A new bioRxiv preprint introduces ACE—Aging Cell Embeddings—an explainable, deep-generative model that claims to pull out universal transcriptional signatures of ageing across datasets. If it holds up, ACE could do something black-box “age clocks” rarely manage: slot neatly into existing FDA/EMA playbooks for qualified biomarkers and even, one day, companion tests for geroprotectors.
What’s new—and why it matters
The ACE paper proposes an explainable framework that learns low-dimensional “age embeddings” from gene-expression data while disentangling nuisance signals (tissue, batch, platform). In plain English: instead of a single opaque score that rises with age,
