title: Machine learning-based methods for prediction of linear B-cell epitopes.
abstract: B-cell epitope prediction facilitates immunologists in designing peptide-based vaccine, diagnostic test, disease prevention, treatment, and antibody production. In comparison with T-cell epitope prediction, the performance of variable length B-cell epitope prediction is still yet to be satisfied. Fortunately, due to increasingly available verified epitope databases, bioinformaticians could adopt machine learning-based algorithms on all curated data to design an improved prediction tool for biomedical researchers. Here, we have reviewed related epitope prediction papers, especially those for linear B-cell epitope prediction. It should be noticed that a combination of selected propensity scales and statistics of epitope residues with machine learning-based tools formulated a general way for constructing linear B-cell epitope prediction systems. It is also observed from most of the comparison results that the kernel method of support vector machine (SVM) classifier outperformed other machine learning-based approaches. Hence, in this chapter, except reviewing recently published papers, we have introduced the fundamentals of B-cell epitope and SVM techniques. In addition, an example of linear B-cell prediction system based on physicochemical features and amino acid combinations is illustrated in details.
authors.affiliation: Department of Computer Science and Engineering, National Taiwan Ocean University, 2 Pei-Ning Road, Keelung, 20224, Taiwan, Republic of China.
authors.forename: Hsin-Wei
authors.initials: HW
authors.lastname: Wang
authors.forename: Tun-Wen
authors.initials: TW
authors.lastname: Pai
mesh_terms: D000818:Animals; D001185:Artificial Intelligence; D018985:Epitopes, B-Lymphocyte / Q000276:immunology*; D006801:Humans; D018448:Models, Immunological; D060388:Support Vector Machine
chemical_list: D018985:Epitopes, B-Lymphocyte