RBNE-CMI: An Efficient Method for Predicting circRNA-miRNA Interactions via Multiattribute Incomplete Heterogeneous Network Embedding,Journal of Chemical Information and Modeling

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RBNE-CMI: An Efficient Method for Predicting circRNA-miRNA Interactions via Multiattribute Incomplete Heterogeneous Network Embedding
Journal of Chemical Information and Modeling ( IF 5.6 ) Pub Date : 2024-09-04 , DOI: 10.1021/acs.jcim.4c01118
Chang-Qing Yu ₁ , Xin-Fei Wang ₂ , Li-Ping Li ₃ , Zhu-Hong You ₄ , Zhong-Hao Ren ₅ , Peng Chu ₁ , Feng Guo ₁ , Zhen-Yu Wang ₆

Affiliation

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.

中文翻译：

RBNE-CMI：一种通过多属性不完全异质网络嵌入预测 circRNA-miRNA 相互作用的有效方法

环状RNA（circRNA）-微小RNA（miRNA）相互作用（CMI）在细胞调节中发挥着至关重要的作用，为疾病诊断和治疗提供了有前景的前景。因此，有必要采用计算方法来快速且经济有效地预测潜在的 circRNA-miRNA 相互作用。然而，现有方法受到数据不完整的限制；因此，很难对具有不同属性的分子进行大规模建模，这极大地阻碍了预测的效率和性能。在这项研究中，我们提出了一种预测 circRNA-miRNA 相互作用的有效方法，称为 RBNE-CMI，并引入了一个可以嵌入不完整的多属性 CMI 异构网络的框架。通过结合所提出的方法，我们将 CMI 预测领域的不同数据集集成到一个不完整网络中进行建模，在 5 倍交叉验证中取得了优异的性能。而且，在基于完整数据的预测任务中，所提出的方法仍然取得了比已知模型更好的性能。此外，在案例研究中，我们成功预测了 20 种潜在癌症生物标志物中的 18 种。数据和源代码可以在https://github.com/1axin/RBNE-CMI找到。

更新日期：2024-09-04

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