Hybrid Deep Learning–Based Rapid Broad-Spectrum Antimicrobial Susceptibility Testing from Whole-Genome Assemblies

Hybrid Deep Learning–Based Rapid Broad-Spectrum Antimicrobial Susceptibility Testing from Whole-Genome Assemblies | 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 Hybrid Deep Learning–Based Rapid Broad-Spectrum Antimicrobial Susceptibility Testing from Whole-Genome Assemblies Linda Osaghale, Abeni Beshiru, Eman Abid Fahad Alhasnawi, David Kanzin, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8779521/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract Antimicrobial resistance (AMR) is a global public health threat. Mortality and poor treatment outcomes are the key consequences of AMR. Conventional antimicrobial susceptibility testing (AST) is slow, limited in coverage, and dependent on laboratory infrastructure, creating delays in clinical decision-making. In this study, we developed a hybrid deep learning model for broad-spectrum antimicrobial resistance prediction by analyzing 699 bacterial genome assemblies and paired antimicrobial susceptibility outcomes across 22 antibiotics. Genome assemblies were encoded using 6-mer frequency and antimicrobial susceptibility phenotypes were engineered into genome–antibiotic pairs for binary prediction. The proposed model integrates convolutional neural networks (CNNs) for local sequence feature extraction, bidirectional long short-term memory (BiLSTM) networks to capture long-range genomic dependencies, and an attention mechanism to improve interpretability. Model evaluation achieved an accuracy of 0.772 and AUROC of 0.77 at a resistance decision threshold of 0.55, with balanced accuracy of 0.697 and AUPRC of 0.489. The results demonstrate variable predictive performance across antibiotics and organism groups. This study demonstrates that a hybrid CNN–BiLSTM–Attention model can rapidly predict antimicrobial resistance from genome-derived k-mer features while incorporating organism and antibiotic metadata for broad-spectrum AST prediction. This framework offers a scalable way to predict susceptibility from genome data and can help advance the development of AMR decision-support tools for clinical use. Antimicrobial resistance machine learning deep learning CNN LSTM attention whole-genome sequencing multilabel prediction rapid AST Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions 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-8779521","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":586253128,"identity":"7bbc1e6f-310a-49c9-a308-4ae4260f3e15","order_by":0,"name":"Linda Osaghale","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYBACPmYGBgkwS4KB8QBDBZDBzNyAVwsbkhaGAwxnQFoYCWhhQNbC2AZiEdLCznvwxo+KOnn56OYHBz7Oq43mbwdq+VGxDY/D+JIte84cNtx455jBwZnbjufOOMzYwNhz5jYeLTxmErxtBxg3zkgwOMy77VhuA1ALM2Mbfi2Sf9vq7DfOSP9w+O+cY7nzidEizdvGnDhfIscAqLgmdwMRWoytZc4cTt4gkVNwsOfYgdyNQC0H8fmFn/+M4c03FXW282ekb3zwo6Yud975wwcf/KjArQUODA6AqcNg8gBh9UAg3wCm6ohSPApGwSgYBSMLAAA8ZVyNO8y2pgAAAABJRU5ErkJggg==","orcid":"","institution":"University of Ibadan","correspondingAuthor":true,"prefix":"","firstName":"Linda","middleName":"","lastName":"Osaghale","suffix":""},{"id":586253129,"identity":"bdf3d0ac-27fb-4fa0-bb16-ae71c8073bc2","order_by":1,"name":"Abeni Beshiru","email":"","orcid":"","institution":"German Federal Institute for Risk Assessment","correspondingAuthor":false,"prefix":"","firstName":"Abeni","middleName":"","lastName":"Beshiru","suffix":""},{"id":586253130,"identity":"3f77b31c-4a08-4bbf-9ae2-9baf0de00da8","order_by":2,"name":"Eman Abid Fahad Alhasnawi","email":"","orcid":"","institution":"Middle Technical University","correspondingAuthor":false,"prefix":"","firstName":"Eman","middleName":"Abid Fahad","lastName":"Alhasnawi","suffix":""},{"id":586253131,"identity":"90754d47-f302-47b9-bb3d-cd1654cb94c4","order_by":3,"name":"David Kanzin","email":"","orcid":"","institution":"University of Texas","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Kanzin","suffix":""},{"id":586253132,"identity":"ad23b67e-df8e-4972-b4ce-f7ac86e82667","order_by":4,"name":"Bolaji Olalere","email":"","orcid":"","institution":"West Georgia Technical College","correspondingAuthor":false,"prefix":"","firstName":"Bolaji","middleName":"","lastName":"Olalere","suffix":""}],"badges":[],"createdAt":"2026-02-03 19:38:35","currentVersionCode":2,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8779521/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-8779521/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[],"financialInterests":"The authors declare no competing interests.","formattedTitle":"Hybrid Deep Learning–Based Rapid Broad-Spectrum Antimicrobial Susceptibility Testing from Whole-Genome Assemblies","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":"Antimicrobial resistance, machine learning, deep learning, CNN, LSTM, attention, whole-genome sequencing, multilabel prediction, rapid AST","lastPublishedDoi":"10.21203/rs.3.rs-8779521/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8779521/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAntimicrobial resistance (AMR) is a global public health threat. 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