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Legrand J, Gogdemir R, Bousquet C, Dalleau K, Devignes MD, Digan W, Lee CJ, Ndiaye NC, Petitpain N, Ringot P, Smaïl-Tabbone M, Toussaint Y, Coulet A. PGxCorpus, a manually annotated corpus for pharmacogenomics. Sci Data 2020; 7:3. [PMID: 31896797 PMCID: PMC6940385 DOI: 10.1038/s41597-019-0342-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Accepted: 12/02/2019] [Indexed: 11/09/2022] Open
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.
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Affiliation(s)
- Joël Legrand
- Université de Lorraine, CNRS, Inria, LORIA, Nancy, France.
| | | | - Cédric Bousquet
- Sorbonne Université, INSERM, Université Paris 13, LIMICS, Paris, France
| | - Kevin Dalleau
- Université de Lorraine, CNRS, Inria, LORIA, Nancy, France
| | | | - William Digan
- Hôpital Européen Georges Pompidou, AP-HP, Université Paris Descartes, Université Sorbonne Paris Cité, Paris, France
- INSERM UMR 1138 Equipe 22, Université Paris Descartes, Université Sorbonne Paris Cité, Paris, France
| | - Chia-Ju Lee
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, USA
| | | | - Nadine Petitpain
- Centre Régional de Pharmacovigilance, CHRU of Nancy, Nancy, France
| | - Patrice Ringot
- Université de Lorraine, CNRS, Inria, LORIA, Nancy, France
| | | | | | - Adrien Coulet
- Université de Lorraine, CNRS, Inria, LORIA, Nancy, France
- Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, USA
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Mahmood ASMA, Rao S, McGarvey P, Wu C, Madhavan S, Vijay-Shanker K. eGARD: Extracting associations between genomic anomalies and drug responses from text. PLoS One 2017; 12:e0189663. [PMID: 29261751 PMCID: PMC5738129 DOI: 10.1371/journal.pone.0189663] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Accepted: 11/29/2017] [Indexed: 12/25/2022] Open
Abstract
Tumor molecular profiling plays an integral role in identifying genomic anomalies which may help in personalizing cancer treatments, improving patient outcomes and minimizing risks associated with different therapies. However, critical information regarding the evidence of clinical utility of such anomalies is largely buried in biomedical literature. It is becoming prohibitive for biocurators, clinical researchers and oncologists to keep up with the rapidly growing volume and breadth of information, especially those that describe therapeutic implications of biomarkers and therefore relevant for treatment selection. In an effort to improve and speed up the process of manually reviewing and extracting relevant information from literature, we have developed a natural language processing (NLP)-based text mining (TM) system called eGARD (extracting Genomic Anomalies association with Response to Drugs). This system relies on the syntactic nature of sentences coupled with various textual features to extract relations between genomic anomalies and drug response from MEDLINE abstracts. Our system achieved high precision, recall and F-measure of up to 0.95, 0.86 and 0.90, respectively, on annotated evaluation datasets created in-house and obtained externally from PharmGKB. Additionally, the system extracted information that helps determine the confidence level of extraction to support prioritization of curation. Such a system will enable clinical researchers to explore the use of published markers to stratify patients upfront for 'best-fit' therapies and readily generate hypotheses for new clinical trials.
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Affiliation(s)
- A. S. M. Ashique Mahmood
- Department of Computer and Information Science, University of Delaware, Newark, Delaware, United States of America
- * E-mail:
| | - Shruti Rao
- Innovation Center For Biomedical Informatics, Georgetown University, Washington D.C, United States of America
| | - Peter McGarvey
- Innovation Center For Biomedical Informatics, Georgetown University, Washington D.C, United States of America
- Protein Information Resource, Georgetown University Medical Center, Washington D.C, United States of America
| | - Cathy Wu
- Department of Computer and Information Science, University of Delaware, Newark, Delaware, United States of America
- Protein Information Resource, Georgetown University Medical Center, Washington D.C, United States of America
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, Delaware, United States of America
| | - Subha Madhavan
- Innovation Center For Biomedical Informatics, Georgetown University, Washington D.C, United States of America
- Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington D.C, United States of America
| | - K. Vijay-Shanker
- Department of Computer and Information Science, University of Delaware, Newark, Delaware, United States of America
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Abstract
Natural language processing employs computational techniques for the purpose of learning, understanding, and producing human language content. Early computational approaches to language research focused on automating the analysis of the linguistic structure of language and developing basic technologies such as machine translation, speech recognition, and speech synthesis. Today's researchers refine and make use of such tools in real-world applications, creating spoken dialogue systems and speech-to-speech translation engines, mining social media for information about health or finance, and identifying sentiment and emotion toward products and services. We describe successes and challenges in this rapidly advancing area.
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Affiliation(s)
- Julia Hirschberg
- Department of Computer Science, Columbia University, New York, NY 10027, USA.
| | - Christopher D Manning
- Department of Linguistics, Stanford University, Stanford, CA 94305-2150, USA. Department of Computer Science, Stanford University, Stanford, CA 94305-9020, USA
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