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While Boolean approaches are based on switching functions and consider networks as logical circuits, other discrete formalisms such as rule-based methods allow for the biochemical and biophysical description of multi-state components [ 19].
The simulation-based methods allow for suitable grouping of uncertain parameters in order to build a simplified model of correlation across structural parameters.
These label-based methods allow for estimation of relative protein abundance [65].
The sparse signal reconstruction-based methods allow for the fusion of data from all bistatic links, which is not possible in traditional time-frequency analysis-based methods.
Affinity-based methods allow for rapid and specific assessment of methylation changes in a gene-specific manner.
Affinity-based methods allow for rapid and specific assessment of the mean methylation levels of large DNA regions.
Interestingly, a number of the training-based methods allow for a prediction knowing only the sequence of a protein [ 17- 26].
The optimized QuEChERS-DESI-HRMS based-method allowed for a rapid reliable screening of the investigated compounds at levels of interest, thus being useful for high-throughput analyses.
The health state-based method allows for an addition to published post-event utilities because the acute values are based on health states that include cardiovascular events.
The aim of the current study is the quantitative analysis of genotypes from low-cost, low-density SNP assays, focusing on maximum likelihood based methods that allow for the possibility of genotyping errors, e.g. [ 14– 20].
Furthermore, Bayesian methods allow for better uncertainty quantification than likelihood based estimation, as the coverage probability is closer to the nominal 95% level.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com