A Machine Learning Approach to Predict and Evaluate the Human Immune System Response to Intrusion with B –Cell Epitopes as Protein-Protein Interaction

Abstract

The immune system relies on its capacity to identify substances, for its functioning but this ability weakens over time. B cells and T cells the elements of the system actively search for antigens. An antigen typically carries a membrane with a sequence of amino acids on its surface. These amino acid clusters, known as epitopes act as identifiers for antigens. B Cell Epitopes are epitopes that B cells recognize. In this study we. Evaluate a model that distinguishes between epitopes and non epitopes offering insight into the systems recognition process involving antigenic components. Various machine learning models have been utilized in our analysis, such, as regression, random forests, decision trees, Bernoulli Navie Bayes and XGBoost.