Ensemble Approach for Antigenic Epitopes Prediction using Physicochemical Properties

Abstract

Accurate and efficient prediction of antigenic epitopes are essential for the medical applications and immunologic research. The prediction of antigenic epitopes are challenging as compared to other bio-informatics issues. Because antigenic epitopes have many variabilities where an paratope which is a part of antibody binds to a given epitope with high accuracy. Although, continuous efforts are invested in this field for the improvement but the problem is still unsolved and attracts attention of the researchers. To improve the results of antigenic epitopes prediction, an adaptive system needs to be constructed by using machine learning techniques. The pathogen or invader which is identifiable as a foreign substance by the adaptive immune system that is known as antigen. Normally, antigens are the structural proteins which include portion of bacterium cell membranes and spike proteins of viruses. Epitopes are the part of antigens which bind to the helper T-cells, Cytotoxic T-lymphocytes, B-cells, antibodies and antigenic molecule based upon the type of antigen. Therefore, to predict antigenic epitopes, analyze and predict diseases, to group similar genetic elements, and to find relationships or associations in biological data, machine learning techniques can be used to improve the results of such type of problems. There are many studies exist to predict antigenic epitopes. But these studies have some limitations including use of single model, fixed length of epitopes, lack of data preprocessing and fixed data partitioning approach to train the models. Because of such issues, the trained model may or may not produce a reliable and efficient prediction. Single model can be replaced with the ensemble model to predict antigenic epitopes. Ensemble learning is a process of combining more than one model to solve a given computational intelligence problem. Generally, it is used to enhance the predictability as well as to improve the robustness of a model.