| library_name: pyaging | |
| tags: | |
| - pyaging | |
| - aging-clock | |
| - biology | |
| - transcriptomics | |
| # tagemortality | |
| Elastic Net over 10,487 mouse-Entrez genes, multispecies multi-tissue, scaleddiff variant. Cohort-relative: predict_age runs the tAge cohort preprocessing on the raw RNA-seq counts itself, so a prediction is a hazard shift against the reference group rather than an absolute risk. Name the cohort's species with a 0/1 column among var_names (mouse, rat, macaque or human; absent or all-zero means mouse) and the samples to centre against with a truthy adata.obs["tage_reference_group"] (absent centres on the whole cohort); at least two samples are needed. The published pipeline's SimpleImputer, mean-only StandardScaler and pass-through SelectKBest are folded into the packaged linear layer, and the imputer medians are carried as reference_values so a gene the sample does not measure contributes its training median. Output is log10(hazard ratio), using the base-10 logarithm -- and unlike the chronological clocks it is never rescaled by species maximum lifespan, so it is directly comparable across species. Released under the MGB Open Access License 1.0: non-commercial academic research use only. | |
| Model weights retain the original authors' terms; the pyaging software license does not relicense them. These weights are restricted to research use under the authors' terms. | |
| | | | | |
| |---|---| | |
| | **Predicts** | mortality risk | | |
| | **Species** | multiple species | | |
| | **Tissue** | multi-tissue | | |
| | **Data type** | transcriptomics | | |
| | **Model type** | elastic net regression | | |
| | **Year** | 2026 | | |
| ## Use with pyaging | |
| ```python | |
| import pyaging as pya | |
| pya.pred.predict_age(adata, ["tagemortality"]) | |
| ``` | |
| Browse every clock in the [pyaging Clock Catalogue](https://pyaging.readthedocs.io). | |
| ## Citation | |
| Tyshkovskiy, Alexander, et al. "Universal transcriptomic hallmarks of mammalian ageing and mortality." Nature 654 (2026): 173-188. | |
| https://doi.org/10.1038/s41586-026-10542-3 | |