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This tool uses a negative binomial distribution model to test for differential gene expression [ 55].
Cuffdiff [ 7] uses a beta negative binomial distribution model to test the significance of change between samples.
The R (version 2.15.2) Bioconductor package EdgeR (v2.4.6) (Robinson et al. 2010), which uses a negative binomial distribution model to account for both biological and technical variability, was applied to identify statistically significant differentially expressed (DE) genes.
The read counts for each miRNA were compared between conditions using the Bioconductor DESeq package, which uses a negative binomial distribution model to test for differential expression in deep sequencing datasets (Additional file 7) [ 44, 45].
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The choice of a binomial distribution model was adequate to assess overdispersion of A-type particles (Rosner, 2011 ).
Then, as a sequence quality filtering, we applied our binomial distribution model (see above) to the reduced set of HCDiffs.
P values were estimated using a negative binomial distribution model and local regression to estimate the relationship between the dispersion and the mean of each miRNA.
It is shown in Fig. 3(a) that all transcriptional networks (and only them) have sign arrangements on the edges that are more skewed than expected (with respect to a binomial distribution model, see Supplementary Notes in Additional File 1 and Table S6) and also this property contributes to their monotonicity (Fig. S5).
Enrichment of tags within a 150 bp target window after subtraction of normalized input tags was gauged relative to a 200 kb background window using a binomial distribution model.
We propose a new collection frequency weighting scheme derived from the negative binomial distribution model of term occurrences.
This method was based on a negative binomial distribution model; the read count of gene i in the sample of j was designated Kij.
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