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Contributor Name: CGIAR Research Program on Wheat (WHEAT) Publication Date: 2018 Contributor Name: CGIAR Author Name: Crossa, Jose Contributor Name: CGIAR Research Program on Maize (MAIZE)

1 to 3 of 3 Results

Dataset / Software A Bayesian genomic multi-output regressor stacking model for predicting multi-trait multi-environment plant breeding data Oct 26, 2018 - CIMMYT Research Data Montesinos-López, Osval A ; Montesinos-López, Abelardo; Crossa, Jose; Cuevas, Jaime; Montesinos-López, José Cricelio; Gutiérrez, Zitlalli Salas; Lillemo, Morten; Juliana, Philomin; Singh, Ravi, 2018, "A Bayesian genomic multi-output regressor stacking model for predicting multi-trait multi-environment plant breeding data", hdl:11529/10548141, CIMMYT Research Data & Software Repository Network, V1 A new statistical model is presented for genomic prediction on maize and wheat data comprising multi-trait, multi-environment data.
Dataset / Software Supplemental Materials for The Relative Efficiency of Three Constrained Multistage Linear Phenotypic Selection Indices Oct 8, 2018 - CIMMYT Research Data Cerón-Rojas, J.Jesús; Toledo, Fernando; Crossa, Jose, 2018, "Supplemental Materials for The Relative Efficiency of Three Constrained Multistage Linear Phenotypic Selection Indices", hdl:11529/10548136, CIMMYT Research Data & Software Repository Network, V1 This dataset provides supplemental information related to an investigation of constrained multistage linear phenotypic selection indices.
Dataset / Software Supplemental data for multi-trait, multi-environment deep learning modeling for genomic-enabled prediction of plant traits Sep 28, 2018 - CIMMYT Research Data Montesinos-López, Osval A ; Montesinos-López, Abelardo; Crossa, Jose; Gianola, Daniel ; Hernández-Suárez, Carlos M.; Martín-Vallejo, Javier, 2018, "Supplemental data for multi-trait, multi-environment deep learning modeling for genomic-enabled prediction of plant traits", hdl:11529/10548134, CIMMYT Research Data & Software Repository Network, V1 This study provides supplemental data to support an investigation of the power of multi-trait deep learning (MTDL) models in terms of genomic-enabled prediction accuracy.

Dataset / Software

A Bayesian genomic multi-output regressor stacking model for predicting multi-trait multi-environment plant breeding data

Oct 26, 2018 - CIMMYT Research Data

Montesinos-López, Osval A ; Montesinos-López, Abelardo; Crossa, Jose; Cuevas, Jaime; Montesinos-López, José Cricelio; Gutiérrez, Zitlalli Salas; Lillemo, Morten; Juliana, Philomin; Singh, Ravi, 2018, "A Bayesian genomic multi-output regressor stacking model for predicting multi-trait multi-environment plant breeding data", hdl:11529/10548141, CIMMYT Research Data & Software Repository Network, V1

A new statistical model is presented for genomic prediction on maize and wheat data comprising multi-trait, multi-environment data.

Dataset / Software

Supplemental Materials for The Relative Efficiency of Three Constrained Multistage Linear Phenotypic Selection Indices

Oct 8, 2018 - CIMMYT Research Data

Cerón-Rojas, J.Jesús; Toledo, Fernando; Crossa, Jose, 2018, "Supplemental Materials for The Relative Efficiency of Three Constrained Multistage Linear Phenotypic Selection Indices", hdl:11529/10548136, CIMMYT Research Data & Software Repository Network, V1

This dataset provides supplemental information related to an investigation of constrained multistage linear phenotypic selection indices.

Dataset / Software

Supplemental data for multi-trait, multi-environment deep learning modeling for genomic-enabled prediction of plant traits

Sep 28, 2018 - CIMMYT Research Data

Montesinos-López, Osval A ; Montesinos-López, Abelardo; Crossa, Jose; Gianola, Daniel ; Hernández-Suárez, Carlos M.; Martín-Vallejo, Javier, 2018, "Supplemental data for multi-trait, multi-environment deep learning modeling for genomic-enabled prediction of plant traits", hdl:11529/10548134, CIMMYT Research Data & Software Repository Network, V1

This study provides supplemental data to support an investigation of the power of multi-trait deep learning (MTDL) models in terms of genomic-enabled prediction accuracy.

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