M. Adams, J. Kelley, J. Gocayne, M. Dubnick, M. Polymeropoulos et al., Complementary DNA sequencing: expressed sequence tags and human genome project, Science, vol.252, issue.5013, pp.252-1651, 1991.
DOI : 10.1126/science.2047873

M. Ashburner, C. Ball, J. Blake, D. Botstein, H. Butler et al., Gene ontology: Tool for the unification of biology. the gene ontology consortium, Nat Genet, issue.1, p.25, 2000.

M. Asyali, D. Colak, O. Demirkaya, and M. Inan, Gene Expression Profile Classification: A Review, Current Bioinformatics, vol.1, issue.1, pp.55-73, 2006.
DOI : 10.2174/157489306775330615

C. Becquet, S. Blachon, B. Jeudy, J. Boulicaut, and O. Gandrillon, Strong-association-rule mining for large-scale gene-expression data analysis: A case study on human sage data, Genome Biol, issue.12, p.3, 2002.
URL : https://hal.archives-ouvertes.fr/hal-00194295

S. Benyahia, T. Hamrouni, M. Nguifo, and E. , Frequent closed itemset based algorithms: A thorough structural and analytical survey, SIGKDD Explorations, vol.8, issue.1, pp.93-104, 2006.

M. Bilenko, S. Basu, and R. Mooney, Integrating constraints and metric learning in semi-supervised clustering, Twenty-first international conference on Machine learning , ICML '04, 2004.
DOI : 10.1145/1015330.1015360

A. Boulesteix, G. Tutz, and K. Strimmer, A CART-based approach to discover emerging patterns in microarray data, Bioinformatics, vol.19, issue.18, pp.2465-2472, 2003.
DOI : 10.1093/bioinformatics/btg361

A. Brazma, P. Hingamp, J. Quackenbush, G. Sherlock, P. Spellman et al., Minimum information about a microarray experiment (miame)-toward standards for microarray data, Nat Genet, issue.4, pp.29-365, 2001.

R. Breitling, A. Amtmann, and P. Herzyk, Iterative group analysis (iga): A simple tool to enhance sensitivity and facilitate interpretation of microarray experiments, BMC Bioinformatics, p.5, 2004.

S. Brenner, M. Johnson, J. Bridgham, G. Golda, D. Lloyd et al., Gene expression analysis by massively parallel signature sequencing (MPSS) on microbead arrays, Nature Biotechnology, vol.311, issue.6, pp.18-630, 2000.
DOI : 10.1038/76469

L. Brisson and M. Collard, An ontology driven data mining process, Proc. ICEIS conference, 2008.
URL : https://hal.archives-ouvertes.fr/ird-00842979

P. Carmona-saez, M. Chagoyen, A. Rodríguez, O. Trelles, J. Carazo et al., Integrated analysis of gene expression by association rules discovery, BMC Bioinformatics, vol.7, issue.1, pp.54-69, 2006.
DOI : 10.1186/1471-2105-7-54

A. Ceglar and J. Roddick, Association mining, ACM Computing Surveys, vol.38, issue.2, 2006.
DOI : 10.1145/1132956.1132958

J. Chen, V. Agrawal, M. Rattray, M. West, S. Clair et al., A comparison of microarray and MPSS technology platforms for expression analysis of Arabidopsis, BMC Genomics, vol.8, issue.1, p.414, 2007.
DOI : 10.1186/1471-2164-8-414

S. Chung, Z. Deng, C. Shu, and D. Hu, Clustering analysis of gene expression data based on semisupervised visual clustering algorithm, Soft Comput, issue.11, pp.10-981, 2006.

G. Cong, K. Tan, A. Tung, and X. Xu, Mining top-K covering rule groups for gene expression data, Proceedings of the 2005 ACM SIGMOD international conference on Management of data , SIGMOD '05, pp.670-681, 2005.
DOI : 10.1145/1066157.1066234

C. Creighton and S. Hanash, Mining gene expression databases for association rules, Bioinformatics, vol.19, issue.1, pp.79-86, 2003.
DOI : 10.1093/bioinformatics/19.1.79

M. Eisen, P. Spellman, P. Brown, and D. Botstein, Cluster analysis and display of genome-wide expression patterns, Proc. Natl. Acad. Sci. USA, pp.95-14863, 1998.
DOI : 10.1073/pnas.95.25.14863

H. Fan, C. Zhai, L. Liu, and Y. J. , Subspace clustering for microarray data analysis: Multiple criteria and significance assessment, Proc. IEEE CSB Conference, pp.582-583, 2004.

C. Goble and R. Stevens, State of the nation in data integration for bioinformatics, Journal of Biomedical Informatics, vol.41, issue.5, 2008.
DOI : 10.1016/j.jbi.2008.01.008

A. Gyenesei, U. Wagner, S. Barkow-oesterreicher, E. Stolte, and R. Schlapbach, Mining co-regulated gene profiles for the detection of functional associations in gene expression data, Bioinformatics, vol.23, issue.15, 2007.
DOI : 10.1093/bioinformatics/btm276

D. Hanisch, A. Zien, R. Zimmer, and T. Lengauer, Co-clustering of biological networks and gene expression data, Bioinformatics, vol.18, issue.Suppl 1, pp.18-145, 2002.
DOI : 10.1093/bioinformatics/18.suppl_1.S145

L. Hene, V. Sreenu, M. Vuong, S. Abidi, J. Sutton et al., Deep analysis of cellular transcriptomes ??? LongSAGE versus classic MPSS, BMC Genomics, vol.8, issue.1, p.333, 2007.
DOI : 10.1186/1471-2164-8-333

Z. Huang, J. Li, H. Su, G. Watts, and H. Chen, Large-scale regulatory network analysis from microarray data: modified Bayesian network learning and association rule mining, Decision Support Systems, vol.43, issue.4, pp.43-1207, 2007.
DOI : 10.1016/j.dss.2006.02.002

A. Icev, C. Ruiz, and E. Ryder, Distance-enhanced association rules for gene expression, Proc. BioKDD Conference, pp.34-40, 2003.

D. Jiang, P. J. Ramanathan, M. Lin, C. Tang, C. Zhang et al., Mining gene???sample???time microarray data: a coherent gene cluster discovery approach, Knowledge and Information Systems, vol.17, issue.1, pp.13-305, 2007.
DOI : 10.1007/s10115-006-0031-9

J. Jiang and D. Conrath, Semantic similarity based on corpus statistics and lexical taxonomy. CoRR, cmp-lg, 1997.

X. Jiang and L. Gruenwald, Microarray gene expression data association rules mining based on jg-tree, Proc. DEXA Workshop, p.27, 2003.

P. Jonsson, K. Laurio, Z. Lubovac, B. Olsson, and M. Andersson, Using functional annotation to improve clusterings of gene expression patterns, Information Sciences, vol.145, issue.3-4, pp.3-4, 2002.
DOI : 10.1016/S0020-0255(02)00230-X

J. Jun, S. Chung, and D. Mcleod, Subspace Clustering of Microarray Data Based on Domain Transformation, Lecture Notes in Computer Science, vol.4316, pp.14-28, 2006.
DOI : 10.1007/11960669_3

S. Kim and D. Volsky, Page: Parametric analysis of geneset enrichment, BMC Bioinformatics, issue.8, 2005.

P. Kotala, P. Zhou, S. Mudivarthy, W. Perrizo, and E. Deckard, Gene expression profiling of dna microarray data using peano count trees, Proc. VCGB Conference, 2001.

E. Kotsifakos, G. Marketos, and Y. Theodoridis, A framework for integrating ontologies and patternbases , chapter 12, Information Science Reference, 2007.

C. Leacock and M. Chodorow, Combining local context with wordnet similarity for word sense identification WordNet: A Lexical Reference System and its Application, 1998.

M. Lee, F. Kuo, G. Whitmore, and J. Sklar, Importance of replication in microarray gene expression studies: Statistical methods and evidence from repetitive cDNA hybridizations, Proc. Natl. Acad. Sci. USA, pp.97-9834, 2000.
DOI : 10.1073/pnas.97.18.9834

P. Lenca, P. Meyer, B. Vaillant, and S. Lallich, On selecting interestingness measures for association rules: User oriented description and multiple criteria decision aid, European Journal of Operational Research, vol.184, issue.2, pp.610-626, 2008.
DOI : 10.1016/j.ejor.2006.10.059

J. Li, H. Liu, S. Ng, and L. Wong, Discovery of significant rules for classifying cancer diagnosis data, Bioinformatics, vol.19, issue.Suppl 2, pp.93-102, 2003.
DOI : 10.1093/bioinformatics/btg1066

J. Li and L. Wong, Identifying good diagnostic gene groups from gene expression profiles using the concept of emerging patterns, Bioinformatics, vol.18, issue.5, pp.725-734, 2002.
DOI : 10.1093/bioinformatics/18.5.725

D. Lin, An information-theoretic definition of similarity, Proc. ICML Conference, 1998.

B. Liu, W. Hsu, L. Mun, and H. Lee, Finding interesting patterns using user expectations, Knowledge and Data Engineering, vol.11, issue.6, pp.817-832, 1999.

F. Liu, T. Jenssen, J. Trimarchi, C. Punzo, C. Cepko et al., Comparison of hybridization-based and sequencing-based gene expression technologies on bio, International Journal of Software and Informatics, vol.2, issue.2, 2008.

P. Lord, R. Stevens, A. Brass, and C. Goble, SEMANTIC SIMILARITY MEASURES AS TOOLS FOR EXPLORING THE GENE ONTOLOGY, Biocomputing 2003, 2003.
DOI : 10.1142/9789812776303_0056

A. Maddalena, Pattern Based Management: Data Models and Architectural Aspects, Lecture Notes in Computer Science, vol.3268, pp.54-65, 2005.
DOI : 10.1007/978-3-540-30192-9_6

S. Madeira and A. Oliveira, Biclustering algorithms for biological data analysis: a survey, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.1, issue.1, pp.24-45, 2004.
DOI : 10.1109/TCBB.2004.2

R. Martinez and M. Collard, Extracted knowledge interpretation in mining biological data: A survey, International Journal of Computer Science & Applications, issue.2, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00476710

R. Martinez, C. Pasquier, and N. Pasquier, GenMiner: Mining Informative Association Rules from Genomic Data, 2007 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2007), pp.15-22, 2007.
DOI : 10.1109/BIBM.2007.49

URL : https://hal.archives-ouvertes.fr/hal-00361737

R. Martinez, N. Pasquier, C. Pasquier, M. Collard, and L. Lopez-perez, Co-expressed gene groups analysis (cgga): An automatic tool for the interpretation of microarray experiments, Journal of Integrative Bioinformatics, vol.3, issue.2, pp.1-12, 2006.
URL : https://hal.archives-ouvertes.fr/hal-00172501

T. Mcintosh and S. Chawla, High Confidence Rule Mining for Microarray Analysis, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.4, issue.4, pp.611-623, 2007.
DOI : 10.1109/tcbb.2007.1050

V. Mootha, C. Lindgren, K. Eriksson, A. Subramanian, S. Sihag et al., PGC-1??-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes, Nature Genetics, vol.34, issue.3, pp.267-273, 2003.
DOI : 10.1038/ng1180

F. Pan, G. Cong, A. Tung, J. Yang, and M. Zaki, Carpenter, Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '03, pp.637-642, 2003.
DOI : 10.1145/956750.956832

L. Parsons, E. Haque, and H. Liu, Subspace clustering for high dimensional data, ACM SIGKDD Explorations Newsletter, vol.6, issue.1, pp.90-105, 2004.
DOI : 10.1145/1007730.1007731

C. Pasquier, Biological data integration using Semantic Web technologies, Biochimie, vol.90, issue.4, 2008.
DOI : 10.1016/j.biochi.2008.02.007

URL : https://hal.archives-ouvertes.fr/hal-00362021

J. Pei, D. Jiang, and A. Zhang, Mining cross-graph quasi-cliques in gene expression and protein interaction data, ICDE Conference, pp.353-354, 2005.

J. Pfaltz and C. Taylor, Closed set mining of biological data, Proc. BioKDD Conference, 2002.

A. Prelic, S. Bleuler, P. Zimmermann, A. Wille, P. Bühlmann et al., A systematic comparison and evaluation of biclustering methods for gene expression data, Bioinformatics, vol.22, issue.9, pp.221122-1129, 2006.
DOI : 10.1093/bioinformatics/btl060

P. Resnik, Using information content to evaluate semantic similarity in a taxonomy, Proc. IJCAI Conference, pp.448-453, 1995.

S. Rizzi, E. Bertino, B. Catania, M. Golfarelli, M. Halkidi et al., Towards a Logical Model for Patterns, Proc. ER Conference, pp.77-90, 2003.
DOI : 10.1007/978-3-540-39648-2_9

A. Ruttenberg, T. Clark, W. Bug, M. Samwald, O. Bodenreider et al., Advancing translational research with the Semantic Web, BMC Bioinformatics, vol.8, issue.Suppl 3, 2007.
DOI : 10.1186/1471-2105-8-S3-S2

M. Schena, D. Shalon, R. Davis, and P. Brown, Quantitative Monitoring of Gene Expression Patterns with a Complementary DNA Microarray, Science, vol.270, issue.5235, pp.270-467, 1995.
DOI : 10.1126/science.270.5235.467

A. Schlicker, F. Domingues, J. Rahnenfuhrer, and T. Lengauer, A new measure for functional similarity of gene products based on gene ontology, BMC Bioinformatics, vol.7, issue.1, p.302, 2006.
DOI : 10.1186/1471-2105-7-302

E. Segal, M. Shapira, A. Regev, D. Pe-'er, D. Botstein et al., Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data, Nature Genetics, vol.291, issue.2, pp.34-166, 2003.
DOI : 10.1038/35057062

E. Segal, H. Wang, and D. Koller, Discovering molecular pathways from protein interaction and gene expression data, Bioinformatics, vol.19, issue.Suppl 1, pp.264-272, 2003.
DOI : 10.1093/bioinformatics/btg1037

A. Silberschatz and A. Tuzhilin, What makes patterns interesting in knowledge discovery systems, IEEE Transactions on Knowledge and Data Engineering, vol.8, issue.6, pp.970-974, 1996.
DOI : 10.1109/69.553165

A. Tanay, R. Sharan, and R. Shamir, Discovering statistically significant biclusters in gene expression data, Bioinformatics, vol.18, issue.Suppl 1, pp.136-144, 2002.
DOI : 10.1093/bioinformatics/18.suppl_1.S136

M. Thattai and A. Van-oudenaarden, Intrinsic noise in gene regulatory networks, Proc. Natl. Acad
DOI : 10.1073/pnas.151588598

A. Tuzhilin and G. Adomavicius, Handling very large numbers of association rules in the analysis of microarray data, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '02, pp.396-404, 2002.
DOI : 10.1145/775047.775104

V. Velculescu, L. Zhang, B. Vogelstein, and K. Kinzler, Serial Analysis of Gene Expression, Science, vol.270, issue.5235, pp.484-491, 1995.
DOI : 10.1126/science.270.5235.484

T. Werner, Bioinformatics applications for pathway analysis of microarray data, Current Opinion in Biotechnology, vol.19, issue.1, pp.50-54, 2008.
DOI : 10.1016/j.copbio.2007.11.005

H. Yoon, S. Lee, and J. Kim, Application of Emerging Patterns for Multi-source Bio-Data Classification and Analysis, Lecture Notes in Computer Science, p.3610, 2005.
DOI : 10.1007/11539087_128

J. Zhong, H. Zhu, J. Li, and Y. Yu, Conceptual Graph Matching for Semantic Search, Proc. ICCS Conference, pp.92-196, 2002.
DOI : 10.1007/3-540-45483-7_8

URL : http://apex.sjtu.edu.cn/docs/iccs2002.pdf

P. Ziegler and K. Dittrich, Three decades of data integration -all problems solved?, Proc. IFIP Congess Topical Sessions, pp.3-12, 2004.