Machine Learning Applications in Genomic Data Analysis

ABSTRACT

The rapid advancement of next-generation sequencing technologies has led to the generation of massive genomic datasets, creating more opportunities and challenges in biological research. Analyzing such complex and high-dimensional data requires advanced computational methods. Machine learning (ML), a branch of artificial intelligence, has emerged as a powerful tool for extracting meaningful insights from genomic data. ML algorithms enable the identification of patterns, prediction of gene functions, classification of biological sequences, and detection of genetic variations associated with diseases. This paper reviews the role of machine learning techniques in genomic data analysis and highlights their applications in gene prediction, gene expression analysis, disease diagnosis, and personalized medicine. The study also discusses commonly used machine learning algorithms such as support vector machines, decision trees, neural networks, and deep learning models. Furthermore, challenges and future prospects of applying machine learning in genomics are discussed. The integration of machine learning with bioinformatics tools offers promising opportunities for improving genomic research, accelerating biomedical discoveries, and supporting the development of precision medicine