Scientific article
Open access

Using binary classification to prioritize and curate articles for the Comparative Toxicogenomics Database

Published inDatabase, vol. 2012, bas050
Publication date2012

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.

  • Data Mining/methods
  • Databases, Genetic/classification
  • Internet
  • Molecular Sequence Annotation
  • Periodicals as Topic
  • Semantics
  • Support Vector Machines
  • Toxicogenetics
  • Workflow
Citation (ISO format)
VISHNYAKOVA, Dina, PASCHE, Emilie, RUCH, Patrick. Using binary classification to prioritize and curate articles for the Comparative Toxicogenomics Database. In: Database, 2012, vol. 2012, p. bas050. doi: 10.1093/database/bas050
Main files (1)
Article (Published version)
ISSN of the journal1758-0463

Technical informations

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