W. M. Aalst and . Van-der, Process mining: Discovery, conformance and enhancement of business processes, pp.1-352, 2011.

M. Adda, P. Valtchev, R. Missaoui, and C. Djeraba, A framework for mining meaningful usage patterns within a semantically enhanced web portal, Proceedings of the Third C* Conference on Computer Science and Software Engineering, C3S2E '10, pp.138-147, 2010.
DOI : 10.1145/1822327.1822347

URL : https://hal.archives-ouvertes.fr/hal-00812300

. Aggarwal, C. Charu, A. Mansurul, M. A. Bhuiyan, and . Hasan, Frequent Pattern Mining Algorithms: A Survey, pp.19-64, 2014.
DOI : 10.1007/978-3-319-07821-2_2

R. Agrawal, T. Imielinski, and A. Swami, Database mining: a performance perspective, IEEE Transactions on Knowledge and Data Engineering, vol.5, issue.6, pp.914-925, 1993.
DOI : 10.1109/69.250074

R. Agrawal and R. Srikant, Fast algorithms for mining association rules, Proc. 20th int. conf. very large data bases, VLDB, pp.487-499, 1994.

E. Ahlberg, L. Carlsson, and S. Boyer, Computational Derivation of Structural Alerts from Large Toxicology Data Sets, Journal of Chemical Information and Modeling, vol.54, issue.10, pp.2945-2952, 2014.
DOI : 10.1021/ci500314a

M. Alam, A. Buzmakov, V. Codocedo, and A. Napoli, Mining Definitions from RDF Annotations Using Formal Concept Analysis, Proc. Twenty-Fourth Int. Jt. Conf. Artif. Intell. IJCAI 2015, pp.823-829, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01186204

B. N. Ames, D. Frank, . Lee, E. William, and . Durston, An Improved Bacterial Test System for the Detection and Classification of Mutagens and Carcinogens, Proc. Natl, 1973.
DOI : 10.1073/pnas.70.3.782

J. Ashby and R. Tennant, Definitive relationships among chemical structure, carcinogenicity and mutagenicity for 301 chemicals tested by the U.S. NTP, Mutation Research/Reviews in Genetic Toxicology, vol.257, issue.3, pp.229-306, 1991.
DOI : 10.1016/0165-1110(91)90003-E

Y. Asses, A. Buzmakov, T. Bourquard, S. O. Kuznetsov, and A. Napoli, A Hybrid Classification Approach based on FCA and Emerging Patterns-An application for the classification of biological inhibitors, Proc. 9th Int. Conf. Concept Lattices Their Appl. Pp, pp.211-222, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00761586

Y. Asses, V. Leroux, S. Tairi-kellou, R. Dono, F. Maina et al., Analysis of c-Met Kinase Domain Complexes: A New Specific Catalytic Site Receptor Model for Defining Binding Modes of ATP-Competitive Ligands, Chemical Biology & Drug Design, vol.1697, issue.6, pp.560-570, 2009.
DOI : 10.1111/j.1747-0285.2009.00895.x

URL : https://hal.archives-ouvertes.fr/hal-00507523

Y. Bibliography-asses, V. Venkatraman, V. Leroux, W. David, B. Ritchie et al., Exploring c-Met kinase flexibility by sampling and clustering its conformational space, Proteins: Structure, Function, and Bioinformatics, vol.51, issue.4, pp.1227-1238, 2012.
DOI : 10.1002/prot.24021

J. Auer and J. Bajorath, Emerging Chemical Patterns:?? A New Methodology for Molecular Classification and Compound Selection, Journal of Chemical Information and Modeling, vol.46, issue.6, pp.2502-2514, 2006.
DOI : 10.1021/ci600301t

J. Ayres, J. Flannick, J. Gehrke, and T. Yiu, Sequential PAttern mining using a bitmap representation, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '02, pp.429-435, 2002.
DOI : 10.1145/775047.775109

P. J. Azevedo, M. Alípio, and . Jorge, Comparing Rule Measures for Predictive Association Rules, Mach. Learn. ECML Lecture Notes in Computer Science, vol.4701, pp.510-517, 2007.
DOI : 10.1007/978-3-540-74958-5_47

M. A. Babin and S. O. Kuznetsov, Approximating Concept Stability, Ed. by Florent Domenach, DmitryI. Ignatov, and Jonas Poelmans. Lecture Notes in Computer Science, vol.7278, pp.7-15, 2012.
DOI : 10.1007/978-3-642-29892-9_7

J. L. Balcázar, A. Bifet, and A. Lozano, Intersection Algorithms and a Closure Operator on Unordered Trees, p.1, 2006.

M. Barbut and B. Monjardet, Ordre et classification algèbre et combinatoirs, 1970.

R. Bêlohlávek, Fuzzy Galois Connections, Math. Log. Q. 45.4, pp.497-504, 1999.
DOI : 10.1002/malq.19990450408

R. B?lohlávek and V. Sklená?, Formal Concept Analysis Constrained by Attribute-Dependency Formulas, Lecture Notes in Computer Science, vol.3403, pp.176-191, 2005.
DOI : 10.1007/978-3-540-32262-7_12

R. Belohlavek and M. Trnecka, Basic Level in Formal Concept Analysis: Interesting Concepts and Psychological Ramifications, Proc. Twenty-Third Int. Jt. Conf, 2013.

R. Belohlavek and V. Vychodil, Formal Concept Analysis With Background Knowledge: Attribute Priorities, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol.39, issue.4, pp.399-409, 2009.
DOI : 10.1109/TSMCC.2008.2012168

R. B?lohlávek and V. Vychodil, Formal Concept Analysis with Constraints by Closure Operators, Lecture Notes in Computer Science, vol.4068, issue.1, pp.131-143, 2006.
DOI : 10.1007/11787181_10

R. Benigni, The Benigni/Bossa Rulebase for Mutagenicity and Carcinogenicity ? a Module of Toxtree, JRC Sci. Tech. Reports, pp.1-70, 2008.

R. Benigni and C. Bossa, Mechanisms of Chemical Carcinogenicity and Mutagenicity: A Review with Implications for Predictive Toxicology, Chemical Reviews, vol.111, issue.4, pp.2507-2536, 2011.
DOI : 10.1021/cr100222q

D. Benz, A. Hotho, and G. Stumme, Semantics made by you and me: Self-emerging ontologies can capture the diversity of shared knowledge, Proceedings of the 2nd Web Science Conference, 2010.

J. Besson, R. G. Pensa, C. Robardet, and J. Boulicaut, Constraint-Based Mining of Fault-Tolerant Patterns from Boolean Data, Knowl. Discov. Inductive Databases, pp.55-71, 2006.
DOI : 10.1007/978-94-009-7798-3_15

J. Besson, C. Robardet, and J. Boulicaut, Mining Formal Concepts with a Bounded Number of Exceptions from Transactional Data, Bart Goethals and Arno Siebes. Lecture Notes in Computer Science, vol.3377, pp.33-45, 2005.
DOI : 10.1007/978-3-540-31841-5_3

R. Bissell-siders, B. Cuissart, and B. Crémilleux, On the Stimulation of Patterns -Definitions, Calculation Method and First Usages, 18th Int. Conf. Concept. Struct. ICCS 2010, Proc. Pp, pp.56-69, 2010.
URL : https://hal.archives-ouvertes.fr/hal-01016911

C. Bizer, T. Heath, and T. Berners-lee, Linked Data - The Story So Far, International Journal on Semantic Web and Information Systems, vol.5, issue.3, pp.1-22, 2009.
DOI : 10.4018/jswis.2009081901

J. Björne, A. Airola, T. Pahikkala, and T. Salakoski, Drug-drug interaction extraction from biomedical texts with svm and rls classifiers, Proceedings of DDIExtraction-2011 challenge task, pp.35-42, 2011.

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.
DOI : 10.1093/bioinformatics/btg096

N. G. Boldyrev, Minimization of Boolean Partial Functions with a Large Number of "Don't Care" Conditions and the Problem of Feature Extraction, Proc. Int. Symp. "Discrete Syst, pp.101-109, 1974.

M. Boley, T. Horváth, A. Poigné, and S. Wrobel, Listing closed sets of strongly accessible set systems with applications to data mining, Theor. Comput. Sci. 411.3, pp.691-700, 2010.
DOI : 10.1016/j.tcs.2009.10.024

C. Borgelt, Combining Ring Extensions and Canonical Form Pruning, Work. Min. Learn. with Graphs, pp.109-116, 2006.

C. Borgelt and M. R. Berthold, Mining molecular fragments: finding relevant substructures of molecules, 2002 IEEE International Conference on Data Mining, 2002. Proceedings., pp.51-58, 2002.
DOI : 10.1109/ICDM.2002.1183885

J. Boulicaut, A. Bykowski, and C. Rigotti, Approximation of Frequency Queries by Means of Free-Sets, In: Princ. Data Min. Knowl. Discov Lecture Notes in Computer Science, pp.75-85, 1910.
DOI : 10.1007/3-540-45372-5_8

D. Burdick, M. Calimlim, and J. Gehrke, MAFIA: a maximal frequent itemset algorithm for transactional databases, Proceedings 17th International Conference on Data Engineering, pp.443-452, 2001.
DOI : 10.1109/ICDE.2001.914857

A. Buzmakov, E. Egho, N. Jay, S. O. Kuznetsov, A. Napoli et al., FCA and pattern structures for mining care trajectories, Work. Notes FCA4AI, pp.7-14, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00910290

A. Buzmakov, S. O. Kuznetsov, and A. Napoli, Concept Stability as a Tool for Pattern Selection, Work. Notes FCA4AI, pp.51-58, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01095903

A. Buzmakov, S. O. Kuznetsov, and A. Napoli, Fast Generation of Best Interval Patterns for Nonmonotonic Constraints, Mach. Learn. Knowl. Discov. Databases, 2015.
DOI : 10.1007/978-3-319-23525-7_10

URL : https://hal.archives-ouvertes.fr/hal-01186718

T. Calders and B. Goethals, Mining All Non-derivable Frequent Itemsets, Data Min. Knowl. Discov. Ed. by Tapio Elomaa, Heikki Mannila, and Hannu Toivonen. Lecture Notes in Computer Science, vol.2431, pp.74-86, 2002.
DOI : 10.1007/3-540-45681-3_7

URL : http://arxiv.org/abs/cs/0206004

T. Calders, C. Rigotti, and J. Boulicaut, A Survey on Condensed Representations for Frequent Sets, Lecture Notes in Computer Science, vol.3848, pp.64-80, 2006.
DOI : 10.1007/11615576_4

J. Cao, Z. Wu, and J. Wu, Scaling up cosine interesting pattern discovery: A depth-first method, Information Sciences, vol.266, pp.31-46, 2014.
DOI : 10.1016/j.ins.2013.12.062

R. E. Carhart, H. Dennis, R. Smith, and . Venkataraghavan, Atom pairs as molecular features in structure-activity studies: definition and applications, Journal of Chemical Information and Modeling, vol.25, issue.2, 1985.
DOI : 10.1021/ci00046a002

D. R. Carvalho, A. A. Freitas, and N. Ebecken, Evaluating the Correlation Between Objective Rule Interestingness Measures and Real Human Interest, In: Knowl. Discov. Databases PKDD Lecture Notes in Computer Science, vol.3721, pp.453-461, 2005.
DOI : 10.1007/11564126_45

G. Casas-garriga, Summarizing Sequential Data with Closed Partial Orders, Proc. 5th SIAM Int'l Conf. Data Min, 2005.
DOI : 10.1137/1.9781611972757.34

P. Cellier, S. Ferré, O. Ridoux, and M. Ducasse, A PARAMETERIZED ALGORITHM TO EXPLORE FORMAL CONTEXTS WITH A TAXONOMY, International Journal of Foundations of Computer Science, vol.19, issue.02, pp.2-319, 2008.
DOI : 10.1142/S012905410800570X

URL : https://hal.archives-ouvertes.fr/inria-00363594

D. Chiu, Y. Wu, and A. L. Chen, An Efficient Algorithm for Mining Frequent Sequences by a New Strategy without Support Counting, pp.375-386, 2004.

F. Chowdhury, A. B. Mahbub, A. Abacha, P. Lavelli, and . Zweigenbaum, Two different machine learning techniques for drug-drug interaction extraction, Challenge Task on Drug-Drug Interaction Extraction, pp.19-26, 2011.

M. Chowdhury, A. Faisal-mahbub, and . Lavelli, Drug-drug interaction extraction using composite kernels, Challenge Task on Drug-Drug Interaction Extraction, pp.27-33, 2011.

V. Codocedo and A. Napoli, A Proposition for Combining Pattern Structures and Relational Concept Analysis, 12th International Conference on Formal Concept Anal- ysis, 2014.
DOI : 10.1007/978-3-319-07248-7_8

URL : https://hal.archives-ouvertes.fr/hal-01095870

F. Coenen, The LUCS-KDD Discretised and normalised ARM and CARM Data Library 25, 2003.

E. Commission, REACH: Registration, Evaluation, Authorisation and Restriction of Chemicals, 2007.

D. J. Cook and L. B. Holder, Substructure discovery using minimum description length and background knowledge, J. Artif. Intell. Res, vol.1, pp.231-255, 1994.

L. Coquin, S. J. Canipa, W. C. Drewe, L. Fisk, V. J. Gillet et al., New structural alerts for Ames mutagenicity discovered using emerging pattern mining techniques, Toxicol. Res., vol.539, issue.1, pp.46-56, 2015.
DOI : 10.1039/C4TX00071D

A. Coulet, F. Domenach, M. Kaytoue, and A. Napoli, Using Pattern Structures for Analyzing Ontology-Based Annotations of Biomedical Data, Lecture Notes in Computer Science, vol.7880, pp.76-91, 2013.
DOI : 10.1007/978-3-642-38317-5_5

URL : https://hal.archives-ouvertes.fr/hal-00922392

B. Cuissart, G. Poezevara, and B. Crémilleux, Emerging Patterns as Structural Alerts for Computational Toxicology, Alban Lepailleur, and Ronan Bureau, pp.269-282, 2013.
DOI : 10.1201/b12986-25

URL : https://hal.archives-ouvertes.fr/hal-01024000

M. Deshpande, M. Kuramochi, N. Wale, and G. Karapis, Frequent substructure-based approaches for classifying chemical compounds, IEEE Transactions on Knowledge and Data Engineering, vol.17, issue.8, pp.1036-1050, 2005.
DOI : 10.1109/TKDE.2005.127

J. Devillers and A. T. Balaban, Topological Indices and Related Descrip-tors in QSAR and QSPR, 1999.

S. M. Dias and N. J. Vieira, Applying the JBOS reduction method for relevant knowledge extraction, Expert Systems with Applications, vol.40, issue.5, pp.1880-1887, 2013.
DOI : 10.1016/j.eswa.2012.10.010

. Ding, D. Bolin, J. Lo, S. Han, and . Khoo, Efficient Mining of Closed Repetitive Gapped Subsequences from a Sequence Database, 2009 IEEE 25th International Conference on Data Engineering, pp.1024-1035, 2009.
DOI : 10.1109/ICDE.2009.104

G. Dong and J. Bailey, Contrast Data Mining: Concepts, Algorithms, and Applications, 2013.

G. Dong and J. Li, Efficient mining of emerging patterns, Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '99, pp.43-52, 1999.
DOI : 10.1145/312129.312191

I. Dussault, F. Steven, and . Bellon, c-Met inhibitors with different binding modes: Two is better than one, Cell Cycle, vol.7, issue.9, pp.1157-1160, 2008.
DOI : 10.4161/cc.7.9.5827

S. Eathiraj, R. Palma, E. Volckova, M. Hirschi, S. Dennis et al., Discovery of a Novel Mode of Protein Kinase Inhibition Characterized by the Mechanism of Inhibition of Human Mesenchymal-epithelial Transition Factor (c-Met) Protein Autophosphorylation by ARQ 197, Journal of Biological Chemistry, vol.286, issue.23, pp.20666-20676, 2011.
DOI : 10.1074/jbc.M110.213801

E. Egho, N. Jay, C. Raïssi, D. Ienco, and P. Poncelet, A contribution to the discovery of multidimensional patterns in healthcare trajectories, Maguelonne Teisseire, and Amedeo Napoli, pp.283-305, 2014.
DOI : 10.1007/s10844-014-0309-4

URL : https://hal.archives-ouvertes.fr/hal-01094377

E. Egho, C. Raïssi, N. Jay, and A. Napoli, Mining Heterogeneous Multidimensional Sequential Patterns, ECAI 2014 -21st Eur. Conf. Artif. Intell. Pp, pp.279-284, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01094365

J. L. Faulon, C. J. Churchwell, and D. Visco, The signature molecular descriptor. 2. Enumerating molecules from their extended valence sequences, In: J. Chem. Inf. Comput, 2003.

J. L. Faulon, D. Visco, and R. Pophale, The Signature Molecular Descriptor. 1. Using Extended Valence Sequences in QSAR and QSPR Studies, Journal of Chemical Information and Computer Sciences, vol.43, issue.3, pp.707-720, 2003.
DOI : 10.1021/ci020345w

B. Fayyad, G. Usama, P. Piatetsky-shapiro, and . Smyth, From data mining to knowledge discovery in databases, p.37, 1996.

. Fda and . Gov, Genetic Toxicity, Reproductive and Developmental Toxicity, and Carcinogenicity Database

J. Feng, L. Lurati, H. Ouyang, T. Robinson, Y. Wang et al., Predictive Toxicology:??? Benchmarking Molecular Descriptors and Statistical Methods, Journal of Chemical Information and Computer Sciences, vol.43, issue.5, pp.1463-1470, 2003.
DOI : 10.1021/ci034032s

S. Ferré, The Efficient Computation of Complete and Concise Substring Scales with Suffix Trees, Lecture Notes in Computer Science, vol.4390, pp.98-113, 2007.
DOI : 10.1007/978-3-540-70901-5_7

S. Ferré and O. Ridoux, The Use of Associative Concepts in the Incremental Building of a Logical Context, Lecture Notes in Computer Science, vol.2393, pp.299-313, 2002.
DOI : 10.1007/3-540-45483-7_23

R. B. Fetter, Y. Shin, J. L. Freeman, R. F. Averill, and J. D. Thompson, Case mix definition by diagnosis-related groups, In: Med Care, vol.18, issue.2, pp.1-53, 1980.

V. K. Finn, Plausible reasoning in systems of JSM type, Itogi Nauk. i Tekhniki, Seriya Inform. 15, pp.54-101, 1991.

A. Frank and A. Asuncion, UCI Machine Learning Repository, 2010.

C. Fürber and M. Hepp, Swiqa -a semantic web information quality assessment framework, 19th European Conference on Information Systems, 2011.

B. A. Galitsky, D. Ilvovsky, S. O. Kuznetsov, and F. Strok, Finding Maximal Common Sub-parse Thickets for Multi-sentence Search, In: Graph Struct. Knowl. Represent . Reason. Ed. by Madalina Croitoru Lecture Notes in Computer Science, vol.8323, pp.39-57, 2014.
DOI : 10.1007/978-3-319-04534-4_4

B. A. Galitsky, O. Sergei, D. Kuznetsov, and . Usikov, Parse Thicket Representation for Multi-sentence Search, Concept. Struct. STEM Res. Educ. Ed. by HeatherD. Pfeiffer, DmitryI. Ignatov, Jonas Poelmans, and Nagarjuna Gadiraju. Lecture Notes in Computer Science, vol.7735, pp.153-172, 2013.
DOI : 10.1007/978-3-642-35786-2_12

B. Ganter, Two Basic Algorithms in Concept Analysis, In: Form. Concept Anal. Ed. by Léonard Kwuida and Baris Sertkaya. Lecture Notes in Computer Science, vol.5986, 1984.
DOI : 10.1007/978-3-642-11928-6_22

B. Ganter, P. A. Grigoriev, S. O. Kuznetsov, and M. V. Samokhin, Concept-Based Data Mining with Scaled Labeled Graphs, Concept. Struct. Work SE, 2004.
DOI : 10.1007/978-3-540-27769-9_6

B. Ganter and S. O. Kuznetsov, Formalizing Hypotheses with Concepts, Lecture Notes in Computer Science, vol.1867, pp.342-356, 2000.
DOI : 10.1007/10722280_24

B. Ganter and R. Wille, Formal Concept Analysis: Mathematical Foundations. 1st, pp.1-284, 1999.

. Garcia-blasco, . Sandra, M. Santiago, R. Mola-velasco, P. Danger et al., Automatic Drug-Drug Interaction Detection: A Machine Learning Approach With Maximal Frequent Sequence Extraction, Challenge Task on Drug-Drug Interaction Extraction, pp.51-58, 2011.

. Garofalakis, N. Minos, R. Rastogi, and K. Shim, SPIRIT: Sequential pattern mining with regular expression constraints, In: VLDB, vol.99, pp.7-10, 1999.

G. C. Garriga, R. Khardon, and L. English, Mining closed patterns in relational, graph and network data, Annals of Mathematics and Artificial Intelligence, vol.4, issue.4, pp.1-28, 2012.
DOI : 10.1007/s10472-012-9324-8

URL : https://hal.archives-ouvertes.fr/hal-00754967

L. Geng and H. J. Hamilton, Interestingness measures for data mining, ACM Computing Surveys, vol.38, issue.3, p.9, 2006.
DOI : 10.1145/1132960.1132963

K. Gouda and M. J. Zaki, GenMax: An Efficient Algorithm for Mining Maximal Frequent Itemsets, Data Mining and Knowledge Discovery, vol.129, issue.2, pp.223-242, 2005.
DOI : 10.1007/s10618-005-0002-x

G. Grahne and J. Zhu, Efficiently Using Prefix-trees in Mining Frequent Itemsets, In: FIMI, vol.90, 2003.

T. Guns, S. Nijssen, and L. De-raedt, Itemset mining: A constraint programming perspective, Artificial Intelligence, vol.175, issue.12-13, pp.1951-1983, 2011.
DOI : 10.1016/j.artint.2011.05.002

P. S. Guzelian, M. Victoroff, N. Halmes, R. James, and C. Guzelian, Evidence-based toxicology: a comprehensive framework for causation, Human & Experimental Toxicology, vol.24, issue.4, pp.161-201, 2005.
DOI : 10.1191/0960327105ht517oa

M. Hacene, M. Rouane, A. Huchard, P. Napoli, and . Valtchev, Relational concept analysis: mining concept lattices from multi-relational data, Annals of Mathematics and Artificial Intelligence, vol.5, issue.1, pp.81-108, 2013.
DOI : 10.1007/s10472-012-9329-3

URL : https://hal.archives-ouvertes.fr/lirmm-00816300

N. Haider, Functionality Pattern Matching as an Efficient Complementary Structure/Reaction Search Tool: an Open-Source Approach, Molecules, vol.15, issue.8, pp.5079-5092, 2010.
DOI : 10.3390/molecules15085079

J. Han, H. Cheng, D. Xin, and X. Yan, Frequent pattern mining: current status and future directions, Data Mining and Knowledge Discovery, vol.1, issue.1, pp.55-86, 2007.
DOI : 10.1007/s10618-006-0059-1

J. Han and J. Pei, Mining frequent patterns by pattern-growth, ACM SIGKDD Explor. Newsl. 2.2, pp.14-20, 2000.
DOI : 10.1145/380995.381002

J. Han, J. Pei, B. Mortazavi-asl, Q. Chen, U. Dayal et al., FreeSpan, Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '00, pp.355-359, 2000.
DOI : 10.1145/347090.347167

J. Han, J. Wang, Y. Lu, and P. Tzvetkov, Mining top-k frequent closed patterns without minimum support, Data Mining, 2002. ICDM 2003. Proceedings. 2002 IEEE Int. Conf. Pp, pp.211-218, 2002.

J. A. Hanley and B. Mcneil, The meaning and use of the area under a receiver operating characteristic (ROC) curve., Radiology, vol.143, issue.1, pp.29-36, 1982.
DOI : 10.1148/radiology.143.1.7063747

K. Hansen, . Mika, . Schroeter, . Sutter, T. Ter-laak et al., Benchmark Data Set for in Silico Prediction of Ames Mutagenicity, Journal of Chemical Information and Modeling, vol.49, issue.9, pp.2077-2081, 2009.
DOI : 10.1021/ci900161g

M. Hasan, V. Al, S. Chaoji, J. Salem, M. J. Besson et al., ORIGAMI: Mining Representative Orthogonal Graph Patterns, Seventh IEEE International Conference on Data Mining (ICDM 2007), pp.153-162, 2007.
DOI : 10.1109/ICDM.2007.45

URL : https://hal.archives-ouvertes.fr/hal-01511074

M. Hasan, M. J. Al, and . Zaki, MUSK: Uniform Sampling of k Maximal Patterns, Proc. SDM, pp, pp.650-661, 2009.

M. Hasan, M. J. Al, and . Zaki, Output space sampling for graph patterns, Proc. VLDB Endow. 2.1, pp.730-741, 2009.
DOI : 10.14778/1687627.1687710

Z. He, J. Zhang, X. Shi, L. Hu, X. Kong et al., Predicting Drug-Target Interaction Networks Based on Functional Groups and Biological Features, PLoS ONE, vol.5, issue.3, p.9603, 2010.
DOI : 10.1371/journal.pone.0009603.s006

C. Hébert and B. Crémilleux, Mining Frequent ??-Free Patterns in Large Databases, Lecture Notes in Computer Science, vol.3735, pp.124-136, 2005.
DOI : 10.1007/11563983_12

C. Helma, Lazy structure-activity relationships (lazar) for the prediction of rodent carcinogenicity and Salmonella mutagenicity, Molecular Diversity, vol.19, issue.2, pp.147-158, 2006.
DOI : 10.1007/s11030-005-9001-5

C. Helma, T. Cramer, S. Kramer, and L. De-raedt, Data Mining and Machine Learning Techniques for the Identification of Mutagenicity Inducing Substructures and Structure Activity Relationships of Noncongeneric Compounds, J. Chem. Inf. Comput. Sci, pp.1402-1411, 2004.

R. J. Hilderman and H. J. Hamilton, Heuristic Measures of Interestingness, Princ, pp.232-241, 1999.
DOI : 10.1007/978-3-540-48247-5_25

J. Huan and W. Wang, SPIN, Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '04, p.581, 2004.
DOI : 10.1145/1014052.1014123

N. Jay, F. Kohler, and A. Napoli, Analysis of Social Communities with Iceberg and Stability-Based Concept Lattices, Lecture Notes in Computer Science, vol.4933, pp.258-272, 2008.
DOI : 10.1007/978-3-540-78137-0_19

URL : https://hal.archives-ouvertes.fr/hal-00608180

C. Jiang, F. Coenen, and M. Z. English, A survey of frequent subgraph mining algorithms, The Knowledge Engineering Review, vol.66, issue.01, pp.75-105, 2013.
DOI : 10.1093/bioinformatics/bth919

J. Wang, Z. Zeng, and L. Zhou, CLAN: An Algorithm for Mining Closed Cliques from Large Dense Graph Databases, 22nd Int. Conf. Data Eng. IEEE, pp.73-73, 2006.

N. Jin, C. Young, and W. Wang, Graph classification based on pattern cooccurrence, Proceeding 18th ACM Conf. Inf. Knowl. Manag. -CIKM '09, p.573, 2009.

P. N. Judson, N. Cooke, . Doerrer, R. Greene, C. Hanzlik et al., Towards the creation of an international toxicology information centre, Toxicology, vol.213, issue.1-2, pp.1-2, 2005.
DOI : 10.1016/j.tox.2005.05.014

K. Jung, B. Park, and S. Hong, Progress in cancer therapy targeting c-Met signaling pathway, Archives of Pharmacal Research, vol.67, issue.4, pp.595-604, 2012.
DOI : 10.1007/s12272-012-0402-6

T. B. Kaiser and S. E. Schmidt, Some Remarks on the Relation between Annotated Ordered Sets and Pattern Structures, Pattern Recognit. Mach. Intell, vol.18, issue.2, p.9, 2011.
DOI : 10.1007/978-3-642-10646-0_4

. Ed, O. Sergei, and . Kuznetsov, Lecture Notes in Computer Science x, DebaP. Mandal, MalayK. Kundu, and SankarK. Pal, vol.6744, pp.43-48

M. Kaytoue, S. O. Kuznetsov, and A. Napoli, Revisiting Numerical Pattern Mining with Formal Concept Analysis, IJCAI 2011, Proc. 22nd Int. Jt. Conf. Artif. Intell, pp.1342-1347, 2011.
URL : https://hal.archives-ouvertes.fr/inria-00584371

M. Kaytoue and S. O. Kuznetsov, Mining gene expression data with pattern structures in formal concept analysis, Information Sciences, vol.181, issue.10, pp.1989-2001, 2011.
DOI : 10.1016/j.ins.2010.07.007

URL : https://hal.archives-ouvertes.fr/hal-00541100

J. Kazius, R. Mcguire, and R. Bursi, Derivation and Validation of Toxicophores for Mutagenicity Prediction, Journal of Medicinal Chemistry, vol.48, issue.1, pp.312-320, 2005.
DOI : 10.1021/jm040835a

J. Kazius, S. Nijssen, J. Kok, A. Bäck, and . Ijzerman, Substructure Mining Using Elaborate Chemical Representation, Journal of Chemical Information and Modeling, vol.46, issue.2, pp.597-605, 2006.
DOI : 10.1021/ci0503715

R. D. King, A. Srinivasan, and L. English, Warmr: a data mining tool for chemical data, Journal of Computer-Aided Molecular Design, vol.15, issue.2, pp.173-181, 2001.
DOI : 10.1023/A:1008171016861

D. Klein, D. Christopher, and . Manning, Accurate unlexicalized parsing, Proceedings of the 41st Annual Meeting on Association for Computational Linguistics , ACL '03, pp.423-430, 2003.
DOI : 10.3115/1075096.1075150

URL : http://acl.ldc.upenn.edu/acl2003/main/pdf/Klein.pdf

M. Klimushkin, S. A. Obiedkov, and C. Roth, Approaches to the Selection of Relevant Concepts in the Case of Noisy Data, Proc. 8th Int. Conf. Form. Concept Anal. ICFCA'10, pp.255-266, 2010.
DOI : 10.1007/978-3-642-11928-6_18

G. J. Klopman, Artificial Intelligence Approach to Structure-Activity Studies: Computer Automated Structure Evaluation of Biological Activity of Organic Molecules, In: J. Am. Chem. Soc, vol.10624, pp.7315-7321, 1984.

P. Krajca, J. Outrata, and V. Vychodil, Advances in Algorithms Based on CbO, Proc. 8th Int. Conf. Concept Lattices Their Appl. (CLA'10). Pp, pp.325-337, 2010.

. Krishna, N. N. Varun, G. Suri, and . Athithan, A comparative survey of algorithms for frequent subgraph discovery, Curr. Sci, vol.100, issue.2, pp.190-198, 2011.

N. L. Kruhlak, J. Contrera, R. Benz, and E. Matthews, Progress in QSAR toxicity screening of pharmaceutical impurities and other FDA regulated products?????????, Advanced Drug Delivery Reviews, vol.59, issue.1, pp.43-55, 2007.
DOI : 10.1016/j.addr.2006.10.008

H. Kum, W. Chang, and . Wang, Benchmarking the effectiveness of sequential pattern mining methods, Data & Knowledge Engineering, vol.60, issue.1, pp.30-50, 2007.
DOI : 10.1016/j.datak.2006.01.004

M. Kuramochi and G. Karypis, Frequent subgraph discovery, Proceedings 2001 IEEE International Conference on Data Mining, pp.313-320, 2001.
DOI : 10.1109/ICDM.2001.989534

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.20.7488

. English, Finding Frequent Patterns in a Large Sparse Graph, Data Min. Knowl. Discov, vol.11, issue.3, pp.243-271, 2005.

S. O. Kuznetsov, Interpretation on graphs and complexity characteristics of a search for specific patterns, Nauchno-Tekhnicheskaya Informatsiya Seriya 2 (Autom. Doc. Mathem. Ling.) 23.1, pp.23-27, 1989.

B. Kuznetsov and S. O. , A fast algorithm for computing all intersections of objects from an arbitrary semilattice, Nauchno-Tekhnicheskaya Informatsiya Seriya 2 (Autom. Doc. Mathem. Ling.) 1, pp.17-20, 1993.

S. O. Kuznetsov and S. A. Obiedkov, Comparing performance of algorithms for generating concept lattices, Journal of Experimental & Theoretical Artificial Intelligence, vol.21, issue.2-3, pp.2-3, 2002.
DOI : 10.1016/S0020-0190(99)00108-8

S. O. Kuznetsov, A. Sergei, C. Obiedkov, and . Roth, Reducing the Representation Complexity of Lattice-Based Taxonomies, In: Concept. Struct. Knowl. Archit. Smart Appl Lecture Notes in Computer Science, vol.4604, pp.241-254, 2007.
DOI : 10.1007/978-3-540-73681-3_18

S. O. Kuznetsov and J. Poelmans, Knowledge representation and processing with formal concept analysis, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol.133, issue.3, pp.200-215, 2013.
DOI : 10.1002/widm.1088

S. O. Kuznetsov, V. Mikhail, and . Samokhin, Learning Closed Sets of Labeled Graphs for Chemical Applications, Lecture No. Lecture Notes in Computer Science, vol.3625, pp.190-208, 2005.
DOI : 10.1007/11536314_12

L. Kwuida, R. Missaoui, L. Beligh-ben-amor, J. Boumedjout, and . Vaillancourt, Restrictions on Concept Lattices for Pattern Management, pp.235-246, 2010.

A. Lagunin, . Stepanchikova, V. Filimonov, and . Poroikov, PASS: prediction of activity spectra for biologically active substances, Bioinformatics, vol.16, issue.8, pp.747-748, 2000.
DOI : 10.1093/bioinformatics/16.8.747

D. Y. Lai and Y. Woo, OncoLogic, In: Predict. Toxicol, pp.385-413, 2005.
DOI : 10.1201/9780849350351.ch10

D. Y. Lai, . Yin-tak, M. Woo, J. Argus, and . Arcos, Development of Structure- Activity Relationship Rules for Predicting Carcinogenic Potential of Chemicals, Toxicol. Lett, vol.95, pp.1-3, 1995.

A. R. Leach, K. Brian, C. E. Shoichet, and . Peishoff, Prediction of Protein- Ligand Interactions. Docking and Scoring: Successes and Gaps, J. Med. Chem, vol.4920, pp.5851-5855, 2006.

K. Leeuwen, T. W. Van, T. Schultz, B. Henry, G. D. Diderich et al., Using chemical categories to fill data gaps in hazard assessment, SAR and QSAR in Environmental Research, vol.48, issue.3-4, pp.3-4, 2009.
DOI : 10.1289/ehp.5757

A. Leeuwenberg and A. Buzmakov, Exploring Pattern Structures of Syntactic Trees for Relation Extraction, Yannick Toussaint, and Amedeo Napoli In: Form. Concept Anal. Lecture Notes in Computer Science, vol.9113, pp.153-168, 2015.
DOI : 10.1007/978-3-319-19545-2_10

URL : https://hal.archives-ouvertes.fr/hal-01186717

S. Lozano, G. Poezevara, M. Halm-lemeille, E. Lescot-fontaine, A. Lepailleur et al., Introduction of Jumping Fragments in Combination with QSARs for the Assessment of Classification in Ecotoxicology, Journal of Chemical Information and Modeling, vol.50, issue.8, pp.1330-1339, 2010.
DOI : 10.1021/ci100092x

URL : https://hal.archives-ouvertes.fr/hal-01011322

C. Luo and S. M. Chung, A scalable algorithm for mining maximal frequent sequences using sampling, 16th IEEE International Conference on Tools with Artificial Intelligence, pp.156-165, 2004.
DOI : 10.1109/ICTAI.2004.16

. Mabroukeh, R. Nizar, and C. Ezeife, A taxonomy of sequential pattern mining algorithms, ACM Computing Surveys, vol.43, issue.1, pp.1-3, 2010.
DOI : 10.1145/1824795.1824798

H. Mannila and H. Toivonen, Levelwise search and borders of theories in knowledge discovery, Data Mining and Knowledge Discovery, vol.1, issue.3, pp.241-258, 1997.
DOI : 10.1023/A:1009796218281

H. Mannila, H. Toivonen, and . Verkamo, Efficient Algorithms for Discovering Association Rules, In: Knowl. Discov. Data Min. Pp, pp.181-192, 1994.

C. D. Manning, H. Prabhakar-raghavan, and . Schtze, Introduction to Information Retrieval, 2008.
DOI : 10.1017/CBO9780511809071

T. M. Martin, P. Harten, M. Douglas, . Young, N. Eugene et al., Does Rational Selection of Training and Test Sets Improve the Outcome of QSAR Modeling?, Journal of Chemical Information and Modeling, vol.52, issue.10, pp.2570-2578, 2012.
DOI : 10.1021/ci300338w

A. Masood and S. Soong, Measuring Interestingness ? Perspectives on Anomaly Detection, In: Comput. Eng. Intell. Syst, pp.29-40, 2013.

F. Masseglia, F. Cathala, and P. Poncelet, The PSP approach for mining sequential patterns, pp.176-184, 1998.
DOI : 10.1007/BFb0094818

A. Maunz, C. Helma, T. Cramer, and S. Kramer, Latent Structure Pattern Mining, Mach. Learn. Knowl. Discov. Databases SE - Aristides Gionis, and Michèle Sebag. Lecture Notes in Computer Science, vol.23, issue.6322, pp.353-368, 2010.
DOI : 10.1007/978-3-642-15883-4_23

K. Mcgarry and . English, A survey of interestingness measures for knowledge discovery, The Knowledge Engineering Review, vol.20, issue.01, pp.1-39, 2005.
DOI : 10.1017/S0269888905000408

D. Merwe, S. A. Van-der, D. G. Obiedkov, P. Kourie, and . Eklund, AddIntent: A new incremental algorithm for constructing concept lattices, Concept Lattices, pp.372-385, 2004.

N. Messai, M. Devignes, A. Napoli, and M. Smaïl-tabbone, Extending Attribute Dependencies for Lattice-Based Querying and Navigation, In: Concept. Struct. Knowl. Vis. Reason. Ed. by Peter Eklund and Ollivier Haemmerlé. Lecture Notes in Computer Science, vol.5113, pp.189-202, 2008.
DOI : 10.1007/978-3-540-70596-3_13

URL : https://hal.archives-ouvertes.fr/inria-00338678

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.
DOI : 10.1021/ci500611v

A. Minard, L. Makour, A. Ligozat, and B. Grau, Feature Selection for Drug-Drug Interaction Detection Using Machine-Learning Based Approaches, Challenge Task on Drug-Drug Interaction Extraction, pp.43-50, 2011.

T. Mitchell and . Michael, Version spaces: an approach to concept learning, 1978.

F. Moerchen, M. Thies, and A. Ultsch, Efficient mining of all margin-closed itemsets with applications in temporal knowledge discovery and classification by compression, Knowledge and Information Systems, vol.9, issue.3, pp.55-80, 2011.
DOI : 10.1007/s10115-010-0329-5

C. H. Mooney and J. F. Roddick, Sequential pattern mining -- approaches and algorithms, ACM Computing Surveys, vol.45, issue.2, pp.1-39, 2013.
DOI : 10.1145/2431211.2431218

F. Murtagh, A Survey of Recent Advances in Hierarchical Clustering Algorithms, The Computer Journal, vol.26, issue.4, pp.354-359, 1983.
DOI : 10.1093/comjnl/26.4.354

W. Muster, . Breidenbach, . Fischer, . Kirchner, A. Müller et al., Computational toxicology in drug development, Drug Discovery Today, vol.13, issue.7-8, pp.303-310, 2008.
DOI : 10.1016/j.drudis.2007.12.007

K. A. Najdenova, A formal model of knowledge interpretation on the basis of classification process, pp.175-180, 1963.

S. Nijssen and J. N. Kok, The Gaston Tool for Frequent Subgraph Mining, Electronic Notes in Theoretical Computer Science, vol.127, issue.1, pp.77-87, 2005.
DOI : 10.1016/j.entcs.2004.12.039

N. I. Nikolaev, N. Evgueni, and . Smirnov, Stochastically Guided Disjunctive Version Space Learning, 1996.

M. H. Norman, L. Liu, M. Lee, N. Xi, I. Fellows et al., Structure-Based Design of Novel Class II c-Met Inhibitors: 1. Identification of Pyrazolone-Based Derivatives, Journal of Medicinal Chemistry, vol.55, issue.5, pp.1858-1867, 2012.
DOI : 10.1021/jm201330u

E. M. Norris, An algorithm for computing the maximal rectangles in a binary relation, In: Rev. Roum. Mathématiques Pures Appliquées, vol.23, issue.2, pp.243-250, 1978.

L. Nourine and O. Raynaud, A fast incremental algorithm for building lattices, Journal of Experimental & Theoretical Artificial Intelligence, vol.24, issue.2-3, pp.2-3, 2002.
DOI : 10.1016/S0020-0190(99)00108-8

G. D. Oosthuizen, The use of of a lattice in Knowledge Processing, 1988.

S. Orlando, R. Perego, and C. Silvestri, A new algorithm for gap constrained sequence mining, Proceedings of the 2004 ACM symposium on Applied computing , SAC '04, pp.540-547, 2004.
DOI : 10.1145/967900.968014

A. N. Papadopoulos, A. Lyritsis, and Y. Manolopoulos, SkyGraph: an algorithm for important subgraph discovery in relational graphs, Data Mining and Knowledge Discovery, vol.23, issue.13, pp.57-76, 2008.
DOI : 10.1007/s10618-008-0109-y

N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal, Efficient mining of association rules using closed itemset lattices, Information Systems, vol.24, issue.1, pp.25-46, 1999.
DOI : 10.1016/S0306-4379(99)00003-4

G. M. Pearl, S. Livingston-carr, and S. Durham, Integration of Computational Analysis as a Sentinel Tool in Toxicological Assessments, Current Topics in Medicinal Chemistry, vol.1, issue.4, pp.247-255, 2001.
DOI : 10.2174/1568026013395074

J. Pei, J. Han, B. Mortazavi-asl, H. Pinto, Q. Chen et al., PrefixSpan Mining Sequential Patterns Efficiently by Prefix Projected Pattern Growth, 17th Int. Conf. Data Eng. Pp, pp.215-226, 2001.

J. Pei, J. Han, B. Mortazavi-asl, H. Pinto, Q. Chen et al., PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth, pp.215-224, 2001.

F. Pennerath, G. Niel, P. Vismara, P. Jauffret, C. Laurenço et al., Graph-Mining Algorithm for the Evaluation of Bond Formability, Journal of Chemical Information and Modeling, vol.50, issue.2, pp.221-239, 2010.
DOI : 10.1021/ci9003909

URL : https://hal.archives-ouvertes.fr/hal-00471405

N. Pernelle, M. Rousset, H. Soldano, and V. Ventos, ZooM: a nested Galois lattices-based system for conceptual clustering, Journal of Experimental & Theoretical Artificial Intelligence, vol.5, issue.2-3, pp.2-3, 2002.
DOI : 10.1023/A:1022611825350

H. Pinto, J. Han, J. Pei, K. Wang, Q. Chen et al., Multi-dimensional sequential pattern mining, Proceedings of the tenth international conference on Information and knowledge management , CIKM'01, pp.81-88, 2001.
DOI : 10.1145/502585.502600

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.29.379

M. Plantevit, Y. Wei-choong, A. Laurent, D. Laurent, and M. Teisseire, M2SP: Mining Sequential Patterns Among Several Dimensions, In: Knowl. Discov. Databases PKDD Lecture Notes in Computer Science, vol.3721, pp.205-216, 2005.
DOI : 10.1007/11564126_23

URL : https://hal.archives-ouvertes.fr/lirmm-00106087

M. Plantevit, A. Laurent, D. Laurent, M. Teisseire, and Y. W. Choong, Mining multidimensional and multilevel sequential patterns, ACM Transactions on Knowledge Discovery from Data, vol.4, issue.1, pp.1-37, 2010.
DOI : 10.1145/1644873.1644877

URL : https://hal.archives-ouvertes.fr/hal-01381826

G. D. Plotkin, A note on inductive generalization, Mach. Intell. 5.1, pp.153-163, 1970.

J. Poelmans, D. I. Ignatov, S. O. 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.
DOI : 10.1016/j.eswa.2013.05.009

J. Poelmans, S. O. Kuznetsov, D. I. Ignatov, and G. Dedene, Formal Concept Analysis in knowledge processing: A survey on models and techniques, Expert Systems with Applications, vol.40, issue.16, pp.6601-6623, 2013.
DOI : 10.1016/j.eswa.2013.05.007

G. Poezevara, B. Cuissart, and B. Crémilleux, Extracting and summarizing the frequent emerging graph patterns from a dataset of graphs, Journal of Intelligent Information Systems, vol.17, issue.8, pp.333-353, 2011.
DOI : 10.1007/s10844-011-0168-1

URL : https://hal.archives-ouvertes.fr/hal-01018410

. Qian, S. Gang, Y. Sural, S. Gu, and . Pramanik, Similarity between Euclidean and cosine angle distance for nearest neighbor queries, Proceedings of the 2004 ACM symposium on Applied computing , SAC '04, 2004.
DOI : 10.1145/967900.968151

C. Raïssi, T. Calders, and P. Poncelet, Mining conjunctive sequential patterns, Data Min. Knowl. Discov, vol.171, pp.77-93, 2008.

D. C. Rees, M. Congreve, W. Christopher, R. Murray, and . Carr, Fragment-based lead discovery, Nature Reviews Drug Discovery, vol.2, issue.8, pp.660-672, 2012.
DOI : 10.1038/nrd1467

K. W. Rickert, B. Sangita, T. J. Patel, . Allison, J. Noel et al., Structural Basis for Selective Small Molecule Kinase Inhibition of Activated c-Met, Journal of Biological Chemistry, vol.286, issue.13, pp.11218-11225, 2011.
DOI : 10.1074/jbc.M110.204404

J. E. Ridings, M. D. Barratt, R. Cary, C. G. Earnshaw, C. E. Eggington et al., Computer prediction of possible toxic action from chemical structure: an update on the DEREK system, Toxicology, vol.106, issue.1-3, pp.106-107, 1996.
DOI : 10.1016/0300-483X(95)03190-Q

M. D. Rogers, The European Commission's White Paper "Strategy for a Future Chemicals Policy": A Review, Risk Anal. 23.2, pp.381-388, 2003.
DOI : 10.1080/136698701455997

C. Roth, S. A. Obiedkov, and D. G. Kourie, Towards Concise Representation for Taxonomies of Epistemic Communities, Proc. 4th Int. Conf. Concept lattices their Appl. CLA'06, pp.240-255, 2006.
DOI : 10.1007/978-3-540-78921-5_17

E. Salvemini, F. Fumarola, D. Malerba, and J. Han, FAST Sequence Mining Based on Sparse Id-Lists, Proceedings of the 19th international conference on Foundations of intelligent systems. ISMIS'11, pp.316-325, 2011.
DOI : 10.1007/978-3-540-88636-5_72

D. M. Sanderson and C. G. Earnshaw, Computer Prediction of Possible Toxic Action from Chemical Structure; The DEREK System, Human & Experimental Toxicology, vol.10, issue.4, pp.261-273, 1991.
DOI : 10.1177/096032719101000405

B. Schietgat, J. Leander, M. Ramon, H. Bruynooghe, and . Blockeel, An Efficiently Computable Graph-Based Metric for the Classification of Small Molecules, Lecture Notes in Computer Science, vol.5255, pp.197-209, 2008.
DOI : 10.1007/978-3-540-88411-8_20

A. Seal, A. Passi, U. David, and J. Wild, In-silico predictive mutagenicity model generation using supervised learning approaches, Journal of Cheminformatics, vol.4, issue.1, p.10, 2012.
DOI : 10.1002/em.2860070613

M. Sebag, Delaying the choice of bias: A disjunctive version space approach, pp.444-452, 1996.
URL : https://hal.archives-ouvertes.fr/hal-00116418

. Segura-bedmar, P. Isabel, D. Mart?nez, and . Sánchez-cisneros, The 1st DDIExtraction- 2011 challenge task: Extraction of Drug-Drug Interactions from biomedical texts, Challenge Task on Drug-Drug Interaction Extraction 2011, pp.1-9, 2011.

P. Shelokar, A. Quirin, O. Cordón, and Ó. English, MOSubdue: a Pareto dominance-based multiobjective Subdue algorithm for frequent subgraph mining, Knowledge and Information Systems, vol.7, issue.3, pp.75-108, 2013.
DOI : 10.1007/s10115-011-0452-y

R. Sherhod, V. J. Gillet, P. N. Judson, and J. D. Vessey, Automating Knowledge Discovery for Toxicity Prediction Using Jumping Emerging Pattern Mining, Journal of Chemical Information and Modeling, vol.52, issue.11, pp.3074-3087, 2012.
DOI : 10.1021/ci300254w

R. Sherhod, P. N. Judson, T. Hanser, J. D. Vessey, S. J. Webb et al., Emerging Pattern Mining To Aid Toxicological Knowledge Discovery, Journal of Chemical Information and Modeling, vol.54, issue.7, pp.1864-1879, 2014.
DOI : 10.1021/ci5001828

M. P. Smithing and F. Darvas, HazardExpert, ACS Sym. Ser, vol.484, pp.191-200, 1992.
DOI : 10.1021/bk-1992-0484.ch019

R. Socher, J. Bauer, D. Christopher, . Manning, Y. Andrew et al., Parsing with compositional vector grammars, Proceedings of the ACL conference. Citeseer, 2013.

H. Soldano, Extensional Confluences and Local Closure Operators, -Aciego. Lecture Notes in Computer Science, vol.9113, pp.128-144, 2015.
DOI : 10.1007/978-3-319-19545-2_8

H. Soldano and G. Santini, Graph abstraction for closed pattern mining in attributed network, Eur. Conf. Artif. Intell. (ECAI), pp.849-854, 2014.

H. Soldano and V. Ventos, Abstract Concept Lattices, Petko Valtchev and Robert Jäschke. Lecture Notes in Computer Science, vol.2, issue.14, pp.235-250, 2011.
DOI : 10.1080/10798587.1996.10750660

URL : https://hal.archives-ouvertes.fr/hal-00642105

A. Soulet and B. Crémilleux, Optimizing Constraint-Based Mining by Automatically Relaxing Constraints, Fifth IEEE International Conference on Data Mining (ICDM'05), 2005.
DOI : 10.1109/ICDM.2005.112

URL : https://hal.archives-ouvertes.fr/hal-00324838

A. Soulet, C. Raïssi, M. Plantevit, and B. Cremilleux, Mining Dominant Patterns in the Sky, 2011 IEEE 11th International Conference on Data Mining, pp.655-664, 2011.
DOI : 10.1109/ICDM.2011.100

URL : https://hal.archives-ouvertes.fr/inria-00623566

E. Spyropoulou, M. B. De-bie, and . English, Interesting pattern mining in multirelational data, Data Min. Knowl. Discov, pp.1-42, 2013.

R. Srikant and R. Agrawal, Mining sequential patterns: Generalizations and performance improvements, 1996.
DOI : 10.1007/BFb0014140

L. Szathmary, P. Valtchev, A. Napoli, and R. Godin, Efficient Vertical Mining of Frequent Closures and Generators, Adv. Intell. Data Anal. VIII, 2009.
DOI : 10.1007/978-3-540-88411-8_15

URL : https://hal.archives-ouvertes.fr/inria-00618805

L. Szathmary, P. Valtchev, A. Napoli, R. Godin, V. Boc et al., A fast compound algorithm for mining generators, closed itemsets, and computing links between equivalence classes, Annals of Mathematics and Artificial Intelligence, vol.175, issue.2, pp.1-2, 2014.
DOI : 10.1007/s10472-013-9372-8

URL : https://hal.archives-ouvertes.fr/hal-01101140

N. Tatti, F. Moerchen, and T. Calders, Finding Robust Itemsets under Subsampling, In: ACM Trans. Database Syst, vol.393, pp.1-27, 2014.
DOI : 10.1109/icdm.2011.69

URL : http://repository.tue.nl/782671

L. T. Thomas, R. Satyanarayana, K. Valluri, and . Karlapalem, MARGIN, ACM Transactions on Knowledge Discovery from Data, vol.4, issue.3, pp.1-42, 2010.
DOI : 10.1145/1839490.1839491

URL : https://hal.archives-ouvertes.fr/hal-00118047

P. Thomas, M. Neves, I. Solt, D. Tikk, and U. Leser, Relation extraction for drug-drug interactions using ensemble learning, Challenge Task on Drug- Drug Interaction Extraction, pp.11-18, 2011.

R. Tiedt, E. Degenkolbe, P. Furet, A. Brent, S. Appleton et al., A Drug Resistance Screen Using a Selective MET Inhibitor Reveals a Spectrum of Mutations That Partially Overlap with Activating Mutations Found in Cancer Patients, Cancer Research, vol.71, issue.15, pp.5255-5264, 2011.
DOI : 10.1158/0008-5472.CAN-10-4433

A. Tropsha and A. Golbraikh, Predictive QSAR Modeling Workflow, Model Applicability Domains, and Virtual Screening, Current Pharmaceutical Design, vol.13, issue.34, pp.3494-3504, 2007.
DOI : 10.2174/138161207782794257

S. Tsumoto, H. Iwata, S. Hirano, and Y. Tsumoto, Similarity-based behavior and process mining of medical practices, Future Generation Computer Systems, vol.33, pp.21-31, 2014.
DOI : 10.1016/j.future.2013.10.014

T. Uno, T. Asai, Y. Uchida, and H. Arimura, An Efficient Algorithm for Enumerating Closed Patterns in Transaction Databases, Lecture Notes in Computer Science, vol.3245, pp.16-31, 2004.
DOI : 10.1007/978-3-540-30214-8_2

T. Uno, M. Kiyomi, and H. Arimura, LCM ver.3, Proceedings of the 1st international workshop on open source data mining frequent pattern mining implementations, OSDM '05, pp.77-86, 2005.
DOI : 10.1145/1133905.1133916

L. G. Valerio and . Jr, In silico toxicology for the pharmaceutical sciences???, Toxicology and Applied Pharmacology, vol.241, issue.3, pp.356-370, 2009.
DOI : 10.1016/j.taap.2009.08.022

V. Ventos, H. Soldano, and T. Lamadon, Alpha Galois Lattices, Fourth IEEE International Conference on Data Mining (ICDM'04), pp.555-558, 2004.
DOI : 10.1109/ICDM.2004.10028

URL : https://hal.archives-ouvertes.fr/hal-00077391

N. Villanueva-rosales and M. Dumontier, Describing chemical functional groups in OWL-DL for the classification of chemical compounds, 2007.

J. Vreeken, M. Van-leeuwen, and A. Siebes, Krimp: mining itemsets that compress, Data Mining and Knowledge Discovery, vol.177, issue.1, pp.169-214, 2011.
DOI : 10.1007/s10618-010-0202-x

J. Vreeken and N. Tatti, Interesting Patterns, pp.105-134, 2014.
DOI : 10.1007/978-3-319-07821-2_5

G. I. Webb and . English, Discovering Significant Patterns, Mach. Learn, vol.681, pp.1-33, 2007.
DOI : 10.1007/s10994-007-5006-x

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.69.3886

G. I. Webb and S. English, K-Optimal Rule Discovery, Data Mining and Knowledge Discovery, vol.10, issue.1, pp.39-79, 2005.
DOI : 10.1007/s10618-005-0255-4

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.146.4925

D. Wienand and H. Paulheim, Detecting Incorrect Numerical Data in DBpedia, 11th Extended Semantic Web Conference, 2014.
DOI : 10.1007/978-3-319-07443-6_34

M. Wörlein, T. Meinl, I. Fischer, M. Philippsen, F. et al., A Quantitative Comparison of the Subgraph Miners MoFa, gSpan, FFSM, and Gaston, Knowl. Discov. Databases PKDD 2005 SE -39, pp.392-403, 2005.
DOI : 10.1007/11564126_39

D. Xin, H. Cheng, X. Yan, and J. Han, Extracting redundancy-aware top-k patterns, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '06, p.444, 2006.
DOI : 10.1145/1150402.1150452

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.472.1876

C. Xu, F. Cheng, L. Chen, Z. Du, W. Li et al., In silico Prediction of Chemical Ames Mutagenicity, Journal of Chemical Information and Modeling, vol.52, issue.11, pp.2840-2847, 2012.
DOI : 10.1021/ci300400a

M. Yamagishi, N. Martins, G. Neshich, W. Cai, X. Shao et al., A fast surface-matching procedure for protein???ligand docking, Journal of Molecular Modeling, vol.29, issue.6, pp.965-972, 2006.
DOI : 10.1007/s00894-006-0109-z

X. Yan, H. Cheng, J. Han, and P. S. Yu, Mining significant graph patterns by leap search, Proceedings of the 2008 ACM SIGMOD international conference on Management of data , SIGMOD '08, pp.433-444, 2008.
DOI : 10.1145/1376616.1376662

X. Yan and J. Han, gSpan: Graph-Based Substructure Pattern Mining, Data Mining, 2002. ICDM 2003. Proceedings. . . . Pp, pp.721-724, 2002.

X. Yan, J. Han, and R. Afshar, CloSpan: Mining: Closed Sequential Patterns in Large Datasets, Proc. SIAM Int'l Conf. Data Min. Pp, pp.166-177, 2003.
DOI : 10.1137/1.9781611972733.15

Z. Yang, M. Kitsuregawa, and Y. Wang, PAID: Mining Sequential Patterns by Passed Item Deduction in Large Databases, 2006 10th International Database Engineering and Applications Symposium (IDEAS'06), pp.113-120, 2006.
DOI : 10.1109/IDEAS.2006.34

H. Yao and H. J. Hamilton, Mining itemset utilities from transaction databases, Data & Knowledge Engineering, vol.59, issue.3, pp.603-626, 2006.
DOI : 10.1016/j.datak.2005.10.004

C. Yu and Y. Chen, Mining Sequential Patterns from Multidimensional Sequence Data, IEEE Trans. Knowl. Data Eng, vol.17, issue.1, pp.136-140, 2005.

Y. Yu and J. Heflin, Detecting abnormal data for ontology based information integration, 2011 International Conference on Collaboration Technologies and Systems (CTS), 2011.
DOI : 10.1109/CTS.2011.5928721

M. Zaki and . Javeed, Efficient enumeration of frequent sequences, Proceedings of the seventh international conference on Information and knowledge management , CIKM '98, pp.68-75, 1998.
DOI : 10.1145/288627.288643

M. Zaki, K. Javeed, and . Gouda, Fast vertical mining using diffsets, Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '03, pp.326-335, 2003.
DOI : 10.1145/956750.956788

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.14.5466

M. Zaki, C. J. Javeed, and . Hsiao, Efficient algorithms for mining closed itemsets and their lattice structure, IEEE Transactions on Knowledge and Data Engineering, vol.17, issue.4, pp.462-478, 2005.
DOI : 10.1109/TKDE.2005.60

A. Zaveri, D. Kontokostas, M. A. Sherif, L. Bühmann, M. Morsey et al., User-driven quality evaluation of DBpedia, Proceedings of the 9th International Conference on Semantic Systems, I-SEMANTICS '13, 2013.
DOI : 10.1145/2506182.2506195

N. Zbidi, S. Faiz, and M. Limam, On Mining Summaries by Objective Measures of Interestingness, Machine Learning, vol.5, issue.6, pp.175-198, 2006.
DOI : 10.1007/s10994-005-5066-8

Z. Zeng and J. Wang, FOGGER, Proceedings of the 12th International Conference on Extending Database Technology Advances in Database Technology, EDBT '09, pp.517-528, 2009.
DOI : 10.1145/1516360.1516421

Z. Zeng, J. Wang, L. Zhou, and G. Karypis, Coherent closed quasiclique discovery from large dense graph databases, Proc. 12th ACM SIGKDD Int. Conf. Knowl. Discov. data Min. -KDD '06, p.797, 2006.
DOI : 10.1145/1150402.1150506

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.109.2218

F. Zhu, X. Yan, J. Han, and P. S. Yu, gPrune: A Constraint Pushing Framework for Graph Pattern Mining, Lecture Notes in Computer Science, vol.4426, 2007.
DOI : 10.1007/978-3-540-71701-0_38

A. Zimmermann, Objectively evaluating condensed representations and interestingness measures for frequent itemset mining, Journal of Intelligent Information Systems, vol.12, issue.3, pp.1-19, 2013.
DOI : 10.1007/s10844-013-0297-9

. Mots-clés, Exploration de données, Analyse Formelle de Concepts, Pattern Structures