Applying Machine-Learning to Human Gastrointestinal Microbial Species to Predict Dietary Intake (P20-040-19)

To better understand host-microbe interactions, a more computationally intensive, multivariate, machine learning approach must be utilized. Accordingly, we aimed to identify biomarkers with high predictive accuracy for dietary intake. Data were aggregated from five randomized, controlled, feeding studies in adults (n = 199) that provided avocados, almonds, broccoli, walnuts, or whole grain oats and whole grain barley. Fecal samples were...