Predicting CircRNA-Disease Associations Through Linear Neighborhood Label Propagation Method

Abstract

Identification of circRNA-disease associations provides insight into the mechanism that circRNAs cause diseases. Wet experimental identification of circRNA-disease associations is time-consuming and labor-intensive, and thus developing computational methods for the circRNA-disease association prediction is an urgent task. In this paper, we propose a linear neighborhood label propagation method to predict circRNA-disease associations, named CD-LNLP. First, CD-LNLP uses association profiles based on known associations to calculate circRNA-circRNA similarities and disease-disease similarities. Next, CD-LNLP implements the label propagation based on the circRNA-circRNA similarity-based graph and the disease-disease similarity-based graph respectively to predict circRNA-disease associations. Finally, we combine the outputs from circRNA-circRNA similarity-based graph model and disease-disease similarity-based graph model to produce the results. In the experiments, CD-LNLP achieves impressive performance with the AUPR score of 0.4487 and the AUC score of 0.9007 and outperforms outstanding baseline methods (collaborative filter method, KATZ, nonnegative matrix factorization method, and resource allocation method) and the state-of-the-art method MRLDC. The case studies show that CD-LNLP identifies novel circRNA-disease associations, which are validated by up-to-date circRNA-disease databases and literature respectively. In conclusion, CD-LNLP is a promising method for predicting circRNA-disease associations.