DNA SEQUENCING USING MACHINE LEARNING ALGORITHM
MR.DHINAHARAN S, HARISH.P, ANJUSREE.K, DINESH.A
HEAD OF THE DEPARTMENT, Department of ARTIFICIAL INTELLIGENCE AND DATA SCIENCE
ABSTRACT
DNA sequencing is a fundamental technique in molecular biology that has revolutionized various fields, including medicine, agriculture, and environmental science. Recent advancements in high-throughput sequencing technologies have enabled the generation of vast amounts of genomic data at an unprecedented rate. However, the analysis of these data presents significant challenges due to their complexity and size. Traditional methods for DNA sequencing analysis often struggle to cope with the scale and intricacy of the data, necessitating the adoption of innovative approaches. Machine learning (ML) has emerged as a powerful tool for addressing the challenges associated with DNA sequencing analysis. ML techniques, including deep learning, random forests,and support vector machines, offer the potential to extract meaningful insights from genomic data, improve sequencing accuracy, and accelerate the identification of genetic variations and biomarkers. This paper provides a comprehensive review of the application of ML in DNA sequencing, covering various aspects such as base calling, sequence alignment, variant calling, metagenomic analysis, and personalized medicine. We discuss the different ML algorithms employed in DNA sequencing analysis, highlighting their strengths, limitations, and potential applications. Additionally, we examine the key considerations in data preprocessing, feature selection, model training, and evaluation. Furthermore, we explore the challenges and future directions in the integration of ML with DNA sequencing technologies, including the need for robust and interpretable models, the importance of data privacy and security, and the potential for interdisciplinary collaboration. Overall, this review underscores the transformative impact of ML on DNA sequencing analysis and provides insights into the opportunities and challenges in leveraging ML techniques to unlock the full potential of genomic data for scientific discovery and clinical applications.
