Download config.json from pyaging/pcgrimagegdf15: direct link, hf CLI and curl.
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https://huggingface.co/pyaging/pcgrimagegdf15/resolve/main/config.json
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hf download hf://pyaging/pcgrimagegdf15/config.json
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curl -L -o config.json https://huggingface.co/pyaging/pcgrimagegdf15/resolve/main/config.json
1.31 kB
| { | |
| "approved_by_author": "\u231b", | |
| "citation": "Higgins-Chen, Albert T., et al. \"A computational solution for bolstering reliability of epigenetic clocks: implications for clinical trials and longitudinal tracking.\" Nature Aging 2 (2022): 644\u2013661.", | |
| "citations": 546, | |
| "citations_date": "2026-10-02", | |
| "clock_name": "pcgrimagegdf15", | |
| "data_type": "DNA methylation", | |
| "doi": "https://doi.org/10.1038/s43587-022-00248-2", | |
| "journal": "Nature Aging", | |
| "last_author": "Morgan E. Levine", | |
| "model_type": "PCA + elastic net regression", | |
| "n_features": 78465, | |
| "notes": "Standalone principal-component proxy trained to reproduce the original DNAm GDF15 score. Coefficients are composed exactly from the author PCA rotation and proxy regression. CpGs use the author GSE40279 reference. Sex (female=1, male=0) and chronological age must be supplied when selected by the original component regression; missing demographic inputs are rejected. Selected demographic inputs: age.", | |
| "platform": [ | |
| "Illumina 450K" | |
| ], | |
| "population": "adults", | |
| "predicts": [ | |
| "GDF-15" | |
| ], | |
| "reference_values": true, | |
| "research_only": null, | |
| "species": "Homo sapiens", | |
| "tissue": [ | |
| "whole blood" | |
| ], | |
| "training_target": [ | |
| "DNAm GDF15 output" | |
| ], | |
| "unit": [ | |
| "picograms per milliliter" | |
| ], | |
| "version": "0.5.4", | |
| "year": 2022 | |
| } |