Circular RNA (circRNA) is a common non‐coding RNA and plays an important role in the diagnosis and therapy of human diseases, circRNA‐disease associations prediction based on computational methods can provide a new way for better clinical diagnosis. In this article, we proposed a novel method for circRNA‐disease associations prediction based on ensemble learning, named ELCDA. First, the association heterogeneous network was constructed via collecting multiple information of circRNAs and diseases, and multiple similarity measures are adopted here, then, we use metapath, matrix factorization and GraphSAGE‐based models to extract features of nodes from different views, the final comprehensive features of circRNAs and diseases via ensemble learning, finally, a soft voting ensemble strategy is used to integrate the predicted results of all classifier. The performance of ELCDA is evaluated by fivefold cross‐validation and compare with other state‐of‐the‐art methods, the experimental results show that ELCDA is outperformance than others. Furthermore, three common diseases are used as case studies, which also demonstrate that ELCDA is an effective method for predicting circRNA‐disease associations.

中文翻译：

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通过多模型融合和集成学习识别 circRNA 与疾病的关联

环状RNA（circRNA）是一种常见的非编码RNA，在人类疾病的诊断和治疗中发挥着重要作用，基于计算方法的circRNA与疾病关联预测可以为更好的临床诊断提供新途径。在本文中，我们提出了一种基于集成学习的 circRNA 疾病关联预测新方法，称为 ELCDA。首先，通过收集circRNA和疾病的多种信息构建关联异质网络，并采用多种相似性度量，然后，我们使用元路径、矩阵分解和基于GraphSAGE的模型从不同角度提取节点特征，最终综合通过集成学习来识别circRNA和疾病的特征，最后使用软投票集成策略来整合所有分类器的预测结果。 ELCDA的性能通过五重交叉验证进行评估，并与其他最先进的方法进行比较，实验结果表明ELCDA的性能优于其他方法。此外，以三种常见疾病作为案例研究，也证明ELCDA是预测circRNA与疾病关联的有效方法。