ライフサイエンスコーパス: additive model

1 0.003 and p = 0.04, respectively, using the additive model).

2 nificance (P = 1.5 x 10(-13) for rs12777823, additive model).

3 ff above 60% coverage (p < 0.01; generalized additive models).

4 4.0%; celiac disease, 4.1%) beyond a simple additive model.

5 ubstantially better goodness of fit than the additive model.

6 ion single nucleotide polymorphisms using an additive model.

7 cancer risk allele for each SNP under a log-additive model.

8 using penalized splines within a generalized additive model.

9 deled baseline mortality using a generalized additive model.

10 ect cannot be approximated adequately by the additive model.

11 in ADCYAP1R1 (rs2267735) and asthma under an additive model.

12 34) after adjustment for covariates under an additive model.

13 trends, and day of week using a generalized additive model.

14 risk for a variant SNP allele based on a log-additive model.

15 d percent fat mass was investigated under an additive model.

16 ingle-nucleotide polymorphism (SNP) using an additive model.

17 dominant QTL that were not detectable by an additive model.

18 interval 1.10 to 1.56, P=0.002), assuming an additive model.

19 t PD (age > 59 years) were a recessive or an additive model.

20 e, generalized linear model, and generalized additive model.

21 variation for foliage and tuber blight on an additive model.

22 ts a significantly higher V (G) than does an additive model.

23 and 6 (MLS = 0.61 at 112.5 cM), all under an additive model.

24 locus and multiplicative models than for the additive model.

25 larger sample size to be detected under the additive model.

26 unterfactual emissions using the generalized additive model.

27 Data were fitted by multilevel generalized additive model.

28 ge deletions and total CNV burden support an additive model.

29 AAs; this relationship appears to follow an additive model.

30 .6 +/- 0.4 kJ/mol) when considering a simple additive model.

31 tion in soybean and function in line with an additive model.

32 t climates were examined using a generalized additive model.

33 ned using penalized splines within a general additive model.

34 assessed using log-linear analyses under an additive model.

35 an allelic model, with similar results in an additive model.

36 inations was analyzed by using a generalized additive model.

37 between each SNP and each phenotype under an additive model.

38 ctors than observations for both, linear and additive models.

39 ing stepwise model selection and generalized additive models.

40 , and half require more than five loci under additive models.

41 s, corresponding to dominant, recessive, and additive models.

42 bin transfusion thresholds using generalised additive models.

43 ariation between traits that are not seen in additive models.

44 logistic regression and binomial generalized additive models.

45 d to UKHSA, were estimated using generalised additive models.

46 in each city using overdispersed generalized additive models.

47 r combined exposures under multiplicative or additive models.

48 verlap for each population using generalized additive models.

49 using change-score analysis and generalized additive models.

50 covariate model with the aid of generalized additive modeling.

51 3.1 ms higher versus CC; 1-sided P=0.04) or additive model (0.06 SD [SE, 0.03] or 1.6 ms higher per

52 wide significance level [odds ratio (OR) for additive model = 1.61, 95%CI, 1.36-1.91, P = 3.2 x 10(-8

53 h overall HCC (odds ratio [OR] per G allele, additive model=1.77; 95% confidence interval [CI]: 1.42-

54 Generalized additive models, a type of semiparametric regression mod

55 Thus, a simple additive model accurately predicts flowering time for ma

56 ducted separately in each sample assuming an additive model adjusted for age, sex and relatedness of

57 The study used a logistic additive model adjusted for bacterial species, the McCab

58 ing a quasi-Poisson hierarchical generalized additive model adjusted for population density and the C

59 In generalized additive models adjusted for patient, procedure, and ant

60 s was performed on whites for each SNP in an additive model adjusting for baseline BP, age, sex, and

61 a combined meta-analysis under recessive and additive models after adjusting for age, sex, body mass

62 Generalized additive models allow for non-parametric and non-linear

63 pared with a mutually exclusive approach, an additive model allowing for self-report of multiple iden

64 alence) using this marker were 1.37 under an additive model and 1.36 under a multiplicative model.

65 e statistical model based on the generalized additive model and allows for information sharing across

66 different from those predicted from a purely additive model and could even aid adaptation.

67 The generalized additive model and logistic regression were used to dete

68 Generalized additive models and a 2-piece linear model with a break

69 resistance were evaluated using generalized additive models and Fisher exact tests.

70 The results were analyzed using generalized additive models and logistic regression, adjusting for r

71 Algorithms for generalized additive models and random forest models were developed

72 cal treatment were examined with generalized additive models and receiver operating characteristic an

73 Generalized additive models and segmented regression analysis were u

74 Here we show, using generalized additive models and tissue samples of 814 U.K.-stranded

75 homozygotes, assuming a multiplicative (log-additive) model and attributable fraction of 25% (95% CI

76 ded in an association analysis at 7 951 614 (additive model) and 4 669 537 (genotypic model) loci.

77 ), rs1581479 on 8p22 ($P=1.47\ast{10}^{-8}$, additive model) and rs73367537 on 10q26 ($P=1.21\ast{10}

78 ve model, with p values lower than under the additive model, and >40% of these were novel.

79 m likelihood, crude and adjusted generalized additive models, and a machine learning approach based o

80 Descriptive analysis, generalized additive models, and alternating logistic regression mod

81 Partial Spearman's correlation, generalized additive models, and receiver operating characteristic (

82 hat of the two homozygotes); (2) 6 two-locus additive models; and (3) 16 two-locus heterogeneity mode

83 total incidence, outperforming a generalized additive model approach.

84 , and offspring survival and growth using an additive modelling approach on 21 years of individual-ba

85 Risk prediction evaluation identified the additive model as best for describing the effect of APOE

86 arly focusing on the limitations of existing additive models based on small molecule data.

87 d linear model (NBGLM), Boosting generalized additive model (BGAM), Spline generalized additive model

88 Derivations of additive models by least-squares and ridge-regression me

89 the residual variances estimated by standard additive models can be inflated in the presence of G-C a

90 Therefore, additive models can be very useful for the discovery and

91 ein can identify cases where an independent, additive model cannot be applied and so require addition

92 tion (effective df = 3.83 in the generalized additive model, chi2 test P = .002 for the 4-df cubic sp

93 pared association tests based on a biallelic additive model constraining the effect of a single-nucle

94 Generalized additive models demonstrated an association between the

95 h systolic and diastolic BP when a two-locus additive model developed for ACE concentration was used.

96 Our main aim is to compare the additive model, due to Mesterton-Gibbons, and the multip

97 multivariable-adjusted logistic generalized additive model, elevated pathway-specific polygenic risk

98 Nonlinear generalized additive models examined the association between timing

99 s current strategies typically test only the additive model, exclude the X chromosome, and use only o

100 Quasi-Poisson generalized additive models explored associations between infant lun

101 biomass burning, as detected by generalized additive models fitted to seven pollen and charcoal reco

102 ooled (P = 0.0010-0.00099) samples under the additive model, following correction for multiple testin

103 We used 2-way mixed linear regression and an additive model for all primary analyses.

104 ve, which verifies the practice of using the additive model for analyzing SNP effects on metabolites.

105 ficients of the log-length correction in the additive model for arbitrary sequences and lengths and (

106 Analysis of these two with an additive model for beta(J)(=1) and beta(J)(=3) reveals a

107 A generalized additive model for location, scale, and shape (GAMLSS) w

108 ting yeast growth rates, EFA outperforms the additive model for several traits with large epistasis h

109 th suggestive significance (P < 1E-5) in the additive model for the effect of the GxSex interaction o

110 Finally, we demonstrate that an additive model for these APOE variants is superior to ot

111 We fit generalized additive models for associations between pollutant expos

112 VarWalker fits generalized additive models for each sample based on sample-specific

113 tion accuracy, significantly higher than the additive models for either HILIC or RPLC.

114 ls from 7 cohorts, which were analyzed using additive models for epsilon2 and epsilon4.

115 demonstrate how one can use the generalized additive models for location, scale and shape (GAMLSS) d

116 e 'metamicrobiomeR' that applies Generalized Additive Models for Location, Scale and Shape (GAMLSS) w

117 ls, Generalised Additive Models, Generalised Additive Models for Location, Scale and Shape (GAMLSS),

118 Generalized Additive Models for Location, Scale and Shape were fitte

119 ve models were constructed using Generalized Additive Models for Location, Scale, and Shape (GAMLSS)

120 In generalized additive models for location, scale, and shape (GAMLSS),

121 Generalized additive models for location, scale, and shape were used

122 orporating characteristics using generalized additive models for location, scale, and shape.

123 urves were established using the generalized additive models for location, scale, and shape.

124 mograms were estimated by use of generalized additive models for location, shape, and scale with Box-

125 hat varied by age and site using generalised additive models for location, shape, and scale.

126 s were detected following reanalysis with an additive model (for example, for birth weight, beta = 20

127 ear models (for climate PC1) and generalized additive models (for biology PC1-2) invoking only the cl

128 e introduce tradeSeq, a powerful generalized additive model framework based on the negative binomial

129 son regression analyses within a generalized additive model framework.

130 Two statistical models,a general additive model (GAM) and GAMBOOST model with boosted reg

131 A generalized additive model (GAM) and spline smoothing were employed

132 A Generalized Additive Model (GAM) approach using one year of county d

133 s; (3) a geographically weighted generalized additive model (GAM) ensemble model was used to fuse the

134 obal climate model, and create a generalized additive model (GAM) to examine how future changes in te

135 We used a generalized additive model (GAM) to identify significant change poin

136 A Generalized Additive Model (GAM) was used to calculate Adjusted Tota

137 ic (ROC) curves and multivariate generalized additive model (GAM) were applied to predict outcomes.

138 Logistic regression and Generalized Additive Model (GAM) were used, adjusting for year, moth

139 At the first stage, we used a generalized additive model (GAM) with a Gaussian link to examine the

140 ver in two high-risk areas using Generalized Additive Model (GAM), random forests and Structural Equa

141 General Additive Modeling (GAM) and low-degree polynomials (i.e.

142 A generalized additive modelling (GAM) approach is used to describe th

143 We also fitted generalized additive models (GAM) and performed two-objective optimi

144 The widely used generalized additive models (GAM) method is a flexible and effective

145 Here, we developed generalized additive models (GAM) to interpolate the limited spatio-

146 Generalized additive models (GAM) with features designed to mitigate

147 Based on Generalized additive models (GAM), the proportional influence of reg

148 linear regression and nonlinear generalized additive models (GAMs) to estimate on-road concentration

149 were both linear and nonlinear, generalized additive models (GAMs) were chosen to model response cur

150 Non-linear generalized additive models (GAMs) were used to evaluate the influen

151 (ISR) and second derivatives of generalized additive models (GAMs).

152 t 333 sampling sites by means of generalized additive models (GAMs).

153 uding Generalised Linear Models, Generalised Additive Models, Generalised Additive Models for Locatio

154 y the same role) the predictions differ: the additive model has the same predictions as in the random

155 Additive models, however, can miss loci with recessive e

156 MELD >/=28 was higher than predicted by the additive model (HR=2.38, 95% CI 1.73-3.27, P<0.001) resu

157 rs73367537 on 10q26 ($P=1.21\ast{10}^{-8}$, additive model in GACRS only).

158 anges in ACTN3 expression consistent with an additive model in the human genotype-tissue expression c

159 (95% CI, 0.90 to 1.37; P=0.35), assuming an additive model in the matched analysis.

160 The data support an additive model in which individuals heterozygous for the

161 In a generalized additive model in which the authors accounted for long-t

162 in cation/pi interactions is captured by an additive model in which the substituent is isolated from

163 association studies have focused on testing additive models in cohorts with European ancestry.

164 plotype GC1s) was overtransmitted (P = 0.02, additive model) in the entire Boston cohort, in Whites (

165 An additive model, in which a chemical change in the struct

166 was assessed by linear regression, using an additive model, in which absolute change in the Disease

167 An additive model (incorporating the contributions of all 2

168 Generalized additive models indicate that c.

169 A piecewise exponential additive model indicated incidence rate ratios (IRRs) fo

170 Generalized additive models indicated that %sealed and in some cases

171 Generalized additive models indicated that in situ N(2)O concentrati

172 Generalized additive models indicated that predictable transitions b

173 Generalized additive modeling inferred gestational age-specific resp

174 Specifically, a Generalized Additive Model is implemented to analyze data from the I

175 uman skeletal muscle, but we suggest that an additive model is the most appropriate for use in testin

176 iome and mortality were determined using Cox additive models, Kaplan-Meier analysis, and Cox proporti

177 D = 1.74; P=.024), compared with a two-locus additive model (LOD = 0.90).

178 lues are factorized as a product between the additive model matrix and the h - 1 additive effects, an

179 e.g. maximum likelihood methods, generalized additive models, nonparametric kernel density estimators

180 ated with sCJD risk; within PRNP (rs1799990; additive model odds ratio [OR] 1.23 [95% CI 1.17-1.30],

181 CI], 1.82 to 3.26; P=1.42x10(-9); and in the additive model: odds ratio, 2.19; 95% CI, 1.66 to 2.90;

182 Titration experiments indicate an additive model of HIPK3-T splicing activation, requiring

183 Quantitative trait analysis with the additive model of inheritance was analyzed using linear

184 although the TDT can perform better under an additive model of inheritance.

185 A physically motivated, nonpairwise-additive model of water-mediated interactions added to a

186 cell line-specific manner and that existing additive models of gene knockout effects fail at capturi

187 Generalized additive models of location, scale, and shape (GAMLSS) w

188 tivariate system, assuming an infinitesimal, additive, model of inheritance.

189 curve during an oral glucose tolerance test (additive model, P = 0.022; dominant model, P = 0.010).

190 R8 rs3764880-G (recessive model: p = 0.0173; additive model: p = 0.0161) were associated with pericar

191 cted mortality under medical treatment, with additive model predictive value (all, p </= 0.04) and a

192 by proportional hazards using a generalized additive models procedure.

193 An additive model, rather than a multiplicative or contrast

194 We also note that, under dominant and additive models, regardless of the statistic used, pedig

195 Generalized additive modeling revealed interferon response kinetics.

196 Logistic generalized additive models revealed positive linear associations be

197 Generalized additive models revealed that long-term increase in Vibr

198 ed additive model (BGAM), Spline generalized additive model (SGAM), Spike and slab regression (SSR),

199 Furthermore, the generalized additive model showed that b reached maximum at the inte

200 Generalized additive model showed that worker abundance of this ant

201 Generalized additive models showed nonlinear positive associations b

202 Multivariable generalized additive models showed that RTL did not differ between c

203 Predictions from the additive model significantly overshot the actual respons

204 Data were analyzed with generalized additive models (smoothing).

205 An additive model specifies that the disutility of costs is

206 Generalized additive models stratified by gestational age categories

207 Additive models, such as those produced by gradient boos

208 Generalized additive models suggest that one subcluster, LC1 epithel

209 Generalized additive models supported a linear association between e

210 ted SAR cases was analysed using generalized additive models, taking into account confounding factors

211 Instead, the data support an additive model that best captures abnormal neural patter

212 Using a generalized additive model that controlled for season, region, and l

213 Furthermore, we fit a generalized additive model that incorporates baseline biomarkers as

214 While in additive models the mutational covariance matrix is alwa

215 By using a linear additive model, these 4 TH1 pathway SNPs cumulatively ex

216 ndard deviations were detected; but under an additive model, these did not fully account for the obse

217 ical content on temperature is improved over additive models, though further sampling is required to

218 nalysis because current methods often use an additive model to analyze data.

219 ral trends were analyzed using a Generalized Additive Model to capture the nonlinear dynamics effecti

220 lected outcome, the SLS used the generalized additive model to estimate locally averaged Omicron case

221 upancy model that uses a spatial generalized additive model to estimate non-linear spatial variation

222 We used a generalized additive model to study GFR distribution by age accordin

223 Using generalized additive modeling to fit and compare aging trajectories,

224 ate holistic system indicator variables, non-additive modelling to estimate alternate attractors, and

225 rmed a retrospective study using generalized additive models to analyze three major livestock populat

226 As a first approach, we use additive models to analyze two independent data sets (n

227 We used Generalized Additive Models to assess these relationships across sev

228 We used generalised additive models to evaluate the quantitative relation be

229 x proportional hazard models and generalized additive models to examine multivariable-adjusted associ

230 We used generalized additive models to examine the dose-response curve using

231 of field observations in the North Sea with additive models to infer spatiotemporal trends of chloro

232 We constructed general additive models to investigate relations between pestici

233 d gene coexpression networks and generalized additive models to predict effects on reproduction in th

234 s (generalized linear models and generalized additive models) to test environment-related hypotheses

235 rn North Atlantic Ocean based on generalized additive models, to establish a current habitat suitabil

236 Under various recessive and additive models, TRANSMIT was slightly more powerful tha

237 lies on the use of Time-Varying Group Sparse Additive Models (TV-GroupSpAM) for high-dimensional, fun

238 Using the additive model, two tightly linked functional SNPs in ER

239 (GxE), merits more involved models than the additive model typically used to analyze data from genom

240 The validity of the additive model used in this analysis can be tested by de

241 7.5 million SNPs were examined under the log-additive model using Cox proportional hazards models, ad

242 ation studies on AWT were performed under an additive model using linear regression (adjusted for pac

243 d glaucoma was obtained with the generalized additive model using only three parameters (AROC = 0.854

244 est evidence of an FcgammaR-SLE association (additive model: V/V 176 versus V/F 176 OR 1.51, V/V 176

245 T. aestivum gene expression was compared to additive model values (mid-parent) calculated from paren

246 justed hazard ratio for each L allele in the additive model was 1.91 (1.20 to 3.06; P=0.01) for the r

247 ariance analysis indicated that a completely additive model was adequate to explain the variances obs

248 A generalised additive model was used to estimate the effects of seven

249 A Poisson generalized additive model was used to evaluate this primary outcome

250 with fixed-effects meta-analysis assuming an additive model was used to test for associations.

251 Generalised additive modelling was used to characterise spatial hete

252 al multivariable regression with generalised additive models was used to investigate the effects of m

253 Using the same covariates in a generalized additive model, we examined the shape of the relationshi

254 s of 272 UM cases and 1782 controls using an additive model, we identified five variants significantl

255 tional GWAS of dominance deviations from the additive model, we identify no genome-wide-significant S

256 Using generalised additive models, we analyzed time-series data representi

257 Employing generalised additive models, we found a lower rate of antidepressant

258 In unadjusted generalized additive models, we observed a significant nonlinear rel

259 ographic and Health Surveys with generalized additive models, we quantify spatial patterns of measles

260 Using Generalized Additive Models, we then modelled these animals' use of

261 nfidence intervals) for each L allele in the additive model were 1.99 (1.27 to 3.14; P=0.003) for the

262 Synergistic interactions on the additive model were observed between heavy alcohol consu

263 Generalized additive models were constructed to estimate age-standar

264 Generalized Additive Models were used to determine the relationship

265 Generalized additive models were used to directly link river flows a

266 Generalized additive models were used to estimate the amount of wate

267 Generalized additive models were used to estimate the association be

268 Generalized additive models were used to estimate the variation in s

269 tivariate linear, piecewise, and generalized additive models were used to examine dose-response relat

270 Nonlinear generalized additive models were used to examine the association bet

271 Generalized additive models were used to examine the relationship be

272 Generalized additive models were used to explore interactive associa

273 Generalized additive models were used to identify structure in the r

274 nal study of 357 Yup'ik Eskimos, generalized additive models were used to plot covariate-adjusted ass

275 Generalized additive models were used to predict ASD odds across the

276 Generalized additive models were used to regress time series of dail

277 Adjusted generalized additive models were used to smooth the association of p

278 Generalized additive models were used to test for threshold effects.

279 Generalized additive models were used, with the burden of lesions (e

280 08 on 20p (location, chi = 19.5 cM) under an additive model, whereas the weighted MLS was 2.06 on 20q

281 The authors evaluated an additive model whereby risk of schizophrenia requires le

282 An additive model, whereby fat is added to the waist and hi

283 polymorphisms (SNPs) and expression under an additive model, which ignores interaction and haplotypic

284 proportional hazards model and a generalized additive model with a logistic link.

285 Cox proportional hazards regression under an additive model with adjustment for age at onset, sex, an

286 A two-locus additive model with an additive x additive interaction o

287 We used a longitudinal general additive model with bootstrapping technique to generate

288 A generalized additive model with penalized splines and independence w

289 The accuracy of a simple additive model with peptide length correction (R(2) valu

290 APOL1 risk alleles were associated under an additive model with systolic blood pressure (SBP) and ag

291 en's cognitive performance using generalized additive models with cyclic cubic splines.

292 tistic is increased by >20%, on average, for additive models with modest genotype relative risks.

293 present study, the authors used generalized additive models with nonparametric smoothing splines to

294 d was not appropriate, we fitted generalised additive models with penalised splines to visualise tren

295 factorization and analyzed using generalized additive models with penalized splines to capture both l

296 ted to trait and state anxiety using general additive models with penalized splines, while controllin

297 t follow-up were evaluated using generalized additive models with penalized splines.

298 In the training data, generalized additive models with splines were plotted for each MELD

299 Logistic regression models and generalized additive models with thin-plate splines were fit to the

300 using PLINK (logistic regression assuming an additive model) with sex, age, smoking and the first thr