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The study design was eventually approved by a global scientific committee and by local scientific committees of each single data provider.
Then, we evaluate the performance of the proposed Weibull method for each single data set.
We generated lists of tissue-specific genes for each single data set and integrated them to a single consolidated list.
We test SSEM on the non-control dataset of each group, where it produces a rank for each single data point.
The analysis shows that our integrative approach has consistently superior power in predicting gene expression levels compared to that from each single data type-based analysis.
In accordance with (10), the likelihood ℒ(M| D) of the model given the data is the product of the likelihood for each single data point, i.e., ℒ (M | D ) = ∏ i = 1 N u (T, ξ i (T ) ).
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Data represent three independent experiments, each collecting single data points.
For in vitro experiments results are expressed as arithmetic means ± SD of duplicates in one experiment (rat whole blood) or as arithmetic means ± SEM of at least two independent experiments, each collecting single data points (human blood cells and isolated 5-LO).
In each step, one single data block is processed by certain slot.
Each participant contributed a single data point for each analysis.
Comparing each single cell data with the YH-mix data as control, we found that DOP-PCR had the best accuracy for CNVs detection (≥1 Mb, Bonferroni-corrected Mann–Whitney-Wilcoxon test, p < 0.05), with a mean sensitivity of 94.15 % (SD 4.84%%) and a mean specificity of 94.00 % (SD 6.51%%).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com