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Predicting Protein Functions in Rare Diseases Using A Deep Learning Approach
Alzaher, Hanin Almahdi, Razan Sidiya, Aichetou
Alzaher, Hanin
Almahdi, Razan
Sidiya, Aichetou
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Abstract
With 6000 to 8000 rare disease types such as Achalasia, Acrodysostosis, and Sirenomelia significantly impacting children and contributing to premature death before the age of five in 30\% of cases, the need for advanced research methodologies is evident. Traditional models for predicting protein function often fall short when applied to poorly characterized proteins associated with these diseases. This study proposes a deep learning model specifically tailored for such proteins, utilizing Generative Adversarial Networks (GANs) to predict their functions. Our approach involves collecting and preprocessing amino acid sequences from rare diseases, developing a predictive model, and evaluating its performance against existing methods. Through an extensive literature review and methodological analysis, this project aims to address current gaps in research, offering a novel solution that could significantly contribute to Sustainable Development Goal 3 of Good Health and Well-being, and align with the objectives of Saudi Vision 2030 in terms of Well-being and Preventive Care.