These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

250 related articles for article (PubMed ID: 16278953)

  • 1. Rank-based methods as a non-parametric alternative of the T-statistic for the analysis of biological microarray data.
    Breitling R; Herzyk P
    J Bioinform Comput Biol; 2005 Oct; 3(5):1171-89. PubMed ID: 16278953
    [TBL] [Abstract][Full Text] [Related]  

  • 2. The Baumgartner-Weiss-Schindler test for the detection of differentially expressed genes in replicated microarray experiments.
    Neuhäuser M; Senske R
    Bioinformatics; 2004 Dec; 20(18):3553-64. PubMed ID: 15284098
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Construction of null statistics in permutation-based multiple testing for multi-factorial microarray experiments.
    Gao X
    Bioinformatics; 2006 Jun; 22(12):1486-94. PubMed ID: 16574697
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Identifying differentially expressed genes from microarray experiments via statistic synthesis.
    Yang YH; Xiao Y; Segal MR
    Bioinformatics; 2005 Apr; 21(7):1084-93. PubMed ID: 15513985
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Spot intensity ratio statistics in two-channel microarray experiments.
    Park T; Kim K; Yi SG; Kim JH; Lee YS; Lee S
    J Bioinform Comput Biol; 2007 Aug; 5(4):865-73. PubMed ID: 17787060
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A new outlier removal approach for cDNA microarray normalization.
    Wu Y; Yan L; Liu H; Sun H; Xie H
    Biotechniques; 2009 Aug; 47(2):691-2, 694-700. PubMed ID: 19737130
    [TBL] [Abstract][Full Text] [Related]  

  • 7. A unified framework for finding differentially expressed genes from microarray experiments.
    Shaik JS; Yeasin M
    BMC Bioinformatics; 2007 Sep; 8():347. PubMed ID: 17877806
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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]  

  • 9. New criteria for selecting differentially expressed genes.
    Loo LH; Roberts S; Hrebien L; Kam M
    IEEE Eng Med Biol Mag; 2007; 26(2):17-26. PubMed ID: 17441605
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Comparison and evaluation of methods for generating differentially expressed gene lists from microarray data.
    Jeffery IB; Higgins DG; Culhane AC
    BMC Bioinformatics; 2006 Jul; 7():359. PubMed ID: 16872483
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Utilization of two sample t-test statistics from redundant probe sets to evaluate different probe set algorithms in GeneChip studies.
    Hu Z; Willsky GR
    BMC Bioinformatics; 2006 Jan; 7():12. PubMed ID: 16403228
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Classification of microarray data with factor mixture models.
    Martella F
    Bioinformatics; 2006 Jan; 22(2):202-8. PubMed ID: 16287938
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Modified nonparametric approaches to detecting differentially expressed genes in replicated microarray experiments.
    Zhao Y; Pan W
    Bioinformatics; 2003 Jun; 19(9):1046-54. PubMed ID: 12801864
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Nonparametric methods for identifying differentially expressed genes in microarray data.
    Troyanskaya OG; Garber ME; Brown PO; Botstein D; Altman RB
    Bioinformatics; 2002 Nov; 18(11):1454-61. PubMed ID: 12424116
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Bayesian variable selection for the analysis of microarray data with censored outcomes.
    Sha N; Tadesse MG; Vannucci M
    Bioinformatics; 2006 Sep; 22(18):2262-8. PubMed ID: 16845144
    [TBL] [Abstract][Full Text] [Related]  

  • 16. A robust neural networks approach for spatial and intensity-dependent normalization of cDNA microarray data.
    Tarca AL; Cooke JE; Mackay J
    Bioinformatics; 2005 Jun; 21(11):2674-83. PubMed ID: 15797913
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. A variational Bayesian mixture modelling framework for cluster analysis of gene-expression data.
    Teschendorff AE; Wang Y; Barbosa-Morais NL; Brenton JD; Caldas C
    Bioinformatics; 2005 Jul; 21(13):3025-33. PubMed ID: 15860564
    [TBL] [Abstract][Full Text] [Related]  

  • 19. 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]  

  • 20. Rank-based clustering analysis for the time-course microarray data.
    Yi SG; Joo YJ; Park T
    J Bioinform Comput Biol; 2009 Feb; 7(1):75-91. PubMed ID: 19226661
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 13.