| --- |
| license: other |
| license_name: original-author-license |
| license_link: https://huggingface.co/pyaging/organagemortalitythyroid/blob/main/organagemortalitythyroid.LICENSE.txt |
| library_name: pyaging |
| tags: |
| - pyaging |
| - aging-clock |
| - biology |
| - proteomics |
| --- |
| |
| # organagemortalitythyroid |
|
|
| Thyroid OrganAge mortality model for Olink Explore 3072, using author-recommended fold 1. Trained on UK Biobank plasma measured with Olink Explore 3072. Input columns are original case-sensitive protein symbols (e.g. NTproBNP, HLA-DRA), not UniProt IDs or Olink assay IDs. Values are assay-normalized Olink NPX on the log2 relative-abundance scale, not raw counts, concentrations, linear abundances or z-scores. No additional centering or cohort scaling is applied. Absent proteins contribute zero; NaNs in supplied proteins propagate, matching the original Example_Script.R scoring expressions. For other assays, the authors recommend a symmetric abundance distribution, protein-wise mean subtraction and SD division, then multiplication by the full-model UKB protein SDs in Table S3. This cohort-dependent cross-platform preparation is not performed by pyaging. The optional Example_Script.R SD-matching step is also external; NPX labeling alone does not establish comparability across Olink panels, batches, plasma and serum. Output is relative natural-log mortality hazard, not age in years. The 2026 Insilico trial used conventional OrganAge models on Olink Explore 3072 serum; organ-specific variants are not the six-clock comparison endpoints. pyaging returns the original raw score and does not apply the trial wrapper's cohort harmonization or mortality-to-years conversion. Models labelled Female and Male contain protein features; neither requires a sex covariate. Academic/non-commercial research use only; commercial use requires a separate license from the authors. |
|
|
| Model weights retain the original authors' terms; the pyaging software license does not relicense them. See the [original author license](organagemortalitythyroid.LICENSE.txt). These weights are restricted to research use under the authors' terms. |
|
|
| | | | |
| |---|---| |
| | **Predicts** | mortality risk | |
| | **Species** | Homo sapiens | |
| | **Tissue** | plasma | |
| | **Data type** | proteomics | |
| | **Model type** | elastic net Cox regression | |
| | **Year** | 2025 | |
|
|
| ## Use with pyaging |
|
|
| ```python |
| import pyaging as pya |
| |
| pya.pred.predict_age(adata, ["organagemortalitythyroid"]) |
| ``` |
|
|
| Browse every clock in the [pyaging Clock Catalogue](https://pyaging.readthedocs.io). |
|
|
| ## Citation |
|
|
| Goeminne, L. J. E., et al. "Plasma protein-based organ-specific aging and mortality models unveil diseases as accelerated aging of organismal systems." Cell Metabolism 37, 205–222.e6 (2025). |
|
|
| https://doi.org/10.1016/j.cmet.2024.10.005 |
|
|
