BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

319 related articles for article (PubMed ID: 16940966)

  • 1. Selection of differentially expressed genes in microarray data analysis.
    Chen JJ; Wang SJ; Tsai CA; Lin CJ
    Pharmacogenomics J; 2007 Jun; 7(3):212-20. PubMed ID: 16940966
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Significance analysis of ROC indices for comparing diagnostic markers: applications to gene microarray data.
    Tsai CA; Chen JJ
    J Biopharm Stat; 2004 Nov; 14(4):985-1003. PubMed ID: 15587976
    [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. 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]  

  • 5. 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]  

  • 6. 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]  

  • 7. Identification of differentially expressed genes and false discovery rate in microarray studies.
    Gusnanto A; Calza S; Pawitan Y
    Curr Opin Lipidol; 2007 Apr; 18(2):187-93. PubMed ID: 17353668
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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]  

  • 9. A framework to identify physiological responses in microarray-based gene expression studies: selection and interpretation of biologically relevant genes.
    Rodenburg W; Heidema AG; Boer JM; Bovee-Oudenhoven IM; Feskens EJ; Mariman EC; Keijer J
    Physiol Genomics; 2008 Mar; 33(1):78-90. PubMed ID: 18162501
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Functional genomics and proteomics in the clinical neurosciences: data mining and bioinformatics.
    Phan JH; Quo CF; Wang MD
    Prog Brain Res; 2006; 158():83-108. PubMed ID: 17027692
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Statistical analysis of microarray data: a Bayesian approach.
    Gottardo R; Pannucci JA; Kuske CR; Brettin T
    Biostatistics; 2003 Oct; 4(4):597-620. PubMed ID: 14557114
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. Selecting differentially expressed genes using minimum probability of classification error.
    Mahata P; Mahata K
    J Biomed Inform; 2007 Dec; 40(6):775-86. PubMed ID: 17950675
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. An integrated algorithm for gene selection and classification applied to microarray data of ovarian cancer.
    Lee ZJ
    Artif Intell Med; 2008 Jan; 42(1):81-93. PubMed ID: 18006289
    [TBL] [Abstract][Full Text] [Related]  

  • 16. An investigation of two multivariate permutation methods for controlling the false discovery proportion.
    Korn EL; Li MC; McShane LM; Simon R
    Stat Med; 2007 Oct; 26(24):4428-40. PubMed ID: 17357994
    [TBL] [Abstract][Full Text] [Related]  

  • 17. A microarray gene expression study of the molecular pharmacology of lithium carbonate on mouse brain mRNA to understand the neurobiology of mood stabilization and treatment of bipolar affective disorder.
    McQuillin A; Rizig M; Gurling HM
    Pharmacogenet Genomics; 2007 Aug; 17(8):605-17. PubMed ID: 17622937
    [TBL] [Abstract][Full Text] [Related]  

  • 18. [Identification of the differentially expressed genes between primary breast cancer and paired lymph node metastasis through combining mRNA differential display and gene microarray].
    Feng YM; Gao G; Zhang F; Chen H; Wan YF; Li XQ
    Zhonghua Yi Xue Za Zhi; 2006 Oct; 86(39):2749-55. PubMed ID: 17199993
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Sample size calculations based on ranking and selection in microarray experiments.
    Matsui S; Zeng S; Yamanaka T; Shaughnessy J
    Biometrics; 2008 Mar; 64(1):217-26. PubMed ID: 17680829
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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]  

    [Next]    [New Search]
    of 16.