Application of supervised and unsupervised machine learning approaches to identify somatic T‐cell receptor sequence variation in Alzheimer disease among Midwestern Amish
Decreased TCR diversity is marginally associated with having LOAD or MCI. Our ongoing work focuses on applying ML approaches to TCR CD3 amino acid sequences to identify disease-associated clonotypes and develop predictive models for LOAD diagnosis.
Authors
- Kristy Miskimen
- Lauren A. Cruz
- Dana C. Crawford (ORCID: https://orcid.org/0000-0002-6437-6248)
- Sherri D. Hochstetler
- Jacob L. McCauley (ORCID: https://orcid.org/0000-0002-1805-1733)
- Reneé A. Laux
- Yeunjoo E. Song (ORCID: https://orcid.org/0000-0002-7452-3731)
- Ioanna Konidari (ORCID: https://orcid.org/0000-0003-1531-8559)
- William K. Scott (ORCID: https://orcid.org/0000-0001-9336-6404)
- Sarada L. Fuzzell
- Audrey Lynn
- Margaret Pericak‐Vance
- Alan J. Lerner
- Jonathan L Haines
- Shiying Liu
Institutions
- University of Miami (US)
- University Memory and Aging Center (US)
- Dr. John T. Macdonald Foundation (US)
- Case Western Reserve University (US)
Publication Details
- Journal
- Alzheimer s & Dementia
- Published
- 2025-12-01
- DOI
- https://doi.org/10.1002/alz70855_102239
- Primary Topic
- Neuroinflammation and Neurodegeneration Mechanisms
- Type
- article
- Field-Weighted Citation Impact
- 0.00