| library_name: pyaging | |
| tags: | |
| - pyaging | |
| - aging-clock | |
| - biology | |
| - dna-methylation | |
| # encen40 | |
| Elastic-net DNAm-age clock trained in 7,039 people aged 40–115, including centenarians, to reduce extreme-old-age underestimation. | |
| Model weights retain the original authors' terms; the pyaging software license does not relicense them. | |
| | | | | |
| |---|---| | |
| | **Predicts** | chronological age | | |
| | **Species** | Homo sapiens | | |
| | **Tissue** | whole blood, saliva, buccal epithelium | | |
| | **Data type** | DNA methylation | | |
| | **Model type** | elastic net regression | | |
| | **Year** | 2023 | | |
| ## Use with pyaging | |
| ```python | |
| import pyaging as pya | |
| pya.pred.predict_age(adata, ["encen40"]) | |
| ``` | |
| Browse every clock in the [pyaging Clock Catalogue](https://pyaging.readthedocs.io). | |
| ## Citation | |
| Dec, Eric, et al. "Centenarian clocks: epigenetic clocks for validating claims of exceptional longevity." GeroScience 45 (2023): 1817–1835. | |
| https://doi.org/10.1007/s11357-023-00731-7 | |