Open AccessDrugMetab: An Integrated Machine Learning and Lexicon Mapping Named Entity Recognition Method for Drug Metabolite
Author(s) -
Wu HengYi,
Lu Deshun,
Hyder Mustafa,
Zhang Shijun,
Quinney Sara K.,
Desta Zeruesenay,
Li Lang
Publication year - 2018
Publication title -
cpt: pharmacometrics and systems pharmacology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.53
H-Index - 37
ISSN - 2163-8306
DOI - 10.1002/psp4.12340
Subject(s) - computer science , artificial intelligence , named entity recognition , natural language processing , recall , precision and recall , classifier (uml) , terminology , lexicon , metabolite , machine learning , task (project management) , chemistry , biochemistry , engineering , linguistics , philosophy , systems engineering
Drug metabolites ( DM s) are critical in pharmacology research areas, such as drug metabolism pathways and drug‐drug interactions. However, there is no terminology dictionary containing comprehensive drug metabolite names, and there is no named entity recognition ( NER ) algorithm focusing on drug metabolite identification. In this article, we developed a novel NER system, DrugMetab, to identify DM s from the PubMed abstracts. DrugMetab utilizes the features characterized from the Part‐of‐Speech, drug index, and pre/suffix, and determines DM s within context. To evaluate the performance, a gold‐standard corpus was manually constructed. In this task, DrugMetab with sequential minimal optimization ( SMO ) classifier achieves 0.89 precision, 0.77 recall, and 0.83 F‐measure in the internal testing set; and 0.86 precision, 0.85 recall, and 0.86 F‐measure in the external validation set. We further compared the performance between DrugMetab and whatizitChemical, which was designed for identifying small molecules or chemical entities. DrugMetab outperformed whatizitChemical, which had a lower recall rate of 0.65.