1. A computer implemented method that facilitates epitope prediction, comprising: training, using a processing unit, a logistic regression (LR) model for epitope prediction using information from a plurality of sources representative of standard and special features of a desired epitope, wherein the standard features comprise, alone or in conjunction, data representative of an identity and/or supertype of a major histocompatibility complex (MHC) allele, and data representative of the identity and/or a chemical property of an amino acid at a certain position of an epitope, wherein the special features comprise, alone or Boolean combinations of, data representative of the standard features, the identity of an amino acid and/or the chemical property of the amino acid at a given position along either region that flanks an epitope and data representative of an amino acid and/or the chemical property of the amino acid at a given position along a MHC molecule; employing hidden variables that represent an absence of supertypes among MHC molecules; employing a shift variable that represents a position of a peptide within a groove of the MHC molecule; and performing a multi-factor cross validation to confirm the epitope prediction.