Sync cellpopage
Browse files- README.md +38 -0
- cellpopage.pt +3 -0
- config.json +34 -0
README.md
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---
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license: bsd-3-clause
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library_name: pyaging
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tags:
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- pyaging
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- aging-clock
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- biology
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- dna-methylation
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---
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# cellpopage
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CellPopAge is an elastic-net DNA-methylation clock using 42 selected CpGs to predict passage-based age of serially cultured adult primary human fibroblast populations and screen compounds that decelerate this measure.
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|---|---|
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| **Predicts** | cell-population passage age |
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| **Species** | Homo sapiens |
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| **Tissue** | cultured fibroblasts |
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| **Data type** | DNA methylation |
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| **Model type** | elastic net regression |
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| **Year** | 2024 |
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## Use with pyaging
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```python
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import pyaging as pya
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pya.pred.predict_age(adata, ["cellpopage"])
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```
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Browse every clock in the [pyaging Clock Catalogue](https://pyaging.readthedocs.io).
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## Citation
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Lujan, Celia, et al. "An expedited screening platform for the discovery of anti-ageing compounds in vitro and in vivo." Genome Medicine 16 (2024): 85.
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https://doi.org/10.1186/s13073-024-01349-w
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cellpopage.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:0ef38f53a061e3a649fd6a2f51ebeae8253674f7196da26805ad70931d3cb9bc
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size 75621
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config.json
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{
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"approved_by_author": "\u231b",
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"citation": "Lujan, Celia, et al. \"An expedited screening platform for the discovery of anti-ageing compounds in vitro and in vivo.\" Genome Medicine 16 (2024): 85.",
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"citations": 6,
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"citations_date": "2026-07-05",
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"clock_name": "cellpopage",
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"data_type": "DNA methylation",
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"doi": "https://doi.org/10.1186/s13073-024-01349-w",
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"journal": "Genome Medicine",
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"last_author": "Ivana Bjedov",
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"model_type": "elastic net regression",
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"n_features": 42,
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"notes": "CellPopAge is an elastic-net DNA-methylation clock using 42 selected CpGs to predict passage-based age of serially cultured adult primary human fibroblast populations and screen compounds that decelerate this measure.",
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"platform": [
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"Illumina EPIC"
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],
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"population": "human cell cultures",
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"predicts": [
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"cell-population passage age"
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],
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"research_only": null,
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"species": "Homo sapiens",
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"tissue": [
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"cultured fibroblasts"
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],
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"training_target": [
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"cell passage number"
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],
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"unit": [
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"passages"
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],
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"version": "v0.3.0",
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"year": 2024
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}
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