| library_name: pyaging | |
| tags: | |
| - pyaging | |
| - aging-clock | |
| - biology | |
| - dna-methylation | |
| # cpgptgrimage3 | |
| CpGPTGrimAge3 implementation combining chronological age, GrimAge2 DNAm proxies, and CpGPT-predicted plasma-protein proxies in a Cox linear predictor that is calibrated to years. Supply derived methylation protein/lifestyle proxies and age, not a raw CpG matrix or measured proteomics. CpGPT protein proxies come from the proteins checkpoint on its trained standardized scale; required proxies must be present. | |
| Model weights retain the original authors' terms; the pyaging software license does not relicense them. These weights are restricted to research use under the authors' terms. | |
| | | | | |
| |---|---| | |
| | **Predicts** | biological age, mortality risk | | |
| | **Species** | Homo sapiens | | |
| | **Tissue** | whole blood | | |
| | **Data type** | DNA methylation | | |
| | **Model type** | Cox proportional hazards regression | | |
| | **Year** | 2024 | | |
| ## Use with pyaging | |
| ```python | |
| import pyaging as pya | |
| pya.pred.predict_age(adata, ["cpgptgrimage3"]) | |
| ``` | |
| Browse every clock in the [pyaging Clock Catalogue](https://pyaging.readthedocs.io). | |
| ## Citation | |
| de Lima Camillo, L. P., Sehgal, R., Armstrong, J., et al. CpGPT: a foundation model for DNA methylation. bioRxiv 2024.10.24.619766 (2024). | |
| https://doi.org/10.1101/2024.10.24.619766 | |