MSCFS: inferring circRNA functional similarity based on multiple data sources

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 (R= 0.213) and circRNA co-expression similarity (R= 0.8991). 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.