Modeling transcriptomic age using knowledge-primed artificial neural networks.

ABSTRACT: 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 hi

SUBMITTER: Holzscheck N 

PROVIDER: S-EPMC8169742 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature