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encen40 – AI Model by pyaging | AlphaNeural AI
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pyaging
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encen40
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pyaging
elastic net regression
aging-clock
biology
dna-methylation
mit
us
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encen40
Elastic-net DNAm-age clock trained in 7,039 people aged 40–115, including centenarians, to reduce extreme-old-age underestimation.
Predicts
chronological age
Species
Homo sapiens
Tissue
whole blood, saliva, buccal epithelium
Data type
DNA methylation
Model type
elastic net regression
Year
2023
Use with pyaging
python
1
import
pyaging
as
pya
2
3
pya
.
pred
.
predict_age
(
adata
,
[
"encen40"
]
)
Browse every clock in the
pyaging Clock Catalogue
.
Citation
Dec, Eric, et al. "Centenarian clocks: epigenetic clocks for validating claims of exceptional longevity." GeroScience 45 (2023): 1817–1835.
https://doi.org/10.1007/s11357-023-00731-7
