Predicting the pro-longevity or anti-longevity effect of model organism genes with enhanced Gaussian noise augmentation-based contrastive learning on protein-protein interaction networks

Ageing is a highly complex and important biological process that plays major roles in many diseases. Therefore, it is essential to better understand the molecular mechanisms of ageing-related genes. In this work, we proposed a novel enhanced Gaussian noise augmentation-based contrastive learning (EGsCL) framework to predict the pro-longevity or anti-longevity effect of four model organisms’ ageing-related genes by exploiting protein–protein interaction (PPI) networks. The experimental results suggest that EGsCL successfully outperformed the conventional Gaussian noise augmentation-based contrastive learning methods and obtained state-of-the-art performance on three model organisms’ predictive tasks when merely relying on PPI network data. In addition, we use EGsCL to predict 10 novel pro-/anti-longevity mouse genes and discuss the support for these predictions in the literature.