Sentence examples for datasets were calculated as from inspiring English sources

Distances between datasets were calculated as one minus the correlation coefficient of the resulting taxonomic distributions.

Codon usage fractions for individual datasets were calculated as percentage of the respective total amount of counted codons.

Accordingly, the distance between datasets was calculated as the average interpoint distance of all points in one dataset from all points in the other dataset.

The extent of agreement between the Affymetrix and Agilent datasets was calculated as the percentage of genes in common out of the possible maximum (Table 1).

Pairwise trait correlations within each dataset were calculated as the median correlation over 100 repeat random samplings of one individual from each independent sibship (see Appendix S4).

To evaluate the relationship between the fitting performance and sea clutter characteristics, mean value ε d and variance σ d of the fitting RMSE of each dataset are calculated as: {varepsilon}_d={displaystyle sum_l{mathrm{RMSE}}_{d,l}}/7,kern1em {sigma}_d={displaystyle sum_l{left({mathrm{RMSE}}_{d,l}-{varepsilon}_dright)}^2}/7 (18).

Due to the high computational cost of a large splitstree construction, the phylogenetic distance between two sequences of a large dataset is calculated as an average distance between those two sequences in trees of randomly sampled sequence subsets composed in half of NSI/CCR5-only and in half of SI/CXCR4-capable sequences.

Minimal mutual information for each dataset is calculated as the case in which the two lists of n1 transcriptome DEGs and n2 translatome DEGs have null overlap.

The CV of each gene in each dataset is calculated as follows: (1) C V g i j = SD (g i j ) mean (g i j ).

Similarly, recall is defined as the fraction of experimental (true) terms, which were correctly predicted, i.e. As before, precision and recall for the entire dataset are calculated as averages over the entire set of proteins [an alternative definition of precision and recall is given by Verspoor et al. (2006)].

KA/KS values for gene datasets were calculated by summing up over all non-synonymous and synonymous sites in each dataset as reported elsewhere [ 6].