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].

中文翻译：

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使用堆叠泛化改进抗原表位预测：一种集成方法。

肽疫苗设计、免疫诊断和抗体生产的主要目的是准确识别线性 B 细胞表位。通过实验分析确定表位非常昂贵。因此，需要开发一个可靠的模型，在预测模型上有显着改进。在这项研究中，通过使用堆叠泛化集成技术设计了一种混合模型，用于预测线性 B 细胞表位。使用堆叠泛化集成方法的目标是细化基分类器的预测并摆脱更差的预测。在这项研究中，融合了六种机器学习模型来预测可变长度表位（6-49 mers）。所提出的集成模型达到了 76.6% 的准确率，重复 10 次交叉验证的平均准确率为 73.14%。