Integration of Deep Learning and Metaheuristics for Advanced RNA-Seq Data Analysis: A Rigorous Framework for Biomarker Discovery

Integration of Deep Learning and Metaheuristics for Advanced RNA-Seq Data Analysis: A Rigorous Framework for Biomarker Discovery | 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 Integration of Deep Learning and Metaheuristics for Advanced RNA-Seq Data Analysis: A Rigorous Framework for Biomarker Discovery Ahmed Miloudi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7303548/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 Background The analysis of high-throughput RNA sequencing (RNA-Seq) data is hindered by the challenge of simultaneously optimizing for predictive accuracy, feature parsimony, and biological relevance. Conventional statistical and machine learning methods often fail to address these competing objectives, struggling with the high dimensionality of transcriptomic data and the complex, non-linear interactions between genes. This research bridges deep learning architectures with chaotic metaheuristic optimization to resolve these critical limitations. Results We developed Neuro-MetaRNA, a novel hybrid framework integrating biological attention mechanisms with chaotic multi-objective optimization. Comprehensive benchmarking across TCGA ( 10,340 samples), GTEx ( 17,382 samples), and ENCODE ( 1,200 samples) datasets demonstrated a \(\:17.3\%\) mean improvement in classification accuracy (p; 0.001) and an unprecedented \(\:95.8\%\) feature reduction compared to state-of-the-art methods. The framework achieved a Pareto front with a hypervolume of 0.921, significantly outperforming standard optimizers like NSGA-II (0.782) and MOEA/D (0.801). Crucially, the resulting gene signatures showed \(\:92\%\) overlap with established cancer hallmark pathways. Conclusions Neuro-MetaRNA establishes a new paradigm for RNA-Seq analysis by effectively balancing the trade-offs between accuracy, model complexity, and interpretability. The integration of chaotic dynamics within a Pareto optimization framework proves highly effective for navigating complex biological search spaces. This work provides a powerful, validated tool for biomarker discovery and paves the way for extending these methods to single-cell and spatial transcriptomics. metaheuristics deep learning data analysis rna-seq biomarker discovery Full Text Additional Declarations The authors declare no competing interests. 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-7303548","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":496193597,"identity":"655e3b10-d985-4a09-aee4-87de1a58a945","order_by":0,"name":"Ahmed Miloudi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYDACCSDmASI+EDuhAipClBY2sJYzJGhhAGthbCNCC//s7sQHbxi2ybCx9xjeeDjvsL3u7OYHDD9qGKL5G3BYcufsZsM5DLd52HjOGFskbjucuO3OMQPGnmMMuTMO4LDmRu42aR6QFokcMwmglgSzGwkGDLwNDLkNOLTI38jd/huhZc5he7Mb6R8Y/wK1zMehxQBoCzNCS8Nhxm03cgyYQbZswKHF8EbuZsk5BiC/HCu2SDiWDvTLmYLDMsckcjfi0CJ3I3fjhzcVt+352Zs33vxRY21vdrt948M3NTa583B5H+I8NP4BIiJ0FIyCUTAKRgEeAAARrl2g5WLhbwAAAABJRU5ErkJggg==","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Ahmed","middleName":"","lastName":"Miloudi","suffix":""}],"badges":[],"createdAt":"2025-08-05 19:08:24","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7303548/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7303548/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88423624,"identity":"5e1fe488-8285-4123-8377-0b63058ea71a","added_by":"auto","created_at":"2025-08-06 09:40:19","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":925003,"visible":true,"origin":"","legend":"","description":"","filename":"Mthrstcrnaseq32.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7303548/v1_covered_4c1438e7-0c1e-4b9f-a62b-e0499b798652.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eIntegration of Deep Learning and Metaheuristics for Advanced RNA-Seq Data Analysis: A Rigorous Framework for Biomarker Discovery\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Sidi Mohamed Ben Abdellah University","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":"metaheuristics, deep learning, data analysis, rna-seq, biomarker discovery","lastPublishedDoi":"10.21203/rs.3.rs-7303548/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7303548/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis of high-throughput RNA sequencing (RNA-Seq) data is hindered by the challenge of simultaneously optimizing for predictive accuracy, feature parsimony, and biological relevance. Conventional statistical and machine learning methods often fail to address these competing objectives, struggling with the high dimensionality of transcriptomic data and the complex, non-linear interactions between genes. This research bridges deep learning architectures with chaotic metaheuristic optimization to resolve these critical limitations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe developed Neuro-MetaRNA, a novel hybrid framework integrating biological attention mechanisms with chaotic multi-objective optimization. Comprehensive benchmarking across TCGA ( 10,340 samples), GTEx ( 17,382 samples), and ENCODE ( 1,200 samples) datasets demonstrated a \\(\\:17.3\\%\\) mean improvement in classification accuracy (p; 0.001) and an unprecedented \\(\\:95.8\\%\\) feature reduction compared to state-of-the-art methods. The framework achieved a Pareto front with a hypervolume of 0.921, significantly outperforming standard optimizers like NSGA-II (0.782) and MOEA/D (0.801). Crucially, the resulting gene signatures showed \\(\\:92\\%\\) overlap with established cancer hallmark pathways.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNeuro-MetaRNA establishes a new paradigm for RNA-Seq analysis by effectively balancing the trade-offs between accuracy, model complexity, and interpretability. The integration of chaotic dynamics within a Pareto optimization framework proves highly effective for navigating complex biological search spaces. 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