Marcos Guimarães de Andrade Landell

This study uses multiomic approaches to identify genetic variants and genes associated with sucrose accumulation in sugarcane, providing insights for breeding and biotechnology.

This study predicts sugarcane photosynthesis using environmental inputs, finding strong correlations between measured variables and modeled CO2 uptake.

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

This study profiles the kinomes of sugarcane and sorghum, identifying PKs and their evolutionary relationships, and provides expression patterns and coexpression networks.

The paper uses machine learning to identify genomic regions associated with sugarcane brown rust resistance by analyzing SNP data from full-sib progeny.