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Items: 1 to 20 of 175

1.

Bias in random forest variable importance measures: illustrations, sources and a solution.

Strobl C, Boulesteix AL, Zeileis A, Hothorn T.

BMC Bioinformatics. 2007 Jan 25;8:25.

2.

Intervention in prediction measure: a new approach to assessing variable importance for random forests.

Epifanio I.

BMC Bioinformatics. 2017 May 2;18(1):230. doi: 10.1186/s12859-017-1650-8.

3.

Gene selection and classification of microarray data using random forest.

Díaz-Uriarte R, Alvarez de Andrés S.

BMC Bioinformatics. 2006 Jan 6;7:3.

4.

Conditional variable importance for random forests.

Strobl C, Boulesteix AL, Kneib T, Augustin T, Zeileis A.

BMC Bioinformatics. 2008 Jul 11;9:307. doi: 10.1186/1471-2105-9-307.

5.

Unbiased split variable selection for random survival forests using maximally selected rank statistics.

Wright MN, Dankowski T, Ziegler A.

Stat Med. 2017 Apr 15;36(8):1272-1284. doi: 10.1002/sim.7212. Epub 2017 Jan 15.

PMID:
28088842
6.

The behaviour of random forest permutation-based variable importance measures under predictor correlation.

Nicodemus KK, Malley JD, Strobl C, Ziegler A.

BMC Bioinformatics. 2010 Feb 27;11:110. doi: 10.1186/1471-2105-11-110.

7.

Random forest Gini importance favours SNPs with large minor allele frequency: impact, sources and recommendations.

Boulesteix AL, Bender A, Lorenzo Bermejo J, Strobl C.

Brief Bioinform. 2012 May;13(3):292-304. doi: 10.1093/bib/bbr053. Epub 2011 Sep 10.

PMID:
21908865
8.

Modeling X Chromosome Data Using Random Forests: Conquering Sex Bias.

Winham SJ, Jenkins GD, Biernacka JM.

Genet Epidemiol. 2016 Feb;40(2):123-32. doi: 10.1002/gepi.21946. Epub 2015 Dec 7.

9.

A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data.

Menze BH, Kelm BM, Masuch R, Himmelreich U, Bachert P, Petrich W, Hamprecht FA.

BMC Bioinformatics. 2009 Jul 10;10:213. doi: 10.1186/1471-2105-10-213.

10.

Predictor correlation impacts machine learning algorithms: implications for genomic studies.

Nicodemus KK, Malley JD.

Bioinformatics. 2009 Aug 1;25(15):1884-90. doi: 10.1093/bioinformatics/btp331. Epub 2009 May 21.

PMID:
19460890
11.

A comparative study of variable selection methods in the context of developing psychiatric screening instruments.

Lu F, Petkova E.

Stat Med. 2014 Feb 10;33(3):401-21. doi: 10.1002/sim.5937. Epub 2013 Aug 11.

12.

Estimation of a predictor's importance by Random Forests when there is missing data: risk prediction in liver surgery using laboratory data.

Hapfelmeier A, Hothorn T, Riediger C, Ulm K.

Int J Biostat. 2014;10(2):165-83. doi: 10.1515/ijb-2013-0038.

13.

SNP selection and classification of genome-wide SNP data using stratified sampling random forests.

Wu Q, Ye Y, Liu Y, Ng MK.

IEEE Trans Nanobioscience. 2012 Sep;11(3):216-27. doi: 10.1109/TNB.2012.2214232.

PMID:
22987127
14.

Assessing the accuracy and stability of variable selection methods for random forest modeling in ecology.

Fox EW, Hill RA, Leibowitz SG, Olsen AR, Thornbrugh DJ, Weber MH.

Environ Monit Assess. 2017 Jul;189(7):316. doi: 10.1007/s10661-017-6025-0. Epub 2017 Jun 6.

PMID:
28589457
15.

The revival of the Gini Importance?

Nembrini S, König IR, Wright MN.

Bioinformatics. 2018 May 10. doi: 10.1093/bioinformatics/bty373. [Epub ahead of print]

PMID:
29757357
16.
17.

Screening large-scale association study data: exploiting interactions using random forests.

Lunetta KL, Hayward LB, Segal J, Van Eerdewegh P.

BMC Genet. 2004 Dec 10;5:32.

18.

EM-random forest and new measures of variable importance for multi-locus quantitative trait linkage analysis.

Lee SS, Sun L, Kustra R, Bull SB.

Bioinformatics. 2008 Jul 15;24(14):1603-10. doi: 10.1093/bioinformatics/btn239. Epub 2008 May 21.

19.

Pathway analysis using random forests classification and regression.

Pang H, Lin A, Holford M, Enerson BE, Lu B, Lawton MP, Floyd E, Zhao H.

Bioinformatics. 2006 Aug 15;22(16):2028-36. Epub 2006 Jun 29.

PMID:
16809386
20.

Statistical significance of variables driving systematic variation in high-dimensional data.

Chung NC, Storey JD.

Bioinformatics. 2015 Feb 15;31(4):545-54. doi: 10.1093/bioinformatics/btu674. Epub 2014 Oct 21.

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