AI-Enabled Precision Medicine: Optimizing Treatment Strategies Through Genomic Data Analysis

Mingxuan Yang; Decheng Huang; Haodong Zhang; Wenxuan Zheng

doi:10.5281/zenodo.13380619

Authors

Mingxuan Yang Brown University
Decheng Huang University of Pennsylvania
Haodong Zhang New York University
Wenxuan Zheng University of California

DOI:

https://doi.org/10.5281/zenodo.13380619

ARK:

https://n2t.net/ark:/40704/JCTAM.v1n3a10

Keywords:

Artificial intelligence, Precision Medicine, Genomic Data Analysis, Personalized Treatment

Abstract

This research investigates how Artificial Intelligence (AI) can be used in precision medicine to improve treatment strategies by analysing genomic data. We explore sophisticated machine learning methods for examining complicated genomic datasets, such as deep learning models and ensemble techniques. The study focuses on overcoming obstacles in analysing complex genomic data and introduces innovative methods for combining multi-omics data. We create predictive models using AI technology to forecast patient prognosis with better accuracy in predicting disease progression and treatment results. The research also looks into AI's use in finding new uses for drugs, demonstrating how machine learning can speed up the process of finding potential treatments. We introduce a model for personalised treatment planning using AI, which integrates genomic biomarkers and clinical factors to enhance drug combination and dosage selection. The assessment of incorporating AI in clinical decision support systems is conducted, showcasing enhancements in diagnostic accuracy and treatment effectiveness through multiple medical fields. Ethical concerns, such as algorithmic bias and data privacy, are thoroughly examined, focusing on discussing regulatory guidelines for AI in the healthcare sector. Our results suggest precision medicine with AI technology could significantly improve treatment customisation, enhance patient results, and transform healthcare delivery. Nevertheless, it is essential to consider ethical, privacy, and regulatory obstacles when implementing responsible practices.

Author Biographies

Mingxuan Yang, Brown University

Master of Innovation Management and Entrepreneurship, Brown University, RI, USA.

Decheng Huang, University of Pennsylvania

Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, PA, USA.

Haodong Zhang, New York University

Computer Science, New York University, NY, USA.

Wenxuan Zheng, University of California

Applied Math, University of California, Los Angeles, CA, USA.

References

Paranjape, K., Schinkel, M., & Nanayakkara, P. (2020). Short Keynote Paper: Mainstreaming Personalized Healthcare–Transforming Healthcare Through New Era of Artificial Intelligence. IEEE Journal of Biomedical and Health Informatics, 24(7), 1860-1863.

Rawat, B., Joshi, Y., & Kumar, A. (2023). Bio-Marker Cancer Prediction System Using Artificial Intelligence. 2023 International Conference on Integration of Computational Intelligent Systems (ICICIS), 1-5.

Bagirozz, B. B., Doruk, E., & Yildiz, O. (n.d.). Machine Learning In Bioinformatics: Gene Expression And Microarray Studies. IEEE.

Arshi, O., Chaudhary, A., & Singh, R. (2023). Navigating the Future of Healthcare: AI-Powered Solutions, Personalized Treatment Plans, and Emerging Trends in 2023. 2023 International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI), 1-6.

AI in Healthcare: Opportunities and Challenges for Personalized Medicine and Disease Diagnosis. (2023). 2023 5th International Conference on Inventive Research in Computing Applications (ICIRCA), 374-379.

Akbar, A., Peoples, N., Xie, H., Sergot, P., Hussein, H., Peacock IV, W. F., & Rafique, Z. . (2022). Thrombolytic Administration for Acute Ischemic Stroke: What Processes Can Be Optimized? McGill Journal of Medicine, 20(2).

Chen, P., Lam, K. H., Liu, Z., Mindlin, F. A., Chen, B., Gutierrez, C. B., ... & Jin, R. (2019). Structure of the full-length Clostridium difficile toxin B. Nature Structural & Molecular Biology, 26(8), 712-719.

Zhang, X., Wang, C., Chen, B., Wang, Q., Xu, W., Ye, S., ... & Zhang, R. (2019). Crystal structure of refolding fusion core of Lassa virus GP2 and design of Lassa virus fusion inhibitors. Frontiers in microbiology, 10, 1829.

Chen, B., Basak, S., Chen, P., Zhang, C., Perry, K., Tian, S., ... & Jin, R. (2022). Structure and conformational dynamics of Clostridioides difficile toxin A. Life Science Alliance, 5(6).

Chen, B., Zhu, Y., Ye, S., & Zhang, R. (2018). The structure of the DNA-binding domain of the human myelin-gene regulatory factor reveals its potential protein-DNA recognition mode. Journal of Structural Biology, 203(2), 170-178.

Chen, B., Liu, Z., Perry, K., & Jin, R. (2022). The structure of the glucosyltransferase domain of TcdA in complex with RhoA provides insights into substrate recognition. Scientific reports, 12(1), 9028.

Chen, Baohua, Kay Perry, and Rongsheng Jin. "Neutralizing epitopes on Clostridioides difficile toxin A revealed by the structures of two camelid VHH antibodies." Frontiers in Immunology 13 (2022): 978858.

Li, S., Xu, H., Lu, T., Cao, G., & Zhang, X. (2024). Emerging Technologies in Finance: Revolutionizing Investment Strategies and Tax Management in the Digital Era. Management Journal for Advanced Research, 4(4), 35-49.

Shi J, Shang F, Zhou S, et al. Applications of Quantum Machine Learning in Large-Scale E-commerce Recommendation Systems: Enhancing Efficiency and Accuracy[J]. Journal of Industrial Engineering and Applied Science, 2024, 2(4): 90-103.

Wang, S., Zheng, H., Wen, X., & Fu, S. (2024). DISTRIBUTED HIGH-PERFORMANCE COMPUTING METHODS FOR ACCELERATING DEEP LEARNING TRAINING. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 3(3), 108-126.

Zhang, M., Yuan, B., Li, H., & Xu, K. (2024). LLM-Cloud Complete: Leveraging Cloud Computing for Efficient Large Language Model-based Code Completion. Journal of Artificial Intelligence General Science (JAIGS) ISSN: 3006-4023, 5(1), 295-326.

Lei, H., Wang, B., Shui, Z., Yang, P., & Liang, P. (2024). Automated Lane Change Behavior Prediction and Environmental Perception Based on SLAM Technology. arXiv preprint arXiv:2404.04492.

Wang, B., He, Y., Shui, Z., Xin, Q., & Lei, H. (2024). Predictive Optimization of DDoS Attack Mitigation in Distributed Systems using Machine Learning. Applied and Computational Engineering, 64, 95-100.

Wang, B., Zheng, H., Qian, K., Zhan, X., & Wang, J. (2024). Edge computing and AI-driven intelligent traffic monitoring and optimization. Applied and Computational Engineering, 77, 225-230.

Xu, Y., Liu, Y., Xu, H., & Tan, H. (2024). AI-Driven UX/UI Design: Empirical Research and Applications in FinTech. International Journal of Innovative Research in Computer Science & Technology, 12(4), 99-109.

Liu, Y., Xu, Y., & Song, R. (2024). Transforming User Experience (UX) through Artificial Intelligence (AI) in interactive media design. Engineering Science & Technology Journal, 5(7), 2273-2283.

Zhang, P. (2024). A STUDY ON THE LOCATION SELECTION OF LOGISTICS DISTRIBUTION CENTERS BASED ON E-COMMERCE. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 3(3), 103-107.

Zhang, P., & Gan, L. I. U. (2024). Optimization of Vehicle Scheduling for Joint Distribution in the Logistics Park based on Priority. Journal of Industrial Engineering and Applied Science, 2(4), 116-121.

Li, H., Wang, S. X., Shang, F., Niu, K., & Song, R. (2024). Applications of Large Language Models in Cloud Computing: An Empirical Study Using Real-world Data. International Journal of Innovative Research in Computer Science & Technology, 12(4), 59-69.

Ping, G., Wang, S. X., Zhao, F., Wang, Z., & Zhang, X. (2024). Blockchain-Based Reverse Logistics Data Tracking: An Innovative Approach to Enhance E-Waste Recycling Efficiency.

Xu, H., Niu, K., Lu, T., & Li, S. (2024). Leveraging artificial intelligence for enhanced risk management in financial services: Current applications and prospects. Engineering Science & Technology Journal, 5(8), 2402-2426.

Shi, Y., Shang, F., Xu, Z., & Zhou, S. (2024). Emotion-Driven Deep Learning Recommendation Systems: Mining Preferences from User Reviews and Predicting Scores. Journal of Artificial Intelligence and Development, 3(1), 40-46.

Wang, Shikai, Kangming Xu, and Zhipeng Ling. "Deep Learning-Based Chip Power Prediction and Optimization: An Intelligent EDA Approach." International Journal of Innovative Research in Computer Science & Technology 12.4 (2024): 77-87.

Zhang, M., Yuan, B., Li, H., & Xu, K. (2024). LLM-Cloud Complete: Leveraging Cloud Computing for Efficient Large Language Model-based Code Completion. Journal of Artificial Intelligence General Science (JAIGS) ISSN: 3006-4023, 5(1), 295-326.

Liu, B., Zhao, X., Hu, H., Lin, Q., & Huang, J. (2023). Detection of Esophageal Cancer Lesions Based on CBAM Faster R-CNN. Journal of Theory and Practice of Engineering Science, 3(12), 36-42.

Liu, B., Yu, L., Che, C., Lin, Q., Hu, H., & Zhao, X. (2024). Integration and performance analysis of artificial intelligence and computer vision based on deep learning algorithms. Applied and Computational Engineering, 64, 36-41.

Liu, B. (2023). Based on intelligent advertising recommendations and abnormal advertising monitoring systems in the field of machine learning. International Journal of Computer Science and Information Technology, 1(1), 17-23.

Liang, P., Song, B., Zhan, X., Chen, Z., & Yuan, J. (2024). Automating the training and deployment of models in MLOps by integrating systems with machine learning. Applied and Computational Engineering, 67, 1-7.

Xu, K., Zhou, H., Zheng, H., Zhu, M., & Xin, Q. (2024). Intelligent Classification and Personalized Recommendation of E-commerce Products Based on Machine Learning. arXiv preprint arXiv:2403.19345.

Zheng, H., Xu, K., Zhou, H., Wang, Y., & Su, G. (2024). Medication Recommendation System Based on Natural Language Processing for Patient Emotion Analysis. Academic Journal of Science and Technology, 10(1), 62-68.

Wang, S., Xu, K., & Ling, Z. (2024). Deep Learning-Based Chip Power Prediction and Optimization: An Intelligent EDA Approach. International Journal of Innovative Research in Computer Science & Technology, 12(4), 77-87.

Xu, K., Zheng, H., Zhan, X., Zhou, S., & Niu, K. (2024). Evaluation and Optimization of Intelligent Recommendation System Performance with Cloud Resource Automation Compatibility.

Guo, L., Li, Z., Qian, K., Ding, W., & Chen, Z. (2024). Bank Credit Risk Early Warning Model Based on Machine Learning Decision Trees. Journal of Economic Theory and Business Management, 1(3), 24-30.

Xu, Z., Guo, L., Zhou, S., Song, R., & Niu, K. (2024). Enterprise Supply Chain Risk Management and Decision Support Driven by Large Language Models. Applied Science and Engineering Journal for Advanced Research, 3(4), 1-7.

Zhao, F.; Li, H.; Niu, K.; Shi, J.; Song, R. Application of Deep Learning-Based Intrusion Detection System (IDS) in Network Anomaly Traffic Detection. Preprints 2024, 2024070595.