A Comprehensive Multi-Dimensional Disease Similarity Computation Framework for miRNA-Disease Association Prediction

A Comprehensive Multi-Dimensional Disease Similarity Computation Framework for miRNA-Disease Association 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 Comprehensive Multi-Dimensional Disease Similarity Computation Framework for miRNA-Disease Association Prediction Xu Sa Xuan, 刘 健 This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9053024/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract In light of the explosive growth of gene and disease association data driven by the rapid development of high-throughput sequencing technologies, mining potential molecular associations between diseases from massive biomedical data has emerged as a crucial direction in bioinformatics research. To address this, we propose and implement a macroscopic gene-associated disease similarity computational framework tailored for bioinformatics mining. Leveraging the latest miRNA-disease association dataset (HMDD v4.0) [ 1 ] as the core data source, this framework deeply integrates gene-disease association information from the DisGeNET database [ 2 ] and the hierarchical structure data of MeSH (Medical Subject Headings) to construct a multi-source heterogeneous disease association network. The objective is to provide a comprehensive and precise disease similarity prediction strategy for biomedical research through multi-dimensional data fusion analysis. Technically, the framework integrates multiple core computational mechanisms, including target-based disease functional similarity, MeSH-based semantic similarity, and network topology-based Gaussian Interaction Profile similarity. Notably, the semantic similarity algorithm incorporates Information Content (IC) weights upon classical structural decay; the functional similarity algorithm introduces an exponential penalty term to address the long-tail distribution problem, effectively resolving the illusion of false-positive associations caused by unequal research depth between common and rare diseases, thereby achieving a multi-perspective quantification of disease similarity. Based on the calculated similarity matrices, we conducted extensive evaluations (e.g., AUC, AUPR, Accuracy) using state-of-the-art deep learning models, such as graph neural networks (TriFusion) [ 9 ] and Transformer architectures (MDformer) [ 8 ]. Experimental results demonstrate that this multi-dimensional similarity feature enhancement strategy can effectively reveal potential biological associations between diseases, validating its application value in drug repositioning and rare disease auxiliary prediction, while exhibiting robust generalized predictive performance and high scalability. MiRNA-disease association HMDD v4.0 Semantic similarity Machine learning Bioinformatics Open source Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 16 Mar, 2026 Editor invited by journal 11 Mar, 2026 Editor assigned by journal 11 Mar, 2026 Submission checks completed at journal 11 Mar, 2026 First submitted to journal 06 Mar, 2026 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-9053024","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":607281223,"identity":"b302d056-0117-4f88-8b58-f026b3bfc7a0","order_by":0,"name":"Xu Sa Xuan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIie3RsYrCQBDG8Q0Dm2aObXdR7hlWhFQ+zIZAKoUrUwgGPEyhYmvhQ1haJgRis2JrIVxEOCshdlZi+juS2Fnsv/5+xTCEmEzvmB1tiwIfQAGSXAXDBgS1by3bsc1s6slcZw0IVw5gL2ZigY44fUO9kLHyz9g/tmRKnMANKWHRVFUSESbbzkr/dkviH9xNm3C9W1cSZoWKX2fglSQ7uJoSyQfVhEK5+XjAaJ1aky93AvWEUeIIxBTEGChpRMQMvc4SfWDlZ7jSGdbeIn8uSV5gDyjb3273YPjJonk1+RO+NjeZTCbTvz0B7ehJP1dEQhcAAAAASUVORK5CYII=","orcid":"","institution":"Baoding University","correspondingAuthor":true,"prefix":"","firstName":"Xu","middleName":"Sa","lastName":"Xuan","suffix":""},{"id":607281224,"identity":"4096efe2-6589-4024-b3d4-1b615874a0a4","order_by":1,"name":"刘 健","email":"","orcid":"","institution":"Baoding University","correspondingAuthor":false,"prefix":"","firstName":"刘","middleName":"","lastName":"健","suffix":""}],"badges":[],"createdAt":"2026-03-06 17:53:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9053024/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9053024/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105034042,"identity":"85b94ef6-1388-41ae-93fe-2e5b7ba65c72","added_by":"auto","created_at":"2026-03-20 07:22:30","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":697190,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9053024/v1_covered_7ffe8fa5-2c42-4668-9340-e25e63eae9ba.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Comprehensive Multi-Dimensional Disease Similarity Computation Framework for miRNA-Disease Association Prediction","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-bioinformatics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"binf","sideBox":"Learn more about [BMC Bioinformatics](http://bmcbioinformatics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/binf","title":"BMC Bioinformatics","twitterHandle":"@BMC_Bioinformatics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"MiRNA-disease association, HMDD v4.0, Semantic similarity, Machine learning, Bioinformatics, Open source","lastPublishedDoi":"10.21203/rs.3.rs-9053024/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9053024/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn light of the explosive growth of gene and disease association data driven by the rapid development of high-throughput sequencing technologies, mining potential molecular associations between diseases from massive biomedical data has emerged as a crucial direction in bioinformatics research. To address this, we propose and implement a macroscopic gene-associated disease similarity computational framework tailored for bioinformatics mining. Leveraging the latest miRNA-disease association dataset (HMDD v4.0) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] as the core data source, this framework deeply integrates gene-disease association information from the DisGeNET database [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] and the hierarchical structure data of MeSH (Medical Subject Headings) to construct a multi-source heterogeneous disease association network. The objective is to provide a comprehensive and precise disease similarity prediction strategy for biomedical research through multi-dimensional data fusion analysis.\u003c/p\u003e \u003cp\u003eTechnically, the framework integrates multiple core computational mechanisms, including target-based disease functional similarity, MeSH-based semantic similarity, and network topology-based Gaussian Interaction Profile similarity. Notably, the semantic similarity algorithm incorporates Information Content (IC) weights upon classical structural decay; the functional similarity algorithm introduces an exponential penalty term to address the long-tail distribution problem, effectively resolving the illusion of false-positive associations caused by unequal research depth between common and rare diseases, thereby achieving a multi-perspective quantification of disease similarity. Based on the calculated similarity matrices, we conducted extensive evaluations (e.g., AUC, AUPR, Accuracy) using state-of-the-art deep learning models, such as graph neural networks (TriFusion) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and Transformer architectures (MDformer) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Experimental results demonstrate that this multi-dimensional similarity feature enhancement strategy can effectively reveal potential biological associations between diseases, validating its application value in drug repositioning and rare disease auxiliary prediction, while exhibiting robust generalized predictive performance and high scalability.\u003c/p\u003e","manuscriptTitle":"A Comprehensive Multi-Dimensional Disease Similarity Computation Framework for miRNA-Disease Association Prediction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-18 01:41:43","doi":"10.21203/rs.3.rs-9053024/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-03-16T14:58:43+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-11T14:38:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-11T08:54:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-11T08:53:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Bioinformatics","date":"2026-03-06T17:41:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-bioinformatics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"binf","sideBox":"Learn more about [BMC Bioinformatics](http://bmcbioinformatics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/binf","title":"BMC Bioinformatics","twitterHandle":"@BMC_Bioinformatics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"24d9dab2-45f8-4830-b406-2d08e24b085b","owner":[],"postedDate":"March 18th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-18T01:41:43+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-18 01:41:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9053024","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9053024","identity":"rs-9053024","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}