{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T21:22:35Z","timestamp":1775856155656,"version":"3.50.1"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"8","license":[{"start":{"date-parts":[[2025,7,22]],"date-time":"2025-07-22T00:00:00Z","timestamp":1753142400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Cancer subtypes play a critical role in disease progression, prognosis, and treatment, making their detection essential for tailoring precision medicine. Studies have shown that multi-omics integration outperforms single-omics approaches in cancer subtyping tasks. However, due to the high-dimensionality of multi-omics data, many existing studies either fail to capture the correlation between true labels and learned features, or lack sufficient capacity to model complex biological representations. These limitations hinder the full potential of leveraging the rich and complementary information embedded in multi-omics datasets.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Result<\/jats:title>\n                  <jats:p>We propose a framework that leverages supervised feature learning and classification based on a graph-based learning approach with attention mechanism for cancer subtyping. More specifically, we train graph convolutional network models on each omics dataset to extract latent representations, which are then concatenated to form a comprehensive multi-omics feature embedding. We further develop sample fusion network based on the omics-specific graphs, incorporating the derived features and feeding them into a graph attention model for subtype classification. This two-stage multi-omics framework is applied to eight cancer types, with performance evaluated in terms of test accuracy, training time, macro-averaged precision, recall, and F-score. Experimental results show that the proposed method outperforms state-of-the-art approaches across various cancer types. Additionally, we provide empirical evidence supporting the hypothesis that retaining a limited number of high-confidence edges and utilizing enriched embeddings from intermediate graph neural network layers can improve predictive performance.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>Data and the code are available at https:\/\/github.com\/YD-00\/MO-GCAN-Updated.git.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaf405","type":"journal-article","created":{"date-parts":[[2025,7,22]],"date-time":"2025-07-22T05:30:42Z","timestamp":1753162242000},"source":"Crossref","is-referenced-by-count":7,"title":["MO-GCAN: multi-omics integration based on graph convolutional and attention networks"],"prefix":"10.1093","volume":"41","author":[{"given":"Yifan","family":"Dou","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Ohio State University , Columbus, OH 43210,","place":["United States"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1756-7604","authenticated-orcid":false,"given":"Golrokh","family":"Mirzaei","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Ohio State University , Columbus, OH 43210,","place":["United States"]}]}],"member":"286","published-online":{"date-parts":[[2025,7,22]]},"reference":[{"key":"2025081214335225900_btaf405-B1","doi-asserted-by":"crossref","first-page":"10220","DOI":"10.3390\/ijms231810220","article-title":"omicsGAT: graph attention network for cancer subtype analyses","volume":"23","author":"Baul","year":"2022","journal-title":"Int J Mol Sci"},{"key":"2025081214335225900_btaf405-B2","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1038\/s41746-021-00521-5","article-title":"Digital medicine and the curse of dimensionality","volume":"4","author":"Berisha","year":"2021","journal-title":"NPJ Digit Med"},{"key":"2025081214335225900_btaf405-B3","doi-asserted-by":"crossref","first-page":"e1000279","DOI":"10.1371\/journal.pmed.1000279","article-title":"Subtyping of breast cancer by immunohistochemistry to investigate a relationship between subtype and short and long term survival: a collaborative analysis of data for 10,159 cases from 12 studies","volume":"7","author":"Blows","year":"2010","journal-title":"PLoS Med"},{"key":"2025081214335225900_btaf405-B4","first-page":"570","author":"Cao","year":"2024"},{"key":"2025081214335225900_btaf405-B5","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1158\/2159-8290.CD-12-0095","article-title":"The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data","volume":"2","author":"Cerami","year":"2012","journal-title":"Cancer Discov"},{"key":"2025081214335225900_btaf405-B6","doi-asserted-by":"crossref","first-page":"1248","DOI":"10.1158\/1078-0432.CCR-17-0853","article-title":"Deep learning-based multi-omics integration robustly predicts survival in liver cancer","volume":"24","author":"Chaudhary","year":"2018","journal-title":"Clin Cancer Res"},{"key":"2025081214335225900_btaf405-B7","doi-asserted-by":"crossref","first-page":"1476","DOI":"10.1093\/bioinformatics\/btz769","article-title":"Deep-learning approach to identifying cancer subtypes using high-dimensional genomic data","volume":"36","author":"Chen","year":"2020","journal-title":"Bioinformatics"},{"key":"2025081214335225900_btaf405-B8","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1038\/bjc.2012.581","article-title":"Cancer heterogeneity: implications for targeted therapeutics","volume":"108","author":"Fisher","year":"2013","journal-title":"Br J Cancer"},{"key":"2025081214335225900_btaf405-B9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1021\/ci0342472","article-title":"The problem of overfitting","volume":"44","author":"Hawkins","year":"2004","journal-title":"J Chem Inf Comput Sci"},{"key":"2025081214335225900_btaf405-B10","doi-asserted-by":"crossref","first-page":"84","DOI":"10.3389\/fgene.2017.00084","article-title":"More is better: recent progress in multi-omics data integration methods","volume":"8","author":"Huang","year":"2017","journal-title":"Front Genet"},{"key":"2025081214335225900_btaf405-B11","doi-asserted-by":"crossref","first-page":"4636","DOI":"10.1038\/s41598-023-31426-w","article-title":"Machine learning based combination of multi-omics data for subgroup identification in non-small cell lung cancer","volume":"13","author":"Khadirnaikar","year":"2023","journal-title":"Sci Rep"},{"key":"2025081214335225900_btaf405-B12","doi-asserted-by":"crossref","first-page":"lqad063","DOI":"10.1093\/nargab\/lqad063","article-title":"SUPREME: multiomics data integration using graph convolutional networks","volume":"5","author":"Kesimoglu","year":"2023","journal-title":"NAR Genom Bioinform"},{"key":"2025081214335225900_btaf405-B13","author":"Kipf","year":"2017"},{"key":"2025081214335225900_btaf405-B14","first-page":"79","article-title":"The growing role of precision and personalized medicine for cancer treatment","volume":"6","author":"Krzyszczyk","year":"2018","journal-title":"Technology (Singap World Sci)"},{"key":"2025081214335225900_btaf405-B15","doi-asserted-by":"crossref","first-page":"806842","DOI":"10.3389\/fgene.2022.806842","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"},{"key":"2025081214335225900_btaf405-B16","doi-asserted-by":"crossref","first-page":"3060","DOI":"10.3390\/cancers14133060","article-title":"GraphChrom: a novel graph-based framework for cancer classification using chromosomal rearrangement endpoints","volume":"14","author":"Mirzaei","year":"2022","journal-title":"Cancers (Basel)"},{"key":"2025081214335225900_btaf405-B17","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1186\/s12864-023-09780-w","article-title":"Constructing gene similarity networks using co-occurrence probabilities","volume":"24","author":"Mirzaei","year":"2023","journal-title":"BMC Genomics"},{"key":"2025081214335225900_btaf405-B18","doi-asserted-by":"crossref","first-page":"111773","DOI":"10.1016\/j.mrfmmm.2021.111773","article-title":"Distribution of copy number variations and rearrangement endpoints in human cancers with a review of literature","volume":"824","author":"Mirzaei","year":"2022","journal-title":"Mutat Res"},{"key":"2025081214335225900_btaf405-B19","first-page":"3130","author":"Raj","year":"2022"},{"key":"2025081214335225900_btaf405-B20","doi-asserted-by":"crossref","first-page":"3551","DOI":"10.3390\/cimb46040222","article-title":"Multi-omics integration for liver cancer using regression analysis","volume":"46","author":"Raj","year":"2024","journal-title":"Curr Issues Mol Biol"},{"key":"2025081214335225900_btaf405-B21","doi-asserted-by":"crossref","first-page":"1177932219899051","DOI":"10.1177\/1177932219899051","article-title":"Multi-omics data integration, interpretation, and its application","volume":"14","author":"Subramanian","year":"2020","journal-title":"Bioinform Biol Insights"},{"key":"2025081214335225900_btaf405-B22","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1186\/s12911-020-1114-3","article-title":"A multi-omics supervised autoencoder for pan-cancer clinical outcome endpoints prediction","volume":"20","author":"Tan","year":"2020","journal-title":"BMC Med Inform Decis Making"},{"key":"2025081214335225900_btaf405-B23","doi-asserted-by":"crossref","first-page":"2788","DOI":"10.3390\/ijms25052788","article-title":"MOGAT: an improved multi-omics integration framework using graph attention networks","volume":"25","author":"Tanvir","year":"2024","journal-title":"Int J Mol Sci"},{"key":"2025081214335225900_btaf405-B24","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1038\/nature11412","article-title":"Comprehensive molecular portraits of human breast tumours","volume":"490","author":"The Cancer Genome Atlas Network","year":"2012","journal-title":"Nature"},{"key":"2025081214335225900_btaf405-B25","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1038\/nature10166","article-title":"Integrated genomic analyses of ovarian carcinoma","volume":"474","author":"The Cancer Genome Atlas Research Network","year":"2011","journal-title":"Nature"},{"key":"2025081214335225900_btaf405-B26","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1038\/ng.2764","article-title":"The cancer genome atlas pan-cancer analysis project","volume":"45","author":"The Cancer Genome Atlas Research Network","year":"2013","journal-title":"Nat Genet"},{"key":"2025081214335225900_btaf405-B27","first-page":"5998","author":"Vaswani","year":"2017"},{"key":"2025081214335225900_btaf405-B28","author":"Veli\u010dkovi\u0107"},{"key":"2025081214335225900_btaf405-B29","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1038\/nmeth.2810","article-title":"Similarity network fusion for aggregating data types on a genomic scale","volume":"11","author":"Wang","year":"2014","journal-title":"Nat Methods"},{"key":"2025081214335225900_btaf405-B30","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":"2025081214335225900_btaf405-B31","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1186\/s12859-019-3116-7","article-title":"A hierarchical integration deep flexible neural forest framework for cancer subtype classification by integrating multi-omics data","volume":"20","author":"Xu","year":"2019","journal-title":"BMC Bioinformatics"},{"key":"2025081214335225900_btaf405-B32","doi-asserted-by":"crossref","first-page":"e0293607","DOI":"10.1371\/journal.pone.0293607","article-title":"Performance analysis of data resampling on class imbalance and classification techniques on multi-omics data for cancer classification","volume":"19","author":"Yang","year":"2024","journal-title":"PLoS One"},{"key":"2025081214335225900_btaf405-B33","doi-asserted-by":"crossref","first-page":"107223","DOI":"10.1016\/j.compbiomed.2023.107223","article-title":"Multi-omics clustering for cancer subtyping based on latent subspace learning","volume":"164","author":"Ye","year":"2023","journal-title":"Comput Biol Med"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btaf405\/63815009\/btaf405.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/41\/8\/btaf405\/63815009\/btaf405.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/41\/8\/btaf405\/63815009\/btaf405.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,12]],"date-time":"2025-08-12T18:33:59Z","timestamp":1755023639000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/doi\/10.1093\/bioinformatics\/btaf405\/8210085"}},"subtitle":[],"editor":[{"given":"Lenore","family":"Cowen","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2025,7,22]]},"references-count":33,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2025,8,2]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btaf405","relation":{},"ISSN":["1367-4811"],"issn-type":[{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025,8]]},"published":{"date-parts":[[2025,7,22]]},"article-number":"btaf405"}}