pyaging#
GPU-accelerated biological aging clocks in Python — 170+ published clocks across DNA methylation, histone marks, ATAC-seq, RNA-seq, and blood chemistry, behind a one-line prediction API.
Get started
Install pyaging and run your first prediction — the Illumina 450K/EPIC walkthrough.
Clock Catalogue
Filter, sort, and search every available clock.
Tutorials
End-to-end walkthroughs for each data type.
GitHub
Source, issues, and contributions.
Why pyaging#
170+ clocks
A comprehensive, curated collection of published aging clocks, each cross-validated against its source.
Multi-omic
DNA methylation, histone marks, ATAC-seq, RNA-seq, and blood chemistry — one consistent interface.
GPU-optimized
A PyTorch backend runs predictions on CPU or GPU with no code changes.
