Genomic Prediction (GP) is a powerful approach for inferring complex phenotypes from
genetic markers. GP is critical for improving grain yield, particularly for staple crops such as …

Genomic selection (GS) is a strategy to predict the genetic merits of individuals using
genome-wide markers. However, GS prediction accuracy is affected by many factors …

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 …

In the field of plant breeding, various machine learning models have been developed and
studied to evaluate the genomic prediction (GP) accuracy of unseen phenotypes. Deep …

While several statistical machine learning methods have been developed and studied for
assessing the genomic prediction (GP) accuracy of unobserved phenotypes in plant …

With the human population continuing to increase worldwide, there is pressure to employ
novel technologies to increase genetic gain in plant breeding programs that contribute to …

Crop growth models (CGM) can predict the performance of a cultivar in untested
environments by sampling genotype-specific parameters. As they cannot predict the …

Background Multi-layer perceptron (MLP) and radial basis function neural networks
(RBFNN) have been shown to be effective in genome-enabled prediction. Here, we …

The effects of climate change create formidable challenges for breeders striving to produce
sufficient food quantities in rapidly changing environments. It is therefore critical to …

Genomic and pedigree predictions for grain yield and agronomic traits were carried out
using high density molecular data on a set of 803 spring wheat lines that were evaluated in …