Sentence examples for performance of the workflow from inspiring English sources

To evaluate the performance of the workflow we performed a comparative lipid analysis of human milk, cow milk, and Lacprodan® PL-20, a phospholipid-enriched milk protein concentrate for infant formula.

Although a formal evaluation of the accuracy of variant calling pipelines remains unfeasible for nonsimulated sequence data (Li 2014), we estimated the performance of the workflow using both sorghum and Arabidopsis sequence data.

This dataset was chosen to evaluate the performance of this workflow in identifying the known LHON-causative mutations since 42 % of these genomes was expected to harbor at least one of the primary mutations included in the panel of the 'Top 14 LHON' annotated in Mitomap (Lott et al. 2013).

The data included genomes from the 1000 Genomes Project, autism exomes, autism genomes, and Illumina's "Platinum" genomes (Additional file 2: Table S1) (http://www.illumina.com/platinumgenomes/.) This diverse collection enabled assessment of the performance parameters of the workflow with relatively homogeneous versus heterogeneous data, as expected in both biomedical and clinical scenarios.

A recently published solid-state (^{17}hbox {O}) NMR study of (^{17}hbox {O} -enriched uranyl salts serves tO} -enriched the performance of the auranyled workflow [15].

Lastly, we further validated the performance of the RIG workflow using publicly available Sanger sequence and WGS data from Arabidopsis.

As such, we validated the performance of the RIG workflow using only WGS data as both the source of reliable variants and the analysis target.

We evaluate the performance of the RIG workflow for sorghum sequence data using a collection of genetically validated variants, and we compare the output of the RIG workflow with variant calls from a recent sorghum study.

Whereas the performance of the automatic workflow is comparable among the three curators on the 'manual annotation' subset, it deviates significantly on the 'manual correction" subset with the F-score values as high as 95%.

For optimal performance of the entire acquisition reconstruction workflow, the post-processing pipeline can also accept a ZMQ [36] stream directly from a detector instead of reading data from file.

Settings for each step in this process affect the quality of features returned and therefore the overall performance of the untargeted metabolomic workflow.