Improvement in prediction of antigenic epitopes using stacked generalisation: an ensemble approach.
Identifieur interne : 000176 ( Main/Exploration ); précédent : 000175; suivant : 000177Improvement in prediction of antigenic epitopes using stacked generalisation: an ensemble approach.
Auteurs : Divya Khanna [Inde] ; Prashant Singh Rana [Inde]Source :
- IET systems biology [ 1751-8849 ] ; 2020.
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 [inline-formula removed].
DOI: 10.1049/iet-syb.2018.5083
PubMed: 31931475
Affiliations:
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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 [inline-formula removed].</div></front></TEI><affiliations><list><country><li>Inde</li></country></list><tree><country name="Inde"><noRegion><name sortKey="Khanna, Divya" sort="Khanna, Divya" uniqKey="Khanna D" first="Divya" last="Khanna">Divya Khanna</name></noRegion><name sortKey="Rana, Prashant Singh" sort="Rana, Prashant Singh" uniqKey="Rana P" first="Prashant Singh" last="Rana">Prashant Singh Rana</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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