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Article
Multi-trait, multi-environment deep learning modeling for genomic-enabled prediction of plant traits
(Genetics Society of America, 2018)
Article
Genome-enabled prediction of genetic values using radial basis function neural networks
(Springer, 2012)
The availability of high density panels of molecular markers has prompted the adoption of genomic selection (GS) methods in animal and plant breeding. In GS, parametric, semi-parametric and non-parametric regressions models ...
Article
Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat
(Genetics Society of America, 2012)
In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The ...
Article
Genomic-enabled prediction based on molecular markers and pedigree using the Bayesian linear regression package in R
(Crop Science Society of America, 2010)
The availability of dense molecular markers has made possible the use of genomic selection in plant and animal breeding. However, models for genomic selection pose several computational and statistical challenges and require ...
Article
A benchmarking between deep learning, support vector machine and bayesian threshold best linear unbiased prediction for predicting ordinal traits in plant breeding
(Genetics Society of America, 2019)
Genomic selection is revolutionizing plant breeding. However, still lacking are better statistical models for ordinal phenotypes to improve the accuracy of the selection of candidate genotypes. For this reason, in this ...