| { | |
| "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 | |
| } |