Melissa D. Lehti‐Shiu

This study examines how duplicated genes evolve spatial expression patterns after a genome triplication event in tomato, finding that ancestral expression states are often retained, with some experiencing expression gain or loss linked to fruit quality.

This study integrates multi-omic data with interpretable machine learning to identify genetic mechanisms underlying trait variation and phenotypic plasticity in switchgrass across environments.

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 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.

This study uses deep learning to accurately measure Arabidopsis fitness traits, revealing gene function effects previously undetected by traditional methods.