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Items: 1 to 20 of 83

1.

Dynamism in gene expression across multiple studies.

Morgan AA, Dudley JT, Deshpande T, Butte AJ.

Physiol Genomics. 2010 Feb 4;40(3):128-40. doi: 10.1152/physiolgenomics.90403.2008. Epub 2009 Nov 17.

2.

The limit fold change model: a practical approach for selecting differentially expressed genes from microarray data.

Mutch DM, Berger A, Mansourian R, Rytz A, Roberts MA.

BMC Bioinformatics. 2002 Jun 21;3:17.

3.

Bioinformatics approaches for cross-species liver cancer analysis based on microarray gene expression profiling.

Fang H, Tong W, Perkins R, Shi L, Hong H, Cao X, Xie Q, Yim SH, Ward JM, Pitot HC, Dragan YP.

BMC Bioinformatics. 2005 Jul 15;6 Suppl 2:S6.

4.

Mining gene expression data by interpreting principal components.

Roden JC, King BW, Trout D, Mortazavi A, Wold BJ, Hart CE.

BMC Bioinformatics. 2006 Apr 7;7:194.

5.
6.

Microarray data analysis: a practical approach for selecting differentially expressed genes.

Mutch DM, Berger A, Mansourian R, Rytz A, Roberts MA.

Genome Biol. 2001;2(12):PREPRINT0009. Epub 2001 Nov 16.

PMID:
11790248
7.

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 10;6:26.

8.

Bayesian meta-analysis models for microarray data: a comparative study.

Conlon EM, Song JJ, Liu A.

BMC Bioinformatics. 2007 Mar 7;8:80.

9.

Exon and junction microarrays detect widespread mouse strain- and sex-bias expression differences.

Su WL, Modrek B, GuhaThakurta D, Edwards S, Shah JK, Kulkarni AV, Russell A, Schadt EE, Johnson JM, Castle JC.

BMC Genomics. 2008 Jun 4;9:273. doi: 10.1186/1471-2164-9-273.

10.

Reproducibility of microarray data: a further analysis of microarray quality control (MAQC) data.

Chen JJ, Hsueh HM, Delongchamp RR, Lin CJ, Tsai CA.

BMC Bioinformatics. 2007 Oct 25;8:412.

11.

Construction and use of gene expression covariation matrix.

Hennetin J, Pehkonen P, Bellis M.

BMC Bioinformatics. 2009 Jul 13;10:214. doi: 10.1186/1471-2105-10-214.

12.

Microarrays: how many do you need?

Zien A, Fluck J, Zimmer R, Lengauer T.

J Comput Biol. 2003;10(3-4):653-67.

PMID:
12935350
13.

Gene expression profiling of mouse embryos with microarrays.

Sharov AA, Piao Y, Ko MS.

Methods Enzymol. 2010;477:511-41. doi: 10.1016/S0076-6879(10)77025-7.

14.
15.

A global meta-analysis of microarray expression data to predict unknown gene functions and estimate the literature-data divide.

Wren JD.

Bioinformatics. 2009 Jul 1;25(13):1694-701. doi: 10.1093/bioinformatics/btp290. Epub 2009 May 15.

16.

TTCA: an R package for the identification of differentially expressed genes in time course microarray data.

Albrecht M, Stichel D, Müller B, Merkle R, Sticht C, Gretz N, Klingmüller U, Breuhahn K, Matthäus F.

BMC Bioinformatics. 2017 Jan 14;18(1):33. doi: 10.1186/s12859-016-1440-8.

17.

Discovering gene expression patterns in time course microarray experiments by ANOVA-SCA.

Nueda MJ, Conesa A, Westerhuis JA, Hoefsloot HC, Smilde AK, Talón M, Ferrer A.

Bioinformatics. 2007 Jul 15;23(14):1792-800. Epub 2007 May 22.

PMID:
17519250
18.

Ranking analysis for identifying differentially expressed genes.

Qi Y, Sun H, Sun Q, Pan L.

Genomics. 2011 May;97(5):326-9. doi: 10.1016/j.ygeno.2011.03.002. Epub 2011 Mar 22.

19.

Global analysis of gene expression using GeneChip microarrays.

Zhu T.

Curr Opin Plant Biol. 2003 Oct;6(5):418-25. Review.

PMID:
12972041
20.

Expression and microarrays.

Dopazo J, Al-Shahrour F.

Methods Mol Biol. 2008;453:245-55. doi: 10.1007/978-1-60327-429-6_12.

PMID:
18712307

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