Kanser Sınıflandırmasını Geliştirme: RNA-Seq Gen EkspresyonuVerileri Üzerinde Makine Öğrenimi Uygulamaları

Öz

Bu çalışma, kanser sınıflandırmasını geliştirmek amacıyla RNA-seq gen ekspresyonu verilerinin makine öğrenimi teknikleri ile analizini ele almaktadır. UCI Machine Learning Repository'den elde edilen veri seti, farklı kanser türlerine ait gen ifade seviyelerini içermektedir. Bu veriler, makine öğrenimi algoritmalarının kanser teşhisi ve sınıflandırmasındaki etkinliğini değerlendirmek için kullanılmaktadır.

Anahtar Kelimeler

• Kanser
• RNA Sekanslanma
• Veri Analizi
• Makine Öğrenimi

Advancing Cancer Classification: Machine Learning Applications on RNA-Seq Gene Expression Data

Öz

This study addresses the analysis of RNA-seq gene expression data using machine learning techniques to improve cancer classification. The dataset obtained from the UCI Machine Learning Repository contains gene expression levels for different types of cancer. These data are used to evaluate the effectiveness of machine learning algorithms in cancer diagnosis and classification.

Anahtar Kelimeler

• Cancer
• RNA Sequencing
• Data Analysis
• Machine Learning

Kaynakça

1. Ackerknecht, E. H. (1953). Rudolf Virchow: Doctor, statesman, anthropologist. The University of Chicago Press.
2. Aerts, H. J. W. L., et al. (2014). "Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach." Nature Communications, 5, 4006. doi:10.1038/ncomms5006.
3. Ardila, D., Kiraly, A. P., Bharadwaj, S., Choi, B., Reicher, J. J., Peng, L., ... & Corrado, G. (2019). "End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography." Nature Medicine, 25(6), 954-961.
4. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding." arXiv preprint arXiv:1810.04805.
5. Donegan, W. L. (2005). "An overview of long-term survival in breast cancer." Cancer Journal (Sudbury, Mass.), 11(5), 365-373.
6. Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). "Dermatologist-level classification of skin cancer with deep neural networks." Nature, 542(7639), 115–118. doi:10.1038/nature21056.
7. Goertzel, B. (2014). Artificial General Intelligence: Concept, State of the Art, and Future Prospects. Springer.
8. Goodman, L. S., & Gilman, A. (1946). The Pharmacological Basis of Therapeutics. New York: Macmillan.

9. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. [ISBN: 978-0262035613].
10. Hajdu, S. I. (2011). "A note from history: landmarks in history of cancer, part 1." Cancer, 117(5), 1097-1102.
11. Jobin, A., Ienca, M., & Vayena, E. (2019). "The global landscape of AI ethics guidelines." Nature Machine Intelligence, 1(9), 389-399. doi:10.1038/s42256-019-0088-2.
12. Karpozilos, A., & Pavlidis, N. (2004). "The treatment of cancer in Greek antiquity." European Journal of Cancer, 40(14), 2033-2040.
13. Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2015). "Machine learning applications in cancer prognosis and prediction." Computational and Structural Biotechnology Journal, 13, 8–17. doi:10.1016/j.csbj.2014.11.005.
14. LeCun, Y., Bengio, Y., & Hinton, G. (2015). "Deep learning." Nature, 521(7553), 436–444. doi:10.1038/nature14539.
15. Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., ... & van der Laak, J. A. (2017). "A survey on deep learning in medical image analysis." Medical Image Analysis, 42, 60-88.
16. Liu, Y., Gadepalli, K., Norouzi, M., Dahl, G. E., Kohlberger, T., Boyko, A., ... & Stumpe, M. C. (2019). "Detecting cancer metastases on gigapixel pathology images." Medical Image Analysis, 50, 197-209.
17. McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., ... & Suleiman, S. (2020). "International evaluation of an AI system for breast cancer screening." Nature, 577(7788), 89-94.
18. Müller, H. (1981). "Cancer Treatment: From Ancient Times to the 20th Century." Journal of the History of Medicine and Allied Sciences.
19. Nie, K., Shi, L., Chen, Q., Hu, X., & Jabbour, S. K. (2021). "Machine Learning: Automated Analysis of Multimodal Imaging Data for Radiomics to Predict Treatment Response and Prognosis in Cancer." Cancers, 13(7), 1621. doi:10.3390/cancers13071621.
20. Russell, S., & Norvig, P. (2010). Artificial Intelligence: A Modern Approach (3rd ed.). Prentice Hall. [ISBN: 978-0136042594].
21. Silver, D., et al. (2016). "Mastering the game of Go with deep neural networks and tree search." Nature, 529(7587), 484-489. doi:10.1038/nature16961.
22. Sudhakar, A. (2009). "History of cancer, ancient and modern treatment methods." Journal of Cancer Science & Therapy, 1(2), 1-4.
23. Topol, E. J. (2019). Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books.
24. Tubiana, M. (2006). "Wilhelm Conrad Röntgen and the discovery of X-rays." Cancer Radiothérapie, 10(7-8), 475-482.
25. Virchow, R. (1858). Cellular Pathology as Based Upon Physiological and Pathological Histology. (Translated by Frank Chance). J.B. Lippincott & Co.
26. Wang, Z., Gerstein, M., & Snyder, M. (2009). "RNA-Seq: a revolutionary tool for transcriptomics." Nature Reviews Genetics, 10(1), 57-63.
27. World Health Organization (WHO). (2023). "Cancer Fact Sheet." Retrieved from https://www.who.int
28. Yu, K. H., Beam, A. L., & Kohane, I. S. (2018). "Artificial intelligence in healthcare." Nature Biomedical Engineering, 2(10), 719–731. doi:10.1038/s41551-018-0305-z.
29. Zhavoronkov, A., Ivanenkov, Y. A., Aliper, A., Veselov, M. S., Aladinskiy, V. A., Aladinskaya, A. V., ... & Aspuru-Guzik, A. (2020). "Artificial Intelligence for Drug Discovery, Biomarker Development, and Generation of Novel Chemistry." Cell, 180(4), 688-702. doi:10.1016/j.cell.2020.02.034.