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As seen in the table, our method with automated parameter setting, IDD-BM3D and our method with fixed parameter setting achieved 3, 2, and 1 best values out of the 6 scenarios, respectively, but differences among them are small and all of them are significantly better than those of Fnnmm. Figure 3 shows a zoomed area of the body of the plane.
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In the Table, our method-II achieves the best performance (83.7%), implying the majority of predicted complexes are significant.
As seen from these tables, our method can achieve the predicted convergent rates both intime and space.
As we can see from these tables, our method performs better than that of Shotton et al. [4] in both total and average accuracy, especially the average one.
The comparisons of object labeling on Polo and TUD are listed in Tables 3 and 4. According to these tables, our method performs better than that of He and Gould [15] in the four metrics.
By comparing the ROC analysis results in Table 3 (our method) with Table 5 (conventional method), the advantage of using the inversely transformed VOIs of our method is clear.
We used 7 microarray assays (GEO: GSE17796) of bone marrow cells to run our test and the results were listed in Table 2a (our method) and Table 2b (distance comparison method).
As shown in this table, our mixture method achieves significant speedups compared with the original parsimony method.
Obviously from the table, our proposed method is stable and effective.
As one can see from the table, our suggested method again resulted in the numerical solution about one order faster than the PDL1P measured in the CPU time.
As one can see from the table, our suggested method takes about one order less CPU time than the PDL1P for the computation of the numerical solution.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com