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1 th more closely than the often-used negative binomial distribution.
2 epsilona values obeyed laws of binomial distribution.
3 ribution, and in paired data based on a beta-binomial distribution.
4 termined by using an exact method based on a binomial distribution.
5 ng generalized linear models with a negative binomial distribution.
6 -Seq reads were assumed to follow a negative binomial distribution.
7 hedral species, which do not follow a simple binomial distribution.
8 -Seq reads were assumed to follow a negative binomial distribution.
9 given reference position are sampled from a binomial distribution.
10 ds is generally slower than predicted by the binomial distribution.
11 image quality ranks were calculated from the binomial distribution.
12 ly modelled as a random process based on the binomial distribution.
13 entials, EPPs) is well described by a simple binomial distribution.
14 ed by Sr(2+) were best described as a simple binomial distribution.
15 etermined by the normal approximation to the binomial distribution.
16 culated with the normal approximation to the binomial distribution.
17 mer, which is in agreement with the expected binomial distribution.
18 on the translation of rate comparison to two binomial distributions.
19 sed linear model with an underlying negative binomial distribution, adjusted for sex, baseline number
20 dure for power estimation using the negative binomial distribution and assuming a generalized linear
22 bles regression model that uses the negative binomial distribution and draws inference using a parame
23 CANOES models read counts using a negative binomial distribution and estimates variance of the read
24 simulation experiments based on the negative binomial distribution and our proposed nonparametric sim
25 ome among biological samples with a negative binomial distribution and uses a local variance estimati
26 pression in RNA-seq data based on a negative binomial distribution, and in paired data based on a bet
27 cell-free HIV-1 infection follows a negative-binomial distribution, and our model reproduces these da
29 igher masses until they reached the expected binomial distribution at equilibrium after approximately
30 e alternative scenarios was calculated for a binomial distribution by considering currently observed
32 ne expression analysis based on the negative binomial distribution (DESeq) or Empirical analysis of D
33 s, spatial power spectra, and deviation from binomial distribution for C + G% in large moving windows
34 ty is tested for each codon site, assuming a binomial distribution for the probability of obtaining c
37 (end-plate potentials, EPPs) follow a simple binomial distribution in both Ca(2+) and Sr(2+) solution
40 ast such an evaluation was performed using a binomial distribution model equation, which is inappropr
44 lets in the mass spectrum resulting from the binomial distribution of isotopic label in the bis-DNP d
45 activity of these proteins is described by a binomial distribution of proteins on transcripts contain
48 ctivity was recovered (theoretical 25% for a binomial distribution), proving that the functional unit
49 covering the IP samples only with 2 negative binomial distributions, QNB is based on 4 independent ne
51 logues in the 95%(13)C extracts, follows the binomial distribution, showing mirrored peak pairs for t
53 d retention in the mother cell) according to binomial distribution, thus limiting equal segregation o
54 S assumed to be uncorrelated, we adopted the binomial distribution to approximate the statistical sig
56 empirical Bayesian method based on the beta-binomial distribution to model paired data from high-thr
58 sing an existing model (based on Poisson and binomial distributions) to derive an expression for the
59 assessed by using the Student t test, exact binomial distribution, two-sample test of proportions, a
60 a likelihood function based on the negative binomial distribution, use a regularization approach to
63 s in the data, a single GAM using a negative binomial distribution was suitable to make predictions o
64 ized estimating equations (GEE) model with a binomial distribution was used to assess covariates asso
67 zed estimating equations assuming a negative binomial distribution were used to estimate relative rat
69 on may not be as appropriate as the negative binomial distribution when biological replicates are ava
70 urrent densities that approached a predicted binomial distribution where mutant and wild-type subunit
71 particular, we consider weights based on the binomial distribution, where the median of the p-values
72 seq data by sex revealed underlying negative binomial distributions which increased statistical power
73 d on statistical models such as the negative binomial distribution, which is employed by the tools ed
74 consistent with both log-normal and negative binomial distributions, while the mean-variance relation
75 over a distance d can then be described by a binomial distribution with a standard deviation 0.5 x d1
76 ralized linear mixed model assuming negative binomial distribution with log link function on 3-time r
77 major-component distribution is similar to a binomial distribution with low error and low reference b
78 vision is random and asymmetric, following a binomial distribution with mean probability of 0.52-0.72
79 ring self-pollination in pea conforms to the binomial distribution with no evidence of a tetrad-polle
80 ions, QNB is based on 4 independent negative binomial distributions with their variances and means li
81 is and compare the performance of three, the binomial distribution, z scores, and gene set enrichment
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