Identification of disease-related miRNAs based on weighted k-nearest known neighbours and inductive matrix completion

miRNAs, a subtype of non-coding RNAs, have a length of about 18–22 nucleotides. Studies have shown that miRNAs play an important role in the initiation and progression of many human diseases. For this reason, it is very significant to know the miRNAs associated with diseases. Because experimental studies to identify these associations are expensive and time-consuming, many computational methods have been developed to identify disease-related miRNAs. In this study, we propose a calculation method based on nearest known neighbours and matrix completion. ROC curves of our suggested method were plotted using two commonly used cross-validation techniques such as five-fold and LOOCV, and also AUC values were calculated in both validation techniques. Moreover, we carried out case studies on breast cancer, lung cancer, and lymphoma to further demonstrate the predictive accuracy of our method. As a result, our proposed method can be used with confidence to identify possible miRNA-disease associations.