Enhancing Alzheimer Disease risk prediction using machine learning for Pathway Polygenic Risk Scores (p‐PRS)
Utilizing the innovative p-PRS approach has improved the estimation of AD risk in the PR cohort. The application of interpretable machine learning approaches allows identifying the most relevant pathways for effective risk prediction and classification. Importantly, improved precision will provide more effective actionable risk mitigation strategies, optimize the selection process for clinical trials, and contribute to the development of more personalized treatment interventions.
Authors
- Gary W. Beecham (ORCID: https://orcid.org/0000-0002-7061-6332)
- Farid Rajabli (ORCID: https://orcid.org/0000-0001-5229-7254)
- Patrice L. Whitehead
- Michael L. Cuccaro (ORCID: https://orcid.org/0000-0003-4769-0785)
- Jeffery M. Vance (ORCID: https://orcid.org/0000-0003-3815-8199)
- Kara L. Hamilton‐Nelson (ORCID: https://orcid.org/0000-0003-3748-7193)
- Katrina Celis
- Vanessa Aguiar‐Pulido (ORCID: https://orcid.org/0000-0001-5248-6944)
- Briseida E. Feliciano‐Astacio (ORCID: https://orcid.org/0000-0003-4747-8928)
- Michael B. Prough
- Kyle Scott
- Heriberto Acosta
- Pedro Mena (ORCID: https://orcid.org/0000-0003-2150-2977)
- Jóse Javier Sánchez (ORCID: https://orcid.org/0000-0002-6638-177X)
- Larry D. Adams
- Katalina McInerney
- Margaret A Pericak‐Vance
Institutions
- University of Miami (US)
- Central University of the Caribbean (PR)
- Dr. John T. Macdonald Foundation (US)
- Center for Advanced Studies on Puerto Rico and the Caribbean (PR)
Publication Details
- Journal
- Alzheimer s & Dementia
- Published
- 2024-12-01
- DOI
- https://doi.org/10.1002/alz.091886
- Primary Topic
- Genetic Associations and Epidemiology
- Type
- article
- Field-Weighted Citation Impact
- 0.00