Blar i Brage NMBU på forfatter "Montesinos-Lopez, Osval A."
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A Bayesian genomic multi-output regressor stacking model for predicting multi-trait multi-environment plant breeding data
Montesinos-Lopez, Osval A.; Montesinos-Lopez, Abelardo; Crossa, Jose; Cuevas, Jaime; Montesinos-Lopez, Jose C.; Zitlalli, Salas Gutierrez; Lillemo, Morten; Philomin, Juliana; Singh, Ravi P. (Peer reviewed; Journal article, 2019)In this paper we propose a Bayesian multi-output regressor stacking (BMORS) model that is a generalization of the multi-trait regressor stacking method. The proposed BMORS model consists of two stages: in the first stage, ... -
Approximate genome-based kernel models for large data sets including main effects and interactions
Cuevas, Jaime; Montesinos-Lopez, Osval A.; Martini, J.W.R.; Perez-Rodriguez, Paulino; Lillemo, Morten; Crossa, Jose (Peer reviewed; Journal article, 2020)The rapid development of molecular markers and sequencing technologies has made it possible to use genomic prediction (GP) and selection (GS) in animal and plant breeding. However, when the number of observations (n) is ... -
Comparing gradient boosting machine and Bayesian threshold BLUP for genome-based prediction of categorical traits in wheat breeding
Montesinos-Lopez, Osval A.; Gonzales, Henry Nicole; Montesinos-Lopez, Abelardo; Daza-Torres, Maria; Lillemo, Morten; Montesinos-Lopez, Jose Cricelio; Crossa, Jose (Peer reviewed; Journal article, 2022) -
Partial Least Squares Enhances Genomic Prediction of New Environments
Montesinos-Lopez, Osval A.; Montesinos-Lopez, Abelardo; Kismiantini, Kismiantini; Roman-Gallardo, Armando; Gardner, Keith; Lillemo, Morten; Fritsche-Neto, Roberto; Crossa, Jose (Peer reviewed; Journal article, 2022) -
An R Package for Bayesian Analysis of Multi-environment and Multi-trait Multi-environment Data for Genome-based Prediction
Montesinos-Lopez, Osval A.; Montesinos-Lopez, Abelardo; Luna-Vazquez, Francisco Javier; Toledo, Fernando H.; Perez-Rodriguez, Paulino; Lillemo, Morten; Crossa, Jose (Peer reviewed; Journal article, 2019)Evidence that genomic selection (GS) is a technology that is revolutionizing plant breeding continues to grow. However, it is very well documented that its success strongly depends on statistical models, which are used by ...