EDAmame: Interactive exploratory data analyses with explainable models

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dc.contributor.authorChuah, Aaronen
dc.contributor.authorHewitt, Tim C.en
dc.contributor.authorAli, Sidra A.en
dc.contributor.authorMay, Maryamen
dc.contributor.authorXu, Tonyen
dc.contributor.authorChristiadi, Danielen
dc.contributor.authorChoi, Philip Y.I.en
dc.contributor.authorGardiner, Elizabeth E.en
dc.contributor.authorAndrews, T. Danielen
dc.date.accessioned2025-12-16T09:40:33Z
dc.date.available2025-12-16T09:40:33Z
dc.date.issued2025-06-20en
dc.description.abstractComplex 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.en
dc.description.sponsorshipWe 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.en
dc.description.statusPeer-revieweden
dc.format.extent5en
dc.identifier.issn1367-4803en
dc.identifier.otherORCID:/0000-0001-9453-9688/work/187725258en
dc.identifier.scopus105009430033en
dc.identifier.urihttps://hdl.handle.net/1885/733795450
dc.language.isoenen
dc.provenanceThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly citeden
dc.rights © 2025 The Author(s).en
dc.sourceBioinformaticsen
dc.titleEDAmame: Interactive exploratory data analyses with explainable modelsen
dc.typeJournal articleen
dspace.entity.typePublicationen
local.contributor.affiliationChuah, Aaron; Division of Immunology and Infectious Diseases, John Curtin School of Medical Research, ANU College of Science and Medicine, The Australian National Universityen
local.contributor.affiliationHewitt, Tim C.; The John Curtin School of Medical Researchen
local.contributor.affiliationAli, Sidra A.; Genome Sciences and Cancer Division, John Curtin School of Medical Research, ANU College of Science and Medicine, The Australian National Universityen
local.contributor.affiliationMay, Maryam; The John Curtin School of Medical Researchen
local.contributor.affiliationXu, Tony; The John Curtin School of Medical Researchen
local.contributor.affiliationChristiadi, Daniel; Division of Immunology and Infectious Diseases, John Curtin School of Medical Research, ANU College of Science and Medicine, The Australian National Universityen
local.contributor.affiliationChoi, Philip Y.I.; Genome Sciences and Cancer Division, John Curtin School of Medical Research, ANU College of Science and Medicine, The Australian National Universityen
local.contributor.affiliationGardiner, Elizabeth E.; Genome Sciences and Cancer Division, John Curtin School of Medical Research, ANU College of Science and Medicine, The Australian National Universityen
local.contributor.affiliationAndrews, T. Daniel; The John Curtin School of Medical Researchen
local.identifier.citationvolume41en
local.identifier.doi10.1093/bioinformatics/btaf340en
local.identifier.pure72213d16-08c0-4991-8bc9-176293872d3den
local.identifier.urlhttps://www.scopus.com/pages/publications/105009430033en
local.type.statusPublisheden

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