Siddiqui, Mohd FaizanMouna, AzaroualVillela, RicardoKalmatov, RomanBoueri, MyriamBay, SadıkKurbanaliev, Abdikerim2025-12-162025-12-162024Siddiqui, M. F., Mouna, A., Villela, R., Kalmatov, R., Boueri, M., Bay, S. ... Kurbanaliev, A. (2024). Inequality in genetic healthcare: bridging gaps with deep learning innovations in low-income and middle-income countries. Deep Learning in Genetics and Genomics: Volume 1: Foundations and Introductory Applications içinde (397-410. ss.). Elsevier. http://dx.doi.org/10.1016/B978-0-443-27574-6.00003-597804432757469780443275753http://dx.doi.org/10.1016/B978-0-443-27574-6.00003-5https://hdl.handle.net/20.500.12511/13340The field of genomics is progressing via a scientific framework that significantly depends on the analysis and interpretation of large datasets. The development of advanced data creation methods in genomics has resulted in a flood of genetic data. Abundant knowledge of genetic data has enabled artificial intelligence, especially deep learning approaches, to be extremely beneficial in revealing significant discoveries and patterns. On the other hand, in low-income and middle-income countries (LMICs), the lack of clinical genetic resources and restricted access to genetic screening programs increases children's and families' risk of delayed diagnosis. This chapter emphasizes development and utilization of deep learning methodologies in various facets of human genomics to address global health challenges. This necessitates the implementation of screening and risk assessment measures at the point of care, tailored to the specific local, economic, and sociocultural circumstances of LMIC's populations.eninfo:eu-repo/semantics/closedAccessDeep LearningGenetic SyndromesGenomicsGlobal HealthLow- And Middle-Income Countries (Lmics)Book Chapter39741010.1016/B978-0-443-27574-6.00003-52-s2.0-85213195795