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.
Abstract
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.
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Research summary
The paper addresses the prediction of pro-longevity and anti-longevity effects of genes in model organisms, which is directly related to understanding the molecular mechanisms of aging. The use of enhanced Gaussian noise augmentation-based contrastive learning to analyze protein-protein interaction networks is a novel approach that could contribute to the field. However, while the findings are solid, they appear to be incremental rather than groundbreaking, limiting their overall impact.
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