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Items: 1 to 20 of 101

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. doi: 10.1007/s00122-012-1868-9. Epub 2012 May 8.

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. doi: 10.1534/g3.112.003665. Epub 2012 Dec 1.

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. doi: 10.1017/S1751731113001389. Epub 2013 Jul 24.

PMID:
23880322
4.

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 11;4(6):1027-46. doi: 10.1534/g3.114.010298.

5.

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 7;12:87. doi: 10.1186/1471-2156-12-87.

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 28;14(4):18471-84. doi: 10.4238/2015.December.23.35.

7.

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. doi: 10.1007/s00122-014-2411-y. Epub 2014 Oct 24.

PMID:
25341369
8.

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. doi: 10.1515/sagmb-2012-0042.

PMID:
23629460
9.

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. doi: 10.1534/genetics.110.118521. Epub 2010 Sep 2.

10.

Modeling Epistasis in Genomic Selection.

Jiang Y, Reif JC.

Genetics. 2015 Oct;201(2):759-68. doi: 10.1534/genetics.115.177907. Epub 2015 Jul 27.

11.

Genomic prediction in maize breeding populations with genotyping-by-sequencing.

Crossa J, Beyene Y, Kassa S, Pérez P, Hickey JM, Chen C, de los Campos G, Burgueño J, Windhausen VS, Buckler E, Jannink JL, Lopez Cruz MA, Babu R.

G3 (Bethesda). 2013 Nov 6;3(11):1903-26. doi: 10.1534/g3.113.008227.

12.

Genome-wide prediction using Bayesian additive regression trees.

Waldmann P.

Genet Sel Evol. 2016 Jun 10;48(1):42. doi: 10.1186/s12711-016-0219-8.

13.

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. doi: 10.1038/hdy.2013.144. Epub 2014 Jan 15.

14.

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 11;45:34. doi: 10.1186/1297-9686-45-34.

15.

Genome-enabled prediction using probabilistic neural network classifiers.

González-Camacho JM, Crossa J, Pérez-Rodríguez P, Ornella L, Gianola D.

BMC Genomics. 2016 Mar 9;17:208. doi: 10.1186/s12864-016-2553-1.

16.

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. doi: 10.1007/s00122-011-1702-9. Epub 2011 Sep 22.

PMID:
21938474
17.

Effect of genotype imputation on genome-enabled prediction of complex traits: an empirical study with mice data.

Felipe VP, Okut H, Gianola D, Silva MA, Rosa GJ.

BMC Genet. 2014 Dec 29;15:149. doi: 10.1186/s12863-014-0149-9.

18.

Threshold models for genome-enabled prediction of ordinal categorical traits in plant breeding.

Montesinos-López OA, Montesinos-López A, Pérez-Rodríguez P, de Los Campos G, Eskridge K, Crossa J.

G3 (Bethesda). 2014 Dec 23;5(2):291-300. doi: 10.1534/g3.114.016188.

19.

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. doi: 10.1017/S0016672310000285.

PMID:
20943010
20.

A Unified and Comprehensible View of Parametric and Kernel Methods for Genomic Prediction with Application to Rice.

Jacquin L, Cao TV, Ahmadi N.

Front Genet. 2016 Aug 9;7:145. doi: 10.3389/fgene.2016.00145. eCollection 2016.

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