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The advancements in genomics research have led to an exponential growth in the amount of data generated from various sequencing technologies. Analyzing this vast amount of genomic data is a complex task that can provide valuable insights into biological processes, disease mechanisms, and personalized medicine. In recent years, machine learning has emerged as a powerful tool for genomic data analysis, enabling researchers to uncover hidden patterns, make predictions, and gain a deeper understanding of the genome. This review aims to provide an overview of the applications of machine learning in genomics data analysis, highlighting its potential, challenges, and future directions.
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