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Uncertainty in applying the method is quantified.
This is the first time that the amount of bias associated with each method is quantified.
The performance of each method is quantified and compared based on three norms and the best methods are identified.
The computational efficiency of the new method is quantified by comparing its results with other PSO variants found in the literature.
The performance of the method is quantified and it is demonstrated that the obtained results are substantially better when dealing with multi-dimensional data.
The impact of the explicit interface treatment on the stability and accuracy of the multi-block method is quantified for the advection and diffusion equations.
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However, cell migration using this method was quantified by manually counting cells (which is a tedious and time consuming process).
The expression levels of each gene spot measured by the WEnCA method were quantified and then normalized based on reference gene (β-actin) density.
The representation ability of sparse representation-based methods is quantified by defining category concentration index.
Performance goodness of these methods is quantified by calculating two types of errors as follows: error = Q test − Q correl Q test (3) absolute error = | Q test − Q correl Q test | (4).
Performance of different target-prediction methods is quantified using two standard metrics: the area under the ROC curve (AUC) [49], and the enrichment of the prediction ranked test data [50].
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