Kenia Segura Abá

The paper uses machine learning to predict yeast fitness across 35 environments using genetic variants, finding that copy number variants explain most fitness variation and that gene contributions depend on isolate and environment.

This study uses machine learning to identify gene families associated with oxidative stress resistance in yeasts and validates their roles experimentally.

This study evaluates factors affecting genomic prediction accuracy in switchgrass, finding that exome capture and multi-allelic variants improve predictions for complex traits.

The paper reports on a competition to predict maize yield using genetic and environmental data, showing that diverse modeling approaches can achieve good results.

This study integrates multi-omics data to predict complex traits in Arabidopsis, showing that transcriptome- and methylome-based models perform comparably to genome-based models and reveal novel gene interactions.