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Most of the performance comparison shown in this paper is done using the network shown in Figure 5.
Using the network shown in Figure 2 and the parameters estimated for the TCDD network shown in Table 3, we simulated 500 data sets consisting of nine microarrays three for each dioxin dose; that is, we replicated the experiment 500 times using the predicted model.
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Proteins correlating with NFL levels in the CSF were used to create the network shown in Figure 5D.
Note also that this distinction could not have been made using the network projection (shown too in Figure 1).
In order to demonstrate how network performance deteriorates by Jammer's presence, we conduct an experiment using the network layout shown in Figure 1.
Supplementary Table S1 A brief summary of the microarray datasets used for construction of the network shown in Figure 3. Supplementary Table S2 Abbreviations for the node names of the network shown in Figure 3.
To quantify how much is actually gained by using the network and show the manner in which multifunctionality can affect network structure through node degree, we constructed networks entirely according to the model proposed earlier (in which a gene's appearance in a given test is taken to be due to a higher prevalence, pi).
We illustrate the algorithm using the sample network shown in Figure 2a.
In subfigure (b) we see the results of the same simulations using the network of communities shown in Figure 3 produced using preferential attachment.
The statistics from the validation using the first network shows a relative mean bias of about 1%, a relative mean absolute difference of 6%, and a mean absolute difference of 1.0 MJ m−2.
We use the example network shown in Fig. 2 to analyze the local PMC caused by additional vehicle through cell i and j.
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