K. Liolios, N. Tavernarakis, P. Hugenholtz, and N. Kyrpides, The Genomes On Line Database (GOLD) v.2: a monitor of genome projects worldwide, Database, pp.34-332, 2006.
DOI : 10.1093/nar/gkj145

A. Bernal, U. Ear, and N. Kyrpides, Genomes OnLine Database (GOLD): a monitor of genome projects world-wide, Nucleic Acids Research, vol.29, issue.1, pp.126-127, 2001.
DOI : 10.1093/nar/29.1.126

S. Muro, I. Takemasa, S. Oba, R. Matoba, N. Ueno et al., Identification of expressed genes linked to malignancy of human colorectal carcinoma by parametric clustering of quantitative expression data, Genome Biology, vol.4, issue.3, p.21, 2003.
DOI : 10.1186/gb-2003-4-3-r21

C. Perou, T. Sorlie, M. Eisen, R. Van-de, M. Jeffrey et al., Molecular portraits of human breast tumours, Nature, issue.6797, pp.406747-752, 2000.

A. Statnikov, C. Aliferis, I. Tsamardinos, D. Hardin, and S. Levy, A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis, Bioinformatics, vol.21, issue.5, pp.631-643, 2005.
DOI : 10.1093/bioinformatics/bti033

K. Imai, M. Kawai, M. Tada, T. Nagase, O. Ohara et al., Temporal change in mKIAA gene expression during the early stage of retinoic acid-induced neurite outgrowth, Gene, vol.364, pp.114-122, 2005.
DOI : 10.1016/j.gene.2005.05.037

R. Raab, Incorporating genome-scale tools for studying energy homeostasis, Nutrition & Metabolism, vol.3, issue.1, p.40, 2006.
DOI : 10.1186/1743-7075-3-40

K. Fellenberg, C. Busold, O. Witt, A. Bauer, B. Beckmann et al., Systematic interpretation of microarray data using experiment annotations, BMC Genomics, vol.7, issue.1, p.319, 2006.
DOI : 10.1186/1471-2164-7-319

J. Hoheisel, Microarray technology: beyond transcript profiling and genotype analysis, Nature Reviews Microbiology, vol.6, issue.3, pp.200-210, 2006.
DOI : 10.1038/nrg1809

J. Derisi, V. Iyer, and P. Brown, Exploring the Metabolic and Genetic Control of Gene Expression on a Genomic Scale, Science, vol.278, issue.5338, pp.680-686, 1997.
DOI : 10.1126/science.278.5338.680

J. Clarke and T. Zhu, Microarray analysis of the transcriptome as a stepping stone towards understanding biological systems: practical considerations and perspectives, The Plant Journal, vol.136, issue.4 Pt 1, pp.630-650, 2006.
DOI : 10.1111/j.1365-313X.2006.02668.x

W. Zhang, R. Rekaya, and K. Bertrand, A method for predicting disease subtypes in presence of misclassification among training samples using gene expression: application to human breast cancer, Bioinformatics, vol.22, issue.3, pp.317-325, 2006.
DOI : 10.1093/bioinformatics/bti738

A. Alizadeh, M. Eisen, R. Davis, C. Ma, I. Lossos et al., Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling, Nature, vol.303, issue.6769, pp.403503-511, 2000.
DOI : 10.1038/35000501

T. Pham, C. Wells, and D. Crane, Analysis of Microarray Gene Expression Data, Current Bioinformatics, vol.1, issue.1, pp.37-53, 2006.
DOI : 10.2174/157489306775330642

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

J. Wei, B. Greer, F. Westermann, S. Steinberg, C. Son et al., Prediction of Clinical Outcome Using Gene Expression Profiling and Artificial Neural Networks for Patients with Neuroblastoma, Cancer Research, vol.64, issue.19, pp.646883-6891, 2004.
DOI : 10.1158/0008-5472.CAN-04-0695

A. Gruzdz, A. Ihnatowicz, and D. Slezak, Interactive Gene Clustering???A Case Study of Breast Cancer Microarray Data, Information Systems Frontiers, vol.219, issue.1, pp.21-27, 2006.
DOI : 10.1007/s10796-005-6100-x

J. Schuchhardt, D. Beule, A. Malik, E. Wolski, H. Eickhoff et al., Normalization strategies for cDNA microarrays, Nucleic Acids Research, vol.28, issue.10, pp.28-47, 2000.
DOI : 10.1093/nar/28.10.e47

J. Hartigan and M. Wong, Algorithm AS 136: A K-Means Clustering Algorithm, Applied Statistics, vol.28, issue.1, pp.100-115, 1979.
DOI : 10.2307/2346830

T. Kohonen, Self-organized formation of topologically correct feature maps, Biological Cybernetics, vol.13, issue.1, pp.59-69, 1982.
DOI : 10.1007/BF00337288

T. Kohonen, Self-Organizing Maps, 2001.

D. Wang, Y. Lv, Z. Guo, X. Li, Y. Li et al., Effects of replacing the unreliable cDNA microarray measurements on the disease classification based on gene expression profiles and functional modules, Bioinformatics, vol.22, issue.23, pp.222883-2889, 2006.
DOI : 10.1093/bioinformatics/btl339

A. Gru?d?, A. Ihnatowicz, and D. ?l?zak, Gene Expression Clustering: Dealing with the Missing Values. Intelligent Information Processing and Web Mining, p.521, 2005.

E. Fix and J. Hodges, Discriminatory Analysis. Nonparametric Discrimination: Consistency Properties, International Statistical Review / Revue Internationale de Statistique, vol.57, issue.3, 1951.
DOI : 10.2307/1403797

O. Troyanskaya, M. Cantor, G. Sherlock, P. Brown, T. Hastie et al., Missing value estimation methods for DNA microarrays, Bioinformatics, vol.17, issue.6, pp.520-525, 2001.
DOI : 10.1093/bioinformatics/17.6.520

K. Kim, B. Kim, and G. Yi, Reuse of imputed data in microarray analysis increases imputation efficiency, BMC Bioinformatics, vol.5, issue.1, p.160, 2004.
DOI : 10.1186/1471-2105-5-160

T. Bo, B. Dysvik, and I. Jonassen, LSimpute: accurate estimation of missing values in microarray data with least squares methods, Nucleic Acids Research, vol.32, issue.3, p.34, 2004.
DOI : 10.1093/nar/gnh026

S. Oba, M. Sato, I. Takemasa, M. Monden, K. Matsubara et al., A Bayesian missing value estimation method for gene expression profile data, Bioinformatics, vol.19, issue.16, pp.2088-2096, 2003.
DOI : 10.1093/bioinformatics/btg287

Z. Bar-joseph, G. Gerber, D. Gifford, T. Jaakkola, and I. Simon, Continuous Representations of Time-Series Gene Expression Data, Journal of Computational Biology, vol.10, issue.3-4, pp.3-4341, 2003.
DOI : 10.1089/10665270360688057

A. Schliep, A. Schonhuth, and C. Steinhoff, Using hidden Markov models to analyze gene expression time course data, Bioinformatics, vol.19, issue.Suppl 1, pp.255-263, 2003.
DOI : 10.1093/bioinformatics/btg1036

J. Tuikkala, L. Elo, O. Nevalainen, and T. Aittokallio, Improving missing value estimation in microarray data with gene ontology, Bioinformatics, vol.22, issue.5, pp.566-572, 2006.
DOI : 10.1093/bioinformatics/btk019

D. Kim, K. Lee, K. Lee, and D. Lee, Towards clustering of incomplete microarray data without the use of imputation, Bioinformatics, vol.23, issue.1, pp.107-113, 2007.
DOI : 10.1093/bioinformatics/btl555

J. Hu, H. Li, M. Waterman, and X. Zhou, Integrative missing value estimation for microarray data, BMC Bioinformatics, vol.7, issue.1, p.449, 2006.
DOI : 10.1186/1471-2105-7-449

R. Jornsten, M. Ouyang, and H. Wang, A meta-data based method for DNA microarray imputation, BMC Bioinformatics, vol.8, issue.1, p.109, 2007.
DOI : 10.1186/1471-2105-8-109

X. Gan, A. Liew, and H. Yan, Microarray missing data imputation based on a set theoretic framework and biological knowledge, Nucleic Acids Research, vol.34, issue.5, pp.1608-1619, 2006.
DOI : 10.1093/nar/gkl047

D. Hua and Y. Lai, An ensemble approach to microarray data-based gene prioritization after missing value imputation, Bioinformatics, vol.23, issue.6, pp.747-754, 2007.
DOI : 10.1093/bioinformatics/btm010

X. Wang, A. Li, Z. Jiang, and H. Feng, Missing value estimation for DNA microarray gene expression data by Support Vector Regression imputation and orthogonal coding scheme, BMC Bioinformatics, vol.7, issue.1, p.32, 2006.
DOI : 10.1186/1471-2105-7-32

G. Feten, T. Almoy, and A. Aastveit, Prediction of Missing Values in Microarray and Use of Mixed Models to Evaluate the Predictors, Statistical Applications in Genetics and Molecular Biology, vol.4, issue.1, p.10, 2005.
DOI : 10.2202/1544-6115.1120

D. Nguyen, N. Wang, and R. Carroll, Evaluation of Missing Value Estimation for Microarray Data, Journal of Data Science, vol.2, pp.347-370, 2004.

M. Ouyang, W. Welsh, and P. Georgopoulos, Gaussian mixture clustering and imputation of microarray data, Bioinformatics, vol.20, issue.6, pp.917-923, 2004.
DOI : 10.1093/bioinformatics/bth007

R. Jornsten, H. Wang, W. Welsh, and M. Ouyang, DNA microarray data imputation and significance analysis of differential expression, Bioinformatics, vol.21, issue.22, pp.4155-4161, 2005.
DOI : 10.1093/bioinformatics/bti638

M. Sehgal, I. Gondal, and L. Dooley, Collateral missing value imputation: a new robust missing value estimation algorithm for microarray data, Bioinformatics, vol.21, issue.10, pp.2417-2423, 2005.
DOI : 10.1093/bioinformatics/bti345

I. Scheel, M. Aldrin, I. Glad, R. Sorum, H. Lyng et al., The influence of missing value imputation on detection of differentially expressed genes from microarray data, Bioinformatics, vol.21, issue.23, pp.4272-4279, 2005.
DOI : 10.1093/bioinformatics/bti708

E. Tsiporkova and V. Boeva, TWO-PASS IMPUTATION ALGORITHM FOR MISSING VALUE ESTIMATION IN GENE EXPRESSION TIME SERIES, Journal of Bioinformatics and Computational Biology, vol.05, issue.05, pp.1005-1022, 2007.
DOI : 10.1142/S0219720007003053

L. Bras and J. Menezes, Dealing with gene expression missing data, IEE Proceedings - Systems Biology, vol.153, issue.3, pp.105-119
DOI : 10.1049/ip-syb:20050056

L. Bras and J. Menezes, Improving cluster-based missing value estimation of DNA microarray data, Biomolecular Engineering, vol.24, issue.2, pp.273-282, 2007.
DOI : 10.1016/j.bioeng.2007.04.003

A. De-brevern, S. Hazout, and A. Malpertuy, Influence of microarrays experiments missing values on the stability of gene groups by hierarchical clustering, BMC Bioinformatics, vol.5, issue.1, p.114, 2004.
DOI : 10.1186/1471-2105-5-114

URL : https://hal.archives-ouvertes.fr/inserm-00000113

D. Wong, F. Wong, and G. Wood, A multi-stage approach to clustering and imputation of gene expression profiles, Bioinformatics, vol.23, issue.8, pp.998-1005, 2007.
DOI : 10.1093/bioinformatics/btm053

N. Ogawa, J. Derisi, and P. Brown, New Components of a System for Phosphate Accumulation and Polyphosphate Metabolism in Saccharomyces cerevisiae Revealed by Genomic Expression Analysis, Molecular Biology of the Cell, vol.11, issue.12, pp.4309-4321, 2000.
DOI : 10.1091/mbc.11.12.4309

A. Gasch, P. Spellman, C. Kao, O. Eisen, M. Storz et al., Genomic Expression Programs in the Response of Yeast Cells to Environmental Changes, Molecular Biology of the Cell, vol.11, issue.12, pp.114241-4257, 2000.
DOI : 10.1091/mbc.11.12.4241

S. Bohen, O. Troyanskaya, O. Alter, R. Warnke, D. Botstein et al., Variation in gene expression patterns in follicular lymphoma and the response to rituximab, Proceedings of the National Academy of Sciences, vol.100, issue.4, pp.1926-1930, 2003.
DOI : 10.1073/pnas.0437875100

A. Lucau-danila, G. Lelandais, Z. Kozovska, V. Tanty, T. Delaveau et al., Early Expression of Yeast Genes Affected by Chemical Stress, Molecular and Cellular Biology, vol.25, issue.5, pp.1860-1868, 2005.
DOI : 10.1128/MCB.25.5.1860-1868.2005

G. Brock, J. Shaffer, R. Blakesley, M. Lotz, and G. Tseng, Which missing value imputation method to use in expression profiles: a comparative study and two selection schemes, BMC Bioinformatics, vol.9, issue.1, p.12, 2008.
DOI : 10.1186/1471-2105-9-12

J. Tuikkala, L. Elo, O. Nevalainen, and T. Aittokallio, Missing value imputation improves clustering and interpretation of gene expression microarray data, BMC Bioinformatics, vol.9, issue.1, p.202, 2008.
DOI : 10.1186/1471-2105-9-202

H. Kim, G. Golub, and H. Park, Missing value estimation for DNA microarray gene expression data: local least squares imputation, Bioinformatics, vol.21, issue.2, pp.187-198, 2005.
DOI : 10.1093/bioinformatics/bth499

B. Cox, T. Kislinger, and A. Emili, Integrating gene and protein expression data: pattern analysis and profile mining, Methods, vol.35, issue.3, pp.303-314, 2005.
DOI : 10.1016/j.ymeth.2004.08.021

P. Tamayo, D. Slonim, J. Mesirov, Q. Zhu, S. Kitareewan et al., Interpreting patterns of gene expression with selforganizing maps: methods and application to hematopoietic differentiation, Proc Natl Acad Sci, issue.6, pp.962907-2912, 1999.

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.9514863-14868, 1998.
DOI : 10.1073/pnas.95.25.14863

J. Herrero, A. Valencia, and J. Dopazo, A hierarchical unsupervised growing neural network for clustering gene expression patterns, Bioinformatics, vol.17, issue.2, pp.126-136, 2001.
DOI : 10.1093/bioinformatics/17.2.126

J. Dopazo and J. Carazo, Phylogenetic Reconstruction Using an Unsupervised Growing Neural Network That Adopts the Topology of a Phylogenetic Tree, Journal of Molecular Evolution, vol.44, issue.2, pp.226-233, 1997.
DOI : 10.1007/PL00006139

L. Yin, C. Huang, and J. Ni, Clustering of gene expression data: performance and similarity analysis, BMC Bioinformatics, vol.7, issue.Suppl 4, p.19, 2006.
DOI : 10.1186/1471-2105-7-S4-S19

X. Fu, L. Teng, Y. Li, W. Chen, Y. Mao et al., Finding dominant sets in microarray data, Frontiers in Bioscience, vol.10, issue.1-3, pp.3068-3077, 2005.
DOI : 10.2741/1763

G. Tseng and W. Wong, Tight Clustering: A Resampling-Based Approach for Identifying Stable and Tight Patterns in Data, Biometrics, vol.9, issue.1, pp.10-16, 2005.
DOI : 10.1093/bioinformatics/17.10.977

A. Ben-dor, R. Shamir, and Z. Yakhini, Clustering gene expression patterns, J Comput Biol, vol.6, pp.3-4281, 1999.
DOI : 10.1089/106652799318274

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.34.5341

Y. Qu and S. Xu, Supervised cluster analysis for microarray data based on multivariate Gaussian mixture, Bioinformatics, vol.20, issue.12, pp.1905-1913, 2004.
DOI : 10.1093/bioinformatics/bth177

K. Yeung, C. Fraley, A. Murua, A. Raftery, and W. Ruzzo, Model-based clustering and data transformations for gene expression data, Bioinformatics, vol.17, issue.10, pp.977-987, 2001.
DOI : 10.1093/bioinformatics/17.10.977

K. Yeung, D. Haynor, and W. Ruzzo, Validating clustering for gene expression data, Bioinformatics, vol.17, issue.4, pp.309-318, 2001.
DOI : 10.1093/bioinformatics/17.4.309

URL : http://bauhaus.cs.washington.edu/homes/kayee/cluster2.pdf

J. Kim and H. Kim, Clustering of change patterns using Fourier coefficients, Bioinformatics, vol.24, issue.2, 2007.
DOI : 10.1093/bioinformatics/btm568

C. Huttenhower, A. Flamholz, J. Landis, S. Sahi, C. Myers et al., Nearest Neighbor Networks: clustering expression data based on gene neighborhoods, BMC Bioinformatics, vol.8, issue.1, p.250, 2007.
DOI : 10.1186/1471-2105-8-250

L. Fu and E. Medico, FLAME, a novel fuzzy clustering method for the analysis of DNA microarray data, BMC Bioinformatics, vol.8, issue.1, p.3, 2007.
DOI : 10.1186/1471-2105-8-3

G. Lelandais, P. Vincens, A. Badel-chagnon, S. Vialette, C. Jacq et al., Comparing gene expression networks in a multi-dimensional space to extract similarities and differences between organisms, Bioinformatics, vol.22, issue.11, pp.221359-1366, 2006.
DOI : 10.1093/bioinformatics/btl087

URL : https://hal.archives-ouvertes.fr/hal-00091646

S. Datta and S. Datta, Evaluation of clustering algorithms for gene expression data, BMC Bioinformatics, vol.7, issue.Suppl 4, p.17, 2006.
DOI : 10.1186/1471-2105-7-S4-S17

D. Allison, X. Cui, G. Page, and M. Sabripour, Microarray data analysis: from disarray to consolidation and consensus, Nature Reviews Genetics, vol.12, issue.1, pp.55-65, 2006.
DOI : 10.1038/nrg1749

J. Handl, J. Knowles, and D. Kell, Computational cluster validation in post-genomic data analysis, Bioinformatics, vol.21, issue.15, pp.3201-3212, 2005.
DOI : 10.1093/bioinformatics/bti517

L. Wu, T. Hughes, A. Davierwala, M. Robinson, R. Stoughton et al., Large-scale prediction of Saccharomyces cerevisiae gene function using overlapping transcriptional clusters, Nature Genetics, vol.31, issue.3, pp.31255-265, 2002.
DOI : 10.1038/ng906

A. Thalamuthu, I. Mukhopadhyay, X. Zheng, and G. Tseng, Evaluation and comparison of gene clustering methods in microarray analysis, Bioinformatics, vol.22, issue.19, pp.2405-2412, 2006.
DOI : 10.1093/bioinformatics/btl406

S. Swift, A. Tucker, V. Vinciotti, N. Martin, C. Orengo et al., Consensus clustering and functional interpretation of gene-expression data, Genome Biology, vol.5, issue.11, p.94, 2004.
DOI : 10.1186/gb-2004-5-11-r94

X. Zhang, X. Song, H. Wang, and H. Zhang, Sequential local least squares imputation estimating missing value of microarray data, Computers in Biology and Medicine, vol.38, issue.10, pp.1112-1120, 2008.
DOI : 10.1016/j.compbiomed.2008.08.006

J. Gollub, C. Ball, G. Binkley, J. Demeter, D. Finkelstein et al., The Stanford Microarray Database: data access and quality assessment tools, Nucleic Acids Research, vol.31, issue.1, pp.3194-96, 2003.
DOI : 10.1093/nar/gkg078

R. Ihaka and R. Gentleman, R: a language for data analysis and graphics, J Comput Graph Stat, vol.5, pp.299-314, 1996.

J. Quackenbush, COMPUTATIONAL GENETICS: COMPUTATIONAL ANALYSIS OF MICROARRAY DATA, Nature Reviews Genetics, vol.2, issue.6, pp.418-427, 2001.
DOI : 10.1038/35076576

B. Meunier, E. Dumas, I. Piec, D. Bechet, M. Hebraud et al., Assessment of Hierarchical Clustering Methodologies for Proteomic Data Mining, Journal of Proteome Research, vol.6, issue.1, pp.358-366, 2007.
DOI : 10.1021/pr060343h

. Celton, Comparative analysis of missing value imputation methods to improve clustering and interpretation of microarray experiments, BMC Genomics, vol.11, issue.1, p.15, 2010.
DOI : 10.1186/1471-2164-11-15

URL : https://hal.archives-ouvertes.fr/inserm-00663912