NARVGA: A Hybrid Framework Integrating Matrix Factorisation and Adversarial Graph Learning for circRNA-Disease Association Prediction
TL;DR
A computer-based method that combines several types of biological information to predict likely relationships between circular ribonucleic acids and diseases is developed and shows a strong ability to distinguish known associations from unconfirmed ones.
Abstract
Simple Summary Circular ribonucleic acid molecules help regulate gene activity and may contribute to many diseases. However, laboratory experiments can examine only a small fraction of the possible links between these molecules and diseases, making it difficult to identify the most promising candidates for further study. This study developed a computer-based method that combines several types of biological information to predict likely relationships between circular ribonucleic acids and diseases. When tested on a widely used collection of known associations, the method showed a strong ability to distinguish known associations from unconfirmed ones. It also performed well on two related collections involving other types of ribonucleic acid. In a liver cancer case study, 19 of the 20 highest-ranked circular ribonucleic acids were supported by published studies, while one remained a potentially new candidate. These findings suggest that the method can help researchers select promising associations for laboratory testing, reduce unnecessary experimental screening, and support the discovery of disease-related markers and treatment targets.