179 related articles for article (PubMed ID: 19476634)
21. Sample size for identifying differentially expressed genes in microarray experiments.
Wang SJ; Chen JJ
J Comput Biol; 2004; 11(4):714-26. PubMed ID: 15579240
[TBL] [Abstract][Full Text] [Related]
22. Practical guidelines for assessing power and false discovery rate for a fixed sample size in microarray experiments.
Tong T; Zhao H
Stat Med; 2008 May; 27(11):1960-72. PubMed ID: 18338314
[TBL] [Abstract][Full Text] [Related]
23. An improved procedure for gene selection from microarray experiments using false discovery rate criterion.
Yang JJ; Yang MC
BMC Bioinformatics; 2006 Jan; 7():15. PubMed ID: 16405735
[TBL] [Abstract][Full Text] [Related]
24. Estimating effect sizes of differentially expressed genes for power and sample-size assessments in microarray experiments.
Matsui S; Noma H
Biometrics; 2011 Dec; 67(4):1225-35. PubMed ID: 21627629
[TBL] [Abstract][Full Text] [Related]
25. Analysis of oligonucleotide array experiments with repeated measures using mixed models.
Li H; Wood CL; Getchell TV; Getchell ML; Stromberg AJ
BMC Bioinformatics; 2004 Dec; 5():209. PubMed ID: 15626348
[TBL] [Abstract][Full Text] [Related]
26. Multiple testing for gene sets from microarray experiments.
Sohn I; Owzar K; Lim J; George SL; Mackey Cushman S; Jung SH
BMC Bioinformatics; 2011 May; 12():209. PubMed ID: 21615889
[TBL] [Abstract][Full Text] [Related]
27. Comparison of microarray designs for class comparison and class discovery.
Dobbin K; Simon R
Bioinformatics; 2002 Nov; 18(11):1438-45. PubMed ID: 12424114
[TBL] [Abstract][Full Text] [Related]
28. A mixture model for estimating the local false discovery rate in DNA microarray analysis.
Liao JG; Lin Y; Selvanayagam ZE; Shih WJ
Bioinformatics; 2004 Nov; 20(16):2694-701. PubMed ID: 15145810
[TBL] [Abstract][Full Text] [Related]
29. Noise sampling method: an ANOVA approach allowing robust selection of differentially regulated genes measured by DNA microarrays.
Draghici S; Kulaeva O; Hoff B; Petrov A; Shams S; Tainsky MA
Bioinformatics; 2003 Jul; 19(11):1348-59. PubMed ID: 12874046
[TBL] [Abstract][Full Text] [Related]
30. Sample size calculation through the incorporation of heteroscedasticity and dependence for a penalized t-statistic in microarray experiments.
Hirakawa A; Hamada C; Yoshimura I
J Biopharm Stat; 2012; 22(2):260-75. PubMed ID: 22251173
[TBL] [Abstract][Full Text] [Related]
31. Estimating p-values in small microarray experiments.
Yang H; Churchill G
Bioinformatics; 2007 Jan; 23(1):38-43. PubMed ID: 17077100
[TBL] [Abstract][Full Text] [Related]
32. Quadratic regression analysis for gene discovery and pattern recognition for non-cyclic short time-course microarray experiments.
Liu H; Tarima S; Borders AS; Getchell TV; Getchell ML; Stromberg AJ
BMC Bioinformatics; 2005 Apr; 6():106. PubMed ID: 15850479
[TBL] [Abstract][Full Text] [Related]
33. Multidimensional local false discovery rate for microarray studies.
Ploner A; Calza S; Gusnanto A; Pawitan Y
Bioinformatics; 2006 Mar; 22(5):556-65. PubMed ID: 16368770
[TBL] [Abstract][Full Text] [Related]
34. FDR-controlling testing procedures and sample size determination for microarrays.
Li SS; Bigler J; Lampe JW; Potter JD; Feng Z
Stat Med; 2005 Aug; 24(15):2267-80. PubMed ID: 15977294
[TBL] [Abstract][Full Text] [Related]
35. Empirical Bayes screening of many p-values with applications to microarray studies.
Datta S; Datta S
Bioinformatics; 2005 May; 21(9):1987-94. PubMed ID: 15691856
[TBL] [Abstract][Full Text] [Related]
36. A statistical framework for the design of microarray experiments and effective detection of differential gene expression.
Zhang SD; Gant TW
Bioinformatics; 2004 Nov; 20(16):2821-8. PubMed ID: 15180939
[TBL] [Abstract][Full Text] [Related]
37. Re-sampling strategy to improve the estimation of number of null hypotheses in FDR control under strong correlation structures.
Lu X; Perkins DL
BMC Bioinformatics; 2007 May; 8():157. PubMed ID: 17509157
[TBL] [Abstract][Full Text] [Related]
38. Multiplicity issues in microarray experiments.
Bretz F; Landgrebe J; Brunner E
Methods Inf Med; 2005; 44(3):431-7. PubMed ID: 16113769
[TBL] [Abstract][Full Text] [Related]
39. A practical false discovery rate approach to identifying patterns of differential expression in microarray data.
Grant GR; Liu J; Stoeckert CJ
Bioinformatics; 2005 Jun; 21(11):2684-90. PubMed ID: 15797908
[TBL] [Abstract][Full Text] [Related]
40. Using weighted permutation scores to detect differential gene expression with microarray data.
Guo X; Pan W
J Bioinform Comput Biol; 2005 Aug; 3(4):989-1006. PubMed ID: 16078371
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
