Integrating random walk with restart and k-Nearest Neighbor to identify novel circRNA-disease association
Integrating random walk with restart and k-Nearest Neighbor to identify novel circRNA-disease association
复制标题
将随机游走与重启和 k 最近邻相结合来识别新的 circRNA 疾病关联
DOI：
10.1038/s41598-020-59040-0
复制
发表时间：
2020-02-06
影响因子：
4.6
通讯作者：
Bian, Chen
中科院分区：
文献类型：
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作者：
Lei, Xiujuan;Bian, Chen
CircRNA是一类特殊的非编码RNA，与人类多种复杂疾病的发生、发展密切相关。然而，通过实验方法确定circRNA与疾病的关联是耗时且昂贵的。因此，我们在现有数据库的基础上，提出了一种结合重启随机游走（RWR）和k-近邻（KNN）的方法RWRKNN来预测circRNA与疾病之间的关联。具体地说，我们使用RWR算法对全局网络拓扑信息进行加权，并使用KNN进行基于特征的分类。最后，获得每个circRNA-疾病对的预测得分。如留一法、5倍交叉验证和10倍交叉验证所示，RWRKNN的AUC值分别为0.9297、0.9333和0.9261。案例研究表明，RWRKNN预测的circRNA与疾病的关联可以成功地证明。总之，RWRKNN是预测circRNA-疾病关联的有用方法。
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.