DeepBeta: A Deep Learning Framework for Nonlinear Feature Fusion in Microbial Beta Diversity Analysis

DeepBeta: A Deep Learning Framework for Nonlinear Feature Fusion in Microbial Beta Diversity Analysis | 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 DeepBeta: A Deep Learning Framework for Nonlinear Feature Fusion in Microbial Beta Diversity Analysis uzma Uzma, Dominic Quinn, Marta Vignola, Jeanine Lenselink, Cindy J Smith, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9072949/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 beta diversity is fundamental to assessing how ecological communities respond to environmental change. However, traditional linear methods often fail to capture the high-dimensional, non-linear complexities inherent in multi-source microbial data. Here, we introduce DeepBeta, an algorithmic framework for non-linear information fusionleveraging deep undercomplete autoencoders. By integrating and compressing ecological dissimilarity matrices through a constrained neural bottleneck, DeepBeta filters stochastic noise and fuses latent features that conventional methods overlook. We evaluate the fusion performance across bacterial, eukaryotic, and integrated datasets. Results demonstrate that DeepBeta consistently extracts a higher density of information, identifying stronger community separation across temporal and depth gradients compared to standard approaches. By providing superior algorithmic resolution for detecting subtle shifts, DeepBeta offers a robust, integrative fusion solution for analyzing complex high-dimensional systems. This framework establishes a new benchmark for representation learning in microbial biogeography and the analysis of community assembly. Biological sciences/Computational biology and bioinformatics/Data mining Biological sciences/Computational biology and bioinformatics/Computational models Information Fusion Deep Learning Nonlinear Representation Learning Deep Autoencoders Feature Extraction Beta Diversity Microbial Community Analysis 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. We do this by developing innovative software and high quality services for the global research community. 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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-9072949","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":603779123,"identity":"e6a8cd03-8ad5-4db3-81a3-838439753999","order_by":0,"name":"uzma Uzma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFElEQVRIie2RMUvEMBiGv1DQ5eoc8aR/4SuFqlDs6N9oEJx6IriceOBNutT9RNC/ECncHCnUwZSuhVt07xDhhi6C5hBxiZ6bYJ7hGwIP7xteAIvlzyIAEYA8wdGXR7qE4iDgL5UVupSC9SNTICHYWq3Kkznuxt5ehtCNgF2NDYqocgoNhDvZ4cGsj/vsTkokWQns2pRyf8kpKIhQpOGMopP4kwEHdwzsxqQUbt4tlLoNjymexVohr98ppTtdFMMmDYnCgtzSAXd0iqnYunSn24mkATZtsAH4wHivVkW/pIHp+2t1lTeqjHxep/5LNzyNvYtz9tyOos2JMMRoko8NnJ6uKvROPwz5Cenej2foY7FYLP+XN/bHXbJ3WrYPAAAAAElFTkSuQmCC","orcid":"","institution":"University of Glasgow","correspondingAuthor":true,"prefix":"","firstName":"uzma","middleName":"","lastName":"Uzma","suffix":""},{"id":603779124,"identity":"d7ecf7d8-951a-4e94-b138-590220ece14a","order_by":1,"name":"Dominic Quinn","email":"","orcid":"","institution":"University of Glasgow","correspondingAuthor":false,"prefix":"","firstName":"Dominic","middleName":"","lastName":"Quinn","suffix":""},{"id":603779125,"identity":"b9ccac03-18ba-474b-a2f0-db1514e42ae0","order_by":2,"name":"Marta Vignola","email":"","orcid":"","institution":"University of Glasgow","correspondingAuthor":false,"prefix":"","firstName":"Marta","middleName":"","lastName":"Vignola","suffix":""},{"id":603779126,"identity":"296af995-cdf1-4769-885f-cfc19c3ed493","order_by":3,"name":"Jeanine Lenselink","email":"","orcid":"","institution":"University of Glasgow","correspondingAuthor":false,"prefix":"","firstName":"Jeanine","middleName":"","lastName":"Lenselink","suffix":""},{"id":603779127,"identity":"f68264c6-274f-4e61-8ee1-1472c95768ba","order_by":4,"name":"Cindy J Smith","email":"","orcid":"","institution":"University of Glasgow","correspondingAuthor":false,"prefix":"","firstName":"Cindy","middleName":"J","lastName":"Smith","suffix":""},{"id":603779128,"identity":"c80a9099-0ef9-4c8c-ade5-d420af894441","order_by":5,"name":"William Sloan","email":"","orcid":"","institution":"University of Glasgow","correspondingAuthor":false,"prefix":"","firstName":"William","middleName":"","lastName":"Sloan","suffix":""}],"badges":[],"createdAt":"2026-03-09 12:35:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9072949/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9072949/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105034147,"identity":"61e50125-8b7a-4539-a355-dc5dbd1f4d7a","added_by":"auto","created_at":"2026-03-20 07:22:45","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":801221,"visible":true,"origin":"","legend":"Article File","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9072949/v1_covered_793b2f7d-acd7-4b13-a0e0-6080b36db14e.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"DeepBeta: A Deep Learning Framework for Nonlinear Feature Fusion in Microbial Beta Diversity Analysis","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":"Information Fusion, Deep Learning, Nonlinear Representation Learning, Deep Autoencoders, Feature Extraction, Beta Diversity, Microbial Community Analysis","lastPublishedDoi":"10.21203/rs.3.rs-9072949/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9072949/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Understanding beta diversity is fundamental to assessing how ecological communities respond to environmental change. 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