School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, China.
School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, China.
J Cell Mol Med. 2024 Oct;28(19):e18590. doi: 10.1111/jcmm.18590.
Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) are two typical types of non-coding RNAs that interact and play important regulatory roles in many animal organisms. Exploring the unknown interactions between lncRNAs and miRNAs contributes to a better understanding of their functional involvement. Currently, studying the interactions between lncRNAs and miRNAs heavily relies on laborious biological experiments. Therefore, it is necessary to design a computational method for predicting lncRNA-miRNA interactions. In this work, we propose a method called MPGK-LMI, which utilizes a graph attention network (GAT) to predict lncRNA-miRNA interactions in animals. First, we construct a meta-path similarity matrix based on known lncRNA-miRNA interaction information. Then, we use GAT to aggregate the constructed meta-path similarity matrix and the computed Gaussian kernel similarity matrix to update the feature matrix with neighbourhood information. Finally, a scoring module is used for prediction. By comparing with three state-of-the-art algorithms, MPGK-LMI achieves the best results in terms of performance, with AUC value of 0.9077, AUPR of 0.9327, ACC of 0.9080, F1-score of 0.9143 and precision of 0.8739. These results validate the effectiveness and reliability of MPGK-LMI. Additionally, we conduct detailed case studies to demonstrate the effectiveness and feasibility of our approach in practical applications. Through these empirical results, we gain deeper insights into the functional roles and mechanisms of lncRNA-miRNA interactions, providing significant breakthroughs and advancements in this field of research. In summary, our method not only outperforms others in terms of performance but also establishes its practicality and reliability in biological research through real-case analysis, offering strong support and guidance for future studies and applications.
长非编码 RNA（lncRNA）和 microRNA（miRNA）是两种典型的非编码 RNA，它们在许多动物生物中相互作用并发挥重要的调节作用。探索 lncRNA 和 miRNA 之间未知的相互作用有助于更好地理解它们的功能参与。目前，研究 lncRNA 和 miRNA 之间的相互作用严重依赖于费力的生物学实验。因此，有必要设计一种预测 lncRNA-miRNA 相互作用的计算方法。在这项工作中，我们提出了一种称为 MPGK-LMI 的方法，该方法利用图注意网络（GAT）来预测动物中的 lncRNA-miRNA 相互作用。首先，我们基于已知的 lncRNA-miRNA 相互作用信息构建了一个元路径相似性矩阵。然后，我们使用 GAT 聚合构建的元路径相似性矩阵和计算的高斯核相似性矩阵，使用邻域信息更新特征矩阵。最后，使用评分模块进行预测。通过与三种最先进的算法进行比较，MPGK-LMI 在性能方面取得了最佳结果，AUC 值为 0.9077，AUPR 值为 0.9327，ACC 值为 0.9080，F1 分数为 0.9143，精度为 0.8739。这些结果验证了 MPGK-LMI 的有效性和可靠性。此外，我们进行了详细的案例研究，以展示我们的方法在实际应用中的有效性和可行性。通过这些经验结果，我们深入了解了 lncRNA-miRNA 相互作用的功能作用和机制，为该研究领域提供了重大突破和进展。总之，我们的方法不仅在性能方面表现出色，而且通过实际案例分析在生物研究中建立了其实用性和可靠性，为未来的研究和应用提供了强有力的支持和指导。
