A transformer-based graph local-global fusion model for interpretable circRNA-miRNA interaction prediction

A transformer-based graph local-global fusion model for interpretable circRNA-miRNA interaction prediction | 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 A transformer-based graph local-global fusion model for interpretable circRNA-miRNA interaction prediction Meng-Meng Wei, Lei Wang, Xiao-Rui Su, Bo-Wei Zhao, Zhu-Hong You This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6229563/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Aberrant circRNA–miRNA interactions (CMIs) play a pivotal role in the pathogenesis of major diseases. Although several computational models have been proposed for CMI prediction with considerable results, their inter-pretability in elucidating the underlying mechanisms of CMI remains limited. Therefore, developing a highly interpretable CMI prediction model is of great significance for uncovering the molecular mechanisms and biological implications. Results We propose TransFusion, a transformer-based graph fusion model for predicting CMI. TransFusion focuses on molecular biological characteristics, extracting intrinsic semantic features using advanced natural language processing techniques, which are subsequently integrated into the network. By combining graph convolutional networks with graph transformers, the model captures both local and global structural information, facilitating the fusion of multi-source features. We visualized the process of molecule representation to explore the inter-pretability of TransFusion in capturing and distinguishing molecular features. Disease-related case studies validate the practicality of TransFusion from both computational and biological perspectives. Conclusions TransFusion is capable of revealing molecular interaction mechanisms , providing valuable insights for the design of targeted cancer therapies. To facilitate communication with relevant researchers, we developed an online prediction server (http://39.106.153.201/TransFusion/home.html) and open-sourced the code on GitHub (https://github.com/591286260/TransFusion). circRNA–miRNA interactions semantic features natural language processing graph convolutional networks graph transformers local and global structural information Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-6229563","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":434472203,"identity":"d2ed09d4-6b59-448c-bffe-629a7492d43b","order_by":0,"name":"Meng-Meng Wei","email":"","orcid":"","institution":"China University of Mining and Technology","correspondingAuthor":false,"prefix":"","firstName":"Meng-Meng","middleName":"","lastName":"Wei","suffix":""},{"id":434472204,"identity":"839e1c7f-eb13-4d51-98aa-3d7a30d37770","order_by":1,"name":"Lei Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYBACPgbGBhAtB+UzE9bCBtViDMQgFlFaICCxgXgt7M1tEj931Kb3z8hOf8BQYZ3YwH72AH4tPAfbJHvPHM+dcSN3YwPDmfTEBp68BPxaJBLbbvC2HcvdIAHUwth2OLFBgscAvxb5h203/7YdSzcAa/lHjBYJxrbbvG01CRAtDcRo4Uls/y3bdsBwxpm3G2ckHEs3buPJwa+Fn/34Y8O3bXXy/O25Gz58qLGW7Wc/g18LFByGUAkMiJgiBOqIVDcKRsEoGAUjEgAAcFdGk7fiMBIAAAAASUVORK5CYII=","orcid":"","institution":"China University of Mining and Technology","correspondingAuthor":true,"prefix":"","firstName":"Lei","middleName":"","lastName":"Wang","suffix":""},{"id":434472206,"identity":"0158f4bb-b8b0-4a65-b808-a71797fdc9a7","order_by":2,"name":"Xiao-Rui Su","email":"","orcid":"","institution":"Harvard Medical School","correspondingAuthor":false,"prefix":"","firstName":"Xiao-Rui","middleName":"","lastName":"Su","suffix":""},{"id":434472210,"identity":"8ae2a45a-591e-43c0-9e8f-8b6f273a8d59","order_by":3,"name":"Bo-Wei Zhao","email":"","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Bo-Wei","middleName":"","lastName":"Zhao","suffix":""},{"id":434472211,"identity":"b7a1785a-5e66-429c-943e-9635f552c967","order_by":4,"name":"Zhu-Hong You","email":"","orcid":"","institution":"Guangxi Academy of Science","correspondingAuthor":false,"prefix":"","firstName":"Zhu-Hong","middleName":"","lastName":"You","suffix":""}],"badges":[],"createdAt":"2025-03-15 00:08:09","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6229563/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6229563/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80155240,"identity":"66d70f87-7089-47b7-88f7-9c6107ea01bb","added_by":"auto","created_at":"2025-04-08 14:17:07","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7441978,"visible":true,"origin":"","legend":"","description":"","filename":"snarticle.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6229563/v1_covered_98339bc5-5451-4441-af75-c5d3a01940fb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A transformer-based graph local-global fusion model for interpretable circRNA-miRNA interaction prediction","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"circRNA–miRNA interactions, semantic features, natural language processing, graph convolutional networks, graph transformers, local and global structural information","lastPublishedDoi":"10.21203/rs.3.rs-6229563/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6229563/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground Aberrant circRNA–miRNA interactions (CMIs) play a pivotal role in the pathogenesis of major diseases. Although several computational models have been proposed for CMI prediction with considerable results, their inter-pretability in elucidating the underlying mechanisms of CMI remains limited. Therefore, developing a highly interpretable CMI prediction model is of great significance for uncovering the molecular mechanisms and biological implications.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults We propose TransFusion, a transformer-based graph fusion model for predicting CMI. TransFusion focuses on molecular biological characteristics, extracting intrinsic semantic features using advanced natural language processing techniques, which are subsequently integrated into the network. By combining graph convolutional networks with graph transformers, the model captures both local and global structural information, facilitating the fusion of multi-source features. We visualized the process of molecule representation to explore the inter-pretability of TransFusion in capturing and distinguishing molecular features. Disease-related case studies validate the practicality of TransFusion from both computational and biological perspectives.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusions TransFusion is capable of revealing molecular interaction mechanisms , providing valuable insights for the design of targeted cancer therapies. 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