Macnee, MariePérez Palma, EduardoSchumacher-Bass, SarahDalton, JarrodLeu, CostinBlankenberg, DanielLal, Dennis2022-01-112022-01-112021Marie Macnee, Eduardo Pérez-Palma, Sarah Schumacher-Bass, Jarrod Dalton, Costin Leu, Daniel Blankenberg, Dennis Lal, SimText: a text mining framework for interactive analysis and visualization of similarities among biomedical entities, Bioinformatics, Volume 37, Issue 22, 15 November 2021, Pages 4285–4287, https://doi.org/10.1093/bioinformatics/btab365https://doi.org/10.1093/bioinformatics/btab365http://hdl.handle.net/11447/5425Literature exploration in PubMed on a large number of biomedical entities (e.g. genes, diseases or experiments) can be time-consuming and challenging, especially when assessing associations between entities. 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 text. SimText can be used for (i) text collection from PubMed and extraction of words with different text mining approaches, and (ii) interactive analysis and visualization of data using unsupervised learning techniques in an interactive app.enBiomedical entitiesSimilaritiesText miningArticle