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

• 作者: Xiu-Juan Lei Chen Bian Yi Pan
• 来源期刊: 计算机科学技术学报（英文版）
• 年，卷（期）: 2021,（2）
• 所属期刊栏目: Special Section on AI Big Data Analytics in Biology and Medicine
• 页码范围: 288-298
• 页数: 11页
• 语种: 英文

摘要

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 0.9216,0.9172 and 0.9005,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.