Predicting miRNA-disease interaction based on recommend method
Information Discovery and Delivery
ISSN: 2398-6247
Article publication date: 17 September 2019
Issue publication date: 19 February 2020
Abstract
Purpose
MicroRNAs (miRNAs) have been proved to be a significant type of non-coding RNAs related to various human diseases. This paper aims to identify the potential miRNA–disease interactions.
Design/methodology/approach
A computational framework, MDIRM is presented to predict miRNAs-disease interactions. Unlike traditional approaches, the miRNA function similarity is calculated by miRNA–disease interactions. The k-mean method is further used to cluster miRNA similarity network. For miRNAs in the same cluster, their similarities are enhanced, as the miRNAs from the same cluster may be reliable. Further, the potential miRNA–disease association is predicted by using recommend method.
Findings
To evaluate the performance of our model, the fivefold cross validation is implemented to compare with two state-of-the-art methods. The experimental results indicate that MDIRM achieves an AUC of 0.926, which outperforms other methods.
Originality/value
This paper proposes a novel computational method for miRNA–disease interaction prediction based on recommend method. Identifying the relationship between miRNAs and diseases not only helps us better understand the disease occurrence and mechanism through the perspective of miRNA but also promotes disease diagnosis and treatment.
Keywords
Acknowledgements
This paper was partially supported by the National Natural Science Foundation of China projects 61702122 and 61751314, the Natural Science Foundation of Guangxi projects 2017GXNSFDA198033 and 2018GXNSFBA281193, the key R&D plan of Guangxi AB17195055, the Scientific Research Fund of Hunan Provincial Education Department 18B469 and the Doctoral research fund of Guangxi University XBZ180476.
Citation
Chen, Q., Zhao, Z., Lan, W., Zhang, R. and Liang, J. (2020), "Predicting miRNA-disease interaction based on recommend method", Information Discovery and Delivery, Vol. 48 No. 1, pp. 35-40. https://doi.org/10.1108/IDD-04-2019-0026
Publisher
:Emerald Publishing Limited
Copyright © 2019, Emerald Publishing Limited