title: k-Neighborhood decentralization: a comprehensive solution to index the UMLS for large scale knowledge discovery.
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.
authors.affiliation: Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA. yxiang@bmi.osu.edu
authors.forename: Yang
authors.initials: Y
authors.lastname: Xiang
authors.forename: Kewei
authors.initials: K
authors.lastname: Lu
authors.forename: Stephen L
authors.initials: SL
authors.lastname: James
authors.forename: Tara B
authors.initials: TB
authors.lastname: Borlawsky
authors.forename: Kun
authors.initials: K
authors.lastname: Huang
authors.forename: Philip R O
authors.initials: PR
authors.lastname: Payne
mesh_terms: D000043:Abstracting and Indexing / Q000379:methods*; D000465:Algorithms; D008490:Medical Informatics / Q000379:methods*; D011057:Politics; D017432:Unified Medical Language System / Q000592:standards*