RKNNMDA: Ranking-based KNN for MiRNA-Disease Association prediction

Cumulative verified experimental studies have demonstrated that microRNAs (miRNAs) could be closely related with the development and progression of human complex diseases. Based on the assumption that functional similar miRNAs may have a strong correlation with phenotypically similar diseases and vice versa, researchers developed various effective computational models which combine heterogeneous biologic data sets including disease similarity network, miRNA similarity network, and known disease-miRNA association network to identify potential relationships between miRNAs and diseases in biomedical research. Considering the limitations in previous computational study, we introduced a novel computational method of Ranking-based KNN for miRNA-Disease Association prediction (RKNNMDA) to predict potential related miRNAs for diseases, and our method obtained an AUC of 0.8221 based on leave-one-out cross validation. In addition, RKNNMDA was applied to 3 kinds of important human cancers for further performance evaluation. The results showed that 96%, 80% and 94% of predicted top 50 potential related miRNAs for Colon Neoplasms, Esophageal Neoplasms, and Prostate Neoplasms have been confirmed by experimental literatures, respectively. Moreover, RKNNMDA could be used to predict potential miRNAs for diseases without any known miRNAs, and it is anticipated that RKNNMDA would be of great use for novel miRNA-disease association identification.

多项经实验验证的累积研究均证实，微小核糖核酸（microRNAs，miRNAs）与人类复杂疾病的发生发展密切相关。基于“功能相似的miRNAs与表型相似的疾病存在强相关性，反之亦然”这一假设，研究人员已构建多种高效计算模型，整合疾病相似性网络、miRNA相似性网络以及已知疾病-miRNA关联网络等异质生物数据集，用于生物医学研究中miRNA与疾病潜在关联的挖掘。鉴于现有计算研究存在的局限，本研究提出一种全新的基于排序的K近邻（Ranking-based KNN）miRNA-疾病关联预测方法（RKNNMDA），用于预测疾病的潜在相关miRNAs；经留一交叉验证（leave-one-out cross validation），该方法的受试者工作特征曲线下面积（Area Under Curve, AUC）达0.8221。此外，本研究将RKNNMDA应用于3种重要的人类癌症以进一步评估其性能，结果显示：针对结肠肿瘤、食管肿瘤与前列腺肿瘤，该方法预测得到的前50个潜在相关miRNAs中，分别有96%、80%与94%已被实验文献证实。不仅如此，RKNNMDA还可用于预测无任何已知关联miRNAs的疾病的潜在相关miRNAs，预计该方法将在新型miRNA-疾病关联挖掘中发挥重要作用。