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pyaging/phenoagesaopaulo
phenoagesaopaulo is a machine learning model from pyaging. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for pyaging.
PhenoAge refit on NHANES III adults aged 20-84 with BioAge::phenoagecalc(), dropping creatinine, albumin, and alkaline phosphatase and keeping the remaining six biomarkers plus chronological age. The coefficients and…
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From the Hugging Face model README
PhenoAge refit on NHANES III adults aged 20-84 with BioAge::phenoage_calc(), dropping creatinine, albumin, and alkaline phosphatase and keeping the remaining six biomarkers plus chronological age. The coefficients and the mortality-to-age constants are refit, so they differ from the published PhenoAge and its constants do not apply. Biomarkers are on BioAge's SI-unit variants, which are natively pyaging's unit convention, and C-reactive protein is supplied raw in mg/dL and log1p-transformed inside the clock. The refit is pooled across sexes, so unlike kdmage and homeostaticdysregulation this clock takes no female column. The refit has no publication of its own: it was produced for pyaging 0.5.0 by clocks/notebooks/phenoagesaopaulo.ipynb with the BioAge toolkit, so the DOI, journal, last author and citation count are the toolkit's, and Levine 2018 is listed beside it as the method reference that defines PhenoAge.
Model weights retain the original authors' terms; the pyaging software license does not relicense them.
| Predicts | phenotypic age |
| Species | Homo sapiens |
| Tissue | blood |
| Data type | clinical biomarkers |
| Model type | Gompertz hazards regression with age calibration |
| Year | 2026 |
import pyaging as pya
pya.pred.predict_age(adata, ["phenoagesaopaulo"])
Browse every clock in the pyaging Clock Catalogue.
['Kwon, Dayoon, and Daniel W. Belsky. "A toolkit for quantification of biological age from blood chemistry and organ function test data: BioAge." GeroScience 43.6 (2021): 2795-2808.', 'Levine, M. E., et al. "An epigenetic biomarker of aging for lifespan and healthspan." Aging 10.4 (2018): 573-591.']