Sentence examples for a random dataset from inspiring English sources

We conduct six experiments based on KB-II, a random dataset and a real product dataset, and the results show that these metrics can be used to roughly filter a big number of design concepts, and then expert-based method can be further used.

In order to assess the relationship between the distributions of access scores and a Laplace, we created a random dataset from an ideal Laplace distribution.

A relative abundance window centered at unity was used to generate a random dataset of control proteins numbering the same as the proteins showing differences in relative abundance.

Comparison of the nucleotide frequency at each position to the value of a random dataset generated in silico led to identification of a 5'-CTVB consensus sequence (P<0.0001).

And finally, parallel analysis was conducted to compare eigenvalues of the exploratory factor analysis with eigenvalues of a random dataset.

To ensure that modules were not being detected by chance, we simulated a random dataset containing same number of samples and genes as our test dataset.

Then, we iteratively replace genes from the original dataset by random ones, in 20% increments, and the dataset is progressively converted to a fully random dataset.

We generated a two-component random dataset as described above, a random miRNA-mRNA expression dataset and a random miRNA target prediction dataset.

To do this, two reference datasets were used in the KCCA, including the real reference dataset and a random reference dataset of IRGs constructed by randomly placing a similar number of IRGs from the real reference in five categories to emulate five types of IRGs.

Each of the simulated datasets was paired with a 'true negative' random dataset with the same query protein but in which all other proteins were selected randomly from the proteome.

To demonstrate this, I created a null (or random) dataset that could be analyzed with the approaches used in Chari et al. This "null" dataset was generated by combining the SAGE data from all 24 of their analyzed samples and then randomly re-distributing this "meta-transcriptome" into libraries with sizes equal to those in the original dataset.