Abstract
Large-scale genomic studies generate vast amounts of data, which present both opportunities and challenges for extracting meaningful insights. Data mining techniques play a crucial role in analyzing and interpreting these large datasets, enabling the discovery of patterns, associations, and relationships between genetic variants and diseases. This article explores the role of data mining in large-scale genomic studies, reviewing key techniques, tools, and applications, and highlighting the challenges in handling complex genomic data. We also discuss future directions for data mining in genomics and its potential to drive advances in personalized medicine, disease prevention, and drug discovery.
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