Open AccessPredicting microRNA‐disease association based on microRNA structural and functional similarity network
Author(s) -
Ding Tao,
Gao Jie,
Zhu Shanshan,
Xu Junhua,
Wu Min
Publication year - 2019
Publication title -
quantitative biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.707
H-Index - 15
eISSN - 2095-4697
pISSN - 2095-4689
DOI - 10.1007/s40484-019-0170-0
Subject(s) - microrna , similarity (geometry) , disease , computational biology , biomarker , bioinformatics , biology , computer science , medicine , artificial intelligence , genetics , gene , pathology , image (mathematics)
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 disease‐related 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 leave‐one‐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.