ライフサイエンスコーパス: principal component analysis

1 procedures used in glycomics, including PCA (principal components analysis).

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

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

4 thern, and alcohol/salads) were derived from principal component analysis.

5 ng and defining both genera was supported by Principal Component Analysis.

6 on exhibited a similar peptide expression by principal component analysis.

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

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

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

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

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

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

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

14 The first two dimensions of the Principal component analysis accounted for 57% of the va

15 A combined principal component analysis across the two aetiologies

16 The rice mineral profile evaluated by principal component analysis allowed the identification

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

18 A principal component analysis along with k-means clusteri

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

20 equations using two machine learning models (principal component analysis and a convolutional neural

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

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

23 patterns among sites by cluster analysis and principal component analysis and grouped the pPLA into b

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

25 Principal component analysis and hierarchical clustering

26 Multipollutant analysis using principal component analysis and hierarchical clustering

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

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

29 Oblique principal component analysis and point biserial correlat

30 Principal component analysis and Spearman correlations i

31 A principal component analysis and Spearman's rank correla

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

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

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

35 using a constrained combination approach and principal component analysis, and did a network analysis

36 rared spectromicroscopy, factorial analysis, principal component analysis, and stepwise-cluster analy

37 Principal component analysis applied to power spectra of

38 81 vs 0.80, respectively; P = .76) or RA and principal component analysis (AUC, 0.78 vs 0.78, respect

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

40 Principal component analysis based on volatile compounds

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

42 Principal component analysis-based outlier detection ana

43 The charts, which were constructed using principal component analysis, can only be used to identi

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

45 Principal component analysis coupled with linear discrim

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

47 Principal component analysis demonstrated relationships

48 Principal component analysis demonstrated that each text

49 Principal component analysis demonstrated that mothers w

50 Principal component analysis demonstrated that peroxisom

51 Principal component analysis detected distinguishable po

52 Based on a Principal Component Analysis different migration pattern

53 Principal component analysis displayed that red wines we

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

55 A Principal Component Analysis established that nine volat

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

57 Principal component analysis explained 81.8% of the tota

58 The principal component analysis explained between 88.3% and

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

60 Data were analyzed using principal component analysis followed by machine learnin

61 Principal components analysis followed by unsupervised k

62 Analytical methods included principal component analysis for ancestral matching and

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

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

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

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

67 Principal component analysis grouped the single cells in

68 Principal component analysis has been applied to dimensi

69 Principal component analysis has shown some tendencies t

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

71 spectra was performed using Independent and Principal Components Analysis (ICA, PCA) as well as Orth

72 Principal component analysis identified a single compone

73 Rotated principal component analysis identified five significant

74 Principal component analysis identified patterns of vari

75 A principal component analysis identified symptom dimensio

76 Principal component analysis identified two dimensions o

77 Principal component analysis identified two latent facto

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

79 A unique population structure using principal component analysis illustrated clear distincti

80 The principal component analysis illustrated that controlled

81 Principal component analysis illustrated that the aroma

82 The principal component analysis illustrated the colour valu

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

84 pon strategies from analysis of variance and principal component analysis in order to reduce dimensio

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

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

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

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

89 Principal component analysis indicated that MCC under ma

90 Principal component analysis indicates that the resistom

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

92 Principal Component Analysis is a key tool in the study

93 Principal component analysis is used to highlight the re

94 Based on principal component analysis it could be concluded that

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

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

97 al-world biospectroscopic applications using principal component analysis linear discriminant analysi

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

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

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

101 Principal component analysis, multilocus genotype assign

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

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

104 ression/gradient tree boosting with a sparse principal component analysis/non-negative matrix factori

105 Principal component analysis of 12 subscales taken from

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

107 A Principal Component Analysis of all expressed genes was

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

109 Principal component analysis of behavioural scores confi

110 domized Subspace Iteration method to perform Principal Component Analysis of large-scale datasets.

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

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

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

114 The principal component analysis of the correlation matrices

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

116 Principal component analysis of the genetic distances, p

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

118 Principal Component Analysis of the measured spectra is

119 Principal component analysis of the model weights reveal

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

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

122 Principal component analysis of the volatilome indicated

123 Principal component analysis of the voltammetric fingerp

124 cipal component score of the graph-Laplacian Principal Component Analysis on 112 grey matter region-o

125 Principal component analysis on all detected metabolites

126 Principal component analysis on the mCRPC plasma methylo

127 We applied principal component analysis on the measures and subsequ

128 Using principal-component analysis on the binding potentials i

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

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

131 analysis (HCA) (OPUS Version 7.2 software), principal component analysis (PCA) (OPUS Version 7.2 sof

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

133 pression signature (BPGES) was derived using principal component analysis (PCA) and evaluated for ass

134 Principal component analysis (PCA) and explanatory facto

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

136 Principal component analysis (PCA) and hierarchical clus

137 applied on this spectral data set including principal component analysis (PCA) and hierarchical clus

138 Application of principal component analysis (PCA) and hierarchical clus

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

140 Principal component analysis (PCA) and linear discrimina

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

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

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

144 Principal component analysis (PCA) and orthogonal partia

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

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

147 Principal component analysis (PCA) and partial least squ

148 Principal component analysis (PCA) and partial least-squ

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

150 re processed using two statistical methods - principal component analysis (PCA) and self-organized ma

151 Principal component analysis (PCA) and supervised partia

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

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

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

155 q, and unsupervised clustering combined with Principal Component Analysis (PCA) can be used to overco

156 Principal component analysis (PCA) determined that the g

157 Principal component analysis (PCA) dimensionality reduct

158 Two principal component analysis (PCA) dimensions summarised

159 Principal component analysis (PCA) discriminated the mor

160 Principal component analysis (PCA) extracted distinct co

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

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

163 Principal component analysis (PCA) including phenolics a

164 Principal component analysis (PCA) indicated a clear dis

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

166 Principal component analysis (PCA) is a standard method

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

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

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

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

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

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

173 Principal component analysis (PCA) of the tissue samples

174 Principal component analysis (PCA) projection of the inp

175 Principal component analysis (PCA) revealed clear discri

176 Principal component analysis (PCA) revealed correlations

177 Principal component analysis (PCA) revealed that some of

178 Principal component analysis (PCA) separated SD from HP

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

180 as determined by multivariate statistics and principal component analysis (PCA) showed that hydroxybe

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

182 Application of Principal Component Analysis (PCA) to experimental data

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

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

185 Principal component analysis (PCA) was employed for imag

186 Principal component analysis (PCA) was employed to creat

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

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

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

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

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

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

193 h as hierarchical cluster analysis (HCA) and principal component analysis (PCA) were successfully app

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

195 Principal component analysis (PCA) with promax rotation

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

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

198 ng multivariate, discriminant analysis (DA), principal component analysis (PCA), and cluster analysis

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

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

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

202 Principal component analysis (PCA), hierarchical cluster

203 ivariate statistical analysis (MVSA), namely Principal Component Analysis (PCA), Hierarchical Cluster

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

205 analysis, heatmap, clustering, biclustering, Principal Component Analysis (PCA), Multidimensional Sca

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

207 Principal component analysis (PCA), partial least square

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

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

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

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

212 lymphoma samples and compare it to those of principal component analysis (PCA), t-distributed stocha

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

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

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

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

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

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

219 Dietary patterns were identified with principal component analysis (PCA).

220 n-6 PUFAs were analyzed using SD scores and principal component analysis (PCA).

221 mpeting methods, including the commonly used principal component analysis (PCA).

222 es were accompanied by distinct outcome from principal component analysis (PCA).

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

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

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

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

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

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

229 as achieved, analyzing the experimental with principal components analysis (PCA) demonstrating that H

230 Principal Components Analysis (PCA) of flavour volatile

231 Principal components analysis (PCA) produced three clear

232 Principal components analysis (PCA) was used to characte

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

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

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

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

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

238 A principal component analysis performed on the dataset sh

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

240 Evaluation by principal component analysis revealed clear separation a

241 Principal component analysis revealed clustering of the

242 Principal component analysis revealed gene clusters asso

243 Principal component analysis revealed that flowers occup

244 Principal component analysis revealed that invader popul

245 Principal component analysis revealed that the total rel

246 Principal component analysis reveals that such a bi-pola

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

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

249 Principal component analysis score plots and orthogonal

250 Principal component analysis separated HFpEF from HFrEF

251 cans, and local IV curves reconstructed from principal component analysis show minimal hysteresis of

252 Principal component analysis showed a significant relati

253 A Principal Component Analysis showed that beta ionone, be

254 The Principal Component Analysis showed that blueberry group

255 A principal component analysis showed that both types of v

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

257 Principal component analysis showed that for all six sam

258 Principal Component Analysis showed that similarities in

259 Principal component analysis showed that the control and

260 A Principal Component Analysis showed that the samples fro

261 y preprocessing, MICE imputation, and sparse principal component analysis (SPCA) to improve the usabi

262 Principal component analysis suggested that program cult

263 Principal component analysis suggested that TFC is a key

264 were significantly better represented using principal component analysis than by just counting spike

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

266 Coupled with principal component analysis, the spectral biomarkers th

267 Applying principal component analysis to a large neuroimaging dat

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

269 subjected to PTR-Quadrupole MS (PTR-QMS) and Principal Component Analysis to compare the groups and e

270 Survey items were analyzed using principal component analysis to derive composite measure

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

272 We used principal component analysis to describe countries using

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

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

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

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

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

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

279 ped a novel visual feedback system that uses principal component analysis to weight four features of

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

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

282 Principal component analysis was implemented to describe

283 Nonsupervised principal component analysis was performed for all nasal

284 Principal component analysis was performed on registered

285 Principal component analysis was used for data explorati

286 A principal component analysis was used on each imaging mo

287 Principal component analysis was used to assess the effe

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

289 Principal component analysis was used to correlate the b

290 Principal component analysis was used to identify the ch

291 Using principal component analysis we find that the network dy

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

293 at had more weight in the differentiation by principal component analysis were Castanea, Cytisus type

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

295 Dimension scores from principal component analysis were then correlated with w

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

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

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

299 Principal component analysis with autosomal SNPs separat

300 Principal component analysis with rotation and factor ex