Search Results - "Hilbert space."

Genomic Prediction in Faba bean for Heat and Herbicide Tolerance by Abou-Khater, Lynn, Maalouf, Fouad, Hamwieh, Aladdin, Jighly, Abdul-Qader, Joukhadar, Reem, Alsamman, Alsamman M., Ahmed, Zayed Babiker Mahgoub, Balech, Rind, Hu, Jinguo, Ma, Y., Sanchez-Garcia, Miguel, Agrawal, Shiv Kumar

“…Prediction accuracy (PA) was evaluated using the reproducing kernel Hilbert space model with and without considering genotype by environment interaction and considering two cross-validation strategies (CV1: predicting new lines; CV2: predicting complete records from unbalanced data). …”
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Response to early generation genomic selection for yield in wheat by Bonnett, David G., Yongle Li, Crossa, José, Dreisigacker, Susanne, Basnet, Bhoja Raj, Pérez Rodriguez, Paulino, Alvarado Beltrán, Gregorio, Jannink, Jean-Luc, Poland, Jesse A., Sorrells, Mark Earl

“…A training set of 1,334 elite wheat breeding lines tested over three field seasons was used to generate Genomic Estimated Breeding Values (GEBVs) for grain yield under irrigated conditions applying markers and three different prediction methods: (1) Genomic Best Linear Unbiased Predictor (GBLUP), (2) GBLUP with the imputation of missing genotypic data by Ridge Regression BLUP (rrGBLUP_imp), and (3) Reproducing Kernel Hilbert Space (RKHS) a.k.a. Gaussian Kernel (GK). F2 GEBVs were generated for 1,924 individuals from 38 biparental cross populations between 21 parents selected from the training set. …”
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Unraveling the genetic basis of general combining ability in CIMMYT elite bread wheat germplasm: implications for breeding strategies optimization by Saavedra-Avila, José Ignacio, Gerard, Guillermo Sebastián, Esposito, Salvatore, Velu, Govindan, Huerta-Espino, Julio, Tarekegn, Zerihun Tadesse, Dreisigacker, Susanne, Saint Pierre, Carolina, Pacheco Gil, Rosa Angela, Toledo, Fernando Henrique, Gardner, Keith, Crespo Herrera, Leonardo Abdiel, Crossa, Jose, Vitale, Paolo

“…The highest PA was reached by using the reproducing kernel Hilbert space (RKHS) model for the trait GY (0.34). The identification of MTAs for PNC and GY provided insight into the biological pathways underpinning combining ability and demonstrated the potential for predicting the ability of the genotypes to be crossed. …”
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Multiple‑trait analyses improved the accuracy of genomic prediction and the power of genome‑wide association of productivity and climate change‑adaptive traits in lodgepole pine by Cappa, Eduardo Pablo, Chen, Charles, Klutsch, Jennifer G., Sebastian-Azcona, Jaime, Ratcliffe, Blaise, Wei, Xiaojing, Da Ros, Letitia, Ullan, Aziz, Liu, Yang, Bernowicz, Andy, Sadoway, Shane, Mansfield, Shawn D., Erbilgin, Nadir, Thomas, Barb R., El-Kassaby, Yousry A.

“…GP, ST and MT parametric and non-parametric (Reproducing Kernel Hilbert Spaces, RKHS) models were compared in terms of prediction accuracy (PA) and control of bias.…”
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Genome-wide association mapping and genomic prediction for CBSD resistance in Manihot esculenta by Kayondo, S.I., Carpio, D.P. del, Lozano, R., Ozimati, A.A., Wolfe, M., Baguma, Yona K., Gracen, V., Offei, S., Ferguson, Morag E., Kawuki, R.S., Jannink, Jean-Luc

“…For all traits, Random Forest and reproducing kernel Hilbert spaces regression showed the highest predictive accuracies. …”
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Investigating genomic prediction strategies for grain carotenoid traits in a tropical/subtropical maize panel by LaPorte, Mary-Francis, Suwarno, Willy Bayuardi, Hannok, Pattama, Koide, Akiyoshi, Bradbury, Peter, Crossa, José, Palacios-Rojas, Natalia, Diepenbrock, Christine

“…Ridge Regression-Best Linear Unbiased Prediction, Elastic Net, and Reproducing Kernel Hilbert Spaces had high predictive abilities for all tested traits (beta-carotene, beta-cryptoxanthin, provitamin A, lutein, and zeaxanthin) and outperformed Least Absolute Shrinkage and Selection Operator. …”
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Multiple-trait analyses improved the accurary of genomic prediction and the power of genome-wide association of productivity and climate change-adaptive traits in lodgepole pine by Cappa, Eduardo Pablo, Chen, Charles, Klutsch, Jennifer G., Azcona, Jaime Sebastián, Ratcliffe, Blaise, Wei, Xiaojing, Da Ros, Letitia, Ullah, Aziz, Liu, Yang, Benowicz, Andy, Sadoway, Shane, Mansfield, Shawn D., Erbilgin, Nadir, Thomas, Barb R., El-Kassaby, Yousry A.

“…GP, ST and MT parametric and non-parametric (Reproducing Kernel Hilbert Spaces, RKHS) models were compared in terms of prediction accuracy (PA) and control of bias. …”
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