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Article
Genome-enabled prediction using probabilistic neural network classifiers
(BioMed Central, 2016)
Article
Applications of machine learning methods to genomic selection in breeding wheat for rust resistance
(Crop Science Society of America, 2018)
New methods and algorithms are being developed for predicting untested phenotypes in schemes commonly used in genomic selection (GS). The prediction of disease resistance in GS has its own peculiarities: a) there is consensus ...
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
Joint use of genome, pedigree, and their interaction with environment for predicting the performance of wheat lines in new environments
(Genetics Society of America, 2019)
Genome-enabled prediction plays an essential role in wheat breeding because it has the potential to increase the rate of genetic gain relative to traditional phenotypic and pedigree-based selection. Since the performance ...