{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,26]],"date-time":"2026-07-26T01:50:20Z","timestamp":1785030620936,"version":"3.55.0"},"reference-count":48,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T00:00:00Z","timestamp":1778716800000},"content-version":"vor","delay-in-days":13,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"University of Western Brittany"},{"name":"The French Ministry of Research","award":["2023\/0522"],"award-info":[{"award-number":["2023\/0522"]}]},{"name":"Innovative Medicines Initiative Joint Undertaking","award":["115565"],"award-info":[{"award-number":["115565"]}]},{"name":"European Union\u2019s Seventh Framework Program","award":["FP7\/2007\u20132013"],"award-info":[{"award-number":["FP7\/2007\u20132013"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,5,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Inferring gene module activity is key to understanding transcriptomic dysregulation in case-control studies. Most existing gene set analysis methods focus on differences between groups without considering the complex geometry of the data. We introduce GEDO, a graph and topology-based method that infers gene module activity through a transition score, quantifying the shift from healthy controls to diseased individuals. When applied to bulk RNA-seq data from Sj\u00f6gren\u2019s disease patients and healthy controls (PRECISESADS cohort), and a breast cancer dataset from The Cancer Genome Atlas, GEDO was benchmarked against Principal Component Analysis (PCA), the mean of z-scores, Single Sample Gene Set Enrichment Analysis (ssGSEA), and Gene Set Variation Analysis (GSVA) in classification, unsupervised clustering tasks and robustness against noise and bias. On the PRECISESADS cohort, GEDO outperformed the other approaches in predicting disease status, interferon signature, enhancing subgroup separability, and robustness against noise and bias. The biological signal captured was aligned with the knowledge and clinical features of Sj\u00f6gren\u2019s disease. In the breast cancer dataset, GEDO\u2019s embeddings better represent PAM50 molecular subtypes. Its supervised and topology-based design enables finer resolution of disease-related transcriptomic alterations. GEDO offers a robust, interpretable framework for quantifying gene modules\u2019 activity, with applications in single and potentially in multi-omics integration.<\/jats:p>","DOI":"10.1093\/bib\/bbag230","type":"journal-article","created":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T11:41:15Z","timestamp":1776944475000},"source":"Crossref","is-referenced-by-count":1,"title":["GEDO: topology-based inference of gene module activity in Sj\u00f6gren\u2019s disease"],"prefix":"10.1093","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-5547-6338","authenticated-orcid":false,"given":"Cl\u00e9ment","family":"B\u00e9zier","sequence":"first","affiliation":[{"name":"LBAI, UMR1227, Univ Brest , Inserm, Brest,","place":["France"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7504-888X","authenticated-orcid":false,"given":"Jakez","family":"Rolland","sequence":"additional","affiliation":[{"name":"Bio Logbook, 1 rue Julien Videment 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