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

dc.creatorMacnee, Marie
dc.creatorPérez Palma, Eduardo
dc.creatorSchumacher-Bass, Sarah
dc.creatorDalton, Jarrod
dc.creatorLeu, Costin
dc.creatorBlankenberg, Daniel
dc.creatorLal, Dennis
dc.date.accessioned2022-01-11T17:26:07Z
dc.date.accessioned2023-05-19T14:48:22Z
dc.date.available2022-01-11T17:26:07Z
dc.date.available2023-05-19T14:48:22Z
dc.date.created2022-01-11T17:26:07Z
dc.date.issued2021
dc.identifierMarie 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/btab365
dc.identifierhttps://doi.org/10.1093/bioinformatics/btab365
dc.identifierhttp://hdl.handle.net/11447/5425
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/6302233
dc.description.abstractLiterature 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.
dc.languageen
dc.subjectBiomedical entities
dc.subjectSimilarities
dc.subjectText mining
dc.titleSimText: a text mining framework for interactive analysis and visualization of similarities among biomedical entities
dc.typeArticle


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