Background
MicroRNAs (miRNAs) are a significant type of non-coding RNAs, which usually were encoded by endogenous genes with about ~22 nt nucleotides. Accumulating biological experiments have shown that miRNAs have close associations with various human diseases. Although traditional experimental methods achieve great successes in miRNA-disease interaction identification, these methods also have some limitations. Therefore, it is necessary to develop computational method to predict miRNA-disease interactions.
Methods
Here, we propose a computational framework (MDVSI) to predict interactions between miRNAs and diseases by integrating miRNA topological similarity and functional similarity. Firstly, the CosRA index is utilized to measure miRNA similarity based on network topological feature. Then, in order to enhance the reliability of miRNA similarity, the functional similarity and CosRA similarity are integrated based on linear weight method. Further, the potential miRNA-disease associations are predicted by using recommendation method. In addition, in order to overcome limitation of recommendation method, for new disease, a new strategy is proposed to predict potential interactions between miRNAs and new disease based on disease functional similarity.
Results
To evaluate the performance of different methods, we conduct ten-fold cross validation and de novo test in experiment and compare MDVSI with two the-state-of-art methods. The experimental result shows that MDVSI achieves an AUC of 0.91, which is at least 0.012 higher than other compared methods.
Conclusion
In summary, we propose a computational framework (MDSVI) for miRNA-disease interaction prediction. The experiment results demonstrate that it outperforms other the-state-of-the-art methods. Case study shows that it can effectively identify potential miRNA-disease interactions.
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14 September 2020
The original version of this article unfortunately contained a mistake in the name of the last author. The corrected name is Yi-Ping Phoebe Chen.
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Acknowledgements
The work reported in this paper was partially supported by the National Natural Science Foundation of China (Nos. 61702122, 61751314 and 31560317), the Natural Science Foundation of Guangxi (Nos. 2017GXNSFDA198033 and 2018GXNSFBA281193), the Key Research and Development Plan of Guangxi (No. AB17195055), the Bossco Project of Guangxi University (No. 20190240), the Hunan Provincial Science and Technology Program (No. 2018WK4001) and 111 Project (No. B18059).
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The authors Qingfeng Chen, Zhao Zhe,Wei Lan, Ruchang Zhang, Zhiqiang Wang, Cheng Luo and Yi-Ping Pheobe Chen declare that they have no conflict of interests.
This article does not contain any studies with human or animal subjects performed by any of the authors.
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Author summary: Rapidly increasing evidences show that miRNAs have close relationships with many diseases. Therefore, identifying miRNA-disease interactions may contribute to understand the pathogenesis of diseases. In this paper, we propose a computational framework to predict miRNA-disease interactions based on recommendation method. The experiment results show that our method outperforms other the-state-of-the-art methods.
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Chen, Q., Zhe, Z., Lan, W. et al. Identifying MiRNA-disease association based on integrating miRNA topological similarity and functional similarity. Quant Biol 7, 202–209 (2019). https://doi.org/10.1007/s40484-019-0176-7
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DOI: https://doi.org/10.1007/s40484-019-0176-7