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Items: 16

1.

A novel method for cross-species gene expression analysis.

Kristiansson E, Ă–sterlund T, Gunnarsson L, Arne G, Larsson DG, Nerman O.

BMC Bioinformatics. 2013 Feb 27;14:70. doi: 10.1186/1471-2105-14-70.

2.

Identification of human HK genes and gene expression regulation study in cancer from transcriptomics data analysis.

Chen M, Xiao J, Zhang Z, Liu J, Wu J, Yu J.

PLoS One. 2013;8(1):e54082. doi: 10.1371/journal.pone.0054082.

3.

Harnessing the complexity of gene expression data from cancer: from single gene to structural pathway methods.

Emmert-Streib F, Tripathi S, de Matos Simoes R.

Biol Direct. 2012 Dec 10;7:44. doi: 10.1186/1745-6150-7-44. Review.

4.

Reproducible cancer biomarker discovery in SELDI-TOF MS using different pre-processing algorithms.

Zou J, Hong G, Guo X, Zhang L, Yao C, Wang J, Guo Z.

PLoS One. 2011;6(10):e26294. doi: 10.1371/journal.pone.0026294.

5.

Hierarchical parallelization of gene differential association analysis.

Needham M, Hu R, Dwarkadas S, Qiu X.

BMC Bioinformatics. 2011 Sep 21;12:374. doi: 10.1186/1471-2105-12-374.

6.

Heading down the wrong pathway: on the influence of correlation within gene sets.

Gatti DM, Barry WT, Nobel AB, Rusyn I, Wright FA.

BMC Genomics. 2010 Oct 18;11:574. doi: 10.1186/1471-2164-11-574.

7.

Circulating brain-derived neurotrophic factor and indices of metabolic and cardiovascular health: data from the Baltimore Longitudinal Study of Aging.

Golden E, Emiliano A, Maudsley S, Windham BG, Carlson OD, Egan JM, Driscoll I, Ferrucci L, Martin B, Mattson MP.

PLoS One. 2010 Apr 9;5(4):e10099. doi: 10.1371/journal.pone.0010099.

8.

Balancing Type One and Two Errors in Multiple Testing for Differential Expression of Genes.

Gordon A, Chen L, Glazko G, Yakovlev A.

Comput Stat Data Anal. 2009 Mar 15;53(5):1622-1629.

9.

Evaluating reproducibility of differential expression discoveries in microarray studies by considering correlated molecular changes.

Zhang M, Zhang L, Zou J, Yao C, Xiao H, Liu Q, Wang J, Wang D, Wang C, Guo Z.

Bioinformatics. 2009 Jul 1;25(13):1662-8. doi: 10.1093/bioinformatics/btp295.

10.

Detecting intergene correlation changes in microarray analysis: a new approach to gene selection.

Hu R, Qiu X, Glazko G, Klebanov L, Yakovlev A.

BMC Bioinformatics. 2009 Jan 15;10:20. doi: 10.1186/1471-2105-10-20.

11.

A general framework for multiple testing dependence.

Leek JT, Storey JD.

Proc Natl Acad Sci U S A. 2008 Dec 2;105(48):18718-23. doi: 10.1073/pnas.0808709105.

12.

Gene and pathway identification with Lp penalized Bayesian logistic regression.

Liu Z, Gartenhaus RB, Tan M, Jiang F, Jiao X.

BMC Bioinformatics. 2008 Oct 3;9:412. doi: 10.1186/1471-2105-9-412.

14.
15.

Is there an alternative to increasing the sample size in microarray studies?

Klebanov L, Yakovlev A.

Bioinformation. 2007 Apr 10;1(10):429-31.

16.

Utility of correlation measures in analysis of gene expression.

Almudevar A, Klebanov LB, Qiu X, Salzman P, Yakovlev AY.

NeuroRx. 2006 Jul;3(3):384-95. Review.

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