Assessing the Need of Discourse-Level Analysis in Identifying Evidence of Drug-Disease Relations in Scientific Literature.
Relation extraction typically involves the extraction of relations between two or more entities occurring within a single or multiple sentences. In this study, we investigated the significance of extracting information from multiple sentences specifically in the context of drug-disease relation discovery. We used multiple resources such as Semantic Medline, a literature based resource, and Medline search (for filtering spurious results) and inferred 8,772 potential drug-disease pairs. Our analysis revealed that 6,450 (73.5%) of the 8,772 potential drug-disease relations did not occur in a single sentence. Moreover, only 537 of the drug-disease pairs matched the curated gold standard in Comparative Toxicogenomics Database (CTD), a trusted resource for drug-disease relations. Among the 537, nearly 75% (407) of the drug-disease pairs occur in multiple sentences. Our analysis revealed that the drug-disease pairs inferred from Semantic Medline or retrieved from CTD could be extracted from multiple sentences in the literature. This highlights the significance of the need of discourse-level analysis in extracting the relations from biomedical literature.
Biomedical Statistics & Informatics, Mayo Clinic, Rochester, MN, USA.
Majid
M
Rastegar-Mojarad
Biomedical Statistics & Informatics, Mayo Clinic, Rochester, MN, USA.
Ravikumar
R
Komandur Elayavilli
Biomedical Statistics & Informatics, Mayo Clinic, Rochester, MN, USA.
Dingcheng
D
Li
Biomedical Statistics & Informatics, Mayo Clinic, Rochester, MN, USA.
Hongfang
H
Liu
D057225:Data Mining / Q000379:methods*; D064420:Drug-Related Side Effects and Adverse Reactions / Q000145:classification*; D006801:Humans; D016239:MEDLINE; D000069550:Machine Learning; D009323:Natural Language Processing; D020380:Needs Assessment; D010506:Periodicals as Topic / Q000145:classification*; D012586:Science; D012660:Semantics