Bioinformatics Research Laboratory, Department of Biological Sciences, University of Cyprus, CY 1678, Nicosia, Cyprus, Greece.
Bioinformatics. 2011 Dec 1;27(23):3327-8. doi: 10.1093/bioinformatics/btr564. Epub 2011 Oct 12.
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.
http://biotextquest.biol.ucy.ac.cy
vprobon@ucy.ac.cy; iliopj@med.uoc.gr
Supplementary data are available at Bioinformatics online.
BioTextQuest 通过命名实体识别服务，将文章聚类中重要术语的自动发现与结构化知识注释相结合，提供了交互友好的可视化界面。基于标签云的术语展示对每个文档聚类进行语义标注，根据生物实体进行标注，并且提供文档标题列表，使用户能够同时比较每个聚类的术语和文档，促进概念关联和假设生成。BioTextQuest 允许自定义分析参数，例如聚类/词干算法、排除文档/重要术语，以更好地匹配所解决的生物学问题。
http://biotextquest.biol.ucy.ac.cy
vprobon@ucy.ac.cy；iliopj@med.uoc.gr
补充数据可在Bioinformatics 在线获取。
