Machine‐learning derived proxy phenotypes for Alzheimer’s disease genome‐wide association study by proxy
Preliminary analysis of ML-derived proxy phenotypes of AD indicates may be a promising approach to improve AD genomic studies in middle age cohorts. Further analysis of the results and potential biases of this approach are needed to fully characterise this method.
Authors
- Martin Saint‐Jalmes (ORCID: https://orcid.org/0000-0001-9012-3626)
- Colin L. Masters (ORCID: https://orcid.org/0000-0003-3072-7940)
- Simon M. Laws (ORCID: https://orcid.org/0000-0002-4355-7082)
- Benjamin Goudey (ORCID: https://orcid.org/0000-0002-2318-985X)
- Shu Liu (ORCID: https://orcid.org/0000-0002-9576-9335)
Institutions
- Edith Cowan University (AU)
- The University of Melbourne (AU)
- Curtin University (AU)
- Florey Institute of Neuroscience and Mental Health (AU)
Publication Details
- Journal
- Alzheimer s & Dementia
- Published
- 2024-12-01
- DOI
- https://doi.org/10.1002/alz.093165
- Primary Topic
- Genetic Associations and Epidemiology
- Type
- article
- Field-Weighted Citation Impact
- 0.00