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Combining automatic table classification and relationship extraction in extracting anticancer drug-side effect pairs from full-text articles.

Abstract：

:Anticancer drug-associated side effect knowledge often exists in multiple heterogeneous and complementary data sources. A comprehensive anticancer drug-side effect (drug-SE) relationship knowledge base is important for computation-based drug target discovery, drug toxicity predication and drug repositioning. In this study, we present a two-step approach by combining table classification and relationship extraction to extract drug-SE pairs from a large number of high-profile oncological full-text articles. The data consists of 31,255 tables downloaded from the Journal of Oncology (JCO). We first trained a statistical classifier to classify tables into SE-related and -unrelated categories. We then extracted drug-SE pairs from SE-related tables. We compared drug side effect knowledge extracted from JCO tables to that derived from FDA drug labels. Finally, we systematically analyzed relationships between anti-cancer drug-associated side effects and drug-associated gene targets, metabolism genes, and disease indications. The statistical table classifier is effective in classifying tables into SE-related and -unrelated (precision: 0.711; recall: 0.941; F1: 0.810). We extracted a total of 26,918 drug-SE pairs from SE-related tables with a precision of 0.605, a recall of 0.460, and a F1 of 0.520. Drug-SE pairs extracted from JCO tables is largely complementary to those derived from FDA drug labels; as many as 84.7% of the pairs extracted from JCO tables have not been included a side effect database constructed from FDA drug labels. Side effects associated with anticancer drugs positively correlate with drug target genes, drug metabolism genes, and disease indications.

journal_name

J Biomed Inform

journal_title

Journal of biomedical informatics

authors

Xu R,Wang Q

doi

10.1016/j.jbi.2014.10.002

subject

Has Abstract

pub_date

2015-02-01 00:00:00

pages

128-35

eissn

1532-0464

issn

1532-0480

pii

S1532-0464(14)00221-4

journal_volume

53

pub_type

杂志文章

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