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Gene Ontology Summarization to Support Visualization and Quality Assurance

机译：基因本体概述支持可视化和质量保证

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The Gene Ontology (GO) is used extensively in the field of genomics. Like other large and complex ontologies, GO is difficult to maintain. In particular, quality assurance (QA) efforts for GO's content can be laborious and time consuming. Abstraction networks are summarization networks that reveal and highlight high-level structural and hierarchical aggregation patterns in an ontology. They have been shown to successfully support QA work in the context of OWL-format ontologies and SNOMED CT. A kind of abstraction network, called a partial-area taxonomy, is developed for GO hierarchies. The Biological process (BP) taxonomy is derived. Within this framework, several QA heuristics based on the identification of anomalous groups of terms are introduced. Such groups are expected to have higher error rates compared to the general population of terms. The results of a preliminary QA review, based on the BP taxonomy, are presented. It is observed that various inconsistencies in the modeling of GO are exposed with the use of the taxonomy-based QA heuristics. Some anomalies repeatedly revealed errors in the underlying GO.
机译：基因本体（GO）广泛用于基因组学领域。与其他大型和复杂的本体一样，难以维护。特别是，GO含量的质量保证（QA）努力可能是费力和耗时的。抽象网络是概括网络，其揭示和突出显示本体中的高级结构和分层聚合模式。他们已被证明可以在OWL-Format Intolologies和Snomed CT的背景下成功支持QA工作。一种被称为部分地区分类的抽象网络是为GO层次结构开发的。生物过程（BP）分类学是衍生的。在此框架内，介绍了几个基于异常术语术语识别的QA启发式。与一般术语相比，这些群体预计将具有更高的误差率。提出了基于BP分类法的初步QA审查的结果。观察到，GO建模中的各种不一致是利用基于分类的QA启发式的使用。一些异常反复揭示了潜在的出现错误。

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