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One can then integrate the multiple datasets to increase the statistical power of allele-specificity analysis.
Because detection of alternative splicing events is critically limited by the number of junction fragments, we first combined the datasets to increase the sensitivity of detection.
We have demonstrated a simple approach to leveraging multiple gene expression datasets to increase the robustness of inferred links between genes, by focusing on links supported by multiple gene expression datasets.
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Further studies need to be conducted on a larger dataset to increase the robustness of the volume estimation.
In our previous study, we found that the ComBat algorithm could remove such noise (batch signal or each individual study) from the dataset with powerful efficiency when adjusted with additional and multiple effects of the batch information [ 15, 16], which provide the prerequisite to combine the methylation array dataset to increase the sample size of the statistical analysis.
The present analysis and similar efforts [for example [ 35] also demonstrate not only the power of large (but well-managed) datasets to increase phylogenetic resolution, but the risk of relying on single sources of data, as inconsistencies between organellar- and nuclear-based analyses can remain even with greatly increased sampling.
Addition of new PPI datasets to increase coverage, for example, could change the values of the topological attributes for many PPI enabling an update to all of the scores.
The fictitious data records need to be added to the given datasets to increase occurrences for preserving l-diversity and also need to compensate the effects of these record values when performing some useful computations.
In this study, we integrated biological knowledge to overcome the variability of diagnostic predictors across gene expression datasets and to increase the predictive power of gene expression data.
For the latter dataset, and to increase the number of observations (holdings) in our lists, we joined the two years of data, 2005 and 2006.
The goal of the second multi-genotype dataset was to increase the number genotypes, tissues, and physiological conditions, while also increasing sequence depth and coverage by using 454 pyrosequencing.
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