Liang Shu1, Cheng Zhou1, Xinxu Yuan2, Jingpu Zhang3, Lei Deng4. 1. School of Computer Science and Engineering, Central South University, Lushangnan Road, Changsha, China. 2. Department of Chemical and Life Science Engineering, Virginia Commonwealth University, Richmond, VA, 23284, USA. 3. School of Computer and Data Science, Henan University of Urban Construction, Longxiang Road, Pingdingshan, 467000, China. 4. School of Computer Science and Engineering, Central South University, Lushangnan Road, Changsha, China. leideng@csu.edu.cn.
Abstract
BACKGROUND: More and more evidence shows that circRNA plays an important role in various biological processes and human health. Therefore, inferring the circRNA's potential functions and obtaining circRNA functional similarity has become more and more significant. However, there is no effective approach to explore the functional similarity of circRNAs. METHODS: In this paper, we propose a new approach, called MSCFS, to calculate the functional similarity of circRNA by integrating multiple data sources. We combine circRNA-disease association, circRNA-gene-Gene Ontology association, and circRNA sequence information to explore the functional similarity of circRNA. Firstly, we employ different learning representation methods from three data sources to establish three circRNA functional similarity networks. Then we integrate the three networks to obtain the final circRNA functional similarity. RESULTS: We utilize circRNA-miRNA association similarity and circRNA co-expression similarity to evaluate the performance of MSCFS. The results show a positive correlation with miRNA association ([Formula: see text]) and circRNA co-expression similarity ([Formula: see text]). Finally, we construct a circRNA functional similarity network and perform case analysis. The result shows our method can be applied to infer new potential functions of circRNA and other associations. CONCLUSIONS: MSCFS combines multiple data sources related to circRNA functions. Correlation analysis and case analyses prove that MSCFS is a useful method to explore circRNA functional similarity.
BACKGROUND: More and more evidence shows that circRNA plays an important role in various biological processes and human health. Therefore, inferring the circRNA's potential functions and obtaining circRNA functional similarity has become more and more significant. However, there is no effective approach to explore the functional similarity of circRNAs. METHODS: In this paper, we propose a new approach, called MSCFS, to calculate the functional similarity of circRNA by integrating multiple data sources. We combine circRNA-disease association, circRNA-gene-Gene Ontology association, and circRNA sequence information to explore the functional similarity of circRNA. Firstly, we employ different learning representation methods from three data sources to establish three circRNA functional similarity networks. Then we integrate the three networks to obtain the final circRNA functional similarity. RESULTS: We utilize circRNA-miRNA association similarity and circRNA co-expression similarity to evaluate the performance of MSCFS. The results show a positive correlation with miRNA association ([Formula: see text]) and circRNA co-expression similarity ([Formula: see text]). Finally, we construct a circRNA functional similarity network and perform case analysis. The result shows our method can be applied to infer new potential functions of circRNA and other associations. CONCLUSIONS: MSCFS combines multiple data sources related to circRNA functions. Correlation analysis and case analyses prove that MSCFS is a useful method to explore circRNA functional similarity.
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