MLACNN: an attention mechanism based CNN architecture for predicting genome-wide DNA methylation

MLACNN: an attention mechanism based CNN architecture for predicting genome-wide DNA methylation | 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 MLACNN: an attention mechanism based CNN architecture for predicting genome-wide DNA methylation JianGuo Bai, Hai Yang, ChangDe Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1997163/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Aug, 2023 Read the published version in Theory in Biosciences → Version 1 posted You are reading this latest preprint version Abstract Methylation, as an important epigenetic regulation of methylation genes, plays an important role in the regulation of some biological processes. Although the traditional methods of detecting methylation in biological experiments are constantly improving, with the development of artificial intelligence, using deep learning and machine learning methods to detect methylation has gradually become a new trend. Traditional machine learning based methods rely too much on manual feature extraction. Although there are some deep learning methods to study methylation, most network architectures extract fewer features due to the simple network structure. We propose a bottleneck network based on attention mechanism, and use some new methods to ensure that the deep network can learn more effective features and minimize overfitting to get more significant prediction results. The model uses three coding methods to encode the original DNA sequence, and then uses the feature fusion based on the attention mechanism to get the best fusion method. The results show that MLACNN is superior to the previous methods and can obtain more satisfactory performance. Genome wide methylation detection attension CNN Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 30 Aug, 2023 Read the published version in Theory in Biosciences → 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-1997163","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":132054478,"identity":"1d0d5c6f-eb65-4912-9f66-9e5cd354d27c","order_by":0,"name":"JianGuo Bai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYBAC+/bG9h8fDGzkGNsbiNRiwHO4QXJGRZoxc88BYrVIpDdI85w5lMg+I4FILeYSiQ3GvG0HEnhnPt54g6HGJpqgFsuehw2Jc9vu5EnOTiu2YDiWlttAUM/xxIYDb9ueFRvOzjGTYGw4TISWA4mNDbxthxP33zxDpBaDE4nNjDxnDic2zuAhUotkz8E2RlAgM/YA/ZJAjF/42dufMUCi8vDGGx9qbIjwC7IjJRJIUQ7RQqqOUTAKRsEoGBkAAEJ9SFHBtyP8AAAAAElFTkSuQmCC","orcid":"","institution":"Shandong Jiaotong University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"JianGuo","middleName":"","lastName":"Bai","suffix":""},{"id":132054479,"identity":"f99c8207-380c-46b1-9af2-cfae22a6ffce","order_by":1,"name":"Hai Yang","email":"","orcid":"","institution":"Shandong Jiaotong University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hai","middleName":"","lastName":"Yang","suffix":""},{"id":132054480,"identity":"801acbd1-5922-4c1d-9023-2e9ea450ad2f","order_by":2,"name":"ChangDe Wu","email":"","orcid":"","institution":"Shandong Jiaotong University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"ChangDe","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2022-08-25 09:29:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1997163/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1997163/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12064-023-00402-3","type":"published","date":"2023-08-30T15:10:12+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":25862963,"identity":"4f0434e0-3026-49f4-aa61-c5cce07a6fe2","added_by":"auto","created_at":"2022-08-30 19:20:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1136447,"visible":true,"origin":"","legend":"","description":"","filename":"MLACNN.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1997163/v1_covered.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"MLACNN: an attention mechanism based CNN architecture for predicting genome-wide DNA methylation","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-1997163/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"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":"Genome wide methylation detection, attension CNN","lastPublishedDoi":"10.21203/rs.3.rs-1997163/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1997163/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMethylation, as an important epigenetic regulation of methylation genes, plays an important role in the regulation of some biological processes. Although the traditional methods of detecting methylation in biological experiments are constantly improving, with the development\u0026nbsp;of artificial intelligence, using deep learning and machine learning methods to detect methylation has gradually become a new trend. Traditional machine learning based methods rely too much on manual feature extraction. Although there are some deep learning methods to study methylation, most network architectures extract fewer features due to the simple network structure. We propose a bottleneck network based on attention mechanism, and use some new methods to ensure that the deep network can learn more effective features and minimize overfitting to get more significant prediction results. The model uses three coding methods to encode the original DNA sequence, and then uses the feature fusion based on the attention mechanism to get the best fusion method. 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