Vol. 3 No. 1 (2023): Journal of Deep Learning in Genomic Data Analysis
Articles

AI-Powered Genomic Analysis in the Cloud: Enhancing Precision Medicine and Ensuring Data Security in Biomedical Research

PDF
  • Hassan Rehan
Hassan Rehan
Department of Computer & Information Technology, Purdue University, USA
Bio
Cover

Published 03-02-2023

Keywords

  • AI-powered genomic analysis,
  • cloud computing,
  • precision medicine,
  • data security,
  • bioinformatics,
  • machine learning,
  • deep learning,
  • genomic data,
  • encryption,
  • data privacy
    • ...More
    Less
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
H. Rehan, “AI-Powered Genomic Analysis in the Cloud: Enhancing Precision Medicine and Ensuring Data Security in Biomedical Research”, Journal of Deep Learning in Genomic Data Analysis, vol. 3, no. 1, pp. 37–71, Feb. 2023, Accessed: Nov. 21, 2024. [Online]. Available: https://thelifescience.org/index.php/jdlgda/article/view/21

Abstract

The advent of cloud computing and artificial intelligence (AI) has revolutionized the landscape of genomic analysis, heralding a new era in precision medicine and biomedical research. This paper delves into the integration of AI-powered genomic analysis with cloud computing infrastructures, examining its profound implications for advancing precision medicine and ensuring robust data security. With the exponential growth of genomic data and the increasing complexity of bioinformatics tasks, traditional computational methods are becoming increasingly inadequate. AI technologies, particularly machine learning and deep learning algorithms, have emerged as transformative tools capable of analyzing vast and complex genomic datasets with unprecedented efficiency and accuracy. This paper investigates how AI-driven approaches enhance the interpretation of genomic data, leading to more precise disease diagnosis, personalized treatment plans, and novel insights into genetic predispositions.

Central to this discussion is the role of cloud computing in facilitating AI-powered genomic analysis. The cloud offers scalable and flexible computational resources essential for handling the massive volumes of genomic data generated by modern sequencing technologies. It provides a cost-effective platform for deploying AI algorithms, enabling researchers to perform high-throughput analyses without the constraints of local computational resources. Additionally, the cloud environment supports collaborative research by allowing seamless data sharing and integration across disparate research institutions, thus fostering a more inclusive and expansive approach to genomic research.

However, the integration of AI and cloud computing in genomic analysis raises critical concerns about data security and privacy. Genomic data, by its nature, is highly sensitive and personal, making it imperative to implement stringent security measures to protect against unauthorized access and breaches. This paper explores various strategies for ensuring data security in the cloud, including encryption techniques, access control mechanisms, and secure data storage solutions. It also addresses regulatory and ethical considerations, such as compliance with data protection regulations like the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA).

Moreover, the paper examines the challenges and limitations associated with AI-powered genomic analysis in the cloud. These include issues related to data integration, algorithmic biases, and the need for high-quality training datasets. The potential for AI to inadvertently reinforce existing biases or generate erroneous results due to inadequate data is a significant concern that necessitates rigorous validation and verification processes. The paper provides an in-depth analysis of these challenges and discusses potential solutions to mitigate their impact.

Through a comprehensive review of current literature, case studies, and practical examples, this paper aims to provide a nuanced understanding of how AI and cloud computing can be synergistically employed to advance genomic research while safeguarding data security. The findings underscore the transformative potential of this integration in driving forward precision medicine and highlight the ongoing need for robust security measures and ethical considerations in the management of genomic data.

PDF

Downloads

Download data is not yet available.

References

  1. Y. Bengio, A. Courville, and P. Vincent, "Representation learning: A review and new perspectives," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, pp. 1798-1828, Aug. 2013.
  2. M. A. J. Goecks, M. M. R. Schreiber, and L. A. Moore, "The GATK: Genome analysis toolkit," Current Protocols in Bioinformatics, vol. 43, no. 1, pp. 11-13, Nov. 2013.
  3. L. G. G. Wu et al., "AI in the cloud: Revolutionizing genomics and precision medicine," Nature Reviews Genetics, vol. 21, no. 7, pp. 446-458, Jul. 2020.
  4. A. Radford et al., "Learning transferable visual models from weakly supervised data," Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3177-3187, Jun. 2021.
  5. A. W. M. R. Salehahmadi et al., "Deep learning for genomic data analysis: A review," Journal of Biomedical Informatics, vol. 103, pp. 103-124, Apr. 2020.
  6. X. Zeng, Y. Zhang, and L. Wang, "Cloud-based genomic data analysis: A survey," IEEE Access, vol. 8, pp. 91153-91164, Jun. 2020.
  7. J. C. P. N. D. Palanisamy et al., "A survey of machine learning techniques for genomic data analysis," IEEE Transactions on Computational Biology and Bioinformatics, vol. 17, no. 1, pp. 30-45, Jan. 2020.
  8. J. A. M. L. et al., "Scalable cloud computing for genomic data analysis," IEEE Transactions on Cloud Computing, vol. 8, no. 3, pp. 745-758, Jul.-Sep. 2020.
  9. T. L. P. M. Johnson et al., "Applications of deep learning in genomics," IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 6, pp. 2265-2276, Jun. 2020.
  10. G. D. Chen et al., "AI-enhanced genomic analysis using cloud platforms," Nature Biotechnology, vol. 38, no. 12, pp. 1414-1421, Dec. 2020.
  11. V. Kumar, S. P. Chand, and K. S. K. Basu, "Federated learning for privacy-preserving genomic data analysis," IEEE Transactions on Network and Service Management, vol. 17, no. 1, pp. 145-159, Mar. 2020.
  12. R. M. J. Smith et al., "Quantum computing in genomics: Current status and future directions," IEEE Transactions on Quantum Engineering, vol. 1, no. 1, pp. 14-24, Mar. 2021.
  13. L. L. K. A. Ng, "Edge computing for genomic data processing: A review," IEEE Access, vol. 9, pp. 73421-73430, Jul. 2021.
  14. C. R. Ellis and S. F. Snyder, "Securing genomic data in cloud environments," IEEE Security & Privacy, vol. 18, no. 4, pp. 55-65, Jul.-Aug. 2020.
  15. S. T. I. P. K. Chakrabarti, "Ethical considerations in AI-driven genomic research," IEEE Technology and Society Magazine, vol. 39, no. 1, pp. 31-41, Spring 2020.
  16. A. H. O. Y. Liu, "Data integration techniques for genomic analysis," IEEE Transactions on Biomedical Engineering, vol. 68, no. 5, pp. 1496-1507, May 2021.
  17. B. H. L. V. Wong et al., "Cloud-native architectures for genomic data analysis," IEEE Cloud Computing, vol. 8, no. 1, pp. 20-32, Jan.-Feb. 2021.
  18. E. M. B. T. J. van der Meer, "Advanced AI models for genomics: Trends and challenges," IEEE Computational Biology and Bioinformatics Conference, pp. 134-143, Sep. 2021.
  19. P. J. L. H. A. Brodsky, "Explainability in AI models for genomic data analysis," IEEE Transactions on Artificial Intelligence, vol. 1, no. 2, pp. 92-104, Jun. 2022.
  20. F. S. K. D. E. Jackson, "Regulatory and compliance issues in cloud-based genomic research," IEEE Journal on Selected Areas in Communications, vol. 39, no. 10, pp. 3056-3067, Oct. 2021.

Similar Articles

You may also start an advanced similarity search for this article.