Direct prediction of T-cell epitopes using support vector machines with novel sequence encoding schemes.
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.
Department of Bioengineering, University of Illinois at Chicago, Chicago, IL 60607, USA. lhuang7@uic.edu
Lei
L
Huang
Yang
Y
Dai
D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D019295:Computational Biology; D018984:Epitopes, T-Lymphocyte / Q000737:chemistry* / Q000235:genetics*; D020539:Sequence Analysis, Protein / Q000706:statistics & numerical data; D013601:T-Lymphocytes / Q000276:immunology*
D018984:Epitopes, T-Lymphocyte