{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:39:00Z","timestamp":1792791540207,"version":"4.5.0"},"reference-count":46,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Information Sciences"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.ins.2026.123935","type":"journal-article","created":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T06:51:29Z","timestamp":1784789489000},"page":"123935","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Advancing multi-omics analysis via dynamic labeling with shared-specific information"],"prefix":"10.1016","volume":"757","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-6092-4664","authenticated-orcid":false,"given":"Jiecheng","family":"Wu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaoliang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yali","family":"Pu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weihong","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanfei","family":"Dai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Genggeng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjie","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5195-9682","authenticated-orcid":false,"given":"Shiping","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ins.2026.123935_bib0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2023.107377","article-title":"Multi-omics integration method based on attention deep learning network for biomedical data classification","volume":"231","author":"Gong","year":"2023","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.ins.2026.123935_bib0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2024.121864","article-title":"A deep contrastive multi-modal encoder for multi-omics data integration and analysis","volume":"700","author":"Yinghua","year":"2025","journal-title":"Inf. Sci."},{"key":"10.1016\/j.ins.2026.123935_bib0015","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-017-1215-1","article-title":"Multi-omics approaches to disease","volume":"18","author":"Hasin","year":"2017","journal-title":"Genome Biol."},{"issue":"6","key":"10.1016\/j.ins.2026.123935_bib0020","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbad378","article-title":"Multiomics dynamic learning enables personalized diagnosis and prognosis for pancancer and cancer subtypes","volume":"24","author":"Lu","year":"2023","journal-title":"Brief. Bioinform."},{"key":"10.1016\/j.ins.2026.123935_bib0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2024.108478","article-title":"DEDUCE: multi-head attention decoupled contrastive learning to discover cancer subtypes based on multi-omics data","volume":"257","author":"Pan","year":"2024","journal-title":"Comput. Methods Programs Biomed."},{"issue":"8","key":"10.1016\/j.ins.2026.123935_bib0030","doi-asserted-by":"crossref","first-page":"888","DOI":"10.3390\/genes11080888","article-title":"Classifying breast cancer subtypes using deep neural networks based on multi-omics data","volume":"11","author":"Lin","year":"2020","journal-title":"Genes"},{"issue":"8","key":"10.1016\/j.ins.2026.123935_bib0035","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isci.2023.107378","article-title":"MOCSS: multi-omics data clustering and cancer subtyping via shared and specific representation learning","volume":"26","author":"Chen","year":"2023","journal-title":"iScience"},{"key":"10.1016\/j.ins.2026.123935_bib0040","series-title":"Proceedings of the 41st International Conference on Machine Learning","first-page":"1","article-title":"Decouple then classify: a dynamic multi-view labeling strategy with shared and specific information","author":"Wan","year":"2024"},{"key":"10.1016\/j.ins.2026.123935_bib0045","article-title":"MoGCN: a multi-omics integration method based on graph convolutional network for cancer subtype analysis","volume":"13","author":"Li","year":"2022","journal-title":"Front. Genet."},{"issue":"1","key":"10.1016\/j.ins.2026.123935_bib0050","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12864-024-11112-5","article-title":"DeepMoIC: multi-omics data integration via deep graph convolutional networks for cancer subtype classification","volume":"25","author":"Wu","year":"2024","journal-title":"BMC Genom."},{"key":"10.1016\/j.ins.2026.123935_bib0055","doi-asserted-by":"crossref","first-page":"37724","DOI":"10.1109\/ACCESS.2025.3540769","article-title":"Comparative analysis of multi-omics integration using graph neural networks for cancer classification","author":"Alharbi","year":"2025","journal-title":"IEEE Access"},{"key":"10.1016\/j.ins.2026.123935_bib0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2024.120792","article-title":"Co-augmentation of structure and feature for boosting graph contrastive learning","volume":"676","author":"Bao","year":"2024","journal-title":"Inf. Sci."},{"issue":"9","key":"10.1016\/j.ins.2026.123935_bib0065","first-page":"5042","article-title":"Learning deep sparse regularizers with applications to multi-view clustering and semi-supervised classification","volume":"44","author":"Wang","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.ins.2026.123935_bib0070","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.inffus.2023.02.013","article-title":"Learnable graph convolutional network and feature fusion for multi-view learning","volume":"95","author":"Chen","year":"2023","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.ins.2026.123935_bib0075","doi-asserted-by":"crossref","first-page":"25171","DOI":"10.52202\/068431-1825","article-title":"Deep active learning by leveraging training dynamics","volume":"35","author":"Wang","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.ins.2026.123935_bib0080","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2023.122356","article-title":"Integrating active learning and semi-supervised learning for improved data-driven HVAC fault diagnosis performance","volume":"356","author":"Fan","year":"2024","journal-title":"Appl. Energy"},{"issue":"16","key":"10.1016\/j.ins.2026.123935_bib0085","doi-asserted-by":"crossref","first-page":"2626","DOI":"10.1093\/bioinformatics\/bth294","article-title":"A statistical framework for genomic data fusion","volume":"20","author":"Lanckriet","year":"2004","journal-title":"Bioinformatics"},{"issue":"14","key":"10.1016\/j.ins.2026.123935_bib0090","doi-asserted-by":"crossref","first-page":"i501","DOI":"10.1093\/bioinformatics\/btz318","article-title":"MOLI: multi-omics late integration with deep neural networks for drug response prediction","volume":"35","author":"Sharifi-Noghabi","year":"2019","journal-title":"Bioinformatics"},{"key":"10.1016\/j.ins.2026.123935_bib0095","doi-asserted-by":"crossref","first-page":"166","DOI":"10.3389\/fgene.2019.00166","article-title":"SALMON: survival analysis learning with multi-omics neural networks on breast cancer","volume":"10","author":"Huang","year":"2019","journal-title":"Front. Genet."},{"key":"10.1016\/j.ins.2026.123935_bib0100","doi-asserted-by":"crossref","first-page":"10912","DOI":"10.1109\/ACCESS.2023.3240515","article-title":"Moanna: multi-omics autoencoder-based neural network algorithm for predicting breast cancer subtypes","volume":"11","author":"Lupat","year":"2023","journal-title":"IEEE Access"},{"key":"10.1016\/j.ins.2026.123935_bib0105","series-title":"Proceedings of the International Symposium on Bioinformatics Research and Applications","first-page":"495","article-title":"A novel approach for subtype identification via multi-omics data using adversarial autoencoder","author":"Chen","year":"2024"},{"key":"10.1016\/j.ins.2026.123935_bib0110","article-title":"MMHA: a multi-modal data fusion algorithm with hierarchical attention for disease diagnosis and cancer subtype prediction","author":"Wang","year":"2025","journal-title":"Expert Syst. Appl."},{"issue":"2","key":"10.1016\/j.ins.2026.123935_bib0115","doi-asserted-by":"crossref","first-page":"1830","DOI":"10.1109\/JBHI.2025.3596817","article-title":"MOKAN: a multi-omics data analysis framework using Kolmogorov-Arnold networks","volume":"30","author":"He","year":"2026","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"1","key":"10.1016\/j.ins.2026.123935_bib0120","doi-asserted-by":"crossref","first-page":"3445","DOI":"10.1038\/s41467-021-23774-w","article-title":"MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification","volume":"12","author":"Wang","year":"2021","journal-title":"Nat. Commun."},{"key":"10.1016\/j.ins.2026.123935_bib0125","series-title":"Proceedings of the 35th AAAI Conference on Artificial Intelligence, 37","first-page":"11381","article-title":"Multi-level confidence learning for trustworthy multimodal classification","author":"Zheng","year":"2023"},{"key":"10.1016\/j.ins.2026.123935_bib0130","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.125772","article-title":"MOHGCN: a trustworthy multi-omics data integration framework based on specificity-aware heterogeneous graph convolutional neural networks for disease diagnosis","volume":"263","author":"Wu","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.ins.2026.123935_bib0135","first-page":"1","article-title":"GL-fusion: a multi-omics integration method based on graph-level structure fusion and locus-level feature fusion for cancer subtype classification","author":"Tan","year":"2025","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"1","key":"10.1016\/j.ins.2026.123935_bib0140","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbaf735","article-title":"MoAGNN: a multi-omics hierarchical graph neural network for subtype classification and prognosis prediction in lung adenocarcinoma","volume":"27","author":"Lin","year":"2026","journal-title":"Brief. Bioinform."},{"issue":"7","key":"10.1016\/j.ins.2026.123935_bib0145","doi-asserted-by":"crossref","first-page":"2496","DOI":"10.1109\/TPAMI.2020.2973634","article-title":"Semi-supervised multi-view deep discriminant representation learning","volume":"43","author":"Jia","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"6","key":"10.1016\/j.ins.2026.123935_bib0150","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btad353","article-title":"MRGCN: cancer subtyping with multi-reconstruction graph convolutional network using full and partial multi-omics dataset","volume":"39","author":"Yang","year":"2023","journal-title":"Bioinformatics"},{"issue":"9","key":"10.1016\/j.ins.2026.123935_bib0155","doi-asserted-by":"crossref","first-page":"6425","DOI":"10.1109\/TCSVT.2022.3159371","article-title":"MMatch: semi-supervised discriminative representation learning for multi-view classification","volume":"32","author":"Wang","year":"2022","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"9","key":"10.1016\/j.ins.2026.123935_bib0160","doi-asserted-by":"crossref","first-page":"5624","DOI":"10.1109\/JBHI.2024.3415641","article-title":"MOSGAT: uniting specificity-aware GATs and cross modal-attention to integrate multi-omics data for disease diagnosis","volume":"28","author":"Wu","year":"2024","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"1","key":"10.1016\/j.ins.2026.123935_bib0165","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbae658","article-title":"MORE: a multi-omics data-driven hypergraph integration network for biomedical data classification and biomarker identification","volume":"26","author":"Wang","year":"2024","journal-title":"Brief. Bioinform."},{"issue":"1","key":"10.1016\/j.ins.2026.123935_bib0170","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-023-50039-x","article-title":"Comprehensive analysis of the potential biological significance of cuproptosis-related gene LIPT2 in pan-cancer prognosis and immunotherapy","volume":"13","author":"Wang","year":"2023","journal-title":"Sci. Rep."},{"issue":"3","key":"10.1016\/j.ins.2026.123935_bib0175","doi-asserted-by":"crossref","first-page":"95","DOI":"10.3892\/mmr.2022.12611","article-title":"FRAT1 promotes the angiogenic properties of human Glioblastoma cells via VEGFA","volume":"25","author":"Yang","year":"2022","journal-title":"Mol. Med. Rep."},{"issue":"2","key":"10.1016\/j.ins.2026.123935_bib0180","doi-asserted-by":"crossref","first-page":"427","DOI":"10.3892\/or.2020.7892","article-title":"IGFBP2 in cancer: pathological role and clinical significance","volume":"45","author":"Wei","year":"2021","journal-title":"Oncol. Rep."},{"key":"10.1016\/j.ins.2026.123935_bib0185","doi-asserted-by":"crossref","DOI":"10.1016\/j.biopha.2021.111507","article-title":"The emerging role of KIAA1199 in cancer development and therapy","volume":"138","author":"Liu","year":"2021","journal-title":"Biomed. Pharmacother."},{"issue":"10","key":"10.1016\/j.ins.2026.123935_bib0190","doi-asserted-by":"crossref","first-page":"1428","DOI":"10.3390\/cancers11101428","article-title":"STAT3 and STAT5 activation in solid cancers","volume":"11","author":"Igelmann","year":"2019","journal-title":"Cancers"},{"issue":"6","key":"10.1016\/j.ins.2026.123935_bib0195","doi-asserted-by":"crossref","first-page":"512","DOI":"10.3892\/ol.2023.14099","article-title":"PEX13 is a potential immunotherapeutic indicator and prognostic biomarker for various tumors including PAAD","volume":"26","author":"Dong","year":"2023","journal-title":"Oncol. Lett."},{"issue":"1","key":"10.1016\/j.ins.2026.123935_bib0200","doi-asserted-by":"crossref","first-page":"1523","DOI":"10.1038\/s41467-019-09234-6","article-title":"Metascape provides a biologist-oriented resource for the analysis of systems-level datasets","volume":"10","author":"Zhou","year":"2019","journal-title":"Nat. Commun."},{"issue":"4","key":"10.1016\/j.ins.2026.123935_bib0205","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1042\/EBC20220016","article-title":"WNT signaling and cancer stemness","volume":"66","author":"Katoh","year":"2022","journal-title":"Essays Biochem."},{"issue":"1","key":"10.1016\/j.ins.2026.123935_bib0210","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1038\/sj.cr.7290105","article-title":"MAPK signal pathways in the regulation of cell proliferation in mammalian cells","volume":"12","author":"Zhang","year":"2002","journal-title":"Cell Res."},{"issue":"10","key":"10.1016\/j.ins.2026.123935_bib0215","doi-asserted-by":"crossref","first-page":"2485","DOI":"10.1002\/j.1460-2075.1986.tb04525.x","article-title":"Cadherin cell adhesion molecules with distinct binding specificities share a common structure","volume":"5","author":"Shirayoshi","year":"1986","journal-title":"EMBO J."},{"issue":"5","key":"10.1016\/j.ins.2026.123935_bib0220","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1210\/endrev\/bnad008","article-title":"Signaling pathways of the insulin-like growth factor binding proteins","volume":"44","author":"Baxter","year":"2023","journal-title":"Endocr. Rev."},{"issue":"1","key":"10.1016\/j.ins.2026.123935_bib0225","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1124\/pharmrev.120.000086","article-title":"Subcellular organization of the cAMP signaling pathway","volume":"73","author":"Zaccolo","year":"2021","journal-title":"Pharmacol. Rev."},{"issue":"5","key":"10.1016\/j.ins.2026.123935_bib0230","doi-asserted-by":"crossref","DOI":"10.1101\/cshperspect.a013201","article-title":"Protein folding in the endoplasmic reticulum","volume":"5","author":"Braakman","year":"2013","journal-title":"Cold Spring Harb. Perspect. Biol."}],"container-title":["Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0020025526008662?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0020025526008662?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:53:12Z","timestamp":1792788792000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0020025526008662"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":46,"alternative-id":["S0020025526008662"],"URL":"https:\/\/doi.org\/10.1016\/j.ins.2026.123935","relation":{},"ISSN":["0020-0255"],"issn-type":[{"value":"0020-0255","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Advancing multi-omics analysis via dynamic labeling with shared-specific information","name":"articletitle","label":"Article Title"},{"value":"Information Sciences","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ins.2026.123935","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"123935"}}