Sentence examples for sparse analyses from inspiring English sources

As it results from sparse analyses of the content and composition of strawberry pomace polyphenols, about half of the content of ellagitannins in processed strawberry fruits is to be found in juice and the rest remains in pomace, out of which 35%% in the pulp and ca. 15 % in the seeds [ 6, 30].

We continued the meta-analyses to 12 weeks since age-specific data was sparse for the analyses after this age.

Because these last two pancreatitis characteristics were not asked in the MDACC study and numbers were too sparse for meaningful analyses in the UCSF San Francisco Bay Area dataset, they are not addressed in the analyses presented here.

Due to the sparse literature, the analyses were basically explorative, although it was possible to make a few expectations.

We did not include Archaea in this paper because the taxon sampling of available genome sequence is still too sparse to perform analyses that involve closely related pairs.

Meta-analyses may result in type I errors owing to an increased risk of random error when sparse data are analysed 23 and due to repeated significance testing when a cumulative meta-analysis is updated with new trials.

This novel approach to sensitivity analysis provides a formal statement of confidence in our findings based on statistical power, and a solution for analysing sparse data sets of disease prevalence.

Here we present an integrated approach to analyse sparse, heterogeneous data from a largely unknown organism.

Because of the possibility of analysing sparse and unbalanced datasets, thereby minimising the burden for each child, population PK PD modelling and simulation using non-linear mixed effect modelling has become the preferred tool to develop effective and safe dosing regimens for children.

Moreover, under the homoscedasticity variance assumption across subjects and groups that is often used in SPM analysis, the aforementioned individual and group analyses using sparse dictionary learning can be accurately modeled by a mixed-effect model, which also facilitates a standard SPM-type group-level inference using summary statistics.

Linear discriminant analyses (LDA), sparse partial least squares (SPLS) and pairwise comparisons of cell line data were used to build resistance signatures from both cell line panels.