M. Eisen, P. Spellman, P. Brown, and D. Botstein, Cluster analysis and display of genome-wide expression patterns, Proceedings of the National Academy of Sciences, vol.95, issue.25, pp.85-14863, 1998.
DOI : 10.1073/pnas.95.25.14863

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

E. Blalock, Harnessing the power of gene microarrays for the study of brain aging and Alzheimer's disease: Statistical reliability and functional correlation, Ageing Research Reviews, vol.4, issue.4, pp.482-512, 2005.
DOI : 10.1016/j.arr.2005.06.006

F. Hoerndli, D. David, and J. Götz, Functional genomics meets neurodegenerative disorders. part ii: Application and data integration, Progress Neurobiol, pp.76169-188, 2005.

G. Cong, X. Tung, F. Pan, and Y. J. , FARMER, Proceedings of the 2004 ACM SIGMOD international conference on Management of data , SIGMOD '04, pp.143-154, 2004.
DOI : 10.1145/1007568.1007587

URL : https://hal.archives-ouvertes.fr/hal-01234831

J. Khan, Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks, Nature Medicine, pp.673-679, 2001.

R. Pensa, J. Besson, and J. Boulicaut, A Methodology for Biologically Relevant Pattern Discovery from Gene Expression Data, Discovery Science, pp.3245230-241, 2004.
DOI : 10.1007/978-3-540-30214-8_18

M. Korotkiy, R. Middelburg, H. Dekker, F. Van-harmelen, and J. Lankelma, A tool for gene expression based PubMed search through combining data sources, Bioinformatics, vol.20, issue.12, pp.201980-1982, 2004.
DOI : 10.1093/bioinformatics/bth183

P. Salle, GeneMining: Identification, Visualization, and Interpretation of, Brain Ageing Signatures. Stud Health Technol Inform, vol.150, pp.767-71, 2009.
URL : https://hal.archives-ouvertes.fr/lirmm-00395142

P. Salle, S. Bringay, and M. Teisseire, Mining Discriminant Sequential Patterns for Aging Brain, Proceedings of the 12th, pp.365-369, 2009.
DOI : 10.1073/pnas.091062498

URL : https://hal.archives-ouvertes.fr/lirmm-00395128

L. Tanabe, U. Scherf, L. Smith, J. Lee, L. Hunter et al., MedMiner: an Internet text-mining tool for biomedical information, with application to gene expression profiling, Biotechniques, vol.27, pp.1210-1214, 1999.

B. Zeeberg, GoMiner: a resource for biological interpretation of genomic and proteomic data, Genome Biology, vol.4, issue.4, p.28, 2003.
DOI : 10.1186/gb-2003-4-4-r28

U. Leser and J. Hakenberg, What makes a gene name? Named entity recognition in the biomedical literature, Briefings in Bioinformatics, vol.6, issue.4, pp.357-69, 2005.
DOI : 10.1093/bib/6.4.357

P. Collet and J. Rennard, Introduction to Stochastic Optimization Algorithms, Handbook of Research on Nature- Inspired Computing for Economics and Management, JP. Rennard, IDEA Group Inc, 2006.

V. Pillet, M. Zehnder, A. Seewald, A. Veuthey, and J. Petrak, GPSDB: a new database for synonyms expansion of gene and protein names, Bioinformatics, vol.21, issue.8, pp.1743-1744, 2005.
DOI : 10.1093/bioinformatics/bti235