EG-BINN: Parameter-Efficient Ensembling of Gated Biologically Informed Neural Networks for Epigenetic Age Estimation

Abstract

Epigenetic aging clocks are widely used to evaluate biological aging, disease risk, and clinical interventions. However, most existing clocks rely on linear models that overlook nonlinear relationships between age and methylation patterns, or on deep learning architectures that are parameter-intensive and difficult to interpret. We developed EG-BINN, a parameter-efficient ensemble of biologically informed neural networks that uses sample-specific gating to modulate representations across biologically annotated nodes. Trained on 93 multi-tissue datasets, EG-BINN achieved the lowest mean and median absolute errors among matched linear and nonlinear baselines in five-fold cross-validation, as well as the lowest mean absolute and root mean squared errors across eight held-out Gene Expression Omnibus (GEO) datasets. Analysis of the gating mechanism revealed widespread associations between pathway gate values and age, including trajectories that varied across blood, adipose tissue, skeletal muscle, and liver. Across 11 TCGA cohorts, EG-BINN identified significant tumor--normal differences in residual epigenetic age acceleration in six cohorts, whereas Shapley additive explanations (SHAP) highlighted recurrent pathway nodes contributing to tumor age predictions. EG-BINN combines accurate pan-tissue age prediction with an explainable architecture for examining biologically labeled nodes across age, tissue, and disease contexts.