Abstract
The analysis of large genomic datasets is fundamental in understanding the genetic basis of complex diseases such as cancer, diabetes, and cardiovascular disorders. With the advent of high-throughput sequencing technologies, the availability of large-scale genomic data has increased exponentially. Bioinformatics approaches play a crucial role in managing, processing, and analyzing these datasets to uncover genetic variants, gene expression patterns, and regulatory networks associated with complex diseases. This article reviews the methods and challenges involved in analyzing large genomic datasets, including statistical models, machine learning algorithms, and integrative approaches. We also discuss the future directions and opportunities in complex disease studies using genomic data.
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