Speaker
Description
Digital twins are emerging as a powerful paradigm for modeling complex biological systems by integrating data-driven and mechanistic approaches. In this talk, we present recent developments in constructing machine learning-enabled digital twins for Alzheimer’s disease (AD), a highly heterogeneous neurodegenerative disorder characterized by diverse biomarker trajectories, progression rates, and treatment responses.
We develop a unified framework that combines mechanistic mathematical modeling with data-driven learning to build patient-specific digital twins capable of simulating disease progression and predicting individualized outcomes. This approach enables the integration of multimodal clinical and biomarker data, providing a more comprehensive representation of disease dynamics.
We demonstrate how these digital twins can be used to explore the underlying biomarker cascade, quantify patient variability, and evaluate personalized therapeutic strategies in silico. This work highlights the potential of digital twin technologies to advance precision medicine in AD and reduce the cost and time associated with clinical trials.
