CRII: III: A Scalable Framework for Debugging Large Biological Ontologies

为了利用生物研究界产生的越来越多的数字数据带来的变革机会，我们需要系统地采用数据和元数据标准，例如基因本体（GO）。因为GO？质量问题是编纂、管理和共享生物学知识的基础，如果不加以解决，可能会导致误导性的结果或错过生物学发现。提高像GO这样的本体系统的质量，虽然是一项具有挑战性和艰巨的任务，但可以直接影响数据密集型研究发现的基础。大多数现有的GO质量保证方法都集中在概念的丰富上，以跟上快速发展的生物学知识的步伐。然而，在现有的质量保证方法中，以关系为代表的关键结构信息在很大程度上被忽略了，使它们无法发挥其预期的作用。有原则的、可扩展的和自动化的方法，可以调试围棋以生成可编程的（而不是手动的）建议，如果成功，可以在开发新一代提高围棋质量的方法方面改变游戏规则。PI提出了一个基于包容的子项推理框架（SSIF），通过利用GO的底层图结构和一个新的项代数来审计GO。SSIF结合了在GO中捕获的术语、子术语和关系中嵌入的生物学知识，可以自动检测语义不一致，并为GO的未来版本生成更改建议。为了提高基因本体和其他生物医学本体的质量，PI提出了一个基于包容的子术语推理框架（SSIF）的发展。SSIF包括三个主要组成部分：(1)使用词性分析和子概念匹配对GO概念术语进行基于序列的表示；(2)将这种基于序列的表示与反义词和基于包容的最长子序列对齐相结合，为发展项代数而制定代数运算；(3)构建一套用于后向包含推理的条件规则，旨在揭示GO和其他本体结构中的语义不一致。SSIF将使用可扩展的计算算法实现，并应用于基因本体联盟提供的GO分布。将探索两种算法策略，使用条件规则在围棋上执行大规模的向后包容推理：(1)穷举，所有概念对，以及(2)在称为非格子图的特殊类型诱导子结构中的概念对的子空间。如果GO中存在的关系与条件规则的结果不一致，则表示可能存在错误。基于条件规则集合的未发现的语义不一致有可能自动揭示局部bug。以及潜在的系统模式进行审查和修订，以提高氧化石墨烯和其他生物医学本体的质量。

To capitalize on the transformative opportunities of the increasingly large amounts of digital data produced by the biological research community, we need to systematically adopt data and metadata standards, such as the Gene Ontology (GO). Because of GO?s fundamental role in codifying, managing, and sharing biological knowledge, quality issues, if not addressed, can cause misleading results or missed biological discoveries. Enhancing the quality of ontological systems such as GO, though a challenging and arduous task, can directly impact the very foundation of data-intensive research discovery. Most existing quality assurance approaches for GO have focused on the enrichment of concepts in order to keep pace with the rapidly evolving biological knowledge. However, critical structural information represented by relations has been largely ignored in existing quality assurance approaches, making them inadequate for their intended roles. Principled, scalable, and automated approaches that can debug GO to generate programmable (rather than manual) suggestions, if successful, can be a game changer in developing a new generation of methods for enhancing the quality of GO. The PI proposes a Subsumption-based Sub-term Inference Framework, SSIF, for auditing the GO by leveraging both its underlying graph structure and a novel term-algebra. SSIF combines the biological knowledge embedded in terms, sub-terms, and relationships captured in GO that can automatically detect semantic inconsistencies and generate change suggestions for future versions of GO.In order to enhance the quality of the Gene Ontology and other biomedical ontologies, the PI proposes development of a Subsumption-based Sub-term Inference Framework, SSIF. The SSIF includes three main components: (1) a sequence-based representation of GO concept terms by using part-of-speech parsing and sub-concept matching; (2) the formulation of algebraic operations for the development of a term-algebra combining this sequence-based representation with antonyms and subsumption-based longest subsequence alignment; and (3) the construction of a set of conditional rules for backward subsumption inference aimed at uncovering semantic inconsistencies in GO and other ontological structures. SSIF will be implemented using scalable computational algorithms and applied to the GO distributions provided by the Gene Ontology Consortium. Two algorithmic strategies will be explored to perform large-scale, backward subsumption inference on GO using the conditional rules: (1) exhaustive, all concept pairs, and (2) the subspace of concept pairs within a special type of induced substructures called non-lattice subgraphs. If an existing relation in GO is inconsistent with the consequence of the conditional rules, it represents a likely candidate of error. The uncovered semantic inconsistencies based on a collection of conditional rules have the potential to automatically reveal local ?bugs? as well as potential systemic patterns for review and revision, to enhance the quality of GO and other biomedical ontologies.