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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Genet. | doi: 10.3389/fgene.2019.00832

Predicting CircRNA-Disease Associations Based on CircRNA Expression Similarity and Functional Similarity

 Yongtian Wang1*,  Chenxi Nie1, Tianyi Zang1* and Yadong Wang1
  • 1Harbin Institute of Technology, China

Circular RNAs (circRNAs) are a novel class of endogenous noncoding RNAs that have well-conserved sequences. Emerging evidence has shown that circRNAs can be novel biomarkers or therapeutic targets for many diseases and play an important role in the development of various pathological conditions. Therefore, identifying potential disease-related circRNAs is helpful in improving the efficiency of finding therapeutic targets for diseases. Here, we propose a computational model (PreCDA) to predict potential circRNA-disease associations. First, we calculated the circRNA expression similarity based on circRNA expression profiles. The circRNA functional similarity is calculated based on cosine similarity, and the disease similarity is used as the dimension of each circRNA vector. The associations between circRNAs and diseases are defined based on the circRNA functional similarity and expression similarity. We constructed a disease-related circRNA association network and used a graph-based recommendation algorithm (PersonalRank) to sort candidate disease-related circRNAs. As a result, PreCDA has an average area under the receiver operating characteristic curve (AUC) value of 78.15% in predicting candidate disease-related circRNAs. In addition, we discuss the factors that affect the performance of this method and find some unknown circRNAs related to diseases, with several common diseases used as case studies. These results show that PreCDA has good performance in predicting potential circRNA-disease associations and is helpful for the diagnosis and treatment of human diseases.

Keywords: circRNA, Disease, circRNA expression similarity, circRNA functional similarity, PersonalRank

Received: 24 Jun 2019; Accepted: 13 Aug 2019.

Copyright: © 2019 Wang, Nie, Zang and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Mx. Yongtian Wang, Harbin Institute of Technology, Harbin, China, ytwang@hit.edu.cn
Mx. Tianyi Zang, Harbin Institute of Technology, Harbin, China, tianyi.zang@hit.edu.cn