Optimized Gene Selection Model for Accurate Classification of Microarray Gene Expression Data

Optimized Gene Selection Model for Accurate Classification of Microarray Gene Expression Data | 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 Optimized Gene Selection Model for Accurate Classification of Microarray Gene Expression Data Abrar Yaqoob, Nasreena Bashir, Mushtaq Ahmad mir, R. Vijaya Lakshmi, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6388180/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 In the realm of gene expression analysis, isolating significant genes from high-dimensional biological datasets remains a critical challenge, often hindered by redundancy and the presence of irrelevant features. To address this, we propose a novel hybrid gene selection algorithm that integrates Harris Hawk Optimization (HHO) with Cuckoo Search Algorithm (CSA), termed HHOCSA, synergized with the Support Vector Machine (SVM) classifier for the effective classification of biomedical data. This method is evaluated on the benchmark Lung Cancer dataset, demonstrating its superiority over traditional feature selection (FS) methods. The HHOCSA algorithm effectively identifies relevant gene subsets by leveraging a hybrid strategy, achieving notable improvements in classification accuracy, specificity, and sensitivity. Experimental results reveal that HHOCSA achieves mean classification accuracies of 100%, 96.70%, and 99.39% in Experiments 1, 2, and 3, respectively, outperforming mRMR, mRMR + HHO, and mRMR + CSA in all tested scenarios. The findings underscore the robustness and efficiency of HHOCSA in handling high-dimensional data, making it a valuable tool for bioinformatics and biotechnology researchers engaged in gene selection and classification tasks. Metaheuristic Algorithm Harris Hawk Optimization Cuckoo Search Algorithm Feature Selection Biomedical Data 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-6388180","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":451619390,"identity":"61f4c56a-cc0a-47ed-aa31-ce3af9c973cf","order_by":0,"name":"Abrar Yaqoob","email":"","orcid":"","institution":"VIT Bhopal University's School of Advanced Science and Language, located at Kothrikalan","correspondingAuthor":false,"prefix":"","firstName":"Abrar","middleName":"","lastName":"Yaqoob","suffix":""},{"id":451619391,"identity":"5fc0bb78-9cee-440a-9859-9c2474836c41","order_by":1,"name":"Nasreena Bashir","email":"","orcid":"","institution":"King Khalid University","correspondingAuthor":false,"prefix":"","firstName":"Nasreena","middleName":"","lastName":"Bashir","suffix":""},{"id":451619395,"identity":"d87c1079-6344-4e69-9eb2-063a555bfbf3","order_by":2,"name":"Mushtaq Ahmad mir","email":"","orcid":"","institution":"King Khalid University","correspondingAuthor":false,"prefix":"","firstName":"Mushtaq","middleName":"Ahmad","lastName":"mir","suffix":""},{"id":451619396,"identity":"1cf7d063-cb0b-4388-9938-85a1c369320d","order_by":3,"name":"R. Vijaya Lakshmi","email":"","orcid":"","institution":"ICFAI University","correspondingAuthor":false,"prefix":"","firstName":"R.","middleName":"Vijaya","lastName":"Lakshmi","suffix":""},{"id":451619398,"identity":"5c4d5f23-da87-4658-a4d3-f09f2e70d9f4","order_by":4,"name":"Tejaswini Pradhan","email":"","orcid":"","institution":"Kalinga University","correspondingAuthor":false,"prefix":"","firstName":"Tejaswini","middleName":"","lastName":"Pradhan","suffix":""},{"id":451619399,"identity":"749efa9b-887b-406c-985d-35e325bb631d","order_by":5,"name":"GVV Jagannadha Rao","email":"","orcid":"","institution":"Kalinga University","correspondingAuthor":false,"prefix":"","firstName":"GVV","middleName":"Jagannadha","lastName":"Rao","suffix":""},{"id":451619400,"identity":"3a351a22-4f8a-4250-b27f-579cfa78bc01","order_by":6,"name":"Ghanshyam G. Tejani","email":"","orcid":"","institution":"Jadara University","correspondingAuthor":false,"prefix":"","firstName":"Ghanshyam","middleName":"G.","lastName":"Tejani","suffix":""},{"id":451619401,"identity":"d13febe6-0444-43f7-91d0-739a48e75e2d","order_by":7,"name":"Nashwa Hassan Babiker Eisa","email":"","orcid":"","institution":"King Khalid University","correspondingAuthor":false,"prefix":"","firstName":"Nashwa","middleName":"Hassan Babiker","lastName":"Eisa","suffix":""},{"id":451619402,"identity":"0e5c6f2d-ac26-4b0f-a93e-03cbfd6d3ee5","order_by":8,"name":"Hind Mamoun Hussien Osman","email":"","orcid":"","institution":"King Khalid University","correspondingAuthor":false,"prefix":"","firstName":"Hind","middleName":"Mamoun Hussien","lastName":"Osman","suffix":""},{"id":451619403,"identity":"b4a4426c-8d7b-47b9-a461-2d595406c5c0","order_by":9,"name":"Mohd Asif Shah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIie3OMQrCMBSA4SeFujxwFUrxBEJKIA4GzxIRMiuCOHZzc1YEz9DiBQIZugQ9QBfFC1RcBAeNowgNOjnkhyQk8PEC4PP9a4JzYo/XgkZqt8pNpHwjjZV7jNRfkG4anKujONDWRufXMfA4U8EprSNMhawtRMnaezmNViBppsLEQYDB8FZyMEgiBD3MFPSO9aR5rYTY845Bekd4WNK8OKYgsR9TjBhkdoqyBB0f0zizZEQTE876SEZ0rXFST4rF7nITg2Rrgl2J80G8LBZ5LYHg7UY+Xnw+n8/3S08ubkrTnLI70AAAAABJRU5ErkJggg==","orcid":"","institution":"Kardan University","correspondingAuthor":true,"prefix":"","firstName":"Mohd","middleName":"Asif","lastName":"Shah","suffix":""}],"badges":[],"createdAt":"2025-04-06 18:08:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6388180/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6388180/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82191569,"identity":"ea3410ce-e2ed-400b-8672-794544542606","added_by":"auto","created_at":"2025-05-07 14:17:10","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":714343,"visible":true,"origin":"","legend":"","description":"","filename":"GeneSelectionforHighDimensionalBiologicalDatasetsUsingHybridHarrisHawkoptimizationandCuckooSearchAlgorithm.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6388180/v1_covered_28414785-cf79-4809-87e6-4428aab1fc26.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimized Gene Selection Model for Accurate Classification of Microarray Gene Expression Data","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":"Metaheuristic Algorithm, Harris Hawk Optimization, Cuckoo Search Algorithm, Feature Selection, Biomedical Data","lastPublishedDoi":"10.21203/rs.3.rs-6388180/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6388180/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the realm of gene expression analysis, isolating significant genes from high-dimensional biological datasets remains a critical challenge, often hindered by redundancy and the presence of irrelevant features. To address this, we propose a novel hybrid gene selection algorithm that integrates Harris Hawk Optimization (HHO) with Cuckoo Search Algorithm (CSA), termed HHOCSA, synergized with the Support Vector Machine (SVM) classifier for the effective classification of biomedical data. This method is evaluated on the benchmark Lung Cancer dataset, demonstrating its superiority over traditional feature selection (FS) methods. The HHOCSA algorithm effectively identifies relevant gene subsets by leveraging a hybrid strategy, achieving notable improvements in classification accuracy, specificity, and sensitivity. Experimental results reveal that HHOCSA achieves mean classification accuracies of 100%, 96.70%, and 99.39% in Experiments 1, 2, and 3, respectively, outperforming mRMR, mRMR\u0026thinsp;+\u0026thinsp;HHO, and mRMR\u0026thinsp;+\u0026thinsp;CSA in all tested scenarios. The findings underscore the robustness and efficiency of HHOCSA in handling high-dimensional data, making it a valuable tool for bioinformatics and biotechnology researchers engaged in gene selection and classification tasks.\u003c/p\u003e","manuscriptTitle":"Optimized Gene Selection Model for Accurate Classification of Microarray Gene Expression Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 11:21:54","doi":"10.21203/rs.3.rs-6388180/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"a59b29ce-add2-4441-865a-03f52eaf51a1","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-07T14:09:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-07 11:21:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6388180","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6388180","identity":"rs-6388180","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}