Precision Medicine Prediction Model for Alzheimer’s Disease Using Cooperative Learning Approaches for Multi-Omic Data


Using cooperative learning/artificial intelligence (AI) approaches, we will develop risk-prediction tools that can integrate different types of omic data to foster a better biological understanding of the genetic and genomic features of Alzheimer’s disease (AD). The approach also will provide patients with highly personalized predictions for their age-specific AD risk and forecasts for age of onset of AD. As the approach will be able to fully integrate all the different data sources, e.g., genetic and genomic, of the Cure Alzheimer’s Fund Alzheimer’s Genome Project™ (AGP), we will be able to take full advantage of all the investments Cure Alzheimer’s Fund has made into this project over the years and provide patients with personalized risk assessment/prediction for onset of AD, based on their genetic and genomic profiles. This will enable personalized medicine approaches for AD, allowing patients to understand their specific risk profile/trajectory and potential intervention that may bring changes to their disease trajectory.

Funding to Date



Biomarkers/Diagnostics/Studies of Risk & Resilience, Foundational


Christoph Lange, Ph.D.

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