Formal axioms in biomedical ontologies improve analysis and interpretation of associated data

Abstract

Motivation Over 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 of biomedical ontologies may have also the potential to provide background knowledge for machine learning and predictive modelling. Results 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. Availability and implementation https://github.com/bio-ontology-research-group/tsoe. Supplementary information Supplementary data are available at Bioinformatics online.

Design and Use of Semantic Resources: Findings from the Section on Knowledge Representation and Management of the 2020 International Medical Informatics Association Yearbook

“…Section editors achieved a first selection of 148 papers based on titles and abstracts. After a second review of this set of papers, including full text reviews, a selection of 15 candidate best papers was established [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. Five reviewers reviewed these pre-selected papers to best four best papers [4][5][6][7].…”

“…Smaili et al, [17] propose to use formal axioms in biomedical ontologies to improve the analysis and interpretation of biomedical data. The general principle is to consider each axiom of the ontology as a sentence processed by an algorithm similar to Word2vec.…”

RTX-KG2: a system for building a semantically standardized knowledge graph for translational biomedicine

“…The RDF-based method merges all of the OWL and TTL sources (class-based), without flattening the ontologies (i.e., preserving rdfs:subClassOf relationships) and generates a single JSON file. The hybrid design of RTX-KG2 balances the benefits of modularity (where it is feasible in the direct-to-JSON method) with the need for a monolithic ingestion module for ontologies where inter-ontology axioms are needed for determining semantic types at the ETL stage [ 101 ].…”

RTX-KG2: a system for building a semantically standardized knowledge graph for translational biomedicine

“…The RDF-based method merges all of the OWL and TTL sources and generates a single JSON file. The hybrid design for the ETL layer for RTX-KG2 balances the benefits of modularity (where it is feasible in the direct-to-JSON method) with the need for a monolithic ingestion module for ontologies due to their extensive use of inter-ontology axioms [89]. RTX-KG2 integrates 70 knowledge sources (Table 1), 50 of them via a resource description framework (RDF)-based ingestion method and 20 of them via a direct-to-JSON ingestion method.…”