Document Detail


k-Neighborhood decentralization: A comprehensive solution to index the UMLS for scale knowledge discovery.
MedLine Citation:
PMID:  22154838     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
The Unified Medical Language System (UMLS) is the largest thesaurus in the biomedical informatics domain. Previous works have shown that knowledge constructs comprised of transitively-associated UMLS concepts are effective for discovering potentially novel biomedical hypotheses. However, the extremely large size of the UMLS becomes a major challenge for these applications. To address this problem, we designed a k-neighborhood Decentralization Labeling Scheme (kDLS) for the UMLS, and the corresponding method to effectively evaluate the kDLS indexing results. kDLS provides a comprehensive solution for indexing the UMLS for very efficient large scale knowledge discovery. We demonstrated that it is highly effective to use kDLS paths to prioritize disease-gene relations across the whole genome, with extremely high fold-enrichment values. To our knowledge, this is the first indexing scheme capable of supporting efficient large scale knowledge discovery on the UMLS as a whole. Our expectation is that kDLS will become a vital engine for retrieving information and generating hypotheses from the UMLS for future medical informatics applications.
Authors:
Yang Xiang; Kewei Lu; Stephen L James; Tara B Borlawsky; Kun Huang; Philip R O Payne
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2011-12-2
Journal Detail:
Title:  Journal of biomedical informatics     Volume:  -     ISSN:  1532-0480     ISO Abbreviation:  -     Publication Date:  2011 Dec 
Date Detail:
Created Date:  2011-12-13     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  100970413     Medline TA:  J Biomed Inform     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Copyright Information:
Copyright © 2011 Elsevier Inc. All rights reserved.
Affiliation:
Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, United States.
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