ArticleJournal of biomedical semantics2017
Matching disease and phenotype ontologies in the ontology alignment evaluation initiative.
Article in Journal of biomedical semantics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Applicability Assessment of Technologies for Predictive and Prescriptive Analytics of Nephrology Big Data.Proteomics · 2025Review
- Network location and clustering of genetic mutations determine chronicity in a stylized model of genetic diseases.Scientific reports · 2022Article
- Performance assessment of ontology matching systems for FAIR data.Journal of biomedical semantics · 2022Article
- Language model-based automatic prefix abbreviation expansion method for biomedical big data analysis.Future generations computer systems : FGCS · 2019Article
- Integrating ontologies of human diseases, phenotypes, and radiological diagnosis.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- An Effective Method to Measure Disease Similarity Using Gene and Phenotype Associations.Frontiers in genetics · 2019Article
- Matching biomedical ontologies based on formal concept analysis.Journal of biomedical semantics · 2018Article
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Authors and funding
10 authors.
Funding
Abstract
backgroundThe disease and phenotype track was designed to evaluate the relative performance of ontology matching systems that generate mappings between source ontologies. Disease and phenotype ontologies are important for applications such as data mining, data integration and knowledge management to support translational science in drug discovery and understanding the genetics of disease.
resultsEleven systems (out of 21 OAEI participating systems) were able to cope with at least one of the tasks in the Disease and Phenotype track. AML, FCA-Map, LogMap(Bio) and PhenoMF systems produced the top results for ontology matching in comparison to consensus alignments. The results against manually curated mappings proved to be more difficult most likely because these mapping sets comprised mostly subsumption relationships rather than equivalence. Manual assessment of unique equivalence mappings showed that AML, LogMap(Bio) and PhenoMF systems have the highest precision results.
conclusionsFour systems gave the highest performance for matching disease and phenotype ontologies. These systems coped well with the detection of equivalence matches, but struggled to detect semantic similarity. This deserves more attention in the future development of ontology matching systems. The findings of this evaluation show that such systems could help to automate equivalence matching in the workflow of curators, who maintain ontology mapping services in numerous domains such as disease and phenotype.
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