Articles
| Open Access |
DOI: CANCER DRUG SENSITIVITY THROUGH GENOMIC DATA: INTEGRATING INSIGHTS FOR PERSONALIZED MEDICINE IN THE USA HEALTHCARE SYSTEM
Ekramul Hasan , College of Engineering and Technology, Westcliff University, Irvine, California, USA Md Musa Haque , School of Business, International American University, Los Angeles, California, USA Shah Foysal Hossain , School of IT, Washington University of Science and Technology, Alexandria, Virginia, USA Md Al Amin , School of Business, International American University, Los Angeles, California, USA Shahriar Ahmed , School of Business, International American University, Los Angeles, California, USA Md Azharul Islam , College of Business, Westcliff University, Irvine, California, USA Irin Akter Liza , College of Graduate and Professional Studies (CGPS), Trine University, Detroit, Michigan, USA Sarmin Akter , School of Business, International American University, Los Angeles, California, USAAbstract
Despite the significant progress in cancer genomics in America, there is still a noteworthy gap regarding genomic markers that predict drug sensitivity which presents a major obstacle to personalized oncology care. Tumors This research project aims to identify a set of genetic variations or mutations that influence the individual response of a cancer patient to certain drugs. This study also aims to develop machine learning models that can analyze a patient's genomic data to predict their likely response to different therapies. This study utilized the Genomic of Drug Sensitivity in Cancer (GDSC). The GDSC dataset is a very valued resource in therapeutic biomarker discovery in cancer research. This dataset combined drug response data with genomic profiles of cancer cell lines, enabling investigations into the relationship between genetic features and drug sensitivity. The main task associated with this dataset was to predict drug sensitivity, measured as IC50 values, from genomic features of cancer cell lines. Several accredited and proven Machine Learning algorithms were utilized in the study, particularly, Linear Regression, Ridge Regression, and SGD Regression. The most important regression model evaluation metrics deployed in a drug sensitivity prediction included the Mean Squared Error- MSE, Root Mean Squared Error- RMSE, and Mean Absolute Error- MAE. The Ridge Regression model outperformed the Linear Regression and the SGD algorithm, particularly, the Ridge Regression model captured excellently the hidden trends in the data much better compared to the other two models. Predictive analytics can significantly enhance clinical decision-making in the USA by providing health professionals with data-driven insights into the best available treatment options. As patient complexity and treatment options continue to grow, such models will help clinicians choose the most appropriate interventions for individual patients, informed by historical data on their disease course and other individual patient factors, including genetic profiling and comorbid status.
Keywords
Genomic markers, Oncology, Drug sensitivity
References
Acanda De La Rocha, A. M., Berlow, N. E., Fader, M., Coats, E. R., Saghira, C., Espinal, P. S., ... & Azzam, D. J. (2024). Feasibility of functional precision medicine for guiding treatment of relapsed or refractory pediatric cancers. Nature Medicine, 1-11.
Alam, S., Hider, M. A., Al Mukaddim, A., Anonna, F. R., Hossain, M. S., khalilor Rahman, M., & Nasiruddin, M. (2024). Machine Learning Models for Predicting Thyroid Cancer Recurrence: A Comparative Analysis. Journal of Medical and Health Studies, 5(4), 113-129.
Alipour, S. (2024, August 13). Genomics of Drug Sensitivity in Cancer (GDSC). Kaggle. https://www.kaggle.com/datasets/samiraalipour/genomics-of-drug-sensitivity-in-cancer-gdsc
Al Amin, M., Liza, I. A., Hossain, S. F., Hasan, E., Haque, M. M., & Bortty, J. C. (2024). Predicting and Monitoring Anxiety and Depression: Advanced Machine Learning Techniques for Mental Health Analysis. British Journal of Nursing Studies, 4(2), 66-75.
Bhowmik, P. K., Miah, M. N. I., Uddin, M. K., Sizan, M. M. H., Pant, L., Islam, M. R., & Gurung, N. (2024). Advancing Heart Disease Prediction through Machine Learning: Techniques and Insights for Improved Cardiovascular Health. British Journal of Nursing Studies, 4(2), 35-50.
Brancato, V., Esposito, G., Coppola, L., Cavaliere, C., Mirabelli, P., Scapicchio, C., ... & Aiello, M. (2024). Standardizing digital biobanks: integrating imaging, genomic, and clinical data for precision medicine. Journal of Translational Medicine, 22(1), 136.
Bortty, J. C., Bhowmik, P. K., Reza, S. A., Liza, I. A., Miah, M. N. I., Chowdhury, M. S. R., & Al Amin, M. (2024). Optimizing Lung Cancer Risk Prediction with Advanced Machine Learning Algorithms and Techniques. Journal of Medical and Health Studies, 5(4), 35-48.
Chafai, N., Bonizzi, L., Botti, S., & Badaoui, B. (2024). Emerging applications of machine learning in genomic medicine and healthcare. Critical Reviews in Clinical Laboratory Sciences, 61(2), 140-163.
Dutta, S., Sikder, R., Islam, M. R., Al Mukaddim, A., Hider, M. A., & Nasiruddin, M. (2024). Comparing the Effectiveness of Machine Learning Algorithms in Early Chronic Kidney Disease Detection. Journal of Computer Science and Technology Studies, 6(4), 77-91.
Fawaz, A., Ferraresi, A., & Isidoro, C. (2023). Systems Biology in Cancer Diagnosis Integrating Omics Technologies and Artificial Intelligence to Support Physician Decision Making. Journal of Personalized Medicine, 13(11), 1590.
Hider, M. A., Nasiruddin, M., & Al Mukaddim, A. (2024). Early Disease Detection through Advanced Machine Learning Techniques: A Comprehensive Analysis and Implementation in Healthcare Systems. Revista de Inteligencia Artificial en Medicina, 15(1), 1010-1042.
Hossain, M. S., Rahman, M. K., & Dalim, H. M. (2024). Leveraging AI for Real-Time Monitoring and Prediction of Environmental Health Hazards: Protecting Public Health in the USA. Revista de Inteligencia Artificial en Medicina, 15(1), 1117-1145.
Islam, M. Z., Nasiruddin, M., Dutta, S., Sikder, R., Huda, C. B., & Islam, M. R. (2024). A Comparative Assessment of Machine Learning Algorithms for Detecting and Diagnosing Breast Cancer. Journal of Computer Science and Technology Studies, 6(2), 121-135.
Kang, C. C., Lee, T. Y., Lim, W. F., & Yeo, W. W. Y. (2023). Opportunities and challenges of 5G network technology toward precision medicine. Clinical and Translational Science, 16(11), 2078-2094.
Laxmi Pant, Abdullah Al Mukaddim, Md Khalilor Rahman, Abdullah AL Sayeed, Md Sazzad Hossain, MD Tushar Khan, & Adib Ahmed. (2024). Genomic predictors of drug sensitivity in cancer: Integrating genomic data for personalized medicine in the USA. Computer Science & IT Research Journal, 5(12), 2682-2702. https://doi.org/10.51594/csitrj.v5i12.1760
Joshi, I., Bhrdwaj, A., Khandelwal, R., Pande, A., Agarwal, A., Srija, C. D., ... & Singh, S. K. (2023). Artificial intelligence, big data and machine learning approaches in genome-wide SNP-based prediction for precision medicine and drug discovery. In Big data analytics in chemoinformatics and bioinformatics (pp. 333-357). Elsevier.
Obijuru, A., Arowoogun, J. O., Onwumere, C., Odilibe, I. P., Anyanwu, E. C., & Daraojimba, A. I. (2024). Big data analytics in healthcare: a review of recent advances and potential for personalized medicine. International Medical Science Research Journal, 4(2), 170-182.
Rahman, A., Karmakar, M., & Debnath, P. (2023). Predictive Analytics for Healthcare: Improving Patient Outcomes in the US through Machine Learning. Revista de Inteligencia Artificial en Medicina, 14(1), 595-624.
Restrepo, J. C., Dueñas, D., Corredor, Z., & Liscano, Y. (2023). Advances in genomic data and biomarkers: revolutionizing NSCLC diagnosis and treatment. Cancers, 15(13), 3474.
Sadee, W., Wang, D., Hartmann, K., & Toland, A. E. (2023). Pharmacogenomics: driving personalized medicine. Pharmacological reviews, 75(4), 789-814.
Tong, L., Shi, W., Isgut, M., Zhong, Y., Lais, P., Gloster, L., ... & Wang, M. D. (2023). Integrating multi-omics data with EHR for precision medicine using advanced artificial intelligence. IEEE Reviews in Biomedical Engineering.
Udegbe, F. C., Ebulue, O. R., Ebulue, C. C., & Ekesiobi, C. S. (2024). Precision Medicine and Genomics: A comprehensive review of IT-enabled approaches. International Medical Science Research Journal, 4(4), 509-520.
Wang, R. C., & Wang, Z. (2023). Precision medicine: disease subtyping and tailored treatment. Cancers, 15(15), 3837.
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Copyright (c) 2024 Ekramul Hasan, Md Musa Haque , Shah Foysal Hossain, Md Al Amin , Shahriar Ahmed , Md Azharul Islam , Irin Akter Liza , Sarmin Akter
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