A meta-learning approach for B-cell conformational epitope prediction.

ABSTRACT:

Background

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. We propose a meta-learning approach for epitope prediction based on stacked and cascade generalizations. Through meta learning, we expect a meta learner to be able integrate multiple prediction models, and outperform the single best-performing model. The objective of this study is twofold: (1) to analyze the complementary predictive strengths in different prediction tools, and (2) to introduce a generic computational model to exploit the synergy among various prediction tools. O

SUBMITTER: Hu YJ 

PROVIDER: S-EPMC4237749 | biostudies-literature | 2014 Nov

REPOSITORIES: biostudies-literature