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

1 ysis (Kruskal-Wallis test, cluster analysis, principal component analysis).

2 hical clustering, phylogenetic analysis, and principal component analysis.

3 triplex detection also being confirmed using principal component analysis.

4 he results obtained by FTIR-ATR coupled with principal component analysis.

5 of neuronal responses were identified using principal component analysis.

6 with configurational entropy calculation and principal component analysis.

7 confirmed by separation of the samples using principal component analysis.

8 hilia as second most important identifier in principal component analysis.

9 derive periodontal complex traits (PCTs) via principal component analysis.

10 epresented a discriminating feature of LN in principal component analysis.

11 ger "co-aging" than other tissues based on a principal component analysis.

12 alyzing agent-based simulation results using principal component analysis.

13 nd a global cognition score was derived from principal component analysis.

14 mation on genetic ancestry was derived using principal component analysis.

15 ains) dietary patterns were identified using principal component analysis.

16 in the loading patterns as observed through principal component analysis.

17 forward stepwise regression, the lasso, and principal components analysis.

18 uit/low-fat dairy," "desserts/sweets") using principal components analysis.

19 than 200 organic ions from these samples and principal component analysis allowed clear separation of

20 ation of the multielemental composition with principal component analysis allowed to discriminate the

21 cluding content analysis of social media and principal components analysis analysis of data sites dis

22 Both Principal Component Analysis and Bayesian clustering app

23 The chemical data was examined by principal component analysis and cluster analysis, revea

24 Offline principal component analysis and discrimination of the l

25 d dimensions of apathy and impulsivity using principal component analysis and employed these in volum

26 ene expression profiling analysis, including principal component analysis and hierarchical clustering

27 Principal component analysis and multivariate regression

28 Principal component analysis and sensitivity analysis ou

29 On the basis of a principal component analysis and subsequent target testi

30 Principal component analysis and unsupervised hierarchic

31 ture of the New Caledonian crow's bill using Principal Components Analysis and Computed Tomography wi

32 c origin of extra virgin olive oils based on principal components analysis and discriminant analysis

33 Our novel integration of principal components analysis and hierarchical clusterin

34 Principal components analysis and linear model selection

35 n monomer-dimer mixtures were analysed using Principal Components Analysis and Multiple Regression to

36 man microscopy combined chemometrics of PCA (Principal Component Analysis) and HCA (Hierarchical Clus

37 Treatment of the data with unsupervised (Principal Component Analysis) and supervised (Partial Le

38 the temporal information based on functional principal component analysis, and disentangles the effec

39 n 1.5 hr, included loading data, annotation, principal component analysis, and single variant and rar

40 ure of grey matter volume by graph-Laplacian principal component analysis, and then fitted a linear m

41 Principal component analysis applied on FT-IR spectral d

42 ted by linear discriminant analysis based on principal component analysis applied to SFS recorded wit

43 Principal component analysis applied to the chromatograp

44 Exploratory principal component analysis applied to the UV-vis spect

45 Nutrient patterns were derived from principal component analysis based on energy-adjusted in

46 Olive oils were clearly classified by principal component analysis based on fatty acid and TAG

47 MANCIE uses a Bayesian-supported principal component analysis-based approach to adjust th

48 First, we show how principal component analysis can be utilized to estimate

49 The Principal Component Analysis carried out with these arom

50 Applying principal component analysis, change-point analysis and

51 Different statistical approaches (ANOVA, Principal Components Analysis, Cluster Analysis) have be

52 Principal-component analysis clustered patient samples i

53 ave employed Molecular Dynamics simulations, Principal Component Analysis, Community Analysis and mea

54 re positively associated with each other and principal component analysis confirmed that one generali

55 A principal component analysis confirmed the importance of

56 Principal component analysis demonstrated a significant

57 Principal component analysis demonstrated clearly which

58 Principal component analysis demonstrated significant di

59 The unsupervised principal components analysis demonstrated a high propor

60 response inhibition, and relationships with principal component analysis derived impulsivity-related

61 Five principal components analysis-derived factors were signi

62 cle we show that univariate and multivariate principal component analysis-discriminant analysis (PCA-

63 Principal Component Analysis distributed heat treated ca

64 dimensionality reduction technique, demixed principal component analysis (dPCA), that decomposes pop

65 Principal component analysis effectively distinguished S

66 The principal component analysis, factor analysis and discri

67 ed in a linked workflow involving non-linear principal component analysis followed by hypothesis test

68 in identifying the spectral biomarkers, and principal component analysis followed by linear discrimi

69 Analogy (SIMCA), k-Nearest Neighbor (k-NN), Principal Component Analysis followed by Linear Discrimi

70 pulation heterogeneity was assessed by using principal component analysis, followed by unsupervised k

71 sis (e.g., differential expression analysis, principal component analysis, gene ontology analysis, an

72 on analysis, interactive heatmap production, principal component analysis, gene ontology analysis, an

73 nds were scored for analyses of dendrograms, principal component analysis, genetic diversity, allele

74 alyzed using pattern recognition techniques, principal component analysis, hierarchic cluster analysi

75 A preliminary principal component analysis highlighted the dominant ef

76 Principal component analysis identified 3 major anatomic

77 Principal component analysis identified an immune signat

78 Principal component analysis identified five groups, and

79 hophysical metrics (precision and accuracy), principal component analysis (in the analysis of spatial

80 and by employing a new adaptive generalized principal components analysis, incorporated modulated ph

81 Principal component analysis indicated positive relation

82 Principal component analysis indicated that the producti

83 Principal component analysis indicated the appropriate s

84 identified as the most effective elicitor by principal component analysis, induced a significant incr

85 An unsupervised principal component analysis integrating iDSA IgG subcla

86 We include principal component analysis into an automated empirical

87 of multivariate analysis techniques, such as principal component analysis-inverse least-squares (PCA-

88 The spatial Principal Component Analysis is designed to investigate

89 In a principal component analysis, LADA patients overlapping

90 TR-FTIR) or Raman spectroscopy combined with principal component analysis-linear discriminant analysi

91 localization of lipids and proteins by using principal component analysis-linear discriminant analysi

92 Principal component analysis loaded 24 questions into 6

93 DeltaF signals analyzed by principal component analysis matched the virtually scree

94 Eigenstrat uses a principal component analysis method to model all sources

95 y multivariate analysis techniques including principal component analysis, non-negative matrix factor

96 r without CO2 pressure is only achieved by a principal component analysis of 15 selected minor compou

97 The first principal component derived by principal component analysis of 27 individual fatty acid

98 tmaps of the differentially expressed genes; principal component analysis of all signatures; enrichme

99 Here, we used principal component analysis of available GRK and protei

100 Principal component analysis of data on sera from patien

101 loop, we incorporated motion modes based on principal component analysis of existing crystal structu

102 Principal component analysis of headspace volatiles reve

103 lective domain motions are identified by the principal component analysis of MD trajectories and redo

104 enerated a progression score on the basis of principal component analysis of prospectively acquired l

105 Principal component analysis of recycled and reloaded ca

106 The Principal Component Analysis of six enological parameter

107 Principal component analysis of sugar, organic acid, and

108 (up to 100 times, for drug delivery) and the principal component analysis of the fluorescence respons

109 edly biased away from calcium signaling, and principal component analysis of the full data set reveal

110 Principal component analysis of the movements of the rec

111 Principal component analysis of the resulting data set c

112 However, brain-wide voxel-based principal component analysis of the same data set reveal

113 Principal Component Analysis of the semi-quantitative da

114 Principal component analysis of the transcriptomes showe

115 A principal component analysis of the volumetric BMD and b

116 The Principal Component Analysis of the whole dataset pointe

117 Principal component analysis of time-domain THz signal (

118 interrelate yield components are measured by principal components analysis of contour point sets.

119 vivors, we used a data-driven approach using principal components analysis of lesion-symptom mapping

120 A principal components analysis of symptoms presented at i

121 ent interactions, and the origin of these, a principal components analysis of the datasets found no s

122 Principal components analysis of the performance measure

123 identification of batch effects is aided by principal components analysis of these metrics.

124 e chemometric analysis (cluster analysis and principal component analysis) of the chromatographic dat

125 Principal component analysis on 38 GroEL experimental st

126 Principal component analysis on the transcriptomes of th

127 the airway immune profile was up-regulated (principal component analysis, P = .04).

128 Principal component analysis (PCA) allowed for spatial d

129 Principal component analysis (PCA) and analysis of varia

130 Principal component analysis (PCA) and cluster analysis

131 We introduce ReFACTor, a method based on principal component analysis (PCA) and designed for the

132 30 samples were analyzed, and then principal component analysis (PCA) and hierarchical clus

133 Principal component analysis (PCA) and hierarchical clus

134 nsionality reduction methods as well, robust Principal Component Analysis (PCA) and Isomap, to genera

135 ervised multivariate data analysis including principal component analysis (PCA) and k-means clusterin

136 ograph-mass spectrometry were analysed using principal component analysis (PCA) and linear discrimina

137 ce spectroscopy was used in combination with principal component analysis (PCA) and linear discrimina

138 rumental work and implement quality control, principal component analysis (PCA) and linear discrimina

139 l multivariate curve resolution method (CR), principal component analysis (PCA) and linear discrimina

140 different multivariate analysis techniques, principal component analysis (PCA) and multivariate curv

141 Principal Component Analysis (PCA) and neural networks (

142 t developmental stages were discriminated by principal component analysis (PCA) and orthogonal partia

143 s revealed by multivariate statistics, i.e., principal component analysis (PCA) and partial least squ

144 Further principal component analysis (PCA) and partial least squ

145 s between 400 and 2500nm were analysed using principal component analysis (PCA) and quality attribute

146 mpared: (i) hyperspectral CARS combined with principal component analysis (PCA) and SFG imaging and (

147 The combination of principal component analysis (PCA) and two-dimensional (

148 Principal component analysis (PCA) applied to physico-ch

149 ing molecular descriptors and identified the principal component analysis (PCA) as the best approach.

150 Principal component analysis (PCA) classified samples ac

151 Principal component analysis (PCA) clearly defined the i

152 Principal component analysis (PCA) confirmed decreased b

153 Pattern recognition with chemometrics using principal component analysis (PCA) demonstrated an excel

154 A complementary approach is to apply principal component analysis (PCA) directly to the matri

155 Principal component analysis (PCA) discovers patterns in

156 Principal component analysis (PCA) facilitated visualiza

157 t, a multivariate analysis was applied using principal component analysis (PCA) followed by a linear

158 tical procedure was used to compare samples: principal component analysis (PCA) followed by linear di

159 ods such as hierarchical clustering (HC) and principal component analysis (PCA) have been used to ide

160 Clustering analysis by Principal Component Analysis (PCA) identified two geneti

161 We have used Principal Component Analysis (PCA) in combination with a

162 The resulting XRD spectra were subjected to principal component analysis (PCA) in order to determine

163 Principal component analysis (PCA) indicated that only (

164 ly suitable for long-term recording by using principal component analysis (PCA) instead of fluorescen

165 Principal component analysis (PCA) is a crucial step in

166 Sparse principal component analysis (PCA) is a popular tool for

167 An exploratory principal component analysis (PCA) model showed a reason

168 The principal component analysis (PCA) of the acquired data

169 Principal component analysis (PCA) of the processed LC-M

170 Principal component analysis (PCA) on the collected fluo

171 Initially, principal component analysis (PCA) revealed clear differ

172 Receiver operating characteristics (ROC) and principal component analysis (PCA) revealed neutrophil r

173 ensus clustering showed unstable subtype and principal component analysis (PCA) showed a continuous s

174 Hierarchical clustering and Principal Component Analysis (PCA) showed an influence o

175 Principal component analysis (PCA) showed clear clusteri

176 Principal component analysis (PCA) showed that fresh pro

177 The potential of intrinsic fluorescence and principal component analysis (PCA) to characterize the a

178 We used principal component analysis (PCA) to decompose the arra

179 volatiles previously reported) were used in Principal Component Analysis (PCA) to determine variable

180 ial Dynamics (ED) is a common application of principal component analysis (PCA) to extract biological

181 ngerprints were then analyzed by exploratory principal component analysis (PCA) to extract informatio

182 ultiple comparison issues, we initially used principal component analysis (PCA) to identify major pat

183 ) of 0.53 +/- 0.03 comparatively a CBIR with Principal Component Analysis (PCA) to learn a low dimens

184 noninterpreted, complex 2D NMR spectra using principal component analysis (PCA) to reveal the largest

185 Here, we applied principal component analysis (PCA) to trial-averaged neu

186 A principal component analysis (PCA) using these genes fur

187 Principal Component Analysis (PCA) was also applied to t

188 When principal component analysis (PCA) was applied, high cor

189 Principal component analysis (PCA) was employed in clust

190 nance (NMR) spectroscopy in combination with principal component analysis (PCA) was employed to chara

191 CE combined with principal component analysis (PCA) was used for classifi

192 Principal component analysis (PCA) was used to evaluate

193 tools, such as analysis of variance (ANOVA), principal component analysis (PCA), and ANOVA simultaneo

194 Principal component analysis (PCA), Bayesian model based

195 ed to evaluation using multifactor ANOVA and principal component analysis (PCA), both showing that ly

196 Through the application of Principal Component Analysis (PCA), derivative voltammog

197 The results were evaluated employing Principal Component Analysis (PCA), Hierarchical Cluster

198 Principal component analysis (PCA), homozygosity rate es

199 Principal component analysis (PCA), least squares-suppor

200 Principal component analysis (PCA), linear discriminant

201 iate analysis (for example, memory efficient principal component analysis (PCA), non-negative matrix

202 e statistical analysis techniques, including principal component analysis (PCA), principal component-

203 these genes/isoforms to HCC are supported by principal component analysis (PCA), read coverage visual

204 On principal component analysis (PCA), stool and saliva mic

205 hrough three-pattern recognition techniques: principal component analysis (PCA), support vector machi

206 tivariate analysis of variance (MANOVA), the principal component analysis (PCA), the generalizing est

207 According to Principal Component Analysis (PCA), the inoculation sequ

208 After the preliminary examination by principal component analysis (PCA), three supervised pat

209 ise the base ciders, and data analysed using principal component analysis (PCA).

210 nical RNA secondary structure motifs through principal component analysis (PCA).

211 estimated from binding isotherms obtained by principal component analysis (PCA).

212 Potential relationships were studied by principal component analysis (PCA).

213 ly resembles neural networks used to perform Principal Component Analysis (PCA).

214 investigated by the statistical approach of principal component analysis (PCA).

215 valuated by analysis of variance (ANOVA) and principal component analysis (PCA).

216 ass cytometry data analysis tools, including principal component analysis (PCA); spanning-tree progre

217 re are several batch evaluation methods like principal component analysis (PCA; mostly based on visua

218 using either model-free algorithms, such as principal components analysis (PCA) and multidimensional

219 Principal components analysis (PCA) applied to LLME/GC-q

220 Furthermore, data analysis using image principal components analysis (PCA) showed that methylph

221 Principal components analysis (PCA) was applied to the i

222 prove melissopalynological routine analysis, principal components analysis (PCA) was used.

223 Boxplot and principal components analysis (PCA) were performed for c

224 ies of the questionnaire were analysed using principal components analysis (PCA).

225 e chemometric methods of analysis, including principal-component analysis (PCA) and partial least-squ

226 sed using multivariate statistical analysis (Principal Component Analysis, PCA) to evaluate chemical

227 Principal component analysis permitted an overview of th

228 Through principal component analysis, precursor and successor so

229 Twelve meal types were identified from the principal component analysis ranging in meal-type inclus

230 cemia as demonstrated by T-wave symmetry and principal component analysis ratio compared with control

231 Principal component analysis resolved samples from diffe

232 Principal component analysis revealed clustering of gene

233 Principal component analysis revealed insights into meta

234 Principal component analysis revealed significant relati

235 Principal component analysis revealed that a fraction of

236 Principal component analysis revealed that flavones and

237 Principal component analysis revealed that the cytokine

238 Principal component analysis revealed two separate phase

239 Principal components analysis revealed distinct clusteri

240 Principal components analysis revealed distinct differen

241 Principal-component analysis revealed key cell- and acti

242 In characterizing mRNA expression using principal component analysis, S100 calcium-binding prote

243 Principal component analysis showed a clear separation a

244 Principal component analysis showed a clear separation o

245 A principal component analysis showed a relationship betwe

246 Principal component analysis showed a serum amino acid s

247 Principal component analysis showed discrete clustering

248 PCA (Principal Component Analysis) showed that there were no

249 -individual variability was observed through principal component analysis, showing that some vegetari

250 The Principal Component Analysis shown that the groups of co

251 rs, local least squares regression, Bayesian principal components analysis, singular value decomposit

252 Principal component analysis suggested that volume of La

253 ies, obtained from phylogenetically informed principal component analysis: the fast-slow and reproduc

254 ion methods for face recognition, we applied principal component analysis to a large set of face imag

255 iroxicam using THz spectroscopy and employed Principal Component Analysis to build similarity maps in

256 We use a principal component analysis to describe individual vari

257 We used principal component analysis to determine a composite co

258 to phytochemical content and sensory data in Principal Component Analysis to determine compounds infl

259 We performed a principal component analysis to extract major dimensions

260 mpowerment present in most surveys, and used principal component analysis to extract the components.

261 We applied demixed principal components analysis to define kinematics syner

262 inematics and vice versa, we applied demixed principal components analysis to define kinematics syner

263 Collected data were analyzed by principal-component analysis to identify whether there i

264 developed a robust in vitro assay that uses principal-component analysis to integrate multidimension

265 One applies a novel time series CPCA (common principal components analysis) to generate scores for ge

266 based on the transmission matrix method and principal component analysis, to realize a broadband and

267 By application of clustering algorithms and principal component analysis visible homogenous clusters

268 Finally, principal component analysis was applied to a single dat

269 Principal component analysis was applied to assess the e

270 Principal component analysis was performed to investigat

271 Principal component analysis was used to capture the ove

272 A principal component analysis was used to derive 2 altern

273 Principal component analysis was used to identify the su

274 Principal component analysis was used to interpret the c

275 Principal component analysis was used to investigate pat

276 Principal component analysis was used to perform an over

277 Principal component analysis was used to reduce features

278 Principal components analysis was performed to distingui

279 Principal components analysis was used for data reductio

280 At baseline, principal components analysis was used to derive factor

281 Food groups were created, and principal components analysis was used to develop "healt

282 Supervised principal components analysis was used to identify patte

283 Principal Components Analysis was used to select a sub-s

284 The protein profile, as revealed by principal component analysis, was variable among the thr

285 ing longitudinal profiles, sparse functional principal components analysis, was used to classify pati

286 Combining quantitative NMR spectroscopy with principal component analysis we have identified and quan

287 bining spatial autocorrelation detection and principal component analysis, we could remove most of th

288 Using a principal component analysis, we found a new metric of c

289 Using a principal component analysis, we found that robust MHC c

290 Using principal component analysis, we identified 255 molecula

291 Through a collective principal component analysis, we identify sequence-depen

292 y combined with gravimetric measurements and principal component analysis, we observe that significan

293 By lineage-guided principal components analysis, we uncover novel relatedn

294 Through principal-component-analysis, we demonstrated highly ove

295 trait data into the analysis), 2 papers used principal components analysis, weighted gene coexpressio

296 e scores derived with the expert opinion and principal component analysis weighting schemes (Pearson

297 Clustering methods and principal component analysis were applied in mouse to an

298 stimated from a composite index derived from principal component analysis, which included bilirubin l

299 Wines were distinguished by principal components analysis, which was carried out usi

300 ology was used, based on the combined use of principal component analysis with discriminant analysis