Fusing literature and full network data improves disease similarity computation
Li, Ping1,2; Nie, Yaling1,2; Yu, Jingkai1
刊名BMC BIOINFORMATICS
2016-08-30
卷号17期号:AUG页码:326
关键词Disease similarity MedSim NetSim MedNetSim Random walk with Restart
ISSN号1471-2105
英文摘要

Background: Identifying relatedness among diseases could help deepen understanding for the underlying pathogenic mechanisms of diseases, and facilitate drug repositioning projects. A number of methods for computing disease similarity had been developed; however, none of them were designed to utilize information of the entire protein interaction network, using instead only those interactions involving disease causing genes. Most of previously published methods required gene-disease association data, unfortunately, many diseases still have very few or no associated genes, which impeded broad adoption of those methods. In this study, we propose a new method (MedNetSim) for computing disease similarity by integrating medical literature and protein interaction network. MedNetSim consists of a network-based method (NetSim), which employs the entire protein interaction network, and a MEDLINE-based method (MedSim), which computes disease similarity by mining the biomedical literature.

WOS标题词Science & Technology ; Life Sciences & Biomedicine
类目[WOS]Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology
研究领域[WOS]Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology
关键词[WOS]PROTEIN-INTERACTION NETWORKS ; SEMANTIC SIMILARITY ; GENE ONTOLOGY ; UPDATE ; PRIORITIZATION ; FIBROMYALGIA ; INFORMATION ; UNIFICATION ; SEARCHES ; BIOLOGY
收录类别SCI
语种英语
WOS记录号WOS:000382832300002
内容类型期刊论文
源URL[http://ir.ipe.ac.cn/handle/122111/21463]  
专题过程工程研究所_生化工程国家重点实验室
作者单位1.Chinese Acad Sci, Inst Proc Engn, State Key Lab Biochem Engn, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714
Li, Ping,Nie, Yaling,Yu, Jingkai. Fusing literature and full network data improves disease similarity computation[J]. BMC BIOINFORMATICS,2016,17(AUG):326.
APA Li, Ping,Nie, Yaling,&Yu, Jingkai.(2016).Fusing literature and full network data improves disease similarity computation.BMC BIOINFORMATICS,17(AUG),326.
MLA Li, Ping,et al."Fusing literature and full network data improves disease similarity computation".BMC BIOINFORMATICS 17.AUG(2016):326.
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