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Items: 1 to 20 of 129

1.

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.

2.

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.

3.

Performance of random forest when SNPs are in linkage disequilibrium.

Meng YA, Yu Y, Cupples LA, Farrer LA, Lunetta KL.

BMC Bioinformatics. 2009 Mar 5;10:78. doi: 10.1186/1471-2105-10-78.

4.

Statistical geometry based prediction of nonsynonymous SNP functional effects using random forest and neuro-fuzzy classifiers.

Barenboim M, Masso M, Vaisman II, Jamison DC.

Proteins. 2008 Jun;71(4):1930-9. doi: 10.1002/prot.21838.

PMID:
18186470
5.

An application of Random Forests to a genome-wide association dataset: methodological considerations & new findings.

Goldstein BA, Hubbard AE, Cutler A, Barcellos LF.

BMC Genet. 2010 Jun 14;11:49. doi: 10.1186/1471-2156-11-49.

6.

Permutation importance: a corrected feature importance measure.

Altmann A, Toloşi L, Sander O, Lengauer T.

Bioinformatics. 2010 May 15;26(10):1340-7. doi: 10.1093/bioinformatics/btq134. Epub 2010 Apr 12.

7.

A random forest approach to the detection of epistatic interactions in case-control studies.

Jiang R, Tang W, Wu X, Fu W.

BMC Bioinformatics. 2009 Jan 30;10 Suppl 1:S65. doi: 10.1186/1471-2105-10-S1-S65.

8.

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.

9.

MegaSNPHunter: a learning approach to detect disease predisposition SNPs and high level interactions in genome wide association study.

Wan X, Yang C, Yang Q, Xue H, Tang NL, Yu W.

BMC Bioinformatics. 2009 Jan 9;10:13. doi: 10.1186/1471-2105-10-13.

10.

An AUC-based permutation variable importance measure for random forests.

Janitza S, Strobl C, Boulesteix AL.

BMC Bioinformatics. 2013 Apr 5;14:119. doi: 10.1186/1471-2105-14-119.

11.

Predictive rule inference for epistatic interaction detection in genome-wide association studies.

Wan X, Yang C, Yang Q, Xue H, Tang NL, Yu W.

Bioinformatics. 2010 Jan 1;26(1):30-7. doi: 10.1093/bioinformatics/btp622. Epub 2009 Oct 30.

12.

A machine learning pipeline for quantitative phenotype prediction from genotype data.

Guzzetta G, Jurman G, Furlanello C.

BMC Bioinformatics. 2010 Oct 26;11 Suppl 8:S3. doi: 10.1186/1471-2105-11-S8-S3.

13.

Capturing the spectrum of interaction effects in genetic association studies by simulated evaporative cooling network analysis.

McKinney BA, Crowe JE, Guo J, Tian D.

PLoS Genet. 2009 Mar;5(3):e1000432. doi: 10.1371/journal.pgen.1000432. Epub 2009 Mar 20.

15.

Genome-wide prediction of discrete traits using Bayesian regressions and machine learning.

González-Recio O, Forni S.

Genet Sel Evol. 2011 Feb 17;43:7. doi: 10.1186/1297-9686-43-7.

16.

Weighted rank aggregation of cluster validation measures: a Monte Carlo cross-entropy approach.

Pihur V, Datta S, Datta S.

Bioinformatics. 2007 Jul 1;23(13):1607-15. Epub 2007 May 5.

17.

Random forests on Hadoop for genome-wide association studies of multivariate neuroimaging phenotypes.

Wang Y, Goh W, Wong L, Montana G; Alzheimer's Disease Neuroimaging Initiative.

BMC Bioinformatics. 2013;14 Suppl 16:S6. doi: 10.1186/1471-2105-14-S16-S6. Epub 2013 Oct 22.

18.

CGHScan: finding variable regions using high-density microarray comparative genomic hybridization data.

Anderson BD, Gilson MC, Scott AA, Biehl BS, Glasner JD, Rajashekara G, Splitter GA, Perna NT.

BMC Genomics. 2006 Apr 25;7:91.

19.

Assessment of algorithms for high throughput detection of genomic copy number variation in oligonucleotide microarray data.

Baross A, Delaney AD, Li HI, Nayar T, Flibotte S, Qian H, Chan SY, Asano J, Ally A, Cao M, Birch P, Brown-John M, Fernandes N, Go A, Kennedy G, Langlois S, Eydoux P, Friedman JM, Marra MA.

BMC Bioinformatics. 2007 Oct 2;8:368.

20.

SNPHarvester: a filtering-based approach for detecting epistatic interactions in genome-wide association studies.

Yang C, He Z, Wan X, Yang Q, Xue H, Yu W.

Bioinformatics. 2009 Feb 15;25(4):504-11. doi: 10.1093/bioinformatics/btn652. Epub 2008 Dec 19.

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