ライフサイエンスコーパス: factor analysis

1 omposition methods such as PARAFAC (parallel factor analysis).

2 tatistical methods such as PARAFAC (parallel factor analysis).

3 ulting model was tested through confirmatory factor analysis.

4 d develop a valid and reliable scale through factor analysis.

5 dimensionality was confirmed by higher order factor analysis.

6 h, dairy, starch foods, and snacks) by using factor analysis.

7 etary patterns were derived from exploratory factor analysis.

8 pilepsy identified in a recent combined risk factor analysis.

9 nsionality of the syndrome with confirmatory factor analysis.

10 and a hybrid method that creates an ROI from factor analysis.

11 principal components analysis and subsequent factor analysis.

12 using nonmetric multidimensional scaling and factor analysis.

13 ly cluster into two correlated dimensions in factor analysis.

14 , and mood) were identified with exploratory factor analysis.

15 ponent traits were replicated in our GWA and factor analysis.

16 of oppositional behavior were derived using factor analysis.

17 ized them using PCR-ribotyping and virulence factor analysis.

18 nical findings, treatment outcomes, and risk factor analysis.

19 s and estimated dietary pattern scores using factor analysis.

20 ded on to one of seven factors identified by factor analysis.

21 Dietary patterns were derived by factor analysis.

22 ese scales have never been validated through factor analysis.

23 imentally by a normalized crystallographic B-factor analysis.

24 years and perinatal data assessment for risk factor analysis.

25 se results were confirmed by IF (interaction factor) analysis.

26 Using factor analysis, 2 major dietary patterns (healthy and W

27 By using factor analysis, 2 major dietary patterns were identifie

28 Second, based on an exploratory factor analysis, a two-factor model described the data w

29 ere identified by using principal components factor analysis: a plant-based diet, high in fruit and v

30 A hierarchical factor analysis across multiple cognitive tasks was used

31 Iterative target factor analysis allowed exclusion of reduced inorganic S

32 myocardial blood flows were calculated with factor analysis and a 2-compartment kinetic model and we

33 Dimensional approaches including factor analysis and canonical correlation analysis aim t

34 ar association between dietary patterns from factor analysis and depression risk.

35 The principal component analysis, factor analysis and discriminant analysis were used for

36 m (PS) symptom severity was summarized using factor analysis and evaluated dimensionally.

37 and ascertainment combined with confirmatory factor analysis and general SEM.

38 Factor analysis and generalized estimating equation mode

39 A combination of unsupervised exploratory factor analysis and hierarchical clustering was used to

40 arvested; biochemistry, cytokine, and growth factor analysis and histology evaluations were performed

41 Single factor analysis and logistic regression were performed,

42 Exploratory factor analysis and longitudinal growth modeling documen

43 tasks each loaded on unique dimensions in a factor analysis and punishment learning and future expec

44 Using form factor analysis and quantitative Western blotting of nor

45 Factor analysis and Rasch modeling were used to validate

46 Factor analysis and stepwise selection found Feno levels

47 Confirmatory factor analysis and structural equation modeling were us

48 We used factor analysis and the Mantel-Cox test to explore the a

49 Exploratory factor analysis and the Mokken Scaling Procedure support

50 Exploratory factor analysis and twin model fitting were performed us

51 We derived dietary patterns with factor analysis and used Cox proportional hazards regres

52 ly placed in the region of the mitral valve, factor analysis, and a hybrid method that creates an ROI

53 ed with multiple MetS component traits using factor analysis, and built a genetic risk score for MetS

54 l logistic regression was performed for risk factor analysis, and Cox proportional hazards regression

55 ators were subjected to principal components factor analysis, and factor scores representing 9 dimens

56 ed dietary pattern methods-cluster analysis, factor analysis, and index analysis-with colorectal canc

57 atent variable measurement models, including factor analysis, and indirect effect models were used in

58 g principle components analysis, exploratory factor analysis, and Pearson correlations for caudate, p

59 Factor analysis applied to the top 25 most abundant taxa

60 GN framework is based on a flexible Bayesian factor analysis approach that allows for simultaneous pr

61 as performed by means of impact analysis and factor analysis as well as by checking for content and f

62 Combining transcription factor analysis at the single cell and the single nucleu

63 Confirmatory Factor Analysis based on the covariance matrix was used

64 to correct for known confounders outperforms factor analysis-based methods that estimate hidden confo

65 ovel allergens can improve diagnostics, risk factor analysis can aid preventative approaches, and stu

66 Factor analysis can be used to investigate this structur

67 Human factors analysis can provide insight into how system ele

68 This 2-factor analysis captures the full risk-factor effect on

69 Confirmatory factor analysis (CFA) examined whether the methods measu

70 We used confirmatory factor analysis (CFA) models for KDQOL-36 kidney-targete

71 proposed the alternative use of confirmatory factor analysis (CFA) to define such patterns.

72 Confirmatory factor analysis (CFA) was conducted to examine the fit o

73 atory factor analysis (EFA) and confirmatory factor analysis (CFA).

74 Confirmatory factor analysis characterised the relationship between E

75 d levels of a microbial, humic-like parallel factor analysis component (C6).

76 Our risk factor analysis contributed to the development of the re

77 Six dietary patterns were identified from factor analysis: cooked vegetables, fruit, Mediterranean

78 EEMs are analyzed using parallel factor analysis, decomposing the signal in its independe

79 Principal component factor analysis demonstrated substantial, but unique, cl

80 citation-emission spectroscopy with parallel factor analysis demonstrated the large mineralization of

81 Hierarchal cluster analysis and factor analysis demonstrated the potential of these 34 i

82 Factor analysis demonstrated two factors with eigenvalue

83 In multivariable risk factor analysis, developmental delay (adjusted HR 18.92,

84 by both excitation-emission matrix-parallel factor analysis (EEM-PARAFAC) and ultrahigh-resolution m

85 WF using excitation-emission matrix parallel factor analysis (EEM-PARAFAC).

86 erential item functioning (DIF), exploratory factor analysis (EFA) and confirmatory factor analysis (

87 Local rank exploratory methods like Evolving Factor Analysis (EFA) method provide local rank maps in

88 et of biomarkers was used for an exploratory factor analysis (EFA) to select patients with BD.

89 Exploratory factor analysis (EFA) was employed to identify groupings

90 Exploratory factor analysis (EFA) was used to characterize underlyin

91 of chromatography-coupled SAXS with Evolving Factor Analysis (EFA), a powerful method for separating

92 addition to equivalence testing, exploratory factor analysis (EFA), and diagnostic analysis.

93 nion in BMImPF6 were obtained using evolving factor analysis (EFA).

94 Principal components exploratory factor analysis evaluated the interrelatedness of frailt

95 Exploratory factor analysis (FA) and parallel analysis (PA), and Ras

96 onolayers on metal surfaces were analyzed by factor analysis (FA) to determine the spatial distributi

97 st independent component analysis (FastICA), factor analysis (FA), or parallel factor analysis (PARAF

98 tary patterns were derived using exploratory factor analysis for 2139 non-small cell lung cancer (NSC

99 t and 1 year later, as well as baseline risk factor analysis for severe dry eye symptoms at 1 year, d

100 t and 1 year later, as well as baseline risk factor analysis for severe dry eye symptoms at 1 year, d

101 kal-Wallis, and chi(2) tests) and predictive factor analysis for tumor growth and viability were perf

102 matory cell assessments, were selected using factor analysis for unsupervised cluster analysis.

103 this approach, we introduce a new 'logistic factor analysis' framework that seeks to directly model

104 PET by using our methodology for generalized factor analysis (generalized factor analysis of dynamic

105 Factor analysis has successfully been applied to SFG ima

106 ave developed a method for Hidden Expression Factor analysis (HEFT) that identifies individual and pl

107 We further quantified, using parallel factor analysis, how tVNS modulates idle occipital alpha

108 Factor analysis identified 2 patterns of intake: 1) high

109 Factor analysis identified 3 components associated with

110 Factor analysis identified a single underlying construct

111 Bayesian sparse factor analysis identified sets of coexpressed transcrip

112 Principal factor analysis identified three factors each in the env

113 Factor analysis identified three representative ECG para

114 ntitative approaches have proven useful: (i) factor analysis, (ii) information theory, (iii) determin

115 Upstream regulatory factor analysis implicated the bile acid/farnesoid X rec

116 were derived by using a principal components factor analysis in 1097 breast cancer cases and an age-s

117 We conducted a confirmatory factor analysis in 49,410 subjects in the National Breas

118 ulations that, on the basis of transcription factor analysis, include both regulatory and follicular

119 ase that could not be found without a hidden factor analysis, including cis-eQTL for GTF2H1 and MTRR,

120 The nonnegative sparse parallel factor analysis indicated a complex latent structure inv

121 Confirmatory factor analysis indicated a seven-factor model (chi(2) (

122 Factor analysis indicated that the scale is unidimension

123 Factor analysis indicates that much of the variability a

124 Factor analysis informed the variables used in a k-means

125 Exploratory factor analysis is a commonly used statistical technique

126 ng Principal Component Analysis and Parallel Factor Analysis it was possible to discriminate musts ac

127 psychometric testing, including exploratory factor analysis, item calibration using item response th

128 quantified with the iterative transformation factor analysis (ITFA) method.

129 Factor analysis largely confirmed the proposed scale str

130 Factor analysis method using principal component was app

131 Here we introduce a model-based factor analysis method, SDA, to analyze a novel 57,600 c

132 Carbon mole fraction plots show how factor analysis methods such as the Adaptive Resonance T

133 performance to other mixed model confounding factor analysis methods when identifying such eQTL.

134 Spectra were deconvolved using multivariate factor analysis (MFA) into 3 "factor score spectra" (tha

135 Multiple factor analysis (MFA) of elemental composition with juic

136 The confirmatory factor analysis model had good approximate fit (comparat

137 We have developed iFad, a Bayesian sparse factor analysis model to jointly analyze the paired gene

138 sent a fully Bayesian formulation of a group factor analysis model.

139 nsisting of a non-parametric sparse Bayesian factor analysis model.

140 mposition based on the PARAFAC (for Parallel Factor analysis) model], we demonstrate that (4) these d

141 variation explained (AJIVE) and multi-omics factor analysis (MOFA) using a cross-validation approach

142 Factor analysis of 11 cognitive variables was performed

143 Data from 185 patients were analysed using factor analysis of 17 questions cited as present in 30%

144 A factor analysis of 18 tests was performed to identify se

145 cutive function, could be identified through factor analysis of a deeply phenotyped clinical sample.

146 Factor analysis of ACS NSQIP postoperative complication

147 orithm or methodology is available for multi-factor analysis of differential co-expression.

148 for generalized factor analysis (generalized factor analysis of dynamic sequences [GFADS]) and compar

149 disorders) factors were found in exploratory factor analysis of lifetime disorders.

150 A multivariate risk factor analysis of lower-respiratory tract disease (LRTD

151 Factor analysis of MR spectroscopic imaging data is a us

152 Factor analysis of our analytic sample (n = 3,566) estab

153 Exploratory factor analysis of pooled questions of CNS-LS and PHQ-9

154 Factor analysis of QCT parameters in asthmatic patients

155 CONCLUSIONS/SIGNIFICANCE: Factor analysis of the AIRS is consistent with a circump

156 four factors were deduced from the evolving factor analysis of the data, and their concentrations an

157 Factor analysis of the first two factors associated with

158 Furthermore, parallel factor analysis of the fluorescence spectra enabled moni

159 e factor structure of the SHPC, confirmatory factor analysis of the resulting 18-item questionnaire (

160 Factor analysis of the signs and symptoms of schizophren

161 nsity and energy cost were analyzed with one-factor analysis of variance.

162 thermomechanical cycling and analyzed with 3-factor analysis of variance.

163 Through a detailed human factors analysis of elective laparoscopic general surger

164 for nuisance technical effects by performing factor analysis on suitable sets of control genes (e.g.,

165 A principal axis factor analysis on the Brief Assessment of Cognition in

166 Dietary pattern analyses by factor analysis or partial least squares may overcome th

167 Following weighted factor analysis, p(EoE) was determined by random forest

168 ix fluorescence in combination with parallel factor analysis (PARAFAC) and partial least squares (PLS

169 orescence spectroscopy coupled with parallel factor analysis (PARAFAC) and Partial least squares Disc

170 urces was measured and modeled with parallel factor analysis (PARAFAC) and the resulting model ("Fluo

171 Parallel factor analysis (PARAFAC) and unfolded-partial least squ

172 Parallel factor analysis (PARAFAC) applied to fluorescence excita

173 PARAllel FACtor analysis (PARAFAC) extracted the potential fluoro

174 rescence spectroscopy combined with parallel-factor analysis (PARAFAC) for seawater samples obtained

175 orescence spectroscopy coupled with parallel factor analysis (PARAFAC) has been widely used to charac

176 A parallel factor analysis (PARAFAC) model developed from the leach

177 atrices using a 7- and 13-component parallel factor analysis (PARAFAC) model showed low PARAFAC sensi

178 ce spectroscopy in combination with Parallel Factor Analysis (PARAFAC) modeling attributed DOM sample

179 Parallel factor analysis (PARAFAC) modeling of DOM fluorophores i

180 matrix (EEM) technique coupled with parallel factor analysis (PARAFAC) modeling, measurements of bulk

181 Parallel Factor Analysis (PARAFAC) of FDOM determined components

182 tal of four models developed by the parallel factor analysis (PARAFAC) of fluorescence excitation and

183 This paper presents parallel factor analysis (PARAFAC) of fluorescence of cereal flou

184 ncipal component analysis (PCA) and parallel factor analysis (PARAFAC) performed on each data set, re

185 Deconvolution with parallel factor analysis (PARAFAC) resulted in three hydrolysis c

186 ploratory study of the spectra with parallel factor analysis (PARAFAC) revealed three groups of fluor

187 n emission matrix (EEM) spectra and parallel factor analysis (PARAFAC) to determine fluorescent DOM (

188 EEM) fluorescence was combined with parallel factor analysis (PARAFAC) to model base-extracted partic

189 tistically modeled EEMF components (parallel factor analysis (PARAFAC)) and the exact mass informatio

190 and multivariate data analysis, as Parallel Factor Analysis (PARAFAC), N-way partial least squares a

191 cessed with the aid of unsupervised parallel factor analysis (PARAFAC), PARAFAC supervised by linear

192 e analyzed by means of unsupervised parallel factor analysis (PARAFAC), PARAFAC supervised by linear

193 ce on an individual sample basis by parallel factor analysis (PARAFAC).

194 h paprika were analyzed by means of parallel factor analysis (PARAFAC).

195 rescence spectroscopy combined with PARAllel FACtor analysis (PARAFAC).

196 tation-emission matrices (EEMs) and parallel factor analysis (PARAFAC).

197 ncipal Component Analysis (PCA) and Parallel Factor Analysis (PARAFAC).

198 estrial and marine components using Parallel Factor Analysis (PARAFAC).

199 ission matrices (EEMs) coupled with parallel factor analysis (PARAFAC).

200 ks and background are modeled using parallel factor analysis (PARAFAC).

201 (FastICA), factor analysis (FA), or parallel factor analysis (PARAFAC).

202 Exploratory factor analysis (principal component analysis) was used

203 ittsburgh, a researcher could apply the same factor analysis procedure to compare data sets for diffe

204 Confirmatory factor analysis produced 7 scales, displaying internal c

205 Phenotypic factor analysis produced evidence for two correlated fac

206 Confirmatory factor analysis provided a solution composed of five mul

207 were performed by means of impact analysis, factor analysis, regression analysis, and by checking fo

208 n studies were conducted, including item and factor analysis, reliability testing, Rasch modeling, an

209 analysed scale structure using confirmatory factor analysis; reliability using Cronbach's alpha valu

210 The application of factor analysis requires methodological decisions that r

211 ponent analysis and maximum auto correlation factor analysis resulted in detection of more than 400 m

212 Exploratory factor analysis revealed 4 underlying factors of quality

213 Transcription factor analysis revealed that SPM and LPM express abunda

214 Exploratory factor analysis revealed that this instrument was unidim

215 Factor analysis revealed that, although they defined dif

216 The factor analysis revealed the retention of 29 items to fo

217 Factor analysis revealed three domains: language and rea

218 iple statistical methods such as significant factor analysis (SFA), principle component analysis (PCA

219 Factor analysis showed good separation between healthy p

220 Exploratory factor analysis showed that 88-94% of the total variance

221 Parallel factor analysis showed that the fluorescent dissolved or

222 However, factor analysis showed that within each sample, FAs cons

223 range of situations, concepts and cultures, factor analysis shows that 50% of the variance in rating

224 Factor analysis shows that smiles sort into three social

225 adjustment for dietary pattern variables by factor analysis significantly shifted the hazard ratio a

226 Factor analysis solutions for 5 to 9 latent factors were

227 Transcription factor analysis suggested that cell death in female pati

228 Upper bounding factor analysis suggested that the results are robust to

229 l experiences (e.g., anger and sadness) in a factor analysis, suggesting that each subregion particip

230 Risk factor analysis suggests that older age (risk ratio = 0.

231 Factor analysis suggests that steroids covary within and

232 emonstrate our approach by comparing various factor analysis techniques on RNA-seq datasets.

233 ed a variety of exploratory and confirmatory factor analysis techniques to their self-reported well-b

234 nt rigorous psychometric analysis, including factor analysis, test-retest reliability, internal consi

235 Chemometric Target Factor Analysis (TFA) and Hierarchical Cluster Analysis

236 ll latent variable model), a method based on factor analysis that uses pathway annotations to guide t

237 extracted and according to the confirmatory factor analysis the three-factor model had adequate fitn

238 on attachment construct through confirmatory factor analysis; the three-factor model adequately fit t

239 In a 2-factor analysis, there was a significant main effect of

240 Even without factor analysis, these samples demonstrated almost ident

241 n countries during 1992-2000, we conducted a factor analysis to delineate important components that s

242 We used factor analysis to derive Western and "Prudent" dietary

243 These were used systematically as inputs for factor analysis to determine a final model that could po

244 First, we use factor analysis to extract the three worldviews or ways

245 The authors applied factor analysis to identify naturally occurring diagnost

246 We use factor analysis to identify temporally correlated assemb

247 We address these questions by applying factor analysis to recordings in the visual cortex of no

248 With window factor analysis to resolve component spectra, temperatur

249 ntext, we used factor scores, derived from a factor analysis using census tract-level characteristics

250 We carried out Confirmatory Factor Analysis using cross loadings and modification in

251 We present Multi-Omics Factor Analysis v2 (MOFA+), a statistical framework for

252 Bootstrap-based exploratory factor analysis was applied to 49 phenotypic subscales f

253 Factor analysis was applied to standard measures of sexu

254 In addition, factor analysis was applied to the sensitivity values (i

255 Factor analysis was conducted on the physical and cognit

256 An exploratory factor analysis was conducted on the Social and Domestic

257 Factor analysis was conducted to delineate the interacti

258 Exploratory factor analysis was conducted to explore the Moral Distr

259 nd rigour of the methods used for prognostic factor analysis was found compared with the previous rev

260 Transcription factor analysis was performed in a microarray data set p

261 A factor analysis was performed on global expression of 21

262 Factor analysis was performed to identify independent do

263 Factor analysis was performed to identify unidimensional

264 llous disease refined the pilot ABQOL before factor analysis was performed to yield the final ABQOL q

265 Risk factor analysis was performed using multivariate logisti

266 Factor analysis was used to assess spatial covariation o

267 A factor analysis was used to characterize correlations be

268 Exploratory factor analysis was used to characterize DHOS exposure.

269 evels and sPTB at <34 weeks and 34-36 weeks; factor analysis was used to characterize patterns of bio

270 Separate shape factor analysis was used to characterize steady vs accel

271 Factor analysis was used to create scales scored to 100

272 Factor analysis was used to derive food patterns based o

273 Exploratory factor analysis was used to determine whether underlying

274 An unbiased data-driven approach using factor analysis was used to develop a GRSS.

275 Factor analysis was used to develop a series of complica

276 Factor analysis was used to estimate blood-pool time-act

277 Principal component factor analysis was used to generate a composite index o

278 em food-frequency questionnaire in 2003, and factor analysis was used to identify dietary patterns.

279 A factor analysis was used to identify factors that best d

280 Exploratory factor analysis was used to identify inflammatory proces

281 Factor analysis was used to identify latent factors amon

282 Factor analysis was utilized to determine associations b

283 imensional data (nonnegative sparse parallel factor analysis) was used to extract latent patterns exp

284 With factor analysis we assessed the correlational structures

285 Using so-called Phi-factor analysis, we analyzed the kinetic and thermodynam

286 Through factor analysis, we classified the quinoa varieties into

287 Through machine learning and multi-omics factor analysis, we compare and contrast the genomic pro

288 With FFQ and factor analysis, we determined 2 dietary patterns consis

289 Using parallel factor analysis, we were able to assign the increase in

290 e scores for each factor from a confirmatory factor analysis were analyzed for association with 696,4

291 pal component analysis (PCA) and explanatory factor analysis were used to consolidate correlated meta

292 Principal components and common factor analysis were used to identify symptom dimensions

293 ychological tests and 4 factors derived from factor analysis were used: executive and visuospatial ab

294 el is a combined multivariate regression and factor analysis, where the complete likelihood of the mo

295 he aim of this study was to determine, using factor analysis, whether these GI symptom factors (clust

296 and we also provide the corresponding shape factor analysis, which can be used synergistically with

297 e AIRS was assessed using Maximum Likelihood Factor Analysis with Oblimin rotation.

298 The exploratory factor analysis with oblique rotation suggested an overl

299 We used principal factor analysis with promax rotation to identify dietary

300 Parallel factor analysis with soft independent modeling by class

301 ensionality of the items was evaluated using factor analysis, with results suggesting four factors: c