DEMLP: DeepWalk Embedding in MLP for miRNA-Disease Association Prediction
DEMLP: DeepWalk Embedding in MLP for miRNA-Disease Association Prediction
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DOI：
10.1155/2021/9678747
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发表时间：
2021-10
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影响因子：
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通讯作者：
Xun Wang;Fu-Yan Wang;Xinzeng Wang;Sibo Qiao;Zhuang Yu
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作者：
Xun Wang;Fu-Yan Wang;Xinzeng Wang;Sibo Qiao;Zhuang Yu
miRNAs显著影响涉及人类疾病的多种生物学过程。生物实验总是需要巨大的资金支持和时间成本。考虑到费用和难度，为了预测潜在的miRNA-疾病关联，基于miRNA-疾病关联数据集生成的网络，已经开发了许多高效的计算机计算方法。然而，存在着许多挑战。首先，miRNAs与疾病之间的关联是错综复杂的。这些方法应该考虑网络中每个节点的邻域的影响。其次，如何度量网络中两个节点之间是否存在关联也是一个重要的问题。在我们的研究中，我们创新地将图节点嵌入与多层感知器相结合，提出了一种方法DEMLP。开始，我们利用miRNA-disease adjacency matrix（MDA）构建了一个miRNA-disease网络。然后，通过DeepWalk从miRNA疾病网络中学习节点的低维嵌入表示向量。最后，我们使用这些低维嵌入表示向量作为输入来训练多层感知器。实验表明，该方法仅利用miRNA与疾病的关联信息，就能有效预测miRNA与疾病的关联。为了评估DEMLP在来自HMDD v3.2的miRNA疾病网络中的有效性，我们在我们的研究中应用了五重交叉验证。DEMLP的ROC-AUC计算结果值为0.943，DEMLP的PR-AUC值为0.937。与其他最先进的方法相比，我们的方法仅使用miRNA-疾病相互作用网络表现出良好的性能。
miRNAs significantly affect multifarious biological processes involving human disease. Biological experiments always need enormous financial support and time cost. Taking expense and difficulty into consideration, to predict the potential miRNA-disease associations, a lot of high-efficiency computational methods by computer have been developed, based on a network generated by miRNA-disease association dataset. However, there exist many challenges. Firstly, the association between miRNAs and diseases is intricate. These methods should consider the influence of the neighborhoods of each node from the network. Secondly, how to measure whether there is an association between two nodes of the network is also an important problem. In our study, we innovatively integrate graph node embedding with a multilayer perceptron and propose a method DEMLP. To begin with, we construct a miRNA-disease network by miRNA-disease adjacency matrix (MDA). Then, low-dimensional embedding representation vectors of nodes are learned from the miRNA-disease network by DeepWalk. Finally, we use these low-dimensional embedding representation vectors as input to train the multilayer perceptron. Experiments show that our proposed method that only utilized the miRNA–disease association information can effectively predict miRNA-disease associations. To evaluate the effectiveness of DEMLP in a miRNA-disease network from HMDD v3.2, we apply fivefold crossvalidation in our study. The ROC-AUC computed result value of DEMLP is 0.943, and the PR-AUC value of DEMLP is 0.937. Compared with other state-of-the-art methods, our method shows good performance using only the miRNA-disease interaction network.