Sync stoch
Browse files- README.md +38 -0
- config.json +34 -0
- stoch.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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# stoch
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Stochastic chronological-age clock built from simulated methylation trajectories at the Horvath clock CpGs; it is a stochastic counterpart, not the original Horvath clock.
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|---|---|
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| **Predicts** | chronological age |
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| **Species** | Homo sapiens |
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| **Tissue** | sorted monocytes |
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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, ["stoch"])
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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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Tong, Huige, et al. "Quantifying the stochastic component of epigenetic aging." Nature Aging 4 (2024): 886–901.
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https://doi.org/10.1038/s43587-024-00600-8
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config.json
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{
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"approved_by_author": "\u2705",
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"citation": "Tong, Huige, et al. \"Quantifying the stochastic component of epigenetic aging.\" Nature Aging 4 (2024): 886\u2013901.",
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"citations": 66,
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"citations_date": "2026-07-05",
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"clock_name": "stoch",
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"data_type": "DNA methylation",
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"doi": "https://doi.org/10.1038/s43587-024-00600-8",
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"journal": "Nature Aging",
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"last_author": "Andrew E. Teschendorff",
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"model_type": "elastic net regression",
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"n_features": 353,
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"notes": "Stochastic chronological-age clock built from simulated methylation trajectories at the Horvath clock CpGs; it is a stochastic counterpart, not the original Horvath clock.",
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"platform": [
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"Illumina 450K"
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],
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"population": "adults",
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"predicts": [
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"chronological 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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"sorted monocytes"
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],
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"training_target": [
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"chronological 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": 2024
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}
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stoch.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:fdd9315b785bbb89a4be11aaf26894047be9208b7c2ccf711100dcb00a7e1c86
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size 14245
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