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    Results: 1 to 20 of 121

    1.

    How many replicates of arrays are required to detect gene expression changes in microarray experiments? A mixture model approach.

    Pan W, Lin J, Le CT.

    Genome Biol. 2002;3(5):research0022. Epub 2002 Apr 22.PMID: 12049663 [PubMed - indexed for MEDLINE]Related articlesFree article

    2.

    A mixture model approach to detecting differentially expressed genes with microarray data.

    Pan W, Lin J, Le CT.

    Funct Integr Genomics. 2003 Jul;3(3):117-24. Epub 2003 Jul 1.PMID: 12844246 [PubMed - indexed for MEDLINE]Related articles

    3.

    Sample size for detecting differentially expressed genes in microarray experiments.

    Wei C, Li J, Bumgarner RE.

    BMC Genomics. 2004 Nov 8;5(1):87.PMID: 15533245 [PubMed - indexed for MEDLINE]Related articlesFree article

    4.

    Model-based cluster analysis of microarray gene-expression data.

    Pan W, Lin J, Le CT.

    Genome Biol. 2002;3(2):RESEARCH0009. Epub 2002 Jan 29.PMID: 11864371 [PubMed - indexed for MEDLINE]Related articlesFree article

    5.

    An improved nonparametric approach for detecting differentially expressed genes with replicated microarray data.

    Zhang S.

    Stat Appl Genet Mol Biol. 2006;5:Article30. Epub 2007 Jan 2.PMID: 17402914 [PubMed - indexed for MEDLINE]Related articlesFree article

    6.

    Powers of multiple-testing procedures for identification of genes significantly differentially expressed in microarray experiments.

    Tan YD, Yan HM.

    Yi Chuan Xue Bao. 2006 Dec;33(12):1132-40.PMID: 17185174 [PubMed - indexed for MEDLINE]Related articles

    7.

    Sample size for identifying differentially expressed genes in microarray experiments.

    Wang SJ, Chen JJ.

    J Comput Biol. 2004;11(4):714-26.PMID: 15579240 [PubMed - indexed for MEDLINE]Related articles

    8.

    Replication, variation and normalisation in microarray experiments.

    Altman N.

    Appl Bioinformatics. 2005;4(1):33-44.PMID: 16000011 [PubMed - indexed for MEDLINE]Related articles

    9.

    Noise sampling method: an ANOVA approach allowing robust selection of differentially regulated genes measured by DNA microarrays.

    Draghici S, Kulaeva O, Hoff B, Petrov A, Shams S, Tainsky MA.

    Bioinformatics. 2003 Jul 22;19(11):1348-59.PMID: 12874046 [PubMed - indexed for MEDLINE]Related articlesFree article

    10.

    Statistical analysis of high-density oligonucleotide arrays: a multiplicative noise model.

    Sásik R, Calvo E, Corbeil J.

    Bioinformatics. 2002 Dec;18(12):1633-40.PMID: 12490448 [PubMed - indexed for MEDLINE]Related articlesFree article

    11.

    A conditional density error model for the statistical analysis of microarray data.

    Love B, Rank DR, Penn SG, Jenkins DA, Thomas RS.

    Bioinformatics. 2002 Aug;18(8):1064-72.PMID: 12176829 [PubMed - indexed for MEDLINE]Related articlesFree article

    13.

    Leveraging two-way probe-level block design for identifying differential gene expression with high-density oligonucleotide arrays.

    Barrera L, Benner C, Tao YC, Winzeler E, Zhou Y.

    BMC Bioinformatics. 2004 Apr 20;5:42.PMID: 15099405 [PubMed - indexed for MEDLINE]Related articlesFree article

    14.

    Rank Difference Analysis of Microarrays (RDAM), a novel approach to statistical analysis of microarray expression profiling data.

    Martin DE, Demougin P, Hall MN, Bellis M.

    BMC Bioinformatics. 2004 Oct 11;5:148.PMID: 15476558 [PubMed - indexed for MEDLINE]Related articlesFree article

    15.

    Intensity-based hierarchical Bayes method improves testing for differentially expressed genes in microarray experiments.

    Sartor MA, Tomlinson CR, Wesselkamper SC, Sivaganesan S, Leikauf GD, Medvedovic M.

    BMC Bioinformatics. 2006 Dec 19;7:538.PMID: 17177995 [PubMed - indexed for MEDLINE]Related articlesFree article

    16.

    Methods for evaluating gene expression from Affymetrix microarray datasets.

    Jiang N, Leach LJ, Hu X, Potokina E, Jia T, Druka A, Waugh R, Kearsey MJ, Luo ZW.

    BMC Bioinformatics. 2008 Jun 17;9:284.PMID: 18559105 [PubMed - indexed for MEDLINE]Related articlesFree article

    17.

    The Global Error Assessment (GEA) model for the selection of differentially expressed genes in microarray data.

    Mansourian R, Mutch DM, Antille N, Aubert J, Fogel P, Le Goff JM, Moulin J, Petrov A, Rytz A, Voegel JJ, Roberts MA.

    Bioinformatics. 2004 Nov 1;20(16):2726-37. Epub 2004 May 14.PMID: 15145801 [PubMed - indexed for MEDLINE]Related articlesFree article

    18.

    Accuracy of cDNA microarray methods to detect small gene expression changes induced by neuregulin on breast epithelial cells.

    Yao B, Rakhade SN, Li Q, Ahmed S, Krauss R, Draghici S, Loeb JA.

    BMC Bioinformatics. 2004 Jul 23;5:99.PMID: 15272935 [PubMed - indexed for MEDLINE]Related articlesFree article

    19.

    Improving the statistical detection of regulated genes from microarray data using intensity-based variance estimation.

    Comander J, Natarajan S, Gimbrone MA Jr, García-Cardeña G.

    BMC Genomics. 2004 Feb 27;5(1):17.PMID: 15113402 [PubMed - indexed for MEDLINE]Related articlesFree article

    20.

    Microarray standard data set and figures of merit for comparing data processing methods and experiment designs.

    He YD, Dai H, Schadt EE, Cavet G, Edwards SW, Stepaniants SB, Duenwald S, Kleinhanz R, Jones AR, Shoemaker DD, Stoughton RB.

    Bioinformatics. 2003 May 22;19(8):956-65.PMID: 12761058 [PubMed - indexed for MEDLINE]Related articlesFree article

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