PCAGroupAdam: A PCA-Based Deep Learning Framework with Custom Optimization for Cancer Biomarker Discovery and Classification in High-Dimensional Gene Expression Data

PCAGroupAdam: A PCA-Based Deep Learning Framework with Custom Optimization for Cancer Biomarker Discovery and Classification in High-Dimensional 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 Article PCAGroupAdam: A PCA-Based Deep Learning Framework with Custom Optimization for Cancer Biomarker Discovery and Classification in High-Dimensional Gene Expression Data Ahmet Emir Şaşmazlar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5810219/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 High-dimensional gene expression datasets present unique challenges in cancer biomarker discovery and classification. Here, we propose a novel deep learning framework incorporating principal component analysis (PCA) for dimensionality reduction and a custom optimizer, PCAGroupAdam, for effective gradient scaling. The framework was tested on multiple gene expression datasets, achieving superior classification performance compared to traditional optimizers (Adam, RMSprop, SGD). Key findings include the identification of biologically relevant genes such as AGR2, TSPAN8, and GAPDH, which were linked to cancer progression using SHAP analysis and validated through functional annotation (GO/KEGG) and STRING protein-protein interaction analysis and an unknown functioned lncRNA found to be correlated with breast cancer. Our approach demonstrates strong performance like high accuracy, f1 scores and significantly reduced loss values, interpretable results, and scalability to various high-dimensional omics datasets. Biological sciences/Computational biology and bioinformatics Biological sciences/Computational biology and bioinformatics/Machine learning Biological sciences/Cancer/Tumour biomarkers Biological sciences/Cancer/Cancer genetics 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-5810219","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":408080040,"identity":"45e1af82-59eb-4c26-9015-0df54ec40c5c","order_by":0,"name":"Ahmet Emir Şaşmazlar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYFACxgYogxnIqADRzA04FaNpATHOgLTARYixjrENxRDsgF/scNvHHzV10fz9Bxs/V86rjeZvB2r5UbENpxbJ2YnNs3mOHc6dceBgs+TZbcdzZxxmbGDsOXMbpxaD24nNzAxsB3IbDjY2SDZuO5bbANTCzNiGW4s9UAvjj391ufMPMzb/bJxzDMTAr8VAOrGZgbeNOXfDMcY2ycaGmtwNhLRIgBzG23c4d+MZxjbLhmMHcjcCtRzE5xf+2emPGX98q8udd/7w4ZsNNWDGwQc/KnBrQQeHweQBotUDQR0pikfBKBgFo2CEAAAwl2ILC9TlnAAAAABJRU5ErkJggg==","orcid":"","institution":"Bahcesehir Science and Technology High School","correspondingAuthor":true,"prefix":"","firstName":"Ahmet","middleName":"Emir","lastName":"Şaşmazlar","suffix":""}],"badges":[],"createdAt":"2025-01-11 15:38:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5810219/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5810219/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74985061,"identity":"9651d1f5-46be-4075-b15f-87b7bf06ef9b","added_by":"auto","created_at":"2025-01-29 05:59:46","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1198360,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5810219/v1_covered_7a65d104-ee00-49e0-a36e-242c7cc108bd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"PCAGroupAdam: A PCA-Based Deep Learning Framework with Custom Optimization for Cancer Biomarker Discovery and Classification in High-Dimensional Gene Expression Data","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":"","lastPublishedDoi":"10.21203/rs.3.rs-5810219/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5810219/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"High-dimensional gene expression datasets present unique challenges in cancer biomarker discovery and classification. 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