ライフサイエンスコーパス: statistical model

1 score of the residuals based on a predefined statistical model.

2 e transcript as determined within a Bayesian statistical model.

3 of interaction between cells we developed a statistical model.

4 BRCA1 using VarCall, a Bayesian integrative statistical model.

5 k of capability to determine the most proper statistical model.

6 ch prevented application of the prespecified statistical model.

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

8 ual-based model, by approximating it using a statistical model.

9 while failing to quantify uncertainty in the statistical model.

10 led XAEM based on a more flexible and robust statistical model.

11 enome and inferring likelihoods from complex statistical models.

12 c addition reactions to imines and developed statistical models.

13 or geographic differences in FA content with statistical models.

14 oftware tools, or a dataset for applying new statistical models.

15 approaches that deploy machine learning and statistical models.

16 red by the piecemeal development of relevant statistical models.

17 wo different environments with two different statistical models.

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

19 test composite null hypotheses in irregular statistical models.

20 mming is a powerful methodology for building statistical models.

21 iable results, which should be compared with statistical models.

22 lysis using EZ-Info, and the creation of the statistical models.

23 ed batch effects can introduce biases in the statistical models.

24 ifferences in gene expression using advanced statistical modeling.

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

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

27 y have potential advantages over traditional statistical modeling.

28 ingly available genomic data and advances in statistical modelling.

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

30 cycle data) in a semiblinded fashion, using statistical models, 25 mug/kg BW/d BPA [BPA(25)], or 250

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

32 the residual contamination and corrected our statistical models accordingly to provide a rigorous ana

33 esponses were analysed using a mixed-effects statistical model accounting for the mean response and v

34 Inclusive, multistage statistical models accurately predicted likelihoods of r

35 To that end, we construct a statistical model addressing joint distributions of part

36 Statistical modelling (adjusted incident rate ratios [IR

37 ass spectrometry imaging in conjunction with statistical modeling allows discrimination of renal tumo

38 uired by DESI-MS imaging in conjunction with statistical modeling allows discrimination of renal tumo

39 The resulting statistical models also allow for extrapolation to out-o

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

41 selection of the most proper multi-modality statistical model and downstream analysis, useful in a s

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

43 ant disconnect is found to exist between the statistical modelling and biological performance of pred

44 h our results are based on inference through statistical modelling and do not provide an absolute pro

45 Using single-cell RNA-seq with statistical modelling and modulation of energy metabolis

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

47 most of them, however, are based on complex statistical models and handle the multi-class case in an

48 aim of building and evaluating multivariate statistical models and machine learning methods for the

49 All statistical models and summary estimates were weighted t

50 The SM has deep connections with parametric statistical models and the theory of phase transitions i

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

52 ng biodiversity data, environmental data and statistical modelling, and could also be adopted by a br

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

54 From its measured performance, we develop a statistical model applicable to much larger datasets.

55 Statistical models applied to Year-3 data could help pre

56 Here, we describe a statistical model approach to reliably transform passive

57 The statistical modeling approach developed in this study re

58 The statistical modeling approach proposed in this work offe

59 ophysical properties, we used a system-based statistical modeling approach to connect the multivariat

60 investigated by a combined experimental and statistical modeling approach.

61 argeted proteomic analyses using a multistep statistical modeling approach.

62 r work proves the strong potential of global statistical modeling approaches to genome-wide coevoluti

63 prediction have been developed, among which statistical models approximating ENSO evolution by linea

64 Different forms of statistical models are now being used to probe the cellu

65 taining and integrating competence data into statistical models as covariates, as the response variab

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

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

68 en contains categorical variables and common statistical model assumptions rarely hold.

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

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

71 Using a statistical model based on the Cox method of modulated r

72 We introduce NBAMSeq, a flexible statistical model based on the generalized additive mode

73 Statistical models built from DESI-MS imaging data allow

74 Data-driven statistical models calculated from the phosphorylation s

75 propose, for the first time, the use of the Statistical Model Checking Engine (SMCE), a probability-

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

77 achine learning algorithm, which optimises a statistical model combining Principal Component Analysis

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

79 Although the planned statistical model could not be applied to the primary en

80 cardiovascular disease or diabetes, multiple statistical models demonstrate that icosapent ethyl subs

81 Multivariable statistical models demonstrated age-related differences

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

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

84 Statistical models detected significant associations bet

85 The statistical models developed to objectively analyze the

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

87 ics) accomplishes this goal by extending the statistical model employed by DEseq, re-purposing the 's

88 This involves applying mechanistic and statistical modeling, establishing consistent and widely

89 Our statistical model estimates that culture-confirmed cases

90 A uniform statistical model explains 34% of these shared variants;

91 A quantitative statistical model explains how for certain reactions, hi

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

93 nosa on five different media and developed a statistical model, FiTnEss, to classify genes as essenti

94 framework with more detail than established statistical modeling fitting methods.

95 a blinded fashion to develop a generalizable statistical model for comparison to extract and marker a

96 es multiple times to improve accuracy, and a statistical model for distinguishing error-enriched regi

97 Based on these findings, we develop a statistical model for each molecular process as well as

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

99 Here, we present a statistical model for estimating a terror group's future

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

101 We first trained a statistical model for obtaining relative brain age (RBA)

102 kelihood theory in the context of a complete statistical model for sequencing counts contributed by c

103 ication from microbiome data, based on solid statistical model for SNP calling, as well as optimized

104 roach Dr Insight implements a frame-breaking statistical model for the 'hand-shake' between disease a

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

106 by extensive co-occurrent resistance, where statistical models for a drug include unrelated mutation

107 We have developed several statistical models for addressing these two challenges b

108 Current statistical models for assessing hotspot significance do

109 trics increase the variance explained by the statistical models for clinical and information processi

110 We developed statistical models for completed cohort fertility at age

111 Placket-Burman and central composite design statistical models for culture condition optimisation pr

112 By using statistical models for dynamic network data, we are able

113 Here we propose an experimental design and statistical models for estimating genetic diversity in a

114 United States, we compare multiple competing statistical models for estimating visitation.

115 PsiCLASS combines mixture statistical models for exonic feature selection across m

116 We build rigorous statistical models for GWAS summary statistics to motiva

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

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

119 -derived parameters used to build predictive statistical models for rates of new ligand/substrate com

120 ome tools are not compatible with downstream statistical models for somatic mutation detection.

121 with individual-level animal movement, most statistical models for telemetry data are not equipped t

122 phore conformation, then used it to generate statistical models for the accurate prediction of lambda

123 stantial literature of ChR variants to train statistical models for the design of high-performance Ch

124 structure of personality traits derived from statistical models (for example, Big Five) is often assu

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

126 To address this, we propose a statistical modeling framework to estimate high frequenc

127 y and patient stratification, and provides a statistical modeling framework to incorporate additional

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

129 This study employs multilevel statistical models from social network analysis to explo

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

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

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

133 A strong statistical model has been established between RS format

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

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

136 Statistical modeling has suggested that this genetic con

137 Various statistical models have been developed to model the sing

138 etic trees, multiple sequence alignments and statistical models (hidden Markov models (HMMs)).

139 Statistical modeling, however, indicates that the FTSJ3

140 Three multivariate statistical models, i.e. PCA/LDA, SELECT/LDA and PLS-DA,

141 Within household compounds, statistical models identified two interfaces for exchang

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

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

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

145 PAIRADISE outperforms alternative statistical models in simulation studies.

146 A statistical model including additive genetic factors and

147 A statistical model including carbon dioxide, temperature,

148 y factor associated with fine-root traits in statistical models including mycorrhizal association and

149 ined differences in total events using other statistical models, including Andersen-Gill, Wei-Lin-Wei

150 Statistical models indicated that increasing atmospheric

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

152 ern discovery and comparison by transferring statistical models into visual clues.

153 h other analytic approaches and note that no statistical model is a panacea to rectify limitations of

154 This statistical model is capable of quantitative prediction

155 A statistical model is developed through training on monom

156 A statistical model is then developed that accounts for bi

157 zed using unsupervised machine learning, and statistical modelling is used to relate compositional va

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

159 Traditional machine learning and statistical models minimize the impact of confounders by

160 it model of pathogen entry and spread with a statistical model of detection and use a stochastic opti

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

162 ons and sequence coevolution, we generated a statistical model of interaction energy for the clustere

163 Joint statistical model of longitudinal prostate-specific anti

164 Here, we present a simple, coarse-grained statistical model of niche construction coupled to speci

165 A statistical model of relative value that includes a term

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

167 Here, we develop a statistical model of the short-term dynamics of spike tr

168 The statistical modeling of CDC kinetics reveals the signifi

169 Statistical modeling of measurements and thorough valida

170 Despite progress in statistical modeling of neural responses and deep learni

171 We show that statistical modeling of single drug response from drug c

172 Statistical modeling of structural features described by

173 Drawing from statistical modeling of survey data from German resident

174 Both statistical modeling of the population level covariation

175 Statistical modelling of absolute growth trajectories re

176 for schizophrenia, with recent evidence from statistical modelling of twin data suggesting direct cau

177 ritability that sometimes arise from popular statistical models of additive genetic variation.

178 erm goal of developing clinically applicable statistical models of biological processes to measure, t

179 In contrast to methods based solely on statistical models of data, the ESS method leverages the

180 Using novel statistical models of different aspects of the recogniti

181 sets are routinely dissected and analyzed by statistical models of ever-increasing complexity.

182 Current statistical models of haplotypes are limited to panels o

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

184 Descriptive statistical models of neural responses generally aim to

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

186 holistic, data-driven workflow for deriving statistical models of one set of reactions that can be u

187 f direct phyletic couplings, based on global statistical models of phylogenetic profiles.

188 ions of surface tumbles set a foundation for statistical models of run-and-tumble surface motion diff

189 Here we present statistical models of severe primary neuronal degenerati

190 rast trees provide a lack-of-fit measure for statistical models of such statistics, or for the comple

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

192 Using a combination of statistical modeling on data from several warming experi

193 sting methods, however, either lack explicit statistical models, or use models based on simplistic as

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

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

196 results exist for radioactive isotopes, and statistical-model predictions typically have large uncer

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

198 unctional HBD catalysis through an iterative statistical modeling process.

199 ack of spatial sense in the method, one uses statistical modeling, reaction-diffusion in continuous m

200 ficient alkenes to develop a three-parameter statistical model relating enantioselectivity to physica

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

202 We developed statistical models relating seasonal temperature and pre

203 A statistical model represents a hypothesis about the unde

204 Via statistical modelling, several characteristics in the po

205 Statistical models showed that surgical residents expose

206 his conclusion was robust to a wide array of statistical model specifications.

207 Parametric statistical models such as ones assuming exponential gro

208 The data show that sequence-based statistical models suffice to specify proteins and provi

209 Our statistical modeling suggests that apparent priming effe

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

211 We develop a parsimonious statistical model that accurately predicts the probabili

212 and trends in neonatal mortality by use of a statistical model that can be used to assess progress in

213 s' variable responses and build a predictive statistical model that can be used to personalize mexile

214 Here, we present a statistical model that captures both the underlying stru

215 A statistical model that combined the outcomes was used to

216 A statistical model that combines these observations corre

217 ted flood thresholds are established using a statistical model that considers predicted tide and proj

218 MRI metrics alone and to determine the best statistical model that explains better EDSS and SDMT.

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

220 ost mixing ratio, which is explained using a statistical model that includes both shifts of the host'

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

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

223 We developed a new statistical model that jointly estimated unintended preg

224 A statistical model that predicts the appearance of strong

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

226 We developed a flexible statistical model that uses patterns of aggregation in p

227 Through statistical modeling that combines individual genetic an

228 ke train recordings often relies on abstract statistical models that allow for principled parameter e

229 We have built statistical models that include confounding factors such

230 Therefore, we developed a statistical model, the separable Nonlinear Input Model,

231 Combined with mathematical and statistical modelling, this has improved the predictabil

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

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

234 In addition, we build a statistical model to characterize correlations in the sp

235 Fitting a statistical model to data from studies of influenza vacc

236 ut of which sounds are composed, we employ a statistical model to extract such components.

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 level Lyme disease case data in a panel data statistical model to investigate prior effects of climat

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

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

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

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

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

245 By fitting a flexible statistical model to tuberculosis drug resistance survei

246 Here, we use live microscopy and statistical modeling to demonstrate that L. monocytogene

247 Cancer drivers require statistical modeling to distinguish them from passenger

248 that integrates dimensionality reduction and statistical modeling to grapple with the heterogeneity.

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

250 detection (DEEPEST), an algorithm that uses statistical modeling to minimize false-positives while i

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

252 GBD 2017 employed a variety of statistical models to determine the number of deaths fro

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

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

255 We apply statistical models to estimate household transmission dy

256 The authors recently developed statistical models to estimate the effect size on IQ of

257 ns in the Amazon River floodplain, we fitted statistical models to explain landscape-scale variation

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

259 We developed statistical models to predict current and reference conc

260 ng station, are identified and used to build statistical models to predict seasonal wind speed and so

261 associated with KIN-193 and further created statistical models to predict the treatment effect of KI

262 ological mechanism, and have led to applying statistical models to quantify causal microbiome effects

263 , and it seeks to develop formal idiographic statistical models to represent these individual process

264 This paper deploys several multivariate statistical models to test whether different aspects of

265 Bayesian networks are powerful statistical models to understand causal relationships in

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

267 in the state of Nebraska, USA, combined with statistical models, to quantify the contribution of cool

268 Point process statistical models trained on initial portions of each p

269 on workflow presents an opportunity to build statistical models unifying various modes of activation

270 The statistical model used here provides a robust framework

271 The statistical models used in previous studies are primaril

272 peculiarities of specific sensor data to the statistical models used, highlighting at the same time t

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

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

275 Statistical modeling was also used to discriminate whisk

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

277 Using a statistical model, we estimated the emergence date and s

278 Using a generalized additive statistical model, we examined the longest data set on f

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

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

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

282 Fitting statistical models, we validate our data and find that h

283 Statistical models were constructed to classify weight m

284 In response to these challenges, statistical models were developed in this paper to predi

285 Cox, logistic, or linear statistical models were used depending on the outcome st

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

287 Statistical models were used to test nine quantitative v

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

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

290 Transcriptomic data is often used to build statistical models which are predictive of a given pheno

291 We aimed to develop and evaluate a statistical model, which included known pre-treatment fa

292 with existing theories of visual processing, statistical modeling will increasingly drive the evoluti

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

294 vious study performed by the authors using a statistical model with similar input features that prese

295 We trained a statistical model with these 52 measurements to infer th

296 hematical analyses and Bayesian hierarchical statistical modeling with diverse data sources.

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

298 We tested statistical models with fusion as a higher-level bistabl

299 By combining statistical models with highly scalable computational to

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