| Title: |
EDAmame: Interactive exploratory data analyses with explainable models |
| Authors: |
Chuah, Aaron; Hewitt, Tim C.; Ali, Sidra A.; May, Maryam; Xu, Tony; Christiadi, Daniel; Choi, Philip Y.I.; Gardiner, Elizabeth E.; Andrews, T. Daniel |
| Source: |
Bioinformatics |
| Publication Year: |
2025 |
| Collection: |
Australian National University: ANU Digital Collections |
| Description: |
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. ; We thank the National Computational Infrastructure (Australia) for continued access to significant computation resources and technical expertise. We are also grateful to Andreas Bachler and Simone Brysland (JCSMR, ANU) for EDAmame software testing and suggestions. This work was supported by Bioplatforms Australia to A.C. and T.C.H. ; Peer-reviewed |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
English |
| Relation: |
https://hdl.handle.net/1885/733795450; 105009430033 |
| DOI: |
10.1093/bioinformatics/btaf340 |
| Availability: |
https://hdl.handle.net/1885/733795450; https://doi.org/10.1093/bioinformatics/btaf340; https://openresearch-repository.anu.edu.au/bitstreams/78fe26bf-a59e-4146-a3dd-1ecae577b08c/download; https://openresearch-repository.anu.edu.au/bitstreams/204b3e8a-4b1b-4107-89bb-76792a224234/download |
| Rights: |
© 2025 The Author(s). |
| Accession Number: |
edsbas.E7726309 |
| Database: |
BASE |