School of Information Engineering, Xijing University, Xi'an, China.
School of Computer Science, Northwestern Polytechnical University, Xi'an, China.
Brief Bioinform. 2023 May 19;24(3). doi: 10.1093/bib/bbad111.
A large number of studies have shown that circular RNA (circRNA) affects biological processes by competitively binding miRNA, providing a new perspective for the diagnosis, and treatment of human diseases. Therefore, exploring the potential circRNA-miRNA interactions (CMIs) is an important and urgent task at present. Although some computational methods have been tried, their performance is limited by the incompleteness of feature extraction in sparse networks and the low computational efficiency of lengthy data.
In this paper, we proposed JSNDCMI, which combines the multi-structure feature extraction framework and Denoising Autoencoder (DAE) to meet the challenge of CMI prediction in sparse networks. In detail, JSNDCMI integrates functional similarity and local topological structure similarity in the CMI network through the multi-structure feature extraction framework, then forces the neural network to learn the robust representation of features through DAE and finally uses the Gradient Boosting Decision Tree classifier to predict the potential CMIs. JSNDCMI produces the best performance in the 5-fold cross-validation of all data sets. In the case study, seven of the top 10 CMIs with the highest score were verified in PubMed.
The data and source code can be found at https://github.com/1axin/JSNDCMI.
大量研究表明，环状 RNA（circRNA）通过竞争性结合 miRNA 来影响生物过程，为人类疾病的诊断和治疗提供了新视角。因此，探索潜在的 circRNA-miRNA 相互作用（CMI）是当前一项重要且紧迫的任务。尽管已经尝试了一些计算方法，但它们的性能受到稀疏网络中特征提取不完整和冗长数据计算效率低的限制。
在本文中，我们提出了 JSNDCMI，它结合了多结构特征提取框架和去噪自动编码器（DAE），以应对稀疏网络中 CMI 预测的挑战。具体来说，JSNDCMI 通过多结构特征提取框架整合了 CMI 网络中的功能相似性和局部拓扑结构相似性，然后通过 DAE 迫使神经网络学习特征的稳健表示，最后使用梯度提升决策树分类器来预测潜在的 CMIs。JSNDCMI 在所有数据集的 5 折交叉验证中均取得了最佳性能。在案例研究中，在 PubMed 中验证了得分最高的前 10 个 CMI 中的 7 个。
数据和源代码可在 https://github.com/1axin/JSNDCMI 上找到。
