212 related articles for article (PubMed ID: 22566067)
1. Genome-enabled prediction of genetic values using radial basis function neural networks.
González-Camacho JM; de Los Campos G; Pérez P; Gianola D; Cairns JE; Mahuku G; Babu R; Crossa J
Theor Appl Genet; 2012 Aug; 125(4):759-71. PubMed ID: 22566067
[TBL] [Abstract][Full Text] [Related]
2. Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat.
Pérez-Rodríguez P; Gianola D; González-Camacho JM; Crossa J; Manès Y; Dreisigacker S
G3 (Bethesda); 2012 Dec; 2(12):1595-605. PubMed ID: 23275882
[TBL] [Abstract][Full Text] [Related]
3. Genome-enabled methods for predicting litter size in pigs: a comparison.
Tusell L; Pérez-Rodríguez P; Forni S; Wu XL; Gianola D
Animal; 2013 Nov; 7(11):1739-49. PubMed ID: 23880322
[TBL] [Abstract][Full Text] [Related]
4. Predicting complex quantitative traits with Bayesian neural networks: a case study with Jersey cows and wheat.
Gianola D; Okut H; Weigel KA; Rosa GJ
BMC Genet; 2011 Oct; 12():87. PubMed ID: 21981731
[TBL] [Abstract][Full Text] [Related]
5. Parametric and nonparametric statistical methods for genomic selection of traits with additive and epistatic genetic architectures.
Howard R; Carriquiry AL; Beavis WD
G3 (Bethesda); 2014 Apr; 4(6):1027-46. PubMed ID: 24727289
[TBL] [Abstract][Full Text] [Related]
6. Genome-wide prediction of maize single-cross performance, considering non-additive genetic effects.
Santos JP; Pereira HD; Von Pinho RG; Pires LP; Camargos RB; Balestre M
Genet Mol Res; 2015 Dec; 14(4):18471-84. PubMed ID: 26782495
[TBL] [Abstract][Full Text] [Related]
7. Comparison of Models and Whole-Genome Profiling Approaches for Genomic-Enabled Prediction of Septoria Tritici Blotch, Stagonospora Nodorum Blotch, and Tan Spot Resistance in Wheat.
Juliana P; Singh RP; Singh PK; Crossa J; Rutkoski JE; Poland JA; Bergstrom GC; Sorrells ME
Plant Genome; 2017 Jul; 10(2):. PubMed ID: 28724084
[TBL] [Abstract][Full Text] [Related]
8. Exploring the areas of applicability of whole-genome prediction methods for Asian rice (Oryza sativa L.).
Onogi A; Ideta O; Inoshita Y; Ebana K; Yoshioka T; Yamasaki M; Iwata H
Theor Appl Genet; 2015 Jan; 128(1):41-53. PubMed ID: 25341369
[TBL] [Abstract][Full Text] [Related]
9. Genomic Prediction of Genotype × Environment Interaction Kernel Regression Models.
Cuevas J; Crossa J; Soberanis V; Pérez-Elizalde S; Pérez-Rodríguez P; Campos GL; Montesinos-López OA; Burgueño J
Plant Genome; 2016 Nov; 9(3):. PubMed ID: 27902799
[TBL] [Abstract][Full Text] [Related]
10. Sensitivity to prior specification in Bayesian genome-based prediction models.
Lehermeier C; Wimmer V; Albrecht T; Auinger HJ; Gianola D; Schmid VJ; Schön CC
Stat Appl Genet Mol Biol; 2013 Jun; 12(3):375-91. PubMed ID: 23629460
[TBL] [Abstract][Full Text] [Related]
11. Modeling Epistasis in Genomic Selection.
Jiang Y; Reif JC
Genetics; 2015 Oct; 201(2):759-68. PubMed ID: 26219298
[TBL] [Abstract][Full Text] [Related]
12. Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers.
Crossa J; Campos Gde L; Pérez P; Gianola D; Burgueño J; Araus JL; Makumbi D; Singh RP; Dreisigacker S; Yan J; Arief V; Banziger M; Braun HJ
Genetics; 2010 Oct; 186(2):713-24. PubMed ID: 20813882
[TBL] [Abstract][Full Text] [Related]
13. Evaluation of genomic selection methods for predicting fiber quality traits in Upland cotton.
Islam MS; Fang DD; Jenkins JN; Guo J; McCarty JC; Jones DC
Mol Genet Genomics; 2020 Jan; 295(1):67-79. PubMed ID: 31473809
[TBL] [Abstract][Full Text] [Related]
14. Comparison of parametric, semiparametric and nonparametric methods in genomic evaluation.
Sahebalam H; Gholizadeh M; Hafezian H; Farhadi A
J Genet; 2019 Nov; 98():. PubMed ID: 31767821
[TBL] [Abstract][Full Text] [Related]
15. Genome-wide prediction using Bayesian additive regression trees.
Waldmann P
Genet Sel Evol; 2016 Jun; 48(1):42. PubMed ID: 27286957
[TBL] [Abstract][Full Text] [Related]
16. Genomic-enabled prediction with classification algorithms.
Ornella L; Pérez P; Tapia E; González-Camacho JM; Burgueño J; Zhang X; Singh S; Vicente FS; Bonnett D; Dreisigacker S; Singh R; Long N; Crossa J
Heredity (Edinb); 2014 Jun; 112(6):616-26. PubMed ID: 24424163
[TBL] [Abstract][Full Text] [Related]
17. Improving genomic prediction accuracy for meat tenderness in Nellore cattle using artificial neural networks.
Brito Lopes F; Magnabosco CU; Passafaro TL; Brunes LC; Costa MFO; Eifert EC; Narciso MG; Rosa GJM; Lobo RB; Baldi F
J Anim Breed Genet; 2020 Sep; 137(5):438-448. PubMed ID: 32020678
[TBL] [Abstract][Full Text] [Related]
18. Bayesian Networks Illustrate Genomic and Residual Trait Connections in Maize (
Töpner K; Rosa GJM; Gianola D; Schön CC
G3 (Bethesda); 2017 Aug; 7(8):2779-2789. PubMed ID: 28637811
[TBL] [Abstract][Full Text] [Related]
19. Predictive ability of genome-assisted statistical models under various forms of gene action.
Momen M; Mehrgardi AA; Sheikhi A; Kranis A; Tusell L; Morota G; Rosa GJM; Gianola D
Sci Rep; 2018 Aug; 8(1):12309. PubMed ID: 30120288
[TBL] [Abstract][Full Text] [Related]
20. Evaluation of genome-wide selection efficiency in maize nested association mapping populations.
Guo Z; Tucker DM; Lu J; Kishore V; Gay G
Theor Appl Genet; 2012 Feb; 124(2):261-75. PubMed ID: 21938474
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
