Direct prediction of T-cell epitopes using support vector machines with novel sequence encoding schemes.

Lei Huang; Yang Dai

doi:10.1142/s0219720006001758

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Direct prediction of T-cell epitopes using support vector machines with novel sequence encoding schemes.

Lei Huang·Yang Dai

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Abstract

New peptide encoding schemes are proposed to use with support vector machines for the direct recognition of T cell epitopes. The methods enable the presentation of information on (1) amino acid positions in peptides, (2) neighboring side chain interactions, and (3) the similarity between amino acids through a BLOSUM matrix. A procedure of feature selection is also introduced to strengthen the prediction. The computational results demonstrate competitive performance over previous techniques.

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Amino Acid Sequence Artificial Intelligence Computational Biology Epitopes, T-Lymphocyte Sequence Analysis, Protein T-Lymphocytes Journal Article Research Support, U.S. Gov't, Non-P.H.S.

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Lei Huang, Yang Dai. 2006. Direct prediction of T-cell epitopes using support vector machines with novel sequence encoding schemes.. https://doi.org/10.1142/s0219720006001758

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