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To determine the diagnostic characteristics, we generated receiver operator characteristic (ROC) curves and evaluated the area under the curves (AUC).
To test whether CD44s regulates tumor-initiating characteristics, we generated stable MHCC97-H CD44s shRNA cell lines and performed microarray analysis.
To visualise the spatial distribution of the area characteristics we generated maps using the 250×250 m map grid over a sample town.
In order to characterize normal protein patterns and improve knowledge of molecular species-specific characteristics, we generated a two-dimensional reference map of sheep serum.
To evaluate methods for linking data with different characteristics, we generated a second "admissions" file of 10,000 records by randomly sampling 10,000 values for each of the identifiers in the original PICANet data.
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Therefore, to test if CNS K and CNS L present this characteristic, we generated constructs where each of the CNSs was cloned downstream of the reporter coding sequence.
Based on the characteristics above, we generated two datasets of simulated spectrum.
To further gain an insight into the characteristics of the proteins, we generated antibodies to five proteins.
We generated baseline characteristics of the 45, 532 participants.
We generated descriptive characteristics of the cases and controls, as well as distributions of risk factors by contraceptive use in the controls to assess potential confounding.
The ERAP1 models we generated exhibit structural characteristics that appear highly relevant to the peptide library screen results.
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CEO of Professional Science Editing for Scientists @ prosciediting.com