{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T19:11:07Z","timestamp":1776107467413,"version":"3.50.1"},"reference-count":55,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T00:00:00Z","timestamp":1740096000000},"content-version":"vor","delay-in-days":91,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Research Training Program Tuition Fee Offset and Stipend Scholarship"},{"name":"AIR@innoHK programme of the Innovation and Technology Commission of Hong Kong"},{"name":"Australian Research Council Discovery Project","award":["DP210100521"],"award-info":[{"award-number":["DP210100521"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,11,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Unraveling the complex interplay between nutrients and drugs via their effects on \u201comics\u201d features could revolutionize our fundamental understanding of nutritional physiology, personalized nutrition, and, ultimately, human health span. Experimental studies in nutrition are starting to use large-scale \u201comics\u201d experiments to pick apart the effects of such interacting factors. However, the high dimensionality of the omics features, coupled with complex fully factorial experimental designs, poses a challenge to the analysis. Current strategies for analyzing such types of data are based on between-feature correlations. However, these techniques risk overlooking important signals that arise from the experimental design and produce clusters that are hard to interpret. We present a novel approach for analyzing high-dimensional outcomes in nutriomics experiments, termed experiment-guided NutriOmics DatA cLustering (\u2018eNODAL\u2019). This three-step hybrid framework takes advantage of both Analysis of Variance (ANOVA)-type analyses and unsupervised learning methods to extract maximum information from experimental nutriomics studies. First, eNODAL categorizes the omics features into interpretable groups based on the significance of response to the different experimental variables using an ANOVA-like test. Such groups may include the main effects of a nutritional intervention and drug exposure or their interaction. Second, consensus clustering is performed within each interpretable group to further identify subclusters of features with similar response profiles to these experimental factors. Third, eNODAL annotates these subclusters based on their experimental responses and biological pathways enriched within the subcluster. We validate eNODAL using data from a mouse experiment to test for the interaction effects of macronutrient intake and drugs that target aging mechanisms in mice.<\/jats:p>","DOI":"10.1093\/bib\/bbaf036","type":"journal-article","created":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T23:22:25Z","timestamp":1740007345000},"source":"Crossref","is-referenced-by-count":1,"title":["eNODAL: an experimentally guided nutriomics data clustering method to unravel complex drug\u2013diet interactions"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1910-6126","authenticated-orcid":false,"given":"Xiangnan","family":"Xu","sequence":"first","affiliation":[{"name":"Chair of Statistics, Humboldt-Universit\u00e4t zu Berlin , Unter den Linden 6, Berlin 10178 ,","place":["Germany"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alistair M","family":"Senior","sequence":"additional","affiliation":[{"name":"Charles Perkins Centre, University of Sydney , Johns Hopkins Drive, NSW 2050 ,","place":["Australia"]},{"name":"Sydney Precision Data Science Centre, University of Sydney , F07 Eastern Avenue, NSW 2050 ,","place":["Australia"]},{"name":"Laboratory of Data Discovery for Health Limited (D24H) , 19 Science Park W Avenue, Hong Kong SAR 999077 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David G","family":"Le Couteur","sequence":"additional","affiliation":[{"name":"Charles Perkins Centre, University of Sydney , Johns Hopkins Drive, NSW 2050 ,","place":["Australia"]},{"name":"Centre for Education and Research on Ageing, Concord RG Hospital , Hospital Road, NSW 2138 ,","place":["Australia"]},{"name":"ANZAC Research Institute, Concord RG Hospital , Hospital Road, NSW 2138 ,","place":["Australia"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Victoria C","family":"Cogger","sequence":"additional","affiliation":[{"name":"Centre for Education and Research on Ageing, Concord RG Hospital , Hospital Road, NSW 2138 ,","place":["Australia"]},{"name":"ANZAC Research Institute, Concord RG Hospital , Hospital Road, NSW 2138 ,","place":["Australia"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Raubenheimer","sequence":"additional","affiliation":[{"name":"Charles Perkins Centre, University of Sydney , Johns Hopkins Drive, NSW 2050 ,","place":["Australia"]},{"name":"School of Life and Environmental Science, University of Sydney , F22 Eastern Avenue, NSW 2050 ,","place":["Australia"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David E","family":"James","sequence":"additional","affiliation":[{"name":"Charles Perkins Centre, University of Sydney , Johns Hopkins Drive, NSW 2050 ,","place":["Australia"]},{"name":"ANZAC Research Institute, Concord RG Hospital , Hospital Road, NSW 2138 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