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DRMDA: deep representations-based miRNA-disease association prediction
Chen, X (Chen, Xing); Gong, Y (Gong, Yao); Zhang, DH (Zhang, De-Hong); You, ZH (You, Zhu-Hong); Li, ZW (Li, Zheng-Wei)
2018
Source PublicationJOURNAL OF CELLULAR AND MOLECULAR MEDICINE
ISSN1582-4934
Volume22Issue:1Pages:472-485
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.

KeywordMirna Disease Mirna-disease Association Deep Representation Auto-encoder
DOI10.1111/jcmm.13336
Indexed BySCI
WOS IDWOS:000418759200042
Citation statistics
Cited Times:14[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.xjipc.cas.cn/handle/365002/5114
Collection多语种信息技术研究室
Affiliation1.China Univ Min & Technol, Sch Informat & Control Engn, Xuzhou, Peoples R China
2.Peking Univ, Sch Life Sci, Beijing, Peoples R China
3.Chinese Acad Sci, Xinjiang Tech Inst Phys & Chem, Urumqi, Peoples R China
4.China Univ Min & Technol, Sch Comp Sci & Technol, Xuzhou, Peoples R China
Recommended Citation
GB/T 7714
Chen, X ,Gong, Y ,Zhang, DH ,et al. DRMDA: deep representations-based miRNA-disease association prediction[J]. JOURNAL OF CELLULAR AND MOLECULAR MEDICINE,2018,22(1):472-485.
APA Chen, X ,Gong, Y ,Zhang, DH ,You, ZH ,&Li, ZW .(2018).DRMDA: deep representations-based miRNA-disease association prediction.JOURNAL OF CELLULAR AND MOLECULAR MEDICINE,22(1),472-485.
MLA Chen, X ,et al."DRMDA: deep representations-based miRNA-disease association prediction".JOURNAL OF CELLULAR AND MOLECULAR MEDICINE 22.1(2018):472-485.
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