Browsing by Author "Crossa, J."

Article

1.4 screening experimental designs for quantitative trait loci, association mapping, genotype-by environment interaction, and other investigations

Federer, W.T.; Crossa, J. (Frontiers, 2012)

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)

Article

A benchmarking between deep learning, support vector machine and bayesian threshold best linear unbiased prediction for predicting ordinal traits in plant breeding

Montesinos-Lopez, O.A.; Martin-Vallejo, J.; Crossa, J.; Gianola, D.; Hernández Suárez, C.M.; Montesinos-Lopez, A.; Juliana, P.; Singh, R.P. (Genetics Society of America, 2019)

Article

A chickpea genetic variation map based on the sequencing of 3,366 genomes

Varshney, R.K.; Roorkiwal, M.; Shuai Sun; Bajaj, P.; Annapurna Chitikineni; Thudi, M.; Singh, N.P.; Xiao Du; Upadhyaya, H.; Khan, A.W.; Yue Wang; Garg, V.; Guangyi Fan; Cowling, W.A.; Crossa, J.; Gentzbittel, L.; Voss-Fels, K.P.; Valluri, V.K.; Sinha, P.; Singh, V.K.; Ben, C.; Rathore, A.; Punna, R.; Muneendra K. Singh; Tar’an, B.; Chellapilla Bharadwaj; Yasin, M.; Pithia, M.S.; Singh, S.; Soren, K.R.; Kudapa, H.; Jarquín, D.; Cubry, P.; Hickey, L.T.; Dixit, G.P.; Thuillet, A.C.; Hamwieh, A.; Kumar, S.; Deokar, A.; Chaturvedi, S.K.; Francis, A.; Howard, R.; Chattopadhyay, D.; Edwards, D.; Lyons, E.; Vigouroux, Y.; Hayes, B.J.; Von Wettberg, E.; Datta, S.K.; Huanming Yang; Nguyen, H.T.; Jian Wang; Siddique, K.H.M.; Mohapatra, T.; Bennetzen, J.L.; Xun Xu; Xin Liu (Nature Publishing Group, 2021)

Article

A comparison between three tuning strategies for gaussian kernels in the context of univariate genomic prediction

Montesinos-Lopez, O.A.; Carter, A.; Bernal Sandoval, D.A.; Cano-Paez, B.; Montesinos-López, A.; Crossa, J. (MDPI, 2022)

Article

A comparison of the adoption of genomic selection across different breeding institutions

Gholami, M.; Wimmer, V.; Sansaloni, C.P.; Petroli, C.D.; Hearne, S.; Covarrubias-Pazaran, G.; Rensing, S.; Heise, J.; Perez-Rodriguez, P.; Dreisigacker, S.; Crossa, J.; Martini, J.W.R. (Frontiers, 2021)

Article

A general-purpose machine learning r library for sparse kernels methods with an application for genome-based prediction

Montesinos-Lopez, O.A.; Mosqueda-Gonzalez, B.A.; Palafox González, A.; Montesinos-Lopez, A.; Crossa, J. (Frontiers, 2022)

Article

A guide for kernel generalized regression methods for genomic-enabled prediction

Montesinos-Lopez, A.; Montesinos-Lopez, O.A.; Montesinos-Lopez, J.C.; Flores-Cortes, C.A.; Rosa, R. de la; Crossa, J. (Springer Nature, 2021)

Article

A linear profit function for economic weights of linear phenotypic selection indices in plant breeding

Ceron Rojas, J.J.; Gowda, M.; Toledo, F.H.; Beyene, Y.; Bentley, A.R.; Crespo Herrera, L.A.; Gardner, K.A.; Crossa, J. (CSSA; Wiley, 2022)

Article

A multi-trait gaussian kernel genomic prediction model under three tunning strategies

Kismiantini; Montesinos-López, A.; Cano-Paez, B.; Montesinos-Lopez, J.C.; Chavira-Flores, M.; Montesinos-Lopez, O.A.; Crossa, J. (MDPI, 2022)

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

A new deep learning calibration method enhances genome-based prediction of continuous crop traits

Montesinos-Lopez, O.A.; Montesinos-Lopez, A.; Mosqueda-Gonzalez, B.A.; Bentley, A.R.; Lillemo, M.; Varshney, R.K.; Crossa, J. (Frontiers, 2021)

Article

A novel method for genomic-enabled prediction of cultivars in new environments

Montesinos-Lopez, O.A.; Ramos-Pulido, S.; Hernández Suárez, C.M.; Mosqueda-Gonzalez, B.A.; Valladares-Anguiano, F.A.; Vitale, P.; Montesinos-López, A.; Crossa, J. (Frontiers Media S.A., 2023)

Article

A regression model for pooled data in a two-stage survey under informative sampling with application for detecting and estimating the presence of transgenic corn

Montesinos-Lopez, O.A.; Eskridge, K.; Montesinos-Lopez, A.; Crossa, J.; Cortés-Cruz, M.A.; Dong Wang (Cambridge University Press, 2016)

Article

A review of deep learning applications for genomic selection

Montesinos-Lopez, O.A.; Montesinos-Lopez, A.; Perez-Rodriguez, P.; Barrón-López, A.; Martini, J.W.R.; Fajardo-Flores, S.B.; Gaytan-Lugo, L.S.; Santana-Mancilla, P.C.; Crossa, J. (BioMed Central, 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

Additive genetic variance and covariance between relatives in synthetic wheat crosses with variable parental ploidy levels

Puhl, L.; Crossa, J.; Munilla, S.; Perez-Rodriguez, P.; Cantet, R. (Oxford University Press, 2021)

Article

Aerial high‐throughput phenotyping enables indirect selection for grain yield at the early generation, seed‐limited stages in breeding programs

Krause, M.; Mondal, S.; Crossa, J.; Singh, R.P.; Pinto Espinosa, F.; Haghighattalab, A.; Shrestha, S.; Rutkoski, J.; Gore, M.A.; Sorrells, M.E.; Poland, J.A. (Crop Science Society of America (CSSA), 2020)

Article

Aerial high‐throughput phenotyping enabling indirect selection for grain yield at the early‐generation seed‐limited stages in breeding programs

Krause, M.; Mondal, S.; Crossa, J.; Singh, R.P.; Pinto Espinosa, F.; Haghighattalab, A.; Shrestha, S.; Rutkoski, J.; Gore, M.A.; Sorrells, M.E.; Poland, J.A. (CSSA; Wiley, 2020)

Report

The AMMI analysis and graphing the biplot

Vargas Hernández, M.; Crossa, J. (CIMMYT, 2000)

CIMMYT Publications Repository

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