187 related articles for article (PubMed ID: 26639183)
1. Modeling X Chromosome Data Using Random Forests: Conquering Sex Bias.
Winham SJ; Jenkins GD; Biernacka JM
Genet Epidemiol; 2016 Feb; 40(2):123-32. PubMed ID: 26639183
[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. Single Nucleotide Polymorphism relevance learning with Random Forests for Type 2 diabetes risk prediction.
López B; Torrent-Fontbona F; Viñas R; Fernández-Real JM
Artif Intell Med; 2018 Apr; 85():43-49. PubMed ID: 28943335
[TBL] [Abstract][Full Text] [Related]
4. Testing and estimation of X-chromosome SNP effects: Impact of model assumptions.
Song Y; Biernacka JM; Winham SJ
Genet Epidemiol; 2021 Sep; 45(6):577-592. PubMed ID: 34082482
[TBL] [Abstract][Full Text] [Related]
5. Detecting associated single-nucleotide polymorphisms on the X chromosome in case control genome-wide association studies.
Chen Z; Ng HK; Li J; Liu Q; Huang H
Stat Methods Med Res; 2017 Apr; 26(2):567-582. PubMed ID: 25253574
[TBL] [Abstract][Full Text] [Related]
6. 2LD, GENECOUNTING and HAP: Computer programs for linkage disequilibrium analysis.
Zhao JH
Bioinformatics; 2004 May; 20(8):1325-6. PubMed ID: 14871868
[TBL] [Abstract][Full Text] [Related]
7. Association tests for X-chromosomal markers--a comparison of different test statistics.
Loley C; Ziegler A; König IR
Hum Hered; 2011; 71(1):23-36. PubMed ID: 21325864
[TBL] [Abstract][Full Text] [Related]
8. Identifying SNPs predictive of phenotype using random forests.
Bureau A; Dupuis J; Falls K; Lunetta KL; Hayward B; Keith TP; Van Eerdewegh P
Genet Epidemiol; 2005 Feb; 28(2):171-82. PubMed ID: 15593090
[TBL] [Abstract][Full Text] [Related]
9. Parent-of-origin, imprinting, mitochondrial, and X-linked effects in traits related to alcohol dependence: presentation Group 18 of Genetic Analysis Workshop 14.
Strauch K; Baur MP
Genet Epidemiol; 2005; 29 Suppl 1():S125-32. PubMed ID: 16342190
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. 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. PubMed ID: 21908865
[TBL] [Abstract][Full Text] [Related]
12. A comparative study of forest methods for time-to-event data: variable selection and predictive performance.
Liu Y; Zhou S; Wei H; An S
BMC Med Res Methodol; 2021 Sep; 21(1):193. PubMed ID: 34563138
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. Multivariate eQTL mapping uncovers functional variation on the X-chromosome associated with complex disease traits.
Brumpton BM; Ferreira MA
Hum Genet; 2016 Jul; 135(7):827-39. PubMed ID: 27155841
[TBL] [Abstract][Full Text] [Related]
15. TRM: a powerful two-stage machine learning approach for identifying SNP-SNP interactions.
Lin HY; Chen YA; Tsai YY; Qu X; Tseng TS; Park JY
Ann Hum Genet; 2012 Jan; 76(1):53-62. PubMed ID: 22150548
[TBL] [Abstract][Full Text] [Related]
16. Prioritizing individual genetic variants after kernel machine testing using variable selection.
He Q; Cai T; Liu Y; Zhao N; Harmon QE; Almli LM; Binder EB; Engel SM; Ressler KJ; Conneely KN; Lin X; Wu MC
Genet Epidemiol; 2016 Dec; 40(8):722-731. PubMed ID: 27488097
[TBL] [Abstract][Full Text] [Related]
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; 5():32. PubMed ID: 15588316
[TBL] [Abstract][Full Text] [Related]
18. Sex chromosome-wide association analysis suggested male-specific risk genes for alcohol dependence.
Zuo L; Wang K; Zhang X; Pan X; Wang G; Krystal JH; Zhang H; Luo X
Psychiatr Genet; 2013 Dec; 23(6):233-8. PubMed ID: 23907288
[TBL] [Abstract][Full Text] [Related]
19. Efficiency and power in genetic association studies.
de Bakker PI; Yelensky R; Pe'er I; Gabriel SB; Daly MJ; Altshuler D
Nat Genet; 2005 Nov; 37(11):1217-23. PubMed ID: 16244653
[TBL] [Abstract][Full Text] [Related]
20. Statistics for X-chromosome associations.
Özbek U; Lin HM; Lin Y; Weeks DE; Chen W; Shaffer JR; Purcell SM; Feingold E
Genet Epidemiol; 2018 Sep; 42(6):539-550. PubMed ID: 29900581
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]