ライフサイエンスコーパス: binomial distribution

1 hurdle regression models using the negative binomial distribution.

2 generalized linear model based on a negative binomial distribution.

3 tructed for each sub-counties based on a log-binomial distribution.

4 and models the cell abundances using a beta-binomial distribution.

5 ell doublets are modeled by employing a Beta-binomial distribution.

6 The 95% CIs were approximated using a binomial distribution.

7 of DGE tools that are based on the negative binomial distribution.

8 th more closely than the often-used negative binomial distribution.

9 epsilona values obeyed laws of binomial distribution.

10 ribution, and in paired data based on a beta-binomial distribution.

11 ting the secondary case data with a negative binomial distribution.

12 termined by using an exact method based on a binomial distribution.

13 ng generalized linear models with a negative binomial distribution.

14 t size, due to the properties of the Poisson-binomial distribution.

15 -Seq reads were assumed to follow a negative binomial distribution.

16 hedral species, which do not follow a simple binomial distribution.

17 given reference position are sampled from a binomial distribution.

18 ds is generally slower than predicted by the binomial distribution.

19 image quality ranks were calculated from the binomial distribution.

20 ly modelled as a random process based on the binomial distribution.

21 entials, EPPs) is well described by a simple binomial distribution.

22 ed by Sr(2+) were best described as a simple binomial distribution.

23 etermined by the normal approximation to the binomial distribution.

24 culated with the normal approximation to the binomial distribution.

25 mer, which is in agreement with the expected binomial distribution.

26 osthospitalization prevalence using Bayesian binomial distributions.

27 on the translation of rate comparison to two binomial distributions.

28 For 43 (43% [binomial distribution 95% CI 33-53]) of 100 infants, we

29 sed linear model with an underlying negative binomial distribution, adjusted for sex, baseline number

30 individual molecules are counted based on a binomial distribution analysis of emission events detect

31 dure for power estimation using the negative binomial distribution and assuming a generalized linear

32 We calculated 95% CIs assuming a binomial distribution and did random-effects meta-regres

33 bles regression model that uses the negative binomial distribution and draws inference using a parame

34 CANOES models read counts using a negative binomial distribution and estimates variance of the read

35 o extend the methods to accommodate negative binomial distribution and implemented these tests in a n

36 A binomial distribution and log link function were used to

37 and generalized linear models with negative binomial distribution and log-link function to examine t

38 ighted random-intercept models with negative binomial distribution and logistic-regression models to

39 eneralised estimating equation models with a binomial distribution and logit link function investigat

40 simulation experiments based on the negative binomial distribution and our proposed nonparametric sim

41 eudo-inferential' replicates from a negative binomial distribution and propose a general procedure fo

42 terpretation for features using a correlated binomial distribution and scales efficiently to analyze

43 ome among biological samples with a negative binomial distribution and uses a local variance estimati

44 generalized linear mixed-effects models with binomial distributions and log link fit to patient-level

45 oising data using the zero-inflated negative binomial distribution, and data enhancement through a di

46 pression in RNA-seq data based on a negative binomial distribution, and in paired data based on a bet

47 cell-free HIV-1 infection follows a negative-binomial distribution, and our model reproduces these da

48 Here, we propose a beta-binomial distribution approach to derive peptide immunog

49 er estimates from the zero-inflated negative binomial distribution are an unreliable indicator of zer

50 The Poisson distribution and negative binomial distribution are commonly used to model count d

51 ere conducted; 95% CIs computed according to binomial distribution are reported.

52 Although negative binomial distributions are well studied, the theoretical

53 ed generalized logistic, gamma, and negative binomial distributions as models for compound behavior.

54 igher masses until they reached the expected binomial distribution at equilibrium after approximately

55 tion data that may not fully follow negative binomial distributions but rather more general mixture d

56 e alternative scenarios was calculated for a binomial distribution by considering currently observed

57 By using binomial distribution, Clopper-Pearson confidence interv

58 ne expression analysis based on the negative binomial distribution (DESeq) or Empirical analysis of D

59 nificance for the difference of two negative binomial distributions (DOTNB).

60 delling of scRNAseq data based on a negative binomial distribution enhances shrinkage performance in

61 of a stable label in precursors following a binomial distribution, estimates depend on the inverse c

62 d points and a generalized linear model with binomial distribution for binary end points, with adjust

63 s, spatial power spectra, and deviation from binomial distribution for C + G% in large moving windows

64 ression with the use of a bivariate negative binomial distribution for paired designs.

65 ty is tested for each codon site, assuming a binomial distribution for the probability of obtaining c

66 ntify the labeled protein population using a binomial distribution function.

67 Probabilistic modeling using binomial distribution functions rejected the hypothesis

68 The negative binomial distribution has been shown to be a good model

69 (end-plate potentials, EPPs) follow a simple binomial distribution in both Ca(2+) and Sr(2+) solution

70 Calculations of p using the simple binomial distribution in Sr(2+) solutions gave theoretic

71 From the binomial distribution in Sr(2+) solutions, values for th

72 ollowed a beta-binomial rather than a simple binomial distribution, indicating that each family may h

73 We show that the negative binomial distribution is a limit case of this model, as

74 The negative binomial distribution is commonly used as a model for tr

75 parameter [Formula: see text] of a negative-binomial distribution is estimated at 0.06 and 0.2 for J

76 ve a closer to fit to data than the negative binomial distribution, it is consistently outperformed b

77 MaxHiC uses a negative binomial distribution model and a maximum likelihood tec

78 ast such an evaluation was performed using a binomial distribution model equation, which is inappropr

79 rediction using deep neural networks and the binomial distribution model.

80 subunits and related these data to predicted binomial distribution models.

81 scribed by Taylor's law (TL) or the negative binomial distribution (NBD).

82 We then derive a binomial distribution of dwell times to describe the sto

83 lets in the mass spectrum resulting from the binomial distribution of isotopic label in the bis-DNP d

84 activity of these proteins is described by a binomial distribution of proteins on transcripts contain

85 is approximated by the concatenation of two binomial distributions of (13)C and (15)N.

86 ng generalized linear models with a negative binomial distribution on the preceding 4 years of data a

87 n interaction matrix that follows a negative binomial distribution or general mixture distribution.

88 rst embryonic cleavage division, following a binomial distribution pattern.

89 ctivity was recovered (theoretical 25% for a binomial distribution), proving that the functional unit

90 covering the IP samples only with 2 negative binomial distributions, QNB is based on 4 independent ne

91 By using the Binomial distribution rather than a normal approximation

92 logues in the 95%(13)C extracts, follows the binomial distribution, showing mirrored peak pairs for t

93 As predicted by the binomial distribution, simultaneous analyte detection at

94 with being drawn from an underlying negative binomial distribution than either a log-normal distribut

95 ditive model framework based on the negative binomial distribution that allows flexible inference of

96 propose a new statistical tool based on beta-binomial distributions that can construct robust gene co

97 d retention in the mother cell) according to binomial distribution, thus limiting equal segregation o

98 kelihood function based on an over-dispersed binomial distribution to aggregate evidence for allelic

99 S assumed to be uncorrelated, we adopted the binomial distribution to approximate the statistical sig

100 and brings them together under a correlated binomial distribution to create an efficient hypothesis

101 mutation counts of the elements with a beta-binomial distribution to handle overdispersion.

102 empirical Bayesian method based on the beta-binomial distribution to model paired data from high-thr

103 edgeR pioneered the use of the negative binomial distribution to model read count data with repl

104 We therefore used the beta-binomial distribution to model the overdispersion.

105 imating equations with binomial and negative binomial distributions to evaluate associations between

106 sing an existing model (based on Poisson and binomial distributions) to derive an expression for the

107 assessed by using the Student t test, exact binomial distribution, two-sample test of proportions, a

108 a likelihood function based on the negative binomial distribution, use a regularization approach to

109 The binomial distribution used to test hypotheses about sequ

110 incidence estimates were computed assuming a binomial distribution using the Wilson score method.

111 as additional mechanistic complexity and the binomial distribution was no longer valid.

112 s in the data, a single GAM using a negative binomial distribution was suitable to make predictions o

113 ized estimating equations (GEE) model with a binomial distribution was used to assess covariates asso

114 Binomial distribution was used to calculate 95% CIs for

115 A customized weighted Negative Binomial distribution was used to describe the radiation

116 A beta-binomial distribution was used to estimate the probabili

117 k with non-informative priors and a negative binomial distribution, we projected [Formula: see text]

118 Deviations from simple binomial distribution were more pronounced when we exclu

119 Spectral counts modeled as a negative binomial distribution were used for statistical comparis

120 xed-effects generalized linear models with a binomial distribution were used to compare outcomes betw

121 zed estimating equations assuming a negative binomial distribution were used to estimate relative rat

122 eneralized estimating equation models with a binomial distribution were used to study longitudinal as

123 Exact binomial distributions were used to establish 95% confid

124 on may not be as appropriate as the negative binomial distribution when biological replicates are ava

125 urrent densities that approached a predicted binomial distribution where mutant and wild-type subunit

126 particular, we consider weights based on the binomial distribution, where the median of the p-values

127 seq data by sex revealed underlying negative binomial distributions which increased statistical power

128 d these with an approximation of the Poisson binomial distribution, which assigns partial incidence t

129 RISPRBetaBinomial or CB(2) Based on the beta-binomial distribution, which is better suited to sgRNA d

130 d on statistical models such as the negative binomial distribution, which is employed by the tools ed

131 consistent with both log-normal and negative binomial distributions, while the mean-variance relation

132 yzed as prevalence ratios calculated using a binomial distribution with a log link or robust Poisson

133 over a distance d can then be described by a binomial distribution with a standard deviation 0.5 x d1

134 The model uses the negative binomial distribution with gamma priors to model sequenc

135 ralized linear mixed model assuming negative binomial distribution with log link function on 3-time r

136 major-component distribution is similar to a binomial distribution with low error and low reference b

137 vision is random and asymmetric, following a binomial distribution with mean probability of 0.52-0.72

138 ring self-pollination in pea conforms to the binomial distribution with no evidence of a tetrad-polle

139 istribution of DCV displacements fits a beta-binomial distribution with the mean and the variance fol

140 ses following either the Poisson or negative binomial distribution with the rate parameter allowed to

141 e distribution should follow a beta-negative binomial distribution with the same parameters as the DC

142 ions, QNB is based on 4 independent negative binomial distributions with their variances and means li

143 gated whether offspring sex follows a simple binomial distribution within families and identified mat

144 is and compare the performance of three, the binomial distribution, z scores, and gene set enrichment