Jeffrey R. Petrella

This study assesses identifiability in digital twins for Alzheimer's Disease to evaluate model validity despite data sparsity and variability.

This paper evaluates the polygenic risk score for Alzheimer's disease in Japanese individuals, showing its utility despite genetic differences from European populations.

This paper develops a blood-based machine learning model to predict cerebral spinal fluid Aβ1-42 levels, showing high accuracy in identifying Alzheimer's disease risk.

The paper develops a computational causal model of the dynamic biomarker cascade in Alzheimer’s disease to simulate biomarker progression and therapy response.