Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat
P Pérez-Rodríguez, D Gianola… - G3: Genes …, 2012 - academic.oup.com
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 have been used. This study assessed the predictive ability of linear and non-linear …
Genome-enabled prediction of genetic values using radial basis function neural networks
JM González-Camacho, G de Los Campos… - Theoretical and Applied …, 2012 - Springer
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 …
genomic selection (GS) methods in animal and plant breeding. In GS, parametric, semi …
Cross-validation without doing cross-validation in genome-enabled prediction
Cross-validation of methods is an essential component of genome-enabled prediction of
complex traits. We develop formulae for computing the predictions that would be obtained …
complex traits. We develop formulae for computing the predictions that would be obtained …
Genome-enabled prediction using probabilistic neural network classifiers
Background Multi-layer perceptron (MLP) and radial basis function neural networks
(RBFNN) have been shown to be effective in genome-enabled prediction. Here, we …
(RBFNN) have been shown to be effective in genome-enabled prediction. Here, we …
Exploring the areas of applicability of whole-genome prediction methods for Asian rice (Oryza sativa L.)
A Onogi, O Ideta, Y Inoshita, K Ebana… - Theoretical and applied …, 2015 - Springer
Key message Our simulation results clarify the areas of applicability of nine prediction
methods and suggest the factors that affect their accuracy at predicting empirical traits …
methods and suggest the factors that affect their accuracy at predicting empirical traits …
Genome-wide prediction using Bayesian additive regression trees
P Waldmann - Genetics Selection Evolution, 2016 - Springer
Background The goal of genome-wide prediction (GWP) is to predict phenotypes based on
marker genotypes, often obtained through single nucleotide polymorphism (SNP) chips. The …
marker genotypes, often obtained through single nucleotide polymorphism (SNP) chips. The …
Genomic prediction and GWAS of yield, quality and disease-related traits in spring barley and winter wheat
Genome-wide association study (GWAS) and genomic prediction (GP) are extensively
employed to accelerate genetic gain and identify QTL in plant breeding. In this study, 1,317 …
employed to accelerate genetic gain and identify QTL in plant breeding. In this study, 1,317 …
Benchmarking parametric and machine learning models for genomic prediction of complex traits
The usefulness of genomic prediction in crop and livestock breeding programs has
prompted efforts to develop new and improved genomic prediction algorithms, such as …
prompted efforts to develop new and improved genomic prediction algorithms, such as …
Genomic prediction of agronomic traits in wheat using different models and cross-validation designs
Key message Genomic predictions across environments and within populations resulted in
moderate to high accuracies but across-population genomic prediction should not be …
moderate to high accuracies but across-population genomic prediction should not be …
Deep kernel and deep learning for genome-based prediction of single traits in multienvironment breeding trials
Deep learning (DL) is a promising method for genomic-enabled prediction. However, the
implementation of DL is difficult because many hyperparameters (number of hidden layers …
implementation of DL is difficult because many hyperparameters (number of hidden layers …