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. 2016 Jun 6;17(Suppl 5):182. doi: 10.1186/s12859-016-1041-6

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© Papanikolaou et al. 2016

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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Fig. 1 — DrugQuest’s workflow. a Queries to DrugBank and retrieval of records related to the query. b DrugBank record mining based on textual information such as: description, toxicology and pharmacology. c Name Entity Recognition techniques to identify genes/proteins, chemicals, diseases, pathways. d TextQuest algorithm to identify non tagged Significant Terms. e Partitional clustering of DrugBank records using various clustering algorithms and similarity measures. f Visual representation of results: Left: Tag cloud example of highly representative terms per cluster. Right: DrugBank records assigned to clusters