title: 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.
authors.affiliation: Medical Informatics Program, Center for Clinical Investigation, Case Western Reserve University, Cleveland, OH 44106, United States. Electronic address: rxx@case.edu.
authors.forename: Rong
authors.initials: R
authors.lastname: Xu
authors.affiliation: ThinTek, LLC, Palo Alto, CA 94306, United States. Electronic address: qwang@thintek.com.
authors.forename: QuanQiu
authors.initials: Q
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D000970:Antineoplastic Agents / Q000627:therapeutic use*; D002986:Clinical Trials as Topic; D019295:Computational Biology / Q000379:methods*; D003625:Data Collection; D057225:Data Mining; D055808:Drug Discovery; D004348:Drug Labeling; D058492:Drug Repositioning; D064420:Drug-Related Side Effects and Adverse Reactions; D006801:Humans; D051188:Knowledge Bases; D016239:MEDLINE; D009369:Neoplasms / Q000188:drug therapy*; D010865:Pilot Projects; D014481:United States; D014486:United States Food and Drug Administration
chemical_list: D000970:Antineoplastic Agents
keywords: Cancer drug side effect; Drug discovery; Drug repositioning; Drug toxicity prediction; Information extraction; Text mining