School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China.
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Int J Mol Sci. 2019 Jul 25;20(15):3648. doi: 10.3390/ijms20153648.
Identification of disease-associated miRNAs (disease miRNAs) are critical for understanding etiology and pathogenesis. Most previous methods focus on integrating similarities and associating information contained in heterogeneous miRNA-disease networks. However, these methods establish only shallow prediction models that fail to capture complex relationships among miRNA similarities, disease similarities, and miRNA-disease associations. We propose a prediction method on the basis of network representation learning and convolutional neural networks to predict disease miRNAs, called CNNMDA. CNNMDA deeply integrates the similarity information of miRNAs and diseases, miRNA-disease associations, and representations of miRNAs and diseases in low-dimensional feature space. The new framework based on deep learning was built to learn the original and global representation of a miRNA-disease pair. First, diverse biological premises about miRNAs and diseases were combined to construct the embedding layer in the left part of the framework, from a biological perspective. Second, the various connection edges in the miRNA-disease network, such as similarity and association connections, were dependent on each other. Therefore, it was necessary to learn the low-dimensional representations of the miRNA and disease nodes based on the entire network. The right part of the framework learnt the low-dimensional representation of each miRNA and disease node based on non-negative matrix factorization, and these representations were used to establish the corresponding embedding layer. Finally, the left and right embedding layers went through convolutional modules to deeply learn the complex and non-linear relationships among the similarities and associations between miRNAs and diseases. Experimental results based on cross validation indicated that CNNMDA yields superior performance compared to several state-of-the-art methods. Furthermore, case studies on lung, breast, and pancreatic neoplasms demonstrated the powerful ability of CNNMDA to discover potential disease miRNAs.
鉴定与疾病相关的 microRNA（疾病 microRNA）对于理解病因和发病机制至关重要。大多数先前的方法都侧重于整合 miRNA 疾病网络中包含的相似性和关联信息。然而，这些方法仅建立了浅层预测模型，无法捕捉 miRNA 相似性、疾病相似性和 miRNA 疾病关联之间的复杂关系。我们提出了一种基于网络表示学习和卷积神经网络的疾病 microRNA 预测方法，称为 CNNMDA。CNNMDA 深度整合了 miRNA 和疾病的相似性信息、miRNA 疾病关联以及 miRNA 和疾病在低维特征空间中的表示。基于深度学习的新框架用于学习 miRNA 疾病对的原始和全局表示。首先，从生物学角度出发，将 miRNA 和疾病的各种生物学前提条件组合起来构建框架左侧的嵌入层。其次，miRNA 疾病网络中的各种连接边，如相似性和关联连接，相互依赖。因此，有必要基于整个网络学习 miRNA 和疾病节点的低维表示。框架的右侧部分基于非负矩阵分解学习每个 miRNA 和疾病节点的低维表示，并使用这些表示来建立相应的嵌入层。最后，左、右嵌入层经过卷积模块，深入学习 miRNA 和疾病之间相似性和关联之间的复杂非线性关系。基于交叉验证的实验结果表明，与几种最先进的方法相比，CNNMDA 具有更好的性能。此外，对肺、乳腺和胰腺肿瘤的案例研究表明，CNNMDA 具有发现潜在疾病 microRNA 的强大能力。
