| library_name: pyaging | |
| tags: | |
| - pyaging | |
| - aging-clock | |
| - biology | |
| - dna-methylation | |
| # zhangblup | |
| High-dimensional chronological-age predictor using best linear unbiased prediction across the full quality-controlled set of 319,607 methylation probes. | |
| 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 | | |
| | **Data type** | DNA methylation | | |
| | **Model type** | best linear unbiased prediction | | |
| | **Year** | 2019 | | |
| ## Use with pyaging | |
| ```python | |
| import pyaging as pya | |
| pya.pred.predict_age(adata, ["zhangblup"]) | |
| ``` | |
| Browse every clock in the [pyaging Clock Catalogue](https://pyaging.readthedocs.io). | |
| ## Citation | |
| Zhang, Q., Vallerga, C.L., Walker, R.M. et al. Improved precision of epigenetic clock estimates across tissues and its implication for biological ageing. Genome Medicine 11, 54 (2019). | |
| https://doi.org/10.1186/s13073-019-0667-1 | |