Predicting CircRNA-Disease Associations via Non-Negative Matrix Factorization Fused with Multiple Similarity Networks

CircRNAs, widely found throughout the human bodies, play a crucial role in regulating various biological processes and are closely linked to complex human diseases. Investigating potential associations between circRNAs and diseases can enhance our understanding of diseases and provide new strategies and tools for early diagnosis, treatment, and disease prevention. However, existing models have limitations in accurately capturing similarities, handling the sparse and noise attributes of association networks, and fully leveraging bioinformatical aspects from multiple viewpoints. To address these issues, this study introduces a new non-negative matrix factorization-based framework called NMFMSN. First, we incorporate circRNA sequence data and disease semantic information to compute circRNA and disease similarity, respectively. Given the sparse known associations between circRNAs and diseases, we reconstruct the network to complete more associations by imputing missing links based on neighboring circRNA and disease interactions. Finally, we integrate these two similarity networks into a non-negative matrix factorization framework to identify potential circRNA-disease associations. Upon conducting 5-fold cross-validation and leave-one-out cross-validation, the AUC values for NMFMSN reach 0.971 2 and 0.976 8, respectively, outperforming the currently most advanced models. Case studies on lung cancer and hepatocellular carcinoma show that NMFMSN is a good way to predict new associations between circRNAs and diseases.