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Measuring all these metrics is difficult and some of them may not have important affect on improving supply chain areas.
Evaluating all these metrics is difficult for engineering managers and they miss the monitoring of effective metrics as they are engaged with all metrics.
Making decision only based on the above metrics is difficult because if one approach performs well with respect to SLA violations at the expense of high power (and vice versa), it is hardly possible to conclude about the preferable approach [18, 23].
Laying a foundation for the selection and estimation of metrics is difficult due to the lack of a conceptual framework.
Answering such questions and establishing robust metrics is difficult due to the lack of publicly available RNA-seq data specifically generated to capture gene fusions.
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But, I admit, standard metrics are difficult to apply to Murakami.
As these five metrics are difficult to be fully understood, it is hard to select the most appropriate one for a specific condition.
Similar to our findings, the results have suggested a modest association, though the different metrics are difficult to compare and no results are statistically significant.
Given the heterogeneity in methods and definitions, however, the different metrics are difficult to compare, and few studies have observed statistically significant results.
However, the lower bound (i.e., the worst-possible score) of the metrics are difficult to be computed since the conservation characteristic of a given motif can not be completely eliminated in any situation.
In general, such metrics are difficult to interpret because a low rate of one false-positive type may imply either a high true-positive rate or a high false-positive rate of the other type.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com