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Volume 7, Issue 1 2008 Article 15 Statistical Applications in Genetics and Molecular Biology Semi-Parametric Differential Expression Analysis via Partial Mixture Estimation David Rossell, Institute for Research in Biomedicine of Barcelona Rudy Guerra, Rice University Clayton Scott, University of Michigan, Ann Arbor Recommended Citation: Rossell, David; Guerra, Rudy; and Scott, Clayton (2008) "Semi-Parametric Differential Expression Analysis via Partial Mixture Estimation," Statistical Applications in Genetics and Molecular Biology: Vol. 7: Iss. 1, Article 15. DOI

), and then analysed tissue distribution and differential expression levels of OaCdc42 mRNA by RT-qPCR between infected sheep challenged with B. melitensis and sheep vaccinated with B . suis S2. Our aims were to discover potential diagnostic biomarkers to discriminate vaccinated sheep from those infected with virulent Brucella . Material and Methods Bacteria B . melitensis (smooth virulent strain, Bm) was isolated from naturally infected sheep. B . suis S2 (live rough avirulent strain, S2) was purchased from the Harbin Pharmaceutical Group (China). The

References 1. Anders, S., W. Huber. Differential Expression Analysis for Sequence Count Data. - Genome Biology, Vol. 11, 2010, R106. https://doi.org/10.1186/gb-2010-11-10-r106 2. Love, M. I., W. Huber, S. Anders. Moderated Estimation of Fold Change and Dispersion for RNA-seq Data with DESeq2. - Genome Biology, Vol. 12, 2014, No 12, 550. https://doi.org/10.1186/s13059-014-0550-8 3. Robinson, M. D., D. J. Mccarthy, G. K. Smyth. EdgeR: A Bioconductor Package for Differential Expression Analysis of Digital Gene Expression Data. - Bioinformatics, Vol. 26, 2010, No 1

known to play an important part in the development and progression of cancer ( Levenson, 2010 ). Here, we present EBADIMEX (Empirical Bayes for Differential Methylation and Expression) to jointly identify differential expression and methylation, and to classify samples as either normal or tumor. We apply the method to a breast cancer (BRCA) cohort from The Cancer Genome Atlas (TCGA) ( http://cancergenome.nih.gov ) with available gene expression and methylation data. EBADIMEX is freely available as an R-package (Appendix B ). DNA methylation plays a critical role

Volume 3, Issue 1 2004 Article 3 Statistical Applications in Genetics and Molecular Biology Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments Gordon K. Smyth, Walter and Eliza Hall Institute Recommended Citation: Smyth, Gordon K. (2004) "Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments," Statistical Applications in Genetics and Molecular Biology: Vol. 3: Iss. 1, Article 3. DOI: 10.2202/1544-6115.1027 ©2004 by the authors. All rights reserved. Linear

C op yr ig ht 20 16 T he A ut ho r( s) .P ub lis he d by Jo ur na lo fI nt eg ra tiv e B io in fo rm at ic s. T hi s ar tic le is lic en se d un de ra C re at iv e C om m on s A ttr ib ut io n- N on C om m er ci al -N oD er iv s 3. 0 U np or te d L ic en se (h ttp :// cr ea tiv ec om m on s. or g/ lic en se s/ by -n c- nd /3 .0 /) . Journal of Integrative Bioinformatics, 13(5):308, 2016 http://journal.imbio.de/ Differential Expression of Hyperhydricity Responsive Peach MicroRNAs Ebru Diler1, Turgay Unver1, Gökhan Karakülah1,* 1 İzmir International

differential expression in both simulated data and a dataset from a two species experiment. Figure 1: Any particular mouse gene or transcript t r may map to a set N r of multiple homologous transcripts in the naked mole-rat (NMR). The rays represent the degree of homology w (), indicating that there are 3 NMR transcripts t n that match the mouse transcript. Modeling the homologous set of transcripts as a cluster can be more powerful than a single feature analysis. 2 Methods Suppose we have a reference species R (e.g. mouse) that is well-annotated relative to a novel

1 Introduction Tests for differential expression allow us to generate hypotheses about the regulatory mechanisms behind the differing phenotypes observed between experimental conditions, and can be applied as a first step towards determining the genes involved and prioritizing targets for further investigation. Tools that allow for the fast and robust determination of genes that are differentially expressed between sets of samples are of great value to experimentalists in identifying the knock-on effects of perturbations, or in identifying candidate genes

Volume 11, Issue 5 2012 Article 8 Statistical Applications in Genetics and Molecular Biology Detecting Differential Expression in RNA- sequence Data Using Quasi-likelihood with Shrunken Dispersion Estimates Steven P. Lund, Statistical Engineering Division, National Institute of Standards and Technology Dan Nettleton, Department of Statistics, Iowa State University Davis J. McCarthy, University of Oxford Gordon K. Smyth, Walter and Eliza Hall Institute of Medical Research Recommended Citation: Lund, Steven P.; Nettleton, Dan; McCarthy, Davis J.; and Smyth, Gordon

Clin Chem Lab Med 2009;47(8):923–929 2009 by Walter de Gruyter • Berlin • New York. DOI 10.1515/CCLM.2009.228 2009/113 Article in press - uncorrected proof Differential expression of microRNAs in the placentae of Chinese patients with severe pre-eclampsia Yali Hu1,2, Pengfei Li3, Sha Hao3, Liu Liu3, Junli Zhao3 and Yayi Hou2,3,* 1 The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, PR China 2 Jiangsu Key Laboratory of Molecular Medicine, Nanjing University, Nanjing 210093, PR China 3 Immunology and Reproductive Biology Lab