Machine Learning Model-based Detection of Potential Genetic Markers Associated with the Diagnosis of Small-cell Lung Cancer

dc.contributor.authorSarihan, Mehmet Ediz
dc.contributor.authorKucukakcali, Zeynep
dc.contributor.authorTekedereli, Ibrahim
dc.date.accessioned2024-08-04T20:59:54Z
dc.date.available2024-08-04T20:59:54Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractBackground: Small-cell lung cancer (SCLC), which is in the category of intractable cancers, has a low survival rate. It is essential to understand the pathophysiological pathways underlying its development to create powerful treatment alternatives for the disease. Objectives: This study aimed to classify gene expression data from SCLC and normal lung tissue and identify the key genes responsible for SCLC. Methods: This study used microarray expression data obtained from SCLC tissue and normal lung tissue (adjacent tissue) from 18 patients. An Extreme Gradient Boosting (XGBoost) model was established for the classification by five-fold cross-validation. Accuracy (AC), balanced accuracy (BAC), sensitivity (Sens), specificity (Spec), positive predictive value (PPV), negative predictive value (NPV), and F1 scores were utilized for performance assessment. Results: AC, BAC, Sens, Spec, PPV, NPV, and F1 scores from the XGBoost model were 90%, 90%, 80%, 100%, 100%, 83.3%, and 88.9%, respectively. Based on variable importance values from the XGBoost, the HIST1H1E, C12orf56, DSTNP2, ADAMDEC1, and HMGB2 genes can be considered potential biomarkers for SCLC. Conclusion: A machine learning-based prediction method discovered genes that potentially serve as biomarkers for SCLC. After clinical confirmation of the acquired genes in the following medical study, their therapeutic use can be established in clinical practice.en_US
dc.identifier.doi10.32592/ircmj.2023.25.8.2618
dc.identifier.issn2074-1804
dc.identifier.issn2074-1812
dc.identifier.issue8en_US
dc.identifier.urihttps://doi.org/10.32592/ircmj.2023.25.8.2618
dc.identifier.urihttps://hdl.handle.net/11616/103635
dc.identifier.volume25en_US
dc.identifier.wosWOS:001099530800007en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherZamensalamati Publ Coen_US
dc.relation.ispartofIranian Red Crescent Medical Journalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectClassificationen_US
dc.subjectMachine learningen_US
dc.subjectPotential biomarkersen_US
dc.subjectSmall-cell lung canceren_US
dc.titleMachine Learning Model-based Detection of Potential Genetic Markers Associated with the Diagnosis of Small-cell Lung Canceren_US
dc.typeArticleen_US

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