Ageing is often characterised by progressive accumulation of damage, and it is one
of the most important risk factors for chronic disease development. Epigenetic mechanisms
including DNA methylation could functionally contribute to organismal aging, however
the key functions and biological processes may govern ageing are still not understood.
Although age predictors called epigenetic clocks can accurately estimate the biological
age of an individual based on cellular DNA methylation, their models have limited
ability to explain the prediction algorithm behind and underlying key biological processes
controlling ageing. Here we present XAI-AGE, a biologically informed, explainable
deep neural network model for accurate biological age prediction across multiple tissue
types. We show that XAI-AGE outperforms the first-generation age predictors and achieves
similar results to deep learning-based models, while opening up the possibility to
infer biologically meaningful insights of the activity of pathways and other abstract
biological processes directly from the model.
