File size: 1,333 Bytes
40aa01e
 
02b6094
bd6bf5e
 
40aa01e
 
 
 
 
 
 
c48895d
40aa01e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
bd6bf5e
40aa01e
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
{
 "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
}