CIMMYT Research Data & Software Repository Network

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Contributor Name: CGIAR Research Program on Wheat (WHEAT) Contributor Name: Genetic Resources Program (GRP) Author Name: Crossa, Jose Author Name: Gianola, Daniel Keyword Term: Wheat Keyword Term: Triticum aestivum

1 to 3 of 3 Results

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 Replication Data for: Joint use of genome, pedigree and their interaction with environment for predicting the performance of wheat lines in new environments Jul 12, 2019 - CIMMYT Research Data Howard, Reka; Gianola, Daniel; Montesinos-López, Osval; Juliana, Philomin; Singh, Ravi; Poland, Jesse; Shrestha, Sandesh; Perez-Rodriguez, Paulino; Crossa, Jose; Jarquin, Diego, 2019, "Replication Data for: Joint use of genome, pedigree and their interaction with environment for predicting the performance of wheat lines in new environments", hdl:11529/10548169, CIMMYT Research Data & Software Repository Network, V3 In this study, we evaluated genome-based prediction using 35,403 wheat lines from the Global Wheat Breeding Program of the International Maize and Wheat Improvement Center (CIMMYT). We implemented eight statistical models that included genome-wide molecular marker and pedigree in...
Dataset / Software Deep kernel and deep learning for genomic-based prediction Aug 15, 2019 - CIMMYT Research Data Crossa, Jose; Martini, Johannes; Gianola, Daniel; Pérez-Rodríguez, Paulino; Burgueño, Juan; Singh, Ravi; Juliana, Philomin; Montesinos-López, Osval; Cuevas, Jaime, 2019, "Deep kernel and deep learning for genomic-based prediction", hdl:11529/10548273, CIMMYT Research Data & Software Repository Network, V1 Deep learning (DL) is a promising method in the context of genomic prediction for selecting individuals early in time without measuring their phenotypes. iI this paper we compare the performance in terms of genome-based prediction of the DL method, deep kernel (arc-cosine kernel,...

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

Replication Data for: Joint use of genome, pedigree and their interaction with environment for predicting the performance of wheat lines in new environments

Jul 12, 2019 - CIMMYT Research Data

Howard, Reka; Gianola, Daniel; Montesinos-López, Osval; Juliana, Philomin; Singh, Ravi; Poland, Jesse; Shrestha, Sandesh; Perez-Rodriguez, Paulino; Crossa, Jose; Jarquin, Diego, 2019, "Replication Data for: Joint use of genome, pedigree and their interaction with environment for predicting the performance of wheat lines in new environments", hdl:11529/10548169, CIMMYT Research Data & Software Repository Network, V3

In this study, we evaluated genome-based prediction using 35,403 wheat lines from the Global Wheat Breeding Program of the International Maize and Wheat Improvement Center (CIMMYT). We implemented eight statistical models that included genome-wide molecular marker and pedigree in...

Dataset / Software

Deep kernel and deep learning for genomic-based prediction

Aug 15, 2019 - CIMMYT Research Data

Crossa, Jose; Martini, Johannes; Gianola, Daniel; Pérez-Rodríguez, Paulino; Burgueño, Juan; Singh, Ravi; Juliana, Philomin; Montesinos-López, Osval; Cuevas, Jaime, 2019, "Deep kernel and deep learning for genomic-based prediction", hdl:11529/10548273, CIMMYT Research Data & Software Repository Network, V1

Deep learning (DL) is a promising method in the context of genomic prediction for selecting individuals early in time without measuring their phenotypes. iI this paper we compare the performance in terms of genome-based prediction of the DL method, deep kernel (arc-cosine kernel,...

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