Sync bohlin
Browse files- README.md +38 -0
- bohlin.pt +3 -0
- config.json +35 -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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# bohlin
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Official minimum-lambda variant of the Bohlin gestational-age LASSO: pyaging implements the 251-CpG lambda.min model and converts its day-scale output to weeks. The paper/package default one-standard-error variant uses 96 CpGs and has nearly identical predictive performance.
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| | |
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|---|---|
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| **Predicts** | gestational age |
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| **Species** | Homo sapiens |
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| **Tissue** | cord blood |
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| **Data type** | DNA methylation |
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| **Model type** | LASSO regression |
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| **Year** | 2016 |
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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, ["bohlin"])
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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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Bohlin, J., Håberg, S. E., Magnus, P. et al. Prediction of gestational age based on genome-wide differentially methylated regions. Genome Biology 17, 207 (2016).
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https://doi.org/10.1186/s13059-016-1063-4
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bohlin.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:769d600b7294ac7030df139f8aa3b0d444b9eb2dc839cb1f8bd37095acdc9a19
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size 11493
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config.json
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{
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"approved_by_author": "\u231b",
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"citation": "Bohlin, J., H\u00e5berg, S. E., Magnus, P. et al. Prediction of gestational age based on genome-wide differentially methylated regions. Genome Biology 17, 207 (2016).",
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"citations": 237,
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"citations_date": "2026-07-05",
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"clock_name": "bohlin",
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"data_type": "DNA methylation",
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"doi": "https://doi.org/10.1186/s13059-016-1063-4",
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"journal": "Genome Biology",
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"last_author": "Wenche Nystad",
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"model_type": "LASSO regression",
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"n_features": 251,
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"notes": "Official minimum-lambda variant of the Bohlin gestational-age LASSO: pyaging implements the 251-CpG lambda.min model and converts its day-scale output to weeks. The paper/package default one-standard-error variant uses 96 CpGs and has nearly identical predictive performance.",
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"platform": [
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"Illumina 450K"
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],
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"population": "newborns",
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"postprocess": "days_to_weeks",
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"predicts": [
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"gestational 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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"cord blood"
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],
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"training_target": [
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"gestational age"
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],
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"unit": [
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"weeks"
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],
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"version": "v0.3.0",
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"year": 2016
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}
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