Formal axioms in biomedical ontologies improve analysis and interpretation of associated data.
KAUST DepartmentComputer Science Program
Computational Bioscience Research Center (CBRC)
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/660553
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AbstractOver the past years, significant resources have been invested into formalizing biomedical ontologies. Formal axioms in ontologies have been developed and used to detect and ensure ontology consistency, find unsatisfiable classes, improve interoperability, guide ontology extension through the application of axiom-based design patterns, and encode domain background knowledge. The domain knowledge in biomedical ontologies may also have the potential to provide background knowledge for machine learning and predictive modelling. We use ontology-based machine learning methods to evaluate the contribution of formal axioms and ontology meta-data to the prediction of protein-protein interactions and gene-disease associations. We find that the background knowledge provided by the Gene Ontology and other ontologies significantly improves the performance of ontology-based prediction models through provision of domain-specific background knowledge. Furthermore, we find that the labels, synonyms and definitions in ontologies can also provide background knowledge that may be exploited for prediction. The axioms and meta-data of different ontologies contribute to improving data analysis in a context-specific manner. Our results have implications on the further development of formal knowledge bases and ontologies in the life sciences, in particular as machine learning methods are more frequently being applied. Our findings motivate the need for further development, and the systematic, application-driven evaluation and improvement, of formal axioms in ontologies. https://github.com/bio-ontology-research-group/tsoe.
CitationSmaili, F. Z., Gao, X., & Hoehndorf, R. (2019). Formal axioms in biomedical ontologies improve analysis and interpretation of associated data. Bioinformatics. doi:10.1093/bioinformatics/btz920
PublisherOxford University Press (OUP)
JournalBioinformatics (Oxford, England)
Except where otherwise noted, this item's license is described as This is a pre-copyedited, author-produced PDF of an article accepted for publication in Bioinformatics (Oxford, England) following peer review. The version of record is available online at: https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btz920/5671694.