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Items: 1 to 20 of 123

1.

The mutual information: detecting and evaluating dependencies between variables.

Steuer R, Kurths J, Daub CO, Weise J, Selbig J.

Bioinformatics. 2002;18 Suppl 2:S231-40.

2.

Fast calculation of pairwise mutual information for gene regulatory network reconstruction.

Qiu P, Gentles AJ, Plevritis SK.

Comput Methods Programs Biomed. 2009 May;94(2):177-80. doi: 10.1016/j.cmpb.2008.11.003. Epub 2009 Jan 22.

PMID:
19167129
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5.

Evaluation of gene-expression clustering via mutual information distance measure.

Priness I, Maimon O, Ben-Gal I.

BMC Bioinformatics. 2007 Mar 30;8:111.

6.

Exploiting sample variability to enhance multivariate analysis of microarray data.

Möller-Levet CS, West CM, Miller CJ.

Bioinformatics. 2007 Oct 15;23(20):2733-40. Epub 2007 Sep 7.

7.

Distance-based clustering of CGH data.

Liu J, Mohammed J, Carter J, Ranka S, Kahveci T, Baudis M.

Bioinformatics. 2006 Aug 15;22(16):1971-8. Epub 2006 May 16.

8.

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.

9.

A novel approach to the clustering of microarray data via nonparametric density estimation.

De Bin R, Risso D.

BMC Bioinformatics. 2011 Feb 8;12:49. doi: 10.1186/1471-2105-12-49.

10.

Modeling and visualizing uncertainty in gene expression clusters using dirichlet process mixtures.

Rasmussen CE, de la Cruz BJ, Ghahramani Z, Wild DL.

IEEE/ACM Trans Comput Biol Bioinform. 2009 Oct-Dec;6(4):615-28. doi: 10.1109/TCBB.2007.70269.

PMID:
19875860
11.

Interactively optimizing signal-to-noise ratios in expression profiling: project-specific algorithm selection and detection p-value weighting in Affymetrix microarrays.

Seo J, Bakay M, Chen YW, Hilmer S, Shneiderman B, Hoffman EP.

Bioinformatics. 2004 Nov 1;20(16):2534-44. Epub 2004 Apr 29.

12.

A variational Bayesian mixture modelling framework for cluster analysis of gene-expression data.

Teschendorff AE, Wang Y, Barbosa-Morais NL, Brenton JD, Caldas C.

Bioinformatics. 2005 Jul 1;21(13):3025-33. Epub 2005 Apr 28.

13.

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.

14.

Query large scale microarray compendium datasets using a model-based bayesian approach with variable selection.

Hu M, Qin ZS.

PLoS One. 2009;4(2):e4495. doi: 10.1371/journal.pone.0004495. Epub 2009 Feb 13.

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16.

goCluster integrates statistical analysis and functional interpretation of microarray expression data.

Wrobel G, Chalmel F, Primig M.

Bioinformatics. 2005 Sep 1;21(17):3575-7. Epub 2005 Jul 14.

17.

Bayesian mixture model based clustering of replicated microarray data.

Medvedovic M, Yeung KY, Bumgarner RE.

Bioinformatics. 2004 May 22;20(8):1222-32. Epub 2004 Feb 10.

18.

Comparisons of graph-structure clustering methods for gene expression data.

Fang Z, Liu L, Yang J, Luo QM, Li YX.

Acta Biochim Biophys Sin (Shanghai). 2006 Jun;38(6):379-84.

19.

Inferential clustering approach for microarray experiments with replicated measurements.

Salicrú M, Vives S, Zheng T.

IEEE/ACM Trans Comput Biol Bioinform. 2009 Oct-Dec;6(4):594-604. doi: 10.1109/TCBB.2008.106.

PMID:
19875858
20.

Bayesian hierarchical error model for analysis of gene expression data.

Cho H, Lee JK.

Bioinformatics. 2004 Sep 1;20(13):2016-25. Epub 2004 Mar 25.

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