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B-cell epitope prediction using Extreme Learning Machine

B-cell epitope is the small portion of antigen surface that is identified by antibodies. Epitope prediction is the task of identification of antigen surface in one of the classes of epitopes and non-epitopes. The prediction of B-cell epitopes is affected by different scales (features) of amino acid samples, such as hydrophobicity, polarity and flexibility, and so, it is necessary to utilize an appropriate model to obtain the maximum accuracy. This paper utilizes Extreme Learning Machine (ELM) as the prediction model to classify the amino acid samples into one of the classes of epitope or non-epitope. Due to the complex relationship between the scales, the extremely fast learning speed and appropriate generalization performance of ELM can be effective on improving the prediction results that is proved by evaluation of this model on a protein dataset with different number of samples and scales.