Open AccessExploring automatic inconsistency detection for literature-based gene ontology annotation
Author(s) -
Jiyu Chen,
Benjamin Goudey,
Justin Zobel,
Nicholas Geard,
Karin Verspoor
Publication year - 2022
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btac230
Subject(s) - computer science , workflow , consistency (knowledge bases) , ontology , context (archaeology) , information retrieval , annotation , quality assurance , task (project management) , biological database , function (biology) , vocabulary , data curation , controlled vocabulary , quality (philosophy) , data mining , database , data science , artificial intelligence , bioinformatics , service (business) , paleontology , philosophy , linguistics , economy , management , epistemology , evolutionary biology , economics , biology
Literature-based gene ontology annotations (GOA) are biological database records that use controlled vocabulary to uniformly represent gene function information that is described in the primary literature. Assurance of the quality of GOA is crucial for supporting biological research. However, a range of different kinds of inconsistencies in between literature as evidence and annotated GO terms can be identified; these have not been systematically studied at record level. The existing manual-curation approach to GOA consistency assurance is inefficient and is unable to keep pace with the rate of updates to gene function knowledge. Automatic tools are therefore needed to assist with GOA consistency assurance. This article presents an exploration of different GOA inconsistencies and an early feasibility study of automatic inconsistency detection.