RKNNMDA: Ranking-based KNN for MiRNA-Disease Association prediction. Issue 7 (3rd July 2017)

• Record Type: Journal Article
• Authors: Chen, Xing; Wu, Qiao-Feng; Yan, Gui-Ying
• Is Part Of: RNA biology. Volume 14:Issue 7(2017)
• Page Start: 952
• Page End: 962
• Publication Date: 2017-07-03
• Subjects: Disease -- disease semantic similarity -- KNN algorithm -- miRNAs -- miRNA-disease association -- SVM Ranking model; RNA -- Periodicals; Molecular biology -- Periodicals; Molecular biology; RNA; Periodicals; 572.8805
• DOI: 10.1080/15476286.2017.1312226
• Languages: English
• ISSNs: 1547-6286

Abstract

ABSTRACT: 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.