Circular RNAs (
circRNAs) are a class of structurally stable endogenous
noncoding RNA molecules. Increasing studies indicate that
circRNAs play vital roles in human diseases. However, validating disease-related
circRNAs in vivo is costly and time-consuming. A reliable and effective computational method to identify
circRNA-disease associations deserves further studies. In this study, we propose a computational method called RNMFLP that combines robust nonnegative matrix factorization (RNMF) and label propagation algorithm (LP) to predict
circRNA-disease associations. First, to reduce the impact of false negative data, the original
circRNA-disease adjacency matrix is updated by matrix multiplication using the integrated
circRNA similarity and the disease similarity information. Subsequently, the RNMF algorithm is used to obtain the restricted latent space to capture potential
circRNA-disease pairs from the association matrix. Finally, the LP algorithm is utilized to predict more accurate
circRNA-disease associations from the integrated
circRNA similarity network and integrated disease similarity network, respectively. Fivefold cross-validation of four datasets shows that RNMFLP is superior to the state-of-the-art methods. In addition, case studies on
lung cancer,
hepatocellular carcinoma and
colorectal cancer further demonstrate the reliability of our method to discover disease-related
circRNAs.