Re-analysis of non-small cell lung cancer and drug resistance microarray datasets with machine learning

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dc.authoridYalçın Özkan / 0000-0001-9922-6592en_US
dc.authorscopusidYalçın Özkan / 57205355640en_US
dc.authorwosidYalçın Özkan / ABE-4721-2021en_US
dc.contributor.authorErol, Çiğdem
dc.contributor.authorBawa, Tchare Adnaane
dc.contributor.authorÖzkan, Yalçın
dc.date.accessioned2023-05-22T12:19:31Z
dc.date.available2023-05-22T12:19:31Z
dc.date.issued2023en_US
dc.departmentİstinye Üniversitesi, İktisadi, İdari ve Sosyal Bilimler Fakültesi, Yönetim Bilişim Sistemleri Bölümüen_US
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, naive 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.en_US
dc.identifier.citationErol, Ç., Bawa, T. A., & Özkan, Y. (2023). Re-Analysis of Non-Small Cell Lung Cancer and Drug Resistance Microarray Datasets with Machine Learning. Cybernetics and Systems, 1-12.en_US
dc.identifier.doi10.1080/01969722.2023.2166251en_US
dc.identifier.issn0196-9722en_US
dc.identifier.issn1087-6553en_US
dc.identifier.scopus2-s2.0-85147566647en_US
dc.identifier.urihttp://dx.doi.org/10.1080/01969722.2023.2166251
dc.identifier.urihttps://hdl.handle.net/20.500.12713/3917
dc.identifier.wosWOS:000924744500001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.institutionauthorÖzkan, Yalçın
dc.language.isoenen_US
dc.publisherTaylor & Francis Incen_US
dc.relation.ispartofCybernetics and Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMachine Learningen_US
dc.subjectMicroarrayen_US
dc.subjectNon-Small Cell Lung Canceren_US
dc.subjectNSCLCen_US
dc.subjectReanalysis of Dataseten_US
dc.titleRe-analysis of non-small cell lung cancer and drug resistance microarray datasets with machine learningen_US
dc.typeArticleen_US

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