, Volume 16, Issue 3–4, pp 363–372 | Cite as

Predict MiRNA-Disease Association with Collaborative Filtering

  • Yatong Jiang
  • Bingtao Liu
  • Linghui Yu
  • Chenggang Yan
  • Hujun Bian
Original Article


The era of human brain science research is dawning. Researchers utilize the various multi-disciplinary knowledge to explore the human brain,such as physiology and bioinformatics. The emerging disease association prediction technology can speed up the study of diseases, so as to better understanding the structure and function of human body. There are increasing evidences that miRNA plays a significant role in nervous system development, adult function, plasticity, and vulnerability to neurological disease states. In this paper ,we proposed the novel improved collaborative filtering-based miRNA-disease association prediction (ICFMDA) approach. Known miRNA-disease associations can be viewed as a bipartite network between diseases and miRNAs. ICFMDA defined significance SIG between pairs of diseases or miRNAs to model the preference on the choices of other entities. The collaborative filtering algorithm is further improved by incorporating similarity matrices to enable the prediction for new miRNA or disease without known associations. Potential miRNA-disease associations are scored with the addition of bidirectional recommendation results with low computational cost. ICFMDA achieved a 0.9076 AUC of ROC curve in global leave-one-out cross validation, which outperformed the state-of-the-art models. ICFMDA is a compact and accurate tool for potential miRNA-disease association prediction. We hope that ICFMDA would be useful in future miRNA and brain researches,and achieve better understanding of the nervous system in molecular level, cellular level, cell change process, and thus can support the research of human brain.


MiRNA-disease associations Collaborative filtering Computational model 



This work is supported by National Nature Science Foundation of China (61525206, 61671196,61327902), Zhejiang Province Nature Science Foundation of China LR17F030006.


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Hangzhou Dianzi UniversityHangzhouChina

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