Journal of Computer Science and Technology ›› 2021, Vol. 36 ›› Issue (2): 288-298.doi: 10.1007/s11390-021-0798-x

Special Issue: Emerging Areas

• Special Section on AI and Big Data Analytics in Biology and Medicine • Previous Articles     Next Articles

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

Xiu-Juan Lei1, Senior Member, CCF, Member, ACM, IEEE, Chen Bian1, and Yi Pan2,3,*, Senior Member, IEEE        

  1. 1 School of Computer Science, Shaanxi Normal University, Xi'an 710119, China;
    2 School of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China;
    3 Department of Computer Science, Georgia State University, Atlanta, GA 30302, U.S.A
  • Received:2020-07-13 Revised:2021-02-23 Online:2021-03-05 Published:2021-04-01
  • Contact: Yi Pan
  • About author:Xiu-Juan Lei is a professor and Ph.D. supervisor at Shaanxi Normal University, Xi'an. She received her Ph.D. degree in Northwestern Polytechnical University, Xi'an, in 2005. Her research interests include bioinformatics and intelligent computing.
  • Supported by:
    The work was supported by the National Natural Science Foundation of China under Grant Nos. 61972451, 61672334 and 61902230, and the Fundamental Research Funds for the Central Universities of China under Grant No. GK201901010.

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.

Key words: circular RNA (circRNA); circRNA-disease association; meta-structure; heterogeneous network;

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