Contrastive Self-Supervised Learning for Predicting Disease-RNA Associations

Predicting disease-RNA associations is important for disease diagnosis and treatment. The traditional biological experiment method has the disadvantage of being time-consuming and laborious. Therefore, a growing number of studies have focused on predicting disease-RNA associations using deep learning methods. Aiming at the characteristics of sparse disease-RNA association data and few labels, we propose a novel prediction method named SCLDA based on contrastive self-supervised learning. This is a new attempt in the field. We also propose a data augmentation method based on RNA similarity. We tested SCLDA and other advanced methods on lncRNA and miRNA datasets, respectively, and the results show that SCLDA achieves the best performance.