Characterization of the mechanism of drug-drug interactions from PubMed using MeSH terms

Identifying drug-drug interaction (DDI) is an important topic for the development of safe pharmaceutical drugs and for the optimization of multidrug regimens for complex diseases such as cancer and HIV. There have been about 150,000 publications on DDIs in PubMed, which is a great resource for DDI s...

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Bibliographic Details
Published in:PloS one Vol. 12; no. 4; p. e0173548
Main Authors: Lu, Yin, Figler, Bryan, Huang, Hong, Tu, Yi-Cheng, Wang, Ju, Cheng, Feng
Format: Journal Article
Language:English
Published: United States Public Library of Science 19.04.2017
Public Library of Science (PLoS)
Subjects:
Algorithms
Biology and Life Sciences
Cancer
Computer and Information Sciences
Computer applications
Cytochrome P-450 Enzyme System - metabolism
Documents
Drug development
Drug interaction
Drug Interactions
Drugs
Glycoproteins
Humans
Indexing
Medical Subject Headings
Medicine and Health Sciences
Metabolism
Metabolites
Network analysis
Optimization
Pharmaceutical industry
Pharmaceutical sciences
Pharmacy
Prescription drugs
Prescription Drugs - pharmacology
Proteins
PubMed - statistics & numerical data
ROC Curve
Social organization
Social Sciences
Thesauri
Florida
United States > US
ISSN:1932-6203, 1932-6203
Online Access:Get full text
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Summary:Identifying drug-drug interaction (DDI) is an important topic for the development of safe pharmaceutical drugs and for the optimization of multidrug regimens for complex diseases such as cancer and HIV. There have been about 150,000 publications on DDIs in PubMed, which is a great resource for DDI studies. In this paper, we introduced an automatic computational method for the systematic analysis of the mechanism of DDIs using MeSH (Medical Subject Headings) terms from PubMed literature. MeSH term is a controlled vocabulary thesaurus developed by the National Library of Medicine for indexing and annotating articles. Our method can effectively identify DDI-relevant MeSH terms such as drugs, proteins and phenomena with high accuracy. The connections among these MeSH terms were investigated by using co-occurrence heatmaps and social network analysis. Our approach can be used to visualize relationships of DDI terms, which has the potential to help users better understand DDIs. As the volume of PubMed records increases, our method for automatic analysis of DDIs from the PubMed database will become more accurate.
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SourceType-Scholarly Journals-1
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Competing Interests: The authors have declared that no competing interests exist.
Conceptualization: YL BF HH YCT JW FC.Data curation: YL BF FC.Formal analysis: YL BF JW FC.Funding acquisition: JW FC YCT.Investigation: YL BF FC.Methodology: YL HH FC.Project administration: HH YCT JW FC.Resources: YCT FC.Software: YL YCT FC.Supervision: YCT FC.Validation: YL BF HH FC.Visualization: YL FC.Writing – original draft: YL BF YCT FC.Writing – review & editing: YL BF YCT JW FC.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0173548