| library_name: pyaging | |
| tags: | |
| - pyaging | |
| - aging-clock | |
| - biology | |
| - dna-methylation | |
| # altumage | |
| Pan-tissue chronological-age predictor using a five-hidden-layer neural network and 20,318 CpGs shared across the 27K, 450K and EPIC manifests; the actual training data came from 27K and 450K datasets. | |
| Model weights retain the original authors' terms; the pyaging software license does not relicense them. | |
| | | | | |
| |---|---| | |
| | **Predicts** | chronological age | | |
| | **Species** | Homo sapiens | | |
| | **Tissue** | multi-tissue | | |
| | **Data type** | DNA methylation | | |
| | **Model type** | deep neural network | | |
| | **Year** | 2022 | | |
| ## Use with pyaging | |
| ```python | |
| import pyaging as pya | |
| pya.pred.predict_age(adata, ["altumage"]) | |
| ``` | |
| Browse every clock in the [pyaging Clock Catalogue](https://pyaging.readthedocs.io). | |
| ## Citation | |
| de Lima Camillo, L.P., Lapierre, L.R. & Singh, R. A pan-tissue DNA-methylation epigenetic clock based on deep learning. npj Aging 8, 4 (2022). | |
| https://doi.org/10.1038/s41514-022-00085-y | |