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1 to 8 of 8 Results
Dataset / Software
Mar 24, 2020
Jarquin, Diego; Howard, Reka; Beyene, Yoseph; Gowda, Manje; Burgueño, Juan; Martini, Johannes; Pacheco, Angela; Covarrubias, Eduardo G.; Crossa, Jose, 2020, "Sparse designs for genomic selection using multi-environment data", hdl:11529/10548369, CIMMYT Research Data & Software Repository Network, V3
This research study the genomic-enabled prediction accuracy of the composition of the following sparse testing allocation design: (1) all non-overlapping (0 overlapping) lines in environments, (2) all overlapping (0 non-overlapping) lines tested in all the environments, and (3) c...
Dataset / Software
May 2, 2020
Villar-Hernández, Bartolo de Jesús; Crossa, Jose; Pérez-Elizalde, Sergio; García-Calvillo, Irma Delia; Toledo, Fernando; Perez-Rodriguez, Paulino, 2020, "Replication Data for: Multi-trait Bayesian decision for parental selection", hdl:11529/10548420, CIMMYT Research Data & Software Repository Network, V1
The files included in this study contains the data used with three promising multivariate loss functions: Kullback-Leibler (KL); the Energy Score; and the Multivariate Asymmetric Loss (MALF); to select the best performing parents for the next breeding cycle in two extensive real...
Dataset / Software
Jul 12, 2019
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
May 11, 2020
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 24, 2020
Cuevas, Jaime; Montesinos-López, Osval A.; Martini, Johannes; Pérez-Rodríguez, Paulino; Lillemo, Morten; Crossa, Jose, 2020, "Replication Data for: Approximate kernels for large data sets In genome-based prediction", hdl:11529/10548425, CIMMYT Research Data & Software Repository Network, V1
The rapid development of molecular markers and sequencing technologies has made it possible to use genomic selection (GS) and genomic prediction (GP) in animal and plant breeding. However, computational difficulties arise when the number of observations is large. This five datase...
Dataset / Software
May 30, 2020
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...
Dataset / Software
Jul 18, 2019
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
Dec 6, 2019
Cerón-Rojas, J. Jesus; Crossa, Jose, 2019, "Combined Multistage Linear Genomic Selection Indices to Predict the Net Genetic Merit in Plant Breeding", hdl:11529/10548356, CIMMYT Research Data & Software Repository Network, V1
Multistage selection is a cost-saving strategy for improving several traits because it is not necessary to measure all traits at each stage. A combined linear genomic selection index is a linear combination of phenotypic and genomic estimated breeding values useful to predict the...
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