201 related articles for article (PubMed ID: 23763755)
1. Predicting complex traits using a diffusion kernel on genetic markers with an application to dairy cattle and wheat data.
Morota G; Koyama M; Rosa GJ; Weigel KA; Gianola D
Genet Sel Evol; 2013 Jun; 45(1):17. PubMed ID: 23763755
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. Application of support vector regression to genome-assisted prediction of quantitative traits.
Long N; Gianola D; Rosa GJ; Weigel KA
Theor Appl Genet; 2011 Nov; 123(7):1065-74. PubMed ID: 21739137
[TBL] [Abstract][Full Text] [Related]
4. Predicting bull fertility using genomic data and biological information.
Abdollahi-Arpanahi R; Morota G; Peñagaricano F
J Dairy Sci; 2017 Dec; 100(12):9656-9666. PubMed ID: 28987577
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. Predicting expected progeny difference for marbling score in Angus cattle using artificial neural networks and Bayesian regression models.
Okut H; Wu XL; Rosa GJ; Bauck S; Woodward BW; Schnabel RD; Taylor JF; Gianola D
Genet Sel Evol; 2013 Sep; 45(1):34. PubMed ID: 24024641
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. Application of neural networks with back-propagation to genome-enabled prediction of complex traits in Holstein-Friesian and German Fleckvieh cattle.
Ehret A; Hochstuhl D; Gianola D; Thaller G
Genet Sel Evol; 2015 Mar; 47(1):22. PubMed ID: 25886037
[TBL] [Abstract][Full Text] [Related]
9. Appraising the Genetic Architecture of Kernel Traits in Hexaploid Wheat Using GWAS.
Muhammad A; Hu W; Li Z; Li J; Xie G; Wang J; Wang L
Int J Mol Sci; 2020 Aug; 21(16):. PubMed ID: 32781752
[TBL] [Abstract][Full Text] [Related]
10. Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models.
Cuevas J; Crossa J; Montesinos-López OA; Burgueño J; Pérez-Rodríguez P; de Los Campos G
G3 (Bethesda); 2017 Jan; 7(1):41-53. PubMed ID: 27793970
[TBL] [Abstract][Full Text] [Related]
11. Bayesian multitrait kernel methods improve multienvironment genome-based prediction.
Montesinos-López OA; Montesinos-López JC; Montesinos-López A; Ramírez-Alcaraz JM; Poland J; Singh R; Dreisigacker S; Crespo L; Mondal S; Govidan V; Juliana P; Espino JH; Shrestha S; Varshney RK; Crossa J
G3 (Bethesda); 2022 Feb; 12(2):. PubMed ID: 34849802
[TBL] [Abstract][Full Text] [Related]
12. Deep learning versus parametric and ensemble methods for genomic prediction of complex phenotypes.
Abdollahi-Arpanahi R; Gianola D; Peñagaricano F
Genet Sel Evol; 2020 Feb; 52(1):12. PubMed ID: 32093611
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. Semi-parametric genomic-enabled prediction of genetic values using reproducing kernel Hilbert spaces methods.
De los Campos G; Gianola D; Rosa GJ; Weigel KA; Crossa J
Genet Res (Camb); 2010 Aug; 92(4):295-308. PubMed ID: 20943010
[TBL] [Abstract][Full Text] [Related]
15. Inferring trait-specific similarity among individuals from molecular markers and phenotypes with Bayesian regression.
Gianola D; Fernando RL; Schön CC
Theor Popul Biol; 2020 Apr; 132():47-59. PubMed ID: 31830483
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. Genomic predictions of growth curves in Holstein dairy cattle based on parameter estimates from nonlinear models combined with different kernel functions.
Yin T; König S
J Dairy Sci; 2020 Aug; 103(8):7222-7237. PubMed ID: 32534925
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. An alternative covariance estimator to investigate genetic heterogeneity in populations.
Heslot N; Jannink JL
Genet Sel Evol; 2015 Nov; 47():93. PubMed ID: 26612537
[TBL] [Abstract][Full Text] [Related]
20. Comparison of genomic predictions using medium-density (∼54,000) and high-density (∼777,000) single nucleotide polymorphism marker panels in Nordic Holstein and Red Dairy Cattle populations.
Su G; Brøndum RF; Ma P; Guldbrandtsen B; Aamand GP; Lund MS
J Dairy Sci; 2012 Aug; 95(8):4657-65. PubMed ID: 22818480
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
