Cangzhi Jia*, Hongyan Gong, Yan Zhu and Yixia Shi Pages 226 - 233 ( 8 )
Background: B-cell epitope prediction is an essential tool for a variety of immunological studies. For identifying such epitopes, several computational predictors have been proposed in the past 10 years.
Objective: In this review, we summarized the representative computational approaches developed for the identification of linear B-cell epitopes.
Methods: We mainly discuss the datasets, feature extraction methods and classification methods used in the previous work.
Results: The performance of the existing methods was not very satisfying, and so more effective approaches should be proposed by considering the structural information of proteins.
Conclusion: We consider existing challenges and future perspectives for developing reliable methods for predicting linear B-cell epitopes.
linear B-cell epitopes, machine learning, bioinformatics, computational, immunological, feature extraction.
School of Science, Dalian Maritime University, No. 1 Linghai Road, Dalian 116026, School of Science, Dalian Maritime University, No. 1 Linghai Road, Dalian 116026, School of Science, Dalian Maritime University, No. 1 Linghai Road, Dalian 116026, Department of Mathematics and Statistics, Lingnan Normal University, Zhanjiang