DRMDA: deep representations‐based miRNA–disease association prediction |
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Authors: | Xing Chen De‐Hong Zhang Zhu‐Hong You Zheng‐Wei Li |
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Institution: | 1. School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, ChinaThe authors wish it to be known that in their opinion, the first two authors should be regarded as joint first authors.;2. School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China;3. Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, ürümqi, China;4. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China |
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Abstract: | Recently, microRNAs (miRNAs) are confirmed to be important molecules within many crucial biological processes and therefore related to various complex human diseases. However, previous methods of predicting miRNA–disease associations have their own deficiencies. Under this circumstance, we developed a prediction method called deep representations‐based miRNA–disease association (DRMDA) prediction. The original miRNA–disease association data were extracted from HDMM database. Meanwhile, stacked auto‐encoder, greedy layer‐wise unsupervised pre‐training algorithm and support vector machine were implemented to predict potential associations. We compared DRMDA with five previous classical prediction models (HGIMDA, RLSMDA, HDMP, WBSMDA and RWRMDA) in global leave‐one‐out cross‐validation (LOOCV), local LOOCV and fivefold cross‐validation, respectively. The AUCs achieved by DRMDA were 0.9177, 08339 and 0.9156 ± 0.0006 in the three tests above, respectively. In further case studies, we predicted the top 50 potential miRNAs for colon neoplasms, lymphoma and prostate neoplasms, and 88%, 90% and 86% of the predicted miRNA can be verified by experimental evidence, respectively. In conclusion, DRMDA is a promising prediction method which could identify potential and novel miRNA–disease associations. |
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Keywords: | miRNA disease miRNA– disease association deep representation auto‐encoder |
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