Innovations In B-Cell Epitope Prediction: A Review Of Machine Learning Techniques And Their Performance
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Abstract
Accurate prediction of conformational B-cell epitopes could play a transformative role in disease diagnosis, drug discovery, and vaccine development. Numerous computational approaches, many leveraging machine learning techniques, have been developed to tackle this challenging problem. This study conducts a comprehensive review of B-cell epitope prediction web servers, encompassing both machine learning and specialized approaches, using data from a unique dataset. The review findings indicate that overall performance remains suboptimal, with some methods performing no better than randomly generated patches of surface residues. These insights underscore the need for advanced evaluation methods in future studies, advise caution in relying on these tools until current limitations are addressed, and highlight potential new strategies for improving the prediction accuracy of conformational B-cell epitope prediction methods.
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References
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