Re-Analysis of Non-Small Cell Lung Cancer and Drug Resistance Microarray Datasets with Machine Learning

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dc.authorscopusidYalçın Özkan / 57205355640
dc.authorwosidYalçın Özkan / JRM-0365-2023
dc.contributor.authorErol, Çiğdem
dc.contributor.authorBawa, Tchare Adnaane
dc.contributor.authorÖzkan, Yalçın
dc.date.accessioned2025-04-18T09:57:49Z
dc.date.available2025-04-18T09:57:49Z
dc.date.issued2025
dc.departmentİstinye Üniversitesi, İktisadi, İdari ve Sosyal Bilimler Fakültesi, Yönetim Bilişim Sistemleri Bölümü
dc.description.abstractNon-small cell lung cancer is the most common type of lung cancer. Identification of genes associated with this disease may contribute to the treatment of the disease. Therefore, a lot of work is being done. In some of these studies, genetic data is obtained by microarray analysis and shared publicly in databases such as NCBI Gene Expression Omnibus. In today’s big data era, machine learning algorithms are frequently used to access valuable information from data stacks. Within the scope of this study, all (6 pieces) microarray datasets related to NSCLC and drug resistance in the NCBI GEO database were analyzed by R Studio. With support vector machine, k nearest neighbor, naïve Bayes, random forest, C5.0 decision tree, multilayer perceptron, and artificial neural network algorithms with principal component step, the datasets were analyzed separately and related genes were determined through the caret package, and the top 10 genes for each algorithm were given in the findings section in order of importance. In this resulting gene table, ELOVL7, HMGA2, SAT1, RRM1, IER3, SLC7A11, and U2AF1 genes are included in at least 2 different datasets. These identified genes are recommended to researchers working in a wet laboratory environment to be validated experimentally. © 2023 Taylor & Francis Group, LLC.
dc.description.sponsorshipThis work was supported by the Health Institutes of T\u00FCrkiye (T\u00DCSEB) [grant number 4590].
dc.identifier.citationErol, Ç., Bawa, T. A., & Özkan, Y. (2025). Re-analysis of non-small cell lung cancer and drug resistance microarray datasets with machine learning. Cybernetics and Systems, 56(1), 69-80.
dc.identifier.doi10.1080/01969722.2023.2166251
dc.identifier.endpage80
dc.identifier.issn01969722
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85147566647
dc.identifier.scopusqualityQ2
dc.identifier.startpage69
dc.identifier.urihttp://dx.doi.org/10.1080/01969722.2023.2166251
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6902
dc.identifier.volume56
dc.indekslendigikaynakScopus
dc.institutionauthorÖzkan, Yalçın
dc.institutionauthoridYalçın Özkan / 0000-0002-3551-7021
dc.language.isoen
dc.publisherTaylor and Francis Ltd.
dc.relation.ispartofCybernetics and Systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectMachine Learning
dc.subjectMicroarray
dc.subjectNon-Small Cell Lung Cancer
dc.subjectNSCLC
dc.subjectReanalysis of Dataset
dc.titleRe-Analysis of Non-Small Cell Lung Cancer and Drug Resistance Microarray Datasets with Machine Learning
dc.typeArticle

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