{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T17:06:22Z","timestamp":1784567182961,"version":"3.55.0"},"reference-count":70,"publisher":"Oxford University Press (OUP)","issue":"7","license":[{"start":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T00:00:00Z","timestamp":1782691200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The National Key Research and Development Program of China","award":["2024YFA1306901"],"award-info":[{"award-number":["2024YFA1306901"]}]},{"DOI":"10.13039\/501100001809","name":"The National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["82171526"],"award-info":[{"award-number":["82171526"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Open Fund of National Engineering Laboratory for Big Data System Computing Technology","award":["SZU-BDSC-OF2024-19"],"award-info":[{"award-number":["SZU-BDSC-OF2024-19"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,7,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Multi-omics integration can improve cancer diagnosis and prognosis, but current models are limited by extreme dimensionality, redundant raw-feature similarities, missing assays, and incomplete pathway priors. We ask whether biologically meaningful patient manifolds can be learned directly from high-dimensional multi-omics data without heuristic graph construction or fixed knowledge-base constraints.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We present OmicsTransformer, an end-to-end framework that projects each omics modality into latent patches, enforces masked semantic consistency through an Exponential Cosine Consistency Loss, models global patch dependencies with a Transformer encoder, and fuses modalities by sample-specific uncertainty. Across eight diagnostic and prognostic cohorts, OmicsTransformer achieved strong performance, including 89.4% accuracy for TCGA-BRCA subtyping and 90.6% area under the receiver operating characteristic curve (AUC) for TCGA-LGG grading. It improved recurrence prediction over the pathway-restricted DeepKEGG baseline by approximately 21.5 percentage points in accuracy (ACC) on TCGA-LIHC and 11.1 percentage points in ACC on TCGA-BLCA. Variance-weighted attribution with ensemble stability selection recovered reproducible cross-modal biomarker cores and non-canonical progression drivers.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>Source code and datasets are freely available at https:\/\/github.com\/FFJXX\/OmicTransformer and https:\/\/doi.org\/10.6084\/m9.figshare.31523905. OmicsTransformer is implemented in PyTorch.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag468","type":"journal-article","created":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T16:42:28Z","timestamp":1782751348000},"source":"Crossref","is-referenced-by-count":0,"title":["OmicsTransformer: self-supervised masked consistency and uncertainty-aware fusion for robust multi-omics prediction"],"prefix":"10.1093","volume":"42","author":[{"given":"Junxuan","family":"Feng","sequence":"first","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University , Shenzhen, 518060,","place":["China"]},{"name":"National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University , Shenzhen, 518060,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bingshen","family":"Shan","sequence":"additional","affiliation":[{"name":"College of Computer Science and Software Engineering, 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