Moysés Nascimento

This study evaluates MTME genomic prediction models for Miscanthus sacchariflorus, finding they improve prediction for some traits but not all, depending on the cross-validation scenario.

This study evaluates sparse testing designs for genomic prediction in Miscanthus breeding to reduce phenotyping costs while maintaining predictive accuracy.

This study optimizes genomic prediction in Miscanthus sacchariflorus by incorporating weather data to improve model performance across different locations.

The paper evaluates cross-validation schemes for genomic selection in perennial Miscanthus species, showing improved predictive ability by incorporating genotype-by-environment interactions.

This study applies genomic prediction for Euterpe edulis to improve breeding programs, finding that single-trait models provide greater selection gains than multi-trait models.

This study evaluates methods for genomic prediction using categorical data in breeding programs, finding that mixed models and Bayesian approaches perform well despite normality violations.

The paper evaluates using radial basis function neural networks (RBFNN) with marker subset selection for genome-enabled prediction of genetic values, showing improved accuracy and reduced error compared to RR-BLUP.