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.
Huang
Lei
L
Department of Bioengineering, University of Illinois at Chicago, Chicago, IL 60607, USA. lhuang7@uic.edu
Dai
Yang
Y
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