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known as antigens. It has great pattern recognition capability that may be used to distinguish between foreign cells entering the body (non- self or antigen) and the body cells (self). Any substance like proteins, polysaccharides, lipoproteins, polypeptides, nucleoproteins and nucleic acids that can induce the immune system to produce a corresponding antibody is called an antigen. This ability of antigen is called antigenicity. That portion of the antigen which can bind with the antigen binding site of the antibody is called B-cell epitope or antigenic determinant. B-cell epitopes can be linear or conformational. These epitopes play a vital role in the development of peptide vaccines, in diagnosis of diseases, immune based cancer therapies and also for allergy research. Since experimental methods of identifying epitopes are costly and time consuming, computational methods for prediction are desirable. This paper reviews various approaches like amino acid scale based methods and machine learning methods used for the prediction of linear B-cell epitopes.

Computational Methods in Linear B-cell Epitope Prediction