Integrating Multiomics Data Using a Correlation Based Graph Attention Network for Subtype Classification in Lower Grade Glioma

Integrating Multiomics Data Using a Correlation Based Graph Attention Network for Subtype Classification in Lower Grade Glioma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Integrating Multiomics Data Using a Correlation Based Graph Attention Network for Subtype Classification in Lower Grade Glioma Eman Mohammed Hamid, Murtada K. Elbashir, Nosiba Yousif Ahmed, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7078501/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Accurate classification of cancer subtypes is crucial to personalized therapies and targeted intervention. In this study, we proposed BioGAT-LGG, a deep learning framework that integrates the multi-omics data, namely mRNA, miRNA, and DNA methylation, by a correlation-based Graph Attention Network (GATv2) for biomarker discovery and subtype classification of Lower-Grade Glioma (LGG). Unlike existing methodologies that depend on external biological priors such as protein-protein interaction networks or reference biological graphs, BioGAT-LGG creates sample-driven correlation graphs so that the model can learn biologically meaningful molecular interactions. For the sake of improving feature interpretability and dimensionality reduction, LASSO regression is performed initially before training. The model attains an accuracy of 98.03% among the variables in precision (98.12%), recall (97.74%), and F1-score (97.87%), all supported by stratified 10-fold cross-validation. hsa-mir-3936, MTCO1P40, and CCND2 represent key markers, whereas KEGG enrichment throws insights into other pathways like PI3K-Akt signaling, Small Cell Lung Cancer, and Transcriptional Misregulation in Cancer. The findings thus suggest that BioGAT-LGG can potentially serve to support clinically relevant subtype classification and biomarker-driven decision-making. This framework thus lays a scalable foundation for the multi-omics integration in oncology, which can further be adopted in other tumor types. Biomarker identification Graph Attention Network (GAT) multi-omics data lower-grade glioma (LGG) correlation-based graph Gene Ontology cancer subtype classification KEGG pathways Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 03 Sep, 2025 Reviews received at journal 01 Sep, 2025 Reviewers agreed at journal 25 Aug, 2025 Reviews received at journal 25 Aug, 2025 Reviewers agreed at journal 20 Aug, 2025 Reviewers agreed at journal 20 Aug, 2025 Reviewers agreed at journal 14 Aug, 2025 Reviewers invited by journal 13 Aug, 2025 Editor assigned by journal 06 Aug, 2025 Submission checks completed at journal 28 Jul, 2025 First submitted to journal 28 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7078501","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":503102546,"identity":"4bd6933e-3905-4b8d-ac38-27091f19b7b1","order_by":0,"name":"Eman Mohammed Hamid","email":"","orcid":"","institution":"University of Gezira","correspondingAuthor":false,"prefix":"","firstName":"Eman","middleName":"Mohammed","lastName":"Hamid","suffix":""},{"id":503102547,"identity":"852b16e6-bb01-4213-8fc9-d1d62d86fa4e","order_by":1,"name":"Murtada K. 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