Prediction of miRNA-disease Association Based on Multi-Source Inductive Matrix Completion

Prediction of miRNA-disease Association Based on Multi-Source Inductive Matrix Completion | 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 Article Prediction of miRNA-disease Association Based on Multi-Source Inductive Matrix Completion YaWei Wang, ZhiXiang Yin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4663197/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Nov, 2024 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract MicroRNAs (miRNAs) are endogenous non-coding RNAs of about 23 nucleotides in length that play important roles in a variety of cellular biochemical processes. A large number of studies have demonstrated that miRNAs are involved in the regulation of many human diseases. Accurate and efficient prediction and identification of the association between miRNAs and human diseases will have great significance for the early diagnosis, treatment and prognosis assessment of human diseases. In this paper, we propose a model called Autoencoder Inductive Matrix Completion (AEIMC) to identify potential miRNA-disease associations. Specifically, we first capture the interaction features of miRNA-disease associations based on multi-source similarity networks, including miRNA functional similarity network features, miRNA sequence similarity features, disease semantic similarity features, disease ontology similarity features, and Gauss interaction spectral kernel similarity features between disease and miRNA. Secondly, autoencoders are used to capture more complex and abstract data representations of miRNA and disease. Finally, the learned high-level features are used as inputs to the induction matrix completion model to obtain the miRNA-disease association prediction matrix. At the end of the artical, an ablation experiment was performed to confirm the validity and necessity of introducing miRNA sequence similarity and disease ontology similarity for the first time. miRNA-disease association multi-source information autoencoder inductive matrix completion ablation experiment optimization algorithm Full Text Additional Declarations No competing interests reported. Supplementary Files codedata.zip Cite Share Download PDF Status: Published Journal Publication published 11 Nov, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 26 Sep, 2024 Reviews received at journal 07 Sep, 2024 Reviewers agreed at journal 23 Aug, 2024 Reviews received at journal 30 Jul, 2024 Reviewers agreed at journal 20 Jul, 2024 Reviewers invited by journal 20 Jul, 2024 Editor assigned by journal 14 Jul, 2024 Editor invited by journal 08 Jul, 2024 Submission checks completed at journal 05 Jul, 2024 First submitted to journal 30 Jun, 2024 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-4663197","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":331935800,"identity":"6f5d64c2-bcf4-488f-9097-adb740e0d03a","order_by":0,"name":"YaWei Wang","email":"","orcid":"","institution":"Shanghai University of Enginneering Science","correspondingAuthor":false,"prefix":"","firstName":"YaWei","middleName":"","lastName":"Wang","suffix":""},{"id":331935801,"identity":"6fd39fce-dece-4bea-af77-13c1a2fd2f8c","order_by":1,"name":"ZhiXiang Yin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYBAC+wYGhgMMFTZybOztB4jTwgjWcibNmI/nTALxWhgYWw4lzpNwMCBOC7NEjuHhwoYD6W0SDAkMPyq2EdbCxnPG4PDMHXdy26QbDzD2nLlNWAsPe4/BYd4zz3LbZA4kMDO2EaFFgpkHqKXtcDqbRIIBcVoMwLa0HU4gQQvPsYLDPGfSDNuAgXyQKL/Yz0je/JmnwkZevr394IMfFURoYWDgQETHAWLUAwH7AyIVjoJRMApGwYgFAIrgPnRN5FSNAAAAAElFTkSuQmCC","orcid":"","institution":"Shanghai University of Enginneering Science","correspondingAuthor":true,"prefix":"","firstName":"ZhiXiang","middleName":"","lastName":"Yin","suffix":""}],"badges":[],"createdAt":"2024-06-30 14:36:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4663197/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4663197/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-78212-w","type":"published","date":"2024-11-11T15:57:24+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":69274947,"identity":"535bec4b-f21f-4eae-b5d0-7271d571c6f2","added_by":"auto","created_at":"2024-11-18 16:40:41","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":510639,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4663197/v1_covered_96d2127e-6f51-4e88-99a2-60646e0290b8.pdf"},{"id":61338979,"identity":"ecbc9431-5467-42f6-92a4-1dc007231bdd","added_by":"auto","created_at":"2024-07-29 16:12:46","extension":"zip","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":2056333,"visible":true,"origin":"","legend":"","description":"","filename":"codedata.zip","url":"https://assets-eu.researchsquare.com/files/rs-4663197/v1/df7bf824c3ae92a08017075d.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prediction of miRNA-disease Association Based on Multi-Source Inductive Matrix Completion","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"miRNA-disease association, multi-source information, autoencoder, inductive matrix completion, ablation experiment, optimization algorithm","lastPublishedDoi":"10.21203/rs.3.rs-4663197/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4663197/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMicroRNAs (miRNAs) are endogenous non-coding RNAs of about 23 nucleotides in length that play important roles in a variety of cellular biochemical processes. A large number of studies have demonstrated that miRNAs are involved in the regulation of many human diseases. Accurate and efficient prediction and identification of the association between miRNAs and human diseases will have great significance for the early diagnosis, treatment and prognosis assessment of human diseases. In this paper, we propose a model called Autoencoder Inductive Matrix Completion (AEIMC) to identify potential miRNA-disease associations. Specifically, we first capture the interaction features of miRNA-disease associations based on multi-source similarity networks, including miRNA functional similarity network features, miRNA sequence similarity features, disease semantic similarity features, disease ontology similarity features, and Gauss interaction spectral kernel similarity features between disease and miRNA. Secondly, autoencoders are used to capture more complex and abstract data representations of miRNA and disease. 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