title: Toward creation of a cancer drug toxicity knowledge base: automatically extracting cancer drug-side effect relationships from the literature.
abstract: OBJECTIVE
  A comprehensive and machine-understandable cancer drug-side effect (drug-SE) relationship knowledge base is important for in silico cancer drug target discovery, drug repurposing, and toxicity predication, and for personalized risk-benefit decisions by cancer patients. While US Food and Drug Administration (FDA) drug labels capture well-known cancer drug SE information, much cancer drug SE knowledge remains buried the published biomedical literature. We present a relationship extraction approach to extract cancer drug-SE pairs from the literature.
  
  
  METHODS
  We used 21,354,075 MEDLINE records as the text corpus. We extracted drug-SE co-occurrence pairs using a cancer drug lexicon and a clean SE lexicon that we created. We then developed two filtering approaches to remove drug-disease treatment pairs and subsequently a ranking scheme to further prioritize filtered pairs. Finally, we analyzed relationships among SEs, gene targets, and indications.
  
  
  RESULTS
  We extracted 56,602 cancer drug-SE pairs. The filtering algorithms improved the precision of extracted pairs from 0.252 at baseline to 0.426, representing a 69% improvement in precision with no decrease in recall. The ranking algorithm further prioritized filtered pairs and achieved a precision of 0.778 for top-ranked pairs. We showed that cancer drugs that share SEs tend to have overlapping gene targets and overlapping indications.
  
  
  CONCLUSIONS
  The relationship extraction approach is effective in extracting many cancer drug-SE pairs from the literature. This unique knowledge base, when combined with existing cancer drug SE knowledge, can facilitate drug target discovery, drug repurposing, and toxicity prediction.
authors.affiliation: Medical Informatics Program, Center for Clinical Investigation, Case Western Reserve University, Cleveland, Ohio, USA.
authors.forename: Rong
authors.initials: R
authors.lastname: Xu
authors.forename: QuanQiu
authors.initials: Q
authors.lastname: Wang
mesh_terms: D000970:Antineoplastic Agents / Q000009:adverse effects*; D057225:Data Mining / Q000379:methods*; D006801:Humans; D051188:Knowledge Bases; D016239:MEDLINE
chemical_list: D000970:Antineoplastic Agents
keywords: Cancer Drug Toxicity; Drug Repurposing; Drug Target Discovery; Information Extraction; Natural Language Processing; Text Mining