Fusing literature and full network data improves disease similarity computation
文献类型:期刊论文
作者 | Li, Ping1,2; Nie, Yaling1,2; Yu, Jingkai1 |
刊名 | BMC BIOINFORMATICS
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出版日期 | 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 |
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. |
入库方式: OAI收割
来源:过程工程研究所
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