EDAmame: interactive exploratory data analyses with explainable models.
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Where this comes from
- Record sourced from PubMed, PMID 40579229.
- Also identified by DOI 10.1093/bioinformatics/btaf340 and PMC identifier 12205176.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
Complex tabular datasets comprising many diverse features can require specific expertise to interpret, posing a barrier to researchers with minimal data science experience. EDAmame is an interactive tool that simplifies initial analysis and visualization of these datasets, providing insights into data quality and feature relationships. By leveraging open-source machine learning frameworks in R, EDAmame allows researchers to perform effective exploratory data analysis without command-line or coding requirements. A limited online version can be accessed at https://edamame.org.au/ or can be downloaded from https://doi.org/10.5281/zenodo.15356492. The app is developed in R Shiny and implements tidyverse and tidymodels packages.
Medical subject headings
- Machine Learning
- Software
- Computational Biology
- Data Analysis