Sentence examples for an iterative screening from inspiring English sources

However, in an iterative screening scenario, when data from the first screening iteration is available, there are a number of practical considerations of a somewhat different nature, such as how large a portion of the database should be screened in the next iteration, that are not answered directly by the enrichment and related metrics.

To achieve this, we employed an iterative screening approach across multiple siRNAs to arrive at advanced designs with low 2′-deoxy-2′-fluoro content that yield significantly improved potency and duration in preclinical species, including non-human primate.

In an iterative screening setup, this has implications on how to select the compounds for the initial round of screening, and the only way to guarantee exchangeability (as long as the sample is large enough) would be to draw a random sample of the available data.

Aptamers are single-stranded DNA (ssDNA) or RNA typically generated by an iterative screening process termed Systemic Evolution of Ligands by Exponential Enrichment (SELEX) [ 16].

Under the assumption that all parameters but those describing the dynamics of chromatin would be identical in all the clones, we performed an iterative screening of model parameters.

In designing right aptamers for interactions with binding sites in specific targets an iterative screening process termed SELEX on complex nucleic acid libraries has been used since its discovery more than two decades ago [ 19, 20].

We performed an iterative screen in Jurkat T-cells with a short-hairpin-RNA (shRNA) library highly enriched for human kinases and phosphatases.

In the present study, we developed a new iterative screening algorithm, F ast a lgorithm of knockout screening for t arget Pro duction based on s hadow price analysis (FastPros), which uses biomass production maximization to identify sets of metabolic reactions whose simultaneous knockouts result in the production of a target metabolite.

With the advancements in screening technology and automation, it has become feasible to screen libraries in an iterative fashion, screening a small part of the library and using the result to make inferences about what compounds to screen next [2 5].

For example, when target production yields under biomass maximization are used as a score for screening, iterative screening fails to identify the combinations of two knockouts that show higher production yields only when they are disrupted simultaneously.

This approach represents a new way of evaluating iterative screening campaigns and optimizing screening efficiency.