ArticleNAR genomics and bioinformatics2024
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.
Article in NAR genomics and bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
3 authors.
Funding
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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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Registered trials
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