BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

127 related articles for article (PubMed ID: 12507787)

  • 1. Partitioning large-sample microarray-based gene expression profiles using principal components analysis.
    Peterson LE
    Comput Methods Programs Biomed; 2003 Feb; 70(2):107-19. PubMed ID: 12507787
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Factor analysis of cluster-specific gene expression levels from cDNA microarrays.
    Peterson LE
    Comput Methods Programs Biomed; 2002 Nov; 69(3):179-88. PubMed ID: 12204446
    [TBL] [Abstract][Full Text] [Related]  

  • 3. CLUSFAVOR 5.0: hierarchical cluster and principal-component analysis of microarray-based transcriptional profiles.
    Peterson LE
    Genome Biol; 2002 Jun; 3(7):SOFTWARE0002. PubMed ID: 12184816
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Identifying temporally differentially expressed genes through functional principal components analysis.
    Liu X; Yang MC
    Biostatistics; 2009 Oct; 10(4):667-79. PubMed ID: 19602570
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Vector algebra in the analysis of genome-wide expression data.
    Kuruvilla FG; Park PJ; Schreiber SL
    Genome Biol; 2002; 3(3):RESEARCH0011. PubMed ID: 11897023
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Gene function inference from gene expression of deletion mutants.
    Bidaut G
    Methods Mol Biol; 2007; 408():1-18. PubMed ID: 18314574
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Application of independent component analysis to microarrays.
    Lee SI; Batzoglou S
    Genome Biol; 2003; 4(11):R76. PubMed ID: 14611662
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Inference of a genetic network by a combined approach of cluster analysis and graphical Gaussian modeling.
    Toh H; Horimoto K
    Bioinformatics; 2002 Feb; 18(2):287-97. PubMed ID: 11847076
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Statistical significance of variables driving systematic variation in high-dimensional data.
    Chung NC; Storey JD
    Bioinformatics; 2015 Feb; 31(4):545-54. PubMed ID: 25336500
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Toxicogenomics using yeast DNA microarrays.
    Yasokawa D; Iwahashi H
    J Biosci Bioeng; 2010 Nov; 110(5):511-22. PubMed ID: 20624688
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Combined analysis of expression data and transcription factor binding sites in the yeast genome.
    Nagaraj VH; O'Flanagan RA; Bruning AR; Mathias JR; Vershon AK; Sengupta AM
    BMC Genomics; 2004 Aug; 5(1):59. PubMed ID: 15331021
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Extracting dynamics from static cancer expression data.
    Gupta A; Bar-Joseph Z
    IEEE/ACM Trans Comput Biol Bioinform; 2008; 5(2):172-82. PubMed ID: 18451427
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Principal component analysis for clustering gene expression data.
    Yeung KY; Ruzzo WL
    Bioinformatics; 2001 Sep; 17(9):763-74. PubMed ID: 11590094
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Partial least squares dimension reduction for microarray gene expression data with a censored response.
    Nguyen DV
    Math Biosci; 2005 Jan; 193(1):119-37. PubMed ID: 15681279
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Time ordering of gene coexpression.
    Leng X; Müller HG
    Biostatistics; 2006 Oct; 7(4):569-84. PubMed ID: 16495429
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Defining transcriptional networks through integrative modeling of mRNA expression and transcription factor binding data.
    Gao F; Foat BC; Bussemaker HJ
    BMC Bioinformatics; 2004 Mar; 5():31. PubMed ID: 15113405
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data.
    Segal E; Shapira M; Regev A; Pe'er D; Botstein D; Koller D; Friedman N
    Nat Genet; 2003 Jun; 34(2):166-76. PubMed ID: 12740579
    [TBL] [Abstract][Full Text] [Related]  

  • 18. CIT: identification of differentially expressed clusters of genes from microarray data.
    Rhodes DR; Miller JC; Haab BB; Furge KA
    Bioinformatics; 2002 Jan; 18(1):205-6. PubMed ID: 11836234
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Possibilistic approach for biclustering microarray data.
    Cano C; Adarve L; López J; Blanco A
    Comput Biol Med; 2007 Oct; 37(10):1426-36. PubMed ID: 17346690
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Using biplots to interpret gene expression patterns in plants.
    Chapman S; Schenk P; Kazan K; Manners J
    Bioinformatics; 2002 Jan; 18(1):202-4. PubMed ID: 11836233
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.