Biologically informed deep learning for explainable epigenetic clocks

• Published in: Scientific Reports
• Main Authors: Aurel Prosz, Orsolya Pipek, Judit Börcsök, Gergely Palla, Zoltan Szallasi, Sandor Spisak, István Csabai
• Format: Article
• Language: English
• Published: Nature Portfolio 2024-01-01
• Online Access: https://doi.org/10.1038/s41598-023-50495-5

Abstract 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.