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. © The Author 2011. Published by Oxford University Press. All rights reserved.
CITATION STYLE
Papanikolaou, N., Pafilis, E., Nikolaou, S., Ouzounis, C. A., Iliopoulos, I., & Promponas, V. J. (2011). Biotextquest: A web-based biomedical text mining suite for concept discovery. Bioinformatics, 27(23), 3327–3328. https://doi.org/10.1093/bioinformatics/btr564
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