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These metrics enable the development of a method that identifies efficient selection choices for sequencing.
Here, we present a method that identifies protein mutants with improved overall cellular efficacy, an objective not feasible with previous approaches.
We developed a method that identifies clusters of bursts that share a similar activation motif throughout the culture based on the fact that the culture morphology remains relatively unchanged for an extended time interval and that neurons fire in a recognizable and precise manner during a burst initiation.
In that sense, it is desirable to have a method that identifies genetic subgroups supervised by their survival times.
Here, I report a method that identifies biologically informative genes, i.e., candidate hubs, based on their co-expression values and corresponding indicators of statistical relevance.
MARS is a method that identifies hinge points in a time series that automatically minimize the residual sum of squares (RSS).
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One example is 'medicines supply and storage' issues, which feature strongly (n = 27), largely due to data derived from direct observation alone (~50%% of studies)—a method that identified a limited number of MAE causes restricted to the latent pathway.
More recently, Kappeler et al. (2009) discussed a method that identified organism names (referred to in this article as species words), with an aim to detect the 'focus' species at document level.
We first applied a standard method that identifies an SSOG as a gene encoding a protein that lacks homology to any predicted peptide from other arthropod genomes (table 1, SSOGs Standard).
The first is a quantitative method that identifies differentially methylated regions using an entropy-based algorithm [ 15] (see Methods).
Here we present a Bayesian method that identifies which known transcriptional relationships in a regulatory network are consistent with a given body of static gene expression data by eliminating the non-relevant ones.
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