In comparison with direct calculation of all features in a single layer of classification, the two-layer architecture employed in this work reduced average total computational time by a factor of two (S7 Fig).

In this case, the computational time savings associated with the two-layer architecture increases with the complexity of the second-layer relationships and can result in large, roughly sixfold speed improvements in the case of two cell pair identification (88 Fig).

This two-tier scheme allows significant reduction in computational time .

Computational time savings associated with two-layer classification architecture for head versus tail detection.

Computational time savings associated with two-layer classification architecture for cell identification.

Unfortunately, once the model is learned, selecting peptides having the greatest predicted bioactivity often requires a prohibitive amount of computational time .

Indeed, applying the model to every peptides would take an astronomical amount of computer time .

Therefore, given a model, is it possible to determine, using reasonable computational time , the peptide that has the best properties and chance for success?

I n-silico predictions are faster and cheaper than in-vitro assays, however, predicting the bioactivity of all possible peptide to select the most bioactive ones would require a prohibitive amount of computational time .

Although INRICH and ALIGATOR show comparable computation times at their lowest SNP p-value cutoff, the need to repeat the analysis at multiple cutoffs means the total analysis for both takes considerably longer.

The low MAGMA computation times are largely due to the choice of statistical model.

Note however that the permutation-based SNP-wise analyses in MAGMA also show very reasonable computation times .

However, even the permutation-based SNP-wise models implemented in MAGMA outperformed their equivalents in other software and yielded very reasonable computation times .