A Multitask Prediction Framework for CircRNAs, Drugs, and Diseases Based on Multi-View Information Integration and Graph Contrastive Learning

Abstract

As a part of drug discovery, the exploration of circRNA-drug sensitivity associations (CRAs), drug-disease associations (RDAs) has been an important means of understanding disease mechanisms and proposing new treatment strategies. Due to the time-consuming and labor-intensive nature of biological experiments, computational methods have become effective solutions to this problem. However, existing computational methods for inferring CRAs, RDAs typically operate in isolation, failing to exploit the rich interplay across circRNAs, drugs, and diseases. In this paper, we propose MVII-GCL, a novel multitask prediction framework built upon multiview information integration and graph contrastive learning. MVII-GCL first constructs a multiview network encompassing attribute, topology, and association perspectives, explicitly tailored to capture the unique characteristics of each domain. To map multiview information into a unified latent space, our multiview attention encoder module employs domain-specific encoders to learn specialized representations, followed by a Top-k sparse attention mechanism. To further bolster robustness against data sparsity and noise, a symmetric graph contrastive learning objective is seamlessly integrated as a regularization term within the multitask learning paradigm. 5-fold cross-validation demonstrates our model’s strong performance, achieving AUC, AUPR, and F1 scores of 0.9764, 0.9750, and 0.9234 in drug-disease association prediction, and 0.9301, 0.9339, and 0.8609 in circRNA-drug sensitivity prediction.Furthermore, case studies further support the model’s ability to discover potential associations.