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.

环状RNA（circRNA）是具有特殊闭环结构的RNA，在肿瘤和其他疾病中起重要作用。由于生物学实验耗时，因此预测circRNA与疾病之间关联的计算方法成为更好的选择。考虑到有限数量的已验证circRNA-疾病关联，我们提出了一种名为CDWBMS的方法，该方法将少量已验证的circRNA-疾病关联与大量circRNA信息整合在一起，从而发现新的circRNA-疾病关联。CDWBMS在异类网络上采用了改进的加权偏向元结构搜索算法，以预测circRNA与疾病之间的关联。在留一法交叉验证（LOOCV），10倍交叉验证和5倍交叉验证方面，CDWBMS得出的接收器工作特性曲线（AUC）值分别为0.921 6、0.917 2和0.900 5。此外，案例研究表明CDWBMS可以预测未知的circRNA-疾病关联。总之，CDWBMS是探索疾病相关circRNA的有效方法。