Integration of background knowledge for automatic detection of inconsistencies in gene ontology annotation.

Journal: Bioinformatics (Oxford, England)
PMID: 38940182

Abstract

MOTIVATION: Biological background knowledge plays an important role in the manual quality assurance (QA) of biological database records. One such QA task is the detection of inconsistencies in literature-based Gene Ontology Annotation (GOA). This manual verification ensures the accuracy of the GO annotations based on a comprehensive review of the literature used as evidence, Gene Ontology (GO) terms, and annotated genes in GOA records. While automatic approaches for the detection of semantic inconsistencies in GOA have been developed, they operate within predetermined contexts, lacking the ability to leverage broader evidence, especially relevant domain-specific background knowledge. This paper investigates various types of background knowledge that could improve the detection of prevalent inconsistencies in GOA. In addition, the paper proposes several approaches to integrate background knowledge into the automatic GOA inconsistency detection process.

Authors

  • Jiyu Chen
    School of Computing and Information Systems, The University of Melbourne, Parkville 3010, VIC, Australia.
  • Benjamin Goudey
    Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia.
  • Nicholas Geard
    School of Computing and Information Systems, The University of Melbourne, Parkville 3010, VIC, Australia.
  • Karin Verspoor
    Dept of Computing and Information Systems, School of Engineering, University of Melbourne, Melbourne, Australia.

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