247 related articles for article (PubMed ID: 20546594)
1. 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; 11():49. PubMed ID: 20546594
[TBL] [Abstract][Full Text] [Related]
2. Genome-wide association data classification and SNPs selection using two-stage quality-based Random Forests.
Nguyen TT; Huang J; Wu Q; Nguyen T; Li M
BMC Genomics; 2015; 16 Suppl 2(Suppl 2):S5. PubMed ID: 25708662
[TBL] [Abstract][Full Text] [Related]
3. 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. PubMed ID: 22987127
[TBL] [Abstract][Full Text] [Related]
4. Random Forests approach for identifying additive and epistatic single nucleotide polymorphisms associated with residual feed intake in dairy cattle.
Yao C; Spurlock DM; Armentano LE; Page CD; VandeHaar MJ; Bickhart DM; Weigel KA
J Dairy Sci; 2013 Oct; 96(10):6716-29. PubMed ID: 23932129
[TBL] [Abstract][Full Text] [Related]
5. Random forests for genetic association studies.
Goldstein BA; Polley EC; Briggs FB
Stat Appl Genet Mol Biol; 2011; 10(1):32. PubMed ID: 22889876
[TBL] [Abstract][Full Text] [Related]
6. Performance of random forest when SNPs are in linkage disequilibrium.
Meng YA; Yu Y; Cupples LA; Farrer LA; Lunetta KL
BMC Bioinformatics; 2009 Mar; 10():78. PubMed ID: 19265542
[TBL] [Abstract][Full Text] [Related]
7. Genome-wide association studies.
Yang TH; Kon M; DeLisi C
Methods Mol Biol; 2013; 939():233-51. PubMed ID: 23192550
[TBL] [Abstract][Full Text] [Related]
8. An integrated approach to reduce the impact of minor allele frequency and linkage disequilibrium on variable importance measures for genome-wide data.
Walters R; Laurin C; Lubke GH
Bioinformatics; 2012 Oct; 28(20):2615-23. PubMed ID: 22847933
[TBL] [Abstract][Full Text] [Related]
9. Random forests on Hadoop for genome-wide association studies of multivariate neuroimaging phenotypes.
Wang Y; Goh W; Wong L; Montana G;
BMC Bioinformatics; 2013; 14 Suppl 16(Suppl 16):S6. PubMed ID: 24564704
[TBL] [Abstract][Full Text] [Related]
10. Were genome-wide linkage studies a waste of time? Exploiting candidate regions within genome-wide association studies.
Yoo YJ; Bull SB; Paterson AD; Waggott D; Sun L;
Genet Epidemiol; 2010 Feb; 34(2):107-18. PubMed ID: 19626703
[TBL] [Abstract][Full Text] [Related]
11. From disease association to risk assessment: an optimistic view from genome-wide association studies on type 1 diabetes.
Wei Z; Wang K; Qu HQ; Zhang H; Bradfield J; Kim C; Frackleton E; Hou C; Glessner JT; Chiavacci R; Stanley C; Monos D; Grant SF; Polychronakos C; Hakonarson H
PLoS Genet; 2009 Oct; 5(10):e1000678. PubMed ID: 19816555
[TBL] [Abstract][Full Text] [Related]
12. Alternative methods for H1 simulations in genome-wide association studies.
Perduca V; Sinoquet C; Mourad R; Nuel G
Hum Hered; 2012; 73(2):95-104. PubMed ID: 22472690
[TBL] [Abstract][Full Text] [Related]
13. A Markov blanket-based method for detecting causal SNPs in GWAS.
Han B; Park M; Chen XW
BMC Bioinformatics; 2010 Apr; 11 Suppl 3(Suppl 3):S5. PubMed ID: 20438652
[TBL] [Abstract][Full Text] [Related]
14. A new permutation strategy of pathway-based approach for genome-wide association study.
Guo YF; Li J; Chen Y; Zhang LS; Deng HW
BMC Bioinformatics; 2009 Dec; 10():429. PubMed ID: 20021635
[TBL] [Abstract][Full Text] [Related]
15. Application and interpretation of genome-wide association (GWA) studies for informing pharmacogenomic research - examples from the field of age-related macular degeneration.
SanGiovanni JP; Rosen R; Kaushal S
Curr Mol Med; 2014; 14(7):814-32. PubMed ID: 25109799
[TBL] [Abstract][Full Text] [Related]
16. Integrating genome-wide association studies and gene expression data highlights dysregulated multiple sclerosis risk pathways.
Liu G; Zhang F; Jiang Y; Hu Y; Gong Z; Liu S; Chen X; Jiang Q; Hao J
Mult Scler; 2017 Feb; 23(2):205-212. PubMed ID: 27207450
[TBL] [Abstract][Full Text] [Related]
17. Genotype distribution-based inference of collective effects in genome-wide association studies: insights to age-related macular degeneration disease mechanism.
Woo HJ; Yu C; Kumar K; Gold B; Reifman J
BMC Genomics; 2016 Aug; 17(1):695. PubMed ID: 27576376
[TBL] [Abstract][Full Text] [Related]
18. Identification of a Bipolar Disorder Vulnerable Gene CHDH at 3p21.1.
Chang H; Li L; Peng T; Grigoroiu-Serbanescu M; Bergen SE; Landén M; Hultman CM; Forstner AJ; Strohmaier J; Hecker J; Schulze TG; Müller-Myhsok B; Reif A; Mitchell PB; Martin NG; Cichon S; Nöthen MM; Jamain S; Leboyer M; Bellivier F; Etain B; Kahn JP; Henry C; Rietschel M; ; ; Xiao X; Li M
Mol Neurobiol; 2017 Sep; 54(7):5166-5176. PubMed ID: 27562178
[TBL] [Abstract][Full Text] [Related]
19. Multigenic modeling of complex disease by random forests.
Sun YV
Adv Genet; 2010; 72():73-99. PubMed ID: 21029849
[TBL] [Abstract][Full Text] [Related]
20. 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. PubMed ID: 19300503
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
