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Open AccessArticle

Predicting Disease Related microRNA Based on Similarity and Topology

by Zhihua Chen 1,†, Xinke Wang 2,†, Peng Gao 2,†, Hongju Liu 3,† and Bosheng Song 4,*
Institute of Computing Science and Technology, Guangzhou University, Guangzhou 510006, China
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
College of Information Technology and Computer Science, University of the Cordilleras, Baguio 2600, Philippines
School of Information Science and Engineering, Hunan University, Changsha 410082, China
Author to whom correspondence should be addressed.
All authors contributed equally to this work.
Cells 2019, 8(11), 1405;
Received: 4 July 2019 / Revised: 31 October 2019 / Accepted: 5 November 2019 / Published: 7 November 2019
(This article belongs to the Special Issue Biocomputing and Synthetic Biology in Cells)
It is known that many diseases are caused by mutations or abnormalities in microRNA (miRNA). The usual method to predict miRNA disease relationships is to build a high-quality similarity network of diseases and miRNAs. All unobserved associations are ranked by their similarity scores, such that a higher score indicates a greater probability of a potential connection. However, this approach does not utilize information within the network. Therefore, in this study, we propose a machine learning method, called STIM, which uses network topology information to predict disease–miRNA associations. In contrast to the conventional approach, STIM constructs features according to information on similarity and topology in networks and then uses a machine learning model to predict potential associations. To verify the reliability and accuracy of our method, we compared STIM to other classical algorithms. The results of fivefold cross validation demonstrated that STIM outperforms many existing methods, particularly in terms of the area under the curve. In addition, the top 30 candidate miRNAs recommended by STIM in a case study of lung neoplasm have been confirmed in previous experiments, which proved the validity of the method. View Full-Text
Keywords: miRNA; network embedding; heterogeneous network; link prediction; topology information; machine learning miRNA; network embedding; heterogeneous network; link prediction; topology information; machine learning
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Chen, Z.; Wang, X.; Gao, P.; Liu, H.; Song, B. Predicting Disease Related microRNA Based on Similarity and Topology. Cells 2019, 8, 1405.

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