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1 to 6 of 6 Results
Dataset / Software
Oct 22, 2018 - CIMMYT Research Data
Montesinos-López, Osval A ; Martín-Vallejo, Javier; Crossa, Jose; Gianola, Daniel ; Hernández-Suárez, Carlos M.; Montesinos-López, Abelardo; Juliana, Philomin; Singh, Ravi, 2018, "New deep learning genomic prediction model for multi-traits with mixed binary, ordinal, and continuous phenotypes", hdl:11529/10548140, CIMMYT Research Data & Software Repository Network, V1
The seven data sets are wheat data from CIMMYT Global Wheat Breeding program. They comprise different traits, like days to heading, days to maturity, grain yield, grain color, different type of leaf and stripe rust in wheat. Also the trials were run in different environments.
Dataset / Software
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
Jul 18, 2019 - CIMMYT Research Data
Cuevas, Jaime; Montesinos-López, Osval A; Juliana, Philomin; Pérez-Rodríguez, Paulino; Burgueño, Juan; Guzman, Carlos; Montesinos-López, Abelardo; Crossa, Jose, 2019, "Deep kernel of genomic and near infrared predictions in multi-environment breeding trials", hdl:11529/10548180, CIMMYT Research Data & Software Repository Network, V4
In genomic prediction deep learning artificial neural network are part of machine learning methods that incorporate parametric, non-parametric and semi-parametric statistical models. Kernel methods are seeing more flexible, and easier to interpret than neural networks. Kernel met...
Dataset / Software
Aug 12, 2019 - CIMMYT Research Data
Montesinos-López, Osval A ; Montesinos-López, Abelardo; Tuberosa, Roberto; Maccaferri, Marco; Sciara, Giuseppe; Ammar, Karim; Crossa, Jose, 2019, "Multi-trait multi-environment genomic prediction of durum wheat", hdl:11529/10548262, CIMMYT Research Data & Software Repository Network, V1
In this paper we cover multi-trait prediction of grain yield (GY), days to heading (DH) and plant height (PH) of 270 durum wheat lines that were evaluated in 43 environments (location-year combinations) in Bologna, Italy. The results of the multi-trait deep learning method also w...
Dataset / Software
May 11, 2020 - CIMMYT Research Data
Ibba, Maria Itria; Crossa, Jose; Montesinos-López, Osval A.; Montesinos-López, Abelardo; Juliana, Philomin; Guzman, Carlos; Dolorean, Emily; Dreisigacker, Susanne ; Poland, Jesse, 2020, "Replication Data for: Genome-based prediction of multiple wheat quality traits in multiple years", hdl:11529/10548423, CIMMYT Research Data & Software Repository Network, V1
The use of genomic prediction could greatly help to increase the efficiency of selecting for wheat quality traits by reducing the cost and time required for this analysis. This study contains data used to evaluate the prediction performances of 13 wheat quality traits under two m...
Dataset / Software
May 30, 2020 - CIMMYT Research Data
Montesinos-López, Osval A ; Montesinos-López, José Cricelio; Singh, Pawan; Lozano-Ramirez, Nerida; Barrón-López, Alberto; Montesinos-López, Abelardo; Crossa, Jose, 2020, "Replication Data for: A multivariate Poisson deep learning model for genomic prediction of count data", hdl:11529/10548438, CIMMYT Research Data & Software Repository Network, V1
Genomic selection (GS) is an important method used in plant and animal breeding. The experimental data provided in this study contain counting data. These datasets were used to support research on efficient methodologies for multivariate count data outcomes including a multivaria...
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