Sentence examples for in the case of datasets from inspiring English sources

In the case of datasets from organisms with non-existent or incomplete reference genomes, namely ones that exist as unscaffolded contigs, algorithms designed for humans fail entirely.

Calculating the amount of variance explained by a set of eigenvectors in a given dataset is straightforward in the case of datasets containing the phenotypic measures used to obtain P matrices.

In K n network, the number of edges is equal to n* n-1)/2; this implies that in* n-1case of dathists wimpliesarge number of genes, analysis of thatK n network can be computatinnally theing when high performancaseofputing options are not available.

Here, we propose a modification to the original energy function such that (20) G = 1 γ N (∑ k = A T θ 0, k  ln   θ 0, k + ∑ j = 1 W ∑ k = A T θ j, k  ln   θ j, k ) This modified energy function is maximized with a perfectly conserved motif occurring in each input sequence, and the γ N factor cancels in the case of datasets derived by the cut heuristic.

For example, in the case of Dataset One, the importance of 'investment in young children', 'public funding' or 'economic return' on investment in ECCE was mentioned in all but two of the presentations/papers, regardless of the session or specific Forum theme in which the presentations/paper was delivered.

For example, in the case of Dataset Two, information related to population data was compared against statistics available online to gain further insights, and policy reviews/reports from countries that presented, where accessible, were also accessed and reviewed as a means of cross-checking categories and themes that were identified.

In the case of dataset A, the total number of tags was 620,696, with 131,623 (21%) tags corresponding to normal tissues and the remaining 489,073 (79%) tags corresponding to cancerous tissues.

In the case of dataset 1 (no CWS and CBS), the last three methods perform similarly.

Two parallel tests were run for 20 million generations in the case of dataset (a) and dataset (b) to achieve convergence.

In the case of dataset GSE3325 each signal intensity from the primary tumour samples was scaled to the corresponding median gene signal intensity across the benign tumour samples.

As in the cases of datasets A and B, a bootstrap analysis was performed to obtain confidence intervals for the parameters of interest and to test the differences in specialization between normal and tumor tissues.