=Paper=
{{Paper
|id=Vol-1747/IP02_ICBO2016
|storemode=property
|title=Adding Evidence Type Representation to DIDEO
|pdfUrl=https://ceur-ws.org/Vol-1747/IP02_ICBO2016.pdf
|volume=Vol-1747
|authors=Mathias Brochhausen,Philip E. Empey,Jodi Schneider,William R. Hogan,Richard D. Boyce
|dblpUrl=https://dblp.org/rec/conf/icbo/BrochhausenESHB16
}}
==Adding Evidence Type Representation to DIDEO ==
https://ceur-ws.org/Vol-1747/IP02_ICBO2016.pdf
Adding evidence type representation to DIDEO
Mathias Brochhausen William R. Hogan
Department of Biomedical Informatics Department of Health Outcomes and Policy
University of Arkansas for Medical Sciences University of Florida
Little Rock, AR USA Gainesville, FL USA
mbrochhausen@uams.edu
Philip E. Empey Jodi Schneider, Richard D. Boyce
Department of Pharmacy and Therapeutics Department of Biomedical Informatics
University of Pittsburgh University of Pittsburgh
Pittsburgh, PA USA Pittsburgh, PA USA
Abstract—In this poster we present novel development and DDIs and PDDIs. An important use case for the new
extension of the Drug-drug Interaction and Drug-drug representation is to automatically categorize evidence items
Interaction Evidence Ontology (DIDEO). We demonstrate how into multilevel taxonomy of evidence types. We plan for
reasoning over this extension of DIDEO can a) automatically curators of DDI and PDDI information to use a web-based data
create a multi-level hierarchy of evidence types from descriptions entry form to enter information about a scientific observation
of the underlying scientific observations and b) automatically that the particular evidence item is about (e.g. an experiment, a
subsume individual evidence items under the correct evidence clinical study, a case report, etc.). Examples of the aspects of
type. Thus DIDEO will enable evidence items added manually by scientific observations relevant to our use case include among
curators to be automatically categorized into a drug-drug
others: group randomization, targeting pharmacokinetics,
interaction framework with precision and minimal effort from
curators. As with all previous DIDEO development this extension
number of drugs involved, enzymes involved, inclusion of
is consistent with OBO Foundry principles. antibodies, etc. Based on information about these aspects we
want to enable automatic categorization of our evidence items
Keywords—drug-drug interaction; potential drug-drug into the DIKB evidence type taxonomy [9]. The top level of
interaction; evidence types; biomedical ontologies this evidence taxonomy is:
I. INTRODUCTION Statements of various kinds
The Drug-drug Interaction and Drug-drug Interaction Metabolic enzyme identification experiments
Evidence Ontology (DIDEO) is an ontology aimed at
Metabolic enzyme inhibition experiments
representing drug-drug interactions, potential drug-drug
interactions and the underlying phenomena from physiology, Transport protein identification experiments
anatomy, pharmacology and laboratory science. The goal in
creating DIDEO is to provide a realism-based, semantically Transport protein inhibition experiments
rich, and logically consistent OWL representation for the Drug Prospective clinical studies
Interaction Knowledge Base (DIKB) [1,2]. DIDEO is based on
Basic Formal Ontology [3] and is compliant with the OBO Non-randomized studies and case reports
Foundry [4] principles [5]. It is coded in Web Ontology
Language (OWL2) [6] and is freely accessible from Observational studies
http://purl.obolibrary.org/obo/dideo.owl. II. METHODS
A key achievement of the initial version of DIDEO [7] was The key strategy for achieving automatic categorization of
to establish a clear distinction between drug-drug interactions evidence is to use a) necessary and sufficient conditions of
or DDIs (biological processes) and potential drug-drug evidence types and b) property assertions for evidence items
interactions or PDDIs (information content entities) based on and the related scientific observations. Fig. 1 shows the classes
the paradigm of ontological realism [8]. This deliberate and relations used to create the necessary and sufficient axiom
separation of representations of physiological processes and of the class randomized drug-drug interaction trial.
material entities, as opposed to the representation of
information about physiological processes has been a core To represent the scientific observations and their
strategy in developing DIDEO. properties, we imported terms from the following ontologies:
Chemical Entities of Biological Importance (ChEBI) [10],
In this poster we present the development of a new, Drug Ontology (DRON) [11], Gene Ontology (GO) [12],
semantically rich OWL representation of types of evidence for
IV. CONCLUSION
Based on these results we conclude that the attributes of
evidence as used by the DIKB are sufficient to infer a
taxonomy of evidence types automatically. We also conclude
that it is feasible to use these attributes to automatically
categorize individual evidence items using OWL reasoning.
ACKNOWLEDGEMENT
For all authors: This project is supported by a grant from
the National Library of Medicine: “Addressing gaps in
clinically useful evidence on drug-drug interactions”
(R01LM011838). JS is supported by training grant
T15LM007059 from the National Library of
Fig. 1. The formal definition of randomized drug-drug interaction trial in
DIDEO. The boxes represent classes; the arrows represent object properties.
Medicine/National Institute of Dental and Craniofacial
All depicted object properties are used in existential statements (SOME). Research.
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Fig. 2. View of the inferred evidence type taxonomy in Protégé