title: Predicting the probability of helper T cell immunodominant sites through discriminant analysis.
abstract: Bayesian discriminant analysis is used to predict whether or not a given protein segment will activate helper T cells. The predictor variables are drawn from the products of frequencies of amino acid residues. The model's predictive validity compares favourably with that of alternative modelling strategies, suggesting that this approach merits further investigation.
authors: Computer Center, School of Medicine, University of California, San Francisco, Fresno 93703.
authors: R R
authors: RR
authors: Mallios
mesh_terms: D000595:Amino Acid Sequence; D001499:Bayes Theorem; D016002:Discriminant Analysis; D006801:Humans; D016056:Immunodominant Epitopes / Q000032:analysis*; D008969:Molecular Sequence Data; D006377:T-Lymphocytes, Helper-Inducer / Q000737:chemistry*
chemical_list: D016056:Immunodominant Epitopes