Zheng Kai, You Zhu-Hong, Wang Lei, Guo Zhen-Hao
School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China.
Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Science, Urumqi 830011, China.
Comput Struct Biotechnol J. 2020 Sep 2;18:2391-2400. doi: 10.1016/j.csbj.2020.08.023. eCollection 2020.
Benefiting from advances in high-throughput experimental techniques, important regulatory roles of miRNAs, lncRNAs, and proteins, as well as biological property information, are gradually being complemented. As the key data support to promote biomedical research, domain knowledge such as intermolecular relationships that are increasingly revealed by molecular genome-wide analysis is often used to guide the discovery of potential associations. However, the method of performing network representation learning from the perspective of the global biological network is scarce. These methods cover a very limited type of molecular associations and are therefore not suitable for more comprehensive analysis of molecular network representation information. In this study, we propose a computational model based on the Biological network for predicting potential associations between miRNAs and diseases called iMDA-BN. The iMDA-BN has three significant advantages: I) It uses a new method to describe disease and miRNA characteristics which analyzes node representation information for disease and miRNA from the perspective of biological networks. II) It can predict unproven associations even if miRNAs and diseases do not appear in the biological network. III) Accurate description of miRNA characteristics from biological properties based on high-throughput sequence information. The iMDA-BN predictor achieves an AUC of 0.9145 and an accuracy of 84.49% on the miRNA-disease association baseline dataset, and it can also achieve an AUC of 0.8765 and an accuracy of 80.96% when predicting unknown diseases and miRNAs in the biological network. Compared to existing miRNA-disease association prediction methods, iMDA-BN has higher accuracy and the advantage of predicting unknown associations. In addition, 45, 49, and 49 of the top 50 miRNA-disease associations with the highest predicted scores were confirmed in the case studies, respectively.
受益于高通量实验技术的进步，miRNA、lncRNA和蛋白质的重要调控作用以及生物学特性信息正在逐渐得到补充。作为推动生物医学研究的关键数据支持，分子全基因组分析日益揭示的分子间关系等领域知识常被用于指导潜在关联的发现。然而，从全局生物网络角度进行网络表示学习的方法却很稀缺。这些方法涵盖的分子关联类型非常有限，因此不适用于对分子网络表示信息进行更全面的分析。在本研究中，我们提出了一种基于生物网络的计算模型，用于预测miRNA与疾病之间的潜在关联，称为iMDA - BN。iMDA - BN具有三个显著优点：I）它使用一种新方法来描述疾病和miRNA特征，即从生物网络的角度分析疾病和miRNA的节点表示信息。II）即使miRNA和疾病未出现在生物网络中，它也能预测未经验证的关联。III）基于高通量序列信息从生物学特性准确描述miRNA特征。iMDA - BN预测器在miRNA - 疾病关联基线数据集上的AUC为0.9145，准确率为84.49%，在预测生物网络中未知疾病和miRNA时，AUC也能达到为0.8765，准确率为80.96%。与现有的miRNA - 疾病关联预测方法相比，iMDA - BN具有更高的准确率以及预测未知关联的优势。此外，在案例研究中，预测得分最高的前50个miRNA - 疾病关联中，分别有45个、49个和从从49个得到了证实。
