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. 2017 Nov 3;7:14482. doi: 10.1038/s41598-017-15235-6

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© The Author(s) 2017

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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The flowchart of proposed DeepMDA. The miRNA similarity was integrated using miRNA-functional similarity and miRNA-disease association. As for disease similarity, we adopted DAG information and Gaussian interaction profile similarity information. The two input data was fed into two stacked autoencoders to learn high-level features, then merged and finally utilized a 3-layer network to infer the association between miRNAs and diseases.