School of Information Engineering, Xijing University, Xi'an 710123 China.
College of Computer Science and Technology, Jilin University, Changchun 130012 China.
J Chem Inf Model. 2024 Sep 23;64(18):7163-7172. doi: 10.1021/acs.jcim.4c01118. Epub 2024 Sep 4.
Circular RNA (circRNA)-microRNA (miRNA) interaction (CMI) plays crucial roles in cellular regulation, offering promising perspectives for disease diagnosis and therapy. Therefore, it is necessary to employ computational methods for the rapid and cost-effective prediction of potential circRNA-miRNA interactions. However, the existing methods are limited by incomplete data; therefore, it is difficult to model molecules with different attributes on a large scale, which greatly hinders the efficiency and performance of prediction. In this study, we propose an effective method for predicting circRNA-miRNA interactions, called RBNE-CMI, and introduce a framework that can embed incomplete multiattribute CMI heterogeneous networks. By combining the proposed method, we integrate different data sets in the CMI prediction field into one incomplete network for modeling, achieving superior performance in 5-fold cross-validation. Moreover, in the prediction task based on complete data, the proposed method still achieves better performance than the known model. In addition, in the case study, we successfully predicted 18 of the 20 potential cancer biomarkers. The data and source code can be found at https://github.com/1axin/RBNE-CMI.
环状 RNA（circRNA）-microRNA（miRNA）相互作用（CMI）在细胞调控中发挥着关键作用，为疾病诊断和治疗提供了有前景的视角。因此，有必要采用计算方法来快速、经济有效地预测潜在的 circRNA-miRNA 相互作用。然而，现有的方法受到数据不完整的限制，因此很难在大规模上对具有不同属性的分子进行建模，这极大地阻碍了预测的效率和性能。在这项研究中，我们提出了一种有效的 circRNA-miRNA 相互作用预测方法，称为 RBNE-CMI，并引入了一个可以嵌入不完整多属性 CMI 异构网络的框架。通过结合提出的方法，我们将 CMI 预测领域中的不同数据集整合到一个不完整的网络中进行建模，在 5 倍交叉验证中实现了卓越的性能。此外，在基于完整数据的预测任务中，所提出的方法仍然比已知模型具有更好的性能。此外，在案例研究中，我们成功预测了 20 个潜在癌症生物标志物中的 18 个。数据和源代码可以在 https://github.com/1axin/RBNE-CMI 上找到。
