Circular RNAs (circRNAs) are a special class of non-coding RNAs with covalently closed-loop structures. Studies prove that circRNAs perform critical roles in various biological processes, and the aberrant expression of circRNAs is closely related to tumorigenesis. Therefore, identifying potential circRNA-disease associations is beneficial to understand the pathogenesis of complex diseases at the circRNA level and helps biomedical researchers and practitioners to discover diagnostic biomarkers accurately. However, it is tremendously laborious and time-consuming to discover disease-related circRNAs with conventional biological experiments. In this study, we develop an integrative framework, called iCDA-CMG, to predict potential associations between circRNAs and diseases. By incorporating multi-source prior knowledge, including known circRNA-disease associations, disease similarities and circRNA similarities, we adopt a collective matrix completion-based graph learning model to prioritize the most promising disease-related circRNAs for guiding laborious clinical trials. The results show that iCDA-CMG outperforms other state-of-the-art models in terms of cross-validation and independent prediction. Moreover, the case studies for several representative cancers suggest the effectiveness of iCDA-CMG in screening circRNA candidates for human diseases, which will contribute to elucidating the pathogenesis mechanisms and unveiling new opportunities for disease diagnosis and targeted therapy.

环状RNA（circRNA）是一类特殊的非编码RNA，具有共价闭环结构。研究证明circRNA在各种生物学过程中起关键作用，并且circRNA的异常表达与肿瘤发生密切相关。因此，鉴定潜在的circRNA-疾病关联有助于在circRNA一级了解复杂疾病的发病机理，并有助于生物医学研究人员和从业人员准确发现诊断性生物标志物。但是，通过常规生物学实验发现与疾病相关的circRNA非常费力且费时。在这项研究中，我们开发了一个称为iCDA-CMG的整合框架，以预测circRNA与疾病之间的潜在关联。通过整合多源先验知识，包括已知的circRNA-疾病关联，疾病相似性和circRNA相似性，我们采用基于集合矩阵完成的图学习模型来对最有希望的与疾病相关的circRNA进行优先排序，以指导费力的临床试验。结果表明，在交叉验证和独立预测方面，iCDA-CMG优于其他最新模型。此外，对几种代表性癌症的病例研究表明，iCDA-CMG在筛选人类疾病的circRNA候选物中的有效性，这将有助于阐明发病机理，为疾病诊断和靶向治疗提供新的机会。我们采用基于集体矩阵完成度的图形学习模型来确定最有前途的疾病相关circRNA的优先级，以指导费力的临床试验。结果表明，在交叉验证和独立预测方面，iCDA-CMG优于其他最新模型。此外，对几种代表性癌症的病例研究表明，iCDA-CMG在筛选人类疾病的circRNA候选物中的有效性，这将有助于阐明发病机理，为疾病诊断和靶向治疗提供新的机会。我们采用基于集体矩阵完成度的图形学习模型来确定最有前途的疾病相关circRNA的优先级，以指导费力的临床试验。结果表明，在交叉验证和独立预测方面，iCDA-CMG优于其他最新模型。此外，对几种代表性癌症的病例研究表明，iCDA-CMG在筛选人类疾病的circRNA候选物中的有效性，这将有助于阐明发病机理，为疾病诊断和靶向治疗提供新的机会。