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Regular Paper

Predicting CircRNA-Disease Associations Based on Improved Weighted Biased Meta-Structure

School of Computer Science, Shaanxi Normal University, Xi’an 710119, China
School of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Department of Computer Science, Georgia State University, Atlanta, GA 30302, U.S.A.
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Abstract

Circular RNAs (circRNAs) are RNAs with a special closed loop structure, which play important roles in tumors and other diseases. Due to the time consumption of biological experiments, computational methods for predicting associations between circRNAs and diseases become a better choice. Taking the limited number of verified circRNA-disease associations into account, we propose a method named CDWBMS, which integrates a small number of verified circRNA-disease associations with a plenty of circRNA information to discover the novel circRNA-disease associations. CDWBMS adopts an improved weighted biased meta-structure search algorithm on a heterogeneous network to predict associations between circRNAs and diseases. In terms of leave-one-out-cross-validation (LOOCV), 10-fold cross-validation and 5-fold cross-validation, CDWBMS yields the area under the receiver operating characteristic curve (AUC) values of 0.921 6, 0.917 2 and 0.900 5, respectively. Furthermore, case studies show that CDWBMS can predict unknow circRNA-disease associations. In conclusion, CDWBMS is an effective method for exploring disease-related circRNAs.

Keywords

circular RNA (circRNA)circRNA-disease associationmeta-structureheterogeneous network

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Journal of Computer Science and Technology
Volume 36 Issue 2,
March 2021
Pages 288-298
DOI: 10.1007/s11390-021-0798-x
Cite this article:
Lei X-J, Bian C, Pan Y. Predicting CircRNA-Disease Associations Based on Improved Weighted Biased Meta-Structure. Journal of Computer Science and Technology, 2021, 36(2): 288-298. https://doi.org/10.1007/s11390-021-0798-x

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Received: 13 July 2020
Accepted: 23 February 2021
Published: 05 March 2021
©Institute of Computing Technology, Chinese Academy of Sciences 2021
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