School of Computer Science, Shaanxi Normal University, Xi'an, 710119, China.
Sci Rep. 2020 Feb 6;10(1):1943. doi: 10.1038/s41598-020-59040-0.
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.
环状 RNA 是一种特殊的非编码 RNA，与许多复杂人类疾病的发生和发展密切相关。然而，通过实验方法确定环状 RNA-疾病的关联是耗时且昂贵的。因此，我们基于现有的数据库提出了一种名为 RWRKNN 的方法，该方法结合了随机游走与重启动（RWR）和 k-最近邻（KNN）来预测环状 RNA 和疾病之间的关联。具体来说，我们将 RWR 算法应用于具有全局网络拓扑信息的加权特征，并使用 KNN 基于特征进行分类。最后，获得每个环状 RNA-疾病对的预测分数。通过留一法、5 折交叉验证和 10 折交叉验证证明，RWRKNN 的 AUC 值分别为 0.9297、0.9333 和 0.9261。案例研究表明，RWRKNN 预测的环状 RNA-疾病关联可以得到成功验证。总之，RWRKNN 是一种预测环状 RNA-疾病关联的有效方法。
