A novel collaborative filtering model for LncRNA-disease association prediction based on the Naïve Bayesian classifier

• Main Authors: Jingwen Yu, Zhanwei Xuan, Xiang Feng, Quan Zou, Lei Wang
• Format: Article
• Language: English
• Published: BMC 2019-07-01
• Series: BMC Bioinformatics
• Subjects: lncRNA-disease associations; Original tripartite network; Item-based collaborative filtering; Updated tripartite network; naïve Bayesian classifier
• Online Access: http://link.springer.com/article/10.1186/s12859-019-2985-0

Abstract Background Since the number of known lncRNA-disease associations verified by biological experiments is quite limited, it has been a challenging task to uncover human disease-related lncRNAs in recent years. Moreover, considering the fact that biological experiments are very expensive and time-consuming, it is important to develop efficient computational models to discover potential lncRNA-disease associations. Results In this manuscript, a novel Collaborative Filtering model called CFNBC for inferring potential lncRNA-disease associations is proposed based on Naïve Bayesian Classifier. In CFNBC, an original lncRNA-miRNA-disease tripartite network is constructed first by integrating known miRNA-lncRNA associations, miRNA-disease associations and lncRNA-disease associations, and then, an updated lncRNA-miRNA-disease tripartite network is further constructed through applying the item-based collaborative filtering algorithm on the original tripartite network. Finally, based on the updated tripartite network, a novel approach based on the Naïve Bayesian Classifier is proposed to predict potential associations between lncRNAs and diseases. The novelty of CFNBC lies in the construction of the updated lncRNA-miRNA-disease tripartite network and the introduction of the item-based collaborative filtering algorithm and Naïve Bayesian Classifier, which guarantee that CFNBC can be applied to predict potential lncRNA-disease associations efficiently without entirely relying on known miRNA-disease associations. Simulation results show that CFNBC can achieve a reliable AUC of 0.8576 in the Leave-One-Out Cross Validation (LOOCV), which is considerably better than previous state-of-the-art results. Moreover, case studies of glioma, colorectal cancer and gastric cancer demonstrate the excellent prediction performance of CFNBC as well. Conclusions According to simulation results, due to the satisfactory prediction performance, CFNBC may be an excellent addition to biomedical researches in the future.