HAMDA: Hybrid Approach for MiRNA-Disease Association prediction

doi:10.1016/j.jbi.2017.10.014

CSpace > 应用数学研究所

	HAMDA: Hybrid Approach for MiRNA-Disease Association prediction
	Chen, Xing 1; Niu, Ya-Wei 2; Wang, Guang-Hui 2; Yan, Gui-Ying3
	2017-12-01
发表期刊	JOURNAL OF BIOMEDICAL INFORMATICS
ISSN	1532-0464
卷号	76 页码:50-58
摘要	For 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.
关键词	miRNA Disease miRNA-disease association Hybrid prediction approach Recommendation systems
DOI	10.1016/j.jbi.2017.10.014
语种	英语
资助项目	Fundamental Research Funds for the Central Universities[2017XKQY083]
WOS研究方向	Computer Science ; Medical Informatics
WOS类目	Computer Science, Interdisciplinary Applications ; Medical Informatics
WOS记录号	WOS:000426221400006
出版者	ACADEMIC PRESS INC ELSEVIER SCIENCE
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/29621
专题	应用数学研究所
通讯作者	Chen, Xing; Wang, Guang-Hui
作者单位	1.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
推荐引用方式 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.