CFMDA: collaborative filtering-based MiRNA-disease association prediction
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MicroRNAs (miRNAs) are increasingly becoming the focus in a number of researches because abundant studies certify miRNAs play vital roles and have critical functions in various biologic processes. Considering the high cost of experiment research to miRNA-disease association, we explore the way to predict the miRNA-disease association using the extensive collaborative filtering in order to diagnose the diseases better. Specifically, we introduce the prediction model of collaborative filtering-based miRNA-disease association prediction (CFMDA) and verify the model by leave-one-out cross validation(LOOCV) and case validation. The CFMDA considers the miRNA functional similarity and disease similarity while uses minimal amount of related information. CFMDA achieves AUCs of 0.8364 using leave-one-out cross validation, which is the highest AUCs compared to other 5 methods. Meanwhile, we obtain more than 85% confirmation of predicted associations using three kinds of case validations. Generally, our method is faster and more effective than other state-of-the-art methods while it doesn’t need any negative samples.
KeywordsmiRNA-disease association prediction Collaborative filtering Computational model
This work is supported by National Nature Science Foundation of China (61671196, 61327902) Zhejiang Province Nature Science Foundation of China LR17F030006.
- 6.Chen X, Yan CC, Xu Z, You Z-H, Deng L, Liu Y, Zhang Y, Dai Q (2016) Wbsmda: within and between score for MiRNA-disease association prediction. Sci Rep 6Google Scholar
- 7.Chen X, Yan CC, Xu Z, You Z-H, Huang Y-A, Yan G-Y (2016) Hgimda: Heterogeneous graph inference for mirna-disease association prediction. Oncotarget 7 (40):65257–65269Google Scholar
- 12.Li X, Xu J, Li Ys (2012) Prioritizing candidate disease mirnas by topological features in the mirna-target dysregulated network. In: Systems biology in cancer research and drug discovery. Springer, Berlin, pp 289–306Google Scholar
- 19.Pan R, Zhou Y, Cao B, Liu NN, Lukose R, Scholz M, Yang Q (2008) One-class collaborative filtering. In: Eighth IEEE international conference on data Mining 2008. ICDM’08. IEEE, USA, pp 502–511Google Scholar
- 20.Schaefer A, Jung M, Mollenkopf H-J, Wagner I, Stephan C, Jentzmik F, Miller K, Lein M, Kristiansen G, Jung K (2010) Diagnostic and prognostic implications of microrna profiling in prostate carcinoma. Int J Cancer 126(5):1166–1176Google Scholar
- 27.Yan C, Xie H, Yang D, Yin J, Zhang Y, Dai Q (2017) Supervised hash coding with deep neural network for environment perception of intelligent vehicles. IEEE Trans Intell Trans Syst PP(99):1–12Google Scholar
- 28.Yan C, Xie H, Yang D, Yin J, Zhang Y, Dai Q (2017) Effective Uyghur language text detection in complex background images for traffic prompt identification. IEEE Trans Intell Trans Syst PP(99):1–10Google Scholar
- 30.Zhu X, Zi H, Shen HT, Zhao X (2013) Linear cross-modal hashing for efficient multimedia search. In: Proceedings of the 21st ACM international conference on multimedia, pp 143–152Google Scholar