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For male CV1, all four methods yielded significant phylogenetic signal (Table 4).
All methods yielded significant deviation from the uniform distribution in each tissue (P < 0.05 by the KS test).
When comparing two random samples of trials from the same experimental condition, all methods yielded significant results at the rate prescribed by the error rate of the statistical test (1000 repetitions, expected number of significant tests: 50; observed number of significant tests for SCRalyze/DDA 1/DDA 2/Peak1/CDA 2/Peak scoring: 53/60/55/47/56/57).
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We observe that lasso and dirty regularization-based regression methods yield significant improvements in performance, both yielding 78.4% prediction accuracy.
Examples from the literature are given, in which conventional parameter estimation methods yield significant qualitative and quantitative errors in the prediction of the phase behavior using the NRTL, UNIQUAC and Wilson models.
When used on human vessels, the same myography method yielded significant results for SNP and CGRP, where a strong dilatation was observed.
Initial experiments with the method yielded significant problems with primer dimerization (Bybee et al. 2011).
While the transporter A gave no apparent improvement with rMMS, the method yielded significant increased crystallization successes for the cytochrome (16 extra new crystallization conditions), the enzyme protein (12 extra new crystallization conditions) and Mhp1 (8 extra new crystallization conditions).
The neural sources with the LORETA method yielded significant electrical activity in the supplementary motor area (Brodmann area 6), the posterior cingulate gyrus (Brodmann area 31/23) and the parietal lobe (Precuneus/Brodmann area 7/5).
In experiments with yeast and human, the networks optimized by our method yielded significant improvements in terms of AUC scores, and the learning time was acceptable even for the large human genome.
However, a comparison of data obtained through both methods yielded no significant differences in measurement of the signal fall off features as shown in Fig. 3b and d.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com