title: Discovering and visualizing indirect associations between biomedical concepts.
abstract: BACKGROUND
  Discovering useful associations between biomedical concepts has been one of the main goals in biomedical text-mining, and understanding their biomedical contexts is crucial in the discovery process. Hence, we need a text-mining system that helps users explore various types of (possibly hidden) associations in an easy and comprehensible manner.
  
  
  RESULTS
  This article describes FACTA+, a real-time text-mining system for finding and visualizing indirect associations between biomedical concepts from MEDLINE abstracts. The system can be used as a text search engine like PubMed with additional features to help users discover and visualize indirect associations between important biomedical concepts such as genes, diseases and chemical compounds. FACTA+ inherits all functionality from its predecessor, FACTA, and extends it by incorporating three new features: (i) detecting biomolecular events in text using a machine learning model, (ii) discovering hidden associations using co-occurrence statistics between concepts, and (iii) visualizing associations to improve the interpretability of the output. To the best of our knowledge, FACTA+ is the first real-time web application that offers the functionality of finding concepts involving biomolecular events and visualizing indirect associations of concepts with both their categories and importance.
  
  
  BACKGROUND
  FACTA+ is available as a web application at http://refine1-nactem.mc.man.ac.uk/facta/, and its visualizer is available at http://refine1-nactem.mc.man.ac.uk/facta-visualizer/.
  
  
  BACKGROUND
  tsuruoka@jaist.ac.jp.
authors.affiliation: School of Information Science, Japan Advanced Institute of Science and Technology (JAIST), Nomi, Japan. tsuruoka@jaist.ac.jp
authors.forename: Yoshimasa
authors.initials: Y
authors.lastname: Tsuruoka
authors.forename: Makoto
authors.initials: M
authors.lastname: Miwa
authors.forename: Kaisei
authors.initials: K
authors.lastname: Hamamoto
authors.forename: Jun'ichi
authors.initials: J
authors.lastname: Tsujii
authors.forename: Sophia
authors.initials: S
authors.lastname: Ananiadou
mesh_terms: D001185:Artificial Intelligence; D057225:Data Mining; D020407:Internet; D016239:MEDLINE; D008491:Medical Informatics Applications; D039781:PubMed; D014481:United States