Search

Now showing items 1-10 of 10

Article

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

Montesinos-Lopez, O.A.; Montesinos-Lopez, A.; Crossa, J.; Cuevas, J.; Montesinos-Lopez, J.C.; Salas Gutiérrez, Z.; Lillemo, M.; Juliana, P.; Singh, R.P. (Genetics Society of America, 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, ...

Article

Deep kernel and deep learning for genome-based prediction of single traits in multienvironment breeding trials

Crossa, J.; Martini, J.W.R.; Gianola, D.; Perez-Rodriguez, P.; Jarquín, D.; JULIANA P.; Montesinos-Lopez, O.A.; Cuevas, J. (Frontiers, 2019)

Article

Multi-trait, multi-environment genomic prediction of durum wheat with genomic best linear unbiased predictor and deep learning methods

Montesinos-Lopez, O.A.; Montesinos-Lopez, A.; Tuberosa, R.; Maccaferri, M.; Sciara, G.; Ammar, K.; Crossa, J. (Frontiers, 2019)

Although durum wheat (Triticum turgidum var. durum Desf.) is a minor cereal crop representing just 5-7% of the world's total wheat crop, it is a staple food in Mediterranean countries, where it is used to produce pasta, ...

Article

CIMMYT Publications Repository

Seeding innovation ... Nourishing hope

Search

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

Deep kernel and deep learning for genome-based prediction of single traits in multienvironment breeding trials

Multi-trait, multi-environment genomic prediction of durum wheat with genomic best linear unbiased predictor and deep learning methods

Prediction of count phenotypes using high-resolution images and genomic data

A zero altered Poisson random forest model for genomic-enabled prediction

A review of deep learning applications for genomic selection

Maximum a posteriori Threshold Genomic Prediction model for ordinal traits

Implementation of genomic selection in the CIMMYT Global Wheat Program, learnings from the past 10 years

A multivariate Poisson deep learning model for genomic prediction of count data

Prospects and challenges of applied genomic selection-a new paradigm in breeding for grain yield in bread wheat

Search

Filters

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

Deep kernel and deep learning for genome-based prediction of single traits in multienvironment breeding trials

Multi-trait, multi-environment genomic prediction of durum wheat with genomic best linear unbiased predictor and deep learning methods

Prediction of count phenotypes using high-resolution images and genomic data

A zero altered Poisson random forest model for genomic-enabled prediction

A review of deep learning applications for genomic selection

Maximum a posteriori Threshold Genomic Prediction model for ordinal traits

Implementation of genomic selection in the CIMMYT Global Wheat Program, learnings from the past 10 years

A multivariate Poisson deep learning model for genomic prediction of count data

Prospects and challenges of applied genomic selection-a new paradigm in breeding for grain yield in bread wheat