ライフサイエンスコーパス: statistical method

1 infer a mean serial interval using a common statistical method.

2 the population activity space using a novel statistical method.

3 raft thickness, and 8 studies used different statistical methods.

4 hich brings challenges to the development of statistical methods.

5 limited datasets and have used inappropriate statistical methods.

6 ression analysis and analyzed by appropriate statistical methods.

7 tlesx3 repeats) are analyzed by multivariate statistical methods.

8 limit was 0.056 mumol L(-1), obtained using statistical methods.

9 rious similarity criteria are aggregated via statistical methods.

10 Results were compared with appropriate statistical methods.

11 lts obtained through the application of both statistical methods.

12 This finding was consistent across all 3 statistical methods.

13 rs: the data were analysed with multivariate statistical methods.

14 ies, conducted with rigorous methodology and statistical methods.

15 ed to be familiar with at least 15 different statistical methods.

16 examples to show limitations of some popular statistical methods.

17 hould extend beyond 7 d and use time-varying statistical methods.

18 easures of hand function using 2 independent statistical methods.

19 erties that distinguish it from better-known statistical methods.

20 variants were misclassified by conventional statistical methods.

21 the current UK PNF criteria and is based on statistical methods.

22 s using cross-population (XP-EHH and XP-CLR) statistical methods.

23 es heavy-duty computational tools, and novel statistical methods.

24 immune mediators were analyzed using various statistical methods.

25 are based on clinical experience rather than statistical methods.

26 atically examined due to a lack of available statistical methods.

27 yses with dense marker panel data and recent statistical methods.

28 s were calculated using the Hospital Compare statistical method, a well-validated hierarchical genera

29 of this study is the application of a novel statistical method accounting for censoring in the follo

30 uares Discriminant Analysis) multiparametric statistical methods allowed the cocoa beans from differe

31 The introduction of multivariate statistical methods allows investigators to utilize data

32 To address this problem, we developed a statistical method and software called OEFinder to ident

33 eliable (R(2)=0.992), it was validated using statistical methods and a mixture of 14 synthetic peptid

34 n of mild iodine deficiency, with a focus on statistical methods and approaches.

35 To address this, we used statistical methods and Bayesian phylogenetic approaches

36 le approach facilitates the dissemination of statistical methods and codes to independent researchers

37 rriers in advancing the development of novel statistical methods and computational algorithms for gen

38 There is a need to develop efficient statistical methods and computational algorithms to cons

39 ude 7 studies (815 eyes) that used different statistical methods and did not find significant associa

40 We used both statistical methods and dynamic mathematical models to (

41 To assist with this goal, new statistical methods and frameworks have been developed,

42 Using classical statistical methods and machine learning to combine ChIP

43 d cell types, recent work employing unbiased statistical methods and more diverse tasks reveals unsus

44 Descriptive statistical methods and multilevel linear regression mod

45 he long-term benefits were coherent by all 3 statistical methods and persisted among patient subgroup

46 cover distortions of conclusions produced by statistical methods and psychosocial forces.

47 rkflow management, as well as improvement in statistical methods and study design, there have been gr

48 mmarize 3 GAW19 contributions applying novel statistical methods and testing previously proposed tech

49 the misapplication and misinterpretation of statistical methods and tests are long-standing and wide

50 from reference data sets using multivariate statistical methods and the subsequent classification of

51 he RV144 trial, this work describes a set of statistical methods and tools applicable to analysis of

52 We compare multiple statistical methods and use simulations to investigate t

53 ormation on study design, methods, outcomes, statistical methods, and conclusions.

54 he unique challenges in exposure assessment, statistical methods, and methodology that epidemiologist

55 The scores for the domains study design, statistical methods, and reporting methods were 44% (19-

56 ubstantial clinical heterogeneity, differing statistical methods, and variable methodological quality

57 in 3 methodologic domains ("study design," "statistical methods," and "reporting methods") were asse

58 Statistical methods applicable to DNA methylation data a

59 s to evaluate the effectiveness of different statistical methods applied for urinary proteomic biomar

60 es (SVM) and artificial neuron network (ANN) statistical methods applied to the spectroscopic data al

61 A number of robust statistical methods are available to identify genes show

62 Although statistical methods are available to investigate the rol

63 The statistical methods are based on mixture modeling and re

64 Statistical methods are implemented for treating outlier

65 Conventional statistical methods are less than ideal because they eit

66 Many of the computational and statistical methods are plagued by fundamental identifia

67 familywise error rate of 5%, the parametric statistical methods are shown to be conservative for vox

68 Typically, conventional statistical methods are used in an attempt to mitigate p

69 Statistical methods are used to accommodate possible err

70 sted necessitates rigorous computational and statistical methods as well as scalable pipelines to int

71 Here we develop a statistical method based on characteristics known to inf

72 gh specific density feedbacks, and show that statistical methods based on model averaging provide rel

73 Statistical methods based on population genetics theory

74 power to identify risk variants compared to statistical methods based on smaller number of GWAS data

75 findings speak to the need of validating the statistical methods being used in the field of neuroimag

76 Nonetheless, selection of an optimal statistical method can be challenging when different met

77 ations in scRNA-seq data, such that existing statistical methods can be improved.

78 -up should be considered so that appropriate statistical methods can be incorporated into the design.

79 infrared spectra and the use of multivariate statistical methods can be useful for studying the compo

80 Standard statistical methods can have difficulty learning discret

81 We developed a novel statistical method, ChromNet, to infer a network of thes

82 Our statistical methods controlled for temporal patterns in

83 and treatment of human disease, but require statistical methods designed to find the most relevant p

84 bias has not previously been highlighted and statistical methods designed to minimize such biases hav

85 Here, we provide an overview of the statistical methods developed to identify archaic introg

86 Statistical methods development for differential express

87 through ages 26, 32, and 38 years, but those statistical methods disregarded the data's hierarchical

88 tools, but applying scoring procedures with statistical methods does not eliminate the fundamental p

89 zing such data are very limited.We develop a statistical method, DSS-single, for detecting DMRs from

90 We develop a statistical method, epiG, to infer and differentiate bet

91 We introduce a statistical method exploiting both pathogen sequences an

92 A recently published set of statistical methods exploits this association to infer c

93 Existing statistical methods extract insufficient information fro

94 Here we propose a novel statistical method, finding batch effect (findBATCH), to

95 hus, there is an acute need for an objective statistical method for classifying whether an experiment

96 r to fill this important gap, we introduce a statistical method for cross-phenotype analysis of rare

97 We developed Monovar, a statistical method for detecting and genotyping single-n

98 te analysis of transcript splicing (MATS), a statistical method for detecting differential alternativ

99 We present a statistical method for determining whether phenotypicall

100 q-value is a widely used statistical method for estimating false discovery rate (

101 Here we present a fast and accurate statistical method for high-dimensional heritability ana

102 We present EVmutation, an unsupervised statistical method for predicting the effects of mutatio

103 a analysis (e.g. 'peak detection'), rigorous statistical method for quantitative comparison of multip

104 We propose a statistical method for selecting genes based on overlapp

105 We proposed a statistical method for the conservative adjustment of q-

106 ion for Ecological Association Inference), a statistical method for the inference of microbial ecolog

107 Employing a novel statistical method for the study of the health effects o

108 Novel statistical method for Tn-seq data analysis is needed to

109 he Dashboard is complementary to traditional statistical methods for analysis of gene-expression data

110 des a graphical interface to three different statistical methods for analyzing TnSeq data.

111 Current statistical methods for assessing this excess are based

112 Here we use statistical methods for causal inference to investigate

113 However, statistical methods for classifying tumors by subtype ba

114 Statistical methods for CNV association analysis can be

115 lculation of false discovery rates providing statistical methods for comparing retroviral vectors.

116 Recent developments in statistical methods for computation of direct evolutiona

117 th codes) and small area estimation methods (statistical methods for estimating rates in small subpop

118 spectrum match scores exist, the field lacks statistical methods for estimating the false discovery r

119 TopDom, to identify TDs, along with a set of statistical methods for evaluating their quality.

120 examined single-trial responses in LIP using statistical methods for fitting and comparing latent dyn

121 omplete code and illustrations of new sparse statistical methods for high-dimensional, longitudinal m

122 icle, we provide a comprehensive overview of statistical methods for meta-analysis of sequencing stud

123 gic quality of included studies, appropriate statistical methods for meta-analysis, and conclusions b

124 ring, comparing the results from an array of statistical methods for optimal causal inference.

125 opment for genotype data has concentrated on statistical methods for phasing and imputation, based on

126 We developed new statistical methods for pipeline design and data analysi

127 asuring, and dealing with heterogeneity; and statistical methods for pooling results.

128 Statistical methods for prediction of putative drug-drug

129 rtance of family-based samples analysis, few statistical methods for rare variant association analysi

130 address these challenges, the performance of statistical methods for rare variants analysis still nee

131 imulation experiments over a small subset of statistical methods for RNA-seq analysis available in th

132 The commonly used statistical methods for RNA-seq differential expression

133 quality control procedure and development of statistical methods for RNA-seq downstream analyses.

134 s for rare variants is that most traditional statistical methods for testing interactions were origin

135 However, related statistical methods for testing SNP-SNP interactions are

136 Statistical methods for the analysis and design of exper

137 ifferential susceptibility is discussed, and statistical methods for the analysis of differential sus

138 We present a set of statistical methods for the analysis of DNA methylation

139 Statistical methods for the analysis of gender differenc

140 cates per group and integrates sophisticated statistical methods for the detection of differential co

141 Furthermore we provide new statistical methods for use in future analyses of multip

142 Among various statistical methods for WGP, multiple-trait model and an

143 t populations to which these OPC apply, and (statistical) methods for OPC development.

144 elected with information theoretic-based and statistical methods from participants' SPECT data.

145 mass spectrometry (QTOF-MS/MS) and advanced statistical methods has been used to extract and identif

146 During the past few years, various novel statistical methods have been developed for fine-mapping

147 Although several statistical methods have been developed to control for p

148 Although statistical methods have been developed to detect CNVs u

149 Statistical methods have been developed to determine whe

150 Recently, several statistical methods have been developed to improve stati

151 Statistical methods have been developed to test for comp

152 cal and accessible, detailed descriptions of statistical methods have been omitted.

153 While many statistical methods have been proposed for identifying n

154 Various statistical methods have been used in order to get the m

155 l (e.g., velocimetry and transit timing) and statistical methods have confirmed and characterized hun

156 odern genetic data combined with appropriate statistical methods have the potential to contribute sub

157 n analysis appears as the most commonly used statistical method in the area.

158 Using statistical methods in evenly split development and vali

159 Most conventional statistical methods in GWAS only investigate one phenoty

160 strategies have the potential to complement statistical methods in high-throughput phenotyping studi

161 ecent years, but studies have generally used statistical methods incapable of confirming this.

162 Statistical methods included chi(2) test and logistic re

163 Statistical methods included discrete-time survival anal

164 Statistical methods included Poisson models, comparison

165 Statistical methods included the Wilcoxon rank-sum test,

166 Statistical methods including Pearson's correlation, lin

167 ected the data were coded and analysed using statistical methods including t-tests, ANOVA and the Kru

168 We used robust statistical methods including the Cause of Death Ensembl

169 over discrete timepoints were combined with statistical methods including the following longitudinal

170 ge is especially relevant, because no single statistical method is sufficient to evaluate a novel bio

171 Our computational statistical method is well suited to meta-analyses as th

172 odology in conjunction with the conventional statistical methods is not only ripe for actual use in c

173 However, currently available statistical methods lack power in detecting differential

174 Statistical methods leveraging the tissue-specificity of

175 As with all inferential statistical methods, maximum likelihood is based on an a

176 ogical information, the results of different statistical methods may best be combined into a master p

177 However, an absence of model-based statistical methods means that researchers are often not

178 d Oryza sativa (rice), a C(3) plant, using a statistical method named the unified developmental model

179 A secondary aim was to test if the statistical method of elastic net regularization would i

180 g and variable selection methods and finally statistical methods of analysis and validation.

181 onite fossil record is commonly used to test statistical methods of evaluating mass extinctions to ac

182 However, statistical methods often treat cellular heterogeneity a

183 s of genome-wide data, using multiple robust statistical methods, on (i) 367 unrelated individuals dr

184 t levels is difficult, however, because most statistical methods only consider when the microbiota ar

185 and CUSUM+, a version of the cumulative sum statistical method optimized for longer events that do.

186 We have developed a novel statistical method, Phantom, to investigate gene set het

187 ion on means and variances of traits and the statistical method presented can be used to estimate tra

188 ould be expected to be able to interpret the statistical methods presented in only 20.8% of articles.

189 In this study, a new statistical method, probability binning signature quadra

190 most of this difference disappears, and the statistical methods produce similar results.

191 on together with advances in maximum entropy statistical methods provide a rich complementary source

192 les identifies and discusses the most common statistical methods reported in IAI and provides example

193 average expression of a biomarker, standard statistical methods require that variance be approximate

194 a better understanding of the procedures and statistical methods required to achieve statistically re

195 n part because of the absence of appropriate statistical methods required to assess attractor-like be

196 mputationally scalable and widely applicable statistical method (SEER) for the identification of sequ

197 , these results provide guidance for optimal statistical methods selection under different scenarios.

198 Analysis with a statistical method specifically designed for such data e

199 udies that identify in a unbiased way--using statistical methods such as clusters analysis--different

200 cting reporter metabolites: (i) conventional statistical methods suffer from small sample sizes, (ii)

201 Here we report a statistical method SURVIV (Survival analysis of mRNA Iso

202 The authors reanalyzed the data using statistical methods tailored to accumulation of evidence

203 The study used several statistical methods tailored to address the age at onset

204 s self-contained multivariate non-parametric statistical methods testing a complex null hypothesis ag

205 imized prion amplification procedures with a statistical method that accounts for false-positive and

206 In this article, we present PennSeq, a statistical method that allows each isoform to have its

207 This study aims to develop a statistical method that can accurately genotype tumor sa

208 To reduce bias, previous studies have used a statistical method that directly estimates the SFS from

209 probabilistic approach is followed, the only statistical method that is capable of estimating the pro

210 190723 binding pocket using PocketFEATURE, a statistical method that scores the similarity between pa

211 Here we adopt an alternative statistical method that substitutes space for time to es

212 In this article, we propose a new statistical method that will infer likely upstream regul

213 We introduce a novel statistical method that, by focusing on individuals, ena

214 We introduce a general statistical method that, given many noisy observables, d

215 ife scenario in all populations, needing new statistical methods that can assess their complex effect

216 Thus, new statistical methods that can control for population stra

217 Multivariate statistical methods that combine information from all bi

218 data were analyzed using recently developed statistical methods that demonstrate improvements over c

219 nance imaging (fMRI) analyses use parametric statistical methods that depend on a variety of assumpti

220 ality measurement network, we present simple statistical methods that do not require extensive traini

221 There are many statistical methods that either summarize gene-level sta

222 Although statistical methods that have been developed for microar

223 parts on patterns of DNA sequence variation, statistical methods that have been developed to leverage

224 d straightforward application of traditional statistical methods that ignore this two-way dependence

225 prove upon existing models by applying novel statistical methods that incorporate longitudinal data.

226 Here, we review recent progress on statistical methods that leverage summary association da

227 We introduce statistical methods that leverage the correlation betwee

228 We describe statistical methods that leverage the large amount of sm

229 rovides a set of multivariate non-parametric statistical methods that test a complex null hypothesis

230 Using novel statistical methods, they detected similar deficits acro

231 We present a statistical method to compare different patterns of ASE

232 We built upon a statistical method to describe metacommunity structure t

233 Here we propose a novel statistical method to detect DML when comparing two trea

234 The standard statistical method to determine whether a gene is an eGe

235 ute shrinkage and selection operator (Lasso) statistical method to diagnose pancreatic tissue section

236 In this study, we present a statistical method to link upstream signaling to downstr

237 In this work, we develop a statistical method to perform quantitative comparison of

238 We developed a statistical method to probe for a transition from classi

239 We use a state-of-the-art statistical method to quantify a CRM's sequence similari

240 Module network inference is an established statistical method to reconstruct co-expression modules

241 Here we present a novel adaptive statistical method to simultaneously address both proble

242 Here we used a tract-based spatial statistical method to uncover potential white matter tis

243 iables of greater interest, and use data and statistical methods to account for the impact of the oth

244 We use haplotype-based statistical methods to analyse genome-wide single nucleo

245 rkov-chain Monte Carlo, and advanced spatial statistical methods to analyse rates and extent of sprea

246 is a growing demand to develop and benchmark statistical methods to analyse these data.

247 We used three statistical methods to analyze a total of 748 time serie

248 challenge, and there are still only very few statistical methods to analyze more than two genomic var

249 We applied competing risk statistical methods to analyze patient outcomes.

250 cial neural network (ANN) modeling and other statistical methods to analyze relationships between a h

251 econdary outcomes in advance, specifying the statistical methods to be applied, and making all data o

252 ose full search details or apply appropriate statistical methods to combine study findings.

253 f combinatorial binding patterns, we develop statistical methods to detect clustering and ordering pa

254 some success in using such multidimensional statistical methods to determine details about the histo

255 or imaging) in combination with multivariate statistical methods to differentiate patients diagnosed

256 phic surveillance systems and develop robust statistical methods to establish and validate causal lin

257 entify periods of vulnerability, we used two statistical methods to estimate phthalate-adiposity asso

258 We extend statistical methods to estimate the frequency, i.e. the

259 We applied robust statistical methods to evaluate a large neutralization d

260 y measure phenotypes, and developed a set of statistical methods to extract genetic interactions from

261 m genotyping arrays, such as the Immunochip, statistical methods to identify credible sets of causal

262 Current statistical methods to identify differential methylation

263 gh representational capacity of multivariate statistical methods to identify neuroimaging-based bioma

264 cellular recording of spike trains and apply statistical methods to model and infer functional connec

265 It uses a range of experimental and statistical methods to quantitate and integrate intermed

266 Here, we describe statistical methods to quantitatively assess the amplifi

267 We have used sophisticated but accessible statistical methods to reveal that spatial components-wh

268 ch describes non-canonical phenotypes, using statistical methods to suggest GO functional annotations

269 We employ a statistical method, TROM, to identify both protein-codin

270 age) patients was compared using 3 different statistical methods: univariable logistic regression, mu

271 ection of bias were complex functions of the statistical method used, mortality rates and distributio

272 A 2-tailed Fisher exact test was the statistical method used.

273 lso discuss how new data sets may change the statistical methods used by economists and the types of

274 mutations, posing a fundamental problem for statistical methods used in cancer genomics.

275 Typically, the statistical methods used in metabolomics consider spectr

276 By applying the statistical methods used in previous studies [1-4, 6] to

277 limitations of various traditional and novel statistical methods used in the literature for biomarker

278 er, it remains controversial in part because statistical methods used to analyze readmission, primari

279 This paper discusses statistical methods used to reconstruct gene regulatory

280 sources used to construct gene sets and the statistical methods used to test for gene set associatio

281 though there is not yet a consistency in the statistical methods used.

282 We demonstrate our statistical method using HapMap-simulated and yeast eQTL

283 -boosted model and compared with traditional statistical methods using 2 independently derived logist

284 A general statistical method was developed for calculating the unc

285 Knowledge of 21 categories of statistical methods was necessary to comprehend 70.9% of

286 TwoPhaseInd implements a number of efficient statistical methods we developed for estimating subgroup

287 Using newer statistical methods, we avoid relying on hazard ratios (

288 Using four different statistical methods, we developed a composite score of t

289 Using well-established statistical methods, we developed a novel framework for

290 Using data-adaptive statistical methods, we identified combinations of antib

291 llating a large database, and using suitable statistical methods, we obtain a 95% upper bound of 0.26

292 Phylogenetically based statistical methods were applied to infer ancestral char

293 Standard statistical methods were applied.

294 ession and receiver operating characteristic statistical methods were employed to determine the assoc

295 Statistical methods were then used to identify proteins

296 Multivariate statistical methods were used to classify samples and id

297 Statistical methods were used to determine confounders a

298 Bayesian statistical methods were used, with the pre-specified me

299 Here, we present a novel statistical method, which we refer to as scPLS (single c

300 basis for subsequent application of rigorous statistical methods, with the ultimate goal being the cl