Sentence examples for many datasets the from inspiring English sources

For many datasets the calculation of meaningful over-representation scores requires correction of bias.

To compute N01 for many datasets, the previous procedure becomes computationally intensive: the permutation step takes long time; unstable: some null density estimates have negative values.

For many datasets, the number of GO terms with noise model-dependent equivocal assessment is larger than the number of GO terms we can unambiguously assign to these experiments irrespective of the chosen noise model.

For example, we have found that in many datasets, the default costs used in TreeMap (Charleston, 1998) and Jane Conoww et al., 2010) give rise to different reconciliations than those using the default costs in AnGST (David and Alm, 2011) and RANGER-DTL (Bansal et al., 2012).

When the training data are sparse, as is the case for many datasets in the life sciences, the Bayesian approach can be beneficial.

After Bayesian integration, we use center bias to conduct post-processing to obtain the final stereo saliency map, because many datasets place the salient object or region in the center of the image [37].

Our reasons are (1) Affymetrix is the common platform used in our three training datasets which ensures the inclusion of the entire set of our 56 genes; (2) the data preprocessing procedure is standardized; (3) existence of many datasets in the public domain for validation and (4) high reproducibility.

Experiments on many datasets demonstrate the usefulness of MOEASSC and PSVIndex, and show that our algorithm is insensitive to its parameters and is scalable to large datasets.

This contention has been corroborated through performance testing using lambda gDNA as a quantitative standard, which has generated many datasets illustrating the exceptional quantitative capabilities of real-time qPCR (e.g. Figures 5, 6 and 7; also see [7]; data not shown).

One particular approach consists in gathering as many datasets from the literature as possible, pool them together and treat them as just one large data set, an approach that has given positive and encouraging results [ 8, 10, 11] In a previous work we applied this technique to two phylogenetically widely different bacteria, E. coli and B. subtilis [ 12].

This result was found to be quite consistent across the many datasets generated in the field trial.