The development of ‘age clocks’, machine learning models predicting age from biological data, has been a major milestone in the search for reliable markers of biological age and has since become an invaluable tool in aging research. However, beyond their unquestionable utility, current clocks offer little insight into the molecular biological processes driving aging, and their inner workings often remain non-transparent. Here we propose a new type of age clock, one that couples predictivity with interpretability of the underlying biology, achieved through the incorporation of prior knowledge into the model design. The clock, an artificial neural network constructed according to well-described biological pathways, allows the prediction of age from gene expression data of skin tissue with high accuracy, while at the same time capturing and revealing aging states of the pathways driving the prediction. The model recapitulates known associations of aging gene knockdowns in simulation experiments and demonstrates its utility in deciphering the main pathways by which accelerated aging conditions such as Hutchinson–Gilford progeria syndrome, as well as pro-longevity interventions like caloric restriction, exert their effects.

“年龄时钟”（根据生物数据预测年龄的机器学习模型）的发展是寻找可靠的生物年龄标记的一个重要里程碑，并已成为衰老研究中的宝贵工具。然而，除了其无可争议的实用性之外，当前的时钟对驱动衰老的分子生物学过程几乎没有提供任何见解，而且它们的内部运作通常仍然是不透明的。在这里，我们提出了一种新型的年龄时钟，它将预测性与基础生物学的可解释性结合起来，通过将先验知识纳入模型设计来实现。该时钟是一种根据详细描述的生物途径构建的人工神经网络，可以根据皮肤组织的基因表达数据高精度地预测年龄，同时捕获和揭示驱动预测的途径的衰老状态。该模型概括了模拟实验中衰老基因敲低的已知关联，并展示了其在破译哈钦森-吉尔福德早衰综合症等加速衰老状况以及热量限制等长寿干预措施发挥作用的主要途径方面的实用性。