Abstract The identification of disease-associated miRNAs can help people better understand the pathogenesis of diseases from a genetic perspective. Therefore, the prediction of miRNA-disease associations has received increasing attention. In this paper, we propose a new computational method NCFM (Neural network-based Collaborative Filtering Method) to predict miRNA-disease associations based on deep neural network. Firstly, high-dimensional sparse vectors of diseases and miRNAs are mapped into low-dimensional dense vectors in implicit semantic space via embedding layer, which called disease embedding and miRNA embedding, respectively. Secondly, the neural collaborative filter layers model the latent feature interactions between miRNAs and diseases. Then, different from other methods using square error loss function, we propose a new pairwise loss function to optimizes our model from a ranking perspective. Finally, experiments show that our proposed method can effectively prioritize disease-related miRNAs with the highest AUC of 0.912 and 0.921 compared with other recent methods in 5-fold cross validation and LOOCV framework. In addition, we implement two types of case studies, including four diseases. For a disease, more than 90% of predicted miRNAs are validated by another official dataset, which further illustrates the effectiveness of NCFM.

中文翻译：

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一种识别miRNA-疾病关联的神经协同过滤方法

摘要 疾病相关miRNA的鉴定可以帮助人们从遗传学的角度更好地了解疾病的发病机制。因此，miRNA-疾病关联的预测受到越来越多的关注。在本文中，我们提出了一种新的计算方法 NCFM（基于神经网络的协同过滤方法）来预测基于深度神经网络的 miRNA-疾病关联。首先，通过嵌入层将疾病和miRNA的高维稀疏向量映射到隐语义空间中的低维密集向量，分别称为疾病嵌入和miRNA嵌入。其次，神经协同过滤层对 miRNA 与疾病之间的潜在特征相互作用进行建模。那么，不同于其他使用平方误差损失函数的方法，我们提出了一个新的成对损失函数，从排名的角度优化我们的模型。最后，实验表明，与 5 倍交叉验证和 LOOCV 框架中的其他近期方法相比，我们提出的方法可以有效地优先考虑具有最高 AUC 为 0.912 和 0.921 的疾病相关 miRNA。此外，我们实施了两种类型的案例研究，包括四种疾病。对于一种疾病，超过 90% 的预测 miRNA 得到了另一个官方数据集的验证，这进一步说明了 NCFM 的有效性。我们实施两种类型的案例研究，包括四种疾病。对于一种疾病，超过 90% 的预测 miRNA 得到了另一个官方数据集的验证，这进一步说明了 NCFM 的有效性。我们实施两种类型的案例研究，包括四种疾病。对于一种疾病，超过 90% 的预测 miRNA 得到了另一个官方数据集的验证，这进一步说明了 NCFM 的有效性。