{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T02:38:25Z","timestamp":1792636705827,"version":"4.5.0"},"reference-count":24,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T00:00:00Z","timestamp":1791763200000},"content-version":"vor","delay-in-days":11,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,10,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Paired spatial omics technologies measure transcriptomic profiles together with epigenomic or proteomic profiles while preserving tissue context. Integration remains challenging because modalities differ in feature distributions, sparsity, and spatial dependencies. Existing methods may not fully model complementary graph views or adapt spatial constraints, limiting preservation of spatial organization and modality-specific information.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We present smiMAD for paired spatial omics integration. smiMAD models spatial-neighborhood and molecular-feature graphs through graph-view-specific and common-information branches. An adaptive gate integrates graph-view and modality information, while an alignment\u2013decorrelation objective promotes common-information embedding consistency and reduces branch redundancy. A dynamic spatial-consistency strategy progressively relaxes spatial constraints during training. Across 17 simulated and 15 real RNA\u2013ATAC (assay for transposase-accessible chromatin) and RNA\u2013protein datasets, smiMAD achieved competitive clustering accuracy, spatial coherence, and multimodal integration performance. Molecular marker patterns provided support for the inferred spatial regions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The source code is available at https:\/\/github.com\/fdu-wangfeilab\/smiMAD, with the version used in this study archived at https:\/\/zenodo.org\/records\/22931624. Data are available at https:\/\/zenodo.org\/records\/17207079.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag758","type":"journal-article","created":{"date-parts":[[2026,10,11]],"date-time":"2026-10-11T11:37:28Z","timestamp":1791718648000},"source":"Crossref","is-referenced-by-count":0,"title":["smiMAD: adaptive multi-view graph learning for paired spatial omics integration"],"prefix":"10.1093","volume":"42","author":[{"given":"Yuhao","family":"Tao","sequence":"first","affiliation":[{"name":"College of Computer Science and Artificial Intelligence, Fudan University , Shanghai 200433,","place":["China"]},{"name":"Shanghai Key Laboratory of Intelligent Information Processing, Fudan University , Shanghai 200433,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5890-0448","authenticated-orcid":false,"given":"Fei","family":"Wang","sequence":"additional","affiliation":[{"name":"College of 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