An evidence-based lexical pattern approach for quality assurance of Gene Ontology relations.
An evidence-based lexical pattern approach for quality assurance of Gene Ontology relations.
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DOI：
10.1093/bib/bbac122
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发表时间：
2022-05-13
影响因子：
9.5
通讯作者：
中科院分区：
文献类型：
--
作者：
基因本体（GO）在生物学领域有着广泛的应用。它是最全面的本体，提供了基因功能（GO概念）和它们之间关系的形式化表示。然而，由于GO概念的大尺寸和GO结构的复杂性，GO中可能存在无意的质量缺陷（例如，缺失或错误的关系）。这种质量缺陷将影响基于GO的分析和应用的结果。在这项工作中，我们介绍了一种新的证据为基础的词汇模式的质量保证GO关系的方法。我们利用两层证据来表明GO中可能缺失的关系，如下所示。我们首先利用相关的概念对（即现有的关系）在GO提取关系特定的词汇模式，作为第一层证据，自动建议不相关的概念对之间的潜在缺失的关系。对于每个建议的缺失关系，我们进一步识别两个其他现有关系作为第二层证据，其类似于缺失关系与建议缺失关系所基于的现有关系之间的差异。应用于2021年12月15日发布的GO，这种方法总共提出了866个潜在的缺失关系。本地领域专家评估了整组潜在缺失关系，并确定821个为缺失关系，45个表示存在错误关系。我们将这些发现提交给GO联盟进行进一步验证，并收到了令人鼓舞的反馈。这些表明，我们的循证方法可以用来发现丢失的关系和错误的现有关系在GO。
Gene Ontology (GO) is widely used in the biological domain. It is the most comprehensive ontology providing formal representation of gene functions (GO concepts) and relations between them. However, unintentional quality defects (e.g. missing or erroneous relations) in GO may exist due to the large size of GO concepts and complexity of GO structures. Such quality defects would impact the results of GO-based analyses and applications. In this work, we introduce a novel evidence-based lexical pattern approach for quality assurance of GO relations. We leverage two layers of evidence to suggest potentially missing relations in GO as follows. We first utilize related concept pairs (i.e. existing relations) in GO to extract relationship-specific lexical patterns, which serve as the first layer evidence to automatically suggest potentially missing relations between unrelated concept pairs. For each suggested missing relation, we further identify two other existing relations as the second layer of evidence that resemble the difference between the missing relation and the existing relation based on which the missing relation is suggested. Applied to the 15 December 2021 release of GO, this approach suggested a total of 866 potentially missing relations. Local domain experts evaluated the entire set of potentially missing relations, and identified 821 as missing relations and 45 indicate erroneous existing relations. We submitted these findings to the GO consortium for further validation and received encouraging feedback. These indicate that our evidence-based approach can be utilized to uncover missing relations and erroneous existing relations in GO.
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DOI：
10.1093/jamia/ocw175
发表时间：
2017-07-01
期刊：
Journal of the American Medical Informatics Association : JAMIA
影响因子：
--
作者：
Cui L;Zhu W;Tao S;Case JT;Bodenreider O;Zhang GQ
通讯作者：
Zhang GQ
影响因子：
14.9
作者：
The Gene Ontology Consortium
通讯作者：
The Gene Ontology Consortium
影响因子：
4.8
作者：
Agrawal, Ankur;Qazi, Kashifuddin
通讯作者：
Qazi, Kashifuddin
DOI：
10.1093/bioinformatics/btp536
发表时间：
2009-11-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Binns D;Dimmer E;Huntley R;Barrell D;O'Donovan C;Apweiler R
通讯作者：
Apweiler R
影响因子：
4.6
作者：
Klopfenstein DV;Zhang L;Pedersen BS;Ramírez F;Warwick Vesztrocy A;Naldi A;Mungall CJ;Yunes JM;Botvinnik O;Weigel M;Dampier W;Dessimoz C;Flick P;Tang H
通讯作者：
Tang H