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HGIMDA: Heterogeneous graph inference for miRNA-disease association prediction
Chen, Xing1; Yan, Chenggang Clarence2; Zhang, Xu3; You, Zhu-Hong4; Huang, Yu-An5; Yan, Gui-Ying6
Source PublicationONCOTARGET
AbstractRecently, 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.
KeywordmicroRNA disease microRNA-disease association heterogeneous network similarity
Funding ProjectNational 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 Research AreaOncology ; Cell Biology
WOS SubjectOncology ; Cell Biology
WOS IDWOS:000387281000057
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Document Type期刊论文
Corresponding AuthorChen, Xing; Yan, Gui-Ying
Affiliation1.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
Recommended Citation
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.
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