With the development of biotechnology and the accumulation of theories, many studies have found that microRNAs (miRNAs) play an important role in various diseases. Uncovering the potential associations between miRNAs and diseases is helpful to better understand the pathogenesis of complex diseases. However, traditional biological experiments are expensive and time-consuming. Therefore, it is necessary to develop more efficient computational methods for exploring underlying disease-related miRNAs. In this paper, we present a new computational method based on positive point-wise mutual information (PPMI) and attention network to predict miRNA-disease associations (MDAs), called PATMDA. Firstly, we construct the heterogeneous MDA network and multiple similarity networks of miRNAs and diseases. Secondly, we respectively perform random walk with restart and PPMI on different similarity network views to get multi-order proximity features and then obtain high-order proximity representations of miRNAs and diseases by applying the convolutional neural network to fuse the learned proximity features. Then, we design an attention network with neural aggregation to integrate the representations of a node and its heterogeneous neighbor nodes according to the MDA network. Finally, an inner product decoder is adopted to calculate the relationship scores between miRNAs and diseases. PATMDA achieves superior performance over the six state-of-the-art methods with the area under the receiver operating characteristic curve of 0.933 and 0.946 on the HMDD v2.0 and HMDD v3.2 datasets, respectively. The case studies further demonstrate the validity of PATMDA for discovering novel disease-associated miRNAs.

中文翻译：

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基于 PPMI 和注意力网络预测 miRNA-疾病关联

随着生物技术的发展和理论的积累，许多研究发现微小RNA（miRNA）在多种疾病中发挥着重要作用。揭示 miRNA 与疾病之间的潜在关联有助于更好地理解复杂疾病的发病机制。然而，传统的生物实验既昂贵又费时。因此，有必要开发更有效的计算方法来探索与潜在疾病相关的 miRNA。在本文中，我们提出了一种基于正点互信息 (PPMI) 和注意力网络来预测 miRNA 疾病关联 (MDA) 的新计算方法，称为 PATMDA。首先，我们构建了异构 MDA 网络和 miRNA 与疾病的多重相似网络。第二，我们分别在不同的相似性网络视图上执行带重启的随机游走和 PPMI 以获得多阶邻近特征，然后通过应用卷积神经网络融合学习到的邻近特征来获得 miRNA 和疾病的高阶邻近表示。然后，我们设计了一个具有神经聚合的注意力网络，以根据 MDA 网络整合节点及其异构邻居节点的表示。最后，采用内积解码器来计算 miRNA 与疾病之间的关系分数。PATMDA 在 HMDD v2.0 和 HMDD v3.2 数据集上的接收器操作特征曲线下面积分别为 0.933 和 0.946，实现了优于六种最先进方法的性能。