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EDAmame: Interactive exploratory data analyses with explainable models

  • Aaron Chuah
  • , Tim C. Hewitt*
  • , Sidra A. Ali
  • , Maryam May
  • , Tony Xu
  • , Daniel Christiadi
  • , Philip Y.I. Choi
  • , Elizabeth E. Gardiner
  • , T. Daniel Andrews
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Article numberbtaf340
Number of pages5
JournalBioinformatics
Volume41
Issue number6
DOIs
Publication statusPublished - 20 Jun 2025

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