Huang Chen, Cen Keliang, Zhang Yang, Liu Bo, Wang Yadong, Li Junyi
School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China.
College of Science, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China.
Life (Basel). 2022 Oct 11;12(10):1578. doi: 10.3390/life12101578.
Correct prediction of potential miRNA-disease pairs can considerably accelerate the experimental process in biomedical research. However, many methods cannot effectively learn the complex information contained in multisource data, limiting the performance of the prediction model. A heterogeneous network prediction model (MEAHNE) is proposed to make full use of the complex information contained in multisource data. To fully mine the potential relationship between miRNA and disease, we collected multisource data and constructed a heterogeneous network. After constructing the network, we mined potential associations in the network through a designed heterogeneous network framework (MEAHNE). MEAHNE first learned the semantic information of the metapath instances, then used the attention mechanism to encode the semantic information as attention weights and aggregated nodes of the same type using the attention weights. The semantic information was also integrated into the node. MEAHNE optimized parameters through end-to-end training. MEAHNE was compared with other state-of-the-art heterogeneous graph neural network methods. The values of the area under the precision-recall curve and the receiver operating characteristic curve demonstrated the superiority of MEAHNE. In addition, MEAHNE predicted 20 miRNAs each for breast cancer and nasopharyngeal cancer and verified 18 miRNAs related to breast cancer and 14 miRNAs related to nasopharyngeal cancer by consulting related databases.
准确预测潜在的miRNA-疾病对能够显著加速生物医学研究中的实验进程。然而，许多方法无法有效学习多源数据中包含的复杂信息，从而限制了预测模型的性能。为此，我们提出了一种异质网络预测模型（MEAHNE），以充分利用多源数据中包含的复杂信息。为了全面挖掘miRNA与疾病之间的潜在关系，我们收集了多源数据并构建了一个异质网络。构建网络后，我们通过设计的异质网络框架（MEAHNE）在网络中挖掘潜在关联。MEAHNE首先学习元路径实例的语义信息，然后使用注意力机制将语义信息编码为注意力权重，并使用注意力权重聚合相同类型的节点。语义信息也被整合到节点中。MEAHNE通过端到端训练优化参数。我们将MEAHNE与其他先进的异质图神经网络方法进行了比较。精确率-召回率曲线下面积和受试者工作特征曲线的值证明了MEAHNE的优越性。此外，MEAHNE分别为乳腺癌和鼻咽癌预测了20个miRNA，并通过查阅相关数据库验证了18个与乳腺癌相关的miRNA和14个与鼻咽癌相关的miRNA。
