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, Volume 78, Issue 1, pp 605–618 | Cite as

CFMDA: collaborative filtering-based MiRNA-disease association prediction

  • ZhiSheng Li
  • Bingtao LiuEmail author
  • Chenggang YanEmail author
Article
  • 162 Downloads

Abstract

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.

Keywords

miRNA-disease association prediction Collaborative filtering Computational model 

Notes

Acknowledgments

This work is supported by National Nature Science Foundation of China (61671196, 61327902) Zhejiang Province Nature Science Foundation of China LR17F030006.

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Hangzhou Dianzi UniversityHangzhouChina

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