Abstract
Artificial Intelligence (AI) is increasingly being applied in the field of genomics to interpret large-scale genomic data, enabling advancements in personalized medicine, disease prediction, and drug discovery. Genomic data interpretation requires the integration of complex, high-dimensional datasets, and AI approaches such as machine learning and deep learning have shown promise in extracting meaningful patterns and insights. This article reviews the various applications of AI in genomic data interpretation, including gene expression analysis, variant interpretation, and the identification of biomarkers. We also explore the challenges and future directions for integrating AI techniques into genomic research and clinical practice.
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