ライフサイエンスコーパス: PCA

1 PCA (Principal Component Analysis) showed that there wer

2 PCA allowed an acceptable separation but some sausage ty

3 PCA analysis of wine revealed association of young jamun

4 PCA analysis showed separation of arabica and robusta.

5 PCA analysis was performed and presented the potential f

6 PCA and cluster analysis were performed in order to exam

7 PCA and DLB showed overlapping patterns of hypometabolis

8 PCA and IBS were used in a mixed linear model of capsaic

9 PCA and Kohonen self-organizing maps showed the formatio

10 PCA and LDA confirmed the differences in the volatile pr

11 PCA identified a DEHP component and a non-DEHP component

12 PCA indicated that samples could be clustered according

13 PCA obviates the customary focus on specific peaks or re

14 PCA of the fluorescence EEMs revealed two principal comp

15 PCA plot proved the potential of reproducibility of anal

16 PCA results also showed two different affection levels w

17 PCA revealed a separation of VOC profiles according to t

18 PCA revealed strong, positive correlations between gluco

19 PCA score plot based on both HPLC and UV spectroscopy sh

20 PCA was applied to delineate the provenance of samples a

21 PCA-2 was often accompanied by additional neural autoant

22 d Purkinje cell cytoplasmic antibody type 2 (PCA-2) antibody, its frequency, and clinical, oncologica

23 d was discharged on the 91th day of life (39 PCA) with a recommendation of permanent neurological sur

24 Finally, a PCA analysis revealed that VOCs profile significantly co

25 Thus grouping of the variables in a PCA plot indicated that each cultivar has specific chara

26 The safety of percutaneous carotid access (PCA) in children is unknown.

27 distribution of phenazine-1-carboxylic acid (PCA) throughout the colony, with 5-methylphenazine-1-car

28 and the yellow phenazine-1-carboxylic acid (PCA).

29 ntents of FBPs (pyrrolidone carboxylic acid [PCA] and urocanic acid [UCA]) using UPLC-MS/MS, transepi

30 platelet clearance on procoagulant activity (PCA) in sepsis.

31 , a product complex analogue AK:pAIE:Mg.ADP (PCA), and the transition state analogue AK:Arg:Mg.ADP:NO

32 g 45 clinical variables (Model 1), and after PCA dimensionality reduction (Model 2).

33 anding of genetic contribution to aggressive PCA, exploring clinical use of genetic testing for PCA m

34 All PCA subjects showed beta-amyloid deposition on PET scann

35 By lateral orbitotomy, all PCAs were cut behind the eyeball in both groups of anima

36 regression and principal component analyses (PCAs).

37 Further principle component analyses(PCA) of RRS simulations agree with the observation of di

38 Principal component analysis (PCA) allowed for spatial differentiation between areas.

39 Principal component analysis (PCA) and analysis of variance (ANOVA) were performed to

40 Principal component analysis (PCA) and cluster analysis were performed in order to exa

41 ethod based on principal component analysis (PCA) and designed for the correction of cell type hetero

42 Principal component analysis (PCA) and hierarchical cluster analysis (HCA) distinguish

43 yzed, and then principal component analysis (PCA) and hierarchical clustering analysis (HCA) were uti

44 s well, robust Principal Component Analysis (PCA) and Isomap, to generate a reduced representation of

45 ysis including principal component analysis (PCA) and k-means clustering was utilized to investigate

46 mbination with principal component analysis (PCA) and linear discriminant analysis (LDA) for the diff

47 n method (CR), principal component analysis (PCA) and linear discriminant analysis (LDA) is proposed

48 ality control, principal component analysis (PCA) and linear discriminant analysis (LDA) were perform

49 analysed using principal component analysis (PCA) and linear discriminant analysis (LDA).

50 re examined by principle component analysis (PCA) and linear discriminant analysis (LDA).

51 is techniques, principal component analysis (PCA) and multivariate curve resolution (MCR), were perfo

52 Principal Component Analysis (PCA) and neural networks (NN) have been used to analyze

53 scriminated by principal component analysis (PCA) and orthogonal partial least squares discriminant a

54 tistics, i.e., principal component analysis (PCA) and partial least squares discriminant analysis (PL

55 Further principal component analysis (PCA) and partial least squares-discrimination analysis (

56 sis, including principal-component analysis (PCA) and partial least-squares discriminant analysis (PL

57 combined with principal component analysis (PCA) and SFG imaging and (ii) simultaneous narrowband CA

58 Principal component analysis (PCA) applied to physico-chemical and instrumental data s

59 identified the principal component analysis (PCA) as the best approach.

60 Principal component analysis (PCA) classified samples according to their levels of enz

61 Principal component analysis (PCA) clearly defined the independent roles of the zincat

62 Principal component analysis (PCA) confirmed decreased bitter and beany off-flavors of

63 ometrics using principal component analysis (PCA) demonstrated an excellent separation between contro

64 ch is to apply principal component analysis (PCA) directly to the matrix of spectral data, correlatin

65 Principal component analysis (PCA) discovers patterns in multivariate data that includ

66 applied using principal component analysis (PCA) followed by a linear discriminant analysis (LDA).

67 mpare samples: principal component analysis (PCA) followed by linear discriminant analysis (LDA).

68 ering (HC) and principal component analysis (PCA) have been used to identify asthma subtypes, with in

69 ng analysis by Principal Component Analysis (PCA) identified two genetic clusters among Iranian popul

70 e subjected to principal component analysis (PCA) in order to determine if there were any distinguish

71 Principal component analysis (PCA) indicated that only (1)H NMR mobility parameters we

72 rding by using principal component analysis (PCA) instead of fluorescence recording system to avoid t

73 Principal component analysis (PCA) is a crucial step in quality control of genomic dat

74 Sparse principal component analysis (PCA) is a popular tool for dimensionality reduction, pat

75 entified using principle component analysis (PCA) method, and discrimination rate of milk and whey po

76 An exploratory principal component analysis (PCA) model showed a reasonable discrimination of the cou

77 Principal component analysis (PCA) of the processed LC-MS data demonstrated the varyin

78 Initially, principal component analysis (PCA) revealed clear differences between saffron cultivat

79 ic profiles by principle component analysis (PCA) revealed metabolic variety of carrot root compositi

80 tics (ROC) and principal component analysis (PCA) revealed neutrophil rolling as an important functio

81 le subtype and principal component analysis (PCA) showed a continuous spectrum both within and betwee

82 Principal component analysis (PCA) showed clear clustering of samples (p0.05).

83 Principal component analysis (PCA) showed that fresh product possessed a metabolic con

84 uorescence and principal component analysis (PCA) to characterize the antioxidant capacity of soy pro

85 We used principal component analysis (PCA) to decompose the array's responses, and found that

86 ) were used in Principal Component Analysis (PCA) to determine variables statistically important to s

87 application of principal component analysis (PCA) to extract biologically relevant motions from atomi

88 by exploratory principal component analysis (PCA) to extract information of the most significant vari

89 initially used principal component analysis (PCA) to identify major patterns of natural log (ln)-tran

90 ly a CBIR with Principal Component Analysis (PCA) to learn a low dimensional space yielded an AUPRC o

91 spectra using principal component analysis (PCA) to reveal the largest trend(s) across the series.

92 re, we applied principal component analysis (PCA) to trial-averaged neural responses in macaque prima

93 A principal component analysis (PCA) using these genes further confirmed the most optima

94 Principal Component Analysis (PCA) was also applied to the LIBS data for rapid categor

95 Principal component analysis (PCA) was employed in cluster analysis to capture data pa

96 mbination with principal component analysis (PCA) was employed to characterize dry-fermented sausages

97 combined with principal component analysis (PCA) was used for classification of samples untreated or

98 Principal component analysis (PCA) was used to evaluate interrelationships between the

99 iance (ANOVA), principal component analysis (PCA), and ANOVA simultaneous component analysis (ASCA),

100 Principal component analysis (PCA), Bayesian model based population structure analysis

101 ctor ANOVA and principal component analysis (PCA), both showing that lyophilization pretreatment affe

102 application of Principal Component Analysis (PCA), derivative voltammograms were used to discriminate

103 ated employing Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA) and Linear Dis

104 Principal component analysis (PCA), homozygosity rate estimations, and linkage studies

105 Principal component analysis (PCA), least squares-support vector machines (LS-SVM) and

106 Principal component analysis (PCA), linear discriminant analysis (LDA), k-nearest neig

107 mory efficient principal component analysis (PCA), non-negative matrix factorization (NMF), maximum a

108 ues, including principal component analysis (PCA), principal component-discriminant function analysis

109 e supported by principal component analysis (PCA), read coverage visualization, and the biological li

110 on techniques: principal component analysis (PCA), support vector machines (SVMs) and hierarchical cl

111 (MANOVA), the principal component analysis (PCA), the generalizing estimating equations (GEE), the t

112 According to Principal Component Analysis (PCA), the inoculation sequence (co-inoculation and seque

113 examination by principal component analysis (PCA), three supervised pattern recognition techniques, P

114 analysed using principal component analysis (PCA).

115 motifs through principal component analysis (PCA).

116 ms obtained by principal component analysis (PCA).

117 al cluster and principle component analysis (PCA).

118 ere studied by principal component analysis (PCA).

119 sed to perform Principal Component Analysis (PCA).

120 valuated using principle component analysis (PCA).

121 statistics and principle component analysis (PCA).

122 ols, including principal component analysis (PCA); spanning-tree progression analysis of density-norm

123 n methods like principal component analysis (PCA; mostly based on visual inspection), and sometimes t

124 tric methods (principle components analysis (PCA) and hierarchical cluster analysis (HCA)).

125 thms, such as principal components analysis (PCA) and multidimensional scaling, or using explicit spa

126 Principal components analysis (PCA) applied to LLME/GC-qMS data allowed to distinguish

127 Principal components analysis (PCA) was applied to the instrumental and sensory analysi

128 Principle components analysis (PCA) was used to confirm separation of the eight sources

129 Boxplot and principal components analysis (PCA) were performed for clusters identification and outl

130 nalysed using principal components analysis (PCA).

131 confirmed by Principal Composition Analysis (PCA), which grouped the black and green tea samples into

132 UHPLC/TOF-HRMS, multivariate data analysis (PCA, PLS-DA) and metabolomic strategies; the OHC fractio

133 al component analysis-discriminant analysis (PCA-DA) statistics applied to the combined (1)H NMR and

134 is followed by Linear Discriminant Analysis (PCA-LDA) and Partial Least Squares-Discriminant Analysis

135 is followed by linear discriminant analysis (PCA-LDA) was used for the multivariate analysis of the e

136 data treatment using unsupervised analysis (PCA) proved useful to classify peach juices on the basis

137 ical analysis (Principal Component Analysis, PCA) to evaluate chemical differences between each roast

138 n ANNA-2 (also known as anti-Ri; 0.016%) and PCA-Tr (also known as delta/notch-like epidermal growth

139 ent between the axis orientations of ACA and PCA in KC patients (k = 0.077, P < .001), but not in the

140 ACA and PCA magnitude, M, J0, J45, and blur significantly increa

141 The mean magnitude of the ACA and PCA was 4.49 +/- 2.16 diopter (D) and 0.90 +/- 0.43 D, r

142 ominant Abeta40 fibril structure in t-AD and PCA-AD, suggest that r-AD may relate to additional fibri

143 ndant in samples from patients with t-AD and PCA-AD, whereas Abeta40 fibrils from r-AD samples exhibi

144 Hierarchical cluster analysis and PCA principle component analysis techniques were employe

145 Tentative cluster and PCA analyses using the compositional coordinates in the

146 were statistically evaluated by cluster and PCA analysis.

147 CONCISE and PCA of NMR spectra have discovered novel states of a wel

148 For comparison, KNN, CART, SIMCA, PLS-DA and PCA-LDA were also used.

149 late island sign was present in both DLB and PCA, although it was more asymmetric in PCA.

150 AD lesions had lower FBP and PCA contents compared to NC skin (both P < 0.001), and h

151 een place cell to grid cell interactions and PCA.

152 SPA-LDA and PCA-LDA provided significantly better results for tea cl

153 of LNs in patients with LC, MM, GEP NET, and PCA correlated with(18)F-FDG uptake, (68)Ga-DOTATOC upta

154 examination of cheminformatic parameters and PCA loading factors revealed trends in aminoglycoside:RN

155 tients; GEP NET: 217 LNs of 35 patients; and PCA: 254 LNs of 40 patients).

156 oral lobe, and caudate nucleus than PCA, and PCA showed more asymmetric patterns of hypometabolism th

157 erentiation between normal prostate (PN) and PCA.

158 squares-support vector machines (LS-SVM) and PCA-back propagation neural network (PCA-BPNN) models wi

159 Applying PCA to time-domain NMR data also yields binding isotherm

160 Applying PCA-LDA to NIR spectra (5500-6000 cm(-1)), the total cor

161 otherms constructed conventionally, applying PCA directly to these spectra along with Pareto scaling

162 c damage following posterior ciliary artery (PCA) occlusion in old, atherosclerotic, hypertensive mon

163 other classical analysis techniques such as PCA and SIMPLISMA.

164 oncerted effort to create a Precancer Atlas (PCA), integrating multi-omics and immunity - basic tenet

165 Posterior cortical atrophy (PCA) and dementia with Lewy bodies (DLB) have both been

166 amnestic AD, 12 posterior cortical atrophy (PCA), 12 logopenic primary progressive aphasia (lvPPA),

167 Covariance and correlation based PCA are two common approaches to determine PCA modes (ei

168 ng with conventional visual inspection-based PCA, are available as a part of an R package exploring b

169 etabolism overlaps to a large degree between PCA and DLB, although the degree of involvement of the f

170 etabolism overlaps to a large degree between PCA and DLB, although the degree of involvement of the f

171 r the cingulate island sign differed between PCA and DLB.

172 Both PCA and PN were stained with monoclonal anti-PSMA antibo

173 Samples (n=29) were analyzed by PCA originating five groups, especially according to eth

174 ed at 96h, these behaviors were confirmed by PCA analyses.

175 ployment periods were also differentiated by PCA, reflecting seasonal variations in chemical profiles

176 unflower) have been clearly discriminated by PCA analysis.

177 tzky-Golay derivative procedure, followed by PCA exploratory analysis, which showed that NIR spectra

178 e classified into several distinct groups by PCA analysis; this grouping pattern reflects origin and

179 trate how spectral variability identified by PCA may be correlated to structure.

180 othesis testing and multivariate modeling by PCA and partial PLS-DA on the Workflow4Metabolomics infr

181 d overtreatment of indolent prostate cancer (PCA) is a serious health issue in most developed countri

182 rine tumors (GEP NETs), and prostate cancer (PCA), lymph node (LN) staging is often performed by (18)

183 ted for genetic testing for prostate cancer (PCA).

184 in staging and restaging of prostate cancer (PCA).

185 The results show that clustering (PCA and PLS-DA) patterns of soybean metabolites were sig

186 ns were available from 96 of 118 consecutive PCA-2-IgG-seropositive patients identified during 1993-2

187 d prostate-specific membrane antigen PET/CT (PCA) but is sometimes not accurate because of indetermin

188 d PCA are two common approaches to determine PCA modes (eigenvectors) and their eigenvalues.

189 Methods: Sixteen clinically diagnosed PCA and 13 probable DLB subjects underwent (18)F-FDG PET

190 Sixteen clinically diagnosed PCA and 13 probable DLB subjects underwent (18)F-FDG PET

191 latelets by approximately 2-fold, diminished PCA by 70%, prolonged coagulation time, and attenuated f

192 Further, with the established PCA-tdTHz, we evaluated the severity of myelin deficit l

193 Two patients had failed PCA with no sequelae.

194 developing a working definition of familial PCA for clinical genetic testing, expanding understandin

195 -induced vasoconstriction in male and female PCAs, respectively.

196 ivity and specificity, and that of 0.4 D for PCA had 89.5% sensitivity and 85.0% specificity for disc

197 ent of collecting separate training data for PCA-ILS model construction increases experimental comple

198 netic evaluation and testing men at risk for PCA-a clinically heterogeneous disease.

199 xploring clinical use of genetic testing for PCA management, genetic testing of African American male

200 appropriate QC filters, WES can be used for PCA and adjustment for population substructure, estimati

201 phosphosugar-enzyme associations emerge from PCA comparisons of the titrations.

202 It can: 1, generate PCA scores that identify ancestral origins and can be us

203 nsus to test HOXB13 for suspected hereditary PCA, BRCA1/2 for suspected hereditary breast and ovarian

204 , respectively, was optimized using a hybrid PCA-RSM approach.

205 oping an integrated multi-omics and immunity PCA - an immense national resource to interrogate, targe

206 rdiagnosis/treatment highlights an important PCA theme.

207 In PCA sections (n = 31), median IRS was 3, median staining

208 and PCA, although it was more asymmetric in PCA.

209 ol cells, but not with ADMA-treated cells in PCA model.

210 he prevalence of limb apraxia was highest in PCA, amnestic AD, lvPPA and nfvPPA.

211 The mean SUVmax was significantly lower in PCA samples with fewer than 50% stained cells (n = 30; 2

212 ibit a significant bradycardia, reduction in PCA and an increase in ventilatory amplitude (VAMP) with

213 Eligible participants included PCA-free men, 50 years or older, scheduled for an initia

214 a genetic evaluation framework for inherited PCA in the multigene testing era.

215 omprehensive genetic evaluation of inherited PCA in the multigene testing era addressing genetic coun

216 ong consensus to factor BRCA2 mutations into PCA screening discussions.

217 All intraprostatic PCA lesions on PET could be confirmed histopathologicall

218 intravenous patient-controlled analgesia (IV-PCA) for pain control over the first 48 hours after hepa

219 The multivariate methods like PCA, PLS-DA and PLS regression were used for interpretat

220 The MAP1B (PCA-2) autoantibody detection rate, among approximately

221 ctrochemical methods to directly detect 5-Me-PCA and find that it is transported by MexGHI-OpmD in P.

222 We also show that 5-Me-PCA is sufficient to fully induce MexGHI-OpmD expression

223 ediate 5-methylphenazine-1-carboxylate (5-Me-PCA), a reactive compound that has eluded detection in m

224 istent with the high redox potential of 5-Me-PCA, which distinguishes it from other well-studied P. a

225 eral metrics that quantify protein mobility, PCA modes with their eigenvalues, and displacement vecto

226 Applied to multifocal PCA, it resulted in sensitivity and specificity of 87% a

227 ated with sensitization, but multiparametric PCA suggested a specific inflammatory response among sen

228 VM) and PCA-back propagation neural network (PCA-BPNN) models with the first and second derivative an

229 Direct application of PCA to crowded spectra, images, and movies (without sele

230 The first two axes of PCA and PLS-DA describes more than 40% of variances with

231 ng Raman microscopy combined chemometrics of PCA (Principal Component Analysis) and HCA (Hierarchical

232 ginosa phenazine biosynthesis, conversion of PCA to pyocyanin is presumed to proceed through the inte

233 on in primary PCA, enabling the detection of PCA with a high sensitivity and specificity.

234 Retrospective review of PCA at Texas Children's Hospital and Children's Healthca

235 ere was no correlation between the SUVmax of PCA and Gleason score (P = 0.54).

236 The mean SUVmax of PCA and PN for an IRS of less than 2 (n = 26; 2.52 +/- 0

237 aracteristic curve analyses of the SUVmax of PCA, validated by immunohistochemical staining in 62 tis

238 properties were preferred by the panelists (PCA, R(2)X(1)=0.7) while buckwheat and cloudberry-bog ho

239 t FlashPCA2, a tool that can perform partial PCA on 1 million individuals faster than competing appro

240 ingerprint region) was determined to perform PCA and HCA analysis.

241 imination between compounds while performing PCA and also improved the prediction accuracy by 34% whe

242 d a biphasic caudal arterial blood pressure (PCA) response that are in direct conflict with the typic

243 ignificantly with PSMA expression in primary PCA, enabling the detection of PCA with a high sensitivi

244 as engineered to accumulate protocatechuate (PCA) as a soluble intermediate compound.

245 all men with metastatic castration-resistant PCA, regardless of family history, with stronger agreeme

246 Results: PCA and DLB showed overlapping patterns of hypometabolis

247 omponent analysis of time-domain THz signal (PCA-tdTHz) and absorption-refractive index relation of T

248 Since PCA and PC-DFA are categorical classifiers, PLSR modelin

249 es suggest that, compared to existing sparse PCA methods, the proposed methods achieve higher sensiti

250 sparse PCA methods Fused and Grouped sparse PCA can effectively incorporate prior biological informa

251 PCA methods called Fused and Grouped sparse PCA that enable incorporation of prior biological inform

252 In this article, we propose two new sparse PCA methods called Fused and Grouped sparse PCA that ena

253 The proposed sparse PCA methods Fused and Grouped sparse PCA can effectively

254 al component analysis-inverse least-squares (PCA-ILS), has become standard for signal isolation from

255 Of the 45 catheterizations with successful PCA and sheath placement, 44 interventions were performe

256 Sulfurreducens PCA but enhance Hg methylation by D. desulfuricans ND132

257 y decreased Hg sorption by G. sulfurreducens PCA but showed little effect on D. desulfuricans ND132 c

258 -reducing bacterium Geobacter sulfurreducens PCA and a sulfate-reducing bacterium Desulfovibrio desul

259 anaerobic bacteria Geobacter sulfurreducens PCA and Desulfovibrio desulfuricans ND132.

260 s to address genetic counseling and testing, PCA screening, and management informed by evidence revie

261 rior temporal lobe, and caudate nucleus than PCA, and PCA showed more asymmetric patterns of hypometa

262 It was demonstrated that PCA analysis may help to determine the authenticity of o

263 d the pathological analysis, indicating that PCA-tdTHz is a quick, powerful, evolving tool for identi

264 The PCA extracted from the engineered Tobacco was further co

265 The PCA resulted in a questionnaire with three domains (or s

266 The PCA was then externally validated using biologically rel

267 In addition, the PCA-MLR analysis confirmed that the industrial sections;

268 Additionally the PCA derived scores can be applied to multiple data sets

269 were able to inhibit fungal amylase, and the PCA analysis confirmed that the relation between the chl

270 ported by the classification provided by the PCA analysis.

271 This was supported by the PCA showing a highly significant difference between chil

272 Considering the PCA-LDA applied to the SFS of all samples, Czech, Hungar

273 ormal participants (P < .001), while for the PCA it was WTR in KC patients and ATR in normal particip

274 ction abilities (96.7%) was obtained for the PCA-LDA.

275 with volatiles discriminating 'SH-5' in the PCA and PLS-DA, respectively.

276 INTERPRETATION: MAP1B, the PCA-2 autoantigen, represents a novel target in paraneop

277 in two main groups using the results of the PCA and discovered some strong signal to define some loc

278 Here we demonstrate the performance of the PCA method for discriminating structural variation among

279 gher affinity to sorb or take up Hg than the PCA strain.

280 poxia were similar to control fish while the PCA response to hypoxia was further inhibited.

281 frequency > 2% (correlation = 0.89 with the PCA coordinates obtained by GWSA).

282 Cutting all the PCAs resulted in the development of ischemic infarction

283 chemic damage following occlusion of all the PCAs was similar in both the young healthy and the old,

284 hy, before and immediately after cutting the PCAs and serially thereafter during the follow-up period

285 in fundus angiography soon after cutting the PCAs showed no filling of the entire choroid and the opt

286 According to PCA analyses, phenolic acids characterized cold break to

287 n limited work on extending this approach to PCA.

288 ere no neurological sequelae attributable to PCA.

289 As compared to PCA-ILS, which relies on explicit user definition of com

290 ing spatial ancestry coordinates compared to PCA.

291 rve as a useful supplement or replacement to PCA-ILS for signal isolation from FSCV data.

292 that MCR-ALS can produce similar results to PCA-ILS and may serve as a useful supplement or replacem

293 shown to be a better approach to traditional PCA-based correction.

294 Untargeted PCA and OPLS-DA demonstrated clear classifications in th

295 and the posterior cortical atrophy variant (PCA-AD)-with a typical prolonged-duration form (t-AD).

296 underwent 47 attempts at catheterization via PCA.

297 The SERS spectra when combined with PCA and PC-DFA demonstrated clear differentiation of nea

298 e mathematical dispersion model coupled with PCA-LDA showed high similarity to the designed microbiot

299 ment in LWE samples could be determined with PCA of the CE measurements.

300 was also developed to classify samples with PCA and PLS-DA.