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Measuring biological age using a functionally interpretable multi-tissue RNA clock
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DOI：10.1111/acel.13799.png)
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Abstract
En 中文
The quantification of the biological age of cells yields great promises for accelerating the discovery of novel rejuvenation strategies. Here, we present MultiTIMER, the first multi-tissue aging clock that measures the biological, rather than chronological, age of cells from their transcriptional profiles by evaluating key cellular processes. We applied MultiTIMER to more than 70,000 transcriptional profiles and demonstrate that it accurately responds to cellular stressors and known interventions while informing about dysregulated cellular functions.
Keywords:
aging
machine learning
transcriptomics
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