Sync ensembleagestatic
Browse files- README.md +38 -0
- config.json +35 -0
- ensembleagestatic.pt +3 -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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# ensembleagestatic
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Single elastic-net static predictor of the median EnsembleAge.Dynamic calibrated age, trained on perturbed MethylGauge mice.
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|---|---|
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| **Predicts** | intervention-responsive epigenetic age |
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| **Species** | Mus musculus |
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| **Tissue** | multi-tissue |
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| **Data type** | DNA methylation |
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| **Model type** | elastic net regression |
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| **Year** | 2025 |
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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, ["ensembleagestatic"])
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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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Haghani, A. et al. EnsembleAge: enhancing epigenetic age assessment with a multi-clock framework. GeroScience 48, 2873-2886 (2025).
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https://doi.org/10.1007/s11357-025-01808-1
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config.json
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{
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"approved_by_author": "\u231b",
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"citation": "Haghani, A. et al. EnsembleAge: enhancing epigenetic age assessment with a multi-clock framework. GeroScience 48, 2873-2886 (2025).",
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"citations": 3,
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"citations_date": "2026-07-05",
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"clock_name": "ensembleagestatic",
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"data_type": "DNA methylation",
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"doi": "https://doi.org/10.1007/s11357-025-01808-1",
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"journal": "GeroScience",
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"last_author": "Steve Horvath",
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"model_type": "elastic net regression",
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"n_features": 288,
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"notes": "Single elastic-net static predictor of the median EnsembleAge.Dynamic calibrated age, trained on perturbed MethylGauge mice.",
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"platform": [
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"Horvath MammalMethylChip40",
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"Horvath MammalMethylChip320"
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],
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"population": "mice",
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"predicts": [
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"intervention-responsive epigenetic age"
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],
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"research_only": null,
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"species": "Mus musculus",
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"tissue": [
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"multi-tissue"
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],
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"training_target": [
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"intervention-responsive epigenetic age"
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],
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"unit": [
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"years"
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],
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"version": "v0.3.0",
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"year": 2025
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}
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ensembleagestatic.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:61f314e40caded4b249a3e5d320d9df75c935d97f4558aa3ce283168f33b1556
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size 12389
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