Classification of DNA Sequence Using Machine Learning

In the field of medical information research, the genetic series is widely used as a component of a category. One of the applications of ML is biochemistry. Bioinformatics is an interdisciplinary science that uses computers and communication science to understand biological data. One of its most difficult tasks is to distinguish between regular genes and disease-causing genes. The classification of gene sequences into existing categories is utilized in genomic research to discover the functions of novel proteins. As a result, it is critical to identify and categorize such genes. We employ ML approaches to distinguish between infected and normal genes using classification methods. AdaBoost has a high degree of precision; relative to the bagging algorithm and Random Forest Algorithm, AdaBoost fully considers the weight of each classifier. To generate a sequence of weak classifiers, an AdaBoost-based learning approach is used to find the most 'informative' or 'discriminating' features. The identification cascade structure can also help to limit false-positive results. This study provides an overview of the mechanics of gene sequence classification using ML Techniques, including a brief introduction to bioinformatics and important challenges in DNA Sequencing with ML.