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Navigating the uncharted territory of rare diseases: leveraging artificial intelligence to achieve groundbreaking advances, with a focus on Congenital Disorders of Glycosylation (CDG): A comprehensive analysis
Department of Computer Science and Engineering, Pacific Academy of Higher Education and Research University, Udaipur, India.
Research Article
International Journal of Science and Research Archive, 2024, 12(02), 1061–1071.
Article DOI: 10.30574/ijsra.2024.12.2.1255
Publication history:
Received on 31 May 2024; revised on 08 July 2024; accepted on 10 July 2024
Abstract:
Artificial Intelligence (AI) is changing the game when it comes to researching, diagnosing, and treating rare genetic disorders, bringing new hope for better patient outcomes. This paper focuses on how AI is making a big difference in tackling Congenital Disorders of Glycosylation (CDG), a group of over 130 rare genetic disorders that disrupt glycosylation, a critical biological process. By using advanced machine learning algorithms, AI tools like PredictSNP, REVEL, and Face2Gene are improving the accuracy of diagnosing these disorders, making it quicker and easier to identify them early. We explore how AI helps predict glycosylation sites, identify important Golgi proteins, and classify different disease phenotypes, offering new insights into how these diseases work. Additionally, AI is playing a crucial role in finding new treatments, including repurposing existing drugs to treat CDG. Despite the exciting progress, there are still challenges to overcome, such as ensuring high-quality data, making AI models understandable, and addressing ethical concerns. To truly unlock the potential of AI in this field, we need to integrate data from various sources and establish strong ethical guidelines. This paper highlights the importance of collaboration among researchers, clinicians, and policymakers to fully leverage AI's capabilities, paving the way for innovative and effective solutions for managing CDG and other rare genetic disorders.
Keywords:
Artificial intelligence; Big data; Congenital disorders of glycosylation; Diagnosis; Drug repurposing; Machine learning; Personalized medicine; Rare diseases
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
