---
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

```python
import pyaging as pya

pya.pred.predict_age(adata, ["adbahadosingh"])
```

Browse every clock in the [pyaging Clock Catalogue](https://pyaging.readthedocs.io).

## 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.

https://doi.org/10.1371/journal.pone.0248375