Volume 24 , Issue 1 , PP: 269-280, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Alex Fabián S. Moreno 1 , Jessica Johanna S. Moreno 2 , Paul Alejandro C. Maldonado 3 , Saziye Yaman 4 *
Doi: https://doi.org/10.54216/IJNS.240124
This article provides a comprehensive view of the evolution and ethical implications of genetics, from its historical origins to modern advancements like the Human Genome Project. It emphasizes the importance of genetic information in disease prediction and understanding human diversity while highlighting the need to address associated ethical and privacy issues. It introduces Fuzzy Cognitive Maps (FCM) and the Dependent OWA (D-OWA) aggregation operator as innovative tools for analyzing the complex landscape of the Right to Genetic Information (RGI) in an international context, identifying key factors such as legal frameworks, technological advancements, access to health, culture, ethics, and commercial interests. The research reveals that while legal framework and technology are predominant, other factors also play significant roles in managing RGI. The application of D-OWA provides additional insights, confirming that conventional centrality assessments adequately reflect the priorities and influence of factors in RGI. It concludes by underlining the need for specific strategies to address these challenges, such as strengthening the legal framework, promoting ethics in genetics, improving public education, and respecting cultural diversity, to protect individual rights while leveraging the benefits of genetics for society.
Neutrosophic sets , D-OWA technique , Fuzzy Logic , Genetics , right to genetic information , dependent aggregation operator.
[1] R. Cullen and S. Marshall, “Genetic research and genetic information: a health information professional’s perspective on the benefits and risks.,” Health Info. Libr. J., vol. 23, no. 4, pp. 275–282, Dec. 2006, Available: https://pubmed.ncbi.nlm.nih.gov/17177948/.
[2] A. R. Starkweather et al., “Strengthen federal regulation of laboratory-developed and direct-to-consumer genetic testing,” Nurs. Outlook, vol. 66, no. 1, pp. 101–104, 2018, Available: https://www.sciencedirect.com/science/article/abs/pii/S0029655417306292.
[3] Kanika Sharma, Achyut Shankar, Prabhishek Singh, Information Security Assessment in Big Data Environment using Fuzzy Logic, Journal of Journal of Cybersecurity and Information Management, Vol. 5 , No. 1 , (2021) : 29-42 (Doi : https://doi.org/10.54216/JCIM.050103).
[4] J. Kaye, S. Gibbons, C. Heeney, and A. Smart, Governing biobanks: understanding the interplay between law and practice. Bloomsbury Publishing, 2012, Available: https://script-ed.org/wp-content/uploads/2015/06/lohse_grewal.pdf.
[5] A. Sariga, J. Uthayakumar, Type 2 Fuzzy Logic based Unequal Clustering algorithm for multi-hop wireless sensor networks, Journal of International Journal of Wireless and Ad Hoc Communication, Vol. 1 , No. 1 , (2020) : 33-46 (Doi : https://doi.org/10.54216/IJWAC.010102)
[6] W. J. Pavan and R. A. Sturm, “The Genetics of Human Skin and Hair Pigmentation.,” Annu. Rev. Genomics Hum. Genet., vol. 20, pp. 41–72, Aug. 2019, Available: https://pubmed.ncbi.nlm.nih.gov/31100995/.
[7] C. A. Newton et al., “The Role of Genetic Testing in Pulmonary Fibrosis: A Perspective From the Pulmonary Fibrosis Foundation Genetic Testing Work Group,” Chest, vol. 162, no. 2, pp. 394–405, 2022, Available: https://pubmed.ncbi.nlm.nih.gov/35337808/.
[8] M. F. Hatwágner, E. Yesil, M. F. Dodurka, E. Papageorgiou, L. Urbas, and L. T. Kóczy, “Two-stage learning based fuzzy cognitive maps reduction approach,” IEEE Trans. Fuzzy Syst., vol. 26, no. 5, pp. 2938–2952, 2018, Available: https://ieeexplore.ieee.org/abstract/document/8259309.
[9] I. D. Apostolopoulos, N. I. Papandrianos, N. D. Papathanasiou, and E. I. Papageorgiou, “Fuzzy Cognitive Map Applications in Medicine over the Last Two Decades: A Review Study.,” Bioeng. (Basel, Switzerland), vol. 11, no. 2, p. 139, Jan. 2024, Available: https://pubmed.ncbi.nlm.nih.gov/38391626/.
[10] Mohammad Hossein Shafiabadi, Zohre Ahmadi, Mohammad Reza Esfandyari, Solving the Problem of Target k-Coverage in WSNs Using Fuzzy Clustering Algorithm, Journal of Journal of Intelligent Systems and Internet of Things, Vol. 2 , No. 2 , (2021) : 55-76 (Doi : https://doi.org/10.54216/JISIoT.020203)
[11] R. Yager, J. Kacprzyk, and G. Beliakov, Preface: In Recent developments in the ordered weighted averaging operators: theory and practice. Deakin University, 2011, Available: https://link.springer.com/book/10.1007/978-3-642-17910-5.
[12] R. R. Yager, “On ordered weighted averaging aggregation operators in multicriteria decisionmaking,” IEEE Trans. Syst. Man. Cybern., vol. 18, no. 1, pp. 183–190, 1988, Available: https://www.sciencedirect.com/science/article/abs/pii/B9781483214504500110.
[13] Abedallah Z. Abualkishik, Rasha Almajed, Amer Ibrahim, An Integrated Spherical Fuzzy Approach for Global Supplier Selection, Journal of Fusion: Practice and Applications, Vol. 6 , No. 1 , (2021) : 43-61 (Doi : https://doi.org/10.54216/FPA.060105).
