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The three methods had comparable invalid rates.
The two methods had comparable performance, with the only statistically significant difference between the two being on diabetes, where the machine learning method was superior.
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With respect to lipid measurement, the two methods have comparable reproducibility and the experimental errors are relatively small compared to natural variations of fish to fish within a sampling site.
The two methods have comparable performances when applied to simulated signals (Figures 2 and 3).
However, there are 45% participants considering that two methods have comparable performance.
However for N = 1000, the two methods have comparable spreads, while the Fortet method retains a smaller bias, see Table 3.
Power simulations have shown that the new methods have comparable power with a popular method known to work well in low-dimensional situation but the new methods have shown enormous advantage when the dimension is large.
As shown in Table 1, the three GAC methods have comparable peak memory cost to each other, while in terms of running time, H-GAC is more efficient than both S-GAC and F-GAC, since the set of slices and fibers decomposed by H-GAC is usually more compact, thus having lower computational complexity.
This is why all selected methods have comparable performance.
i These methods have comparable success rates, but encompass quite different processes.
Although these methods have comparable prediction performances, they are not sufficient for understanding the complete picture of sumoylation mechanism.
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