ライフサイエンスコーパス: linear model

1 OVA or a Negative Binomial (in a generalized linear model).

2 ng a single locus test in framework of mixed linear model.

3 hment scores were identified using a general linear model.

4 ints, 95% CI -0.23 to 2.07) according to the linear model.

5 and then confirmed the same by a generalised linear model.

6 ive fitness ratios and fitting a generalized linear model.

7 ctivation of study centers using generalized linear model.

8 nfirm the quality adjustment of the proposed linear model.

9 nd the phenotypes as predictors in a general linear model.

10 stimated using a weighted multivariate mixed linear model.

11 een quantile regression and the conventional linear model.

12 o and analyzed using a mixed-effects general linear model.

13 parenchyma-absorbed dose was assessed using linear models.

14 Annual trends were modeled using generalized linear models.

15 -hazards models and multivariate generalized linear models.

16 ior which could not be addressed by existing linear models.

17 sis of case-cohort data involves fitting log-linear models.

18 abundances, by negative binomial generalized linear models.

19 ing generalized estimating equation-adjusted linear models.

20 ous variables improved the fit compared with linear models.

21 and fractional anisotropy (FA) using general linear models.

22 h percentile were estimated using multilevel linear models.

23 asma FA were assessed using adjusted general linear models.

24 of GBCA, age, and sex by using multivariable linear models.

25 nical parameters were analyzed using general linear models.

26 identified by using boosting for generalized linear models.

27 lored with age-, sex- and phenotype-adjusted linear models.

28 and accurate eQTL mapping than conventional linear models.

29 incidence, was determined using generalized linear models.

30 se progression was measured with generalized linear models.

31 age of linear, mixed-integer and general non-linear models.

32 of vision (V(TOT)) was assessed with general linear models.

33 life sciences often lead to high-dimensional linear models.

34 stic regression procedures followed by mixed linear modeling.

35 advanced with the development of functional linear modeling.

36 8%]), and basic inferential tests or general linear models (10 trials [40%]).

37 These studies led to a linear model, according to which in the absence of ethyl

38 CRP and/or AGP, were estimated from general linear models, accounting for repeated measures.

39 Linear models across the entire study cohort indicated s

40 We used the generalized linear model adjusted by sex, age, and body mass index f

41 In a general linear model adjusted for age, sex, and body mass index,

42 d cell counts were assessed by multivariable linear models adjusted for relevant risk factors.

43 tabolite profiles at 1-year was evaluated in linear models adjusting for baseline metabolite levels,

44 c steatosis (LPR </= 0.33) using generalized linear models, adjusting for demographics, individual an

45 he included studies using 3 models: the area linear model (ALM), radius linear model (RLM), and area

46 RPE decline pattern using 3 models: the area linear model (ALM), radius linear model (RLM), and area

47 Applying a standard generalized linear model analysis approach, our results indicate tha

48 sed on the social game of Pictionary General linear model analysis revealed increased activation in t

49 ome-wide differential PTM quantitation using linear models analysis (limma).

50 m to find better parameters for the existing linear model and advanced non-linear multi-loop models.

51 onducted using the repeated measures general linear model and the generalized logit model for binomia

52 ferable DR was estimated using a generalised linear model and was used to calculate the intervals nee

53 Using both linear models and a genome-scale metabolic network recon

54 larities in all datasets, comparing to other linear models and coexpression analysis methods.

55 ed and examined several types of generalised linear models and determined the best-fit model accordin

56 Associations were tested using general linear models and logistic regression.

57 Using generalized linear models and model selection techniques, we used 12

58 d MetaPhlAn2, and analyzed using generalized linear models and random effects meta-analyses.

59 and without a sand barrier using multilevel linear models and reported cluster robust standard error

60 ed spiking networks with Poisson generalized linear models and suggests several promising avenues for

61 using the linear models for microarray data (linear modeling) and Boruta (decision trees) R packages,

62 model parameters may be near optimal for the linear model, and that no advanced model performs better

63 main analysis using a logistic, rather than linear, model, and with a lead indicator on PDMP mandate

64 oximate Posterior Estimation for generalized linear model, apeglm, has lower bias than previously pro

65 Using a dyadic multilevel linear modeling approach, treating body mass index (BMI;

66 rofiled the performance of deep, kernel, and linear models as a function of sample size on UKBiobank

67 Linear models assessed changes in protein concentrations

68 We used generalized linear models, assuming a Poisson distribution and log l

69 from 8-weeks in 2016 was used to train three linear models based on drinking water production, electr

70 Comparison of teeth and implants via general linear models based on orthogonal polynomials showed sim

71 Here, we propose a mixed-linear-model-based method called MOMENT that tests for a

72 Ridge regression (RR), Boosting generalized linear model (BGLM), Negative binomial generalized linea

73 c, HapReg, Bayesian hierarchical generalized linear model (BhGLM) and logistic Bayesian LASSO (LBL).

74 rd-order model performs better, but only non-linear models can account for frequency-dependent change

75 We illustrate how robust linear and non-linear models can be constructed to accurately predict t

76 -species community, highlighting that simple linear models can in some cases provide powerful insight

77 ssociations were estimated using Poisson log-linear models controlling for continuous air temperature

78 ted outcomes were analyzed using generalized linear models, controlling for confounding variables.

79 g principal component ordination and general linear modeling, correlations with the North Atlantic Os

80 We find that BTH and a double generalized linear model (dglm) outperform classical tests used for

81 However, linear models did demonstrate better predictive capabili

82 A multikernel linear model distinguished the relative influences of ex

83 We used a Bliss-based linear model, effectively borrowing data from the drug p

84 time than Bayesian hierarchical generalized linear model, efficient mixed model association (EMMA) a

85 Generalized linear models estimated correlations with postoperative

86 A general linear model evaluated the interactions between maternal

87 General linear models examined associations between SVD and [(11

88 Mixed linear models examined the association of midlife social

89 Linear models examined the association of pathology with

90 geographic approach coupled to a generalized linear model extension to characterize the dynamics and

91 variables accounted for by the multivariate linear model, female patients more strongly agreed that

92 Generalized linear model fitting showed decreasing percentage of sma

93 In a phantom study, we estimated a linear model fitting the CZT camera data to the planar d

94 ity patterns using actigraphy and functional linear modeling (FLM), for healthy, adult companion dogs

95 We used functional linear models (FLMs) to determine the critical windows o

96 proposed a network module-based generalized linear model for differential expression analysis of the

97 LINSIGHT combines a generalized linear model for functional genomic data with a probabil

98 We developed a new multivariable linear model for GFR using statistical regression analys

99 A generalized linear model for log-transformed 3MSE scores was used fo

100 We used a hierarchical generalised linear model for meta-analysis of individual participant

101 rtate aminotransferase (AST) using a general linear model for repeated measurements at 5 clinical tim

102 he zero proportion and a semi-parametric log-linear model for the possibly non-normally distributed n

103 een the sepsis and control groups, using the linear models for microarray data (linear modeling) and

104 models analysis dof variance and generalized linear models for multiple repeated measurements.

105 used to construct auto-correlation corrected linear models for pertussis incidence in 2004-2011 for t

106 measures over time were assessed by general linear models for repeated measures.

107 We determined best-fitting linear models for the rates over the entire series based

108 deled change through time using hierarchical linear models for total nitrogen (TN), total phosphorus

109 ng with a novel extension of the generalized linear model framework (GLM), termed the sparse-variable

110 multiHiCcompare uses the general linear model framework for comparative analysis of multi

111 With general and mixed linear model genome-wide associations, we identified 29

112 al interpretation of the Poisson generalized linear model (GLM) as a special case of the CBEM in whic

113 el (GLMM) is an extension of the generalized linear model (GLM) in which the linear predictor takes r

114 rix between discrete states as a generalized linear model (GLM) of genetic, geographic, demographic,

115 A Generalized Linear Model (GLM) revealed a significant difference in

116 We extend a generalized linear model (GLM) that predicts postsynaptic spiking as

117 neuronal circuitry by applying a generalized linear model (GLM) to spike cross-correlations.

118 A generalized linear model (GLM) was used to test the association betw

119 osted regression tree (BRT), and generalized linear model (GLM).

120 Generalized-linear models (GLM) and multi-level Cox-regression analy

121 We apply generalized linear models (GLM) to estimate presence probabilities f

122 eijing, China (2009-2012), using generalized linear models (GLM).

123 n hierarchical models, including generalized linear models (GLMs) and Cox survival models, with four

124 Regularized generalized linear models (GLMs) are popular regression methods in b

125 Generalized linear models (GLMs) are used in high-dimensional machin

126 ive binomial to provide flexible generalized linear models (GLMs) on both the mean and dispersion.

127 We also used generalized linear models (GLMs) to examine female reproductive succ

128 energy change have been suggested, a simple, linear model has been used since the 1980s.

129 In a generalized linear model, higher TTV levels were associated with a d

130 nformation from neural data sets relative to linear models (i.e., higher predictive accuracy), we nex

131 In best-fit general linear models, I(1670)/I(1640,) age, and volumetric bone

132 A generalized linear model identified the following factors to be asso

133 Finally, results from the linear modelling identified metabolites which could be u

134 nt: one commonly applied form of the general linear model, impulse response models, and network contr

135 ed by common assumptions in the literature-a linear model in a log-ratio transformed space, and a lin

136 odel in a log-ratio transformed space, and a linear model in the space of relative abundances-and pro

137 Linear models in combination with multiple-imputation co

138 erpolation results showed deviations for non-linear models in the prediction of EC(50) values of grap

139 In adjusted generalized linear models, in addition to MELD (P < .001), factors i

140 th groups were compared using a multivariate linear model, including variables that were significantl

141 Linear models indicated that dolphin abundance was signi

142 cancer care were estimated using generalised linear models, informed by a representative dataset of c

143 A consequence of linear models is that faster translation of a given mRNA

144 In sum, linear models keep improving as the sample size approach

145 We used a binomial generalized linear model (log-binomial model) to examine the associa

146 We used a generalised linear model (logit function) to estimate odds ratios fo

147 Using linear models, LysoPC and LysoPE groups in CB were posit

148 r can be approximated sufficiently well by a linear model, methods exist to identify the number and c

149 model (BGLM), Negative binomial generalized linear model (NBGLM), Boosting generalized additive mode

150 eralized estimating equation and generalized linear models (non-ART group pVL and hemoglobin) in as-t

151 Different from the general linear model of fMRI that predicts responses directly fr

152 e structural component was calculated with a linear model of Heidelberg Retina Tomograph (Heidelberg

153 e functional component was calculated with a linear model of VF measurements over time.

154 Both models are coupled to a generalised linear model of yellow fever occurrence which uses envir

155 Linear modeling of cellular traits associated with CXCL1

156 expression analysis technology developed for linear modeling of gene expression data was used in comb

157 Here we use non-linear modeling of neuronal activity and bifurcation the

158 d assessed differences between conditions by linear modeling of the data.

159 Linear modelling of our time-series data revealed that t

160 General linear modelling of the movement task functional MRI dat

161 d method has similar performance with simple linear model on computational efficiency.

162 and the initial language impairment (general linear model overall significant at P < 0.0001; ExpB 1.0

163 Status Scale scores in surface-based general linear modelling (P < 0.05).

164 highest discriminative capability of the non-linear model parameter (Parameter A) for the tissue stru

165 nced model performs better than the existing linear model parameters even after parameter optimizatio

166 We find that the current linear model parameters may be near optimal for the line

167 er to characterize the capability of the non-linear model parameters to discriminate structural chang

168 structural or functional brain scans, simple linear models perform on par with more complex, highly p

169 nd associated precision weights in a general linear model pipeline with continuous autoregressive str

170 Point process generalized linear models (PP-GLMs) provide an important statistical

171 rea and relating that to carbon lost using a linear model (r(2) = 0.41), we found 1.1% outlying PAs (

172 odel (r(2) = 0.88 and 0.93, respectively), a linear model (r(2) = 0.87 and 0.92, respectively), or a

173 Multivariable linear models regressed 2-y glucose change onto baseline

174 portance of each predictor using generalized linear model regression of distances between nearest-nei

175 ional ANOVAs, and multivariate analyses with linear models, respectively.

176 Together with linear modeling results, these findings suggest that mos

177 Hierarchical linear modeling revealed that improved treatment respons

178 models: the area linear model (ALM), radius linear model (RLM), and area exponential model (AEM), in

179 models: the area linear model (ALM), radius linear model (RLM), and area exponential model (AEM), in

180 Generalized linear model showed that enamel lesions were significant

181 Generalized linear models showed that only one of the two stressors

182 Intention-to-treat analysis using mixed linear models showed that PBT was noninferior to FBT on

183 Linear models showed that while averaged peripapillary R

184 Mixed linear-model showed around 29% lower average creatinine

185 tic (ROC) curves in high-dimensional, sparse linear model simulations, including a wide range of miss

186 Kallisto TPM data gives the best fit to the linear model studied.

187 is utilized as a covariate in a generalized linear model, successfully remove the influence of techn

188 plicable to other designs within the general linear model such as linear regression and analysis of c

189 Multivariable generalized linear modeling suggests an independent association betw

190 we describe the development of a generalized linear model (termed a pathotyping model) to predict the

191 Vertex-wise linear model tests were conducted across the cortical su

192 With a linear model that combines chromatin annotations and seq

193 ion matrix from DMS datasets and then fits a linear model that combines these substitution scores wit

194 of normalized batch corrected data, using a linear model that included considerations for disease, a

195 A linear model that incorporates the traditional equalizat

196 Using stepwise regression and general linear models that accommodate correlations between meas

197 have developed a framework based on simple, linear models that allows prediction of the monoisotopic

198 We used a linear model to assess log-transformed C-reactive protei

199 orrection factors were calculated by using a linear model to convert each radiomic feature to its est

200 prior information from multiple domains in a linear model to derive a composite score, which, togethe

201 In addition, we used a Poisson generalized linear model to estimate excess perforations attributed

202 ayment data were imputed using a generalized linear model to estimate overall PrEP medication payment

203 To test these predictions, we used a linear model to fit the fMRI response of human participa

204 DeconvSeq utilizes a generalized linear model to model effects of tissue type on feature

205 We train a linear model to predict expression effects of rare CNVs

206 Our aim was to apply functional linear modeling to accelerometry data from osteoarthriti

207 strapping in conjunction with response error linear modeling to decouple biological variance from inf

208 We used generalised linear modelling to assess FeNO as a predictor of respon

209 d a metabolomics-driven analysis followed by linear modelling to dissect the molecular processes in s

210 test for multiple comparison and generalized linear models to adjust for confounding factors such as

211 We used mixed-effects linear models to analyze associations of changes in stan

212 We used mixed effects linear models to analyze associations of changes in stan

213 ctional analysis using general least-squares linear models to assess group differences and associatio

214 Results were analysed using linear models to compare effectiveness of three differen

215 ed lag models and over-dispersed generalized linear models to estimate the cumulative effects of ozon

216 RiboDiff uses generalized linear models to estimate the over-dispersion of RNA-Seq

217 and 2016 antibiotic prescription rates, and linear models to evaluate temporal trends throughout the

218 e) were analyzed using multivariable general linear models to evaluate the relationship between comor

219 We used similarly adjusted generalised linear models to examine association with 5-year healthc

220 sensitivity indices (SSI) under generalized linear models to existing liver RNA expression microarra

221 We then used linear models to explain variabilities in the connection

222 We then used generalised linear models to investigate the associations between ho

223 zel test stratified by site, and generalised linear models to obtain relative risk (RR) estimates and

224 y consistently improves when escalating from linear models to shallow-nonlinear models, and further i

225 We implemented linear models to test differentially methylated position

226 ed univariable and multivariable generalised linear models to test for associations between the age i

227 We used generalized linear models to test the null hypothesis that condition

228 Multilevel mixed linear models (to account for the inclusion of 2 eyes of

229 OCT reliability, we (1) created a multilevel linear model using measured RNFL thickness values and de

230 This linear model was tested experimentally, as well as in si

231 A generalized linear model was used to estimate incremental costs (201

232 A mixed effects multivariable generalized linear model was used to estimate the mean relative incr

233 A generalized linear model was used to model the mean function and var

234 General linear modeling was used to 1) estimate the association

235 Mixed linear modeling was used to compare H1 levels between gr

236 Linear modeling was used to compare vessel densities amo

237 Using linear models, we built a metabolic clock with five meta

238 imaging classification in logistic and mixed linear models, we compared predictions for developing CK

239 Using multivariable generalized linear models, we estimated adjusted risk differences, a

240 Using hierarchical linear models, we examined the relationships between chi

241 Using generalized linear models, we identified significant associations be

242 Using linear models, we identify distinct dose responses to ei

243 Using dynamic linear models, we investigated whether seasonal variatio

244 Generalized linear models were adjusted for age, age squared, sex, h

245 Generalized linear models were applied to assess de novo variant bur

246 Multivariable mixed-effects log-linear models were constructed to determine the associat

247 Multivariable logistic and linear models were created to compare the effect of oper

248 Multivariable linear models were developed to predict OP based on vari

249 Multilevel mixed linear models were performed for analyses.

250 Multivariable generalized linear models were performed to analyze the quality of S

251 d to reconstruct HIV spread, and generalized linear models were tested for viral factors associated w

252 Mixed-effects linear models were tested with visual field mean deviati

253 Mixed-effects linear models were used to analyze the data.

254 Univariate and multivariate generalized linear models were used to analyze the relationships of

255 Adjusted Generalized Linear Models were used to compare matched patients.

256 Linear and generalized linear models were used to determine whether diarrhea wa

257 Repeated-measures analyses using mixed linear models were used to estimate and compare study en

258 Hierarchical linear models were used to estimate pointwise VF progres

259 Log-linear models were used to estimate prevalence ratios (P

260 Generalised linear models were used to estimate trends in annual hos

261 Generalized linear models were used to evaluate direct and indirect

262 Hierarchical linear models were used to evaluate intervention effects

263 Multilevel generalized linear models were used to evaluate trends in the risk-a

264 Generalized linear models were used to examine whether these changes

265 Hierarchical linear models were used to investigate percent total wei

266 Generalized linear models were used to predict the network statistic

267 Generalized linear models were used to predict the spiking activity

268 Linear models were used to regress quantitative CT measu

269 Generalized linear models were used, adjusting for age and sex.

270 In contrast to linear models where translation is largely limited by in

271 eptide charge are well described by a simple linear model, which should help improve current coiled-c

272 A general linear model with age and sex as covariates was used to

273 iffered between SZ and HCs, we implemented a linear model with DeltaBPND as dependent variable, time

274 cific means were compared by using a general linear model with false discovery rate control for multi

275 ression, Poisson regression, and generalized linear model with gamma distribution and log link, respe

276 RNA-seq counts, we recommend the generalized linear model with negative binomial count distribution,

277 of quantitative trait data unchanged under a linear model with normally distributed errors.

278 We developed a linear model with six parameters that can predict 38% of

279 measure, and (3) created a second multilevel linear model with splines and interaction terms that mod

280 We used a generalized linear model with splines to simultaneously capture 2 ty

281 Using a linear model with terms corresponding to the visual stim

282 It is found that a linear model with the resonant frequency peaks as predic

283 69 years of age with WMHV using generalised linear models with a gamma distribution and log link fun

284 eeism rates using generalized linear and log-linear models with a population offset for incidence out

285 mic and videometric data were analyzed using linear models with conduit as the fixed effect of intere

286 Two generalized linear models with elastic net regularization (14VF and

287 Most of these moderated methods are based on linear models with fixed effects where residual variance

288 Linear models with generalised estimating equations desc

289 Linear models with generalized estimating equations were

290 Generalized linear models with log link, Poisson distributions, and

291 herefore application of moderated methods to linear models with mixed effects are needed for differen

292 f the fully moderated t-statistic method for linear models with mixed effects, where both residual va

293 Hierarchical linear models with patients clustered within cancer care

294 General linear models with restricted maximum likelihood estimat

295 nd compared between groups using generalized linear models with robust SEs.

296 e between EXER and CONTROL, mixed-regression linear models with subject variable as random factor and

297 Generalized linear models with variable selection possessed relative

298 compared these intervals using a generalized linear model (with compound symmetry correlation structu

299 and with severity of disease by generalised linear modelling, with and without adjustment for age, s

300 Existing methods are essentially based on a linear model Xbeta, where the design matrix X is known a