{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T18:30:27Z","timestamp":1786559427420,"version":"build-2736575974"},"reference-count":68,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T00:00:00Z","timestamp":1786492800000},"content-version":"vor","delay-in-days":103,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"King\u2019s College London and the A*STAR Research Attachment Programme"},{"name":"Horizontal Technology Programme Office","award":["C211118015"],"award-info":[{"award-number":["C211118015"]}]},{"DOI":"10.13039\/501100000296","name":"British Skin Foundation","doi-asserted-by":"publisher","award":["006\/R\/22"],"award-info":[{"award-number":["006\/R\/22"]}],"id":[{"id":"10.13039\/501100000296","id-type":"DOI","asserted-by":"publisher"}]},{"name":"UK Royal Society","award":["IES\u2216R2\u2216222084"],"award-info":[{"award-number":["IES\u2216R2\u2216222084"]}]}],"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>Network-based analyses of omics data are widely used and, while many of these methods have been adapted to single-cell scenarios, they often remain memory- and space-intensive. As a result, they are better suited to batch data or smaller datasets. Furthermore, the application of network-based methods in multi-omics often relies on similarity-based networks, which lack structurally discrete topologies. This limitation may reduce the effectiveness of graph-based methods that were initially designed for topologies with better defined structures. We propose Subset-Contrastive multi-Omics Network Embedding (SCONE), a method that employs contrastive learning techniques on large datasets through a scalable subgraph contrastive approach. By exploiting the pairwise similarity basis of many network-based omics methods, we transformed this characteristic into a strength, developing an approach that aims to achieve scalable and effective analysis. Our method demonstrates synergistic omics integration for cell type clustering in single-cell data. Additionally, we evaluate its performance in a bulk multi-omics integration scenario, where SCONE performs comparable with the state-of-the-art despite utilizing limited views of the original data. We anticipate that our findings will motivate further research into the use of subset contrastive methods for omics data.<\/jats:p>","DOI":"10.1093\/bib\/bbag381","type":"journal-article","created":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T11:52:22Z","timestamp":1782388342000},"source":"Crossref","is-referenced-by-count":0,"title":["SCONE: a subset-contrastive method for multi-omics network embedding"],"prefix":"10.1093","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0347-7002","authenticated-orcid":false,"given":"Pedro Henrique","family":"da Costa Avelar","sequence":"first","affiliation":[{"name":"Department of Informatics, Faculty of Natural, Mathematical and Engineering Sciences, King\u2019s College London , Bush House, 30 Aldwych, London WC2B 4BG,","place":["United Kingdom"]},{"name":"Institute for Advanced Intelligence and Computing, Agency for Science Technology and Research (A*STAR) , 1 Fusionopolis Way, #21-01 Connexis, 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