| --- |
| license: mit |
| library_name: pyaging |
| tags: |
| - pyaging |
| - aging-clock |
| - biology |
| - dna-methylation |
| --- |
| |
| # yingcausage |
|
|
| Causality-enriched chronological-age clock using EWMR-prioritized CpGs and feature-specific penalties derived from causality scores. |
|
|
| | | | |
| |---|---| |
| | **Predicts** | chronological age | |
| | **Species** | Homo sapiens | |
| | **Tissue** | whole blood | |
| | **Data type** | DNA methylation | |
| | **Model type** | causality-weighted elastic net regression | |
| | **Year** | 2024 | |
|
|
| ## Use with pyaging |
|
|
| ```python |
| import pyaging as pya |
| |
| pya.pred.predict_age(adata, ["yingcausage"]) |
| ``` |
|
|
| Browse every clock in the [pyaging Clock Catalogue](https://pyaging.readthedocs.io). |
|
|
| ## Citation |
|
|
| Ying, K., Liu, H., Tarkhov, A.E. et al. Causality-enriched epigenetic age uncouples damage and adaptation. Nature Aging 4, 231–246 (2024). |
|
|
| https://doi.org/10.1038/s43587-023-00557-0 |
|
|
