Deep Learning-based Biomarker Discovery for Precision Oncology: Utilizing deep learning techniques to discover novel biomarkers for precision oncology, enabling targeted therapies and personalized treatment plans for cancer patients
Published 06-09-2024
Keywords
- Deep learning,
- biomarker discovery
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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Abstract
Deep learning has emerged as a powerful tool in the field of oncology, offering new avenues for biomarker discovery that can revolutionize precision medicine. This paper explores the application of deep learning techniques for the discovery of novel biomarkers in oncology, with a focus on enabling personalized treatment plans for cancer patients. By analyzing large-scale genomics, transcriptomics, and proteomics data, deep learning models can identify subtle patterns and associations that may serve as biomarkers for specific cancer types or subtypes. This paper reviews the current state of deep learning-based biomarker discovery in oncology, discusses challenges and opportunities, and provides insights into future directions in this rapidly evolving field.
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References
- Saeed, A., Zahoor, A., Husnain, A., & Gondal, R. M. (2024). Enhancing E-commerce furniture shopping with AR and AI-driven 3D modeling. International Journal of Science and Research Archive, 12(2), 040-046.
- Shahane, Vishal. "A Comprehensive Decision Framework for Modern IT Infrastructure: Integrating Virtualization, Containerization, and Serverless Computing to Optimize Resource Utilization and Performance." Australian Journal of Machine Learning Research & Applications 3.1 (2023): 53-75.
- Biswas, Anjanava, and Wrick Talukdar. "Guardrails for trust, safety, and ethical development and deployment of Large Language Models (LLM)." Journal of Science & Technology 4.6 (2023): 55-82.
- N. Pushadapu, “Machine Learning Models for Identifying Patterns in Radiology Imaging: AI-Driven Techniques and Real-World Applications”, Journal of Bioinformatics and Artificial Intelligence, vol. 4, no. 1, pp. 152–203, Apr. 2024
- Pelluru, Karthik. "Prospects and Challenges of Big Data Analytics in Medical Science." Journal of Innovative Technologies 3.1 (2020): 1-18.
- Chen, Jan-Jo, Ali Husnain, and Wei-Wei Cheng. "Exploring the Trade-Off Between Performance and Cost in Facial Recognition: Deep Learning Versus Traditional Computer Vision." Proceedings of SAI Intelligent Systems Conference. Cham: Springer Nature Switzerland, 2023.
- Alomari, Ghaith, et al. “AI-Driven Integrated Hardware and Software Solution for EEG-Based Detection of Depression and Anxiety.” International Journal for Multidisciplinary Research, vol. 6, no. 3, May 2024, pp. 1–24.
- Choi, J. E., Qiao, Y., Kryczek, I., Yu, J., Gurkan, J., Bao, Y., ... & Chinnaiyan, A. M. (2024). PIKfyve, expressed by CD11c-positive cells, controls tumor immunity. Nature Communications, 15(1), 5487.
- Borker, P., Bao, Y., Qiao, Y., Chinnaiyan, A., Choi, J. E., Zhang, Y., ... & Zou, W. (2024). Targeting the lipid kinase PIKfyve upregulates surface expression of MHC class I to augment cancer immunotherapy. Cancer Research, 84(6_Supplement), 7479-7479.
- Gondal, Mahnoor Naseer, and Safee Ullah Chaudhary. "Navigating multi-scale cancer systems biology towards model-driven clinical oncology and its applications in personalized therapeutics." Frontiers in Oncology 11 (2021): 712505.
- Saeed, Ayesha, et al. "A Comparative Study of Cat Swarm Algorithm for Graph Coloring Problem: Convergence Analysis and Performance Evaluation." International Journal of Innovative Research in Computer Science & Technology 12.4 (2024): 1-9.
- Pelluru, Karthik. "Enhancing Cyber Security: Strategies, Challenges, and Future Directions." Journal of Engineering and Technology 1.2 (2019): 1-11.
- Tatineni, Sumanth. Integrating Artificial Intelligence with DevOps: Advanced Techniques, Predictive Analytics, and Automation for Real-Time Optimization and Security in Modern Software Development. Libertatem Media Private Limited, 2024.