{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T10:03:36Z","timestamp":1790244216518,"version":"4.1.0"},"reference-count":28,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Program of Guangzhou","award":["2024A04J6551"],"award-info":[{"award-number":["2024A04J6551"]}]},{"DOI":"10.13039\/501100007162","name":"Natural Science Foundation of Guangdong Province, China","doi-asserted-by":"publisher","award":["2022B1515020094"],"award-info":[{"award-number":["2022B1515020094"]}],"id":[{"id":"10.13039\/501100007162","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Mol. Sci.","IJMS"],"abstract":"<jats:p>Multi-omics molecular subtyping is complicated by small cohorts, heterogeneous data layers, and sensitivity to data splitting and feature selection. We developed multi-channel attention-based omics fusion (MCOF), an end-to-end framework that combines omics-specific projection, one-dimensional convolution, and integrated gradients for candidate biomarker prioritization. MCOF was evaluated against six baselines using five repeated stratified holdouts across four multi-omics datasets. In the restricted four-class breast invasive carcinoma task, MCOF achieved the highest observed mean for all five metrics, including an accuracy of 0.900 \u00b1 0.022 and a macro-averaged area under the receiver operating characteristic curve (macro-AUC) of 0.982 \u00b1 0.003. In the restricted three-class stomach adenocarcinoma task, it achieved the highest observed macro-F1 (0.786 \u00b1 0.035), macro-AUC (0.934 \u00b1 0.020), and weighted-AUC (0.930 \u00b1 0.016), whereas support vector machines had a slightly higher accuracy and weighted F1. It also achieved the highest or near-highest AUC on the two non-cancer datasets. Compared with MOGONET, MCOF used 30.6% fewer trainable parameters on both cancer tasks. Sensitivity analyses indicated that the contribution of multi-omics fusion varied by dataset and metric. Excluding the 28 prediction analysis of microarray 50 (PAM50) genes present in the retained BRCA RNA panel produced only a modest reduction in the macro-AUC, from 0.982 \u00b1 0.003 to 0.978 \u00b1 0.004. Implementation tests across 20 trained checkpoints detected no batch-order effect or cross-sample gradient, while the IG rankings were moderately stable across repeated holdouts and numerically consistent across class-weighting schemes and 25\u2013100 integration steps. MCOF provides a practical framework for multi-omics classification and candidate prioritization.<\/jats:p>","DOI":"10.3390\/ijms27198519","type":"journal-article","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T09:07:54Z","timestamp":1790240874000},"page":"8519","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MCOF: A Multi-Channel Attention-Based Omics Fusion Framework for Cancer Subtyping and Candidate Biomarker Prioritization"],"prefix":"10.3390","volume":"27","author":[{"given":"Xuezhi","family":"Liang","sequence":"first","affiliation":[{"name":"Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China"},{"name":"Center for Migrant Health Policy, Sun Yat-sen University, Guangzhou 510080, 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China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Tingting","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China"},{"name":"Center for Migrant Health Policy, Sun Yat-sen University, Guangzhou 510080, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Yi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China"},{"name":"Center for Migrant Health Policy, Sun Yat-sen University, Guangzhou 510080, 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(2020). State of the Field in Multi-Omics Research: From Computational Needs to Data Mining and Sharing. Front. Genet., 11.","DOI":"10.3389\/fgene.2020.610798"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1016\/j.ccell.2022.09.012","article-title":"Artificial Intelligence for Multimodal Data Integration in Oncology","volume":"40","author":"Lipkova","year":"2022","journal-title":"Cancer Cell"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3055","DOI":"10.1093\/bioinformatics\/bty1054","article-title":"DIABLO: An Integrative Approach for Identifying Key Molecular Drivers from Multi-Omics Assays","volume":"35","author":"Singh","year":"2019","journal-title":"Bioinformatics"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"10546","DOI":"10.1093\/nar\/gky889","article-title":"Multi-Omic and Multi-View Clustering Algorithms: Review and Cancer Benchmark","volume":"46","author":"Rappoport","year":"2018","journal-title":"Nucleic Acids Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"100461","DOI":"10.1016\/j.crmeth.2023.100461","article-title":"Systematic Comparison of Multi-Omics Survival Models Reveals a Widespread Lack of Noise Resistance","volume":"3","author":"Wissel","year":"2023","journal-title":"Cell Rep. Methods"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1248","DOI":"10.1158\/1078-0432.CCR-17-0853","article-title":"Deep Learning\u2013Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer","volume":"24","author":"Chaudhary","year":"2018","journal-title":"Clin. Cancer Res."},{"key":"ref_7","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":"ref_8","doi-asserted-by":"crossref","first-page":"107303","DOI":"10.1016\/j.compbiomed.2023.107303","article-title":"Integration of Multi-Omics Data Using Adaptive Graph Learning and Attention Mechanism for Patient Classification and Biomarker Identification","volume":"164","author":"Ouyang","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 22). Squeeze-and-Excitation Networks. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_10","first-page":"3319","article-title":"Axiomatic Attribution for Deep Networks","volume":"Volume 70","author":"Sundararajan","year":"2017","journal-title":"Proceedings of the 34th International Conference on Machine Learning"},{"key":"ref_11","first-page":"1","article-title":"On the Stability of Feature Selection Algorithms","volume":"18","author":"Nogueira","year":"2018","journal-title":"J. Mach. Learn. Res."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Reynolds, J., and Pan, C. (2025). Benchmarking Interpretability of Deep Learning for Predictive Genomics: Recall, Precision, and Variability of Feature Attribution. PLoS Comput. Biol., 21.","DOI":"10.1371\/journal.pcbi.1013784"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e725","DOI":"10.1002\/ctm2.725","article-title":"Protein Phosphatase 1 Regulatory Inhibitor Subunit 14C Promotes Triple-Negative Breast Cancer Progression via Sustaining Inactive Glycogen Synthase Kinase 3 Beta","volume":"12","author":"Jian","year":"2022","journal-title":"Clin. Transl. Med."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1530\/ERC-13-0485","article-title":"VGLL1 Expression Is Associated with a Triple-Negative Basal-like Phenotype in Breast Cancer","volume":"21","author":"Castilla","year":"2014","journal-title":"Endocr. Relat. Cancer"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3839","DOI":"10.1245\/s10434-011-1657-8","article-title":"Basal-like Breast Cancer Defined by FOXC1 Expression Offers Superior Prognostic Value: A Retrospective Immunohistochemical Study","volume":"18","author":"Ray","year":"2011","journal-title":"Ann. Surg. Oncol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1002\/path.2811","article-title":"BCL2L10 Protein Regulates Apoptosis\/Proliferation through Differential Pathways in Gastric Cancer Cells","volume":"223","author":"Xu","year":"2011","journal-title":"J. Pathol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1007\/s10120-020-01064-6","article-title":"A Panel of Intestinal Differentiation Markers (CDX2, GPA33, and LI-Cadherin) Identifies Gastric Cancer Patients with Favourable Prognosis","volume":"23","author":"Lopes","year":"2020","journal-title":"Gastric Cancer"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1805","DOI":"10.1016\/j.stem.2021.05.001","article-title":"Restoring miR-132 Expression Rescues Adult Hippocampal Neurogenesis and Memory Deficits in Alzheimer\u2019s Disease","volume":"28","author":"Walgrave","year":"2021","journal-title":"Cell Stem Cell"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1186\/s13195-023-01366-8","article-title":"miR-129-5p as a Biomarker for Pathology and Cognitive Decline in Alzheimer\u2019s Disease","volume":"16","author":"Han","year":"2024","journal-title":"Alzheimers Res. Ther."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2894","DOI":"10.1007\/s12035-015-9156-8","article-title":"MicroRNA-29a Is a Candidate Biomarker for Alzheimer\u2019s Disease in Cell-Free Cerebrospinal Fluid","volume":"53","author":"Bruinsma","year":"2016","journal-title":"Mol. Neurobiol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1545","DOI":"10.2147\/NDT.S52463","article-title":"Reduced Serum Paraoxonase 1 (PON1) Activity in Patients with Schizophrenia Treated with Olanzapine but Not Quetiapine","volume":"9","author":"Albayrak","year":"2013","journal-title":"Neuropsychiatr. Dis. Treat."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.schres.2015.06.009","article-title":"Lowered Paraoxonase 1 (PON1) Activity Is Associated with Increased Cytokine Levels in Drug Na\u00efve First Episode Psychosis","volume":"166","author":"Brinholi","year":"2015","journal-title":"Schizophr. Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1038\/s41593-018-0154-9","article-title":"A Molecular Network of the Aging Human Brain Provides Insights into the Pathology and Cognitive Decline of Alzheimer\u2019s Disease","volume":"21","author":"Mostafavi","year":"2018","journal-title":"Nat. Neurosci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1217","DOI":"10.1038\/s41380-021-01339-z","article-title":"Multi-Omics of Human Plasma Reveals Molecular Features of Dysregulated Inflammation and Accelerated Aging in Schizophrenia","volume":"27","author":"Campeau","year":"2022","journal-title":"Mol. Psychiatry"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1016\/j.cell.2018.03.059","article-title":"SnapShot: TCGA-Analyzed Tumors","volume":"173","author":"Blum","year":"2018","journal-title":"Cell"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"15023","DOI":"10.1038\/npjbcancer.2015.23","article-title":"PAM50 Gene Signatures and Breast Cancer Prognosis with Adjuvant Anthracycline- and Taxane-Based Chemotherapy: Correlative Analysis of C9741 (Alliance)","volume":"2","author":"Liu","year":"2016","journal-title":"npj Breast Cancer"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Chicco, D., and Jurman, G. (2020). The Advantages of the Matthews Correlation Coefficient (MCC) over F1 Score and Accuracy in Binary Classification Evaluation. BMC Genom., 21.","DOI":"10.1186\/s12864-019-6413-7"}],"container-title":["International Journal of Molecular Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1422-0067\/27\/19\/8519\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T09:13:07Z","timestamp":1790241187000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1422-0067\/27\/19\/8519"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,24]]},"references-count":28,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2026,10]]}},"alternative-id":["ijms27198519"],"URL":"https:\/\/doi.org\/10.3390\/ijms27198519","relation":{},"ISSN":["1422-0067"],"issn-type":[{"value":"1422-0067","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,24]]}}}