ライフサイエンスコーパス: Bayesian approach

1 was predicted by a computationally demanding Bayesian approach.

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

3 variance in those rates using a hierarchical Bayesian approach.

4 s threshold was estimated using an empirical Bayesian approach.

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

6 vertebrate CYP3 amino acid sequences using a Bayesian approach.

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

8 ke distributions are obtained from a modular Bayesian approach.

9 o the burden effect sizes using an empirical Bayesian approach.

10 lecular dating was performed using a using a Bayesian approach.

11 eases or traits previously generated using a Bayesian approach.

12 week after injection 1), was estimated by a bayesian approach.

13 t 1 year, tested for non-inferiority using a Bayesian approach.

14 bability of any increase in mortality with a Bayesian approach.

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

16 with associated uncertainties, obtained by a Bayesian approach.

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

18 Each network was analyzed with a Bayesian approach.

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

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

21 nd their uncertainties were analyzed using a Bayesian approach.

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

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

24 ediction error and precision signaling using Bayesian approaches.

25 netic estimates using maximum likelihood and Bayesian approaches.

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

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

28 tionality analysis with both frequentist and Bayesian approaches.

29 d then pooled using random-effects sizes and bayesian approaches.

30 d-effect framework with likelihood-based and Bayesian approaches.

31 h Maximum Parsimony, Maximum Likelihood, and Bayesian approaches.

32 nalyses were performed using frequentist and Bayesian approaches.

33 surface, which was challenging to fit using Bayesian approaches.

34 sing standard frequentist and random-effects Bayesian approaches.

35 ife probabilities, based on constraints from Bayesian approaches.

36 et was analyzed using maximum likelihood and Bayesian approaches.

37 Analyses were done by both frequentist and Bayesian approaches.

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

39 This Bayesian approach allowed us to determine strategies for

40 the usual frequentist testing approach, the Bayesian approach allows one to compare any number of mo

41 This Bayesian approach allows us to accurately quantify uncer

42 The Bayesian approach also captures the expected seasonal va

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

44 184 informative markers were obtained from a Bayesian approach and 2 maximum likelihood approaches an

45 eural networks achieve similar accuracy to a Bayesian approach and are the best-performing methods wh

46 from FAIR-HF2, used a harmonized and robust Bayesian approach and included individual participant da

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

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

49 , the suggested model was analyzed using the Bayesian approach and the Hamiltonian Monte Carlo method

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

51 computational and time cost of conventional Bayesian approaches and does not rely on acquisition con

52 accuracy relative to the more commonly used Bayesian approaches and opens new avenues for exploratio

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

54 s not require many of the priors demanded by Bayesian approaches, and it has light computing requirem

55 Using a Bayesian approach, Andean and Middle American subpopulat

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

57 In one methodology, a Bayesian approach attributes measurements to one of mult

58 Here, we describe a principled Bayesian approach, BANDLE, that uses these data to compu

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

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

61 approach based on profile likelihood, and a Bayesian approach based on Markov-chain Monte Carlo.

62 D occurred in 10.3% of these patients, and a Bayesian approach (BeviMed(4)) identified multiple new c

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

64 Specifically, this Bayesian approach can accurately estimate the epidemic c

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

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

67 Innovations associated with the bayesian approach can improve the efficiency of drug dev

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

69 The Bayesian approach defined in this study allowed for the

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

71 The Bayesian approach enabled coherent propagation of uncert

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

73 This Bayesian approach explicitly allows us to continuously i

74 The Bayesian approach finds distinct reduced models that fit

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

76 nt stoichiometric binding models and using a Bayesian approach for data analysis.

77 We present a Bayesian approach for detecting host influences on patho

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

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

80 We describe a Bayesian approach for evaluating the correlation between

81 chine-learning approaches; and (3) LDpred, a Bayesian approach for genome-wide risk prediction.

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

83 Here, we present lvm-DE, a generic Bayesian approach for performing DE predictions from a f

84 We employed a Bayesian approach for predicting routes of contamination

85 ubject temporal correlation, incorporating a Bayesian approach for process forecasting to predict the

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

87 Here, we introduce bayNorm, a novel Bayesian approach for scaling and inference of scRNA-seq

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

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

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

91 State-of-the-art Bayesian approaches for analysing epidemic survey data w

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

93 The key idea is to use existing Bayesian approaches for phylogenetic dating to sample po

94 ormance of frequentist, empirical Bayes, and Bayesian approaches for providing 95% confidence/credibl

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

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

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

98 Bayesian approaches have previously been applied to esti

99 Methodologically, our Bayesian approach highlights issues with the separabilit

100 These benefits make the Bayesian approach ideal for analyzing any type of single

101 Maximum-likelihood and Bayesian approaches identified shifts in diversification

102 Innovations possible when using the bayesian approach improve the efficiency of drug develop

103 The bayesian approach improved the efficiency of a clinical

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

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

106 licly available cancer registry data using a Bayesian approach in which the observed data are fixed a

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

108 The Bayesian approach includes a Markov chain Monte Carlo im

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

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

111 The Bayesian approach incorporates prior knowledge of the pr

112 Using a Bayesian approach incorporating ethnographic data and pa

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

114 ss this issue, in this study, an alternative Bayesian approach (Integrated Nested Laplace Approximati

115 The Bayesian approach is a tool for including information fr

116 A Bayesian approach is adopted to investigate how accounti

117 The Bayesian approach is also robust to deviations from mode

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

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

120 This Bayesian approach is illustrated using two examples from

121 Bayesian approach is used to estimate parameters of the

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

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

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

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

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

127 identify loci in AAs using a trans-ancestry Bayesian approach (MANTRA) accounting for heterogeneity

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

129 Here, we present a Bayesian approach, NobBS (Nowcasting by Bayesian Smoothi

130 Furthermore, the fully Bayesian approach of our theory enables researchers to m

131 Recently, Bayesian approach-offering distribution of dose estimate

132 when behavior deviates from optimality, the Bayesian approach offers candidate models to account for

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

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

135 In parallel, we used a Bayesian approach (PAINTOR) that combines genetic associ

136 Here, we present a new Bayesian approach, PathFinder, for reconstructing the ro

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

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

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

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

141 Importantly, this large-scale Bayesian approach prioritizes both known and novel annot

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

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

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

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

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

147 This Bayesian approach provides a more accurate estimate of p

148 kelihood estimate approach, showing that the Bayesian approach provides a more complete understanding

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

150 The Bayesian approach provides appropriate and defensible PP

151 The Bayesian approach provides confidence intervals for para

152 95% confidence interval: 1.22, 1.53) and the Bayesian approach (R.R., 1.36; 95% credible interval: 1.

153 However, the Bayesian approach rejected the generative parameter valu

154 The Bayesian approach requires specification of a prior dist

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

156 Furthermore, methods often rely on Bayesian approaches requiring user-defined priors in the

157 Bayesian approaches revealed significant spatial structu

158 Our Bayesian approach scores Medline abstracts for probabili

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

160 (FVC) at 12 weeks, which we analyzed with a Bayesian approach separately according to background non

161 In conclusion, a Bayesian approach sequentially using 2 immunoassays is a

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

163 y slightly lower sensitivity compared to the Bayesian approach, specifically for weakly reactive pept

164 We further adopt an objective Bayesian approach, such that our results would be agreed

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

166 cause death between PCI and CABG, although a Bayesian approach suggested a difference probably exists

167 Bayesian approaches tend to be computationally demanding

168 Here we introduce an orthogonal Bayesian approach termed 'Ouija' that learns pseudotimes

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

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

171 Here, we present a model-based Bayesian approach that can reconstruct contact patterns

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

173 In this paper, we develop a Bayesian approach that dynamically integrates imputation

174 PheScan (Coloc adapted Phenome-wide Scan), a Bayesian approach that enables an intuitive and systemat

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

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

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

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

179 We employed a hierarchical Bayesian approach that incorporated gross primary produc

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

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

182 We applied a Bayesian approach that leverages allelic heterogeneity a

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

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

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

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

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

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

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

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

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

192 ChIP-seq data, BIT offers a fully integrated Bayesian approach to assess genome-wide consistency betw

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

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

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

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

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

198 substantially improved by the ability of the Bayesian approach to capture nested data and by its rigo

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

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

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

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

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

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

205 nd meta-analysis investigates the use of the bayesian approach to estimate the plausible effect of te

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

207 We employ a Bayesian approach to estimate the posterior distribution

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

209 In this work, we propose SynBa, a flexible Bayesian approach to estimate the uncertainty of the syn

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

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

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

213 istic structure of these models, we employ a Bayesian approach to extend size-structured MPMs to capt

214 improve on previous ESS estimates by using a Bayesian approach to fuse deep-time CO(2) and temperatur

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

216 We introduce a novel Bayesian approach to GWAS, called Structured and Non-Loc

217 A Bayesian approach to hypothesis evaluation produced inco

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

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

220 Here, we develop a statistically rigorous, Bayesian approach to infer the optimal partitioning of a

221 We take a Bayesian approach to integrate gene expression profiling

222 We used a Bayesian approach to integrate the phylogenetic profile

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

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

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

226 In addition, we use a Bayesian approach to merge fleet-wide data in the form o

227 We used a Bayesian approach to meta-analysis.

228 We used a Bayesian approach to meta-regression analysis, which use

229 This Bayesian approach to monitoring is simple to implement a

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

231 Fixed-effects models were fit using a bayesian approach to network meta-analysis.

232 We propose a semi-supervised Bayesian approach to novelty detection, allowing the dis

233 The contemporary Bayesian approach to perception implies that human perfo

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

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

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

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

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

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

240 In this work, we propose a joint Bayesian approach to simultaneously estimate these gene

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

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

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

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

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

246 A conceptually new Bayesian approach to this problem, BayesPrism, has recen

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

248 Perspective, we discuss both frequentist and Bayesian approaches to clinical trial analysis, introduc

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

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

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

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

253 mplies-seems to threaten the universality of Bayesian approaches to the mind.

254 In this work, we survey frequentist and Bayesian approaches to uncertainty quantification of mac

255 We fitted the model, using a hierarchical Bayesian approach, to experimental time-series data of a

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

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

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

259 We tested whether bayesian approaches uphold the new recommendation.

260 We evaluate the new Bayesian approach using gamma-ray data and are able to i

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

262 A Bayesian approach using Markov chain Monte Carlo methods

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

264 The model is calibrated via a Bayesian approach, using in vivo data from New Zealand w

265 We take a Bayesian approach, using Markov Chain Monte Carlo to est

266 A Bayesian approach was developed to incorporate expert ju

267 A Bayesian approach was employed to integrate information

268 Our Bayesian approach was integrated into PrePPI, a structur

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

270 A proposed Bayesian approach was used to estimate nAb dynamics in p

271 Hierarchical Bayesian approach was used to estimate the pooled glauco

272 A bayesian approach was used to provide insights into the

273 A fully Bayesian approach was used to select the best explaining

274 Furthermore, by taking a Bayesian approach we can model unequal variances, contro

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

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

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

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

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

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

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

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

283 Hierarchical Bayesian approaches were used to estimate the pooled pre

284 Here we propose a semi-supervised Bayesian approach (wherein model parameters are inferred

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

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

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

288 d Drug Administration, the future use of the bayesian approach will only continue to increase.

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

290 he pre-specified primary analysis utilized a Bayesian approach with borrowing of prior information fr

291 the RNN consistently outperforms a standard Bayesian approach with either flat priors or with memory

292 We illustrate our Bayesian approach with genetic data from the 1,000 genom

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

294 lence estimates of frailty obtained from our Bayesian approach with those obtained from the 2011 and

295 Calibration equations were developed using a Bayesian approach with three different scenarios: i) a r

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

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

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

299 ikelihood framework whereas STRUCTURE uses a Bayesian approach, yet both produce similar results.

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