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.

中文翻译：

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用于预测潜在 miRNA 与疾病关联的深度置信网络。

MicroRNA（miRNA）在疾病的发生、发展、诊断和治疗中发挥着重要作用。越来越多的研究者开始关注miRNA与疾病的关系。与传统的生物实验相比，整合异质生物数据来预测潜在关联的计算方法可以有效地节省时间和成本。考虑到先前计算模型的局限性，我们开发了用于 miRNA 疾病关联预测的深信网络模型（DBNMDA）。我们构建了特征向量来为所有 miRNA 疾病对预训练受限玻尔兹曼机，并应用正样本和相同数量的选定负样本来微调 DBN 以获得最终预测分数。与之前仅使用已知标签对进行训练的监督模型相比，DBNMDA 在预训练过程中创新地利用了所有 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 基于全局留一法交叉验证 (LOOCV) 的 AUC 为 0.9104，基于局部 LOOCV 的 AUC 为 0.8232，基于 5 倍交叉验证的平均 AUC 为 0.9048 ± 0.0026。这些 AUC 优于之前的其他模型。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。因此，最近的文献证实了前 50 名预测的 miRNA 中有 84%（乳腺肿瘤）、100%（肺肿瘤）和 88%（食管肿瘤）。所以，