Skip to main content
Share Dataverse

Share this dataverse on your favorite social media networks.

Featured Repositories

In order to use this feature you must have at least one published dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Cannot publish dataverse.

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

Find Advanced Search

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...
Add Data

You need to Sign Up or Log In to create a dataverse or add a dataset.

Link Dataverse
Reset Modifications

Are you sure you want to reset the selected metadata fields? If you do this, any customizations (hidden, required, optional) you have done will no longer appear.

Contact CIMMYT Research Data & Software Repository Network Support

CIMMYT Research Data & Software Repository Network Support

Please fill this out to prove you are not a robot.

+ =
Send Message