ライフサイエンスコーパス: Bayesian method

1 g sensitivity and specificity values using a Bayesian method.

2 d error due to inconsistency assessed with a Bayesian method.

3 subjects but there were no failures with the Bayesian method.

4 tionary processes using a recently developed Bayesian method.

5 PV type infections were estimated using semi-Bayesian methods.

6 ncluding parsimony, distance, likelihood and Bayesian methods.

7 cess of these sequences were performed using Bayesian methods.

8 dy provides a clinically relevant example of Bayesian methods.

9 y describe the relationships among different Bayesian methods.

10 m implausibly high can be moderated by using Bayesian methods.

11 letidae basal") based both on likelihood and Bayesian methods.

12 state-space models using both classical and Bayesian methods.

13 erivation of trees by maximum-likelihood and Bayesian methods.

14 ad and neck cancer-were instead monitored by Bayesian methods.

15 eath or disabling stroke at 24 months, using Bayesian methods.

16 rom age-stratified seroprevalence data using Bayesian methods.

17 fficiency compared with the state-of-the-art Bayesian methods.

18 gnostic testing accuracy was performed using Bayesian methods.

19 ters is conducted through both classical and Bayesian methods.

20 was established using maximum likelihood and Bayesian methods.

21 ta analysis and population projections using Bayesian methods.

22 dence estimators, Cox regression models, and Bayesian methods.

23 ex sample survey data using design-weighted, Bayesian methods.

24 corrected for temporal sampling biases using Bayesian methods.

25 urable polymer everolimus-eluting stent with Bayesian methods.

26 were performed using maximum likelihood and Bayesian methods.

27 e reconstructed using maximum likelihood and Bayesian methods.

28 throughput biological data through empirical Bayesian methods.

29 eatment comparison (MTC) was performed using Bayesian methods.

30 ve number (R0) of clusters were estimated by Bayesian methods.

31 l connections between maximum likelihood and Bayesian methods.

32 als for all reported effect sizes, none used Bayesian methods, 1 used false-discovery rates, 3 used s

33 nalyse tide gauge observations using spatial Bayesian methods(13) to show that, contrary to current t

34 We develop an agent-based Bayesian method and apply it to Australia's largest erad

35 We present a Bayesian method and associated software to infer how man

36 Here, the basis of differences between the Bayesian method and the classical or frequentist approac

37 tate-level policies from 1970 to 2016, using Bayesian methods and a modeling approach that addresses

38 view recently developed and still developing Bayesian methods and associated computer software for ma

39 nce of edge strengths can be evaluated using Bayesian methods and bootstrapping.

40 Using Bayesian methods and by estimating the basic reproductio

41 l to a multicentre clinical PDMC trial using Bayesian methods and modelled the potential impact of PD

42 We combine non-parametric Bayesian methods and probabilistic programming to infer

43 ssigned in a response-adaptive manner, using Bayesian methods and stratified by age group (<18 years,

44 Then, using Bayesian methods and well-supported fossil calibration c

45 We mapped HIV prevalence with Bayesian methods, and characterised variability across a

46 larval tick burden datasets via hierarchical Bayesian methods, and use it to explore the relative con

47 12, 2p16.3, 16p13.1 and 16p11.2 with a novel Bayesian method applied to pooled data from published ca

48 to large number of false positives, whereas Bayesian methods are computationally very expensive.

49 Bayesian methods are developed to identify fluorophore p

50 Classical and Bayesian methods are presented.

51 Bayesian methods are routinely used to combine experimen

52 These examples demonstrate that hierarchical Bayesian methods are well suited to these data.

53 g ancestral sequences and concludes that the Bayesian method, as implemented by MRBAYES 3.11, is pref

54 We develop an empirical Bayesian method based on the beta-binomial distribution

55 We developed a Bayesian method, based on allelic frequency, to estimate

56 Recently, the Bayesian method becomes more popular for analyzing high

57 e advantages of each data type, we develop a Bayesian method (bMIND) to integrate bulk and scRNA-seq

58 A Bayesian method (BPEC) was used to detect four geographi

59 The Bayesian method builds on a hierarchical model that acco

60 that FastHiC not only speeds up our original Bayesian method by more than five times, bus also achiev

61 provide an accessible tutorial on the use of Bayesian methods by focusing on example applications tha

62 We introduce a Bayesian method called SUBSTRA that uses regularized bic

63 Additionally, Bayesian methods can be computationally expensive, posin

64 use of existing data is appropriate and how Bayesian methods can be implemented more routinely as an

65 A neural network trained using Bayesian methods can correctly predict about 75% of the

66 Bayesian methods can jointly infer the number of QTL, th

67 approaches, including maximum likelihood and Bayesian methods, cannot scale to the size of the datase

68 oduce EPIC-unmix, a novel two-step empirical Bayesian method combining reference single-cell/single-n

69 eotide datasets using maximum likelihood and Bayesian methods, comprising all 13 mitogenomes currentl

70 ene divergence time estimates obtained using Bayesian methods confirm an early origin of Stratum 1 ge

71 Our fully Bayesian method couples the iHMM to a continuous control

72 lements-or even alternatives-to conventional Bayesian methods, delivering effective uncertainty quant

73 tion events, and phylogenetic analysis using Bayesian methods, demonstrated that new KIR were usually

74 hose inferred by commonly-used parsimony and Bayesian methods demonstrates that statistical tests of

75 The new Bayesian methods described here and other related method

76 n sensitivity in a threshold fashion, with a Bayesian method-estimated threshold (325 mg) (beta=0.060

77 ics, non-parametric approaches and empirical Bayesian methods etc.

78 In real applications, the nonparametric Bayesian method fitted transcriptomic imputation models

79 sed on posterior predictive checks (PPCs), a Bayesian method for assessing the quality of fit of a st

80 LDpred2 is a widely used Bayesian method for building polygenic scores (PGSs).

81 stence of this transform is used to derive a Bayesian method for converting noisy measurements of sub

82 A Bayesian method for determining if there are large depar

83 In this paper, we present a fully Bayesian method for directly comparing models of confide

84 We introduce a Bayesian method for estimating parameters for a model of

85 We then illustrate the advantages of a new Bayesian method for estimating period, which outperforms

86 tic model of the rearrangement process and a Bayesian method for estimating posterior probabilities f

87 ating origination and extinction rates and a Bayesian method for estimating sampling-corrected divers

88 We present a Bayesian method for extracting this information from exp

89 monstrate how a recently developed empirical Bayesian method for HMMs can be extended to enable a mor

90 We develop SparScape, a penalized Bayesian method for identifying DBFs active in the consi

91 We present a hierarchical Bayesian method for identifying genetic interactions thr

92 variable selection methodology to develop a Bayesian method for identifying multiple quantitative tr

93 In this study, we developed BISC, a Bayesian method for inferring bursting parameters from s

94 We present a Bayesian method for inferring the phylogenetic relations

95 Specifically, I use the distance-based Bayesian method for inferring the single most likely anc

96 sian entry time realignment), a hierarchical Bayesian method for investigating the long-term natural

97 We present a Bayesian method for linking markers to censored survival

98 A Bayesian method for mapping linked quantitative trait lo

99 We describe a fast hierarchical Bayesian method for mapping quantitative trait loci by h

100 First, we present a Bayesian method for model selection that accounts for re

101 This article presents a Bayesian method for model-based clustering of gene expre

102 Here we present a Bayesian method for multiple-tissue experiments focusing

103 In this article, the authors describe a Bayesian method for obtaining adjusted results from a di

104 We propose a bayesian method for probabilistic population projections

105 A fully Bayesian method for quantitative genetic analysis of dat

106 To quantify this, we developed a Bayesian method for reconstructing natural images from t

107 We propose a fully Bayesian method for reverse engineering a gene interacti

108 We propose a Bayesian method for the problem of multiple hypothesis t

109 ultiple linked genetic markers was used in a Bayesian method for the statistical mapping of quantitat

110 We present a Bayesian method for the unsupervised integrative modelli

111 Few Bayesian methods for analyzing high-dimensional sparse s

112 Some general likelihood and Bayesian methods for analyzing single nucleotide polymor

113 accuracy and power of maximum likelihood and Bayesian methods for detecting site-specific positive Da

114 ated the performances of three BLUP and five Bayesian methods for genomic prediction by using nine ac

115 The authors describe 4 Bayesian methods for imputing the missing data based on

116 pare and contrast three previously published Bayesian methods for inferring haplotypes from genotype

117 However, state-of-the-art Bayesian methods for inferring timetrees are computation

118 We derive Bayesian methods for selecting the best locus order base

119 We develop Bayesian methods for SRB estimation for all countries fr

120 frequentist approaches and survey the use of Bayesian methods for the design and analysis of clinical

121 mation algorithms as well as frequentist and Bayesian methods for uncertainty analysis have been impl

122 ods: The response function is inferred using Bayesian methods from the observed trajectories of bacte

123 Using simulated datasets, the Bayesian method generally fares better than the ML appro

124 Eyes progressing only by the Bayesian method had faster rates of change than those pr

125 n empirical salmon dataset revealed that our Bayesian method had the highest ratio of correct to inco

126 Our Bayesian method harmonized measured and in silico estima

127 The Bayesian method has been used to construct a PGRN based

128 It is little understood that Bayesian methods have a data-based core, which can be us

129 To date, however, Bayesian methods have been largely restricted to the Var

130 Bayesian methods have been shown to provide accurate tra

131 Hierarchical Bayesian methods have been used in previous papers to es

132 Bayesian methods have proven successful in building comp

133 requentist statistics remain predominant and Bayesian methods have recently experienced a resurgence

134 Bayesian methods have seen an increase in popularity in

135 The bayesian method identified a significantly higher propor

136 In addition, the bayesian method identified a significantly higher propor

137 A Bayesian method identified the largest p-value threshold

138 , the multiplicative method outperformed the Bayesian method if actual tap water consumption rates we

139 the QTL parameters is obtained by using the Bayesian method implemented by Markov chain Monte Carlo

140 Here, we present a Bayesian method implemented in the program BORICE (Bayes

141 s study, we investigate the application of a Bayesian method implemented via the Markov chain Monte C

142 both parametric PrediXcan and nonparametric Bayesian methods in a convenient software tool "TIGAR" (

143 easingly accessible, enabling growing use of Bayesian methods in a range of disciplines, including me

144 Here, we highlight the value of Bayesian methods in drug development, discuss barriers t

145 Our results demonstrate the utility of Bayesian methods in genetic epidemiology and provide sup

146 It differs from classical Bayesian methods in which a classification model is assu

147 Specifically, Bayesian methods incorporating rate variation significan

148 talytic models were fitted to the data using Bayesian methods, incorporating uncertainty in diagnosti

149 We here propose GeneCodeq, a Bayesian method inspired by coding theory for adjusting

150 We present a Bayesian method, integrated assembly of phenotype-specif

151 celebrated power analysis, we show that the Bayesian method is comparable to the frequentist one and

152 The nonparametric Bayesian method is flexible and general because it inclu

153 The generality and flexibility of the Bayesian method is illustrated when a Lorenz curve is to

154 simulated data, we show that the variational Bayesian method is more accurate in finding the true num

155 m three different diseases, we show that the Bayesian method is more robust to outliers, creates more

156 array datasets and show that the variational Bayesian method is more sensitive to capturing biologica

157 First, a recursive Bayesian method is presented for probabilistically chara

158 The reanalysis of completed trials using Bayesian methods is becoming increasingly common, partic

159 We propose using a simulation-based, Bayesian method known as Approximate Bayesian Computatio

160 mparative benchmarking with Bayesian and non-Bayesian methods (LSD, TreeTime, and treedater), we foun

161 s jointly with genomic breeding values using Bayesian methods may prevent that decline.

162 teraction term explicitly, our nonparametric Bayesian method measures the importance of each QTL, irr

163 We propose a Bayesian method: mixture-model-based rare-variant analys

164 We present a Bayesian method of base calling, BM-BC, for Solexa-GA se

165 Here we present a new Bayesian method of counting steps in photobleaching time

166 veral other methods, including the empirical Bayesian method of Efron et al. and the Significance Ana

167 Here, we describe a Bayesian method of inferring the positions of the tagged

168 A abundance for 3,143 of these genes using a Bayesian method of statistical analysis.

169 ations within species and are well suited to Bayesian methods of assigning unknown individuals to pop

170 pares parsimony, maximum likelihood, and the Bayesian methods of inferring ancestral sequences and co

171 s were analyzed using maximum likelihood and Bayesian methods of phylogenetic inference.

172 ogeographic analysis was performed through a Bayesian method on 103 Italian and 208 foreign meningoco

173 The Bayesian method outperformed the multiplicative method i

174 Comparative analysis reveals that the Bayesian method outperforms cutting-edge alternatives, d

175 The outperformance of our Bayesian method over the traditional single-marker analy

176 al life analysis, it has been concluded that Bayesian methods performed better as compared to maximum

177 Additionally, Bayesian methods performed better for the highly heritab

178 We demonstrate that our proposed Bayesian method performs favourably against existing fre

179 Using maximum-likelihood and Bayesian methods, phylogenetic trees based on the full-l

180 propose a latent-temporal progression-based Bayesian method, PROB, for inferring GRNs from the cross

181 Statistical inference using Bayesian methods produces a distribution of effect sizes

182 The Bayesian method proposed by Barry and Hartigan is well s

183 Bayesian methods provide a framework for synthesizing da

184 Bayesian methods provide an alternative, probabilistic i

185 Bayesian methods provide natural modeling frameworks for

186 Bayesian methods provide such an alternative, and our ap

187 verage of 70% compared to a state-of-the-art Bayesian method (PRS-CSx) in the African ancestry popula

188 n the BC for PRS construction and (ii) a new Bayesian method (PRS-PGx-Bayesx) to reduce Eurocentric o

189 We have developed a novel Bayesian method, PurBayes, to estimate tumor purity and

190 Notably, we observe that our Bayesian method reduces uncertainty by up to 60% compare

191 f relevant existing data that could be used, Bayesian methods remain underused in the clinical develo

192 n of individual slopes of MD change with the Bayesian method resulted in better prediction of future

193 Here, we present a Bayesian method SCAPE, which enables de novo identificat

194 In addition, we show that the Bayesian method serves as an alternative or even better

195 he competing models were small, although the Bayesian methods showed a modest improvement over other

196 and S segments, using maximum-likelihood and Bayesian methods, showed that NVAV was most closely rela

197 netic analyses, using maximum-likelihood and Bayesian methods, showed that RKPV shared a most recent

198 etic analysis of the DNA (COI) barcodes with Bayesian methods shows that this "species" is a long-sta

199 umans as the primary bacterial vector, and a Bayesian method significantly matched individuals to the

200 Two of the Bayesian methods (STRUCTURE and BAPS) and phylogenetic r

201 Bayesian methods support the current guidelines, which w

202 We describe a new Bayesian method, termed SigNet for significance networks

203 are shrunk by the same factor, we develop a Bayesian method that allows the shrinkage factor to vary

204 BAli-Phy, a popular Bayesian method that co-estimates multiple sequence alig

205 he interactions in PIPs were calculated by a Bayesian method that combines information from expressio

206 ture review-derived, study data-based, and a Bayesian method that combines prior knowledge with study

207 k Estimation across Unequal Sample sizes), a Bayesian method that enables joint learning of multiple

208 come these difficulties, we have developed a Bayesian method that estimates the empirical pattern of

209 in the presence of allelic heterogeneity), a Bayesian method that integrates genetic association data

210 To address this problem, we report JACKS, a Bayesian method that jointly analyzes screens performed

211 ical Systems Inference Engine 2 (MDSINE2), a Bayesian method that learns compact and interpretable ec

212 Therefore, we present BLEND, a hierarchical Bayesian method that leverages multiple single-cell refe

213 Here, we propose a nonparametric Bayesian method that reconstructs the clonal populations

214 o improve on this, we employ a nonparametric Bayesian method that was originally proposed for genetic

215 genetic analyses using maximum parsimony and Bayesian methods that address the origin and diversifica

216 Among the Bayesian methods, the Bayesian ridge regression and Baye

217 We introduce an empirical bayesian method to accurately characterize the molecular

218 Here, we propose and demonstrate a Bayesian method to build statistical libraries of magnet

219 We propose a Bayesian method to call indels from short-read sequence

220 We used a Bayesian method to compare the outcomes of 67 acute myel

221 We developed a rigorous Bayesian method to detect CNVs in the genes, based on re

222 We introduce here a hierarchical Bayesian method to detect local adaptation that can deal

223 DecontX is a novel Bayesian method to estimate and remove contamination in

224 Markov chain Monte Carlo (MCMC) implemented Bayesian method to estimate both the main and the intera

225 binary time series, we used a nonparametric Bayesian method to estimate pairwise directional correla

226 We propose a novel Bayesian method to estimate such population prevalence t

227 To resolve this discrepancy, we developed a Bayesian method to estimate the ages of angiosperm famil

228 Here I apply an extremely conservative Bayesian method to estimate the number of recent amphibi

229 ameters through a maximum a posteriori (MAP) Bayesian method to facilitate the method development pro

230 We present a novel Bayesian method to find SNPs associated with non-Gaussia

231 leobiology Database (paleobiodb.org) using a Bayesian method to identify significant change points in

232 We propose a Bayesian method to include probe-level measurement error

233 Here, we propose a fully Bayesian method to infer ensembles of chromatin structur

234 We propose a new Bayesian method to infer population structures using mul

235 We propose a Bayesian method to infer the perturbation time given tim

236 ostic Factor Impact Calculator, which uses a Bayesian method to make adjustments based on relative ri

237 Here we present a nonparametric Bayesian method to map multiple quantitative trait loci

238 We present a Bayesian method to model the association between an HPO-

239 ble approximate expected loglikelihood-based Bayesian method to perform network inference given this

240 se a hidden Markov random field (HMRF) based Bayesian method to rigorously model interaction probabil

241 In this study, we used a novel Bayesian method to screen mRNAs and estimate the effects

242 ers have been identified, data are fit using Bayesian methods to a population-TK model to estimate ke

243 Here using Bayesian methods to analyse both studies, we find that L

244 using phylogeny-based maximum likelihood and Bayesian methods to detect Darwinian selection on the ch

245 to screen birds for infections and then used Bayesian methods to determine phylogenetic relationships

246 tree contained in the PAML software provides Bayesian methods to estimate divergence times of genomic

247 We used Bayesian methods to estimate the posterior probability d

248 ose of this study is to use updated data and Bayesian methods to evaluate the effectiveness of hypero

249 Using hierarchical Bayesian methods to fit computational reinforcement lear

250 First, we use Bayesian methods to incorporate assumptions about the ba

251 by analyzing ~20,000 fossil occurrences with Bayesian methods to infer dispersal and diversification

252 transmission-dynamic model, calibrated using Bayesian methods to local surveillance data to understan

253 We apply Bayesian methods to multi-valent seroprevalence data for

254 e estimate and analyze our joint model using Bayesian methods to obtain uncertainties and distributio

255 re all two- and three- strain systems, using Bayesian methods to perform model selection, and identif

256 of coalescent, maximum likelihood (ML), and Bayesian methods to population genetic data combined wit

257 ledge of total community sizes and then used Bayesian methods to produce unbiased estimates with quan

258 We then employ hierarchical Bayesian methods to recover the underlying "hyperdistrib

259 f maximum-likelihood, maximum-parsimony, and Bayesian methods to the resulting data sets, P. penetran

260 pproaches for uncertainty quantification use Bayesian methods to weight ESMs based on a balance of hi

261 e uncertainty, were fitted to the data using Bayesian methods, to estimate rates of antibody (Ab)-ser

262 ecision proxy climate data, analyzed through Bayesian methods, to provide evidence for a rapid climat

263 The analysis has been carried out using Bayesian methods under different loss functions and info

264 Starting with an informative prior, our Bayesian method updates a posterior over the perceptual

265 A Bayesian method using prior knowledge for control subjec

266 The Bayesian method was compared with the conventional ordin

267 A significant contribution from the Bayesian method was realizing that the variability of th

268 A nonparametric Bayesian method was used to construct growth velocity tr

269 The Bayesian method was used to perform a network meta-analy

270 The Bayesian method was validated and found to perform bette

271 Using Bayesian methods we infer parameters for each model from

272 Namely, using metainference, a Bayesian method, we reconstruct an ensemble of structure

273 With the Bayesian method, we were able to handle situations in wh

274 ing a vetted fossil dataset and cutting-edge Bayesian methods, we analyzed the dynamics of South Amer

275 Using Bayesian methods, we compared outcomes in patients who h

276 Using Bayesian methods, we examined how pretreatment levels of

277 variate analyses, clustering algorithms, and Bayesian methods, we found evidence for moderately low r

278 Using frequentist and Bayesian methods, we found that autistic adults performe

279 Taking advantage of the merits of empirical Bayesian methods, we have developed EBSeq for identifyin

280 Using direct overlap and Bayesian methods, we identified new potential target gen

281 r mixed model and the accurate prediction of Bayesian methods, we propose a machine learning-based me

282 imates of sensitivity and specificity of the bayesian method were compared with those obtained by the

283 Bayesian methods were used to define the posterior proba

284 genetic analyses, Statistical Parsimony, and Bayesian methods were used to infer genetic diversity, g

285 Maximum likelihood, maximum parsimony, and Bayesian methods were used to infer phylogenies from the

286 TS: In this comparative effectiveness study, bayesian methods were used to model panel data of annual

287 Bayesian methods were used to test for mediation.

288 We developed a nowcasting Bayesian method which incorporates time-varying delays (

289 sample validation suggests that the proposed Bayesian method, which incorporates physics-guidance, ha

290 ovel hidden Markov random field (HMRF) based Bayesian method, which through explicitly modeling the n

291 Bayesian methods, which improve the prediction accuracy

292 These adjustments were implemented through Bayesian methods, which incorporated all available infor

293 [Formula: see text] is based on a sequential Bayesian method, while the parameter [Formula: see text]

294 lmnet is an R package implementing empirical Bayesian method with both lasso (EBlasso) and elastic ne

295 s each individual to a population by using a Bayesian method with multiple tuning parameters.

296 hort-range LD information well, we recommend Bayesian methods with t-distributed priors.

297 These Bayesian methods (with the aid of Markov chain Monte Car

298 A biexponential analysis with the Bayesian method yielded true diffusion coefficient (D),

299 the DNA+morphology data set by parsimony and Bayesian methods yielded a single well supported family-

300 nary search (REBS), and a procedure the uses Bayesian methods (zippy estimation of sequential testing