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Application of Meta Learning to B-Cell Conformational Epitope Prediction

  • Yuh-Jyh HuEmail author
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Part of the Methods in Molecular Biology book series (MIMB, volume 2131)

Abstract

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.

Key words

B-cell epitopes Meta learning Stacking Cascade Meta decision trees 

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© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  1. 1.College of Computer ScienceNational Chiao Tung UniversityHsinchuTaiwan
  2. 2.Institute of Biomedical EngineeringNational Chiao Tung UniversityHsinchuTaiwan

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