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Items: 6

1.

Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis.

Shen R, Olshen AB, Ladanyi M.

Bioinformatics. 2009 Nov 15;25(22):2906-12. doi: 10.1093/bioinformatics/btp543. Epub 2009 Sep 16. Erratum in: Bioinformatics. 2010 Jan 15;26(2):292-3.

2.

Cancer gene prioritization by integrative analysis of mRNA expression and DNA copy number data: a comparative review.

Lahti L, Schäfer M, Klein HU, Bicciato S, Dugas M.

Brief Bioinform. 2013 Jan;14(1):27-35. doi: 10.1093/bib/bbs005. Epub 2012 Mar 22. Review.

3.

Integrating genetics and epigenetics in breast cancer: biological insights, experimental, computational methods and therapeutic potential.

Cava C, Bertoli G, Castiglioni I.

BMC Syst Biol. 2015 Sep 21;9:62. doi: 10.1186/s12918-015-0211-x. Review.

4.

Sparse models for correlative and integrative analysis of imaging and genetic data.

Lin D, Cao H, Calhoun VD, Wang YP.

J Neurosci Methods. 2014 Nov 30;237:69-78. doi: 10.1016/j.jneumeth.2014.09.001. Epub 2014 Sep 9. Review.

5.

Statistical methods for integrating multiple types of high-throughput data.

Xie Y, Ahn C.

Methods Mol Biol. 2010;620:511-29. doi: 10.1007/978-1-60761-580-4_19. Review.

6.

Manipulating measurement scales in medical statistical analysis and data mining: A review of methodologies.

Marateb HR, Mansourian M, Adibi P, Farina D.

J Res Med Sci. 2014 Jan;19(1):47-56. Review.

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