Mateus Figueirêdo Santos

This study uses random regression models to analyze longitudinal dry matter yield data from perennial forage species, identifying genotype adaptability and stability through reaction norms.

This paper develops a joint learning approach to improve genomic prediction in polyploid grasses, achieving over 50% accuracy improvements over traditional methods.

This paper introduces an automated SNP-based method to identify contaminants in polyploid plant progenies using PCA, Mendelian segregation analysis, and clustering, validated on simulated and real data.