were tested at training epoch numbers from 10 to 100 and the best performance was obtained at about 70 epochs after which the performance deteriorated there are several ways for peptides to be incorporated encoded in a statistical prediction algorithm one of the simplest ways is to encode the amino acid composition of the peptides tracking how many times each amino acid residue is found in each peptide one of the most computationally complex ways is by fully describing the peptide sequence i e identifying what amino acid is occupying which position in each peptide in general the larger the training set the more complex the encoding can be without over fitting the data in this study two peptide encoding schemes were examined for input to the neural network fully and partially encoded peptide sequences the fully encoded peptide sequence model described the exact amino acid residue in each position of the peptide sequence up to 50 residues the partially encoded peptide sequence model described the exact amino acid residues for the 3 amino acids at the n terminus and the 3 amino acids at the c terminus of the peptide the remaining amino acid residues are represented by a single vector thus in the fully encoded peptide sequence model used in these experiments each vector was composed of 21 dimensions and each dimension represented an individual amino acid residue all the vectors representing the exact position of an amino acid residue in the peptide contain 20 zero values and one numerical value representing the amino acid residue in contrast the vector in the partially encoded peptide sequence model that is used to describe the amino acid composition of the remainder of the peptide sequence often has more.