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Article

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

Kismiantini; Montesinos-Lopez, O.A.; Crossa, J.; Setiawan, E.P.; Wutsqa, D.U. (Genetics Society of America, 2021)

Article

The modern plant breeding triangle: optimizing the use of genomics, phenomics, and enviromics data

Crossa, J.; Fritsche-Neto, R.; Montesinos-Lopez, O.A.; Costa-Neto, G.; Dreisigacker, S.; Montesinos-Lopez, A.; Bentley, A.R. (Frontiers, 2021)

Article

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

Montesinos-Lopez, O.A.; Montesinos-López, A.; Mosqueda-Gonzalez, B.A.; Montesinos-Lopez, J.C.; Crossa, J.; Lozano-Ramirez, N.; Singh, P.K.; Valladares-Anguiano, F.A. (Genetics Society of America, 2021)

Article

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

Montesinos-Lopez, O.A.; Montesinos-Lopez, J.C.; Singh, P.K.; Lozano-Ramirez, N.; Barrón-López, A.; Montesinos-Lopez, A.; Crossa, J. (Genetics Society of America, 2020)

Article

Approximate genome-based kernel models for large data sets including main effects and interactions

Cuevas, J.; Montesinos-Lopez, O.A.; Martini, J.W.R.; Perez-Rodriguez, P.; Lillemo, M.; Crossa, J. (Frontiers, 2020)

Article

An R package for multitrait and multienvironment data with the Item-based collaborative filtering algorithm

Montesinos-Lopez, O.A.; Luna-Vazquez, F.J.; Montesinos-Lopez, A.; Juliana, P.; Singh, R.P.; Crossa, J. (Crop Science Society of America, 2018)

The Item-Based Collaborative Filtering for Multitrait and Multienvironment Data (IBCF.MTME) package was developed to implement the item-based collaborative filtering (IBCF) algorithm for continuous phenotypic data in the ...

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

The modern plant breeding triangle: optimizing the use of genomics, phenomics, and enviromics data

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

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

Approximate genome-based kernel models for large data sets including main effects and interactions

An R package for multitrait and multienvironment data with the Item-based collaborative filtering algorithm

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

The modern plant breeding triangle: optimizing the use of genomics, phenomics, and enviromics data

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

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

Approximate genome-based kernel models for large data sets including main effects and interactions

An R package for multitrait and multienvironment data with the Item-based collaborative filtering algorithm