ArticleIET systems biology2020
Improvement in prediction of antigenic epitopes using stacked generalisation: an ensemble approach.
Article in IET systems biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Mapping Potential Malaria Vector Larval Habitats for Larval Source Management in Western Kenya: Introduction to Multimodel Ensembling Approaches.The American journal of tropical medicine and hygiene · 2024Article
- Peptide-Based Vaccines for Tuberculosis.Frontiers in immunology · 2022Review
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Authors and funding
2 authors.
Funding
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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].
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