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In addition, scMOG introduces a contrastive learning module and an omics alignment module to preserve differences in expression patterns across distinct omics while extracting consistent information. Experimental results on eight single\u2010cell multi\u2010omics datasets demonstrate that scMOG outperforms existing methods, producing embeddings that capture meaningful biological signals. scMOG provides an effective solution for integrating single\u2010cell multi\u2010omics data, offering a scalable framework that preserves regulatory signals.<\/jats:p>","DOI":"10.1002\/qub2.70041","type":"journal-article","created":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T03:45:07Z","timestamp":1778125507000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["scMOG: A graph neural network method for regulatory relationship\u2010preserving single\u2010cell multi\u2010omics integration"],"prefix":"10.1002","volume":"14","author":[{"given":"Yucheng","family":"Lu","sequence":"first","affiliation":[{"name":"School of Mathematics and Physics China University of Geosciences  Wuhan 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