Intelligent DNA Sequence Classification Using Machine Learning Techniques

Abstract: The purpose of this study is to provide a new method for improving the efficiency and precision of DNA sequence classification by use of Machine Learning algorithms. To sort DNA sequences into predetermined groups, such species identification, the suggested DNA Sequencing Classifier makes use of cutting-edge Machine Learning methods. Sequencing DNA has changed the face of genetics in many fields, including medicine, evolution, and others. Still, DNA sequences may be difficult to accurately classify. Machine learning may automate this process, making it more precise and uncovering hidden patterns. Using state-of-the-art Machine Learning methods, this research seeks to develop a DNA sequence classifier that is both efficient and effective. Genetic research may be accelerated and improved with the use of automated categorisation. The first step in ensuring the accuracy of raw DNA sequences is data preparation. Machine learning models use the extracted characteristics as inputs and choose the most effective classifier according to performance. Using k-mer counting, the DNA Sequencing Classifier is compared to other approaches and reviewed thoroughly. The integration of machine learning with DNA sequencing has great potential for simplified DNA categorisation, which in turn might speed up research and enhance our knowledge of genetics.

Keywords: DNA, Natural Language Processing (NLP), k-mer counting, Naïve Bayes, Bag of words.