Background:Increasing evidences indicate that microRNAs (miRNAs) are functionally related to the development and progression of various human diseases.Inferring disease-related miRNAs can be helpful in promoting disease biomarker detection for the treatment,diagnosis,and prevention of complex diseases.Methods:To improve the prediction accuracy of miRNA-disease association and capture more potential diseaserelated miRNAs,we constructed a precise miRNA global similarity network (MSFSN) via calculating the miRNA similarity based on secondary structures,families,and functions.Results:We tested the network on the classical algorithms:WBSMDA and RWRMDA through the method of leaveone-out cross-validation.Eventually,AUCs of 0.8212 and 0.9657 are obtained,respectively.Also,the proposed MSFSN is applied to three cancers for breast neoplasms,hepatocellular carcinoma,and prostate neoplasms.Consequently,82％,76％,and 82％ of the top 50 potential miRNAs for these diseases are respectively validated by the miRNA-disease associations database miR2Disease and oncomiRDB.Conclusion:Therefore,MSFSN provides a novel miRNA similarity network combining precise function network with global structure network of miRNAs to predict the associations between miRNAs and diseases in various models.