Sentence examples for using a single dataset from inspiring English sources

In Figures 3b and 3c, we show that by using a single dataset those results reduce to 155 of 304 (51%) parks and 24 of 45 countries (53%).

A previous study evaluated the performance of eight published anti-TNF gene expression classifiers using a single dataset, GSE33377 [ 16].

Haughton et al. [ 15] compared three LCA programs (Latent Gold, poLCA and MCLUST) using a single dataset.

The ability to compare mRNA and proteins in identical samples with respect to miRNA allowed us to identify potential miRNA targets, and highlight translational repression targets which could not have been identified using a single dataset.

This study validated six scoring systems and algorithms to risk-stratify patients for MT at a very early stage after trauma, using a single dataset of severely injured patients derived from the civilian TraumaRegister DGU® database.

That is, a signature developed using a single dataset may overfit certain aspects of the dataset that are not of primary scientific interest, leading to poor performance on independent data.

Combining these ideas into a MATLAB® calculation framework that uses a single dataset on properties of AP and binder consistent with burn rate vs. pressure of pure AP and fine AP-binder system allows for making the predictions of propellants with multiple particle sizes and different fractions.

Validation using a single independent dataset from the same population of patients as used for construction of the signature is sufficient if the protocols used are unbiased and appropriately powered.

However, the result of using a single expression dataset could be biased.

An example of the population structure learned by structure from a single set of 2000 SNPs is shown in Figure 2. In Figure 3, we compare the performances of different association methods using a single simulated dataset.

In brief, we initially illustrate the weighting approach and the graphical diagnostics described above using a single simulated dataset, where the missing data in the outcome are weakly MNAR (i.e. small departure from MAR (δ =0.2)).