School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 21116, Jiangsu, China.
BMC Bioinformatics. 2022 Jul 25;23(1):299. doi: 10.1186/s12859-022-04843-3.
A large number of evidences from biological experiments have confirmed that miRNAs play an important role in the progression and development of various human complex diseases. However, the traditional experiment methods are expensive and time-consuming. Therefore, it is a challenging task that how to develop more accurate and efficient methods for predicting potential associations between miRNA and disease.
In the study, we developed a computational model that combined heterogeneous graph convolutional network with enhanced layer for miRNA-disease association prediction (HGCNELMDA). The major improvement of our method lies in through restarting the random walk optimized the original features of nodes and adding a reinforcement layer to the hidden layer of graph convolutional network retained similar information between nodes in the feature space. In addition, the proposed approach recalculated the influence of neighborhood nodes on target nodes by introducing the attention mechanism. The reliable performance of the HGCNELMDA was certified by the AUC of 93.47% in global leave-one-out cross-validation (LOOCV), and the average AUCs of 93.01% in fivefold cross-validation. Meanwhile, we compared the HGCNELMDA with the state‑of‑the‑art methods. Comparative results indicated that o the HGCNELMDA is very promising and may provide a cost‑effective alternative for miRNA-disease association prediction. Moreover, we applied HGCNELMDA to 3 different case studies to predict potential miRNAs related to lung cancer, prostate cancer, and pancreatic cancer. Results showed that 48, 50, and 50 of the top 50 predicted miRNAs were supported by experimental association evidence. Therefore, the HGCNELMDA is a reliable method for predicting disease-related miRNAs.
The results of the HGCNELMDA method in the LOOCV (leave-one-out cross validation, LOOCV) and 5-cross validations were 93.47% and 93.01%, respectively. Compared with other typical methods, the performance of HGCNELMDA is higher. Three cases of lung cancer, prostate cancer, and pancreatic cancer were studied. Among the predicted top 50 candidate miRNAs, 48, 50, and 50 were verified in the biological database HDMMV2.0. Therefore; this further confirms the feasibility and effectiveness of our method. Therefore, this further confirms the feasibility and effectiveness of our method. To facilitate extensive studies for future disease-related miRNAs research, we developed a freely available web server called HGCNELMDA is available at http://124.221.62.44:8080/HGCNELMDA.jsp .
大量生物学实验证据证实，miRNA 在多种人类复杂疾病的发生发展中发挥着重要作用。然而，传统的实验方法既昂贵又耗时。因此，开发更准确、更高效的 miRNA 与疾病潜在关联预测方法是一项具有挑战性的任务。
本研究中，我们开发了一种计算模型，将异构图卷积网络与增强层相结合，用于 miRNA-疾病关联预测（HGCNELMDA）。该方法的主要改进在于通过重新启动随机游走优化节点的原始特征，并在图卷积网络的隐藏层中添加强化层，保留特征空间中节点之间的相似信息。此外，通过引入注意力机制，重新计算了邻近节点对目标节点的影响。HGCNELMDA 在全局留一法交叉验证（LOOCV）中的 AUC 达到 93.47%，在 5 重交叉验证中的平均 AUC 达到 93.01%，证明了其可靠性能。同时，我们将 HGCNELMDA 与最先进的方法进行了比较。比较结果表明，HGCNELMDA 具有很大的潜力，可能为 miRNA-疾病关联预测提供一种具有成本效益的替代方法。此外，我们还将 HGCNELMDA 应用于 3 个不同的案例研究，以预测与肺癌、前列腺癌和胰腺癌相关的潜在 miRNA。结果表明，在 top50 预测 miRNA 中，有 48、50 和 50 个得到了实验关联证据的支持。因此，HGCNELMDA 是一种可靠的预测疾病相关 miRNA 的方法。
HGCNELMDA 方法在 LOOCV（留一法交叉验证，LOOCV）和 5 重交叉验证中的 AUC 分别为 93.47%和 93.01%。与其他典型方法相比，HGCNELMDA 的性能更高。对肺癌、前列腺癌和胰腺癌 3 个病例进行了研究。在预测的 top50 候选 miRNA 中，有 48、50 和 50 个在生物数据库 HDMMV2.0 中得到了验证。因此，这进一步证实了我们方法的可行性和有效性。为了便于未来疾病相关 miRNA 研究的广泛研究，我们开发了一个免费的在线服务器，网址为：http://124.221.62.44:8080/HGCNELMDA.jsp。
