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1 to 4 of 4 Results
Dataset / Software
Sep 14, 2015 - CIMMYT Research Data
Pérez-Elizalde, Sergio; Cuevas, Jaime; Pérez-Rodríguez, Paulino; Crossa, José, 2015, "Selection of The Bandwidth Parameter in a Bayesian Kernel Regression Model for Genomic-Enabled Prediction", hdl:11529/10234, CIMMYT Research Data & Software Repository Network, V8
One of the most widely used kernel functions in genomic-enabled prediction is the Gaussian kernel. Usually selection of the bandwidth parameter for kernel regression is based on cross-validation. In this study, we propose a Bayesian method for selecting the bandwidth parameter h...
Dataset / Software
Sep 21, 2015 - CIMMYT Research Data
Crossa, José; Campos, Gustavo de los; Maccaferri, Marco; Tuberosa, Roberto; Burgueño, Juan; Pérez-Rodríguez, Paulino, 2015, "Extending the Marker × Environment Interaction Model for Genomic-Enabled Prediction and Genome Wide Association Analyses in Durum Wheat", hdl:11529/10233, CIMMYT Research Data & Software Repository Network, V6
The marker × environment interaction (M×E) genomic model can be used to generate predictions for untested individuals and identify genomic regions whose effects are stable across environments and others that show environmental specificity. The objectives of this study were: (1) t...
Dataset / Software
Dec 14, 2015 - CIMMYT Research Data
González-Camacho, Juan Manuel; Crossa, José; Pérez-Rodríguez, Paulino; Ornella, Leonardo; Gianola, Daniel, 2015, "Genome-enabled prediction using probabilistic neural network classifiers", hdl:11529/10576, CIMMYT Research Data & Software Repository Network, V1
Non-parametric methods have been shown to be effective in genome-enabled prediction, in particular, the multi-layer perceptron (MLP) and the radial basis function neural network (RBFNN). In this study, we evaluated and compared the performance of MLP classifier versus the probabi...
Dataset / Software
Dec 18, 2015 - CIMMYT Research Data
Cuevas, Jaime; Crossa, José; Soberanis, Víctor; Pérez-Elizalde, Sergio; Pérez-Rodríguez, Paulino; Campos, Gustavo de los, 2015, "Genomic Prediction of Marker × Environment Interaction Kernel Regression Models", hdl:11529/10580, CIMMYT Research Data & Software Repository Network, V1
The marker × environment interaction (M×E) decomposes the marker effects into main effects and interaction environmental-specific effects. The M×E interaction may be modeled through a linear kernel (Genomic Best Linear Unbiased Predictor, GBLUP) or with non-linear Gaussian kernel...
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