Using IUPAC strings as motif model and using the MapReduce programming model to enable distributed computing, it was shown that it is feasible to compute all genome-wide conserved words in a large dataset in an exhaustive manner.
Once it became feasible to compute different kinds of residual in a straight forward way, various methods have focused on their underlying properties and their effectiveness.
Therefore, it becomes feasible to compute the time offset as an expectation of all possible true channels given the estimated one, which corresponds to a minimization of the following new metric: Deltahat{theta}_{e}=arg min_{Delta theta_{e} in boldsymbol{Lambda}}left{widetilde{D}left(Deltatheta_{e}right)right}, (41).
While previously it was only feasible to compute Pgen of nucleotide sequences, we introduce a novel dynamic programming algorithm that efficiently computes Pgen of amino acid sequences.
For two candidate power levels and small K (e.g., K≤4), it is computationally feasible to compute the global optimum.
In the data, it is not feasible to compute a family specific food price index, but this is feasible for the non-food bundle.
Therefore, p K a prediction by QSPR models based on QM charges cannot be applied in virtual screening, as it is not feasible to compute QM atomic charges for hundreds of thousands of compounds in a reasonable time.
Since ( {boldsymbol{K}}_i^r{left {boldsymbol{K}}_i^rright)}^ne boldsymbol{I} ), it will be feasible to compute the minimum reconstruction error between the original vector and projected vector for determining the classification results.
Since it is not feasible to compute all regression models, they reduce the set of potential regulators to the 10 best predictors based on average expected BIC.
Being an NP-complete problem, it is also not feasible to compute all the independent sets in a general wireless networks as proposed by [5].
