Alsaggaf, Ibrahim and Freitas, A.A. and Wan, Cen (2024) 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. NAR Genomics and Bioinformatics 6 (4), ISSN 2631-9268.
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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 networks. The experimental results suggest that EGsCL successfully outperformed the conventional Gaussian noise augmentationbased contrastive learning methods and obtained state-of-the-art performance on three model organisms’ predictive tasks when merely relying on protein-protein interaction 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.
Metadata
| Item Type: | Article |
|---|---|
| Keyword(s) / Subject(s): | contrastive learning, protein-protein interaction networks, Gaussian noise data augmentation, ageing |
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Depositing User: | Cen Wan |
| Date Deposited: | 06 Jan 2025 14:29 |
| Last Modified: | 07 Jan 2025 14:04 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/54736 |
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