4. A computer implemented method that facilitates epitope prediction, comprising: training, using a processing unit, a logistic regression (LR) model for epitope prediction using one or more hidden variables representative of one or more characteristics of a major histocompatibility complex (MHC) molecule; using special features that include an identity of an amino acid and/or a chemical property of the amino acid at a given position along either region that flanks an epitope and the identity of the amino acid and/or the chemical property of the amino acid at a given position along the MHC molecule; and performing a multi-factor cross validation to confirm the epitope prediction.
4. A computer implemented method that facilitates epitope prediction, comprising: training, using a processing unit, a logistic regression (LR) model for epitope prediction using one or more hidden variables representative of one or more characteristics of a major histocompatibility complex (MHC) molecule; using special features that include an identity of an amino acid and/or a chemical property of the amino acid at a given position along either region that flanks an epitope and the identity of the amino acid and/or the chemical property of the amino acid at a given position along the MHC molecule; and performing a multi-factor cross validation to confirm the epitope prediction. 7. The method as recited in claim 4 , further comprising training the LR model using an expectation-maximization (EM) algorithm wherein a maximization calculus comprises a gradient optimization.