Zhang Xue, Zou Quan, Niu Mengting, Wang Chunyu
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150000, China.
Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan 610000, China.
Bioinformatics. 2025 Mar 4;41(3). doi: 10.1093/bioinformatics/btaf088.
Circular RNAs (circRNAs) have been identified as key players in the progression of several diseases; however, their roles have not yet been determined because of the high financial burden of biological studies. This highlights the urgent need to develop efficient computational models that can predict circRNA-disease associations, offering an alternative approach to overcome the limitations of expensive experimental studies. Although multi-view learning methods have been widely adopted, most approaches fail to fully exploit the latent information across views, while simultaneously overlooking the fact that different views contribute to varying degrees of significance.
This study presents a method that combines multi-view shared units and multichannel attention mechanisms to predict circRNA-disease associations (MSMCDA). MSMCDA first constructs similarity and meta-path networks for circRNAs and diseases by introducing shared units to facilitate interactive learning across distinct network features. Subsequently, multichannel attention mechanisms were used to optimize the weights within similarity networks. Finally, contrastive learning strengthened the similarity features. Experiments on five public datasets demonstrated that MSMCDA significantly outperformed other baseline methods. Additionally, case studies on colorectal cancer, gastric cancer, and nonsmall cell lung cancer confirmed the effectiveness of MSMCDA in uncovering new associations.
The source code and data are available at https://github.com/zhangxue2115/MSMCDA.git.
环状RNA（circRNA）已被确定为多种疾病进展中的关键因素；然而，由于生物学研究的高昂经济负担，它们的作用尚未确定。这凸显了迫切需要开发高效的计算模型来预测circRNA与疾病的关联，从而提供一种替代方法来克服昂贵实验研究的局限性。尽管多视图学习方法已被广泛采用，但大多数方法未能充分利用跨视图的潜在信息，同时忽略了不同视图具有不同程度重要性这一事实。
本研究提出了一种结合多视图共享单元和多通道注意力机制来预测circRNA与疾病关联的方法（MSMCDA）。MSMCDA首先通过引入共享单元构建circRNA和疾病的相似性及元路径网络，以促进跨不同网络特征的交互学习。随后，使用多通道注意力机制优化相似性网络内的权重。最后，对比学习强化了相似性特征。在五个公共数据集上的实验表明，MSMCDA显著优于其他基线方法。此外，对结直肠癌、胃癌和非小细胞肺癌的案例研究证实了MSMCDA在揭示新关联方面的有效性。
