{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T11:48:26Z","timestamp":1782820106833,"version":"3.54.5"},"reference-count":18,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T00:00:00Z","timestamp":1781308800000},"content-version":"vor","delay-in-days":163,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Inovio Pharmaceuticals and the Commonwealth Scientific and Industrial Research Organisation"},{"name":"the U.S. Food and Drug Administration, Office of the Chief Scientist, Medical Countermeasures Initiative","award":["75F40121C00144"],"award-info":[{"award-number":["75F40121C00144"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Machine Learning approaches continue to be critical to the modelling of complex biological pathways, but many of the most-used models suffer from being uninterpretable, providing little insight into underlying mechanisms. Interpretable machine learning (IML) offers a pathway to bridge predictive modeling and biological understanding.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Here, we apply an IML framework combining TreeFARMS and Rashomon Set analysis to multi-omics data from a controlled ferret vaccination study as a case study. The dataset comprised transcriptomic, proteomic, lipidomic, and metabolomic profiles collected across ten time points from animals receiving different Inovio vaccine formulations. Using TreeFARMS, we generated sparse, interpretable decision trees optimized for accuracy and compactness, and explored their Rashomon Sets to identify stable and alternative molecular rules predictive of vaccination status. The resulting models outperformed the ensemble methods while producing concise if\u2013then rules that directly linked measurable molecular features, such as AZGP1 expression and ketoleucine levels, to immune response patterns. This proof-of-concept study demonstrates that interpretable ML can capture biologically meaningful signatures of vaccine response without sacrificing predictive accuracy, providing a transparent and reproducible alternative to black-box approaches in multi-omics analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The datasets generated and\/or analysed during the current study are available at: https:\/\/github.com\/nehlehk\/iml-ferret-vaccine\/tree\/main\/data \u202f<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioadv\/vbag167","type":"journal-article","created":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T11:49:14Z","timestamp":1781178554000},"source":"Crossref","is-referenced-by-count":0,"title":["Interpretable machine learning for low-sample multi-omics: a case study of ferret vaccine response"],"prefix":"10.1093","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8264-7278","authenticated-orcid":false,"given":"Nehleh","family":"Kargarfard","sequence":"first","affiliation":[{"name":"Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation , Sydney, NSW 2145,","place":["Australia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert","family":"Dunne","sequence":"additional","affiliation":[{"name":"Data61, Commonwealth Scientific and Industrial Research Organisation , Sydney, NSW 2015,","place":["Australia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carol","family":"Lee","sequence":"additional","affiliation":[{"name":"Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation , Sydney, NSW 2145,","place":["Australia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5688-5758","authenticated-orcid":false,"given":"Laurence","family":"Wilson","sequence":"additional","affiliation":[{"name":"Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation , Sydney, NSW 2145,","place":["Australia"]},{"name":"Department of Biomedical Sciences, Macquarie University , Sydney, NSW 2109,","place":["Australia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7632-9633","authenticated-orcid":false,"given":"Alexander J","family":"McAuley","sequence":"additional","affiliation":[{"name":"Health & Biosecurity Research Unit, Australian Centre for Disease Preparedness (ACDP), Commonwealth Scientific and Industrial Research Organisation , East Geelong, VIC 3219,","place":["Australia"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,6,12]]},"reference":[{"key":"2026063006350663200_vbag167-B1","doi-asserted-by":"crossref","first-page":"3146","DOI":"10.1609\/aaai.v34i04.5711","article-title":"Learning optimal decision trees using caching branch-and-bound search","volume":"34","author":"Aglin","year":"2020","journal-title":"Aaai"},{"key":"2026063006350663200_vbag167-B2","first-page":"1","article-title":"Learning certifiably optimal rule lists for categorical data","volume":"18","author":"Angelino","year":"2018","journal-title":"J Mach Learn Res"},{"key":"2026063006350663200_vbag167-B3","doi-asserted-by":"crossref","first-page":"588221","DOI":"10.3389\/fonc.2020.588221","article-title":"Artificial intelligence (AI)-based systems biology approaches in multi-omics data analysis of cancer","volume":"10","author":"Biswas","year":"2020","journal-title":"Front 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SARS-CoV-2 infection: an antibody-based diagnostic and machine learning study","volume":"2","author":"Rosado","year":"2021","journal-title":"Lancet Microbe"},{"key":"2026063006350663200_vbag167-B17","doi-asserted-by":"crossref","first-page":"104650","DOI":"10.1016\/j.compbiomed.2021.104650","article-title":"Classification of SARS-CoV-2 and non-SARS-CoV-2 using machine learning algorithms","volume":"136","author":"Singh","year":"2021","journal-title":"Comput Biol Med"},{"key":"2026063006350663200_vbag167-B18","doi-asserted-by":"crossref","first-page":"1625","DOI":"10.1609\/aaai.v33i01.33011624","article-title":"Learning optimal classification trees using a binary linear program formulation","volume":"33","author":"Verwer","year":"2019","journal-title":"Aaai"},{"key":"2026063006350663200_vbag167-B19","doi-asserted-by":"crossref","first-page":"14071","DOI":"10.52202\/068431-1023","article-title":"Exploring the whole rashomon set of sparse decision 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