Samwald Matthias, Miñarro Giménez Jose Antonio, Boyce Richard D, Freimuth Robert R, Adlassnig Klaus-Peter, Dumontier Michel
Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Spitalgasse 23, 1090, Vienna, Austria.
Institute of Medical Informatics, Statistics, and Documentation; Medical University of Graz, Auenbruggerplatz 2, 8036, Graz, Austria.
BMC Med Inform Decis Mak. 2015 Feb 22;15:12. doi: 10.1186/s12911-015-0130-1.
Every year, hundreds of thousands of patients experience treatment failure or adverse drug reactions (ADRs), many of which could be prevented by pharmacogenomic testing. However, the primary knowledge needed for clinical pharmacogenomics is currently dispersed over disparate data structures and captured in unstructured or semi-structured formalizations. This is a source of potential ambiguity and complexity, making it difficult to create reliable information technology systems for enabling clinical pharmacogenomics.
We developed Web Ontology Language (OWL) ontologies and automated reasoning methodologies to meet the following goals: 1) provide a simple and concise formalism for representing pharmacogenomic knowledge, 2) finde errors and insufficient definitions in pharmacogenomic knowledge bases, 3) automatically assign alleles and phenotypes to patients, 4) match patients to clinically appropriate pharmacogenomic guidelines and clinical decision support messages and 5) facilitate the detection of inconsistencies and overlaps between pharmacogenomic treatment guidelines from different sources. We evaluated different reasoning systems and test our approach with a large collection of publicly available genetic profiles.
Our methodology proved to be a novel and useful choice for representing, analyzing and using pharmacogenomic data. The Genomic Clinical Decision Support (Genomic CDS) ontology represents 336 SNPs with 707 variants; 665 haplotypes related to 43 genes; 22 rules related to drug-response phenotypes; and 308 clinical decision support rules. OWL reasoning identified CDS rules with overlapping target populations but differing treatment recommendations. Only a modest number of clinical decision support rules were triggered for a collection of 943 public genetic profiles. We found significant performance differences across available OWL reasoners.
The ontology-based framework we developed can be used to represent, organize and reason over the growing wealth of pharmacogenomic knowledge, as well as to identify errors, inconsistencies and insufficient definitions in source data sets or individual patient data. Our study highlights both advantages and potential practical issues with such an ontology-based approach.
每年都有数十万患者经历治疗失败或药物不良反应（ADR），其中许多情况可通过药物基因组学检测来预防。然而，临床药物基因组学所需的主要知识目前分散在不同的数据结构中，并以非结构化或半结构化形式记录。这是潜在的歧义与复杂性的来源，使得难以创建用于实现临床药物基因组学的可靠信息技术系统。
我们开发了网络本体语言（OWL）本体和自动推理方法以实现以下目标：1）提供一种简单而简洁的形式来表示药物基因组学知识；2）查找药物基因组学知识库中的错误和定义不充分之处；3）自动为患者分配等位基因和表型；4）将患者与临床适用的药物基因组学指南及临床决策支持信息进行匹配；5）促进检测来自不同来源的药物基因组学治疗指南之间的不一致和重叠。我们评估了不同的推理系统，并使用大量公开可用的基因谱测试了我们的方法。
我们的方法被证明是一种用于表示、分析和使用药物基因组学数据的新颖且有用的选择。基因组临床决策支持（Genomic CDS）本体表示336个单核苷酸多态性（SNP）及707个变体；与43个基因相关的665个单倍型；与药物反应表型相关的22条规则；以及308条临床决策支持规则。OWL推理识别出针对重叠目标人群但治疗建议不同的CDS规则。对于943个公共基因谱的集合，仅触发了少量临床决策支持规则。我们发现现有OWL推理器在性能上存在显著差异。
我们开发的基于本体的框架可用于表示、组织和推理日益丰富的药物基因组学知识，以及识别源数据集或个体患者数据中的错误、不一致和定义不充分之处。我们的研究突出了这种基于本体方法的优点和潜在实际问题。
