School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China.
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China.
Biomed Res Int. 2020 Dec 7;2020:6248686. doi: 10.1155/2020/6248686. eCollection 2020.
Successful prediction of miRNA-disease association is nontrivial for the diagnosis and prognosis of genetic diseases. There are many methods to predict miRNA and disease, but biological data are numerous and complex, and they often exist in the form of network. How to accurately use the features of miRNA and disease-related biological networks to predict unknown association has always been a challenge. Here, we propose PmDNE, a method based on network embedding and network similarity analysis, to predict the miRNA-disease association. In PmDNE, the structure of network bipartite graph is improved, and a random walk generator is designed. For embedded vectors, 128 dimensions are used, and the accuracy of prediction is significantly improved. Compared with other network embedding methods, PmDNE is comparable and competitive with the state of art methods. Our method can solve the problem of feature extraction, reduce the dimension of features, and improve the efficiency of miRNA-disease association prediction. This method can also be extended to other area for biomedical network prediction.
成功预测 miRNA 与疾病的关联对于遗传疾病的诊断和预后至关重要。有许多方法可以预测 miRNA 和疾病，但生物数据众多且复杂，它们通常以网络的形式存在。如何准确利用 miRNA 和疾病相关生物网络的特征来预测未知的关联一直是一个挑战。在这里，我们提出了 PmDNE，一种基于网络嵌入和网络相似性分析的方法，来预测 miRNA 与疾病的关联。在 PmDNE 中，改进了网络二分图的结构，并设计了一个随机游走生成器。对于嵌入向量，使用 128 维，预测的准确性得到了显著提高。与其他网络嵌入方法相比，PmDNE 与最先进的方法相当且具有竞争力。我们的方法可以解决特征提取的问题，降低特征的维度，提高 miRNA 与疾病关联预测的效率。该方法还可以扩展到其他生物医学网络预测领域。
