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 *

139 related articles for article (PubMed ID: 19954419)

  • 1. A unified mixed effects model for gene set analysis of time course microarray experiments.
    Wang L; Chen X; Wolfinger RD; Franklin JL; Coffey RJ; Zhang B
    Stat Appl Genet Mol Biol; 2009; 8(1):Article 47. PubMed ID: 19954419
    [TBL] [Abstract][Full Text] [Related]  

  • 2. ARSyN: a method for the identification and removal of systematic noise in multifactorial time course microarray experiments.
    Nueda MJ; Ferrer A; Conesa A
    Biostatistics; 2012 Jul; 13(3):553-66. PubMed ID: 22085896
    [TBL] [Abstract][Full Text] [Related]  

  • 3. A mixture model with random-effects components for clustering correlated gene-expression profiles.
    Ng SK; McLachlan GJ; Wang K; Ben-Tovim Jones L; Ng SW
    Bioinformatics; 2006 Jul; 22(14):1745-52. PubMed ID: 16675467
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Including probe-level measurement error in robust mixture clustering of replicated microarray gene expression.
    Liu X; Rattray M
    Stat Appl Genet Mol Biol; 2010; 9():Article42. PubMed ID: 21194414
    [TBL] [Abstract][Full Text] [Related]  

  • 5. An integrated approach for the analysis of biological pathways using mixed models.
    Wang L; Zhang B; Wolfinger RD; Chen X
    PLoS Genet; 2008 Jul; 4(7):e1000115. PubMed ID: 18852846
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Differential and trajectory methods for time course gene expression data.
    Liang Y; Tayo B; Cai X; Kelemen A
    Bioinformatics; 2005 Jul; 21(13):3009-16. PubMed ID: 15886280
    [TBL] [Abstract][Full Text] [Related]  

  • 7. maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments.
    Conesa A; Nueda MJ; Ferrer A; Talón M
    Bioinformatics; 2006 May; 22(9):1096-102. PubMed ID: 16481333
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Identification of gene expression patterns using planned linear contrasts.
    Li H; Wood CL; Liu Y; Getchell TV; Getchell ML; Stromberg AJ
    BMC Bioinformatics; 2006 May; 7():245. PubMed ID: 16677382
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Clustering of time-course gene expression data using a mixed-effects model with B-splines.
    Luan Y; Li H
    Bioinformatics; 2003 Mar; 19(4):474-82. PubMed ID: 12611802
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Replication, variation and normalisation in microarray experiments.
    Altman N
    Appl Bioinformatics; 2005; 4(1):33-44. PubMed ID: 16000011
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. Pem: a general statistical approach for identifying differentially expressed genes in time-course cDNA microarray experiment without replicate.
    Han X; Sung WK; Feng L
    Comput Syst Bioinformatics Conf; 2006; ():123-32. PubMed ID: 17369631
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A multivariate growth curve model for ranking genes in replicated time course microarray data.
    Hamid JS; Beyene J
    Stat Appl Genet Mol Biol; 2009; 8():Article33. PubMed ID: 19572832
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Ranking analysis of F-statistics for microarray data.
    Tan YD; Fornage M; Xu H
    BMC Bioinformatics; 2008 Mar; 9():142. PubMed ID: 18325100
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Sample size for detecting differentially expressed genes in microarray experiments.
    Wei C; Li J; Bumgarner RE
    BMC Genomics; 2004 Nov; 5():87. PubMed ID: 15533245
    [TBL] [Abstract][Full Text] [Related]  

  • 16. MAID : an effect size based model for microarray data integration across laboratories and platforms.
    Borozan I; Chen L; Paeper B; Heathcote JE; Edwards AM; Katze M; Zhang Z; McGilvray ID
    BMC Bioinformatics; 2008 Jul; 9():305. PubMed ID: 18616827
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Information criterion-based clustering with order-restricted candidate profiles in short time-course microarray experiments.
    Liu T; Lin N; Shi N; Zhang B
    BMC Bioinformatics; 2009 May; 10():146. PubMed ID: 19445669
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Gene Vector Analysis (Geneva): a unified method to detect differentially-regulated gene sets and similar microarray experiments.
    Tanner SW; Agarwal P
    BMC Bioinformatics; 2008 Aug; 9():348. PubMed ID: 18721468
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Identifying periodically expressed transcripts in microarray time series data.
    Wichert S; Fokianos K; Strimmer K
    Bioinformatics; 2004 Jan; 20(1):5-20. PubMed ID: 14693803
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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]  

    [Next]    [New Search]
    of 7.