff, An Introduction to Variable and Feature Selection, Journal of Machine Learning Research, 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.

M. Hilario, P. Nguyen, H. Do, A. Woznica, and A. Kalousis, Ontology-Based Meta-Mining of Knowledge Discovery Workflows, Meta-Learning in Computational Intelligence, pp.273-315, 2011.

A. Holzinger, M. Dehmer, and I. Jurisica, Knowledge Discovery and interactive Data Mining in Bioinformatics -State-of-the-Art, future challenges and research directions, BMC Bioinformatics, vol.15, p.1, 2014.

J. J. Jansen, H. C. Hoefsloot, J. Van-der-greef, M. E. Timmerman, J. A. Westerhuis et al., ASCA: analysis of multivariate data obtained from an experimental design, Journal of Chemometrics, vol.19, issue.9, pp.469-481, 2005.

M. Kaytoue, S. O. Kuznetsov, A. Napoli, and S. Duplessis, Mining Gene Expression Data with Pattern Structures in Formal Concept Analysis, Information Science, vol.181, issue.10, pp.1989-2001, 2011.
URL : https://hal.archives-ouvertes.fr/hal-02651138

S. O. Kuznetsov and M. V. Samokhin, Learning Closed Sets of Labeled Graphs for Chemical Applications, Proceedings of the 15th International Conference on Inductive Logic Programming (ILP), vol.3625, pp.190-208, 2005.

M. Mamas, W. Dunn, L. Neyses, and R. Goodacre, The role of metabolites and metabolomics in clinically applicable biomarkers of disease, Arch Toxicol, vol.85, issue.1, pp.5-17, 2011.

C. Meng, O. A. Zeleznik, G. G. Thallinger, B. Küster, A. M. Gholami et al., Dimension reduction techniques for the integrative analysis of multi-omics data, Briefings in Bioinformatics, vol.17, issue.4, pp.628-641, 2016.

J. Métivier, A. Lepailleur, A. Buzmakov, G. Poezevara, B. Crémilleux et al., Discovering Structural Alerts for Mutagenicity Using Stable Emerging Molecular Patterns, Journal of Chemical Information and Modeling, vol.55, issue.5, pp.925-940, 2015.

P. Nguyen, M. Hilario, and A. Kalousis, Using Meta-mining to Support Data Mining Workflow Planning and Optimization, Journal of Artificial Intelligence Research (JAIR), vol.51, pp.605-644, 2014.

H. Peng, F. Long, and C. Ding, Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, issue.8, pp.1226-1238, 2005.

J. Poelmans, D. Ignatov, S. Kuznetsov, and G. Dedene, Formal Concept Analysis in Knowledge Processing: A Survey on Applications, Expert Systems with Applications, vol.40, issue.16, pp.6538-6560, 2013.

E. Pujos-guillot, M. Brandolini, M. Pétéra, D. Grissa, C. Joly et al., Systems Metabolomics for Prediction of Metabolic Syndrome, Journal of Proteome Research, vol.16, issue.6, pp.2262-2272, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01722032

P. Rinaudo, S. Boudah, C. Junot, and E. A. Thévenot, biosigner: A New Method for the Discovery of Significant Molecular Signatures from Omics Data, Frontiers in Molecular Biosciences, vol.3, issue.26, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01869335

E. Saccenti, H. Hoefsloot, A. Smilde, J. Westerhuis, and M. Hendriks, Reflections on univariate and multivariate analysis of metabolomics data, Metabolomics, vol.10, issue.3, pp.361-374, 2014.

Y. Saeys, I. Inza, and P. Larraaga, A review of feature selection techniques in bioinformatics, Bioinformatics, vol.23, pp.2507-2517, 2007.

P. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining, 2006.

J. W. Tukey, Exploratory Data Analysis, 1977.

M. Van-leeuwen, Interactive Knowledge Discovery and Data Mining in Biomedical Informatics -State-of-the-Art and Future Challenges, Lecture Notes in Computer Science, vol.8401, pp.169-182, 2014.

V. Vapnik, Statistical Learning Theory, 1998.

J. Xia, D. Broadhurst, M. Wilson, and D. Wishart, Translational biomarker discovery in clinical metabolomics: an introductory tutorial, Metabolomics, vol.9, issue.2, pp.280-99, 2013.