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HAMDA: Hybrid Approach for MiRNA-Disease Association prediction
Chen, Xing1; Niu, Ya-Wei2; Wang, Guang-Hui2; Yan, Gui-Ying3
2017-12-01
Source PublicationJOURNAL OF BIOMEDICAL INFORMATICS
ISSN1532-0464
Volume76Pages:50-58
AbstractFor decades, enormous experimental researches have collectively indicated that microRNA (miRNA) could play indispensable roles in many critical biological processes and thus also the pathogenesis of human complex diseases. Whereas the resource and time cost required in traditional biology experiments are expensive, more and more attentions have been paid to the development of effective and feasible computational methods for predicting potential associations between disease and miRNA. In this study, we developed a computational model of Hybrid Approach for MiRNA-Disease Association prediction (HAMDA), which involved the hybrid graph-based recommendation algorithm, to reveal novel miRNA-disease associations by integrating experimentally verified miRNA-disease associations, disease semantic similarity, miRNA functional similarity, and Gaussian interaction profile kernel similarity into a recommendation algorithm. HAMDA took not only network structure and information propagation but also node attribution into consideration, resulting in a satisfactory prediction performance. Specifically, HAMDA obtained AUCs of 0.9035 and 0.8395 in the frameworks of global and local leave-one-out cross validation, respectively. Meanwhile, HAMDA also achieved good performance with AUC of 0.8965 +/- 0.0012 in 5-fold cross validation. Additionally, we conducted case studies about three important human cancers for performance evaluation of HAMDA. As a result, 90% (Lymphoma), 86% (Prostate Cancer) and 92% (Kidney Cancer) of top 50 predicted miRNAs were confirmed by recent experiment literature, which showed the reliable prediction ability of HAMDA.
KeywordmiRNA Disease miRNA-disease association Hybrid prediction approach Recommendation systems
DOI10.1016/j.jbi.2017.10.014
Language英语
Funding ProjectFundamental Research Funds for the Central Universities[2017XKQY083]
WOS Research AreaComputer Science ; Medical Informatics
WOS SubjectComputer Science, Interdisciplinary Applications ; Medical Informatics
WOS IDWOS:000426221400006
PublisherACADEMIC PRESS INC ELSEVIER SCIENCE
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Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/29621
Collection应用数学研究所
Affiliation1.China Univ Min & Technol, Sch Informat & Control Engn, Xuzhou 221116, Peoples R China
2.Shandong Univ, Sch Math, Jinan 250100, Shandong, Peoples R China
3.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Chen, Xing,Niu, Ya-Wei,Wang, Guang-Hui,et al. HAMDA: Hybrid Approach for MiRNA-Disease Association prediction[J]. JOURNAL OF BIOMEDICAL INFORMATICS,2017,76:50-58.
APA Chen, Xing,Niu, Ya-Wei,Wang, Guang-Hui,&Yan, Gui-Ying.(2017).HAMDA: Hybrid Approach for MiRNA-Disease Association prediction.JOURNAL OF BIOMEDICAL INFORMATICS,76,50-58.
MLA Chen, Xing,et al."HAMDA: Hybrid Approach for MiRNA-Disease Association prediction".JOURNAL OF BIOMEDICAL INFORMATICS 76(2017):50-58.
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