Ouafae Kaissi, Ahmed Moussa, Brigitte Vannier, Abdellatif Ghacham


  1. [1] V. Gomases, S. Tagore and K.V. Kale, Microarray:an approach for current Drug targets, Current DrugMetabolism, (9), 2008, 221-31.Figure 5: Hieararchical Biclustering of genes and condition of the second dataset(a): Hieararchical Biclustering of genes selected by SAM in Matlab(b): Hieararchical Biclustering of genes selected by BH-Ttest in Matlab(c): Hieararchical Biclustering of genes selected by SAM in R/Bioconductor(d): Hieararchical Biclustering of genes selected by BH-Ttest in R/Bioconductor$508
  2. [2] Y. F. Leung and D. Cavalieri, Fundamentals of cDNAmicroarray data Analysis, Trends Genet, (19), 2003, 649-659.
  3. [3] D. J Lockhart, H. Dong, M. C. Byrne, and al,Expression monitoring by hybridization to high-densityoligonucleotid arrays,Nat. Biotechnol, (14) , 1996, 1675-1680.
  4. [4] D. J. Duggan, M. Bittner, Y. Chen,P. Meltzer and J.M. Trent, Expression profiling using cDNAMicroarrays,Nat.Genet, (21), 1999, 10-14.
  5. [5] V.Frouin, and X.Gidrol, Analyse des donnéesd'expression issues des puces à ADN, Biofutur ,(252),2005, 22 – 26.
  6. [6] Efron BJ : Gene association network . Stat Assoc2004, 99-96
  7. [7] F. Chu and L. Wang : Applications of support vectormachines to cancer classifi-cation with microarray data.International Journal of Neural Systems, vol. (15) 2005,475-484.
  8. [8] D. Faller H. U. Voss, J. Timmer and U. Hobohm:Normalization of DNA-microarray data by nonlinearcorrelation Maximization .Journal.Comput.Biol. vol.10(2003) 751-762.
  9. [9] .V. Tusher G., R. Tibshirani, G. Chu : Significanceanalysis of microarrays applied to the ionizing radiationresponse.Proc. Nat. Acad. Sci.vol (98) 2001, 5116–5121.
  10. [10] T. Golub R., et al : Molecular classification ofcancer: class discovery and class prediction by geneexpression monitoring. Science vol. (286 )1999, 531–537.
  11. [11] F., Model P. Adorjan, A. Olek, C. Piepenbrock :Feature selection for DNA methylation based cancerclassification Bioinformatics vol. (17) 2001 157–164.
  12. [12]. Y. Benjamini, Y. Hochberg : Controlling the falsediscovery rate: a practical and powerful approach tomultiple testing. Journal of the Roy. Stat. Soc. vol. (B 57)1995; 289–300.
  13. [13]. B. Bolstad M., R. A. Irizarry,M. Astrand ,T. P.Speed: A comparison of normalization methods for highdensity oligonucleotide array data based on variance andbias.2002,43-46.
  14. [14] T. Yuande Yin L: Comparison of methods foridentifying differentially expressed genes across multipleconditions from microarray data, Vol (3) 2011, 21.
  15. [15]. D, Susmita D Somnath: Comparisons and validationof statistical clustering techniques for microarray geneexpression data. Vol (18) 2002, 20.
  16. [16] W. mLiu R. Mei, X. Di, T. Ryder, E. Hubbel, S. Dee,T. Webster, C. Harrington, M. Ho, J. Baid and S.Smeekens Analysis of High Density ExpressionMicroarray with Signed-Rank Calls Algorithmes.Bioinformatics,(vol.12) 2002,1593-1599.
  17. [17] S,. Sanjai L: Alan Aberrant splicing of the E-cadherin transcript is a novel mechanism of genesilencing in chronic lymphocytic leukemia cells, Blood,2009 4179-4185.
  18. [18] B. Bolstad, R Irizarry., MAstrand., and T .Speed, AComparison of Normalization Methods for High DensityOligonucleotide Array Data Based on Bias and Variance.Bioinformatics,(19),2003, 185-193.
  19. [19]
  20. [20] G. K. Smyth ‘Linear Models and Empirical BayesMethods for Assessing Differential Expression inMicroarray Experiments. Statistical Applications’ inGenetics and Molecular Biology,(3),2004.
  21. [21]
  22. [22] R. Sharan, A. Maron-Katz, and R Shamir, ‘CLICKand EXPANDER: a system for clustering and visualizinggene expression data. Bioinformatics (19), 2003, ,1787–1799.
  23. [23] R. Shamir and al., ‘EXPANDER: an integrativeprogram suite for microarray data analysis. BMCBioinformatics (6), 2005

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