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"approved_by_author": "\u2705",
"citation": "de Lima Camillo, L. P., Sehgal, R., Armstrong, J., et al. CpGPT: a foundation model for DNA methylation. bioRxiv 2024.10.24.619766 (2024).",
"citations": 35,
"citations_date": "2026-10-02",
"clock_name": "cpgptpcgrimage3",
"data_type": "DNA methylation",
"doi": "https://doi.org/10.1101/2024.10.24.619766",
"journal": "bioRxiv",
"last_author": "Bo Wang",
"model_type": "PCA + Cox regression",
"n_features": 31,
"notes": "Principal-component CpGPTGrimAge3 implementation combining chronological age, GrimAge2 DNAm proxies, and CpGPT-predicted plasma-protein proxies; 30 proxy inputs are projected to 29 PCs, entered with age into a Cox linear predictor, and calibrated to years. Supply derived methylation protein/lifestyle proxies and age, not a raw CpG matrix or measured proteomics. CpGPT protein proxies come from the proteins checkpoint on its trained standardized scale; required proxies must be present.",
"platform": [
"Illumina 450K"
],
"population": "adults",
"postprocess": "cox_to_years",
"predicts": [
"biological age",
"mortality risk"
],
"preprocess": "scale",
"research_only": true,
"species": "Homo sapiens",
"tissue": [
"whole blood"
],
"training_target": [
"mortality"
],
"unit": [
"years"
],
"version": "0.5.7",
"year": 2024
} |