| library_name: pyaging | |
| tags: | |
| - pyaging | |
| - aging-clock | |
| - biology | |
| - dna-methylation | |
| # epitoc2 | |
| Dynamic methylation-transmission model returning total cumulative stem-cell divisions per stem cell; an intrinsic rate additionally requires chronological age but is not this implementation's returned value. | |
| Model weights retain the original authors' terms; the pyaging software license does not relicense them. | |
| | | | | |
| |---|---| | |
| | **Predicts** | mitotic age | | |
| | **Species** | Homo sapiens | | |
| | **Tissue** | whole blood | | |
| | **Data type** | DNA methylation | | |
| | **Model type** | dynamic methylation transmission model | | |
| | **Year** | 2020 | | |
| ## Use with pyaging | |
| ```python | |
| import pyaging as pya | |
| pya.pred.predict_age(adata, ["epitoc2"]) | |
| ``` | |
| Browse every clock in the [pyaging Clock Catalogue](https://pyaging.readthedocs.io). | |
| ## Citation | |
| Teschendorff, Andrew E. "A comparison of epigenetic mitotic-like clocks for cancer risk prediction." Genome Medicine 12 (2020): 56. | |
| https://doi.org/10.1186/s13073-020-00752-3 | |