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SimText: A text mining framework for interactive analysis and visualization of similarities among biomedical entities

By Marie Gramm, Eduardo PĂ©rez-Palma, Sarah Schumacher-Bass, Jarrod Dalton, Costin Leu, Daniel Blank-enberg, Dennis Lal

Posted 07 Jul 2020
bioRxiv DOI: 10.1101/2020.07.06.190629

Literature exploration in PubMed on a large number of biomedical entities (e.g., genes, diseases, experiments) can be time consuming and challenging comparing many entities to one other. Here, we describe SimText, a user-friendly toolset that provides customizable and systematic workflows for the analysis of similarities among a set of entities based on words from abstracts and/or other text. SimText can be used for (i) data generation: text collection from Pub-Med and extraction of words with different text mining approaches, and (ii) interactive analysis of data using unsuper-vised learning techniques and visualization in a Shiny web application. Availability and Implementation: We developed SimText as an open-source R software and integrated it into Galaxy, an online data analysis platform. A command line version of the toolset is available for download from GitHub at https://github.com/mgramm1/simtext. ### Competing Interest Statement The authors have declared no competing interest.

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