Sentence examples for automated screening of from inspiring English sources

Dr. Gu has initiated a collaboration with Calibr in San Diego to use their library of 14,000 compounds to develop automated screening of complex endothelial functions such as angiogenesis and cell migration.

Several studies evaluated automated screening of electronic records and found this to be a useful strategy for the identification of SSIs that occurred post discharge.

Automated screening of pesticides based on retention time and the exact mass of one diagnostic ion resulted in too many false positives to enable efficient screening.

The work provides a foundation for automated screening of intact metazoan model organisms in drug discovery using Lab-on-a-Chip.

In this article, we present an automated protocol for setting up alchemical free energy calculations for mutations of naturally occurring amino acids (except for proline) that allows an unprecedented, automated screening of large mutant libraries.

The poor suitability of standard hemagglutination based assay techniques for large-scale automated screening of red blood cell antigens severely limits the ability of blood banks to supply extensively phenotype-matched blood.

Since the tissue-like structures assembled on micro-patterned substrates is highly reproducible and requires low cell numbers, it lends itself to high throughput, automated screens of drugs that normalise tissue architecture.

The NEXT-peak program also provides a goodness-of-fit test, automating screening of the spurious binding, and work is in progress to extend its model to locate multiple binding events in a region.

We used automated screening assays to evaluate thousands of chemical compounds and found that diverse classes of neuroactive molecules caused distinct patterns of behavior.

Automated screening based on deviating patterns of reported time to onset [ 22] is conceptually different, but still focuses on a single aspect of strength of evidence.

Here we present a theoretical, fully automated screening method that uses a combination of machine learning and optimization to guide density functional theory calculations, which are then used to predict electrocatalyst performance.