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. 2019 Mar 13;9:4399. doi: 10.1038/s41598-019-40762-9

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© The Author(s) 2019

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Word cloud analysis of all abstracts in GEO database referring to the 17 uncharacterized genes with a differential-expression filter on. Words were increased in size the more times they were mentioned in text. Stress, metal toxicity, development, and E2F transcription factor are seen to be enriched through this text mining approach. It should be noted that words that were important, but extensively repeated due to the subject of their papers, were removed. These words include: heme, HRG, LIN, cell, cadmium, pocket and transcription.