ライフサイエンスコーパス: random

1 ect of missing data (assuming missingness at random).

2 ecies than if their locations were chosen at random.

3 ave greater accuracy than any model taken at random.

4 ps and characteristics and did not occcur at random.

5 in a 3 vs 4 comparison, their performance is random.

6 tions that form building blocks for magnetic random access memories and magnetic sensors.

7 ing designs leveraging nonvolatile resistive random access memory (ReRAM), and with many studies focu

8 Filamentary resistive random access memory (RRAM) suffers from stochastic swit

9 ing efficient compression and providing fast random access to facilitate development of scalable algo

10 augmented to enable higher data volumes and random access to the data and also allows for future seq

11 tic toehold creation that enables single-bit random-access and in-memory computations.

12 Finally, random-access, integrated devices available at the point

13 t) while some others are assigned totally at random (all in all, a paper needs a bibliography), we ha

14 ity of objective response within 6 months of random allocation in each arm.

15 Random allocation to LD-MTX (<=20 mg/wk) or placebo.

16 As such, random alloy and intermetallic nanocrystals have attract

17 principles and strategies developed to form random alloy and intermetallic nanocrystals with enhance

18 y the intricacies of the defect processes on random alloys.

19 f GuHCl (2-4 M), there is an accumulation of random and beta-sheet structures that is mediated by sma

20 community assembly or disassembly may be non-random and influenced by external drivers, such as clima

21 We find that scientists' strategies are not random, and that they are significantly affected by both

22 n centromere features can translate into non-random aneuploidy, a hallmark of cancer and genetic dise

23 tional drop-cast electrodes, which exhibit a random arrangement of the nanosheets and obvious decreas

24 This random assembly has led some ecologists to question the

25 A second random assignment (R-C) compared docetaxel-capecitabine

26 With minimum post-random assignment follow-up of 13.5 months, median PFS w

27 Random assignment of eyes (1 per participant) to afliber

28 We find that random assignment to FT significantly decreases the prob

29 EFS was defined as the time from random assignment to the date of first evidence of disea

30 pass the gestational sac within 7 days after random assignment.

31 ent given by the number of edges added using random attachment.

32 Using random barcode transposon-site sequencing with an analys

33 as used to unravel the complete atomic-level random Bi(3+)/In(3+) cationic mixing in Cs(2)Bi(1-x)In(x

34 iversity-ecosystem function experiments with random biodiversity loss scenarios have demonstrated tha

35 and defibrillation by citizen responders and random bystanders.

36 participants were examined using stratified, random-cluster systematic sampling; in APEDS III, 5,395

37 the metal and synthesis temperature used, as random (Co, Cd, 120 degrees C), short duplicates (Co, Cd

38 We used random coefficient modeling to account for the nesting e

39 ly, slopes that vary by individual, that is, random coefficient models, could be used to accommodate

40 oscopy in vitro displays the properties of a random coil and acts as an entropic spring.

41 lical segments, beta-sheets, beta-turns, and random coil regions were less stable than in C(H)2s and

42 s heterogeneous ensembles with (essentially) random combinations of monomer glycoforms; (4) native to

43 se for numerosity estimation under shape and random configurations and found a larger N2 component fo

44 Inspired by the sparse and random connectivity of real neuronal circuits, we presen

45 Slower, structural changes in random connectivity, consistent with rewiring and prunin

46 bles stereotypy in sensory responses despite random connectivity.

47 picture of switching in molecular films show random current spikes, just opposite to the expectation.

48 n, recent results on stochastic systems with random delays allow us to rigorously obtain expressions

49 n be explained by the combined influences of random diffusive error and systematic drift toward a set

50 ereas adjacent beta-Pix mutant cells move in random directions.

51 d non-coding RNAs (1.44-fold), compared with random distribution (P < 1 x 10(-3)).

52 However, random distribution of masks is generally suboptimal; pr

53 Our experimental results demonstrate a non-random distribution of oxygen species in gamma-Al(2)O(3)

54 xation, and in structural terms from the non-random distribution of the closed RCs during induction.

55 f responses to correlated and anticorrelated random dot correlograms (RDC) revealed that lateromedial

56 ere we approach this problem by adapting the random-dot motion discrimination paradigm, classically u

57 s that are adapted to their environments and random dynamical systems exposed to the same environment

58 me subset of Primed Neurons was induced from random dynamics, which also coincided with mouse freezin

59 using mixed-effect regression models with a random effect for study site hospital.

60 by intention to treat by means of multilevel random effect regression analyses adjusting for clusteri

61 Random effect regression models assessed group differenc

62 s linear or logistic regression, including a random effect to adjust for within-school clustering, mi

63 above, medical comorbidities, and a hospital random effect were used to quantify odds of receipt of L

64 By interpreting the intercepts as a random effect with a large (fixed) variance, inference f

65 xed-effects Cox regression models (center as random effect) to evaluate the association of recipient

66 ffects and a categorical trial variable as a random effect, adjusting for age, cancer type, and metas

67 e and sex with family membership included as random effect.

68 hods that allow one to fix the variance of a random effect.

69 luded as a covariate and individual ICU as a random effect.

70 nically-relevant confounders and including a random-effect to account for potential clustering by cen

71 HR was used as a summary statistic and was random-effect-models tested.

72 n all positive scales combined with both the random effects (g = 0.33; P = 0.015; k = 17; CI = 0.07-0

73 izes with time-step fixed effects and clinic random effects (Model 1).

74 e efficient estimation of RSFs and SSFs with random effects accessible to anyone in the field.

75 verse variance weighting with multiplicative random effects and conducted sensitivity analysis.

76 Random effects dose-response meta-analyses were used to

77 el (GLM) in which the linear predictor takes random effects into account.

78 We used Bayesian multivariate response random effects logistic regression model to simultaneous

79 analyzed using generalized linear models and random effects meta-analyses.

80 We performed a random effects meta-analysis to determine the growth rat

81 ry relative risks (RRs) were calculated in a random effects meta-analysis.

82 The primary meta-analysis using a random effects model assessed AF recurrence stratified b

83 Using the random effects model, we computed the effect sizes (ESs)

84 For meta-analysis, we used a random effects model.

85 Random effects models were used for all analyses.

86 Random effects models were used to synthesize data.

87 e at surgery, and the primary outcomes using random effects multivariable logistic regression to cont

88 he eye as the unit of analysis, with crossed random effects to adjust for correlation between fellow

89 Classic LMMs assume independence between random effects, which can be violated, causing bias.

90 explicitly estimates the covariance between random effects.

91 d by the same set of known fixed effects and random effects.

92 imum likelihood (GREML) allow both fixed and random effects.

93 We conducted a random-effects Bayesian network meta-analysis using stan

94 Random-effects meta regression estimated whether sex dif

95 Estimates of FH prevalence were pooled using random-effects meta-analyses and were 0.32% (95% confide

96 e estimated the effect of corticosteroids by random-effects meta-analyses using the generic inverse v

97 Random-effects meta-analyses were carried out generating

98 ty assumption and examine its performance in random-effects meta-analyses with simulation studies and

99 accuracy of diagnostic tests with bivariate random-effects meta-analyses.

100 Random-effects meta-analysis and qualitative synthesis w

101 Random-effects meta-analysis models were used to report

102 In random-effects meta-analysis of 16 prospective cohort st

103 d as the primary outcomes and analysed via a random-effects meta-analysis of proportions using the De

104 Inverse-variance-weighted random-effects meta-analysis was used to pool reported e

105 l survey odds ratios (ORs) were pooled using random-effects meta-analysis.

106 E decline rate was estimated using a 2-stage random-effects meta-analysis.

107 Multivariable-adjusted RRs were pooled using random-effects meta-analysis.

108 ticipants, with up to 27 studies included in random-effects meta-analysis.

109 justed study-specific effect estimates using random-effects meta-analysis.

110 A random-effects meta-regression analysis of the aggregate

111 es' g coefficients were pooled using 3-level random-effects meta-regression.

112 We conducted separate meta-analyses using a random-effects model for mortality and hospital admissio

113 A random-effects model was used for statistical analysis.

114 Metaanalysis was performed using a bivariate random-effects model when at least 5 studies were includ

115 ds ratio (OR) or mean difference (MD) with a random-effects model.

116 re pooled using an inverse-variance-weighted random-effects model.

117 t network meta-analysis was conducted with a random-effects model.

118 nce intervals (CIs) were calculated with the random-effects model.

119 confidence interval) was performed using the random-effects model.

120 Percentage change in BMD was pooled using random-effects models and reported as weighted mean diff

121 We used random-effects models to summarize the studies.

122 Random-effects models, based on inverse variance weights

123 empirical test of this argument, we apply a random-effects within-between model to two large represe

124 stem using a minimisation method (with a 20% random element) and the following minimisation factors:

125 erapy, using a minimisation algorithm with a random element.

126 r events and bottlenecks, may either promote random evolution or facilitate adaptation, making the re

127 directed exploration and increased levels of random exploration in the conceptual domain.

128 nformation drives exploration by choice, and random exploration, where behavioral variability drives

129 ht dorsolateral prefrontal cortex and drives random exploration.

130 f of the blocks, a buccal bone dehiscence of random extent ("depth") was created and implants were mo

131 These biases are typically ascribed to random factors that occur during and after antigenic sti

132 elated changes in personality occur in a non-random fashion with respect to their direction, timing,

133 th model, combined with both Gaussian Markov random field (GMRF) and horseshoe Markov random field (H

134 kov random field (GMRF) and horseshoe Markov random field (HSMRF) prior distributions, to approximate

135 Random, Fisher's ratio and Holdout samplers were more ac

136 ance determination was limited mostly by the random fluctuations between replicate measurements, and

137 of such "decoy sites" in controlling noise (random fluctuations) in the level of a TF that is synthe

138 phenotype measured in Area Under the Curve: random forest (0.782), XGBoost (0.781), support vector m

139 ralized Linear Machine (GLM) and Distributed Random Forest (DRF).

140 Using powerful bootstrapping and random forest (RF) approaches, we identified reliably di

141 The first ML model included a random forest (RF) classification model, which was used

142 ted Tomography (CT) scan and used to train a Random Forest (RF) classifier.

143 itigate the inadequacies of random sampling, random forest (RF) together with the combined feature se

144 Models resulting from random forest (RF), nonlinear support vector machine (SV

145 Recursive feature elimination using random forest (RFE-RF) was used to identify the best com

146 t various thermodynamic conditions using the random forest algorithm.

147 esulting from a nearest-neighbor bias in the random forest algorithm.

148 Conditional random forest algorithms confirmed mountainousness as a

149 a k-mer-based set difference algorithm, and random forest algorithms to identify swine-associated se

150 Using data from 1000 farms, random forest algorithms were able to replicate the comp

151 Random forest analysis of volatiles from colored carrot

152 ustering of immune cell subsets coupled with random forest analysis shows profound (AUC = 0.92, p-val

153 from the different layers of omics data and random forest analysis to develop the models.

154 As a case study to demonstrate the method, random forest and support vector machine models were tra

155 nfected individuals using the classificatory random forest approach to discriminate between uncontrol

156 Random forest classification models were trained to diff

157 Coexpression network analysis and Random Forest classification were used to discover poten

158 net regression for variable selection, and a random forest classifier for BD vs. MDD classification.

159 takes an input protein target and develops a random forest classifier to predict the effect of an inp

160 essed with the recursive feature elimination random forest classifier.

161 oth linear logistic regression and nonlinear Random Forest classifiers were benchmarked and evaluated

162 A subset of 15 VOCs from analysis with Random Forest discriminated between cultivars.

163 standard Relief-based feature selection and random forest importance, with the additional benefit of

164 ce of daily F(CH4) could be explained by the random forest machine learning algorithm and traditional

165 ic exceeding 10 micrograms per liter using a random forest machine-learning model based on 11 geospat

166 Random forest modeling differentiated active UC from act

167 We used random forest modeling to identify differences in signat

168 Logistic regression models and random forest models classified T cells according to act

169 Random forest models integrated gut microbiome, host gen

170 We used random forest models to calculate adjusted odds ratios (

171 iptors yields the best predictive power with Random Forest models, often boosted by consensus or hybr

172 The random forest procedure selected 6 variables (not growth

173 redict groundwater uranium concentrations by random forest regression.

174 Here we use a random forest to learn a parameterization from coarse-gr

175 odel, normative modeling, and the functional random forest).

176 t 0.87 on accuracy by decision tree, 0.96 by random forest, 0.91 by simple neural network, and 0.95 b

177 k-nearest neighbor, support vector machine, random forest, logistic regression and Naive Bayes.

178 Leveraging logistic regression-, random forest- and gradient boosting models and register

179 mean absolute error (MAE) was evaluated for random forest-based predictions of retinal sensitivity w

180 ified via recursive feature elimination with random forest.

181 sponse prediction model (called PDXGEM) in a random-forest algorithm by using a subset of the drug se

182 t to the hidden units to develop a secondary random-forest classifier for directly predicting asthma

183 learning of the gene expression data, and a random-forest-based feature selection was applied to the

184 machine learning (ML) classifiers including random forests (RFs), elastic net (ELNET), support vecto

185 omplicated machine-learning models including random forests and deep neural nets.

186 qualities of individual protein models using random forests and various novel energy functions.

187 We assessed feature importance using a random forests classifier and performed feature selectio

188 Logistic regression and random forests using diagnostic and procedure codes as w

189 e-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relat

190 were developed based on logistic regression, random forests, gradient boosted trees and a stacked ens

191 selective advantage but may also be due to a random founder effect.

192 ulations are established by few individuals, random founder effects can facilitate rapid phenotypic d

193 Leveraging quasi-random geographic variation in media markets for 771 mat

194 rk has revealed that such variations are not random heterogeneities; rather, synaptic excitation and

195 cally structured channel, four-monomer-based random heteropolymers (RHPs)(14) can mimic membrane prot

196 A random-hinge electron step correction algorithm and a mo

197 acceleration of self-healing in alternating/random hydrophobic acrylic-based copolymers in the prese

198 arning approaches were tested: training from random initialization and transfer learning.

199 erformance in terms of the power spectrum of random input and output processes.

200 powerful computer-who sees the responses to random inputs-still cannot infer responses to new inputs

201 R-Cas9 methods have been applied to generate random insertions and deletions, large deletions, target

202 to repeated measures was accounted for by a random intercept per individual and an unstructured cova

203 d-effects regressions with participant-level random intercept to identify significant Cytosine-phosph

204 linear mixed model with a participant-level random intercept was used to estimate the effect of H py

205 models, which included participant-specific random intercepts and penalized splines on gestational a

206 multivariate logistic regression model with random intercepts was used to compare MSSA risk factors

207 I) system were calculated on a subset of 100 random internal and 100 external test images.

208 suffers from stochastic switching due to the random kinetic motion of discrete defects in the nanomet

209 Coherent random lasing is indicated by the presence of narrow pea

210 uided growth of planar Li layers, instead of random Li dendrites, is achieved on self-assembled reduc

211 Here, we used random lineage tracing to localize and quantify clonal e

212 uring's work on pattern formation with May's random-matrix approach.

213 We discuss important systematic and random measurement errors when using these kits and sugg

214 that whereas the MT cytoskeleton resembles a random meshwork in the cells' interior, MTs near the cel

215 d a penalized EM algorithm incorporating non-random missingness (PEMM).

216 We hypothesized that non-random missingness is a useful biological measure and de

217 rality was 0.88, significantly higher than a random model or models based on gray matter volumes, deg

218 els improves fitness by 70% and 77% over the random models for a discoidal or an ellipsoidal stem cel

219 dification of proteins has been dominated by random modification of lysines or more site-specific lab

220 ee of interactions is suggestive of coherent/random molecular mechanisms, respectively.

221 al stem cells, dependent only on the rate of random movements vs. mitosis-driven advection.

222 cient in small-world networks as compared to random networks.

223 in of this linear relationship in artificial random networks.

224 nt a novel and experimentally verified "True Random Number Generator" that uses exclusively conventio

225 the LRS (lifetime reproductive success), the random number of offspring an individual produces over i

226 Computer-generated random numbers were provided by the study pharmacist.

227 r a particular assumption, either missing at random or under the detection limit.

228 automated GAT measurements were collected in random order by 2 independent masked observers to assess

229 y pressure trial with change of 10 cm H2O in random order.

230 ndomly assigned to receive 3 treatments in a random order: bolus 30-g dose of LNS (Bolus); 3 x 10-g d

231 d each strategy sequentially for 6 months in random order; 25 ICUs were randomized to the sequence wi

232 gh parasites initially adhere to RBCs with a random orientation, they need to align their apex toward

233 avalent UO(2) as nanocrystals (~1-2 nm) with random orientations inside nanowires.

234 thousands of pairs of data sets obtained by random partitions of large studies in several other dise

235 Within basins, a random pattern of genetic patchiness was observed, sugge

236 Fifty rats were subjected to a dorsal random-pattern flap operation and randomly divided into

237 ty peptide arrays, in casu containing 70,000 random peptides in triplicates.

238 Randomisation was computer generated with random permuted block size 2 and 4, and allocation was c

239 a pattern not statistically different from a random positioning of nuclei.

240 gnetic measurements, we show that the subtle random potential of frozen BCO Brownian rotors suppresse

241 Instead, we activate at random, precise locations in time and space and use post

242 ach experimental trial as a realization of a random process more likely reflects the statistical prop

243 fits a binomial model and may result from a random process with very low possibility (the ratio < 0.

244 al communities is driven by deterministic or random processes is one of the most controversial issues

245 ion reduction, we present SHARP, an ensemble random projection-based algorithm that is scalable to cl

246 uts through a combination of low-dimensional random projections and "classical" low-dimensional hexag

247 erally detrimental to the performance of the random ranking, but they are beneficial for the performa

248 bly or more robust than expected in multiple random realizations.

249 We compare the method with a random regression model using MTG2 and BLUPF90 software

250 Random regression models were used to jointly analyse li

251 y exhibit a large range of sizes, shapes and random relative orientations(3-5).

252 multicenter cohort study of patients from a random sample of all admitted patients with laboratory-c

253 are to analyze the media coverage of a large random sample of business, government, and social advoca

254 A systematic assessment of outcomes among a random sample of patients lost to follow up (LTFU) from

255 outcomes ascertained by tracing a multistage random sample of patients lost to follow-up (LTFU, >90 d

256 bject to spectrum bias as we only included a random sample of people without TB from each cohort.

257 ge probability sample including a stratified random sample of schools.

258 Random sample of US and worldwide Internet users who sea

259 en May 19 and October 27, 2016, a systematic random sample was assessed for eligibility (HIV+, age >=

260 Major Histocompatibility Complex (MHC) of a random sample.The application provides users with a simp

261 Gapsplit generates random samples from convex and non-convex constraint-bas

262 A total of 160 random samples were gathered from private dairy farms in

263 Random samples were selected from the lists of the Calif

264 EHR data and a follow-up manual review of a random samples.

265 Systematic random sampling method was employed to select the study

266 studies (necessary to assess causality), non-random sampling of participants by many studies, and the

267 s is applied to mitigate the inadequacies of random sampling, random forest (RF) together with the co

268 amine in China through multi-stage clustered random sampling.

269 oximately 180 times faster than an automated random search of the parameter space, and is suitable fo

270 accuracy by 27.99%, 16.44%, and 13.11% over random selection for a sample size of 100, 500, and 1,00

271 'Clinical notes review' included a random selection of nurse-patient consultations July-Dec

272 s silicon they are not well characterized in random semiconductor alloys such as silicon germanium.

273 Randomisation was by a computer-generated random sequence by means of an interactive web-response

274 ons the pools are typically transcribed from random-sequence DNA templates, yielding a highly diverse

275 exhibits non-distinguishable behaviors from random-sequence genomic DNA, AA-TT condenses in all alka

276 al Component Analysis (CHPCA) and Rotational Random Shuffling (RRS).

277 s considering the self-assembly of different random size particles on the spherical surface.

278 with a large (fixed) variance, inference for random-slope models becomes feasible with standard Bayes

279 ing, and theoretical neuroscience to suggest random sparse connectivity as a key design principle for

280 Binding Transcription Factor (UBF) reveals a random spatial orientation of regular repeats of rDNA co

281 into two heterotic pools were compared: (i) random split; (ii) split based on genetic distance accor

282 o provide a globally optimum solution from a random start in the initial guess of model parameters (i

283 assigning categories of definitions within a random subset of 50 articles.

284 , patterns of community assembly were nearly random, suggesting a strong role of stochasticity in the

285 We also show that TCR chain usage is non-random, suggesting common antigens for Vdelta1 and Vdelt

286 nt at 3 and 12 months posttransplantation by random survival forest analysis.

287 (57.4%) were identified incidentally through random testing campaigns/surveys or contact tracing.

288 ater activity coefficient data using the Non-Random Two-Liquid (NRTL) model.

289 Here we framed the problem as random variable estimation problem and studied the conve

290 ationship is not a constant; rather, it is a random variable whose distribution depends on cell size

291 , which suggests differences within expected random variation.

292 e find that, counterintuitively, despite the random variations in the medium and the linear nature of

293 g the mixed effects from multiple sources of random variations, the method has been widely used in bi

294 In their search, dendritic cells perform a random walk by amoeboid migration.

295 king data were analysed using the Correlated Random Walk framework.

296 We modelled CCF50 as a time-series random walk function of educational attainment and contr

297 After this, IsoFun performs a tailored bi-random walk on the heterogeneous network to predict the

298 he more recent graph embedding methods (e.g. random walk-based and neural network-based) in terms of

299 n detection depends on the properties of the random walk.

300 able to improve epitope identification above random, with the best performance achieved by neural net