Improving the classification of cardinality phenotypes using collections.

Sarah M. Alghamdi, Robert Hoehndorf

Research output: Contribution to journalArticlepeer-review


MotivationPhenotypes are observable characteristics of an organism and they can be highly variable. Information about phenotypes is collected in a clinical context to characterize disease, and is also collected in model organisms and stored in model organism databases where they are used to understand gene functions. Phenotype data is also used in computational data analysis and machine learning methods to provide novel insights into disease mechanisms and support personalized diagnosis of disease. For mammalian organisms and in a clinical context, ontologies such as the Human Phenotype Ontology and the Mammalian Phenotype Ontology are widely used to formally and precisely describe phenotypes. We specifically analyze axioms pertaining to phenotypes of collections of entities within a body, and we find that some of the axioms in phenotype ontologies lead to inferences that may not accurately reflect the underlying biological phenomena.ResultsWe reformulate the phenotypes of collections of entities using an ontological theory of collections. By reformulating phenotypes of collections in phenotypes ontologies, we avoid potentially incorrect inferences pertaining to the cardinality of these collections. We apply our method to two phenotype ontologies and show that the reformulation not only removes some problematic inferences but also quantitatively improves biological data analysis.
Original languageEnglish (US)
JournalJournal of biomedical semantics
Issue number1
StatePublished - Aug 7 2023

Bibliographical note

KAUST Repository Item: Exported on 2023-09-06
Acknowledged KAUST grant number(s): FCC/1/1976-34-01, FCC/1/1976-46-01, REI/1/5334-01-01, URF/1/4355-01-01, URF/1/4675-01-01, URF/1/4697-01-01, URF/1/5041-01-01
Acknowledgements: This work has been supported by funding from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. URF/1/4355-01-01, URF/1/4675-01-01, URF/1/4697-01-01, URF/1/5041-01-01, REI/1/5334-01-01, FCC/1/1976-46-01, and FCC/1/1976-34-01. This research used the resources of the Supercomputing Laboratory at King Abdullah University of Science & Technology (KAUST) in Thuwal, Saudi Arabia.

ASJC Scopus subject areas

  • Health Informatics
  • Information Systems
  • Computer Science Applications
  • Computer Networks and Communications


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