Cell Type‐Aware Multiple Instance Learning Improves Alzheimer’s Disease Prediction from snRNA‐seq
By applying a hierarchical MIL to Alzheimer's snRNA-seq data, we demonstrate enhanced predictive performance and interpretability over existing models. This approach not only improves classification of AD status but also facilitates the identification of cell types most associated with disease, offering insights that may support biomarker discovery and therapeutic development.
Authors
- Dongyun Kim
- Elissa J. Chesler (ORCID: https://orcid.org/0000-0002-5642-5062)
- Daniel M. Gatti (ORCID: https://orcid.org/0000-0003-0667-9926)
- S. J. Park
- AR Dunn
- Kyungwook Lee
- Doyeong Hwang (ORCID: https://orcid.org/0000-0002-1452-9023)
- Soonyoung Lee
- Soorin Yim
- Kiyoung Kim
- Kristen MS O'Connell
Institutions
- LG (United States) (US)
- Jackson Laboratory (US)
Publication Details
- Journal
- Alzheimer s & Dementia
- Published
- 2025-12-01
- DOI
- https://doi.org/10.1002/alz70861_108521
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 0.00