Vanni Bucci

The paper analyzes gut microbiome communities in Alzheimer's disease, finding an altered balance between Phocaeicola vulgatus and Bacteroides fragilis.

This paper introduces DySCoMeMo, a framework that combines dynamical systems and metabolic models to forecast microbiome-host dynamics from ecological data, achieving accurate predictions without requiring direct metabolite measurements.

The paper links gut microbiome functional gene pathways to cognitive performance in older adults with Alzheimer's disease, identifying specific metabolic pathways associated with cognitive decline.

MDITRE predicts host status from microbiome time-series data using interpretable deep learning, outperforming existing methods in speed and accuracy.

The paper introduces a scalable, differentiable model for learning interpretable rules from microbiome time-series data to classify host status, achieving comparable performance to a Bayesian method with faster runtime and more rules.