ライフサイエンスコーパス: principal component

1 ssociated with the first inflammatory marker principal component.

2 ed for age at cancer diagnosis, CED, and top principal components.

3 usting for age, sex, BMI and nine population principal components.

4 s of participants and alternative choices of principal components.

5 d mode) to accurately compute the 10 leading principal components.

6 A group of 45 proteins was identified as a principal component 1 (PC1) with the highest expression

7 Principal Component, ADMIXTURE, and neighbor joining ana

8 Hierarchical agglomerative clustering and principal component analyses (PCA) were conducted to ide

9 d [(18) F]AV-1451 binding were determined by principal component analyses (PCAs), and the loading of

10 Principal component analyses revealed clusters of CSF in

11 We used principal component analyses to identify eating behavior

12 Principal component analyses were performed on the phant

13 We performed two Principal Component Analyses, one using behaviours from

14 Principal components analyses produced 2 factors preoper

15 81 vs 0.80, respectively; P = .76) or RA and principal component analysis (AUC, 0.78 vs 0.78, respect

16 e interval: 0.79, 0.83; P < .001) and RA and principal component analysis (AUC, 0.78; 95% confidence

17 The methods we use are Complex Hilbert Principal Component Analysis (CHPCA) and Rotational Rand

18 We applied rPCA methods and classical principal component analysis (cPCA) on an RNA-Seq data s

19 This study used functional principal component analysis (FPCA) to achieve this aim.

20 e multinomial methods, including generalized principal component analysis (GLM-PCA) for non-normal di

21 We develop the Intensive Principal Component Analysis (InPCA) and demonstrate cle

22 Marchenko-Pastur principal component analysis (MP-PCA) provides a novel s

23 We applied principal component analysis (PCA) and Bayesian kernel m

24 Principal component analysis (PCA) and explanatory facto

25 , following a prefiltering step, featurewise principal component analysis (PCA) and groupwise PCA (GP

26 By using principal component analysis (PCA) and intramolecular an

27 combined with multivariate analysis, such as principal component analysis (PCA) and linear discrimina

28 Principal component analysis (PCA) and linear discrimina

29 Based on a combination of principal component analysis (PCA) and linear discrimina

30 We used principal component analysis (PCA) and multi-dimensional

31 Principal component analysis (PCA) and orthogonal partia

32 ce spectroscopies were applied together with principal component analysis (PCA) and parallel factor a

33 ious multivariate analysis methods including principal component analysis (PCA) and partial least squ

34 Principal component analysis (PCA) and partial least squ

35 omatography-mass spectroscopy, followed by a principal component analysis (PCA) and pearson correlati

36 Principal component analysis (PCA) and supervised partia

37 model readily yields a viable alternative to principal component analysis (PCA) as a dimension reduct

38 In addition, we applied principal component analysis (PCA) as an input to the al

39 hich optimises a statistical model combining Principal Component Analysis (PCA) as an unsupervised le

40 Principal component analysis (PCA) determined that the g

41 Principal component analysis (PCA) dimensionality reduct

42 Two principal component analysis (PCA) dimensions summarised

43 Principal component analysis (PCA) discriminated the mor

44 Principal component analysis (PCA) extracted distinct co

45 Instead, active enhancers were resolved by principal component analysis (PCA) from all accessible r

46 Comparative investigations by Principal Component Analysis (PCA) highlighted pronounce

47 Principal component analysis (PCA) including phenolics a

48 Principal component analysis (PCA) indicated a clear dis

49 Total explained variance of 89.55% in principal component analysis (PCA) indicated high qualit

50 Principal component analysis (PCA) is used to quantify c

51 Unsupervised Principal Component Analysis (PCA) led to a focused core

52 ucose samples are quantified by applying the Principal Component Analysis (PCA) machine learning algo

53 healthy psyllids were processed through the principal component analysis (PCA) method and compared.

54 Principal component analysis (PCA) of 6 measures of cort

55 Principal component analysis (PCA) of the NMR dataset br

56 Principal component analysis (PCA) of the tissue samples

57 Principal component analysis (PCA) projection of the inp

58 Principal component analysis (PCA) revealed clear discri

59 Principal component analysis (PCA) revealed correlations

60 Principal component analysis (PCA) revealed that some of

61 initial exploratory analysis of the data by Principal Component Analysis (PCA) showed a separation t

62 Nutrition researchers use principal component analysis (PCA) to derive dietary pat

63 The device uses principal component analysis (PCA) to reduce spectral di

64 Principal component analysis (PCA) was employed as the s

65 Principal component analysis (PCA) was employed for imag

66 Principal component analysis (PCA) was employed to creat

67 Principal component analysis (PCA) was used as a very he

68 Principal component analysis (PCA) was used to assess th

69 Initially, principal component analysis (PCA) was used to see if cl

70 Exploratory chemometric techniques based on Principal Component Analysis (PCA) were applied to each

71 Pearson correlation and principal component analysis (PCA) were conducted to rev

72 Hierarchical cluster analysis (HCA) and principal component analysis (PCA) were successfully per

73 By combining principal component analysis (PCA) with Raman spectrosco

74 ida et al. introduced the notion of tropical principal component analysis (PCA), a statistical method

75 lyzed using multivariate analysis, including principal component analysis (PCA), and partial least sq

76 Analysis of variance (ANOVA), principal component analysis (PCA), and partial least sq

77 reduction and variable selection algorithms: Principal Component Analysis (PCA), Genetic Algorithm (G

78 Principal component analysis (PCA), hierarchical cluster

79 ous patients into a "pipeline" that included principal component analysis (PCA), manifold learning, a

80 ormed multivariable data analyses, including principal component analysis (PCA), orthogonal partial l

81 Furthermore, Principal component analysis (PCA), soft independent mod

82 unwanted variation, we propose a variant of principal component analysis (PCA), sparse contrastive P

83 well as other genotype-based methods such as Principal Component Analysis (PCA), Support Vector Machi

84 of single cells that can be visualized using principal component analysis (PCA), t-distributed stocha

85 Using the principal component analysis (PCA), the riboflavin and N

86 Using principal component analysis (PCA), the treated cells co

87 e co-expression network analysis (WGCNA) and principal component analysis (PCA), we characterized com

88 was employed to develop score maps based on principal component analysis (PCA), which permitted to m

89 To adjust for PS, principal component analysis (PCA)-based ancestry predic

90 one-hundred saffron samples was examined by principal component analysis (PCA).

91 distinguished by their mineral content using principal component analysis (PCA).

92 All parameters were analyzed using principal component analysis (PCA).

93 from which it was initially invented, namely Principal Component Analysis (PCA).

94 MS/MS followed by pooling the variables with principal component analysis (PCA).

95 We report the use of two robust principal component analysis (rPCA) methods, PcaHubert a

96 We use a statistical approach called robust Principal Component Analysis (rPCA), to decouple and qua

97 A combined principal component analysis across the two aetiologies

98 we submitted the connected speech metrics to principal component analysis alongside an extensive neur

99 We used a multiparameter principal component analysis and an unbiased parameter-a

100 inite crystallite size were examined through principal component analysis and comparison of PDFs.

101 ta undergoes a preliminary exploration using principal component analysis and heat map-based cluster

102 Multipollutant analysis using principal component analysis and hierarchical clustering

103 Principal component analysis and hierarchical clustering

104 activity as well as their time series using principal component analysis and independent component a

105 o adjust for population structure, including principal component analysis and mixed modelling with a

106 Oblique principal component analysis and point biserial correlat

107 Principal component analysis and Spearman correlations i

108 A principal component analysis and Spearman's rank correla

109 filing was performed with Affymetrix arrays, Principal Component Analysis and the bioconductor packag

110 Principal component analysis applied to power spectra of

111 Principal component analysis based on volatile compounds

112 Principal component analysis demonstrated relationships

113 Principal component analysis demonstrated that mothers w

114 Principal component analysis demonstrated that peroxisom

115 Principal component analysis detected distinguishable po

116 Principal component analysis displayed that red wines we

117 A Principal Component Analysis established that nine volat

118 The principal component analysis explained between 88.3% and

119 CT attenuation features including functional principal component analysis features (FPC1 and FPC2) we

120 Analytical methods included principal component analysis for ancestral matching and

121 oney samples were collected and evaluated by principal component analysis from physicochemical analys

122 Principal component analysis has been applied to dimensi

123 Principal component analysis identified a single compone

124 Rotated principal component analysis identified five significant

125 Principal component analysis identified patterns of vari

126 A principal component analysis identified symptom dimensio

127 Principal component analysis identified two latent facto

128 Hierarchical cluster analysis and principal component analysis identified variation in ran

129 The principal component analysis illustrated that controlled

130 mmonly used dimensionality reduction method, Principal Component Analysis in categorizing samples fro

131 the identifiability parameter by including a principal component analysis in the comparison of functi

132 We used principal component analysis in this cohort to control f

133 Principal component analysis indicates that the resistom

134 Principal component analysis is used to highlight the re

135 Based on principal component analysis it could be concluded that

136 Conclusion Denoising with Marchenko-Pastur principal component analysis led to higher task correlat

137 In a principal component analysis of 19 LCVs, the first princ

138 A Principal Component Analysis of all expressed genes was

139 Furthermore, spectral principal component analysis of amino acid fragment peak

140 Principal component analysis of behavioural scores confi

141 SPG indices based on subsurface density and principal component analysis of sea surface height varia

142 clinical groups, agglomerative cluster, and principal component analysis of semiological features we

143 split based on genetic distance according to principal component analysis of SNP genotypes; and (iii)

144 The principal component analysis of the correlation matrices

145 ling steps or addition of standards, and the principal component analysis of the fragment ion intensi

146 Principal component analysis of the genetic distances, p

147 scores for the proteome shifts observed and principal component analysis of the hypoxia-responsive p

148 Principal component analysis of the model weights reveal

149 binding distributions of the two ligands, a principal component analysis of the spatial distribution

150 ior olive firing dynamics, as measured via a principal component analysis of the spike trains in each

151 Principal component analysis of the volatilome indicated

152 Principal component analysis of the voltammetric fingerp

153 Principal component analysis on all detected metabolites

154 Principal component analysis on the mCRPC plasma methylo

155 We applied principal component analysis on the measures and subsequ

156 A principal component analysis performed on the dataset sh

157 Multivariate chemometric analysis through principal component analysis revealed a discrete distrib

158 Evaluation by principal component analysis revealed clear separation a

159 Principal component analysis revealed clustering of the

160 Principal component analysis revealed gene clusters asso

161 Principal component analysis revealed that flowers occup

162 Principal component analysis revealed that invader popul

163 Principal component analysis revealed that the total rel

164 Principal component analysis reveals that such a bi-pola

165 Principal component analysis score plots and orthogonal

166 Principal component analysis separated HFpEF from HFrEF

167 A Principal Component Analysis showed that beta ionone, be

168 The Principal Component Analysis showed that blueberry group

169 A principal component analysis showed that both types of v

170 A principal component analysis showed that cupper, iron, s

171 Principal component analysis showed that for all six sam

172 Principal component analysis showed that the control and

173 A Principal Component Analysis showed that the samples fro

174 Principal component analysis suggested that TFC is a key

175 of the full object distance in the frame of Principal Component Analysis that can be applied to data

176 Applying principal component analysis to a large neuroimaging dat

177 istical feature extraction was combined with principal component analysis to analyze pairs of two-pho

178 We then used functional principal component analysis to derive the time-varying

179 We used principal component analysis to describe countries using

180 We applied varimax-rotated principal component analysis to explore the underlying s

181 w method that combines biological motion and principal component analysis to gradually mesh amputee a

182 innati, Ohio, we used k-means clustering and principal component analysis to investigate whether part

183 aggregated across multiple timescales using Principal Component Analysis to reduce data dimensionali

184 he overall regulatory capacity by applying a principal component analysis to such variability.

185 In a deductive approach, we performed principal component analysis to summarize 47 proteins kn

186 Principal component analysis was implemented to describe

187 Nonsupervised principal component analysis was performed for all nasal

188 Principal component analysis was performed on registered

189 Principal component analysis was used for data explorati

190 A principal component analysis was used on each imaging mo

191 For each site, principal component analysis was used to calculate both

192 Principal component analysis was used to correlate the b

193 Principal component analysis was used to identify the ch

194 nt results when the correlation analysis and principal component analysis were conducted on the unmod

195 Principal component analysis with autosomal SNPs separat

196 Principal component analysis with rotation and factor ex

197 Multivariate, K-mean and PCA (principal component analysis) for solvent*extract yield

198 y using a multivariate data-driven approach (principal component analysis) on an extensive neuropsych

199 Protein expression changes were evaluated by principal component analysis, 1-way ANOVA (significant p

200 Dietary patterns were derived by principal component analysis, based on 27 food groups sh

201 ractility transient parameters, coupled with principal component analysis, enabled the classification

202 uster combines logistic regression modeling, principal component analysis, hierarchical clustering an

203 mages and the MSI data specifically, such as principal component analysis, independent component anal

204 multi-dimensional dataset was explored using principal component analysis, k-means, and hierarchical

205 lso can be applied to other data types (e.g. principal component analysis, multi-dimensional scaling)

206 Principal component analysis, multilocus genotype assign

207 nd analyzed with multivariate data analysis [principal component analysis, orthogonal projections to

208 ernating least squares, MCR-ALS, followed by principal component analysis, PCA, and partial least squ

209 Coupled with principal component analysis, the spectral biomarkers th

210 cluding neural networks, random forests, and principal component analysis, using a toy model with pro

211 the inflammatory markers using probabilistic principal component analysis, we observed that glutamine

212 real one, two hierarchical clustering and a principal component analysis, were performed.

213 laining most of the variance, as assessed by Principal Component Analysis, which we interpret as a me

214 Principal component analysis-based outlier detection ana

215 By means of statistical analysis (principal component analysis-linear discriminant analysi

216 ctorial-ANOVA, response surface analysis and Principal Component Analysis.

217 was performed by using L1 regularization and principal component analysis.

218 derive two statistical shape models using a principal component analysis.

219 nsion reduction was done for the features by principal component analysis.

220 y distinguished from the remaining juices by principal component analysis.

221 ere the dominant variables (r > 0.80) in the principal component analysis.

222 e to traditional exploratory methods such as principal components analysis (PCA) and hierarchical clu

223 Based on Principal Components Analysis (PCA) and Hierarchical Clu

224 Principal Components Analysis (PCA) of flavour volatile

225 Principal components analysis (PCA) produced three clear

226 Principal components analysis (PCA) was used to characte

227 tract was extracted using an application of principal components analysis (PCA), and we demonstrate

228 termine whether dietary patterns, derived by principal components analysis (PCA), are associated with

229 ity indices (HVIs), commonly developed using principal components analysis (PCA), are mapped to ident

230 V . a/Q . , and shunt were identified using principal components analysis and multiple linear regres

231 Principal components analysis followed by unsupervised k

232 ink between typical learning performance and principal components analysis in single cases.

233 Geometric morphometric methods and principal components analysis were used to extract indep

234 Statistical learning methods (elastic nets/principal components analysis) and Cox regression genera

235 ine) is also demonstrated through the use of principal components analysis, a multivariate technique,

236 By means of principal components analysis, esters were related to th

237 of true associations detected as compared to principal components analysis, non-negative matrix facto

238 ere subjected to an analysis of variance and principal components analysis.

239 a data-driven scaled subprofile model (SSM)/principal-component analysis (PCA) identifying spatial c

240 Voxel-based principal-component analysis allows for an identificatio

241 Using principal-component analysis on the binding potentials i

242 We first employ principal-component analysis to stratify the exploration

243 Importantly, protein activities-based principal-component-analysis multivariate clusters analy

244 Principal component and hierarchical cluster analysis re

245 Combination of principal component and linear discriminant analysis and

246 The strength of associations between atlas principal components and cardiovascular risk factors (sm

247 (including weights for death/graft-failure), principal components and combined donor-recipient PRS, w

248 cal flow estimation, descriptive statistics, principal component, and independent component analyses

249 We demonstrated that those who used the principal component-based visual feedback improved their

250 Principal components calculated from rare variants or id

251 abnormal cardiac structure/function and with principal components/clusters of inflammation proteins.

252 cies projected from reference genotypes onto principal component coordinates.

253 Discriminant analysis of principal components (DAPC) revealed that a large propor

254 od with UV-vis detection in association with Principal Component (Data) Analysis for craft beer class

255 Cannabis sativa and its principal components, Delta9-tetrahydrocannabinol (Delta

256 The extracted principal components demonstrated that the major gait de

257 mass index, smoking status, and the first 5 principal components derived from genotypic data.

258 association on 85 single food intake and 85 principal component-derived dietary patterns from food f

259 logistic regression model, comprising of 12 principal components, explained > 65% of the variance, a

260 Principal component factor analysis was used to generate

261 Hierarchical clustering on principal components identified inflammatory clusters.

262 ion analyses were adjusted for age, sex, and principal components in a linear regression model.

263 The principal components involved in these processes are the

264 Principal components linear discriminant analysis (PC-LD

265 Simple projection (SP) based on principal component loadings and the recently developed

266 onnectome elements corresponded closely with principal component loadings reflecting connectome-wide

267 For the latter, we introduce 'annotation principal components', multidimensional summaries of in

268 hese data identify TLR-activated PMos as the principal component of an intravascular process that con

269 k, we showed that this domain of uL10 is the principal component of binding to GCN2; however, the con

270 Golgi to the plasma membrane, whereas VCP-a principal component of endoplasmic reticulum (ER)-associ

271 psid inhibitor GS-6207 is an investigational principal component of long-acting antiretroviral therap

272 Dental enamel is a principal component of teeth(1), and has evolved to bear

273 urce-based "inflammetry" was used to extract principal components of [(11)C]PK11195 PET signal varian

274 rum concentrations adjusted for age, sex and principal components of ancestry were analyzed.

275 egression to model BPH risk as a function of principal components of ancestry, age, and imputed genot

276 ts model adjusting for age, sex, the first 4 principal components of ancestry, empirical relationship

277 h CHIP, adjusted for age, race, the first 10 principal components of ancestry, smoking, diabetes, and

278 ture is recent, it cannot be corrected using principal components of common variants because they are

279 y to replicate and segregate TR DNA, the two principal components of episome persistence, suggesting

280 of high-frequency (130 GHz) D-band EPR, the principal components of the g tensors were determined.

281 he main contributor to methylation variance (principal component one, or PC1) was strongly correlated

282 theoretical interpretations of the tropical principal components over the space of phylogenetic tree

283 from Apulo-Calabrese had higher scores along Principal Component (PC) 2 (P-value = 4.07 x 10(-5)) and

284 A principal component (PC) analysis was performed on 52 in

285 The first 5 principal components (PC) of the PCA explained 65% of va

286 pal component analysis of 19 LCVs, the first principal component (PC1) explained 27.7% of the total v

287 d off linear relationships between different principal components (PCs) and the percentages of these

288 The fusion of the mineral features with the principal components (PCs) obtained from PCA provided cl

289 pe proportion, DNAm-derived negative control principal components (PCs), and genotype-derived PCs.

290 We simplified immune indices into three principal components (PCs), but we explored mechanistic

291 riables (age, sex, T stage, N stage) and top principal components (PCs), with logistic regression cla

292 on, serum biomarkers, and strength using 3DO principal components (PCs).

293 Principal component regression (PCR) analysis of the EEM

294 Also, partial least squares regression and principal component regression (R(2) = 0.99) were applie

295 lap in both individual trait QTL and QTL for principal component scores (PCA QTL), may have been crit

296 The largest principal component separated anterior insula manifestat

297 Regression analysis on the principal components showed that country-level variation

298 gene sets associated with individual sparse principal components (SPCs) are also reported, showing t

299 ortions, smoking status, and the first three principal components to correct for population stratific

300 The capability of the principal components to discriminate between phenotypes