956-scAgeClock: Model for Single-Cell Human Aging
The paper introduces scAgeClock, a sophisticated human aging clock model designed to predict biological age at the single-cell level. Utilizing a gated multi-head attention neural network, this tool was trained on a massive dataset of over 16 million transcriptomes across 44 different human tissues. Researchers can use the model to identify transcriptomic age acceleration in various diseases, including Alzheimer’s and COVID-19, and to track cell dynamics in cancer. The study also proposes a new metric called the Aging Deviation Index (ADI), which quantifies how much a cell's biological age…
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