| library_name: pyaging | |
| tags: | |
| - pyaging | |
| - aging-clock | |
| - biology | |
| - dna-methylation | |
| # adbahadosingh | |
| PyAging implements the paper’s conventional four-CpG logistic-regression equation and applies a sigmoid to return LOAD case probability; it does not implement the separate high-dimensional deep-learning classifiers also evaluated in the paper. | |
| Model weights retain the original authors' terms; the pyaging software license does not relicense them. | |
| | | | | |
| |---|---| | |
| | **Predicts** | late-onset Alzheimer's disease | | |
| | **Species** | Homo sapiens | | |
| | **Tissue** | whole blood | | |
| | **Data type** | DNA methylation | | |
| | **Model type** | logistic regression | | |
| | **Year** | 2021 | | |
| ## Use with pyaging | |
| ```python | |
| import pyaging as pya | |
| pya.pred.predict_age(adata, ["adbahadosingh"]) | |
| ``` | |
| Browse every clock in the [pyaging Clock Catalogue](https://pyaging.readthedocs.io). | |
| ## Citation | |
| Bahado-Singh, R. O., Vishweswaraiah, S., Aydas, B., et al. (2021). Artificial intelligence and leukocyte epigenomics: Evaluation and prediction of late-onset Alzheimer's disease. PLOS ONE, 16(4), e0248375. | |
| https://doi.org/10.1371/journal.pone.0248375 | |