9. 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 shift variables representative of a position of a peptide within a groove of a major histocompatibility complex (MHC) molecule; using data representative of an identity of a major histocompatibility complex (MHC) allele and data representative of the identity of an amino acid at a certain position of an epitope; and performing a multi-factor cross validation to confirm the epitope prediction.
9. 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 shift variables representative of a position of a peptide within a groove of a major histocompatibility complex (MHC) molecule; using data representative of an identity of a major histocompatibility complex (MHC) allele and data representative of the identity of an amino acid at a certain position of an epitope; and performing a multi-factor cross validation to confirm the epitope prediction. 12. The method as recited in claim 9 , further comprising training the LR model using an expectation-maximization (EM) algorithm wherein a maximization calculus comprises a gradient optimization.