ライフサイエンスコーパス: Bayesian approach

1 variance in those rates using a hierarchical Bayesian approach.

2 s threshold was estimated using an empirical Bayesian approach.

3 o multiple QTL analysis is developed using a Bayesian approach.

4 vertebrate CYP3 amino acid sequences using a Bayesian approach.

5 ere are now many practical advantages to the Bayesian approach.

6 owth between birth and 1 year of age using a Bayesian approach.

7 tal registration system (1997-2014), using a Bayesian approach.

8 Each network was analyzed with a Bayesian approach.

9 mmended to use data from multiple years or a Bayesian approach.

10 g the positive and negative ion data using a Bayesian approach.

11 nd their uncertainties were analyzed using a Bayesian approach.

12 network meta-analysis was performed using a Bayesian approach.

13 ared; a traditional method and a mixed model Bayesian approach.

14 ing the same RNA-Seq data using an empirical Bayesian approach.

15 netic estimates using maximum likelihood and Bayesian approaches.

16 eral criterion is comparable in power to two Bayesian approaches.

17 tify the most significant motifs or by using Bayesian approaches.

18 ife probabilities, based on constraints from Bayesian approaches.

19 et was analyzed using maximum likelihood and Bayesian approaches.

20 Analyses were done by both frequentist and Bayesian approaches.

21 sing standard frequentist and random-effects Bayesian approaches.

22 Using a Bayesian approach a multi-locus model is fit to quantita

23 This Bayesian approach allowed us to determine strategies for

24 The Bayesian approach also captures the expected seasonal va

25 (generalized linear model) and multi-marker (Bayesian approach) analyses were applied to the dataset

26 184 informative markers were obtained from a Bayesian approach and 2 maximum likelihood approaches an

27 hese two advantages enable us to revisit the Bayesian approach and incorporate empirical dynamics and

28 They took a Bayesian approach and modeled sex- and age-specific remi

29 Comparisons between the Bayesian approach and the ML approach are facilitated be

30 mony methods using sequence-based encodings, Bayesian approaches, and direct optimization.

31 Using a Bayesian approach, Andean and Middle American subpopulat

32 antages and challenges faced by users of the Bayesian approach are also discussed and the readers poi

33 eveloped and experimentally verified a novel Bayesian approach based on a hidden Markov model that pr

34 the progress curve assay, here we propose a Bayesian approach based on an equation derived with the

35 We demonstrate the utility of the fully Bayesian approach by applying our method to a data set o

36 Here we show how the same empirical Bayesian approach can be applied to any parametric distr

37 In this paper, we demonstrate that a simple Bayesian approach can be taken to eliminate this regular

38 We have developed a novel Bayesian approach (ChIP-BIT) to reliably detect TFBSs an

39 The Bayesian approach defined in this study allowed for the

40 By using an approximate Bayesian approach employing distribution of fragment len

41 n, this study is also the first to apply the Bayesian approach executed with Markov chain Monte Carlo

42 ion in functional genomic data and propose a Bayesian approach for context-sensitive integration and

43 s between the sequences, and develop a fully Bayesian approach for estimation of the model parameters

44 transcripts from sequencing data (BitSeq), a Bayesian approach for estimation of transcript expressio

45 We describe a Bayesian approach for evaluating the correlation between

46 We developed a new Bayesian approach for identifying the loci underlying an

47 We employed a Bayesian approach for predicting routes of contamination

48 y, we outline a hierarchical, non-parametric Bayesian approach for reverse engineering GRNs using mul

49 This paper gives the first Bayesian approach for testing Hardy-Weinberg equilibrium

50 We have developed a Bayesian approach for the estimation of concentrations f

51 n framework, with a better robustness of the Bayesian approach for the sparsest data sets.

52 Among these developments, the evolution of Bayesian approaches for multiple QTL mapping over the pa

53 for rate-variable trees precludes the use of Bayesian approaches for testing hypotheses regarding the

54 We have developed a Bayesian approach (GO-Bayes) to measure overrepresentati

55 Machine learning and Bayesian approaches have been used to identify TF module

56 Maximum-likelihood and Bayesian approaches identified shifts in diversification

57 A non-parametric Bayesian approach in the form of a Bayesian neural netwo

58 inements and potential difficulties with the Bayesian approach in this context, when prior informatio

59 As an alternative, we propose a hierarchical Bayesian approach, in which knowledge on the toxicity of

60 The Bayesian approach includes a Markov chain Monte Carlo im

61 d processes in order to draw inferences, our Bayesian approach includes the unobserved infection time

62 sing traditional frequentist statistical and Bayesian approaches, including random-effects Bayesian n

63 The Bayesian approach incorporates prior knowledge of the pr

64 Using a Bayesian approach incorporating ethnographic data and pa

65 ariate analyses, using both conventional and Bayesian approaches, indicated that PBBs had no effect o

66 The Bayesian approach is a tool for including information fr

67 A Bayesian approach is adopted to investigate how accounti

68 This novel non-parametric Bayesian approach is demonstrated on a variety of data s

69 ment response between adults and children, a Bayesian approach is described to demonstrate rigorous e

70 Bayesian approach is used to estimate parameters of the

71 Finally, a Bayesian approach is used to estimate probable inventory

72 In the second, a Bayesian approach is used to obtain an approximate descr

73 The hierarchical Bayesian approach is used to understand and forecast the

74 Based on a Bayesian approach, it is assumed that probabilities are

75 The significant difference in using a fully Bayesian approach lies in our ability to account for unc

76 The paper includes a discussion of how this Bayesian approach may be useful for the analysis of gene

77 Here, we apply Bayesian approaches (originally developed for inferring

78 on the gene functional association using the Bayesian approach outperforms predictions using only one

79 ckage allows one to infer parameters using a Bayesian approach, perform forward modelling of the like

80 Despite its inherent subjectivity, the Bayesian approach possesses a number of practical advant

81 ycobacterium tuberculosis, we show that this Bayesian approach predicts essential genes that correspo

82 It has been demonstrated that the Bayesian approach presented in this paper follows the ch

83 elations among gene pairs using an empirical Bayesian approach, producing a false discovery rate cont

84 tric method, such as the mixed model and two Bayesian approaches proved to be more conservative.

85 that model probabilities computed using the Bayesian approach provide a reliability test for the dow

86 k structure and probabilistic framework of a Bayesian approach provide advantages over qualitative ap

87 Additionally, the Bayesian approach provided full distributions of decay r

88 This Bayesian approach provides a more accurate estimate of p

89 Although simple, this Bayesian approach provides a robust inference framework

90 The Bayesian approach provides appropriate and defensible PP

91 The Bayesian approach provides confidence intervals for para

92 However, the Bayesian approach rejected the generative parameter valu

93 The Bayesian approach requires specification of a prior dist

94 A full hierarchical Bayesian approach requires the use of computationally in

95 Bayesian approaches revealed significant spatial structu

96 Our Bayesian approach scores Medline abstracts for probabili

97 e noise definitions, we demonstrate that the Bayesian approach selects the simplest hypothesis that d

98 Accordingly, the Bayesian approach should be employed more widely in the

99 r analyses and those reported from using the Bayesian approach suggest that estimates of the quantita

100 Bayesian approaches tend to be computationally demanding

101 More recently, a bayesian approach termed Posterior Probability Mapping (

102 We address this issue by proposing a Bayesian approach that accounts for age uncertainty inhe

103 Here we present a Bayesian approach that can utilize single-cell, k-cell,

104 ssion parameters jointly using a constrained Bayesian approach that ensures that one remains within t

105 We present a Bayesian approach that exploits prior information on und

106 We have developed an efficient Bayesian approach that exploits the genetical genomics m

107 Here we develop a Bayesian approach that formally characterizes learning s

108 lenges, we develop a comprehensive empirical Bayesian approach that incorporates data and regularizat

109 Here we present a bayesian approach that integrates genetic, demographic a

110 We applied a Bayesian approach that leverages allelic heterogeneity a

111 ally efficient than a competing hierarchical Bayesian approach that requires MCMC sampling.

112 ing penalized likelihood methods, we adopt a Bayesian approach that utilizes a mixture of non-local p

113 We propose a Bayesian approach that utilizes genetic information on a

114 so serve as the basis for the development of Bayesian approaches that incorporate experimental design

115 Via a Bayesian approach, the probabilities of the sequential c

116 ver, RR-BLUP B performed equally well as the Bayesian approaches.The genotypic and phenotypic data us

117 , East Asian, and African ancestries using a Bayesian approach to account for heterogeneity in alleli

118 Here, we present a novel Bayesian approach to align the complete spectra.

119 ical, mechanistic path models fitted using a Bayesian approach to analyse explicitly predicted relati

120 ransversal algorithm to detect AS; anchor, a Bayesian approach to assign modalities; and bonvoyage, a

121 Finally, we introduce a Bayesian approach to association analysis by weighting t

122 mation from this procedure to help improve a Bayesian approach to automated peak deconvolution by res

123 Based on a novel Bayesian approach to biotope assessment, we present a st

124 Here, we use a hierarchical Bayesian approach to calculate tree-specific, species-sp

125 We devised an improved model-based Bayesian approach to cluster microarray gene expression

126 In a fully Bayesian approach to clustering problems of this type, o

127 We propose a fully Bayesian approach to constructing probabilistic gene reg

128 We took a hierarchical Bayesian approach to determine the expected distribution

129 We develop a Bayesian approach to determine the most probable structu

130 In this paper, we present a Bayesian approach to estimate a chromosome and a disorde

131 We used a Bayesian approach to estimate the position and effect of

132 We employ a Bayesian approach to estimate the posterior distribution

133 We present SourceTracker, a Bayesian approach to estimate the proportion of contamin

134 Here we present a model-based Bayesian approach to evaluate molecular cluster assignme

135 The primary analysis used a Bayesian approach to evaluate the hypothesis that the pr

136 We next developed a Bayesian approach to evaluate, for each SNP, the overlap

137 Here we report the use of a Bayesian approach to generate calibration curves for the

138 We then apply a Bayesian approach to identify particular sites in each g

139 pression profile for each patient and uses a Bayesian approach to infer corresponding upstream regula

140 We take a Bayesian approach to integrate gene expression profiling

141 We used a Bayesian approach to integrate the phylogenetic profile

142 In this paper, we proposed a simple Bayesian approach to integrate the regularization parame

143 s-derived approach to characterization and a Bayesian approach to laboratory testing.

144 We also used a Bayesian approach to look at clustering of people who se

145 We used a Bayesian approach to meta-analysis.

146 This Bayesian approach to monitoring is simple to implement a

147 Here, we present a systematic Bayesian approach to multiple-hypothesis testing of a ge

148 The contemporary Bayesian approach to perception implies that human perfo

149 MCMC) algorithms play a critical role in the Bayesian approach to phylogenetic inference.

150 published putative cis eQTLs, we developed a Bayesian approach to predict SNP genotypes that is based

151 We also present a Bayesian approach to predict the number of sorting round

152 ere parameterized using a novel hierarchical Bayesian approach to quantify the effects of leaf traits

153 By utilizing a Bayesian approach to rank putative miRNAs, our method is

154 We have developed a Bayesian approach to separately characterize these two l

155 A Bayesian approach to source-tracking was used to compare

156 In this paper we develop a new Bayesian approach to the detection of APOBEC3-mediated h

157 We present a Bayesian approach to the problem of inferring the number

158 se-positive results is then discussed, and a Bayesian approach to this problem is described.

159 They discuss a recent article in which a Bayesian approach to this problem is developed based on

160 used traditional frequentist statistical and Bayesian approaches to address the following questions:

161 sults demonstrate the ability of integrative Bayesian approaches to identify candidate drivers with b

162 implications for optimal decision theory and Bayesian approaches to learning and behavior in general.

163 Additionally, using Bayesian approaches to link the estimates of treatment e

164 Here we develop two related Bayesian approaches to network inference that allow GRNs

165 sent 3CPET, a tool based on a non-parametric Bayesian approach, to infer the set of the most probable

166 In this primer, we present a Bayesian approach toward treating these data, and we dis

167 Our system employs a Bayesian approach, updating a protein's probability of b

168 We tested whether bayesian approaches uphold the new recommendation.

169 P were tested for significant effects with a Bayesian approach using GENSEL software.

170 A Bayesian approach using Markov chain Monte Carlo methods

171 pediatric SLIT trials is challenging, but a Bayesian approach using prior adult data can reduce the

172 A Bayesian approach was developed to incorporate expert ju

173 A Bayesian approach was employed to integrate information

174 Our Bayesian approach was integrated into PrePPI, a structur

175 A hierarchical Bayesian approach was used as the basis for inference, a

176 Hierarchical Bayesian approach was used to estimate the pooled glauco

177 Thus, by taking a Bayesian approach we find that variability in reversal-l

178 Using a Bayesian approach, we defined credible sets for the T1D-

179 Using a rapidly converging Bayesian approach, we precisely measure the splitting in

180 With a hierarchical Bayesian approach, we quantified the distribution of mor

181 Taking a Bayesian approach, we quantify the trade off between dif

182 Using a Bayesian approach, we show that Gaussian processes model

183 Using a Bayesian approach, we show that, in contrast to earlier

184 oth the classical hypothesis-testing and the Bayesian approaches, we found single and multiple trend

185 Hierarchical Bayesian approaches were used to estimate the pooled pre

186 The method uses a novel Bayesian approach which represents continuous allele fre

187 finable parameters, relying on a model-based Bayesian approach which takes full account of the indivi

188 Our flexible Bayesian approach will be especially useful to improve c

189 sampled proportions for each country from a Bayesian approach with 10 000 sampled country estimates

190 e gene expression patterns over time, a full Bayesian approach with proper conjugate priors to make t

191 d using hierarchical models and an empirical bayesian approach with volume-based shrinkage that allow

192 We implemented the model in a fully Bayesian approach, with all parameters of the model cons

193 anobacterial and chloroplast genomes using a Bayesian approach, with the aim of estimating the age fo

194 maximizing the marginal likelihood from the Bayesian approach yields similar results to a profile li