K-Neighborhood decentralization: A comprehensive solution to index the UMLS for large scale knowledge discovery

Yang Xiang, Kewei Lu, Stephen L. James, Tara B. Borlawsky, Kun Huang, Philip R.O. Payne

Research output: Contribution to journalArticle

9 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)323-336
Number of pages14
JournalJournal of Biomedical Informatics
Volume45
Issue number2
DOIs
StatePublished - Apr 1 2012
Externally publishedYes

Keywords

  • Disease gene prioritization
  • Fold enrichment
  • Graph database
  • Knowledge discovery
  • UMLS

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