title: Using binary classification to prioritize and curate articles for the Comparative Toxicogenomics Database.
abstract: We report on the original integration of an automatic text categorization pipeline, so-called ToxiCat (Toxicogenomic Categorizer), that we developed to perform biomedical documents classification and prioritization in order to speed up the curation of the Comparative Toxicogenomics Database (CTD). The task can be basically described as a binary classification task, where a scoring function is used to rank a selected set of articles. Then components of a question-answering system are used to extract CTD-specific annotations from the ranked list of articles. The ranking function is generated using a Support Vector Machine, which combines three main modules: an information retrieval engine for MEDLINE (EAGLi), a gene normalization service (NormaGene) developed for a previous BioCreative campaign and finally, a set of answering components and entity recognizer for diseases and chemicals. The main components of the pipeline are publicly available both as web application and web services. The specific integration performed for the BioCreative competition is available via a web user interface at http://pingu.unige.ch:8080/Toxicat.
authors.affiliation: Bibliomics and Text Mining Group, Geneva, Switzerland. dina.vishnyakova@hcuge.ch
authors.forename: Dina
authors.initials: D
authors.lastname: Vishnyakova
authors.forename: Emilie
authors.initials: E
authors.lastname: Pasche
authors.forename: Patrick
authors.initials: P
authors.lastname: Ruch
mesh_terms: D057225:Data Mining / Q000379:methods*; D030541:Databases, Genetic / Q000145:classification*; D020407:Internet; D058977:Molecular Sequence Annotation; D010506:Periodicals as Topic; D012660:Semantics; D060388:Support Vector Machine; D043922:Toxicogenetics; D057188:Workflow