Text mining for drug-drug interaction

Heng Yi Wu, Chien Wei Chiang, Lang Li

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

In order to understand the mechanisms of drug-drug interaction (DDI), the study of pharmacokinetics (PK), pharmacodynamics (PD), and pharmacogenetics (PG) data are significant. In recent years, drug PK parameters, drug interaction parameters, and PG data have been unevenly collected in different databases and published extensively in literature. Also the lack of an appropriate PK ontology and a well-annotated PK corpus, which provide the background knowledge and the criteria of determining DDI, respectively, lead to the difficulty of developing DDI text mining tools for PK data collection from the literature and data integration from multiple databases.To conquer the issues, we constructed a comprehensive pharmacokinetics ontology. It includes all aspects of in vitro pharmacokinetics experiments, in vivo pharmacokinetics studies, as well as drug metabolism and transportation enzymes. Using our pharmacokinetics ontology, a PK corpus was constructed to present four classes of pharmacokinetics abstracts: in vivo pharmacokinetics studies, in vivo pharmacoge-netic studies, in vivo drug interaction studies, and in vitro drug interaction studies. A novel hierarchical three-level annotation scheme was proposed and implemented to tag key terms, drug interaction sentences, and drug interaction pairs. The utility of the pharmacokinetics ontology was demonstrated by annotating three pharmacokinetics studies; and the utility of the PK corpus was demonstrated by a drug interaction extraction text mining analysis.The pharmacokinetics ontology annotates both in vitro pharmacokinetics experiments and in vivo pharmacokinetics studies. The PK corpus is a highly valuable resource for the text mining of pharmacoki-netics parameters and drug interactions.

Original languageEnglish (US)
Pages (from-to)47-75
Number of pages29
JournalMethods in Molecular Biology
Volume1159
DOIs
StatePublished - 2014
Externally publishedYes

Fingerprint

Data Mining
Drug Interactions
Pharmacokinetics
Pharmaceutical Preparations
Pharmacogenetics
Databases

Keywords

  • Corpus
  • Drug-drug interaction
  • Enzyme
  • Ontology
  • Pharmacodynamics
  • Pharmacokinetics
  • Relation extraction
  • Text mining
  • Transporter

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics

Cite this

Text mining for drug-drug interaction. / Wu, Heng Yi; Chiang, Chien Wei; Li, Lang.

In: Methods in Molecular Biology, Vol. 1159, 2014, p. 47-75.

Research output: Contribution to journalArticle

Wu, Heng Yi ; Chiang, Chien Wei ; Li, Lang. / Text mining for drug-drug interaction. In: Methods in Molecular Biology. 2014 ; Vol. 1159. pp. 47-75.
@article{e2909dc325b44b87a2006f5e6347ede4,
title = "Text mining for drug-drug interaction",
abstract = "In order to understand the mechanisms of drug-drug interaction (DDI), the study of pharmacokinetics (PK), pharmacodynamics (PD), and pharmacogenetics (PG) data are significant. In recent years, drug PK parameters, drug interaction parameters, and PG data have been unevenly collected in different databases and published extensively in literature. Also the lack of an appropriate PK ontology and a well-annotated PK corpus, which provide the background knowledge and the criteria of determining DDI, respectively, lead to the difficulty of developing DDI text mining tools for PK data collection from the literature and data integration from multiple databases.To conquer the issues, we constructed a comprehensive pharmacokinetics ontology. It includes all aspects of in vitro pharmacokinetics experiments, in vivo pharmacokinetics studies, as well as drug metabolism and transportation enzymes. Using our pharmacokinetics ontology, a PK corpus was constructed to present four classes of pharmacokinetics abstracts: in vivo pharmacokinetics studies, in vivo pharmacoge-netic studies, in vivo drug interaction studies, and in vitro drug interaction studies. A novel hierarchical three-level annotation scheme was proposed and implemented to tag key terms, drug interaction sentences, and drug interaction pairs. The utility of the pharmacokinetics ontology was demonstrated by annotating three pharmacokinetics studies; and the utility of the PK corpus was demonstrated by a drug interaction extraction text mining analysis.The pharmacokinetics ontology annotates both in vitro pharmacokinetics experiments and in vivo pharmacokinetics studies. The PK corpus is a highly valuable resource for the text mining of pharmacoki-netics parameters and drug interactions.",
keywords = "Corpus, Drug-drug interaction, Enzyme, Ontology, Pharmacodynamics, Pharmacokinetics, Relation extraction, Text mining, Transporter",
author = "Wu, {Heng Yi} and Chiang, {Chien Wei} and Lang Li",
year = "2014",
doi = "10.1007/978-1-4939-0709-0_4",
language = "English (US)",
volume = "1159",
pages = "47--75",
journal = "Methods in Molecular Biology",
issn = "1064-3745",
publisher = "Humana Press",

}

TY - JOUR

T1 - Text mining for drug-drug interaction

AU - Wu, Heng Yi

AU - Chiang, Chien Wei

AU - Li, Lang

PY - 2014

Y1 - 2014

N2 - In order to understand the mechanisms of drug-drug interaction (DDI), the study of pharmacokinetics (PK), pharmacodynamics (PD), and pharmacogenetics (PG) data are significant. In recent years, drug PK parameters, drug interaction parameters, and PG data have been unevenly collected in different databases and published extensively in literature. Also the lack of an appropriate PK ontology and a well-annotated PK corpus, which provide the background knowledge and the criteria of determining DDI, respectively, lead to the difficulty of developing DDI text mining tools for PK data collection from the literature and data integration from multiple databases.To conquer the issues, we constructed a comprehensive pharmacokinetics ontology. It includes all aspects of in vitro pharmacokinetics experiments, in vivo pharmacokinetics studies, as well as drug metabolism and transportation enzymes. Using our pharmacokinetics ontology, a PK corpus was constructed to present four classes of pharmacokinetics abstracts: in vivo pharmacokinetics studies, in vivo pharmacoge-netic studies, in vivo drug interaction studies, and in vitro drug interaction studies. A novel hierarchical three-level annotation scheme was proposed and implemented to tag key terms, drug interaction sentences, and drug interaction pairs. The utility of the pharmacokinetics ontology was demonstrated by annotating three pharmacokinetics studies; and the utility of the PK corpus was demonstrated by a drug interaction extraction text mining analysis.The pharmacokinetics ontology annotates both in vitro pharmacokinetics experiments and in vivo pharmacokinetics studies. The PK corpus is a highly valuable resource for the text mining of pharmacoki-netics parameters and drug interactions.

AB - In order to understand the mechanisms of drug-drug interaction (DDI), the study of pharmacokinetics (PK), pharmacodynamics (PD), and pharmacogenetics (PG) data are significant. In recent years, drug PK parameters, drug interaction parameters, and PG data have been unevenly collected in different databases and published extensively in literature. Also the lack of an appropriate PK ontology and a well-annotated PK corpus, which provide the background knowledge and the criteria of determining DDI, respectively, lead to the difficulty of developing DDI text mining tools for PK data collection from the literature and data integration from multiple databases.To conquer the issues, we constructed a comprehensive pharmacokinetics ontology. It includes all aspects of in vitro pharmacokinetics experiments, in vivo pharmacokinetics studies, as well as drug metabolism and transportation enzymes. Using our pharmacokinetics ontology, a PK corpus was constructed to present four classes of pharmacokinetics abstracts: in vivo pharmacokinetics studies, in vivo pharmacoge-netic studies, in vivo drug interaction studies, and in vitro drug interaction studies. A novel hierarchical three-level annotation scheme was proposed and implemented to tag key terms, drug interaction sentences, and drug interaction pairs. The utility of the pharmacokinetics ontology was demonstrated by annotating three pharmacokinetics studies; and the utility of the PK corpus was demonstrated by a drug interaction extraction text mining analysis.The pharmacokinetics ontology annotates both in vitro pharmacokinetics experiments and in vivo pharmacokinetics studies. The PK corpus is a highly valuable resource for the text mining of pharmacoki-netics parameters and drug interactions.

KW - Corpus

KW - Drug-drug interaction

KW - Enzyme

KW - Ontology

KW - Pharmacodynamics

KW - Pharmacokinetics

KW - Relation extraction

KW - Text mining

KW - Transporter

UR - http://www.scopus.com/inward/record.url?scp=84927130836&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84927130836&partnerID=8YFLogxK

U2 - 10.1007/978-1-4939-0709-0_4

DO - 10.1007/978-1-4939-0709-0_4

M3 - Article

C2 - 24788261

AN - SCOPUS:84927130836

VL - 1159

SP - 47

EP - 75

JO - Methods in Molecular Biology

JF - Methods in Molecular Biology

SN - 1064-3745

ER -