BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

80 related articles for article (PubMed ID: 22906473)

  • 1. Identification of differentially expressed genes using multi-resolution wavelet transformation analysis combined with SAM.
    Wu Y; Zhang L; Liu L; Zhang Y; Yi D
    Gene; 2012 Nov; 509(2):302-8. PubMed ID: 22906473
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Comparison of seven methods for producing Affymetrix expression scores based on False Discovery Rates in disease profiling data.
    Shedden K; Chen W; Kuick R; Ghosh D; Macdonald J; Cho KR; Giordano TJ; Gruber SB; Fearon ER; Taylor JM; Hanash S
    BMC Bioinformatics; 2005 Feb; 6():26. PubMed ID: 15705192
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. Sample size for FDR-control in microarray data analysis.
    Jung SH
    Bioinformatics; 2005 Jul; 21(14):3097-104. PubMed ID: 15845654
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. False discovery rate, sensitivity and sample size for microarray studies.
    Pawitan Y; Michiels S; Koscielny S; Gusnanto A; Ploner A
    Bioinformatics; 2005 Jul; 21(13):3017-24. PubMed ID: 15840707
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Identification of differentially expressed gene categories in microarray studies using nonparametric multivariate analysis.
    Nettleton D; Recknor J; Reecy JM
    Bioinformatics; 2008 Jan; 24(2):192-201. PubMed ID: 18042553
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A non-transformation method for identifying differentially expressed genes from cDNA microarrays.
    Zhang JG; Yin ZJ; Zhang Q
    Yi Chuan Xue Bao; 2006 Jan; 33(1):80-8. PubMed ID: 16450591
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Practical FDR-based sample size calculations in microarray experiments.
    Hu J; Zou F; Wright FA
    Bioinformatics; 2005 Aug; 21(15):3264-72. PubMed ID: 15932903
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Cross platform microarray analysis for robust identification of differentially expressed genes.
    Bosotti R; Locatelli G; Healy S; Scacheri E; Sartori L; Mercurio C; Calogero R; Isacchi A
    BMC Bioinformatics; 2007 Mar; 8 Suppl 1(Suppl 1):S5. PubMed ID: 17430572
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Work efficiency: a new criterion for comprehensive comparison and evaluation of statistical methods in large-scale identification of differentially expressed genes.
    Tan YD
    Genomics; 2011 Nov; 98(5):390-9. PubMed ID: 21741470
    [TBL] [Abstract][Full Text] [Related]  

  • 12. On the identification of differentially expressed genes: improving the generalized F-statistics for Affymetrix microarray gene expression data.
    Lai Y
    Comput Biol Chem; 2006 Oct; 30(5):321-6. PubMed ID: 16979381
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Determination of the differentially expressed genes in microarray experiments using local FDR.
    Aubert J; Bar-Hen A; Daudin JJ; Robin S
    BMC Bioinformatics; 2004 Sep; 5():125. PubMed ID: 15350197
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Estimating the false discovery rate using nonparametric deconvolution.
    van de Wiel MA; Kim KI
    Biometrics; 2007 Sep; 63(3):806-15. PubMed ID: 17825012
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Detecting differentially expressed genes by relative entropy.
    Yan X; Deng M; Fung WK; Qian M
    J Theor Biol; 2005 Jun; 234(3):395-402. PubMed ID: 15784273
    [TBL] [Abstract][Full Text] [Related]  

  • 16. A statistical method for estimating the proportion of differentially expressed genes.
    Lai Y
    Comput Biol Chem; 2006 Jun; 30(3):193-202. PubMed ID: 16650806
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Evaluation of a statistical equivalence test applied to microarray data.
    Qiu J; Cui X
    J Biopharm Stat; 2010 Mar; 20(2):240-66. PubMed ID: 20309757
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. An investigation on performance of Significance Analysis of Microarray (SAM) for the comparisons of several treatments with one control in the presence of small-variance genes.
    Lin D; Shkedy Z; Burzykowski T; Ion R; Göhlmann HW; Bondt AD; Perer T; Geerts T; Van den Wyngaert I; Bijnens L
    Biom J; 2008 Oct; 50(5):801-23. PubMed ID: 18932139
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A two-step multiple comparison procedure for a large number of tests and multiple treatments.
    Jiang H; Doerge RW
    Stat Appl Genet Mol Biol; 2006; 5():Article28. PubMed ID: 17402912
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 4.