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Abstract
Big Data into precision medicine and genomics has revolutionized the landscape of healthcare by enabling the development of personalized therapies tailored to the unique genetic makeup of individuals. This paper provides a comprehensive analysis of the key innovations driven by Big Data in the field of precision medicine, with a particular focus on genomics. It explores the diverse types of genomic data, including whole-genome sequencing, whole-exome sequencing, and transcriptome sequencing, and discusses the advanced analytical methods used to extract meaningful insights from these complex datasets. Machine learning models, including random forests, support vector machines, and deep learning techniques, are evaluated for their effectiveness in predictive modeling and disease risk assessment. The paper also addresses the challenges associated with data integration, privacy, and the ethical implications of using Big Data in genomics. Despite these challenges, the ongoing advancements in Big Data analytics continue to drive forward the potential of precision medicine, offering new opportunities for early disease detection, targeted treatment, and improved patient outcomes.
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