CAS OpenIR  > 生化工程国家重点实验室
Fusing literature and full network data improves disease similarity computation
Li, Ping1,2; Nie, Yaling1,2; Yu, Jingkai1
2016-08-30
Source PublicationBMC BIOINFORMATICS
ISSN1471-2105
Volume17Issue:AUGPages:326
Abstract

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.

KeywordDisease Similarity Medsim Netsim Mednetsim Random Walk With Restart
SubtypeArticle
WOS HeadingsScience & Technology ; Life Sciences & Biomedicine
DOI10.1186/s12859-016-1205-4
Indexed BySCI
Language英语
WOS KeywordPROTEIN-INTERACTION NETWORKS ; SEMANTIC SIMILARITY ; GENE ONTOLOGY ; UPDATE ; PRIORITIZATION ; FIBROMYALGIA ; INFORMATION ; UNIFICATION ; SEARCHES ; BIOLOGY
WOS Research AreaBiochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology
WOS SubjectBiochemical Research Methods ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology
Funding OrganizationNational Natural Science Foundation of China(61179008)
WOS IDWOS:000382832300002
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ipe.ac.cn/handle/122111/21463
Collection生化工程国家重点实验室
Affiliation1.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
Recommended Citation
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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