Table I. ANN summary.
| Neural Network Architecture | |
|---|---|
| Motif length | 9 |
| Peptide flanking region size | 3 |
| Peptide flanking region length encoding type | 0 |
| Peptide flanking region encoding | SPARSE OR BLOSUM |
| Number of hidden neurons | 2, 10, 20 |
| Burn-in period without insertions or deletions | 10 |
| Impose amino acid preference during burn-in | ILVMFYW |
| Amino acid numerical encoding | SPARSE OR BLOSUM |
| Number of training cycles | 500 |
| Number of random seeds | 10 |
| Number of networks in final ensemble | 150 |
| Cross-Validation | |
|---|---|
| Folds for cross-validation | 5 |
| Method to create subsets | Common motif with maximum overlap of 9 aa |
| Cross-validation setup | Simple |
| Stop training on best test-set performance | No |
Summary of NNAlign architecture and encoding applied to all four models. Datasets were reduced to 50,000 peptide sequences according to the maximum accepted number of inputs by the server.