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

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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.

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Authors and Affiliations

  1. School of Computer Science, Shaanxi Normal University, Xi’an, 710119, China

    Xiu-Juan Lei & Chen Bian

  2. School of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China

    Yi Pan

  3. Department of Computer Science, Georgia State University, Atlanta, GA, 30302, USA

    Yi Pan

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  1. Xiu-Juan Lei
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  2. Chen Bian
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  3. Yi Pan
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Correspondence to Yi Pan.

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Lei, XJ., Bian, C. & Pan, Y. Predicting CircRNA-Disease Associations Based on Improved Weighted Biased Meta-Structure. J. Comput. Sci. Technol. 36, 288–298 (2021). https://doi.org/10.1007/s11390-021-0798-x

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