Gelişen Yüksek Teknoloji ile Gen Madenciliği ve Gizlilik

Year 2025, Volume: 10 Issue: 1, 200 - 233, 29.06.2025

Hakan Yıldırım , Cihan Ünal

https://doi.org/10.33484/sinopfbd.1599550

Abstract

Gen madenciliği, DNA verilerini analiz ederek genetik yatkınlıkların ve hastalık risklerinin belirlenmesini sağlayan kritik bir veri madenciliği sürecidir. Bu süreçte, yapay zekâ (YZ) ve Kuantum Bilgisayarlar (KB) gibi yüksek teknoloji ürünlerinin kullanılması, analitik gücü artıran yenilikçi teknolojiler olarak öne çıkmaktadır. Yapay zekâ algoritmaları ve veri madenciliği teknikleri genetik örüntülerin keşfini kolaylaştırırken, KB’ler karmaşık ve büyük veri setlerinde bile yüksek doğrulukta analizler yapılmasına olanak tanır. Bu gelişmeler, DNA verilerinin daha hızlı, daha derinlemesine ve güvenilir bir şekilde incelenmesini sağlayarak genetik araştırmalara önemli katkılar sunmaktadır. Ancak DNA verilerinin hassas doğası, kişisel gizlilik ve veri güvenliği için sıkı önlemler alınmasını gerektirmektedir. Bu bağlamda, Blok Zincir Teknolojileri (BZ) DNA verilerinin yalnızca yetkili kişilerle güvenli bir şekilde saklanması, anonimleştirilmesi ve kontrollü paylaşımı için etkili bir çözüm sunar. Blok Zincir Teknolojisi’nin dağıtık ve değiştirilemez yapısı verileri korurken, yapay zeka ve kuantum teknolojileri gen madenciliğine hız ve hassasiyet kazandırmaktadır. Bu makale, gen madenciliğine YZ, veri madenciliği ve KB’nin katkılarını incelemekte ve kişisel gizliliğin korunmasında BZ’nin önemini vurgulamaktadır. Bu çalışma, gen madenciliğinde YZ, KB ve BZ’nin katkılarını ele alarak, özellikle DNA gibi kişisel ve hassas verilerin korunmasında yüksek düzeyde güvenlik sağladığını ve bu teknolojilerin entegre kullanımının veri gizliliği için yeni standartlar oluşturmadaki kritik rolünü vurgulamaktadır.

Keywords

Gen madenciliği , hassas veri gizliliği , Yapay Zekâ (YZ) , Kuantum Bilgisayarlar (KB) , Blok Zincir Teknolojileri (BZ)

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

Gene mining , sensitive data privacy , Artificial Intelligence (AI) , Quantum Computers (QC) , Blockchain Technologies (BCT)

References

• 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
• 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.
• Pollard, T. D., Earnshaw, W. C., Lippincott-Schwartz, J., & Johnson, G. (2022). Cell biology e-book. Elsevier Health Sciences.
• Cooper, G. M., & Adams, K. (2022). The cell: a molecular approach. Oxford University Press.
• 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.
• 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.
• 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.
• 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.
• Lazaridis, I., Patterson, N., Mittnik, A., Renaud, G., Mallick, S., Kirsanow, K., Sudmant, P. H., Schraiber, J. G., Castellano, S., Lipson, M., Berger, B., Economou, C., Bollongino, R., Fu, Q., Bos, K., Nordenfelt, S., Li, H., Filippo, C., Prüfer, K., Sawyer, S., Posth, C., Haak, W., Hallgren, F., Fornander, E., Rohland, N., Delsate, D., Francken, M., Guinet, J. M., Wahl, J., Ayodo, G., Babiker, HA.,Bailliet, G., Balanovska, E., Balanovsky, O., Barrantes, R., Bedoya, G., Ben-Ami, H., Bene, J., Berrada, F., Bravi, C. M., Brisighelli, F., Busby, G. B. J., Cali, F., Churnosov, M., Cole, D. E. C., Corach, D., Damba, L., Driem, G., Dryomov, S., Dugoujon, J. M., Fedorova, SA., Romero, I. G., Gubina, M., Hammer, J., Henn, B. M., Hervig, T., Hodoglugil, U., Jha, A. R., ...... & Krause, J. (2014). Ancient human genomes suggest three ancestral populations for present-day Europeans. Nature, 513(7518), 409-413. https://doi.org/10.1038/nature13673.
• Ghanam, J., Chetty, V. K., Barthel, L., Reinhardt, D., Hoyer, P. F., & Thakur, B. K. (2022). DNA in extracellular vesicles: From evolution to its current application in health and disease. Cell & Bioscience, 12(1), 37. https://doi.org/10.1186/s13578-022-00771-0
• Achilli, A., Olivieri, A., Pala, M., Metspalu, E., Fornarino, S., Battaglia, V., Accetturo, M., Kutuev, I., Khusnutdinova, E., Pennarun, E., Cerutti, N., Gaetano, C.D., Crobu, F., Palli, D., Matullo, G., Santachiara-Benerecetti, A.S., Cavalli-Sforza, L.L., Semino, O., Villems, R., Bandelt, H., Piazza, A., & Torroni, A. (2007). Mitochondrial DNA variation of modern Tuscans supports the near eastern origin of Etruscans. The American Journal of Human Genetics, 80(4), 759-768. http://dx.doi.org/10.1086/512822
• Regalado, A. (2019). Rewriting life more than 26 million people have taken an at-home ancestry test. 2019 [cited 20 Apr 2019].
• Eren, E., & Özkan, M. (2019). Forensic DNA analysis in Turkey: The role of public institutions in forensic cases. Turkish Journal of Forensic Science, 11(3), 145-153.
• Genç, S. (2022). Bilişim teknolojilerinde blok zincir ve kuantum hesaplamanın ortak geleceği: kuantum blok zinciri. Veri Bilimi, 5(2), 53-63
• Libbrecht, M. W., & Noble, W. S. (2015). Machine learning applications in genetics and genomics. Nature Reviews Genetics, 16(6), 321-332.
• Khera, A. V., & Kathiresan, S. (2017). Genetics of coronary artery disease: discovery, biology and clinical translation. Nature Reviews Genetics, 18(6), 331-344.
• Klau, J. H., Maj, C., Klinkhammer, H., Krawitz, P. M., Mayr, A., Hillmer, A. M., Schumacher, J., & Heider, D. (2023). AI-based multi-PRS models outperform classical single-PRS models. Frontiers in Genetics, 14, 1217860. https://doi.org/10.3389/fgene.2023.1217860
• Slunecka, J. L., van der Zee, M. D., Beck, J. J., Johnson, B. N., Finnicum, C. T., Pool, R., Hottenga, J. J., de Geus, E.J.C., & Ehli, E. A. (2021). Implementation and implications for polygenic risk scores in healthcare. Human Genomics, 15(1), 46. https://doi.org/10.1186/s40246-021-00339-y
• Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P. M., Zietz, M., Hoffman, M.M., Xie,W., Rosen, G. L., Lengerich, B.J., Israeli, J., Lanchantin, J., Woloszynek, S., Carpenter, A.E., Shrikumar, A., Xu, J., Cofer, E.M., Lavender, C.A., Turuga, S. C., Alexandaril, A. M., Lu, Z., Harris, D. J., DeCaprio, D., Qil, Y., Kundaje, A., Peng, Y., Wiley, L. K., Segler, M. H. S., Boca, S.M., Swamidass, S. J., Huang,A., Gitter, A., & Greene, C. S. (2018). Opportunities and obstacles for deep learning in biology and medicine. Journal of the Royal Society İnterface, 15(141), 20170387. http://dx.doi.org/10.1098/rsif.2017.0387
• Gürsoy, G., Li, T., Liu, S., Ni, E., Brannon, C. M., & Gerstein, M. B. (2022). Functional genomics data: privacy risk assessment and technological mitigation. Nature Reviews Genetics, 23(4), 245-258. https://doi.org/10.1038/s41576-021-00428-7
• Salama, G. M., Mohamed, A., & Abd-Ellah, M. K. (2024). Machine learning and deep learning covid-19 diagnosis system: key achievements, lessons learned, and a transfer learning algorithm. Soft Computing, 28,13715–13742. https://doi.org/10.1007/s00500-024-10362-5(
• Preskill, J. (2018). Quantum computing in the NISQ era and beyond. Quantum, 2, 79.
• Zhong, H. S., Wang, H., Deng, Y. H., Chen, M. C., Peng, L. C., Luo, Y. H., Qin, J., Wu, D., Ding, X., Hu, Y., Hu, P., Yang, X. Y., Zhang, W. J., Li, H., Li, Y., Jiang, ., Gan, L., Yang, G., You, L., Whang, Z., Li, L., Liu, N. L., Lu, Y. C., & Pan, J. W. (2020). Quantum computational advantage using photons. Science, 370(6523), 1460-1463. https://doi.org/10.1126/science.abe8770
• Panda, S. K., Sathya, A. R., & Das, S. (2023). Bitcoin: Beginning of the cryptocurrency era. In Recent advances in blockchain technology: Real-world applications (pp. 25-58). Cham: Springer International Publishing.
• Li, N., Li, T., & Venkatasubramanian, S. (2006, April). t-closeness: Privacy beyond k-anonymity and l-diversity. In 2007 IEEE 23rd international conference on data engineering (pp. 106-115). IEEE.
• Jayasankar, T., Bhavadharini, R. M., Nagarajanz, N. R., Mani, G., & Ramesh, S. (2021). Securing Medical Data using Extended Role Based Access Control Model and Two fish Algorithms on Cloud Platform. European Journal of Molecular and Clinical Medicine, 8(1), 1075-1090. https://link.gale.com/apps/doc/A698747750/AONE?u=anon~1997fbc2&sid=googleScholar&xid=0f27b833
• Gabriel, O. T. (2023). Data privacy and ethical issues in collecting health care data using artificial intelligence among health workers (Master's thesis, Center for Bioethics and Research).
• Myers, C. T., Kumar, R. D., Pilgram, L., Bonomi, L., Thomas, M., Griffith, O. L., Fullerton, S. M., & Gibbs, R. A. (2025). Genomic data and privacy. Clinical Chemistry, 71(1), 10-17. https://doi.org/10.1093/clinchem/hvae184
• Gadotti, A., Rocher, L., Houssiau, F., Creţu, A. M., & De Montjoye, Y. A. (2024). Anonymization: The imperfect science of using data while preserving privacy. Science Advances, 10(29), eadn7053.
• Khan, J. A. (2024). Role-based access control (rbac) and attribute-based access control (abac). In Improving security, privacy, and trust in cloud computing (pp. 113-126). IGI Global Scientific Publishing.
• Amiri, A., Shekarchizadeh, M., Shekarchizadeh Esfahani, A., & Masoud, G. (2021). Analysis of bio-cyber threats and crimes. Medical Law Journal, 15, 535-550. http://ijmedicallaw.ir/article-1-1391-en.html
• Krishnamurthy, O. (2023). Genetic algorithms, data analytics and it’s applications, cybersecurity: verification systems. International Transactions in Artificial Intelligence, 7(7), 1-25.
• Akash, A., & Sarker, S. P. (2024). Analysing discrimination based on genetic ınformation. Lentera Hukum, 11(2), 157-188. https://doi.org/10.19184/ejlh.v11i2.43512
• Sandua, D. (2023). Artificial intelligence, blockchain & quantum computing. Independently Published. [35] Elgendy, N., Elragal, A., & Päivärinta, T. (2022). DECAS: a modern data-driven decision theory for big data and analytics. Journal of Decision Systems, 31(4), 337-373. https://doi.org/10.1080/12460125.2021.1894674
• Alagarswamy, K., Boini, A., Messaoudi, N., Grasso, V., Turner, B., & Gumbs, A. (2024). Can AI-powered whole genome sequencing be used routinely for personalized decision support in surgical oncology?–a scoping review.
• Krittanawong, C., Johnson, K. W., Choi, E., Kaplin, S., Venner, E., Murugan, M., Wang, Z., Glicksberg, B. S., Amos, C. I., Schatz, M. C., & Tang, W. W. (2022). Artificial intelligence and cardiovascular genetics. Life, 12(2), 279. https://doi.org/10.3390/life12020279
• Jeyaraman, N., Jeyaraman, M., Yadav, S., Ramasubramanian, S., & Balaji, S. (2024). Revolutionizing healthcare: the emerging role of quantum computing in enhancing medical technology and treatment. Cureus, 16(8), e67486. https://doi.org/10.7759/cureus.67486
• Soares, B., Ferreira, A., & Veiga, P. M. (2023). The benefits and challenges of blockchain technology and ehealth ımplementation in estonia-a literature review. Applied Medical Informatics, 45(4), 118-131.
• Hoxhaj, I., Castagna, C., Calabrò, G. E., & Boccia, S. (2022). HTA training for healthcare professionals: international overview of initiatives provided by HTA agencies and organizations. Frontiers in Public Health, 10, 795763. https://doi.org/10.3389/fpubh.2022.795763
• Pal, S., Bhattacharya, M., Dash, S., Lee, S. S., & Chakraborty, C. (2024). Future potential of quantum computing and simulations in biological science. Molecular Biotechnology, 66(9), 2201-2218. https://doi.org/10.1007/s12033-023-00863-3