College of Computer Science, National Chiao Tung University, Hsinchu, Taiwan.
Institute of Biomedical Engineering, National Chiao Tung University, Hsinchu, Taiwan.
Methods Mol Biol. 2020;2131:375-397. doi: 10.1007/978-1-0716-0389-5_22.
One of the major challenges in the field of vaccine design is identifying B-cell epitopes in continuously evolving viruses. Various tools have been developed to predict linear or conformational epitopes, each relying on different physicochemical properties and adopting distinct search strategies. In this chapter, we propose different ensemble meta-learning approaches for epitope prediction based on stacked, cascade generalizations, and meta decision trees. Through meta learning, we expect a meta learner to be able to integrate multiple prediction models and outperform the single best-performing model. The objective of this chapter is twofold: (1) to promote the complementary predictive strengths in different prediction tools and (2) to introduce computational models to exploit the synergy among various prediction tools. Our primary goal is not to develop any particular classifier for B-cell epitope prediction, but to advocate the feasibility of meta learning to epitope prediction. With the flexibility of meta learning, the researcher can construct various meta classification hierarchies that are applicable to epitope prediction in different protein domains.
疫苗设计领域的主要挑战之一是在不断进化的病毒中识别 B 细胞表位。已经开发了各种工具来预测线性或构象表位，每种方法都依赖于不同的物理化学性质，并采用不同的搜索策略。在本章中，我们提出了基于堆叠、级联泛化和元决策树的不同集成元学习方法进行表位预测。通过元学习，我们期望元学习者能够整合多个预测模型，并优于单个表现最佳的模型。本章的目标有两个：（1）促进不同预测工具的互补预测优势；（2）引入计算模型以利用各种预测工具之间的协同作用。我们的主要目标不是为 B 细胞表位预测开发任何特定的分类器，而是倡导元学习在表位预测中的可行性。通过元学习的灵活性，研究人员可以构建适用于不同蛋白质域中表位预测的各种元分类层次结构。
