Paulo Izquierdo

Multi-omic integration using interpretable machine learning reveals genetic mechanisms of trait variation and phenotypic plasticity in switchgrass

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

Predictive models of the genetic bases underlying budding yeast fitness in multiple 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.