{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T21:31:44Z","timestamp":1792099904194,"version":"4.4.0"},"reference-count":37,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2024,12,10]],"date-time":"2024-12-10T00:00:00Z","timestamp":1733788800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Biomedical Data Science for Single Cell Omics National Health"},{"DOI":"10.13039\/501100000265","name":"Medical Research Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000265","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,12,26]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>The human gut microbiome, consisting of trillions of bacteria, significantly impacts health and disease. High-throughput profiling through the advancement of modern technology provides the potential to enhance our understanding of the link between the microbiome and complex disease outcomes. However, there remains an open challenge where current microbiome models lack interpretability of microbial features, limiting a deeper understanding of the role of the gut microbiome in disease. To address this, we present a framework that combines a feature engineering step to transform tabular abundance data to image format using functional microbial annotation databases, with a residual spatial attention transformer block architecture for phenotype classification.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Our model, IMPACT, delivers improved predictive accuracy performance across multiclass classification compared to similar methods. More importantly, our approach provides interpretable feature importance through image classification saliency methods. This enables the extraction of taxa markers (features) associated with a disease outcome and also their associated functional microbial traits and metabolites.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>IMPACT is available at https:\/\/github.com\/SydneyBioX\/IMPACT. We providedirect installation of IMPACT via pip.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae702","type":"journal-article","created":{"date-parts":[[2024,12,8]],"date-time":"2024-12-08T23:19:37Z","timestamp":1733699977000},"source":"Crossref","is-referenced-by-count":4,"title":["IMPACT: interpretable microbial phenotype analysis via microbial characteristic traits"],"prefix":"10.1093","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-9995-784X","authenticated-orcid":false,"given":"Daniel","family":"Mechtersheimer","sequence":"first","affiliation":[{"name":"School of Mathematics and Statistics, The University of Sydney, Sydney , NSW 2006,","place":["Australia"]},{"name":"Sydney Precision Data Science Centre, The University of Sydney, Sydney , NSW 2006,","place":["Australia"]},{"name":"Charles Perkins Centre, The University of Sydney, Sydney , NSW 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