title: BioTextQuest: a web-based biomedical text mining suite for concept discovery.
abstract: CONCLUSIONS
  BioTextQuest combines automated discovery of significant terms in article clusters with structured knowledge annotation, via Named Entity Recognition services, offering interactive user-friendly visualization. A tag-cloud-based illustration of terms labeling each document cluster are semantically annotated according to the biological entity, and a list of document titles enable users to simultaneously compare terms and documents of each cluster, facilitating concept association and hypothesis generation. BioTextQuest allows customization of analysis parameters, e.g. clustering/stemming algorithms, exclusion of documents/significant terms, to better match the biological question addressed.
  
  
  BACKGROUND
  http://biotextquest.biol.ucy.ac.cy
  
  
  BACKGROUND
  vprobon@ucy.ac.cy; iliopj@med.uoc.gr
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors: Bioinformatics Research Laboratory, Department of Biological Sciences, University of Cyprus, CY 1678, Nicosia, Cyprus, Greece.
authors: Nikolas
authors: N
authors: Papanikolaou
authors: Evangelos
authors: E
authors: Pafilis
authors: Stavros
authors: S
authors: Nikolaou
authors: Christos A
authors: CA
authors: Ouzounis
authors: Ioannis
authors: I
authors: Iliopoulos
authors: Vasilis J
authors: VJ
authors: Promponas
mesh_terms: D000465:Algorithms; D000818:Animals; D016000:Cluster Analysis; D057225:Data Mining; D004330:Drosophila / Q000196:embryology / Q000235:genetics; D020407:Internet; D009323:Natural Language Processing