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Items: 1 to 20 of 90

1.

Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses.

Stegle O, Parts L, Piipari M, Winn J, Durbin R.

Nat Protoc. 2012 Feb 16;7(3):500-7. doi: 10.1038/nprot.2011.457.

2.

Analyzing gene perturbation screens with nested effects models in R and bioconductor.

Fröhlich H, Beissbarth T, Tresch A, Kostka D, Jacob J, Spang R, Markowetz F.

Bioinformatics. 2008 Nov 1;24(21):2549-50. doi: 10.1093/bioinformatics/btn446. Epub 2008 Aug 21.

3.

A Bayesian framework to account for complex non-genetic factors in gene expression levels greatly increases power in eQTL studies.

Stegle O, Parts L, Durbin R, Winn J.

PLoS Comput Biol. 2010 May 6;6(5):e1000770. doi: 10.1371/journal.pcbi.1000770.

4.

GTI: a novel algorithm for identifying outlier gene expression profiles from integrated microarray datasets.

Mpindi JP, Sara H, Haapa-Paananen S, Kilpinen S, Pisto T, Bucher E, Ojala K, Iljin K, Vainio P, Björkman M, Gupta S, Kohonen P, Nees M, Kallioniemi O.

PLoS One. 2011 Feb 18;6(2):e17259. doi: 10.1371/journal.pone.0017259. Erratum in: PLoS One. 2011;6(4). doi:10.1371/annotation/7d571883-faf0-4f66-86a2-806c36c4741c.

5.

Evaluating heterogeneity in indoor and outdoor air pollution using land-use regression and constrained factor analysis.

Levy JI, Clougherty JE, Baxter LK, Houseman EA, Paciorek CJ; HEI Health Review Committee.

Res Rep Health Eff Inst. 2010 Dec;(152):5-80; discussion 81-91.

PMID:
21409949
7.

Deviance residuals-based sparse PLS and sparse kernel PLS regression for censored data.

Bastien P, Bertrand F, Meyer N, Maumy-Bertrand M.

Bioinformatics. 2015 Feb 1;31(3):397-404. doi: 10.1093/bioinformatics/btu660. Epub 2014 Oct 6.

PMID:
25286920
8.

Adaptive quality-based clustering of gene expression profiles.

De Smet F, Mathys J, Marchal K, Thijs G, De Moor B, Moreau Y.

Bioinformatics. 2002 May;18(5):735-46.

PMID:
12050070
9.

Identifying periodically expressed transcripts in microarray time series data.

Wichert S, Fokianos K, Strimmer K.

Bioinformatics. 2004 Jan 1;20(1):5-20.

PMID:
14693803
10.

A hidden Markov model for progressive multiple alignment.

Löytynoja A, Milinkovitch MC.

Bioinformatics. 2003 Aug 12;19(12):1505-13.

PMID:
12912831
11.

A statistical framework for the design of microarray experiments and effective detection of differential gene expression.

Zhang SD, Gant TW.

Bioinformatics. 2004 Nov 1;20(16):2821-8. Epub 2004 Jun 4.

PMID:
15180939
12.

MACAT--microarray chromosome analysis tool.

Toedling J, Schmeier S, Heinig M, Georgi B, Roepcke S.

Bioinformatics. 2005 May 1;21(9):2112-3. Epub 2004 Nov 30.

PMID:
15572464
13.

GAGE: generally applicable gene set enrichment for pathway analysis.

Luo W, Friedman MS, Shedden K, Hankenson KD, Woolf PJ.

BMC Bioinformatics. 2009 May 27;10:161. doi: 10.1186/1471-2105-10-161.

14.

A probabilistic method to detect regulatory modules.

Sinha S, van Nimwegen E, Siggia ED.

Bioinformatics. 2003;19 Suppl 1:i292-301.

PMID:
12855472
16.

A variance components factor model for genetic association studies: a Bayesian analysis.

Nonyane BA, Whittaker JC.

Genet Epidemiol. 2010 Sep;34(6):529-36. doi: 10.1002/gepi.20503.

PMID:
20718044
17.

A Bayesian missing value estimation method for gene expression profile data.

Oba S, Sato MA, Takemasa I, Monden M, Matsubara K, Ishii S.

Bioinformatics. 2003 Nov 1;19(16):2088-96.

PMID:
14594714
18.

AnovArray: a set of SAS macros for the analysis of variance of gene expression data.

Hennequet-Antier C, Chiapello H, Piot K, Degrelle S, Hue I, Renard JP, Rodolphe F, Robin S.

BMC Bioinformatics. 2005 Jun 16;6:150.

19.

Statistical estimation of gene expression using multiple laser scans of microarrays.

Khondoker MR, Glasbey CA, Worton BJ.

Bioinformatics. 2006 Jan 15;22(2):215-9. Epub 2005 Nov 22.

PMID:
16303798
20.

Combining multiple microarrays in the presence of controlling variables.

Park T, Yi SG, Shin YK, Lee S.

Bioinformatics. 2006 Jul 15;22(14):1682-9. Epub 2006 May 16.

PMID:
16705015

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