Modeling quantitative traits for COVID-19 case reports

medRxiv

Abstract

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.

Authors 6

1. Núria Queralt-Rosiñach Aachen

   Leiden University Medical Center

   Affiliation as printed

   Leiden University Medical Center, Leiden, 2333 ZA, The Netherlands

2. Susan M. Bello

   Jackson Laboratory

   Affiliation as printed

   The Jackson Laboratory, BarHarbor, ME, USA

3. Robert Hoehndorf

   King Abdullah University of Science and Technology

   Affiliation as printed

   Computer, Electrical and Mathematical Sciences & Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia

   Computer, Electrical and Mathematical Sciences & Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal 2955-6900, Kingdom of Saudi Arabia

4. Claus Weiland

   Senckenberg Biodiversity and Climate Research Centre

   Affiliation as printed

   Senckenberg Biodiversity and Climate Research Center, Frankfurt, Germany

5. Philippe Rocca‐Serra

   University of Oxford

   Affiliation as printed

   Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, Oxford, OX1 3QG, UK

6. Paul N. Schofield corresponding

   University of Cambridge · Jackson Laboratory

   Affiliation as printed

   Department of Physiology, Development & Neuroscience, University of Cambridge, Downing Street, Cambridge, CB2 3EG, UK

   The Jackson Laboratory, BarHarbor, ME, USA