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PGxO: A very lite ontology to reconcile pharmacogenomic knowledge units

Authors :
Clement Jonquet
Joël Legrand
Amedeo Napoli
Pierre Monnin
Adrien Coulet
Knowledge representation, reasonning (ORPAILLEUR)
Inria Nancy - Grand Est
Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD)
Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA)
Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA)
Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)
Système Multi-agent, Interaction, Langage, Evolution (SMILE)
Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM)
Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)
Stanford Center for BioMedical Informatics Research (BMIR)
Stanford University
This work is supported by the PractiKPharma project, founded by the French National Research Agency (ANR) under Grant No. ANR-15-CE23-0028 (http://practikpharma.loria.fr/), and by the Snowball Inria Associate Team (http://snowflake.loria.fr/)
Snowball Inria Associate Team
ANR-15-CE23-0028,PractiKPharma,Confrontation entre connaissances de l'état de l'art et connaissances extraites de dossiers patients en pharmacogénomique(2015)
European Project: 701771,H2020,H2020-MSCA-IF-2015,SIFRm(2016)
Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA)
Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)
Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)
Source :
Methods, tools & platforms for Personalized Medicine in the Big Data Era (NETTAB 2017 Workshop), Methods, tools & platforms for Personalized Medicine in the Big Data Era, Methods, tools & platforms for Personalized Medicine in the Big Data Era, Oct 2017, Palermo, Italy. ⟨10.7287/peerj.preprints.3140v1⟩
Publication Year :
2017
Publisher :
PeerJ, 2017.

Abstract

We present in this article a lightweight ontology named PGxO and a set of rules for its instantiation, which we developed as a frame for reconciling and tracing pharmacogenomics (PGx) knowledge. PGx studies how genomic variations impact variations in drug response phenotypes. Knowledge in PGx is typically composed of units that have the form of ternary relationships gene variant–drug–adverse event, stating that an adverse event may occur for patients having the gene variant when being exposed to the drug. These knowledge units (i) are available in reference databases, such as PharmGKB, are reported in the scientific biomedical literature and (ii) may be discovered by mining clinical data such as Electronic Health Records (EHRs). Therefore, knowledge in PGx is heterogeneously described (i.e., with various quality, granularity, vocabulary, etc.). It is consequently worth to extract, then compare, assertions from distinct resources. Using PGxO, one can represent multiple provenances for pharmacogenomic knowledge units, and reconcile duplicates when they come from distinct sources.

Details

Language :
English
Database :
OpenAIRE
Journal :
Methods, tools & platforms for Personalized Medicine in the Big Data Era (NETTAB 2017 Workshop), Methods, tools & platforms for Personalized Medicine in the Big Data Era, Methods, tools & platforms for Personalized Medicine in the Big Data Era, Oct 2017, Palermo, Italy. ⟨10.7287/peerj.preprints.3140v1⟩
Accession number :
edsair.doi.dedup.....16cfd59d1a25fc06f29ab19a0922254f
Full Text :
https://doi.org/10.7287/peerj.preprints.3140