EN
TR
MULTILAYER ANALYSIS OF NICOTINE-INDUCED GENE EXPRESSION ALTERATIONS IN BREAST CANCER CELLS USING CLUSTERING AND SUPERVISED LEARNING METHODS
Abstract
Nicotine is known not only for its addictive properties but also for its potential to alter the genetic structure of cancer cells. This study investigates the genetic alterations caused by chronic nicotine exposure in HCC38 breast cancer cells through a multi-layered computational analysis. Using K-means clustering and seven supervised machine learning algorithms, differentially expressed genes were grouped into three clusters and used as class labels in classification models. Logistic Regression achieved the highest performance with 98.76% accuracy and an F1 score of 0.9869. The Friedman test was applied to evaluate the statistical significance of performance differences among classifiers, and multi-class ROC curves were used to demonstrate their discriminative power. The findings indicate that nicotine exposure leads to genetic reprogramming and activates inflammation-related gene pathways in specific cellular subpopulations. These results highlight the utility of machine learning methods in uncovering biologically meaningful gene expression patterns in cancer research.
Keywords
References
- Ali, F., Kwak, K.-S., & Kim, Y.-G. (2016). Opinion mining based on fuzzy domain ontology and Support Vector Machine: A proposal to automate online review classification. Applied Soft Computing, 47, 235-250. https://doi.org/10.1016/j.asoc.2016.06.003
- Altman, N. S. (1992). An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician, 46(3), 175-185.
- Amasyali, M. F., & Ersoy, O. (2008). The performance factors of clustering ensembles. 2008 IEEE 16th Signal Processing, Communication and Applications Conference,
- Arlot, S., & Celisse, A. (2010). A survey of cross-validation procedures for model selection. https://doi.org/10.1214/09-SS054
- Arslan, R. U., Yapıcı, İ. Ş., & Erkaymaz, O. (2024). Diyabet risk durumunun belirlenmesinde siniflandirma algoritmalarinin performanslarinin kapsamli bir şekilde karşilaştirilmasi. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 27(4), 1320-1333. https://doi.org/10.17780/ksujes.1465177
- Aydın, C. (2018). Makine öğrenmesi algoritmaları kullanılarak itfaiye istasyonu ihtiyacının sınıflandırılması. Avrupa Bilim ve Teknoloji Dergisi(14), 169-175. https://doi.org/10.31590/ejosat.458613
- Başer, B. Ö., Yangın, M., & Sarıdaş, E. S. (2021). Makine öğrenmesi teknikleriyle diyabet hastalığının sınıflandırılması. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 25(1), 112-120. https://doi.org/10.19113/sdufenbed.842460
- Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
Details
Primary Language
English
Subjects
Artificial Intelligence (Other)
Journal Section
Research Article
Publication Date
September 3, 2025
Submission Date
July 1, 2025
Acceptance Date
August 15, 2025
Published in Issue
Year 1970 Volume: 28 Number: 3
APA
Alabed, T., & Servi, S. (2025). MULTILAYER ANALYSIS OF NICOTINE-INDUCED GENE EXPRESSION ALTERATIONS IN BREAST CANCER CELLS USING CLUSTERING AND SUPERVISED LEARNING METHODS. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(3), 1558-1573. https://doi.org/10.17780/ksujes.1730962
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