ライフサイエンスコーパス: principal component

1 ures of transmission intensity and the first principal component.

2 n the timing of feeding was explained by two principal components.

3 etwork analyses and discriminant analysis of principal components.

4 unsupervised k-means cluster analysis of the principal components.

5 PPV) or challenge, corrected for ancestry by principal components.

6 analysis was used to reduce features to 8-12 principal components.

7 lysis, adjusted for confounders, showed that principal component 1, mainly loaded with interleukin-6,

8 esses detected by NMR are easily captured by principal components 1 and 2.

9 The majority of variation (first functional principal component, 94%) among patient profiles was cha

10 pes were tested with logistic regression and principal component analyses (PCAs).

11 Principal component analyses and analyses of variance we

12 We measured this relationship using principal component analyses and methylation-mutation as

13 The quasi-harmonic and principal component analyses of simulations without rigi

14 Cox proportional hazards regression and principal component analyses were also performed.

15 With the use of principal component analyses, the multidimensionality of

16 l/regenerating BCP cells in multidimensional principal component analyses.

17 five major clusters based on dendrogram and principal component analyses.

18 Dietary patterns were assessed by using principal components analyses.Among 18,763 adults, 1 hea

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

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

21 Principal component analysis (PCA) allowed for spatial d

22 Principal component analysis (PCA) and analysis of varia

23 Principal component analysis (PCA) and cluster analysis

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

25 Principal component analysis (PCA) and hierarchical clus

26 30 samples were analyzed, and then principal component analysis (PCA) and hierarchical clus

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

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

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

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

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

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

33 Principal Component Analysis (PCA) and neural networks (

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

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

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

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

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

39 Principal component analysis (PCA) classified samples ac

40 Principal component analysis (PCA) confirmed decreased b

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

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

43 Principal component analysis (PCA) discovers patterns in

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

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

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

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

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

49 Principal component analysis (PCA) indicated that only (

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

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

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

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

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

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

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

57 Principal component analysis (PCA) showed clear clusteri

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

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

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

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

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

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

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

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

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

67 Principal component analysis (PCA) was used to evaluate

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

69 Principal component analysis (PCA), Bayesian model based

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

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

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

73 Principal component analysis (PCA), linear discriminant

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

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

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

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

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

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

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

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

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

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

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

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

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

87 Both Principal Component Analysis and Bayesian clustering app

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

89 Offline principal component analysis and discrimination of the l

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

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

92 Principal component analysis and multivariate regression

93 Principal component analysis and sensitivity analysis ou

94 Principal component analysis and unsupervised hierarchic

95 Principal component analysis applied on FT-IR spectral d

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

97 Principal component analysis applied to the chromatograp

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

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

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

101 The Principal Component Analysis carried out with these arom

102 A principal component analysis confirmed the importance of

103 Principal component analysis demonstrated a significant

104 Principal component analysis demonstrated clearly which

105 Principal component analysis demonstrated significant di

106 Principal Component Analysis distributed heat treated ca

107 Principal component analysis effectively distinguished S

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

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

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

111 A preliminary principal component analysis highlighted the dominant ef

112 Principal component analysis identified 3 major anatomic

113 Principal component analysis indicated that the producti

114 Principal component analysis indicated the appropriate s

115 We include principal component analysis into an automated empirical

116 The spatial Principal Component Analysis is designed to investigate

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

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

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

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

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

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

123 Principal component analysis of data on sera from patien

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

125 Principal component analysis of headspace volatiles reve

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

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

128 Principal component analysis of recycled and reloaded ca

129 Principal component analysis of sugar, organic acid, and

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

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

132 Principal component analysis of the movements of the rec

133 Principal component analysis of the resulting data set c

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

135 Principal Component Analysis of the semi-quantitative da

136 A principal component analysis of the volumetric BMD and b

137 The Principal Component Analysis of the whole dataset pointe

138 Principal component analysis of time-domain THz signal (

139 Principal component analysis on 38 GroEL experimental st

140 Principal component analysis on the transcriptomes of th

141 Principal component analysis permitted an overview of th

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

143 Principal component analysis resolved samples from diffe

144 Principal component analysis revealed clustering of gene

145 Principal component analysis revealed that a fraction of

146 Principal component analysis revealed that flavones and

147 Principal component analysis revealed two separate phase

148 Principal component analysis showed a clear separation a

149 Principal component analysis showed a clear separation o

150 Principal component analysis showed a serum amino acid s

151 Principal component analysis showed discrete clustering

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

153 We use a principal component analysis to describe individual vari

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

155 We performed a principal component analysis to extract major dimensions

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

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

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

159 Principal component analysis was performed to investigat

160 A principal component analysis was used to derive 2 altern

161 Principal component analysis was used to identify the su

162 Principal component analysis was used to perform an over

163 Principal component analysis was used to reduce features

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

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

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

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

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

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

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

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

172 Applying principal component analysis, change-point analysis and

173 The principal component analysis, factor analysis and discri

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

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

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

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

178 In a principal component analysis, LADA patients overlapping

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

180 Through principal component analysis, precursor and successor so

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

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

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

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

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

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

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

188 Using principal component analysis, we identified 255 molecula

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

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

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

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

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

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

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

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

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

198 triplex detection also being confirmed using principal component analysis.

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

200 of neuronal responses were identified using principal component analysis.

201 with configurational entropy calculation and principal component analysis.

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

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

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

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

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

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

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

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

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

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

212 Our novel integration of principal components analysis and hierarchical clusterin

213 Principal components analysis and linear model selection

214 The unsupervised principal components analysis demonstrated a high propor

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

216 A principal components analysis of symptoms presented at i

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

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

219 Principal components analysis revealed distinct clusteri

220 Principal components analysis revealed distinct differen

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

222 We applied demixed principal components analysis to define kinematics syner

223 Principal components analysis was performed to distingui

224 Principal components analysis was used for data reductio

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

226 Supervised principal components analysis was used to identify patte

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

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

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

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

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

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

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

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

235 Principal-component analysis revealed key cell- and acti

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

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

238 Principal component and cluster analyses of ROI volumes

239 Also, using principal component and cluster analyses, we determined

240 evaluate batch effect based on probabilistic principal component and covariates analysis (PPCCA).

241 By using these intensity variations, principal component and discriminant analysis were perfo

242 Principal component and functional analyses grouped the

243 analyzed using chemometrics methods such as principal component and hierarchical clustering analyses

244 the optimal cold plasma treatment parameters principal component and sensitivity analysis were used.

245 Principal components and common factor analysis were use

246 ed counterparts as observed from analysis of principal components and hierarchical clustering sample

247 es, as demonstrated by hierarchical cluster, principal component, and support vector machine analyses

248 In this article, we propose a principal component based nonparametric regression (PC-n

249 PC-Relate uses principal components calculated from genome-screen data

250 ationships with each other and that a single principal component captures around three-quarters of th

251 In 1:1 binding, the first principal component captures the binding isotherm from N

252 The first four principal components covered 92% of the variance in prod

253 d k-means cluster analysis of the 57 largest principal components delivered 4 distinct clusters of pa

254 f their orientations, the magnitude of their principal components (delta11 > delta22 > delta33) and a

255 The first principal component derived by principal component analy

256 ationship was statistically controlled using principal components derived from the gene expression ma

257 Fifty-seven principal components described approximately 90% of the

258 ently represented using only the first three principal components describing 98.29% of total variance

259 yses (adjusted for year of birth, sex, three principal components) examined the association between G

260 se aroma compounds reveal that the first two principal components explain 53.8% and 17.2% of the tota

261 ust unitary factor structure, with the first principal component explaining 30.9% of the variance in

262 Principal components exploratory factor analysis evaluat

263 Principal component factor analysis demonstrated substan

264 or scores in 3D space spanned by these three principal components form a tetrahedral-like arrangement

265 The principal components in the spectra influencing soil hea

266 ved feature selection and more interpretable principal component loadings and potentially providing i

267 representational subspaces of FFA: the first principal component of FFA shows differential connectivi

268 The complement cascade is a principal component of innate immunity.

269 rticularly Zn and Mn, and Zn and Cd, and the principal component of metals differed by stratum of hig

270 e endoplasmic reticular calcium sensor and a principal component of SOCE in the nervous system, alter

271 th lineages and, at the same time, acts as a principal component of the hematopoietic niche by promot

272 Tar DNA binding protein 43 (TDP-43) is the principal component of ubiquitinated protein inclusions

273 ere each cluster branch is associated with a principal component of variation that can be used to dif

274 djusted for age, sex, recruitment site, five principal components of ancestry and additional features

275 justed for age, sex, season, study site, and principal components of ancestry.

276 of genetic variants with arsenic species and principal components of arsenic species in the Strong He

277 nificant for all percent arsenic species and principal components of arsenic species.

278 Strongly anisotropic media where the principal components of electric permittivity or magneti

279 d alpha10 subunits equally contribute to the principal components of the alpha9alpha10 nAChR.

280 kappaB dimers can be found by extracting the principal components of the fluctuations in Cartesian co

281 related metabolites, with the use of either principal components or pathways, revealed coordinated m

282 ostic interaction, the chemical shift tensor principal components orientation (delta22 or delta33 par

283 the spectra were described by the first two principal components (PC).

284 PCA of the fluorescence EEMs revealed two principal components (PC1-tryptophan, PC2-tyrosine) that

285 troduce a method that infers selection using principal components (PCs) by identifying variants whose

286 m magnetic resonance imaging, TREND resolves principal components (PCs) representing breathing and th

287 imultaneously estimated population-structure principal components (PCs) robust to familial relatednes

288 Then, 21 principal components (PCs) were selected in five sets.

289 equencies using multidimensional scaling and principal component plots, supported by an analysis of m

290 he acetylcholine binding site, composed of a principal component provided by one subunit and a comple

291 Partial Least Squares Regression (PLSR), and Principal Component Regression (PCR) were used as the ca

292 Principal component regression analysis, adjusted for co

293 l, and procedure covariates using supervised principal components regression.

294 As%, MMA%, and DMA% (rs12768205) and for the principal components (rs3740394, rs3740393) were located

295 Principal component scores based on measures of intra-po

296 Principal components showed different evolution with age

297 fried oils using hierarchical clustering on principal component space.

298 distinguishing linear arrangement along the principal component that expressed the variation in lipi

299 We created two orthogonal principal components that summarized iAs, MMA, and DMA a

300 A hierarchical clustering on principal components was also done for neurological and