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Biological experiments always need enormous financial support and time cost. Taking expense and difficulty into consideration, to predict the potential miRNA\u2010disease associations, a lot of high\u2010efficiency computational methods by computer have been developed, based on a network generated by miRNA\u2010disease 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\u2010disease network by miRNA\u2010disease adjacency matrix (MDA). Then, low\u2010dimensional embedding representation vectors of nodes are learned from the miRNA\u2010disease network by DeepWalk. Finally, we use these low\u2010dimensional embedding representation vectors as input to train the multilayer perceptron. Experiments show that our proposed method that only utilized the miRNA\u2013disease association information can effectively predict miRNA\u2010disease associations. To evaluate the effectiveness of DEMLP in a miRNA\u2010disease network from HMDD v3.2, we apply fivefold crossvalidation in our study. The ROC\u2010AUC computed result value of DEMLP is 0.943, and the PR\u2010AUC value of DEMLP is 0.937. Compared with other state\u2010of\u2010the\u2010art methods, our method shows good performance using only the miRNA\u2010disease interaction network.<\/jats:p>","DOI":"10.1155\/2021\/9678747","type":"journal-article","created":{"date-parts":[[2021,10,16]],"date-time":"2021-10-16T18:17:04Z","timestamp":1634408224000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["DEMLP: DeepWalk Embedding in MLP for miRNA\u2010Disease Association 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