Cargando…
Genomic-enabled prediction with classification algorithms
Pearson's correlation coefficient (ρ) is the most commonly reported metric of the success of prediction in genomic selection (GS). However, in real breeding ρ may not be very useful for assessing the quality of the regression in the tails of the distribution, where individuals are chosen for se...
Autores principales: | Ornella, L, Pérez, P, Tapia, E, González-Camacho, J M, Burgueño, J, Zhang, X, Singh, S, Vicente, F S, Bonnett, D, Dreisigacker, S, Singh, R, Long, N, Crossa, J |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2014
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4023444/ https://www.ncbi.nlm.nih.gov/pubmed/24424163 http://dx.doi.org/10.1038/hdy.2013.144 |
Ejemplares similares
-
Genomic prediction in CIMMYT maize and wheat breeding programs
por: Crossa, J, et al.
Publicado: (2014) -
Comparison Between Linear and Non-parametric Regression Models for Genome-Enabled Prediction in Wheat
por: Pérez-Rodríguez, Paulino, et al.
Publicado: (2012) -
Genome-enabled prediction using probabilistic neural network classifiers
por: González-Camacho, Juan Manuel, et al.
Publicado: (2016) -
Genomic-Enabled Prediction of Ordinal Data with Bayesian Logistic Ordinal Regression
por: Montesinos-López, Osval A., et al.
Publicado: (2015) -
Increased Prediction Accuracy in Wheat Breeding Trials Using a Marker × Environment Interaction Genomic Selection Model
por: Lopez-Cruz, Marco, et al.
Publicado: (2015)