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

Marie Macnee; Eduardo Pérez-Palma; Sarah Schumacher-Bass; Jarrod Dalton; Costin Leu; Daniel Blankenberg; Dennis Lal

doi:10.1093/bioinformatics/btab365

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

Bioinformatics. 2021 Nov 18;37(22):4285-4287. doi: 10.1093/bioinformatics/btab365.

Authors

Marie Macnee¹, Eduardo Pérez-Palma², Sarah Schumacher-Bass³, Jarrod Dalton⁴, Costin Leu⁵, Daniel Blankenberg⁵, Dennis Lal^{1

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Affiliations

¹ Cologne Center for Genomics (CCG), Medical Faculty of the University of Cologne, University Hospital of Cologne, Cologne 50931, Germany.
² Universidad del Desarrollo, Centro de Genética y Genómica, Facultad de Medicina Clínica Alemana, Santiago 7590943, Chile.
³ Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA.
⁴ Department of Cardiovascular and Metabolic Sciences, Cleveland Clinic, Cleveland, OH, 44195, USA.
⁵ Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA.
⁶ Stanley Center, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
⁷ Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH 44195, USA.

Abstract

Summary: Literature 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.

Availability and implementation: We developed SimText as an open-source R software and integrated it into Galaxy (https://usegalaxy.eu), an online data analysis platform with supporting self-learning training material available at https://training.galaxyproject.org. A command-line version of the toolset is available for download from GitHub (https://github.com/dlal-group/simtext) or as Docker image (https://hub.docker.com/r/dlalgroup/simtext/tags.).

Supplementary information: Supplementary data are available at Bioinformatics online.

MeSH terms

Data Analysis
Data Interpretation, Statistical
Data Mining* / methods
PubMed
Software*