Felipe Roberto Francisco

This study evaluates how marker density and minor allele frequency affect genomic selection accuracy in grapevine using machine learning models and conventional methods.

This study performs a GWAS on a diverse grapevine collection to identify genetic determinants of berry size and cluster architecture traits, finding significant SNPs and candidate genes.

The paper identifies SNPs and genes associated with stem volume in loblolly pine using GWAS and machine learning, revealing regulatory mechanisms and candidate genes for growth traits.

This paper develops a machine learning approach for predicting rubber tree stem circumference using molecular markers, achieving higher accuracy than traditional models in a single-environment scenario.

The paper identifies gene expression patterns and regulatory mechanisms in cold-resistant rubber tree clones using RNA-seq and gene coexpression network analysis.

This study compares genomic selection models for rubber trees, finding that multi-environment models with nonlinear and linear kernels improve prediction accuracy and genetic gain compared to single-environment models.