中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
HGIMDA: Heterogeneous graph inference for miRNA-disease association prediction

文献类型:期刊论文

作者Chen, Xing1; Yan, Chenggang Clarence2; Zhang, Xu3; You, Zhu-Hong4; Huang, Yu-An5; Yan, Gui-Ying6
刊名ONCOTARGET
出版日期2016-10-04
卷号7期号:40页码:65257-65269
关键词microRNA disease microRNA-disease association heterogeneous network similarity
ISSN号1949-2553
DOI10.18632/oncotarget.11251
英文摘要Recently, microRNAs (miRNAs) have drawn more and more attentions because accumulating experimental studies have indicated miRNA could play critical roles in multiple biological processes as well as the development and progression of human complex diseases. Using the huge number of known heterogeneous biological datasets to predict potential associations between miRNAs and diseases is an important topic in the field of biology, medicine, and bioinformatics. In this study, considering the limitations in the previous computational methods, we developed the computational model of Heterogeneous Graph Inference for MiRNA-Disease Association prediction (HGIMDA) to uncover potential miRNA-disease associations by integrating miRNA functional similarity, disease semantic similarity, Gaussian interaction profile kernel similarity, and experimentally verified miRNA-disease associations into a heterogeneous graph. HGIMDA obtained AUCs of 0.8781 and 0.8077 based on global and local leave-one-out cross validation, respectively. Furthermore, HGIMDA was applied to three important human cancers for performance evaluation. As a result, 90% (Colon Neoplasms), 88% (Esophageal Neoplasms) and 88% (Kidney Neoplasms) of top 50 predicted miRNAs are confirmed by recent experiment reports. Furthermore, HGIMDA could be effectively applied to new diseases and new miRNAs without any known associations, which overcome the important limitations of many previous computational models.
资助项目National Natural Science Foundation of China[11301517] ; National Natural Science Foundation of China[61572506] ; National Natural Science Foundation of China[11371355] ; National Center for Mathematics and Interdisciplinary Sciences, CAS
WOS研究方向Oncology ; Cell Biology
语种英语
WOS记录号WOS:000387281000057
出版者IMPACT JOURNALS LLC
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/24102]  
专题应用数学研究所
通讯作者Chen, Xing; Yan, Gui-Ying
作者单位1.China Univ Min & Technol, Sch Informat & Elect Engn, Xuzhou, Peoples R China
2.Hangzhou Dianzi Univ, Inst Informat & Control, Hangzhou, Zhejiang, Peoples R China
3.Shandong Univ, Sch Mech Elect & Informat Engn, Weihai, Peoples R China
4.China Univ Min & Technol, Sch Comp Sci & Technol, Xuzhou, Peoples R China
5.Hong Kong Polytech Univ, Dept Comp, Hong Kong, Hong Kong, Peoples R China
6.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China
推荐引用方式
GB/T 7714
Chen, Xing,Yan, Chenggang Clarence,Zhang, Xu,et al. HGIMDA: Heterogeneous graph inference for miRNA-disease association prediction[J]. ONCOTARGET,2016,7(40):65257-65269.
APA Chen, Xing,Yan, Chenggang Clarence,Zhang, Xu,You, Zhu-Hong,Huang, Yu-An,&Yan, Gui-Ying.(2016).HGIMDA: Heterogeneous graph inference for miRNA-disease association prediction.ONCOTARGET,7(40),65257-65269.
MLA Chen, Xing,et al."HGIMDA: Heterogeneous graph inference for miRNA-disease association prediction".ONCOTARGET 7.40(2016):65257-65269.

入库方式: OAI收割

来源:数学与系统科学研究院

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