1. PGxCorpus, a manually annotated corpus for pharmacogenomics
- Author
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Kevin Dalleau, Joël Legrand, Adrien Coulet, Nadine Petitpain, Malika Smaïl-Tabbone, Romain Gogdemir, Yannick Toussaint, William Digan, Cédric Bousquet, Marie-Dominique Devignes, Patrice Ringot, Ndeye-Coumba Ndiaye, Chia-Ju Lee, Natural Language Processing : representations, inference and semantics (SYNALP), 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)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), 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), 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), Laboratoire d'Informatique Médicale et Ingénierie des Connaissances en e-Santé (LIMICS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Sorbonne Paris Nord, Computational Algorithms for Protein Structures and Interactions (CAPSID), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), Department of Biomedical Informatics and Medical Education, University of Washington, University of Washington [Seattle], Nutrition-Génétique et Exposition aux Risques Environnementaux (NGERE), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Centre Régional de PharmacoVigilance de Lorraine (CRPV Lorraine), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), ANR-15-CE23-0028, Agence Nationale de la Recherche, 15-IDEX-0004, Université de Lorraine, Snowball Inria Associate Team, GRID5000, ANR-15-CE23-0028,PractiKPharma,Confrontation entre connaissances de l'état de l'art et connaissances extraites de dossiers patients en pharmacogénomique(2015), Coulet, Adrien, Interactions humain-machine, objets connectés, contenus numériques, données massives et connaissance - Confrontation entre connaissances de l'état de l'art et connaissances extraites de dossiers patients en pharmacogénomique - - PractiKPharma2015 - ANR-15-CE23-0028 - AAPG2015 - VALID, CRHU Nancy, and Service Informatique de Soutien à la Recherche (SISR)
- Subjects
Data Descriptor ,Computer science ,[INFO.INFO-TT] Computer Science [cs]/Document and Text Processing ,computer.software_genre ,[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] ,0302 clinical medicine ,Resource (project management) ,Drug response ,030212 general & internal medicine ,lcsh:Science ,Data Curation ,ComputingMilieux_MISCELLANEOUS ,[INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM] ,0303 health sciences ,3. Good health ,Computer Science Applications ,[INFO.INFO-TT]Computer Science [cs]/Document and Text Processing ,[SDV.SP.PHARMA] Life Sciences [q-bio]/Pharmaceutical sciences/Pharmacology ,Supervised Machine Learning ,Statistics, Probability and Uncertainty ,Natural language processing ,Information Systems ,[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI] ,Statistics and Probability ,PubMed ,[SDV.SP.MED] Life Sciences [q-bio]/Pharmaceutical sciences/Medication ,[SDV.GEN.GH] Life Sciences [q-bio]/Genetics/Human genetics ,Library and Information Sciences ,Domain (software engineering) ,Education ,03 medical and health sciences ,[SDV.SP.MED]Life Sciences [q-bio]/Pharmaceutical sciences/Medication ,Component (UML) ,Genetics ,Humans ,030304 developmental biology ,business.industry ,Health care ,Significant part ,Precision medicine ,ComputingMethodologies_PATTERNRECOGNITION ,[SDV.GEN.GH]Life Sciences [q-bio]/Genetics/Human genetics ,Pharmacogenetics ,Pharmacogenomics ,[SDV.SP.PHARMA]Life Sciences [q-bio]/Pharmaceutical sciences/Pharmacology ,lcsh:Q ,Artificial intelligence ,[INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM] ,business ,computer - Abstract
Pharmacogenomics (PGx) studies how individual gene variations impact drug response phenotypes, which makes PGx-related knowledge a key component towards precision medicine. A significant part of the state-of-the-art knowledge in PGx is accumulated in scientific publications, where it is hardly reusable by humans or software. Natural language processing techniques have been developed to guide experts who curate this amount of knowledge. But existing works are limited by the absence of a high quality annotated corpus focusing on PGx domain. In particular, this absence restricts the use of supervised machine learning. This article introduces PGxCorpus, a manually annotated corpus, designed to fill this gap and to enable the automatic extraction of PGx relationships from text. It comprises 945 sentences from 911 PubMed abstracts, annotated with PGx entities of interest (mainly gene variations, genes, drugs and phenotypes), and relationships between those. In this article, we present the corpus itself, its construction and a baseline experiment that illustrates how it may be leveraged to synthesize and summarize PGx knowledge., Measurement(s)gene_variant • response to drug • textual entity • chemical entity • haplotype • gene • Pharmacogenomics • Pharmacogenetics • abbreviation textual entity • Pharmacokinetics • Pharmacodynamics • phenotypeTechnology Type(s)digital curationSample Characteristic - OrganismHomo sapiens Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11323724
- Published
- 2020
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