Gianola D - Search Results

1

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. Pérez-Rodríguez P, et al. Among authors: gianola d. G3 (Bethesda). 2012 Dec;2(12):1595-605. doi: 10.1534/g3.112.003665. Epub 2012 Dec 1. G3 (Bethesda). 2012. PMID: 23275882 Free PMC article.

2

Predicting quantitative traits with regression models for dense molecular markers and pedigree.

de los Campos G, Naya H, Gianola D, Crossa J, Legarra A, Manfredi E, Weigel K, Cotes JM. de los Campos G, et al. Among authors: gianola d. Genetics. 2009 May;182(1):375-85. doi: 10.1534/genetics.109.101501. Epub 2009 Mar 16. Genetics. 2009. PMID: 19293140 Free PMC article.

3

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. Crossa J, et al. Among authors: gianola d. Genetics. 2010 Oct;186(2):713-24. doi: 10.1534/genetics.110.118521. Epub 2010 Sep 2. Genetics. 2010. PMID: 20813882 Free PMC article.

4

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. De los Campos G, et al. Among authors: gianola d. Genet Res (Camb). 2010 Aug;92(4):295-308. doi: 10.1017/S0016672310000285. Genet Res (Camb). 2010. PMID: 20943010

5

Genomic-Enabled Prediction Based on Molecular Markers and Pedigree Using the Bayesian Linear Regression Package in R.

Pérez P, de Los Campos G, Crossa J, Gianola D. Pérez P, et al. Among authors: gianola d. Plant Genome. 2010;3(2):106-116. doi: 10.3835/plantgenome2010.04.0005. Plant Genome. 2010. PMID: 21566722 Free PMC article.

6

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. González-Camacho JM, et al. Among authors: gianola d. Theor Appl Genet. 2012 Aug;125(4):759-71. doi: 10.1007/s00122-012-1868-9. Epub 2012 May 8. Theor Appl Genet. 2012. PMID: 22566067 Free PMC article.

7

Technical note: An R package for fitting Bayesian regularized neural networks with applications in animal breeding.

Pérez-Rodríguez P, Gianola D, Weigel KA, Rosa GJ, Crossa J. Pérez-Rodríguez P, et al. Among authors: gianola d. J Anim Sci. 2013 Aug;91(8):3522-31. doi: 10.2527/jas.2012-6162. Epub 2013 May 8. J Anim Sci. 2013. PMID: 23658327

8

Genome-enabled methods for predicting litter size in pigs: a comparison.

Tusell L, Pérez-Rodríguez P, Forni S, Wu XL, Gianola D. Tusell L, et al. Among authors: gianola d. Animal. 2013 Nov;7(11):1739-49. doi: 10.1017/S1751731113001389. Epub 2013 Jul 24. Animal. 2013. PMID: 23880322 Free article.

9

Model averaging for genome-enabled prediction with reproducing kernel Hilbert spaces: a case study with pig litter size and wheat yield.

Tusell L, Pérez-Rodríguez P, Forni S, Gianola D. Tusell L, et al. Among authors: gianola d. J Anim Breed Genet. 2014 Apr;131(2):105-15. doi: 10.1111/jbg.12070. Epub 2014 Jan 8. J Anim Breed Genet. 2014. PMID: 24397267

10

Bayesian genomic-enabled prediction as an inverse problem.

Cuevas J, Pérez-Elizalde S, Soberanis V, Pérez-Rodríguez P, Gianola D, Crossa J. Cuevas J, et al. Among authors: gianola d. G3 (Bethesda). 2014 Aug 25;4(10):1991-2001. doi: 10.1534/g3.114.013094. G3 (Bethesda). 2014. PMID: 25155273 Free PMC article.

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