PREDICTION OF TARGET DRUGS AND PATHWAYS FOR LUNG CANCER WITH MACHINE LEARNING METHODS USING GDSC DATA

Year 2023, , 729 - 736, 21.08.2023

Abdullah Tercan , Gıyasettin Özcan

https://doi.org/10.31796/ogummf.1248489

Abstract

In this study, lung cancer data is collected from literally cited GDSC dataset, and it was aimed to make predictions on the data using machine learning methods. For this purpose, target drug and target pathway estimates were made depending on the half-life of the drug dose. These two predictions are aimed to be used for disease prediction from a dataset called CTDBase, which is also cited in literature. Thus, it can be possible to predict relation between disease and the dose usage information of drugs. The estimation process was made using machine learning algorithms. In this process, coding was done with the Python programming language and machine learning tools of this language were used. According to the results obtained, it was concluded that the kNN algorithm with Neighborhood Components Analysis achieved efficient prediction performance in the GDSC dataset. For this reason, the kNN algorithm was analyzed in more detail with different k values. The estimation results obtained were in the range of 70% - 90%. These results show that machine learning algorithms have the potential to reveal unknown significant patterns in cancer drug data.

Keywords

Machine learning, GDSC2 dataset, Lung adenocarcinoma, Drug-target prediction, Target pathway prediction CTDBase dataset

GDSC VERİLERİNİ KULLANARAK YAPAY ÖĞRENME YÖNTEMLERİ İLE AKCİĞER KANSERİ İÇİN HEDEF İLAÇ VE YOLAK TAHMİNİ

Abstract

Bu çalışmada literatürde yer alan ve uluslararası alanda öneme sahip olan GDSC veri kümesinde yer alan akciğer kanseri verileri toplanmış, ve bu veriler üzerinde yapay öğrenme yöntemleri kullanarak tahmin yapmak hedeflenmiştir. Bu amaçla ilaç dozunun yarılanma süresine bağlı hedef ilaç ve hedef yolak tahminleri yapılmıştır. Elde edilen bu iki tahminin yine literatürde yer alan CTDBase isimli bir veri kümesinden hastalık tahmini için kullanılması amaçlanmıştır. Böylece ilaçların doz kullanım bilgilerinin hangi hastalıkla ilişkili olabileceği sayısal verilerden tahmin edilmeye çalışılmıştır. Yapılan tahmin işlemi makine öğrenmesi algoritmaları kullanılarak yapılmıştır. Bu süreçte Python programlama dili ile kodlama yapılmış ve bu dilin makine öğrenmesi araçlarından faydalanılmıştır. Elde edilen sonuçlara göre Neighborhood Components Analysis temelini kullanan kNN algoritmasının GDSC veri kümesinde verimli tahmin performansına ulaştığı sonucuna varılmıştır. Bu nedenle kNN algoritması farklı k değerleri ile daha detaylı analiz edilmiştir. Elde edilen tahmin sonuçları % 70 - % 90 aralığında bulunmuştur. Bu sonuçlar makine öğrenmesi algoritmalarının kanser ilaç verilerine ait bilinmeyen anlamlı örüntüleri ortaya çıkarma potansiyeli olduğunu göstermektedir.

Keywords

Yapay öğrenme, GDSC2 veri kümesi, Hedef ilaç tahmini, Hedef yolak tahmini, CTDBase veri kümesi, Machine learning, GDSC2 dataset, Lung adenocarcinoma, Drug-target prediction, Target pathway prediction CTDBase dataset

References

• Ali, M., & Aittokallio, T. (2019). Machine learning and feature selection for drug response prediction in precision oncology applications. Biophysical reviews, 11(1), 31-39.
• Alison, S., Papachristodoulou, D.K., Despo, K., Elliott, W.H., & Elliott, D.C. (2014). Biochemistry and molecular biology (Fifth ed.). Oxford. ISBN 978-0-19-960949-9. OCLC 862091499.
• Alpaydin, E. (2020) Introduction to machine learning. 4th ed. MİT press.
• Atwany, M. Z., Sahyoun, A. H., & Yaqub, M. (2022). Deep learning techniques for diabetic retinopathy classification: A survey. IEEE Access. , 10, 28642-28655.
• Bengio, Y. (2008) Learning deep architectures for AI. Foundations and Trends in Machine Learning 2(1): 1– 127.
• Boser, B.E., Guyon, I.M. & Vapnik, V. (1992). "A training algorithm for optimal margin classifiers". Proceedings of the fifth annual workshop on Computational learning theory – COLT '92. p. 144.
• Brent M. K., Park J., Fong, S.H., Sanchez, K.S., Lee, J., Kreisberg, J.F., Jianzhu, M., & Ideker, T. (2020). Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells. Cancer Cell, Volume 38, Issue 5, Pages 672-684.e6, ISSN 1535-6108, https://doi.org/10.1016/j.ccell.2020.09.014.
• Callahan, A., & Shah, N. H. (2017). Machine learning in healthcare. In Key Advances in Clinical Informatics (pp. 279-291). Academic Press.
• Davis, A. P., Grondin, C. J., Johnson, R. J., Sciaky, D., McMorran, R., Wiegers, J., ... & Mattingly, C. J. (2019). The comparative toxicogenomics database: update 2019. Nucleic acids research, 47(D1), D948-D954.
• Erickson, B. J., Korfiatis, P., Akkus, Z., & Kline, T. L. (2017). Machine learning for medical imaging. Radiographics, 37(2), 505-515. https://doi.org/10.1148/rg.2017160130
• Fix, E., & Hodges, J. L. (1951). Discriminatory Analysis. Nonparametric Discrimination: Consistency Properties (PDF Report). USAF School of Aviation Medicine, Randolph Field, Texas.
• Gao, Y., Lyu, Q., Luo, P., Li, M., Zhou, R., Zhang, J., & Lyu, Q. (2021). Applications of Machine Learning to Predict Cisplatin Resistance in Lung Cancer. International Journal of General Medicine, 14, 5911.
• Goldberger, J., Hinton, G. E., Roweis, S., & Salakhutdinov, R. R. (2004). Neighbourhood components analysis. Advances in neural information processing systems, 17.
• Grossman,, R.L., Heath, A.P., Ferretti, V., Varmus, H.E., Lowy, D.R., Kibbe, W.A., & Staudt, L.M., (2016). Toward a Shared Vision for Cancer Genomic Data. N. Engl. J. Med., 375, 1109–1112.
• Hamilton, D., Pacheco, R., Myers, B., & Peltzer, B. (2020). kNN vs. SVM: A comparison of algorithms. In: Hood, Sharon M.; Drury, Stacy; Steelman, Toddi; Steffens, Ron, eds. . Proceedings of the Fire Continuum-Preparing for the future of wildland fire.
• Hastie, T., Tibshirani, R., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction (Vol. 2, pp. 1-758). New York: Springer.
• Huang, C. H., Chang, P. M. H., Hsu, C. W., Huang, C. Y. F., & Ng, K. L. (2016). Drug repositioning for non-small cell lung cancer by using machine learning algorithms and topological graph theory. BMC bioinformatics (Vol. 17, No. 1, pp. 13-26). BioMed Central.
• Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., ... & Liu, T. Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, 30.
• Libbrecht, M. W., & Noble, W. S. (2015). Machine learning applications in genetics and genomics. Nature Reviews Genetics, 16(6), 321-332.
• Kuenzi, B.M., Park, J., Fong, S.H., Sanchez, K.S., Lee, J., Kreisberg, J.F., et al. (2020). Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells. Cancer Cell, 38:672–84.
• McCulloch, & W., Pitts, W. (1943). A Logical Calculus of Ideas Immanent in Nervous Activity. Bulletin of Mathematical Biophysics, 5: 115–133
• Menden, M. P., Iorio, F., Garnett, M., McDermott, U., Benes, & C. H., Ballester, P. J., & Saez-Rodriguez, J. (2013). Machine learning prediction of cancer cell sensitivity to drugs based on genomic and chemical properties. PLoS one, 8(4), e61318.
• Noble, W. S. (2006). What is a support vector machine. Nature biotechnology, 24(12), 1565-1567.
• Özcan G, ve Yazici S. (2022). Açık Erişimli veri kaynakları ve veri analizi. Türsen Ü, editör. Dermatolojide Yapay Zekâ. 1. Baskı. Ankara: Türkiye Klinikleri. p.9-15.
• Paltun, B.G., Kaski, S., & Mamitsuka, H., (2021). Machine learning approaches for drug combination therapies, Briefings in Bioinformatics, Volume 22, Issue 6, November, https://doi.org/10.1093/bib/bbab293
• Rafique, R., Islam, S. R., & Kazi, J. U. (2021). Machine learning in the prediction of cancer therapy. Computational and Structural Biotechnology Journal, 19, 4003-4017.
• Raies, A., Tulodziecka, E., Stainer, J., Middleton, L., Dhindsa, R. S., Hill, P., ... & Vitsios, D. (2022). DrugnomeAI is an ensemble machine-learning framework for predicting druggability of candidate drug targets. Communications Biology, 5(1), 1291.
• Qiu, K., Lee, J., Kim, H., Yoon, S., & Kang, K. (2021). Machine learning based anti-cancer drug response prediction and search for predictor genes using cancer cell line gene expression. Genomics & informatics, 19(1).
• Qureshi, R., Basit, S. A., Shamsi, J. A., Fan, X., Nawaz, M., Yan, H., & Alam, T. (2022). Machine learning based personalized drug response prediction for lung cancer patients. Scientific Reports, 12(1), 18935.
• Shen, D., Wu, G., & Suk, H. I. (2017). Deep learning in medical image analysis. Annual review of biomedical engineering, 19, 221.
• Tan, X., Yu, Y., Duan, K., Zhang, J., Sun, P., & Sun, H. (2020). Current advances and limitations of deep learning in anticancer drug sensitivity prediction. Current Topics in Medicinal Chemistry, 20(21), 1858-1867.
• Tang, Y.C., Powell, R.T. & Gottlieb, A. (2022). Molecular pathways enhance drug response prediction using transfer learning from cell lines to tumors and patient-derived xenografts. Sci Rep, 16109. https://doi.org/10.1038/s41598-022-20646-1
• Tate J.G., Bamford, S., Jubb, H.C., Sondka. Z., Beare, D.M., Bindal. N., et al. (2019). COSMIC: the Catalogue of Somatic Mutations ın Cancer. Nucleic Acids Research, 47(D1):D941-D7. doi: 10.1093/nar/gky1015.
• Xia, F., Allen, J., Balaprakash, P., Brettin, T., Garcia-Cardona, C., Clyde, A., ... & Stevens, R. (2022). A cross-study analysis of drug response prediction in cancer cell lines. Briefings in bioinformatics, 23(1), bbab356.
• Yang, W., Soares, J., Greninger, P., Edelman, E.J., Lightfoot, H., Forbes, S., et al. (2013) Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res. 41 (Database issue):D955-61
• Yu, K. H., Beam, A. L., & Kohane, I. S. (2018). Artificial intelligence in healthcare. Nature biomedical engineering, 2(10), 719-731.
• Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2), 301-320.