Sentence examples for datasets even when the from inspiring English sources

The tree is highly sensitive to changes in input structures and the calculated features, making it very difficult to compare results across datasets even when the same clustering and fingerprinting algorithms are used.

Furthermore, the stochastic coalescent and mutational processes being modeled predict a large amount of variation in possible datasets even when the parameter values are known.

No differences were found by any of the algorithms for the identical datasets, even when considering the second version in reverse order.

MEME's computational requirements allowed us to process only the top 5000 sequences for all datasets, even when using the parallel version running on 24 CPUs.

However, it has been demonstrated that a given clade may receive support from a combined dataset even when that clade lacks support from every partition analyzed in isolation [ 22, 23].

We also sought to demonstrate that this benefit can be realized from existing datasets, even when they were not designed with MVPA in mind.

Our approach clearly assigned equal acquisition probabilities to traits whose timing was linked in the underlying dataset, even when 50% of the data was occluded.

While the sample size of the present study was large enough for replicating known associations such as the loci represented in the unweighted GRS, the sample size is not large enough to perform discovery studies with the entire Metabochip dataset, even when limited to common variation (minor allele frequency >5%).

No recombination was observed for MYC dataset, even when we used a less stringent q-value cutoff (20%%, data not shown).

Thus, not only can K a / K s detect drug resistance mutations in a single dataset (with no requirement for a reference dataset of untreated samples), it can do so even when the primary dataset is contaminated with a large fraction of untreated samples, or samples with different treatments.

The results presented above indicate that ConoSorter is capable of identifying conopeptides at high specificity and sensitivity, and, even when the dataset being analyzed ostensibly includes all known proteins, accurately assigning the appropriate superfamily and class.