Research Article

Gene Mining and Privacy by the Emerging High Tech

Volume: 10 Number: 1 June 29, 2025
TR EN

Gene Mining and Privacy by the Emerging High Tech

Abstract

Gene mining is a critical data mining process that enables the identification of genetic predispositions and disease risks by analyzing Deoxyribonucleic Acid-DNA data. Within this process, using emerging high tech such as artificial intelligence (AI) and Quantum Computers-QC emerge as innovative technologies that enhance analytical power. AI algorithms and data mining techniques facilitate the discovery of genetic patterns, while QC enable highly accurate analyses even in complex and large datasets. These advancements allow for a faster, more in-depth and reliable examination of DNA data, providing substantial contributions to genetic research. However, the sensitive nature of DNA data necessitates stringent measures for personal privacy and data security. In this context, Blockchain Technologies-BCT offers an effective solution for the secure storage, anonymization and controlled sharing of DNA data exclusively with authorized entities. The distributed and immutable structure of Blockchain Technology-BCT safeguards data while AI and quantum technologies contribute speed and precision to gene mining. This article examines the contributions of AI, data mining and QC to gene mining and underscores the importance of BCT in preserving personal privacy. This study examines the contributions of AI, QC, and BCT in gene mining, emphasizing the importance of BCT in ensuring data privacy and security. In particular, while providing a high level of security for the protection of personal and sensitive data such as DNA, the integrated use of these technologies plays a critical role in establishing new standards for data privacy.

Keywords

References

  1. Stelzer, G., Rosen, N., Plaschkes, I., Zimmerman, S., Twik, M., Fishilevich, S., Stein, T. I., Nudel, R., Lieder, I., Mazor, Y., Kaplan, S., Dahary, D., Warshawsky, D., Guan-Golan, Y., Kohn, A., Rappaport, N., Safran, M., & Lancet, D. (2016). The GeneCards suite: from gene data mining to disease genome sequence analyses. Current Protocols in Bioinformatics, 54(1), 1-30. https://doi.org/10.1002/cpbi.5
  2. Khanum, S., & Mustafa, K. (2023). A systematic literature review on sensitive data protection in blockchain applications. Concurrency and Computation: Practice and Experience, 35(1), e7422.
  3. Pollard, T. D., Earnshaw, W. C., Lippincott-Schwartz, J., & Johnson, G. (2022). Cell biology e-book. Elsevier Health Sciences.
  4. Cooper, G. M., & Adams, K. (2022). The cell: a molecular approach. Oxford University Press.
  5. Visscher, P. M., Brown, M. A., McCarthy, M. I., & Yang, J. (2012). Five years of GWAS discovery. The American Journal of Human Genetics, 90(1), 7-24.
  6. Torkamani, A., Wineinger, N. E., & Topol, E. J. (2018). The personal and clinical utility of polygenic risk scores. Nature Reviews Genetics, 19(9), 581-590.
  7. Khera, A. V., Chaffin, M., Aragam, K. G., Haas, M. E., Roselli, C., Choi, S. H., Natarajan, P., Lander, E. S., Lubitz, S. A., Ellinor, P. T., & Kathiresan, S. (2018). Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nature Genetics, 50(9), 1219-1224.
  8. Erlich, Y., Shor, T., Pe’er, I., & Carmi, S. (2018). Identity inference of genomic data using long-range familial searches. Science, 362(6415), 690-694.

Details

Primary Language

English

Subjects

Software Engineering (Other)

Journal Section

Research Article

Publication Date

June 29, 2025

Submission Date

December 10, 2024

Acceptance Date

May 14, 2025

Published in Issue

Year 2025 Volume: 10 Number: 1

APA
Yıldırım, H., & Ünal, C. (2025). Gene Mining and Privacy by the Emerging High Tech. Sinop Üniversitesi Fen Bilimleri Dergisi, 10(1), 200-233. https://doi.org/10.33484/sinopfbd.1599550
AMA
1.Yıldırım H, Ünal C. Gene Mining and Privacy by the Emerging High Tech. Sinop Uni J Nat Sci. 2025;10(1):200-233. doi:10.33484/sinopfbd.1599550
Chicago
Yıldırım, Hakan, and Cihan Ünal. 2025. “Gene Mining and Privacy by the Emerging High Tech”. Sinop Üniversitesi Fen Bilimleri Dergisi 10 (1): 200-233. https://doi.org/10.33484/sinopfbd.1599550.
EndNote
Yıldırım H, Ünal C (June 1, 2025) Gene Mining and Privacy by the Emerging High Tech. Sinop Üniversitesi Fen Bilimleri Dergisi 10 1 200–233.
IEEE
[1]H. Yıldırım and C. Ünal, “Gene Mining and Privacy by the Emerging High Tech”, Sinop Uni J Nat Sci, vol. 10, no. 1, pp. 200–233, June 2025, doi: 10.33484/sinopfbd.1599550.
ISNAD
Yıldırım, Hakan - Ünal, Cihan. “Gene Mining and Privacy by the Emerging High Tech”. Sinop Üniversitesi Fen Bilimleri Dergisi 10/1 (June 1, 2025): 200-233. https://doi.org/10.33484/sinopfbd.1599550.
JAMA
1.Yıldırım H, Ünal C. Gene Mining and Privacy by the Emerging High Tech. Sinop Uni J Nat Sci. 2025;10:200–233.
MLA
Yıldırım, Hakan, and Cihan Ünal. “Gene Mining and Privacy by the Emerging High Tech”. Sinop Üniversitesi Fen Bilimleri Dergisi, vol. 10, no. 1, June 2025, pp. 200-33, doi:10.33484/sinopfbd.1599550.
Vancouver
1.Hakan Yıldırım, Cihan Ünal. Gene Mining and Privacy by the Emerging High Tech. Sinop Uni J Nat Sci. 2025 Jun. 1;10(1):200-33. doi:10.33484/sinopfbd.1599550

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