Integrated Metaheuristic-Deep Learning Framework for Cross-Disorder Genetic Analysis of Schizophrenia and Major Depressive Disorder

Integrated Metaheuristic-Deep Learning Framework for Cross-Disorder Genetic Analysis of Schizophrenia and Major Depressive Disorder | 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 Integrated Metaheuristic-Deep Learning Framework for Cross-Disorder Genetic Analysis of Schizophrenia and Major Depressive Disorder Ahmed Miloudi, Mohamed Chikri, Said Boujraf This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7237657/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 This comprehensive study presents an integrated metaheuristic-deep learning framework for cross-disorder analysis of schizophrenia (SCZ) and major depressive disorder (MDD) using genome-wide association studies (GWAS) data. Our methodology combines: (1) a multi-objective genetic algorithm with biological constraints for SNP selection, (2) a dual-task convolutional neural network for joint disorder prediction, and (3) multi-modal biological validation. The optimized model achieves 93.7% accuracy for SCZ (AUC=0.96) and 90.2% for MDD (AUC=0.93) with 300 training epochs, while identifying 1,842 shared risk SNPs showing significant enrichment in calcium signaling pathways ( \(p=3.2 \times 10^{-14}\) ). These findings establish a scalable framework for cross-disorder genetic analysis in psychiatry with detailed mathematical formulations of optimization objectives and network architecture specifications. Cross-disorder analysis Schizophrenia Major Depressive Disorder Deep Learning Genetic Algorithm GWAS Computational Psychiatry 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-7237657","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":494396107,"identity":"99143ec5-fc7c-4ef9-b183-7652a753cffc","order_by":0,"name":"Ahmed Miloudi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYDACCSDmASI+EDuhAipClBY2sJYzJGhhAGthbCNCC//s7sQHbxi2ybCx9xjeeDjvsL3u7OYHDD9qGKL5G3BYcufsZsM5DLd52HjOGFskbjucuO3OMQPGnmMMuTMO4LDmRu42aR6QFokcMwmglgSzGwkGDLwNDLkNOLTI38jd/huhZc5he7Mb6R8Y/wK1zMehxQBoCzNCS8Nhxm03cgyYQbZswKHF8EbuZsk5BiC/HCu2SDiWDvTLmYLDMsckcjfi0CJ3I3fjhzcVt+352Zs33vxRY21vdrt948M3NTa583B5H+I8NP4BIiJ0FIyCUTAKRgEeAAARrl2g5WLhbwAAAABJRU5ErkJggg==","orcid":"","institution":"Sidi Mohamed Ben Abdellah University","correspondingAuthor":true,"prefix":"","firstName":"Ahmed","middleName":"","lastName":"Miloudi","suffix":""},{"id":494396108,"identity":"c7d14196-c35c-4a93-9f37-4dc01948b3f0","order_by":1,"name":"Mohamed Chikri","email":"","orcid":"","institution":"Sidi Mohamed Ben Abdellah University","correspondingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"","lastName":"Chikri","suffix":""},{"id":494396109,"identity":"ee84ef20-bbb2-4b68-adcf-73a77a893a4c","order_by":2,"name":"Said Boujraf","email":"","orcid":"","institution":"Sidi Mohamed Ben Abdellah University","correspondingAuthor":false,"prefix":"","firstName":"Said","middleName":"","lastName":"Boujraf","suffix":""}],"badges":[],"createdAt":"2025-07-29 00:23:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7237657/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7237657/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100280621,"identity":"c3345f50-d4ae-4abb-be3a-a3c3d2b795a0","added_by":"auto","created_at":"2026-01-15 02:09:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":296016,"visible":true,"origin":"","legend":"","description":"","filename":"SpringerNatureLaTeXTemplatemiloudi1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7237657/v1_covered_e02a6ade-881e-4935-852f-a858018b29df.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrated Metaheuristic-Deep Learning Framework for Cross-Disorder Genetic Analysis of Schizophrenia and Major Depressive Disorder","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":"Cross-disorder analysis, Schizophrenia, Major Depressive Disorder, Deep Learning, Genetic Algorithm, GWAS, Computational Psychiatry","lastPublishedDoi":"10.21203/rs.3.rs-7237657/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7237657/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e This comprehensive study presents an integrated metaheuristic-deep learning framework for cross-disorder analysis of schizophrenia (SCZ) and major depressive disorder (MDD) using genome-wide association studies (GWAS) data. 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