title: Extraction of chemical-induced diseases using prior knowledge and textual information.
abstract: We describe our approach to the chemical-disease relation (CDR) task in the BioCreative V challenge. The CDR task consists of two subtasks: automatic disease-named entity recognition and normalization (DNER), and extraction of chemical-induced diseases (CIDs) from Medline abstracts. For the DNER subtask, we used our concept recognition tool Peregrine, in combination with several optimization steps. For the CID subtask, our system, which we named RELigator, was trained on a rich feature set, comprising features derived from a graph database containing prior knowledge about chemicals and diseases, and linguistic and statistical features derived from the abstracts in the CDR training corpus. We describe the systems that were developed and present evaluation results for both subtasks on the CDR test set. For DNER, our Peregrine system reached anF-score of 0.757. For CID, the system achieved anF-score of 0.526, which ranked second among 18 participating teams. Several post-challenge modifications of the systems resulted in substantially improvedF-scores (0.828 for DNER and 0.602 for CID). RELigator is available as a web service athttp://biosemantics.org/index.php/software/religator.
authors.affiliation: Department of Medical Informatics Department of Radiology, Erasmus University Medical Center, 3000 DR Rotterdam, PO Box 2040, The Netherlands.
authors.forename: Ewoud
authors.initials: E
authors.lastname: Pons
authors.affiliation: Department of Medical Informatics b.becker@erasmusmc.nl.
authors.forename: Benedikt F H
authors.initials: BF
authors.lastname: Becker
authors.affiliation: Department of Medical Informatics.
authors.forename: Saber A
authors.initials: SA
authors.lastname: Akhondi
authors.affiliation: Department of Medical Informatics.
authors.forename: Zubair
authors.initials: Z
authors.lastname: Afzal
authors.affiliation: Department of Medical Informatics.
authors.forename: Erik M
authors.initials: EM
authors.lastname: van Mulligen
authors.affiliation: Department of Medical Informatics.
authors.forename: Jan A
authors.initials: JA
authors.lastname: Kors
mesh_terms: D019295:Computational Biology / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D004194:Disease / Q000209:etiology*; D015386:Hazardous Substances / Q000633:toxicity*; D006801:Humans; D043922:Toxicogenetics
chemical_list: D015386:Hazardous Substances