scAgeClock: a single-cell transcriptome-based human aging clock model using gated multi-head attention neural networks
Researchers developed scAgeClock, a machine learning model that measures aging at the single-cell level by analyzing gene expression patterns. This cellular-resolution aging clock offers a novel method to detect age-related changes before systemic symptoms emerge, with potential applications in monitoring intervention efficacy and identifying individuals at accelerated aging risk.

