| Literature DB >> 21994227 |
Nikolas Papanikolaou1, Evangelos Pafilis, Stavros Nikolaou, Christos A Ouzounis, Ioannis Iliopoulos, Vasilis J Promponas.
Abstract
SUMMARY: 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. AVAILABILITY: http://biotextquest.biol.ucy.ac.cy CONTACT: vprobon@ucy.ac.cy; iliopj@med.uoc.gr SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.Mesh:
Year: 2011 PMID: 21994227 DOI: 10.1093/bioinformatics/btr564
Source DB: PubMed Journal: Bioinformatics ISSN: 1367-4803 Impact factor: 6.937