If no genome exists then this approach might provide information that can be informative.
Finally, we emphasize on the link that exists between this approach and the EKV formalism derived for bulk MOSFETs, which in turn leads to the unique gms/ID design methodology for DG architectures.
A danger exists with this approach in that the NN, during training, may memorise the training data.
Nevertheless, there is no guarantee that a solution that respects the given constraints exists, thus this approach may lead to empty results.
The result of applying this method is the complete set of non-empty C-induced subtraces of sequences sorting π into π T. Generally, we have no guarantee that a sorting sequence that respects all constraints exists, thus this approach can lead to an empty result.
Many applications exist for this approach, in fields ranging from biomedicine to systems biology, where significant gaps in knowledge still exist despite an abundance of data [13], [28], [29].
Considerable uncertainties exist with this approach particularly when the short historical records do not accurately reflect the true variability of the most extreme events.
However, the following drawbacks exist in this approach: (1) This approach is a pure parallel algorithm in view of the computation itself and ignores the physical reality of the underlying problem.
Additionally, no pharmacoeconomic data exist concerning this approach.
