BioTextQuest (BTQ) implements an enhanced version of the TextQuest algorithm (proposed by Iliopoulos et al., 2001), providing a user friendly web-based interface. BTQ collects abstracts fromMedline literature and OMIM databases matching a user query. Identification of relevant terms enables the representation of text records in a Vector Space Model and the calculation of pairwise document similarities. Employing suitable clustering algorithms, results are transformed into clusters of records along with their corresponding terms. BTQ, besides the document processing and clustering algorithms, relies on public web services such as NCBI eSearch, Reflect, and WhatIzIt to query biomedical databases and to annotate and enrich the biomedically significant terms. Data Integration and further bioinformatics analysis related to the tagged bioentities is available through BioCompendium service. Additional added-value features include a variety of clustering, stemming, co-occurence analysis and visualization algorithms/techniques allowing interactive result navigation.
Ioannis Iliopoulos’ Bioinformatics & Computational Biology Lab
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BioTextQuest+: A knowledge integration platform for literature mining and concept discovery.
Papanikolaou N, Pavlopoulos GA, Pafilis E, Theodosiou T, Schneider R, Satagopam VP, Ouzounis CA, Eliopoulos AG, Promponas VJ, Iliopoulos I.
Bioinformatics. 2014 Aug 6. pii: btu524.