An Intelligent Learning Approach for Identifying Genetic Disorder by Detecting Gene Expression Malfunctions

An Intelligent Learning Approach for Identifying Genetic Disorder by Detecting Gene Expression Malfunctions | 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 An Intelligent Learning Approach for Identifying Genetic Disorder by Detecting Gene Expression Malfunctions Vimala K, Shanthi K, Prema S This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6293957/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 Misregulation or gene mutations cause the gene to act differently from other patterns of genes, causing a person to inherit a particular disorder from their parents or through environmental changes. The early detection of disorders is made possible by gene expression analysis, which also provides a pathway for the patient’s diagnosis. This proposed work combines both filter and wrapper methodologies to analyze the differential expression of genes and yield the best classification for genetic diseases. Hybrid Ant Colony Optimization is applied for the optimization process that results in the best features. Feature selection starts with building similarity graphs, which aids in the subset gene selection process, Fisher Score is determined using mean, standard deviation, and class information. Next, the Pearson Correlation Coefficient is generated using the correlation of the gene information, which is estimated. The adjacency matrix is created using the correlation vector value as a base. The value of for r0 the threshold correlation vector is 0.32. Finally, the gene subset was extracted based on the highest significance. The proposed Forest Feedforward Neural Network and Forest Backpropagation Deep Neuro-Fuzzy Network algorithms are used for classification, and the outcomes of the methods are estimated based on their performance comparison, where the accuracy estimated as (91.09%,92.78%,93.06%). The performance of the proposed algorithm is more appropriate than the conventional algorithm for solving complex applications like gene classification because they need access to a massive amount of gene data. The Deep Neural Network algorithm's motivation is that, without being expressly stated, it finds features that correlate and then combines them to enhance faster learning. Biological sciences/Biotechnology Health sciences/Health care Physical sciences/Engineering Gene Expression Gene Expression Omnibus Z-Score Transform Hybrid Ant Colony Optimization Machine Learning Forest Feedforward Neural Network Forest Backpropagation Deep Neuro-Fuzzy Network algorithms Full Text Additional Declarations Competing interest reported. K.Vimala, Dr. K. Shanthi, Dr. S. Prema are employees of Sri Krishna College of Technology, have an interest in the submitted work. The views expressed in this article are those of the authors and do not necessarily reflect the views of the institution. 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. 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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-6293957","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":465604227,"identity":"71f5d1f3-e395-4cb8-a0b7-91286c2388b5","order_by":0,"name":"Vimala K","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIie3QMQqDMBSA4SeCHYq4xsE7RALSIfQsCYJncOggFHTTubcQhM4BQZeWro5OnYVC6dQ2FtvROHbIT8iDkA9CAHS6PwzXcuvldMD6HBjJuJSEyekmkohFREwEix9R5GZrPrAdEHJp+G0A6pXCTPtZsrcrxBoIgi6qkYCIlMLIsIKUiFlAg26VyofVXJIUzRHHtKsHewIlRTuS1yJyRDyFAEPUSCLUxJJkw3NEUBeF6IRDcqhVxDlX3XCnflE0/hDHWy9vs+ssmfreGb/KXHBfp9PpdPO9AURQSgnR/wEcAAAAAElFTkSuQmCC","orcid":"","institution":"Anna University, Chennai","correspondingAuthor":true,"prefix":"","firstName":"Vimala","middleName":"","lastName":"K","suffix":""},{"id":465604228,"identity":"718f1239-fd6b-4760-8d58-0b54a99cfe30","order_by":1,"name":"Shanthi K","email":"","orcid":"","institution":"Anna University, Chennai","correspondingAuthor":false,"prefix":"","firstName":"Shanthi","middleName":"","lastName":"K","suffix":""},{"id":465604239,"identity":"265b064c-666f-4ecd-83c9-5bbcb4deecf8","order_by":2,"name":"Prema S","email":"","orcid":"","institution":"Anna University, Chennai","correspondingAuthor":false,"prefix":"","firstName":"Prema","middleName":"","lastName":"S","suffix":""}],"badges":[],"createdAt":"2025-03-24 09:53:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6293957/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6293957/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90783530,"identity":"d15758d3-b991-4769-a831-66a4a127e2a6","added_by":"auto","created_at":"2025-09-08 06:18:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":632116,"visible":true,"origin":"","legend":"","description":"","filename":"K.Vimalarevised2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6293957/v1_covered_d3e2d9d4-9503-4057-9d2c-c9983022099f.pdf"}],"financialInterests":"Competing interest reported. 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