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Supplementary Tables
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modified on 2020-10-03, 15:04 Supplementary Table S1: Bayesian Regularized Neural Network (BRNN) architectures tested in this study for genomic prediction of complex traits in maize and eucalypt.
Supplementary Table S2: Deep Learning architectures tested in this study for genomic prediction of complex traits in maize and eucalypt.
Supplementary Table S3: Computational time required by the DL architectures in the genomic prediction of complex traits in maize and eucalypt. The time corresponds to the average of 50 cycles of cross-validation.
Supplementary Table S4: Computational time required by the BRNN architectures in the genomic prediction of complex traits in maize and eucalypt. The time corresponds to the average of 50 cycles of cross-validation.
Supplementary Table S5: Computational time required by genomic prediction methods used in this study for the eucalypt traits. The time corresponds to the average of 50 cycles of cross-validation.
Supplementary Table S6: Computational time required by genomic prediction methods used in this study for the maize traits. The time corresponds to the average of 50 cycles of cross-validation.