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In a simple test problem, the present method gives less phase error than the leapfrog method and two other methods, and it does not give any amplitude error.
This exhibits one potential weakness of the Mahalanobis distance if used for outlier detection: the data is expected to form a single cluster and the method gives less reliable results of this is not the case.
Compared to GSEA, our method gives less biased results to a specific subtype, and this is because subtype-centric contextual gene sets are identified based on subtype-specific interactions.
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Compared to the piezoelectric force sensor, the Hopkinson bar-like method gives less-oscillating signals.
Results showed that, in the case of groups having similar and different ability distribution with different sample size (500 and 1000), test equating application with "Stocking-Lord" method gave less equation error has been obtained.
The RF gene selection method gave less performant results, nonetheless, these results were better than using microarray or clinical variables alone.
Both methods gave less biased estimates and lower average absolute error than the usual method of assuming a single CV for all index values.
In contrast, other methods gave less interpretable results.
The FPKM normalization method gives a less amount of DE genes.
Examples show that in many cases our method can give less conservative results than those by the existing methods.
Despite this limitation, the method will give less bias in the corrected ages than alternative correction methods.
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