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 ese scales have never been validated through factor analysis.

4 nsionality of the syndrome with confirmatory factor analysis.

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

6 principal components analysis and subsequent factor analysis.

7 years and perinatal data assessment for risk factor analysis.

8 using nonmetric multidimensional scaling and factor analysis.

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

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

11 of oppositional behavior were derived using factor analysis.

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

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

14 s and estimated dietary pattern scores using factor analysis.

15 osed measure was evaluated using exploratory factor analysis.

16 Dietary patterns were defined via factor analysis.

17 multitrait scaling analysis, and exploratory factor analysis.

18 or dietary patterns, prudent and Western, by factor analysis.

19 ulting model was tested through confirmatory factor analysis.

20 Four dietary patterns were derived by using factor analysis.

21 These factors underwent a reductive factor analysis.

22 s were defined by using principal components factor analysis.

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

24 dimensionality was confirmed by higher order factor analysis.

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

26 etary patterns were derived from exploratory factor analysis.

27 pilepsy identified in a recent combined risk factor analysis.

28 imentally by a normalized crystallographic B-factor analysis.

29 dimensionality was confirmed by higher order factor analysis.

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

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

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

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

34 A hierarchical factor analysis across multiple cognitive tasks was used

35 factor analysis (WTTFA), along with parallel factor analysis - alternating least squares (PARAFAC-ALS

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

37 he SLAQ using Cronbach's alpha and principal factor analysis and ascertained construct validity by st

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

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

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

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

42 Factor analysis and generalized estimating equation mode

43 We also used factor analysis and identified 3 dietary patterns (Weste

44 Exploratory factor analysis and longitudinal growth modeling documen

45 Using form factor analysis and quantitative Western blotting of nor

46 Factor analysis and stepwise selection found Feno levels

47 Exploratory factor analysis and the Mokken Scaling Procedure support

48 Exploratory factor analysis and twin model fitting were performed us

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

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

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

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

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

54 the striatum, and tumors were generated with factor analysis, and from these, input and output functi

55 sting up-front should include von Willebrand factor analysis, and if normal, platelet aggregation and

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 d histologically; data were summarized using factor analysis, and treatment effects were assessed usi

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 We present a modified factor analysis approach.

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

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

64 Confirmatory Factor Analysis based on the covariance matrix was used

65 Dietary patterns were derived by factor analysis based on validated food frequency questi

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

67 Factor analysis can be used to investigate this structur

68 n patterns could be measured by confirmatory factor analysis (CFA) by using a culturally sensitive fo

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

70 Confirmatory factor analysis characterised the relationship between E

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

72 Fano factor analysis confirmed the presence of a temporal org

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

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

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

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

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

78 The factor analysis demonstrated three factors: thermal, mec

79 Factor analysis demonstrated two factors with eigenvalue

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

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

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

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

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

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

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

87 Using factor analysis (eigenvalue = 1.73) to compare character

88 Principal components exploratory factor analysis evaluated the interrelatedness of frailt

89 Factor analysis explained 61.3% of the total variance us

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

91 A factor analysis (FA) technique for extracting the blood

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

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

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

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

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

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

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

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

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

101 Factor analysis identified 3 components associated with

102 Factor analysis identified 3 independent factors likely

103 Principal components factor analysis identified 3 primary dietary patterns: a

104 Factor analysis identified a single underlying construct

105 Factor analysis identified eight latent variables that c

106 Based on marker correlations, factor analysis identified four major coexpression patte

107 Bayesian sparse factor analysis identified sets of coexpressed transcrip

108 Principal factor analysis identified three factors each in the env

109 Q-factor analysis identified three subtypes of narcissisti

110 Exploratory factor analysis identified two discrete clusters of geni

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

112 Using factor analysis, images were processed to obtain 1 blood

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

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

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

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

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

118 Factor analysis indicated that the scale is unidimension

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

120 Exploratory factor analysis is a commonly used statistical technique

121 PARAFAC (parallel factor analysis) is a powerful chemometric method that h

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

123 Factor analysis largely confirmed the proposed scale str

124 Factor analysis method using principal component was app

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

126 We used factor analysis methods to study associations between di

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

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

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

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

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

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

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

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

135 e traits derived from a principal components factor analysis of 73 items from our consensus diagnosti

136 Factor analysis of ACS NSQIP postoperative complication

137 Factor analysis of child-only studies differed in that c

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

139 n, using a generalized form of least-squares factor analysis of dynamic sequences (GFADS) and a novel

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

141 itative dynamic (82)Rb PET using generalized factor analysis of dynamic sequences and compartmental m

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

143 Factor analysis of MR spectroscopic imaging data is a us

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

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

146 Factor analysis of QCT parameters in asthmatic patients

147 Factor analysis of responses to nonsocial novelty identi

148 Factor analysis of studies including adults yielded an i

149 Here, we employ factor analysis of temperature-dependent Raman spectra t

150 ubjects with OCD and included an exploratory factor analysis of the 13 Yale-Brown Obsessive Compulsiv

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

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

153 Factor analysis of the first two factors associated with

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

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

156 Factor analysis of the signs and symptoms of schizophren

157 Factor analysis of the survey items demonstrated high fa

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

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

160 Factor analysis on the asymmetric regions revealed 4 sep

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

162 ted by Cronbach's alpha (0.87) and principal factor analysis (one factor accounted for 92% of the var

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

164 96 vs. 1.43, p = 0.04), and in studies using factor analysis or the World Health Organization definit

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

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

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

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

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

170 Parallel factor analysis (PARAFAC) applied to fluorescence excita

171 PARAllel FACtor analysis (PARAFAC) extracted the potential fluoro

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

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

174 omponent analysis (PCA) followed by parallel factor analysis (PARAFAC) in concert with the LECO Chrom

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

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

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

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

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

180 Parallel Factor Analysis (PARAFAC) of FDOM determined components

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

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

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

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

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

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

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

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

189 analysis techniques, in particular parallel factor analysis (PARAFAC), provide promising results in

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

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

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

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

194 based on the chemometric technique parallel factor analysis (PARAFAC).

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

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

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

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

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

200 imensional multivariate techniques (parallel factor analysis, PARAFAC) to accuracies comparable with

201 Exploratory factor analysis (principal component analysis) was used

202 Exploratory factor analysis (principal components analysis) was perf

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 Factor analysis produced factor images, representing ili

207 Factor analysis provides support for six LDEQ scales: In

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

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

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 subscales with coherent i

218 Factor analysis revealed two dimensions of mind percepti

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

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

221 Factor analysis showed that a subset of the YMRS items p

222 Factor analysis showed that all pain and function items

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

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

225 Factor analysis shows that smiles sort into three social

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

227 Factor analysis solutions for 5 to 9 latent factors were

228 Transcription factor analysis suggested that cell death in female pati

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

232 We report the application of target factor analysis (TFA) to the identification of trace ana

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

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

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

236 Even without factor analysis, these samples demonstrated almost ident

237 Through factor analysis, they found empirical support for dividi

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

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

240 The authors applied factor analysis to identify naturally occurring diagnost

241 ical modules and computational transcription factor analysis to identify putative regulatory factors

242 We use factor analysis to identify temporally correlated assemb

243 were mathematically resolved using parallel factor analysis to positively identify the metabolites a

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

245 With window factor analysis to resolve component spectra, temperatur

246 We used factor analysis to test hypotheses regarding HRQOL domai

247 ts; two evolutionary conserved transcription factor analysis tools, rVista and multiTF; a tool for ex

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

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

250 Factor analysis was applied to standard measures of sexu

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

252 Factor analysis was conducted on the physical and cognit

253 Factor analysis was conducted to delineate the interacti

254 Exploratory factor analysis was conducted to explore the Moral Distr

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

256 Factor analysis was performed on codon usage of 16,654 g

257 A factor analysis was performed on global expression of 21

258 A multiple-factor analysis was performed that included the combined

259 Factor analysis was performed to assess influences on ca

260 Factor analysis was performed to identify independent do

261 Multilevel confirmatory factor analysis was performed to validate the structure

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

263 Risk factor analysis was performed using multivariate logisti

264 Multivariate prognostic factor analysis was used to assess clinical factors for

265 A factor analysis was used to characterize correlations be

266 Exploratory factor analysis was used to characterize DHOS exposure.

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

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

269 Factor analysis was used to create scales scored to 100

270 Factor analysis was used to derive food patterns based o

271 Exploratory factor analysis was used to determine whether underlying

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

273 Factor analysis was used to develop a series of complica

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

275 Factor analysis was used to examine correlates of treatm

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

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

278 Exploratory factor analysis was used to identify inflammatory proces

279 Factor analysis was used to identify latent factors amon

280 Exploratory factor analysis was used to investigate the underlying f

281 Factor analysis was used to test the conceptual structur

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 Through factor analysis, we identified 2 dietary patterns: Weste

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

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

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

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

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

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

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

293 The exploratory factor analysis with oblique rotation suggested an overl

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

295 Parallel factor analysis with soft independent modeling by class

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

297 mometric techniques of window target testing factor analysis (WTTFA), along with parallel factor anal

298 Factor analysis yielded 4 uncorrelated factors (adiposit

299 The factor analysis yielded a five-factor structure.

300 Factor analysis yielded a total of 5 factors, the first