ライフサイエンスコーパス: statistical model

1 d with the use of an interaction term in the statistical model.

2 d points using the fixed- and random-effects statistical model.

3 d to AMD, Ala(69)-->Ser, did not improve the statistical model.

4 t motion, the stretched exponential, and the statistical model.

5 red by the piecemeal development of relevant statistical models.

6 ify probes affected by genome variants using statistical models.

7 llance and removal of trees in orchards, and statistical models.

8 used to compare the performance of different statistical models.

9 significant (P < .05) remained in the final statistical models.

10 cohort size and availability of appropriate statistical models.

11 on in AA compared with EA men in one or more statistical models.

12 uated with simple and multivariable-adjusted statistical models.

13 wo different environments with two different statistical models.

14 raction, dimension reduction, and tree-based statistical models.

15 important in the era of big data and complex statistical modeling.

16 nce of DRM was assessed using Bayesian-based statistical modeling.

17 vers of land-use change supported by spatial statistical modeling.

18 y have potential advantages over traditional statistical modeling.

19 ions play a central role in mathematical and statistical modelling.

20 a novel method to analyse CNP using spatial statistical modelling.

21 Two statistical models,a general additive model (GAM) and GA

22 using a multivariate statistical approach, a statistical model able to discriminate olive oil from It

23 Multilevel statistical models accounted for clustered measurement a

24 r multiple sample SNV callers, the MultiGeMS statistical model accounts for enzymatic substitution se

25 Inclusive, multistage statistical models accurately predicted likelihoods of r

26 Statistical models adjusted for age and follow-up time,

27 Univariate and multivariable statistical models after controlling for other risk fact

28 ed to suitable quantification procedures and statistical models, analytical criteria were defined to

29 ago data sets with human DNA and developed a statistical model and a new software pipeline ("HiRise")

30 We propose a Bayesian statistical model and a variational expectation maximiza

31 We present in this article a novel statistical model and an inference method to estimate ge

32 Here we describe a new statistical model and computer program, replicate MATS (

33 Additionally, we develop a dedicated statistical model and demonstrate its application to the

34 Using statistical modeling and employing the Fokker-Planck for

35 ction that leverages advances in data-driven statistical modeling and mechanism-based multiscale mode

36 ng the directionality of these links through statistical modeling and verifying our findings with com

37 rns Burke et al.'s findings when alternative statistical models and alternative measures of conflict

38 We propose several statistical models and different methods to compute and

39 Using statistical models and morphometric analyses, we demonst

40 All statistical models and summary estimates were weighted t

41 ic modeling were used to further improve the statistical models and vice versa.

42 ntitative information from the mass spectra, statistical modeling, and model-based analysis of LC-MS/

43 aracteristics, sampling, exposure, outcomes, statistical modelling, and parameters from articles.

44 Finally, both SLE elements of the statistical model appear to operate in Sjogren's syndrom

45 Using network statistical modeling applied to a comprehensive ecologic

46 Four statistical models applied previously to the CDS PK data

47 Statistical modeling applying a Bayesian Markov chain Mo

48 erential equation model with a nonparametric statistical modeling approach allowing us to capture a b

49 The statistical modeling approach developed in this study re

50 The statistical modeling approach proposed in this work offe

51 Physical and statistical modeling are commonly used to integrate thes

52 ns in which site-specific trend analyses and statistical models are developed to estimate the CO2 seq

53 monitoring their expected recovery based on statistical models are needed for patient management dur

54 nts and the forecast skills in dynamical and statistical models are similar overall, with some differ

55 based on lossless data compression and uses statistical model as well as arithmetic coding to compre

56 treating hypothetical data distributions and statistical models as if they reflect known physical law

57 APL-Py implements state-of-the-art tools and statistical models, assembled in a comprehensive workflo

58 The mechanistic statistical model based on ecological diffusion led to i

59 th a variety of different structures using a statistical model based on residue-residue co-evolution

60 Here, we apply a combined experimental- and statistical modeling-based approach to identify a set of

61 es (up to 0.15 at alpha=0.05) under standard statistical models, but not when analysed by a permutati

62 n small social structures due to advances in statistical modeling; but such an approach has so far la

63 Building statistical models by mining existing clinical trial dat

64 Data-driven statistical models calculated from the phosphorylation s

65 We introduce a new statistical model, called a sparse conditional Gaussian

66 Thus, simple statistical models can predict rich neural activity elic

67 87 route-specific CAS were classified and a statistical model capable of predicting the method of fe

68 point prior to division were extracted and a statistical model capturing the successive changes of th

69 n only be interpreted in light of additional statistical modeling choices.

70 puting power, and increasingly sophisticated statistical models combine to enable machines to find pa

71 fume and lead dust exposure, derived from a statistical model combining expert lead intensity rating

72 We address this gap by using Bayesian statistical models combining maps of livestock densities

73 We show that current statistical models commonly used in chronic disease epid

74 gatively correlated with male age, all three statistical models concurred that no PLCzeta-related par

75 Statistical modeling confirms that economic development,

76 cancer tissues and cell lines using a global statistical model connecting protein pairs, genes and an

77 The statistical model considers three priors and two posteri

78 The resultant statistical model correctly identified fracture/no fract

79 s (CEGs) are a graphical representation of a statistical model derived from event trees.

80 We have developed a statistical model describing stochastic exciton-photon t

81 hically using interpolation and by fitting a statistical model describing the position and width of t

82 before, during, and after the event, and 3) statistical modeling designed to compare the observed fr

83 The statistical models developed to objectively analyze the

84 he addition of inflammation markers into the statistical models did not attenuate these associations.

85 However, forecasts from statistical models (e.g. species distribution models) ra

86 icult choices surrounding the cVL metric and statistical model employed.

87 Our statistical model estimated a global annual ES of 6.7 +/

88 Specifically, in statistical models evaluating the roles of host biomass

89 In particular, no statistical model exists that takes the underlying depen

90 ack of freshwater sources of Hg in the NWHI, statistical models explaining the variation in tissue Hg

91 Our results show that statistical modeling extends the scope and potential of

92 Out of eleven investigated statistical models fit to 22 predictors, the Random Fore

93 To do this, we develop a Bayesian statistical model for biclustering to infer subsets of c

94 Here we present a statistical model for detecting tree structure in transc

95 Performance of the statistical model for early detection from online search

96 ts in RNA-seq reads, with our rigorous rMATS statistical model for identifying differential isoform r

97 hylation and gestational age and developed a statistical model for predicting gestational age using M

98 In this paper we develop a statistical model for sequence counts that accounts for

99 is a great need for an efficient and robust statistical model for simultaneous recovery of both recu

100 We formulate a statistical model for the regulation of global gene expr

101 To identify MSI target genes, we developed a statistical model for the somatic background indel mutat

102 We develop a statistical model for this dataset by embedding the posi

103 We integrated live-cell imaging data with statistical modelling for quantitative real-time estimat

104 We therefore propose the first predictive statistical models for identifying persistently infected

105 tabolite composition of carrots and to build statistical models for prediction purposes.

106 The results suggest that the accuracy of statistical models for protein-protein affinity predicti

107 We compared statistical models for quantitative microchimerism value

108 nalysis of dPCR data, we describe a class of statistical models for the analysis and design of experi

109 protein characterization, and of appropriate statistical modeling, for reproducible, accurate and eff

110 We present a multivariate statistical modeling framework for developing a quantita

111 mber of samples, (ii) introducing a flexible statistical modeling framework, including multi-group an

112 Statistical modeling further revealed a strong mediating

113 In the primary analysis, a 3-class statistical model generated a p(EoE) score based on comm

114 mpare them with those of other, conventional statistical models (GWR and LME) by within-sample model

115 The developed statistical model has a c-index of 0.895.

116 A strong statistical model has been established between RS format

117 A statistical model has been reported to predict Tmax of i

118 But the module-based statistical model has not been adequately addressed for

119 Statistical modeling has suggested that this genetic con

120 nces in phenotype using the most appropriate statistical model in a given population.

121 The results were used to construct a statistical model in which bouton addition, elimination,

122 ies, EBglmnet will be a very useful tool for statistical modeling in this area.

123 tative approaches that apply mechanistic and statistical models in a systems-wide approach are illumi

124 A statistical model including additive genetic factors and

125 e explanations exist, simulations based on a statistical model indicated that the coronal nanodomains

126 Statistical models indicated that increasing atmospheric

127 ti-modal imaging, combined with multivariate statistical modeling, indicates that the fronto-accumbal

128 he gains in accuracy achieved by introducing statistical models into fusion detection, and pave the w

129 A parameter in a statistical model is identified if its value can be uniq

130 A statistical model is then developed that accounts for bi

131 sequence regularities but, as opposed to the statistical model, it lacks the same type of closed-form

132 experiments and three modelling approaches: statistical models, local crop models and global gridded

133 This work indicates that predictive statistical modeling methods may be complementary to des

134 pressure (BP) goals during VPI and whether a statistical model might be efficacious for advance warni

135 ative prognostic variables were added to the statistical model (n = 87; hazard ratio, 1.31; 95% CI, 0

136 The statistical models obtained by PLS, with the optoelectro

137 The statistical model of formation of this film was successf

138 ar function prediction algorithm that uses a statistical model of function evolution to incorporate a

139 well-predicted from category extensions by a statistical model of how representative a sample is of a

140 In this study, a Statistical Model of Integrated Phenology and Physiology

141 Joint statistical model of longitudinal prostate-specific anti

142 and hypometabolism by means of a data-driven statistical model of non-overlapping intensity correlati

143 Here we outline a statistical model of strength that resembles a fishnet p

144 A statistical model of the biosignature was trained using

145 The method incorporates a statistical model of the contact counts, assuming that t

146 For this purpose, we fitted a statistical model of the genetic distance between 37 tse

147 possible, measure their fitnesses, and fit a statistical model of the landscape that includes additiv

148 We build a statistical model of the recovery kinetics with a two-re

149 The statistical modeling of CDC kinetics reveals the signifi

150 This flexibility allows for more appropriate statistical modeling of complex data structures that are

151 Statistical modeling of measurements and thorough valida

152 distribution can then be used as a basis for statistical modeling of MSI data.

153 Through novel statistical modeling of paired-end DNA-sequencing data u

154 Drawing from statistical modeling of survey data from German resident

155 Statistical modeling of the MDR ER data demonstrated tha

156 Intercomparison Project Phase 5 (CMIP5) and statistical modelling of historical temperatures, we the

157 -resolution measurements of DNA methylation, statistical modelling of such data is still challenging.

158 We use Bayesian statistical modelling of tourist visits to protected are

159 Using novel statistical models of different aspects of the recogniti

160 res in the 3D image sets to build (or learn) statistical models of each tissue class.

161 We are then able to learn reliable statistical models of enhancer activity for over 70 expr

162 Current statistical models of haplotypes are limited to panels o

163 SMBuilder is a software package for building statistical models of high-dimensional time-series data.

164 This was modelled using statistical models of increasing complexity: frequentist

165 the stochastic dynamics of state-of-the-art statistical models of neuronal spiking activity.

166 Here we present statistical models of severe primary neuronal degenerati

167 Independently, we also estimate statistical models of the different transmission pathway

168 to be captured in ways that directly reflect statistical models of trait-fate relationships.

169 Statistical models often use observational data to predi

170 Here, we used statistical modeling on a regional database covering 179

171 regions with distinct patterns of change, a statistical model originating from control theory is app

172 By combining different statistical models, our approach outperforms current sta

173 t neuropeptide measures were included in the statistical model, OXT compared with placebo treatment s

174 stic tractography combined with multivariate statistical modeling (partial least squares regression a

175 The statistical model performs well in the classification of

176 Here, we explored the potential for a statistical model, previously developed for Peruvian sun

177 coregulation between gs and gm The resulting statistical models provide the first hints for coregulat

178 The statistical models provided a basis for assessing the me

179 Statistical modeling reduced the biosignature to 44 mole

180 The approach works by creating statistical models relating gene expression to drug resp

181 We developed statistical models relating seasonal temperature and pre

182 ogic quality standards but also include more statistical modeling results when data allow.

183 Statistical modeling revealed that subjects' grades in t

184 Statistical models revealed that (i) CD8 counts were rel

185 We used a statistical model selection method to identify a global

186 Statistical models showed that surgical residents expose

187 Statistical modeling shows that protective HA imprinting

188 steps: 1) a feature extraction step and 2) a statistical modeling step.

189 These estimates are typically based on statistical models such as the negative binomial distrib

190 Our statistical model suggests that, averaged over India, yi

191 ce for both classes including a parsimonious statistical model suitable for real-time predictions bas

192 r nitrifier-denitrification production while statistical modelling supported production by archaea, p

193 ning-based classification, segmentation, and statistical modelling system was developed to guide colo

194 ols was selected by means of a wide range of statistical modeling techniques, drawing upon 10 029 dis

195 A statistical model, termed Global optimization-based InFe

196 riate data analysis, we were able to build a statistical model that accurately identified the race of

197 Here we describe and validate a generative statistical model that accurately quantifies technical n

198 lop a new method 'contamDE' based on a novel statistical model that associates RNA-seq expression lev

199 We develop a general statistical model that can detect concerted changes in a

200 A statistical model that combines these observations corre

201 We develop a novel statistical model that decomposes the population respons

202 We developed a new deep mutational scanning statistical model that generates error estimates for eac

203 From these observations, we then built a statistical model that included levels of DHA and AA fro

204 We first predict amphibian occupancy with a statistical model that includes all predictors but the s

205 ed variant effect on regulation), a Bayesian statistical model that incorporates expression data to p

206 Finally, we develop a statistical model that incorporates the uncertainty in i

207 We present a rigorous statistical model that infers the structure of P. falcip

208 algorithm, permuted-SVA (pSVA), using a new statistical model that is blind to biological covariates

209 regate independent simulations into a single statistical model that is validated by previous computat

210 A statistical model that predicts the appearance of strong

211 cerebellum using a newly developed Bayesian statistical model that provides unprecedented transcript

212 We construct a statistical model that separates fragile sites from regi

213 We first fit a statistical model that showed that the projection zone o

214 I describe a statistical model that uses association statistics compu

215 roscopy, was followed by calculation of four statistical models that accurately predicted peroxide va

216 Statistical models that controlled for age and the apoli

217 approach lies in its capability to transform statistical models that describe materials into optimize

218 approach is based on progressively detailed statistical models that enable detection of the head and

219 esents a hybrid of epidemiologic designs and statistical models that explicitly consider both genetic

220 We considered whether statistical models that incorporated both risk factors a

221 Using statistical models that integrate ASE information, we id

222 e performance of competing empirically-based statistical models, that aim to approximate the mechanis

223 Based on a multivariate statistical model, the screening takes into account the

224 Here, via statistical modelling, the authors predict a 72% reducti

225 low-up (7/1/2005-12/31/2010) in two types of statistical models, time-to-event and case-crossover.

226 Applying a track-pattern-based statistical model to 22 Coupled Model Intercomparison Pr

227 in the Kingdom of Saudi Arabia, we applied a statistical model to a line list describing 721 MERS-CoV

228 method for assessing the quality of fit of a statistical model to a specific dataset.

229 ss 14 d of 90 mm NaCl stress and developed a statistical model to assess the genetic architecture of

230 the accuracy of cell-scaffold contact for a statistical model to be 62.6% with 76.7% precision and f

231 We present a novel statistical model to detect differentially methylated lo

232 ons, we developed SingleSplice, which uses a statistical model to detect genes whose isoform usage sh

233 ch dimension, and from this we constructed a statistical model to estimate the proportion of the popu

234 We developed (i) a deterministic statistical model to evaluate the potential climatic con

235 in gene, we sought to develop an integrative statistical model to explain the observed pattern of Tit

236 l patient egg reduction rates (ERRs) using a statistical model to explore the influence of covariates

237 Our aim is to build and validate a statistical model to forecast future platelet demand and

238 We developed a statistical model to generate estimates and projections

239 se I clinical trials in oncology that used a statistical model to guide dose escalation to identify t

240 sults of multiple diagnostic tests through a statistical model to obtain estimates of disease prevale

241 Here, we used a statistical model to partition the variability of seven

242 We develop a statistical model to predict June-July-August (JJA) dail

243 etition of multiple miRNAs into account in a statistical model to predict their target sites.

244 self-organizing communities, and developed a statistical model to quantitatively characterize the two

245 d a geographically weighted regression (GWR) statistical model to represent bias of fine particulate

246 gm of uniting simulation and experiment in a statistical model to study the structure of protein exci

247 We use statistical modeling to explore panel data on annual CO2

248 ric Bayesian framework based on multivariate statistical modeling to identify driver genes in an unsu

249 genome sequencing, detailed phenotyping, and statistical modeling to predict biometric traits in a co

250 ticipant were then incorporated into interim statistical modelling to target the two doses most likel

251 ic Health vital records and use longitudinal statistical models to assess whether social media use is

252 Finally, we develop statistical models to calibrate allelic bias in single-c

253 Here we build statistical models to disentangle the effect of 12 recur

254 al of combining new types of data with novel statistical models to enable a more integrative monitori

255 aerosol optical depth (AOD) measurements and statistical models to estimate ground-level PM2.5 is a p

256 amer et al. have used graphical analysis and statistical models to estimate the impact that age, peri

257 erprints has been investigated by developing statistical models to estimate the probability of error

258 We coupled statistical models to inland water area inventories to e

259 Here, we apply flexible statistical models to investigate the issue by using a l

260 We developed multivariate statistical models to predict cancer status with an area

261 of British Columbia to test the capacity of statistical models to predict temporal changes in bird p

262 Here we use molecular simulation and statistical models to show that recovery is hampered by

263 developed and validated new high-performance statistical models to support decision making in patient

264 c trajectories and newly developed localized statistical models, to predict quantitative selectivitie

265 Using a statistical model trained on DNA methylation data from N

266 observational studies because it affects the statistical models used and the decision of whether or n

267 istory, is not commonly accounted for in the statistical models used by malaria researchers.

268 The statistical models used in previous studies are primaril

269 The statistical models used included generalized estimating

270 However, statistical models used to detect interactions can be co

271 The statistical model uses linear and ordinal regression to

272 utcome measures, and factors included in the statistical model using multiple data sources.

273 However, statistical models using basin and well pad characterist

274 nt of blood-brain barrier passage-predictive statistical models using partial least-squares (PLS) reg

275 nderestimates of concentrations and improper statistical model validation that, in turn, can lead to

276 A hierarchical statistical model was developed to use quantitative info

277 A linear mixed-effects statistical model was used to compare detection accuracy

278 The performance of the used statistical model was validated with independent bacteri

279 Longitudinal statistical modeling was performed to integrate multiple

280 ns with clinical outcomes were analyzed, and statistical modeling was used to identify risk factors f

281 Based on the GWAS statistical model, we developed a multi-SNP GWAS analysi

282 Using quantitative imaging and statistical modeling, we demonstrate that denticle numbe

283 nt concentrations combined with multivariate statistical modeling, we fingerprint and quantify the ab

284 lations, quantitative behavioral assays, and statistical modeling, we show that virilis females combi

285 Using statistical models, we analyze multiyear data from 166 l

286 a combination of a phylogeny and appropriate statistical models, we illustrate how data from extant s

287 Statistical models were adjusted for race, sex, smoking,

288 Statistical models were constructed to classify weight m

289 ng Forward Stepwise Discriminant Analysis, 3 statistical models were created based on sugars content,

290 Statistical models were implemented using a generalized

291 Estimates using simple family-based statistical models were inflated on average by approxima

292 Seven statistical models were tested using two random cross-va

293 Automated brain mapping algorithms and statistical models were used to evaluate the relationshi

294 All the statistical models were validated both by using a test s

295 nseling, and genetic ancestry predicted by a statistical model, were compared for concordance.

296 alysis using Ez-Info and the creation of the statistical model with combinations of responses for mol

297 This study demonstrates that combining statistical modeling with public RNA-seq data can be pow

298 imilarity) scores are analyzed by multilevel statistical models with covariates such as time interval

299 re stimuli were generated by sampling from a statistical model, with parameters chosen to match the p

300 olution and HRMAS data produced very similar statistical models, with high classification accuracy.