| library_name: pyaging | |
| tags: | |
| - pyaging | |
| - aging-clock | |
| - biology | |
| - dna-methylation | |
| # zhangmortality | |
| Ten-CpG whole-blood mortality risk score. Pyaging implements the paper supplement's continuous LASSO-weighted score exactly (the sum of ten raw beta values multiplied by their published coefficients). The same study also defines a separate simplified 0-10 aberrant-methylation count based on cohort-specific quartile cutoffs. | |
| Model weights retain the original authors' terms; the pyaging software license does not relicense them. | |
| | | | | |
| |---|---| | |
| | **Predicts** | mortality risk | | |
| | **Species** | Homo sapiens | | |
| | **Tissue** | whole blood | | |
| | **Data type** | DNA methylation | | |
| | **Model type** | weighted linear score | | |
| | **Year** | 2017 | | |
| ## Use with pyaging | |
| ```python | |
| import pyaging as pya | |
| pya.pred.predict_age(adata, ["zhangmortality"]) | |
| ``` | |
| Browse every clock in the [pyaging Clock Catalogue](https://pyaging.readthedocs.io). | |
| ## Citation | |
| Zhang, Y., Wilson, R., Heiss, J. et al. DNA methylation signatures in peripheral blood strongly predict all-cause mortality. Nature Communications 8, 14617 (2017). | |
| https://doi.org/10.1038/ncomms14617 | |