Identifying miRNA‐disease association based on integrating miRNA topological similarity and functional similarity

Quantitative Biology - Tập 7 Số 3 - Trang 202-209 - 2019
Yi‐Ping Phoebe Chen1, Zhe Zhao1, Wei Lan1, Ruchang Zhang1, Qinnan Wang2, Luo Chen3, Yi-Ping Pheobe Chen4
1<!--1--> School of Computer Electronic and Information Guangxi University Nanning 530004 China
2<!--2--> The State Key Laboratory for Conservation and Utilization of Subtropical Agro‐bioresources Guangxi University Nanning 530004 China
3<!--3--> Xingjian College of Science and Liberal Arts Guangxi University Nanning 530004 China
4<!--4--> Department of Computer Science and Computer Engineering La Trobe University Melbourne Vic 3086 Australia

Tóm tắt

BackgroundMicroRNAs (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.MethodsHere, 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.ResultsTo 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.ConclusionsIn 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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Tài liệu tham khảo

10.1038/nature02871

10.1093/nar/gkv1221

10.1158/0008‐5472.CAN‐05‐1783

10.1016/j.ajg.2017.09.001

10.1093/nar/gkp002

10.1016/S0092‐8674(00)81906‐6

10.2174/138920371506140818112552

10.1109/TCBB.2013.5

10.3390/molecules23102439

Lan W. Wang J. Li M. Liu J.andPan Y.(2015)Predicting microRNA‐disease associations by integrating multiple biological information. In:EEE International Conference on Bioinformatics and Biomedicine (BIBM) pp.183–188

10.1016/j.ymeth.2017.05.024

10.1186/1752‐0509‐4‐S1‐S2

10.1371/journal.pone.0070204

10.1093/bioinformatics/btt677

10.1038/srep05501

10.1109/TNB.2016.2633276

10.1109/TCBB.2017.2776101

10.1016/j.jbi.2017.01.008

10.1109/TCBB.2016.2586190

10.1109/TCBB.2016.2550432

10.1155/2015/810514

10.1371/journal.pone.0078649

10.1371/journal.pone.0168284

10.1016/j.ajhg.2008.02.013

10.1093/bioinformatics/btw639

10.1007/978-1-4939-7717-8_12

10.1109/TST.2015.7297749

10.1093/nar/gkt1023

10.1093/nar/gky1126

10.1093/bioinformatics/btq241

10.1016/j.physa.2016.09.057

10.1109/TCBB.2018.2850884

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Quantitative Biology

Tập 7 Số 3

202-209

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