Comparative Analysis of Machine Learning Algorithms for Genomic Data

Genome analysis is a complex and challenging task that concerns the differences of an individual's genetic makeup which requires advanced computational methods. In recent years, artificial intelligence (AI) has appeared as a dynamic and potent tool for genome analysis. In this study, novel approaches to genome analysis using AI techniques, including machine learning algorithms are explained. By using machine learning methods to classify genetic variations in NGS data, such as logistic regression, GuassianNB, KNN, Decision Tree Classifier, Random Forest Classifier, Gradient Booster, Cat Booster, LGBM Classifier, and XGB Classifier, and comparing the accuracy of these techniques. The methodology involves preprocessing the genomic data, training a deep learning model, and then using the model to predict various genomic Disorders. approach to a publicly available dataset of human genomic data and achieved high accuracy in predicting functional genomic elements such as promoters and enhancers. According to the findings, machine learning can be a highly effective tool for analysing NGS data and has the potential to improve our understanding of genetic variation and disease susceptibility. They also highlight the value of using multiple computational approaches to gain a thorough understanding of genomic data. This study provides insight into the potential of machine learning in healthcare, particularly in the interpretation of genomic data, and it may open the door for more investigation in this area.