metadata
library_name: pyaging
tags:
- pyaging
- aging-clock
- biology
- dna-methylation
adbahadosingh
PyAging implements the paper’s conventional four-CpG logistic-regression equation and applies a sigmoid to return LOAD case probability; it does not implement the separate high-dimensional deep-learning classifiers also evaluated in the paper.
Model weights retain the original authors' terms; the pyaging software license does not relicense them.
| Predicts | late-onset Alzheimer's disease |
| Species | Homo sapiens |
| Tissue | whole blood |
| Data type | DNA methylation |
| Model type | logistic regression |
| Year | 2021 |
Use with pyaging
import pyaging as pya
pya.pred.predict_age(adata, ["adbahadosingh"])
Browse every clock in the pyaging Clock Catalogue.
Citation
Bahado-Singh, R. O., Vishweswaraiah, S., Aydas, B., et al. (2021). Artificial intelligence and leukocyte epigenomics: Evaluation and prediction of late-onset Alzheimer's disease. PLOS ONE, 16(4), e0248375.