Hypergraph representation learning for identifying circRNA-disease associations

Abstract

CircRNA-disease associations (CDA) are crucial for identifying circRNA biomarkers, significantly aiding the prevention, diagnosis, and treatment of complex human diseases. Traditional wet-lab methods for CDA prediction, while useful, are time-consuming, labor-intensive, and not always successful. Recently, computational methods have emerged as promising alternatives, offering more efficient CDA detection. Nevertheless, existing computational methods often overlook the multifaceted nature of CDAs, where each circRNA can associate with multiple diseases simultaneously, and vice versa. These methods typically fail to capture the beyond pairwise relationships and higher-order complex associations between circRNA-disease pairs. To this end, we propose a novel and effective biomarker computational method named HyperGRL-CDA, which is based on biological attribute information and hypergraph representation learning strategies. Its cornerstone is a hypergraph representation learning module that employs circRNA and disease similarity attributes to construct biological hypergraphs. This module leverages a symmetric hypergraph convolutional network to learn and reveal hidden, high-quality embedding representations, capturing the complex associations within these hypergraphs. Enhancing computational efficiency, HyperGRL-CDA incorporates the Extra Trees algorithm to determine CDA matching scores. Tested through five-fold cross-validation on the circR2Disease dataset, HyperGRL-CDA achieved an impressive accuracy of 92.22% and an AUC score of 96.08%. Furthermore, it demonstrated superior predictive performance on various related CDA datasets. These extensive experiments confirm HyperGRL-CDA as an efficient, accurate, and robust method for CDA prediction based on hypergraph representation learning.