k-Neighborhood decentralization: a comprehensive solution to index the UMLS for large scale knowledge discovery.
k-Neighborhood decentralization: a comprehensive solution to index the UMLS for large scale knowledge discovery.
复制标题
k-Neighborhood 去中心化：为大规模知识发现索引 UMLS 的综合解决方案。
DOI：
10.1016/j.jbi.2011.11.012
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发表时间：
2012
影响因子：
4.5
通讯作者：
Payne,PhilipRO
中科院分区：
文献类型：
--
作者：
Xiang,Yang;Lu,Kewei;James,StephenL;Borlawsky,TaraB;Huang,Kun;Payne,PhilipRO
统一医学语言系统(UMLS)是生物医学信息学领域最大的主题词库。以前的工作已经表明，由传递性相关的UMLS概念组成的知识结构对于发现潜在的新的生物医学假设是有效的。然而，UMLS的超大尺寸成为这些应用的主要挑战。针对这一问题，我们设计了一种适用于UMLS的k-邻域分散标注方案(KDLS)，并设计了相应的方法来有效地评价kDLS的标引结果。KDLS为索引UMLS提供了一个全面的解决方案，以实现非常高效的大规模知识发现。我们证明了使用kDLS路径对整个基因组中的疾病-基因关系进行优先排序是非常有效的，具有极高的倍增富集值。据我们所知，这是第一个能够在整个UMLS上支持高效的大规模知识发现的索引方案。我们的期望是，kDLS将成为检索信息和从UMLS生成假说的重要引擎，用于未来的医学信息学应用。
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.