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The 20-spot dataset was selected on the basis of the non-parametric Wilcoxon test, since the distribution of spot volumes deviates from normality and intra-group variance values are dissimilar.
The value of k was varied for each imputation, and the value that minimised the RMSE within the validation dataset was selected for prediction.
To create the first series in the benchmark, the first assay in the dataset was selected and an attempt was made to find a set of 5 molecules whose activities differ by at least 0.38 log units (this attempt involved iterating randomly over all possible selections of 5 molecules from the assay several thousand times).
A subset of 130 genes with significant expression changes in the combined dataset was selected (Materials and Methods).
The RDP sequence with the highest number of corresponding pyrosequencing reads, in the entire dataset, was selected to represent each phylotype.
This dataset was selected to determine if it was possible to distinguish the difference between a real dataset that did not undergo a significant perturbation and random responses.
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Second, joints with high discrimination for the whole dataset are selected to design feature.
To confirm the assignation and explore the genetic diversity of Mamiellophyceae, ASVs from Bathycoccus, Micromonas, Ostreococcus and Mantoniella represented by more than 200 reads in either the LW or LGC dataset were selected.
To compute the uncertainty component for any routine gauging, the four most similar gaugings among the reference stream-gaugings dataset are selected using an analog approach, where analogy includes both riverbed shape and flow distribution complexity.
As case of study, the most common 26 of 48 species from the Snapshot Serengeti (SSe) dataset were selected and the potential of the Very Deep Convolutional neural networks framework for the species identification task was analyzed.
The dataset is selected in the first wizard step (see Figure 2).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com