WEBINAR—Neural Network-inspired Models for Understanding Metabolism-behavior Interactions

Using indirect calorimetry and time-series data—including energy expenditure, blood glucose, food intake and locomotion—as a modeling approach captures dynamic temporal relationships among metabolic variables over time, offering a more precise view of metabolic phenotypes. Learn about a novel time-lagged linear mixed model pipeline that moves beyond traditional metrics toward precise, temporarily resolved characterization of metabolic phenotypes.