A Gene Selection Algorithm for Microarray Cancer Classification Using an improved Particle Swarm Optimization

A Gene Selection Algorithm for Microarray Cancer Classification Using an improved Particle Swarm Optimization | 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 A Gene Selection Algorithm for Microarray Cancer Classification Using an improved Particle Swarm Optimization Arfan Ali Nagra, Ali Haider Khan, Muhammad Abubakar, Muhammad Faheem, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4365273/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Aug, 2024 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Gene selection is an essential step for the classification of microarray cancer data. Gene expression cancer data (DNA microarray) facilitates computing the robust and concurrent expression of various genes. Particle swarm optimization (PSO) requires simple operators and less number of parameters for tuning the model in gene selection. The selection of a prognostic gene with small redundancy is a great challenge for the researcher as there are a few complications in PSO based selection method. In this research, a new variant of PSO (Self-inertia weight adaptive PSO) has been proposed. In the proposed algorithm, SIW-APSO-ELM is explored to achieve gene selection prediction accuracies. This novel algorithm establishes a balance between the exploitation and exploration capabilities of the improved inertia weight adaptive particle swarm optimization. The self-inertia weight adaptive particle swarm optimization (SIW-APSO) algorithm is employed for solution explorations. Each particle in the SIW-APSO increases its position and velocity iteratively through an evolutionary process. The extreme learning machine (ELM) has been designed for the selection procedure. The proposed method has been to identify several genes in the cancer dataset. The classification algorithm contains ELM, K- centroid nearest neighbor (KCNN), and support vector machine (SVM) to attain high forecast accuracy as compared to the start-of-the-art methods on microarray cancer datasets that show the effectiveness of the proposed method. Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Physical sciences/Mathematics and computing Microarray cancer improved PSO ELM SVM evolutionary algorithms. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 23 Aug, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 10 Jun, 2024 Reviews received at journal 08 Jun, 2024 Reviews received at journal 02 Jun, 2024 Reviewers agreed at journal 28 May, 2024 Reviewers agreed at journal 28 May, 2024 Reviewers agreed at journal 28 May, 2024 Reviewers invited by journal 27 May, 2024 Editor assigned by journal 23 May, 2024 Editor invited by journal 15 May, 2024 Submission checks completed at journal 15 May, 2024 First submitted to journal 03 May, 2024 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-4365273","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":306115450,"identity":"68ca1189-beb2-432c-b29a-156b425441ec","order_by":0,"name":"Arfan Ali Nagra","email":"","orcid":"","institution":"Lahore Garrison University","correspondingAuthor":false,"prefix":"","firstName":"Arfan","middleName":"Ali","lastName":"Nagra","suffix":""},{"id":306115451,"identity":"48782a4a-1406-4a33-a1df-86245a435e2d","order_by":1,"name":"Ali Haider Khan","email":"","orcid":"","institution":"Lahore Garrison University","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"Haider","lastName":"Khan","suffix":""},{"id":306115452,"identity":"6f545487-c0f2-408c-ade1-ed826dbc9beb","order_by":2,"name":"Muhammad Abubakar","email":"","orcid":"","institution":"Lahore Garrison University","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Abubakar","suffix":""},{"id":306115453,"identity":"d7473a74-3906-44db-ada4-68c570ceefe1","order_by":3,"name":"Muhammad Faheem","email":"","orcid":"","institution":"University of Vaasa","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Faheem","suffix":""},{"id":306115454,"identity":"00d09170-6845-4356-9237-42231d6debcd","order_by":4,"name":"Adil Rasool","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABE0lEQVRIie3QsUrDQBjA8S8cXJYLWS9UkldoKNRF8VUaCvoG4iDhQiAupS+gvoMuOnrhIC5H5wwOzZKpQqciKOJdKw7iB7o53H9IQsiP774AuFz/sqyQ27tPGgC5ewZmXwiUiN1nhB5bwr8IkxiZwCdh49+R8GIq1Nl5HoeEbZYv+ikPL6W3fK4gYcgMrjtR60aNojK4T2dtz/liQtLrCtIZdq42E7WgMrtRwR1na8VBAx0EFXgPiEi25D03hPXRmyGJBv/VkCNsytCSoiKW0AFrFR+aKcSQDCOp3aWY213o/mhPq+hWe2V0teBTjMSPJ10nNuaP+WXfrRoVxprU69XpwSG6/g95wlz4H4DL5XK5vvcBTrVko19L+JIAAAAASUVORK5CYII=","orcid":"","institution":"Bakhtar University","correspondingAuthor":true,"prefix":"","firstName":"Adil","middleName":"","lastName":"Rasool","suffix":""},{"id":306115455,"identity":"aab39a0c-b8fc-4f67-9643-c0a68f35c50e","order_by":5,"name":"Khalid Masood","email":"","orcid":"","institution":"Lahore Garrison University","correspondingAuthor":false,"prefix":"","firstName":"Khalid","middleName":"","lastName":"Masood","suffix":""},{"id":306115456,"identity":"7edae53a-0485-44e1-a233-7d0376833b4a","order_by":6,"name":"Muzammil Hussain","email":"","orcid":"","institution":"University of Central Punjab Pakistan","correspondingAuthor":false,"prefix":"","firstName":"Muzammil","middleName":"","lastName":"Hussain","suffix":""}],"badges":[],"createdAt":"2024-05-03 16:53:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4365273/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4365273/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-68744-6","type":"published","date":"2024-08-23T15:56:57+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":63300242,"identity":"165b5cf6-5510-4796-bf25-7ec744ff4540","added_by":"auto","created_at":"2024-08-26 16:13:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":543351,"visible":true,"origin":"","legend":"","description":"","filename":"GeneSelectionwithChanges.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4365273/v1_covered_6bb35868-47ae-4553-9f97-f5255adb6286.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Gene Selection Algorithm for Microarray Cancer Classification Using an improved Particle Swarm Optimization","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Microarray cancer, improved PSO, ELM, SVM, evolutionary algorithms.","lastPublishedDoi":"10.21203/rs.3.rs-4365273/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4365273/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGene selection is an essential step for the classification of microarray cancer data. Gene expression cancer data (DNA microarray) facilitates computing the robust and concurrent expression of various genes. Particle swarm optimization (PSO) requires simple operators and less number of parameters for tuning the model in gene selection. The selection of a prognostic gene with small redundancy is a great challenge for the researcher as there are a few complications in PSO based selection method. In this research, a new variant of PSO (Self-inertia weight adaptive PSO) has been proposed. In the proposed algorithm, SIW-APSO-ELM is explored to achieve gene selection prediction accuracies. This novel algorithm establishes a balance between the exploitation and exploration capabilities of the improved inertia weight adaptive particle swarm optimization. The self-inertia weight adaptive particle swarm optimization (SIW-APSO) algorithm is employed for solution explorations. Each particle in the SIW-APSO increases its position and velocity iteratively through an evolutionary process. The extreme learning machine (ELM) has been designed for the selection procedure. The proposed method has been to identify several genes in the cancer dataset. The classification algorithm contains ELM, K- centroid nearest neighbor (KCNN), and support vector machine (SVM) to attain high forecast accuracy as compared to the start-of-the-art methods on microarray cancer datasets that show the effectiveness of the proposed method.\u003c/p\u003e","manuscriptTitle":"A Gene Selection Algorithm for Microarray Cancer Classification Using an improved Particle Swarm Optimization","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-24 06:31:42","doi":"10.21203/rs.3.rs-4365273/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-10T05:15:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-08T22:41:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-03T00:47:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"10817324639776962912879186810218417489","date":"2024-05-28T09:39:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"157973713460939377604619636867027058617","date":"2024-05-28T08:32:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"65736688012346613190765333032104327726","date":"2024-05-28T08:17:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-28T00:55:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-23T22:29:42+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-05-15T11:38:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-15T05:26:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-05-03T16:51:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5232ce62-c4d5-4443-95ce-8deb276e879f","owner":[],"postedDate":"May 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":32332281,"name":"Biological sciences/Cancer"},{"id":32332282,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":32332283,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2024-08-26T16:03:23+00:00","versionOfRecord":{"articleIdentity":"rs-4365273","link":"https://doi.org/10.1038/s41598-024-68744-6","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-08-23 15:56:57","publishedOnDateReadable":"August 23rd, 2024"},"versionCreatedAt":"2024-05-24 06:31:42","video":"","vorDoi":"10.1038/s41598-024-68744-6","vorDoiUrl":"https://doi.org/10.1038/s41598-024-68744-6","workflowStages":[]},"version":"v1","identity":"rs-4365273","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4365273","identity":"rs-4365273","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}