Artificial Intelligence Research Institute, China University of Mining and Technology.
School of Information and Control Engineering, China University of Mining and Technology.
Brief Bioinform. 2021 May 20;22(3). doi: 10.1093/bib/bbaa186.
MicroRNA (miRNA) plays an important role in the occurrence, development, diagnosis and treatment of diseases. More and more researchers begin to pay attention to the relationship between miRNA and disease. Compared with traditional biological experiments, computational method of integrating heterogeneous biological data to predict potential associations can effectively save time and cost. Considering the limitations of the previous computational models, we developed the model of deep-belief network for miRNA-disease association prediction (DBNMDA). We constructed feature vectors to pre-train restricted Boltzmann machines for all miRNA-disease pairs and applied positive samples and the same number of selected negative samples to fine-tune DBN to obtain the final predicted scores. Compared with the previous supervised models that only use pairs with known label for training, DBNMDA innovatively utilizes the information of all miRNA-disease pairs during the pre-training process. This step could reduce the impact of too few known associations on prediction accuracy to some extent. DBNMDA achieves the AUC of 0.9104 based on global leave-one-out cross validation (LOOCV), the AUC of 0.8232 based on local LOOCV and the average AUC of 0.9048 ± 0.0026 based on 5-fold cross validation. These AUCs are better than other previous models. In addition, three different types of case studies for three diseases were implemented to demonstrate the accuracy of DBNMDA. As a result, 84% (breast neoplasms), 100% (lung neoplasms) and 88% (esophageal neoplasms) of the top 50 predicted miRNAs were verified by recent literature. Therefore, we could conclude that DBNMDA is an effective method to predict potential miRNA-disease associations.
微小 RNA（miRNA）在疾病的发生、发展、诊断和治疗中起着重要作用。越来越多的研究人员开始关注 miRNA 与疾病之间的关系。与传统的生物实验相比，整合异质生物数据的计算方法来预测潜在的关联可以有效地节省时间和成本。考虑到之前计算模型的局限性，我们开发了用于 miRNA-疾病关联预测的深度置信网络模型（DBNMDA）。我们构建特征向量，对所有 miRNA-疾病对进行受限玻尔兹曼机的预训练，并应用阳性样本和相同数量的选定阴性样本对 DBN 进行微调，以获得最终的预测分数。与之前仅使用具有已知标签的对进行训练的监督模型相比，DBNMDA 在预训练过程中创新性地利用了所有 miRNA-疾病对的信息。这一步可以在一定程度上减少已知关联太少对预测准确性的影响。DBNMDA 在全局留一交叉验证（LOOCV）上的 AUC 为 0.9104，在局部 LOOCV 上的 AUC 为 0.8232，在 5 折交叉验证上的平均 AUC 为 0.9048±0.0026。这些 AUC 优于其他之前的模型。此外，还针对三种疾病实施了三种不同类型的案例研究，以证明 DBNMDA 的准确性。结果，在 top 50 个预测 miRNA 中，有 84%（乳腺癌）、100%（肺癌）和 88%（食管癌）得到了最近文献的验证。因此，我们可以得出结论，DBNMDA 是一种预测潜在 miRNA-疾病关联的有效方法。
