Identifying MiRNA-disease association based on integrating miRNA topological similarity and functional similarity
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RESEARCH ARTICLE Identifying MiRNA-disease association based on integrating miRNA topological similarity and functional similarity Qingfeng Chen1,2, Zhao Zhe1, Wei Lan1,*, Ruchang Zhang1, Zhiqiang Wang2, Cheng Luo3, and Yi-Ping Pheobe Chen4 1
School of Computer, Electronic and Information, Guangxi University, Nanning 530004, China The State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, Guangxi University, Nanning 530004, China 3 Xingjian College of Science and Liberal Arts, Guangxi University, Nanning 530004, China 4 Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, Vic 3086, Australia * Correspondence: [email protected] 2
Received April 28, 2019; Revised June 12, 2019; Accepted June 25, 2019 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. Conclusions: 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.
Keywords: miRNA-disease association; CosRA index; miRNA functional similarity; recommendation method 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 recomm
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