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Baroukh C, Jenkins SL, Dannenfelser R, et al. Genes2WordCloud: a quick way to identify biological themes from gene lists and free text. Source Code Biol Med. 2011;6:15doi: 10.1186/1751-0473-6-15.
Baroukh, C., Jenkins, S. L., Dannenfelser, R., & Ma'ayan, A. (2011). Genes2WordCloud: a quick way to identify biological themes from gene lists and free text. Source code for biology and medicine, 615. https://doi.org/10.1186/1751-0473-6-15
Baroukh, Caroline, et al. "Genes2WordCloud: a quick way to identify biological themes from gene lists and free text." Source code for biology and medicine vol. 6 (2011): 15. doi: https://doi.org/10.1186/1751-0473-6-15
Baroukh C, Jenkins SL, Dannenfelser R, Ma'ayan A. Genes2WordCloud: a quick way to identify biological themes from gene lists and free text. Source Code Biol Med. 2011 Oct 13;6:15. doi: 10.1186/1751-0473-6-15. PMID: 21995939; PMCID: PMC3213042.
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Source Code Biol Med. 2011 Oct 13;6:15. doi: 10.1186/1751-0473-6-15.

Genes2WordCloud: a quick way to identify biological themes from gene lists and free text.

Source code for biology and medicine

Caroline Baroukh, Sherry L Jenkins, Ruth Dannenfelser, Avi Ma'ayan

Affiliations

  1. Department of Pharmacology and Systems Therapeutics, Systems Biology Center New York (SBCNY), Mount Sinai School of Medicine, 1425 Madison Avenue, New York, NY, 10029, USA. [email protected].

PMID: 21995939 PMCID: PMC3213042 DOI: 10.1186/1751-0473-6-15

Abstract

BACKGROUND: Word-clouds recently emerged on the web as a solution for quickly summarizing text by maximizing the display of most relevant terms about a specific topic in the minimum amount of space. As biologists are faced with the daunting amount of new research data commonly presented in textual formats, word-clouds can be used to summarize and represent biological and/or biomedical content for various applications.

RESULTS: Genes2WordCloud is a web application that enables users to quickly identify biological themes from gene lists and research relevant text by constructing and displaying word-clouds. It provides users with several different options and ideas for the sources that can be used to generate a word-cloud. Different options for rendering and coloring the word-clouds give users the flexibility to quickly generate customized word-clouds of their choice.

METHODS: Genes2WordCloud is a word-cloud generator and a word-cloud viewer that is based on WordCram implemented using Java, Processing, AJAX, mySQL, and PHP. Text is fetched from several sources and then processed to extract the most relevant terms with their computed weights based on word frequencies. Genes2WordCloud is freely available for use online; it is open source software and is available for installation on any web-site along with supporting documentation at http://www.maayanlab.net/G2W.

CONCLUSIONS: Genes2WordCloud provides a useful way to summarize and visualize large amounts of textual biological data or to find biological themes from several different sources. The open source availability of the software enables users to implement customized word-clouds on their own web-sites and desktop applications.

References

  1. Adv Exp Med Biol. 2010;680:709-15 - PubMed
  2. AMIA Annu Symp Proc. 2009 Nov 14;2009:563-7 - PubMed
  3. Wiley Interdiscip Rev Syst Biol Med. 2009 Nov-Dec;1(3):390-399 - PubMed
  4. Source Code Biol Med. 2011 Apr 07;6:7 - PubMed
  5. Nucleic Acids Res. 2010 Jan;38(Database issue):D331-5 - PubMed

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