Extracting drug-enzyme relation from literature as evidence for drug drug interaction

Yaoyun Zhang, Heng Yi Wu, Jingcheng Du, Jun Xu, Jingqi Wang, Cui Tao, Lang Li, Hua Xu

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Background: Information about drug-drug interactions (DDIs) is crucial for computational applications such as pharmacovigilance and drug repurposing. However, existing sources of DDIs have the problems of low coverage, low accuracy and low agreement. One common type of DDIs is related to the mechanism of drug metabolism: a DDI relation may be caused by different interactions (e.g., substrate, inhibit) between drugs and enzymes in the drug metabolism process. Thus, information from drug enzyme interactions (DEIs) serves as important supportive evidence for DDIs. Further, potential DDIs present implicitly could be detected by inference and reasoning based on DEIs. Methods: In this article, we propose a hybrid approach to combining machine learning algorithm with trigger words and syntactic patterns, for DEI relation extraction from biomedical literature. The extracted DEI relations are used for reasoning to infer potential DDI relations, based on a defined drug-enzyme ontology incorporating biological knowledge. Results: Evaluation results demonstrate that the performance of DEI relation extraction is promising, with an F-measure of 84.97 % on the in vivo dataset and 65.58 % on the in vitro dataset. Further, the inferred DDIs achieved a precision of 83.19 % on the in vivo dataset and 70.94 % on the in vitro dataset, respectively. A further examination showed that the overlaps between our inferred DDIs and those present in DrugBank were 42.02 % on the in vivo dataset and 19.23 % on the in vitro dataset, respectively. Conclusions: This paper proposed an effective approach to extract DEI relations from biomedical literature. Potential DDIs not present in existing knowledge bases were then inferred based on the extracted DEIs, demonstrating the capability of the proposed approach to detect DDIs with scientific evidence for pharmacovigilance and drug repurposing applications.

Original languageEnglish (US)
Article number11
JournalJournal of Biomedical Semantics
Volume7
Issue number1
DOIs
StatePublished - Mar 7 2016

Fingerprint

Drug interactions
Drug Interactions
Enzymes
Pharmaceutical Preparations
Metabolism
Drug Repositioning
Pharmacovigilance
Syntactics
Biological Ontologies
Learning algorithms
Ontology
Learning systems

Keywords

  • Drug-enzyme interaction
  • Literature mining
  • Ontology-based inference
  • Pharmacokinetic drug-drug interactions
  • Relation extraction
  • Semantic graph kernel

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computer Networks and Communications
  • Health Informatics

Cite this

Extracting drug-enzyme relation from literature as evidence for drug drug interaction. / Zhang, Yaoyun; Wu, Heng Yi; Du, Jingcheng; Xu, Jun; Wang, Jingqi; Tao, Cui; Li, Lang; Xu, Hua.

In: Journal of Biomedical Semantics, Vol. 7, No. 1, 11, 07.03.2016.

Research output: Contribution to journalArticle

Zhang, Yaoyun ; Wu, Heng Yi ; Du, Jingcheng ; Xu, Jun ; Wang, Jingqi ; Tao, Cui ; Li, Lang ; Xu, Hua. / Extracting drug-enzyme relation from literature as evidence for drug drug interaction. In: Journal of Biomedical Semantics. 2016 ; Vol. 7, No. 1.
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abstract = "Background: Information about drug-drug interactions (DDIs) is crucial for computational applications such as pharmacovigilance and drug repurposing. However, existing sources of DDIs have the problems of low coverage, low accuracy and low agreement. One common type of DDIs is related to the mechanism of drug metabolism: a DDI relation may be caused by different interactions (e.g., substrate, inhibit) between drugs and enzymes in the drug metabolism process. Thus, information from drug enzyme interactions (DEIs) serves as important supportive evidence for DDIs. Further, potential DDIs present implicitly could be detected by inference and reasoning based on DEIs. Methods: In this article, we propose a hybrid approach to combining machine learning algorithm with trigger words and syntactic patterns, for DEI relation extraction from biomedical literature. The extracted DEI relations are used for reasoning to infer potential DDI relations, based on a defined drug-enzyme ontology incorporating biological knowledge. Results: Evaluation results demonstrate that the performance of DEI relation extraction is promising, with an F-measure of 84.97 {\%} on the in vivo dataset and 65.58 {\%} on the in vitro dataset. Further, the inferred DDIs achieved a precision of 83.19 {\%} on the in vivo dataset and 70.94 {\%} on the in vitro dataset, respectively. A further examination showed that the overlaps between our inferred DDIs and those present in DrugBank were 42.02 {\%} on the in vivo dataset and 19.23 {\%} on the in vitro dataset, respectively. Conclusions: This paper proposed an effective approach to extract DEI relations from biomedical literature. Potential DDIs not present in existing knowledge bases were then inferred based on the extracted DEIs, demonstrating the capability of the proposed approach to detect DDIs with scientific evidence for pharmacovigilance and drug repurposing applications.",
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