{
 "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": "cpgptgrimage3",
 "data_type": "DNA methylation",
 "doi": "https://doi.org/10.1101/2024.10.24.619766",
 "journal": "bioRxiv",
 "last_author": "Bo Wang",
 "model_type": "Cox proportional hazards regression",
 "n_features": 24,
 "notes": "CpGPTGrimAge3 implementation combining chronological age, GrimAge2 DNAm proxies, and CpGPT-predicted plasma-protein proxies in a Cox linear predictor that is 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
}