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Modeling quantitative traits for COVID-19 case reports
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covidontheweb.inria.fr
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Academic Article
research paper
schema:ScholarlyArticle
isDefinedBy
Covid-on-the-Web dataset
title
Modeling quantitative traits for COVID-19 case reports
Creator
Bello, Susan
Hoehndorf, Robert
Queralt-Rosinach, Núria
Rocca-Serra, Philippe
Schofield, Paul
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source
MedRxiv
abstract
Medical practitioners record the condition status of a patient through qualitative and quantitative observations. The measurement of vital signs and molecular parameters in the clinics gives a complementary description of abnormal phenotypes associated with the progression of a disease. The Clinical Measurement Ontology (CMO) is used to standardize annotations of these measurable traits. However, researchers have no way to describe how these quantitative traits relate to phenotype concepts in a machine-readable manner. Using the WHO clinical case report form standard for the COVID-19 pandemic, we modeled quantitative traits and developed OWL axioms to formally relate clinical measurement terms with anatomical, biomolecular entities and phenotypes annotated with the Uber-anatomy ontology (Uberon), Chemical Entities of Biological Interest (ChEBI) and the Phenotype and Trait Ontology (PATO) biomedical ontologies. The formal description of these relations allows interoperability between clinical and biological descriptions, and facilitates automated reasoning for analysis of patterns over quantitative and qualitative biomedical observations.
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2020-06-20
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bibo:doi
10.1101/2020.06.18.20135103
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medrxiv
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3ee4d4e1c98f9ed56835242af15111e728ed9194
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https://doi.org/10.1101/2020.06.18.20135103
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Modeling quantitative traits for COVID-19 case reports
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covid:3ee4d4e1c98f9ed56835242af15111e728ed9194#body_text
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named entity 'biological'
named entity 'condition'
named entity 'relate'
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named entity 'measurable'
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