Improvement in prediction of antigenic epitopes using stacked generalisation: an ensemble approach. Issue 1 (1st February 2020)

• Record Type: Journal Article
• Authors: Khanna, Divya; Rana, Prashant Singh
• Is Part Of: IET systems biology. Volume 14:Issue 1(2020)
• Page Start: 1
• Page End: 7
• Publication Date: 2020-02-01
• Subjects: generalisation (artificial intelligence) -- support vector machines -- cellular biophysics -- pattern classification -- proteins -- learning (artificial intelligence) -- bioinformatics; antigenic epitopes -- stacked generalisation -- peptide vaccine designs -- immunodiagnosis -- antibody productions -- linear B‐cell epitopes -- generalisation ensemble technique -- generalisation ensemble approach -- machine learning models -- base classifiers; Systems biology -- Periodicals; Cell physiology -- Periodicals; Biological systems -- Mathematical models -- Periodicals; Genetics -- Mathematical models -- Periodicals; Computational biology -- Periodicals; 573
• DOI: 10.1049/iet-syb.2018.5083
• Languages: English
• ISSNs: 1751-8849

Abstract

Abstract : The major intent of peptide vaccine designs, immunodiagnosis and antibody productions is to accurately identify linear B‐cell epitopes. The determination of epitopes through experimental analysis is highly expensive. Therefore, it is desirable to develop a reliable model with significant improvement in prediction models. In this study, a hybrid model has been designed by using stacked generalisation ensemble technique for prediction of linear B‐cell epitopes. The goal of using stacked generalisation ensemble approach is to refine predictions of base classifiers and to get rid of the worse predictions. In this study, six machine learning models are fused to predict variable length epitopes (6–49 mers). The proposed ensemble model achieves 76.6% accuracy and average accuracy of repeated 10‐fold cross‐validation is 73.14%. The trained ensemble model has been tested on the benchmark dataset and compared with existing sequential B‐cell epitope prediction techniques including APCpred, ABCpred, BCpred and AA P BCPred .