HINN: Hierarchical Input Neural Network identifies multi-omics biomarker for cognitive decline

HINN: Hierarchical Input Neural Network identifies multi-omics biomarker for cognitive decline | 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 HINN: Hierarchical Input Neural Network identifies multi-omics biomarker for cognitive decline Yashu Vashishath, Sarah Beaver, Fahad Saeed, Serdar Bozdag This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7576397/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 Understanding complex diseases requires models that can integrate diverse layers of biological data while yielding insights that are biologically interpretable. Although multi-omics integration with machine learning (ML) has advanced disease prediction and biomarker discovery, most existing approaches overlook the hierarchical and regulatory relationships that connect these molecular layers. Here, we present the Hierarchical Input Neural Network (HINN), a deep learning framework that incorporates known cross-omics relationships directly into its architecture, capturing the flow of information from genomics to epigenomics, transcriptomics, and downstream biological processes. By embedding these relationships, HINN improves both predictive performance and biological interpretability. We applied HINN to blood-derived multi-omics data from individuals with Alzheimer’s disease or mild cognitive impairment to predict cognitive scores from standardized assessments. HINN outperformed both baseline and state-of-the-art models and pinpointed multi-omics biomarkers—including SNPs and promoter-region CpG sites in ATP6V1C1 and RCHY1 that were significantly correlated with plasma p-Tau181 levels. These features map to biologically relevant processes with potential implications for cognitive decline. Our findings demonstrate how combining deep learning with biological knowledge can uncover interpretable, blood-based biomarkers for cognitive decline due to complex diseases such as Alzheimer’s. All code and data are openly available at https://github.com/bozdaglab/HINN . Biological sciences/Computational biology and bioinformatics/Computational models Biological sciences/Neuroscience/Cognitive neuroscience/Intelligence Biological sciences/Computational biology and bioinformatics/Machine learning Biological sciences/Computational biology and bioinformatics/Data integration Cognitive decline Alzheimer’s disease multi-omics cognitive scores data integration biologically-related feature selection GWAS machine learning deep neural network Full Text Additional Declarations There is NO Competing Interest. 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. 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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-7576397","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":516056085,"identity":"491b2d02-89f6-42ae-8dd8-3f03bb05478e","order_by":0,"name":"Yashu Vashishath","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCklEQVRIiWNgGAWjYHACNgglwdjADKQS+CFcZqK1GCRINjAwNhCpBazMIMHgAAEtBsePP3vMw3BYXn52c/Pngpo/ecY3co8/YKiwTmzApeVMjrkxUIvhhjsH26RnHDMoNruRB1R9Jh23lgM5bNI8DGmMGyQS25h52AwSt93IMWxgbDuMW8v5589AWuznz0hs/szzzyBx8wyQln94tNxIMANqsUlsuJHYIM3bZpC4QQKkpQG3Fskbb8wk5xjYJG+4kdgmzdtnnDjjzBvDGQnH0o1xaeE7n/5M4k2FhO38GemPP/N8k0vsb88x+PChxloWlxaFA2DnoQsn4FAOAvK4zBoFo2AUjIJRAAcAtgJcxRxDDFsAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0009-9599-9411","institution":"University of North Texas","correspondingAuthor":true,"prefix":"","firstName":"Yashu","middleName":"","lastName":"Vashishath","suffix":""},{"id":516056086,"identity":"99a67143-4720-4a28-986a-dbb51eb8eacc","order_by":1,"name":"Sarah Beaver","email":"","orcid":"","institution":"University of North Texas","correspondingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"","lastName":"Beaver","suffix":""},{"id":516056087,"identity":"dc8537b1-59e7-410f-9a6f-afc3410995ff","order_by":2,"name":"Fahad Saeed","email":"","orcid":"","institution":"Florida International University","correspondingAuthor":false,"prefix":"","firstName":"Fahad","middleName":"","lastName":"Saeed","suffix":""},{"id":516056088,"identity":"3e2883d6-47f9-4005-8d6a-eeac9f0ed79e","order_by":3,"name":"Serdar Bozdag","email":"","orcid":"https://orcid.org/0000-0002-4813-4310","institution":"University of North Texas","correspondingAuthor":false,"prefix":"","firstName":"Serdar","middleName":"","lastName":"Bozdag","suffix":""}],"badges":[],"createdAt":"2025-09-09 18:21:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7576397/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7576397/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91504812,"identity":"ea28338e-510b-4906-a56b-302d903c8d0c","added_by":"auto","created_at":"2025-09-17 08:11:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2665594,"visible":true,"origin":"","legend":"Article File","description":"","filename":"HINNYV.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7576397/v1_covered_8a85d8c7-a5cf-4ced-9d26-e8b9303c1836.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"HINN: Hierarchical Input Neural Network identifies multi-omics biomarker for cognitive decline","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Cognitive decline, Alzheimer’s disease, multi-omics, cognitive scores, data integration, biologically-related feature selection, GWAS, machine learning, deep neural network","lastPublishedDoi":"10.21203/rs.3.rs-7576397/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7576397/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Understanding complex diseases requires models that can integrate diverse layers of biological data while yielding insights that are biologically interpretable. Although multi-omics integration with machine learning (ML) has advanced disease prediction and biomarker discovery, most existing approaches overlook the hierarchical and regulatory relationships that connect these molecular layers. Here, we present the Hierarchical Input Neural Network (HINN), a deep learning framework that incorporates known cross-omics relationships directly into its architecture, capturing the flow of information from genomics to epigenomics, transcriptomics, and downstream biological processes. By embedding these relationships, HINN improves both predictive performance and biological interpretability.\r\n\r\nWe applied HINN to blood-derived multi-omics data from individuals with Alzheimer’s disease or mild cognitive impairment to predict cognitive scores from standardized assessments. 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