A Predictive Sequence Filling Method using Protein Structures for Early Detection of Alzheimer Disease

A Predictive Sequence Filling Method using Protein Structures for Early Detection of Alzheimer Disease | 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 Predictive Sequence Filling Method using Protein Structures for Early Detection of Alzheimer Disease Balamurugan A.G, Gomathi N This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4371183/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 Alzheimer’s disease (AD) is a generic form of dementia causing memory loss and environmental responses. AD detection is pursued using the different protein structures and their intensity based on different physical behaviors. Using the time-series protein structures the detection and is eased through the proposed neural method for structural protein filling (NC-SSF). Structural differentiations are performed using the high and low intensity profiles observed. This analysis identifies the missing inputs and thereby the fillable sequences are identified. The protein biomarker determines the maximum filling requirement as per the changes observed. The neural network is trained using this sequence required under the low and high intensity variations. This process is recurrent until maximum false rate is confined through accuracy improvements. The AD progression detection is performed by estimating the intensity under different profile filling levels. The proposed method improves accuracy, sensitivity, and specificity by 8.74%, 10.29%, and 8.84% respectively. This method reduced the false rate and MMSE by 9.85% and 10.78% respectively. AD Neural Network Protein Structure Sequence Differentiation 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. 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-4371183","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":302676236,"identity":"ea00f4c4-c39a-435d-a9db-98c8e91fa4d5","order_by":0,"name":"Balamurugan A.G","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYFACHgaGDwwMMkAWGxAzgxFBLYwzgCQPSVqYeVC0EAL8/WuPbrZts+GxZ29+9oChwjqxgZ33AF4tEjfepd3ObUvj4eE5Zm7AcCY9sYGZLwG/NTfOmN3OOXOYh0cih02Cse0wUAuPAV4d8iAtFmf+8/DIvwFq+UeEFoPzPWa3GSoOAG3hAWppIEKL4Q2+tJs9Fck8PGfSzCQSjqUbtxHSInf+7LEbPwzs5NjbDz+T+FBjLdvPfwa/FgaJBCQOiM2GXz0Q8B8gqGQUjIJRMApGOgAAtyw+rdsZfMsAAAAASUVORK5CYII=","orcid":"","institution":"Vel Tech Rangarajan Dr. Sagunthala R\u0026D Institute of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Balamurugan","middleName":"","lastName":"A.G","suffix":""},{"id":302676237,"identity":"37474d07-d49d-43f6-b82e-8aa961f2e675","order_by":1,"name":"Gomathi N","email":"","orcid":"","institution":"Vel Tech Rangarajan Dr. Sagunthala R\u0026D Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Gomathi","middleName":"","lastName":"N","suffix":""}],"badges":[],"createdAt":"2024-05-05 09:59:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4371183/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4371183/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57180213,"identity":"77566f7d-255e-4b09-a9a8-2bda35d3958e","added_by":"auto","created_at":"2024-05-27 04:02:03","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1352336,"visible":true,"origin":"","legend":"","description":"","filename":"ResearchPaper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4371183/v1_covered_3079877f-4db3-413d-8f8b-6458280df98a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Predictive Sequence Filling Method using Protein Structures for Early Detection of Alzheimer Disease","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":false,"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":"AD, Neural Network, Protein Structure, Sequence Differentiation ","lastPublishedDoi":"10.21203/rs.3.rs-4371183/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4371183/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAlzheimer’s disease (AD) is a generic form of dementia causing memory loss and environmental responses. AD detection is pursued using the different protein structures and their intensity based on different physical behaviors. Using the time-series protein structures the detection and is eased through the proposed neural method for structural protein filling (NC-SSF). Structural differentiations are performed using the high and low intensity profiles observed. This analysis identifies the missing inputs and thereby the fillable sequences are identified. The protein biomarker determines the maximum filling requirement as per the changes observed. The neural network is trained using this sequence required under the low and high intensity variations. This process is recurrent until maximum false rate is confined through accuracy improvements. The AD progression detection is performed by estimating the intensity under different profile filling levels. \u0026nbsp;The proposed method improves accuracy, sensitivity, and specificity by 8.74%, 10.29%, and 8.84% respectively. 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