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从 Pharmacogenomics Research Network 中协调和语义注释数据字典:一个案例研究。

Harmonization and semantic annotation of data dictionaries from the Pharmacogenomics Research Network: a case study.

机构信息

Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.

出版信息

J Biomed Inform. 2013 Apr;46(2):286-93. doi: 10.1016/j.jbi.2012.11.004. Epub 2012 Nov 29.

Abstract

The Pharmacogenomics Research Network (PGRN) is a collaborative partnership of research groups funded by NIH to discover and understand how genome contributes to an individual's response to medication. Since traditional biomedical research studies and clinical trials are often conducted independently, common and standardized representations for data are seldom used. This leads to heterogeneity in data representation, which hinders data reuse, data integration and meta-analyses. This study demonstrates harmonization and semantic annotation work for pharmacogenomics data dictionaries collected from PGRN research groups. A semi-automated system was developed to support the harmonization/annotation process, which includes four individual steps, (1) pre-processing PGRN variables; (2) decomposing and normalizing variable descriptions; (3) semantically annotating words and phrases using controlled terminologies; (4) grouping PGRN variables into categories based on the annotation results and semantic types, for total 1514 PGRN variables. Our results demonstrate that there is a significant amount of variability in how pharmacogenomics data is represented and that additional standardization efforts are needed. This represents a critical first step toward identifying and creating data standards for pharmacogenomics studies.

摘要

药物基因组学研究网络(PGRN)是由美国国立卫生研究院(NIH)资助的研究小组之间的合作关系,旨在发现和了解基因组如何影响个体对药物的反应。由于传统的生物医学研究和临床试验通常是独立进行的,因此很少使用通用和标准化的数据表示方法。这导致数据表示的异质性,阻碍了数据的重用、数据的集成和元分析。本研究展示了从 PGRN 研究小组收集的药物基因组学数据字典的协调和语义注释工作。开发了一个半自动化系统来支持协调/注释过程,包括四个单独的步骤:(1)预处理 PGRN 变量;(2)分解和规范化变量描述;(3)使用受控术语对单词和短语进行语义注释;(4)根据注释结果和语义类型将 PGRN 变量分组到类别中,共有 1514 个 PGRN 变量。我们的结果表明,药物基因组学数据的表示方式存在很大的差异,需要进一步的标准化工作。这代表着确定和创建药物基因组学研究数据标准的关键第一步。

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