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Economic analyses subsequently compare both methods with respect to payback period and cash flow.
As shown in Table 2, the differences in accuracy between both methods, with respect to the highest correlation obtained for an individual trait, were negligible.
We tested both methods with respect to the number and accuracy of prior known interactions (PKIs), and the effect of the weight of the PKIs.
Our aim was to determine to what extent gene expression profiles were indicative of protein expression profiles and vice versa, and to evaluate both methods with respect to their potential for predicting physiological differences among New Zealand Pachycladon species.
Comparing for each method the two lists of DE genes resulted from the replicates, both FunPat and edgeR show the highest overlap (0.76 in both methods) with respect to maSigPro (0.52) and FPCA (0).
After having compared both methods with respect to cell replication in vitro, we analyzed SA-β-Gal in vivo; in particular, both expression and activity of SA-β-Gal were measured in the omentum specimens as well as in primary cell isolates without any replicative history.
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Experimental evidence shows that the proposed approach outperforms the competing methods with respect to both computing time and solution quality.
One of the proposed methods presented much better results than do the traditional CBWS, EBWS and SNRBWS methods, with respect to both the denoising performance and the runtime.
These data indicate good agreement between methods with respect to both regional differences in expression and the maturational trajectories.
We compared the performance of considered methods with respect to the SSE values and CPU time.
Table 17 Pairwise comparison among the methods with respect to the APFD P03 P04 P05 Alt.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com