Evolutionary Theory and Machine Learning: New Inroads Into Sex‐specific AD Susceptibility Genes and Pathways Significantly Improve Risk Prediction
A new statistical physics approach discovered male and female AD genes, predicting AD risk with very high accuracy. These results identify further genetic differences leading to AD in males and females, and show the power of quantitative phylogenetics to probe complex human diseases.
Authors
- Kevin Wilhelm (ORCID: https://orcid.org/0000-0003-2686-0976)
- Juan Botas (ORCID: https://orcid.org/0000-0001-5476-5955)
- Ismael Al‐Ramahi (ORCID: https://orcid.org/0000-0002-1275-1114)
- Maryam Samieinasab (ORCID: https://orcid.org/0000-0001-6524-2359)
- Olivier Lichtarge (ORCID: https://orcid.org/0000-0003-4057-7122)
- Panagiotis Katsonis (ORCID: https://orcid.org/0000-0002-7172-1644)
- Kwanghyuk Lee (ORCID: https://orcid.org/0000-0001-7781-9696)
- Yashwanth Lagiesetty
- Chen Wang (ORCID: https://orcid.org/0000-0001-5769-2077)
- Fatemeh Nazem
- Jenn Asmussen
Institutions
- Baylor College of Medicine (US)
- Neurological Research Institute (US)
- Texas Children's Hospital (US)
- The University of Texas Health Science Center at Houston (US)
Publication Details
- Journal
- Alzheimer s & Dementia
- Published
- 2024-12-01
- DOI
- https://doi.org/10.1002/alz.087889
- Primary Topic
- CRISPR and Genetic Engineering
- Type
- article
- Field-Weighted Citation Impact
- 0.00