Abstract
CircRNA is a special type of non-coding RNA, which is closely related to the occurrence and development of many complex human diseases. However, it is time-consuming and expensive to determine the circRNA-disease associations through experimental methods. Therefore, based on the existing databases, we propose a method named RWRKNN, which integrates the random walk with restart (RWR) and k-nearest neighbors (KNN) to predict the associations between circRNAs and diseases. Specifically, we apply RWR algorithm on weighting features with global network topology information, and employ KNN to classify based on features. Finally, the prediction scores of each circRNA-disease pair are obtained. As demonstrated by leave-one-out, 5-fold cross-validation and 10-fold cross-validation, RWRKNN achieves AUC values of 0.9297, 0.9333 and 0.9261, respectively. And case studies show that the circRNA-disease associations predicted by RWRKNN can be successfully demonstrated. In conclusion, RWRKNN is a useful method for predicting circRNA-disease associations.
Highlights
CircRNA is a special type of non-coding RNA, which is closely related to the occurrence and development of many complex human diseases
All features of circRNA-disease pairs are weighted using the random walk with restart (RWR) algorithm
Negative circRNA-disease pairs are selected randomly and a k-Nearest Neighbor (KNN) model get trained with weighted features (See Fig. 1)
Summary
CircRNA is a special type of non-coding RNA, which is closely related to the occurrence and development of many complex human diseases. It is time-consuming and expensive to determine the circRNA-disease associations through experimental methods. Based on the existing databases, we propose a method named RWRKNN, which integrates the random walk with restart (RWR) and k-nearest neighbors (KNN) to predict the associations between circRNAs and diseases. It is not very suitable to discover the relationship of new diseases and novel circRNAs. To solve the problem further, Deng et al.[32] proposed a KATZ-based method (KATZCPDA) integrating the information of circRNAs, diseases and proteins. Negative circRNA-disease pairs are selected randomly and a k-Nearest Neighbor (KNN) model get trained with weighted features (See Fig. 1)
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