Sentence examples for binomial distribution to detect from inspiring English sources

Two commonly used approaches which use the negative binomial distribution to detect differential expression are DESeq (Anders and Huber, 2010) and edgeR (Robinson et al., 2010).

Frequency distribution was compared to binomial distribution to evidence the phase separation and atom clustering.

We have devoted two lines in Table 9 to the binomial distribution to illustrate its inaccuracy.

It uses a negative binomial distribution to model total variation.

We used normal approximation of binomial distribution to construct confidence intervals (CI) for each indicator.

Finally, we used a negative binomial distribution to estimate the statistical significance of the peaks.

We used binomial distributions to calculate the joint probabilities of detecting a b transcript given that 28 clones were obtained from 1 5 sampled workers from single source colonies containing different proportions of BB and Bb workers.

We used generalized linear mixed-effect models (GLMM) with binomial distributions to analyze our data set.

Thus, we developed a model that uses a negative-binomial distribution to approximate an overdispersed Poisson distribution.

We demonstrate how the practical application of the binomial distribution can detect response bias in specific psychiatric disorders such as PTSD and can provide empirically grounded probabilistic evidence of malingering.

Yet, at these small discrete numbers, a binomial distribution of detected events when the occurrence probability is 0.5% indicates that any potential mis-sequence event present 3 or fewer times would need to be culled to be >95% confident that all sequences less frequent than 0.5% of the index are eliminated.