Direct prediction of T-Cell epitodes using support vector machines with novel sequence encoding schemes

Journal of Bioinformatics and Computational Biology

Volumn 4, Issue 1, 2006, Pages 93-107

  Huang, Lei ^a   Dai, Yang ^a  

^a University of Illinois at Chicago   (United States)

Author keywords

Feature selection; Side chain interaction; Support vector machine; T cell epitope recognition

Indexed keywords

AMINO ACID; EPITOPE; PEPTIDE DERIVATIVE;

ANALYTIC METHOD; ARTICLE; ARTIFICIAL NEURAL NETWORK; COMPUTER SYSTEM; CONTROLLED STUDY; INFORMATION PROCESSING; INTERMETHOD COMPARISON; MATHEMATICAL COMPUTING; PREDICTION; PROTEIN INTERACTION; SEQUENCE ANALYSIS; SUPPORT VECTOR MACHINE; T LYMPHOCYTE;

AMINO ACID SEQUENCE; ARTIFICIAL INTELLIGENCE; COMPUTATIONAL BIOLOGY; EPITOPES, T-LYMPHOCYTE; SEQUENCE ANALYSIS, PROTEIN; T-LYMPHOCYTES;

EID: 33645307670     PISSN: 02197200     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0219720006001758     Document Type: Article

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