ライフサイエンスコーパス: PCA

1 PCA analysis allowed clear separation of the genotypes b

2 PCA and cluster heatmap analysis highlighted potentials

3 PCA and HCA were applied for natural clustering of the s

4 PCA and hexachlorobenzene comprised at least 63% of all

5 PCA and other phenazines reductively dissolve Fe and Mn

6 PCA model also revealed markers for freshly dried versus

7 PCA models derived from either UHPLC-MS or GC-MS proved

8 PCA of the profiles exhibited a good separation of most

9 PCA only partially identifies population clusters and do

10 PCA was able to classify spectra of normal, early-stage

11 PCA was able to separate wines by their Australian state

12 PCA was applied (explaining 68.43% of the total variance

13 PCA was performed as a multivariate display method on th

14 PCA, together with binding energies measurements and doc

15 PCA-derived factors composed of highly saturated triglyc

16 PCA-derived HVIs address correlation among vulnerability

17 PCA-LDA and PLS-DA were applied, and results showed that

18 PCA-LDA coupled with HSI histograms correctly differenti

19 PCA-PAM50 code is available at ftp://ftp.wriwindber.org/

20 A PCA-based active appearance model was used to capture th

21 2-79 than in rhizospheres inoculated with a PCA-deficient mutant.

22 ater-based ink using pyrene carboxylic acid (PCA) through non-covalent pai-pai stacking interaction b

23 acid (UCA) and pyrrolidone carboxylic acid (PCA), both of which are filaggrin breakdown products; an

24 anthocyanin metabolite protocatechuic acid (PCA) did not alter cytotoxicity of taxanes.

25 additives, particularly protocatechuic acid (PCA), enhance the integration of FL intasomes by prevent

26 dicarboxylic acids and tricarboxylic acids (PCAs).

27 d cells and to assess procoagulant activity (PCA) as a function of the stage of OSCC progression.

28 urvival, and disease-specific survival after PCA.

29 Patient-controlled analgesia (PCA) is an "on-demand" system which allows patients to s

30 clustering and principal component analyses (PCA) were conducted to identify natural clusters in 100

31 determined by principal component analyses (PCAs), and the loading of each spatial component compare

32 7.2 software), principal component analysis (PCA) (OPUS Version 7.2 software) and partial least squar

33 he subject via Principle Component Analysis (PCA) algorithms.

34 We applied principal component analysis (PCA) and Bayesian kernel machine regression (BKMR) to id

35 derived using principal component analysis (PCA) and evaluated for associations with the TNBC phenot

36 Principal component analysis (PCA) and explanatory factor analysis were used to consol

37 p, featurewise principal component analysis (PCA) and groupwise PCA (GPCA) of the matrix (temporal wi

38 Principal component analysis (PCA) and hierarchical cluster analysis (HCA) revealed th

39 Application of principal component analysis (PCA) and hierarchical cluster analysis (HCA) showed a te

40 set including principal component analysis (PCA) and hierarchical cluster analysis (HCA).

41 By using principal component analysis (PCA) and intramolecular angle calculation, this study ob

42 Principal component analysis (PCA) and linear discriminant analysis (LDA) showed bette

43 combination of principal component analysis (PCA) and linear discriminant analysis (LDA), an identifi

44 lysis, such as principal component analysis (PCA) and linear discriminant analysis (LDA), were used t

45 We used principal component analysis (PCA) and multi-dimensional scaling (MDS) to compare netw

46 Principal component analysis (PCA) and orthogonal partial least squares discriminant a

47 together with principal component analysis (PCA) and parallel factor analysis (PARAFAC) performed on

48 hods including principal component analysis (PCA) and partial least squares (PLS) analysis have been

49 Principal component analysis (PCA) and partial least squares discriminant analysis (PL

50 Principal component analysis (PCA) and partial least-squares (PLS) regression was used

51 followed by a principal component analysis (PCA) and pearson correlation analysis.

52 ical methods - principal component analysis (PCA) and self-organized maps (SOM) in order to classify

53 Principal component analysis (PCA) and supervised partial least squares-discriminant a

54 alyses such as principle component analysis (PCA) and t-tests.

55 alternative to principal component analysis (PCA) as a dimension reduction tool that favors direction

56 on, we applied principal component analysis (PCA) as an input to the algorithms to address multidrive

57 odel combining Principal Component Analysis (PCA) as an unsupervised learning method and Linear Discr

58 combined with Principal Component Analysis (PCA) can be used to overcome this limitation.

59 Principal component analysis (PCA) determined that the gene expression profile of Quan

60 Principal component analysis (PCA) dimensionality reduction obtained a 2-dimensional r

61 Two principal component analysis (PCA) dimensions summarised stress tolerance combinations

62 Principle component analysis (PCA) discriminated between blanching treatments while Or

63 Principal component analysis (PCA) discriminated the morphotypes; 50 compounds out of

64 Principal component analysis (PCA) extracted distinct components of general negative a

65 re resolved by principal component analysis (PCA) from all accessible regions by co-varying transcrip

66 estigations by Principal Component Analysis (PCA) highlighted pronounced effect of thermal processing

67 Principle component analysis (PCA) identified safranal and 2-caren-10-al as discrimina

68 le model (SSM)/principal-component analysis (PCA) identifying spatial covariance patterns.

69 Principal component analysis (PCA) including phenolics and volatile compounds suggeste

70 Principal component analysis (PCA) indicated a clear distinction of the PW from the te

71 e of 89.55% in principal component analysis (PCA) indicated high quality of differential expression a

72 Principal component analysis (PCA) is a standard method to correct for population stra

73 Principal component analysis (PCA) is used to quantify cellular response to drug as a

74 Unsupervised Principal Component Analysis (PCA) led to a focused core HOXB13 target gene-set referr

75 y applying the Principal Component Analysis (PCA) machine learning algorithm.

76 ed through the principal component analysis (PCA) method and compared.

77 Principal component analysis (PCA) of 6 measures of cortical morphology and microstruc

78 Principal component analysis (PCA) of the NMR dataset brought further evidence to a sp

79 Principal component analysis (PCA) of the tissue samples showed a clear discrimination

80 Principal component analysis (PCA) projection of the input compounds is also provided,

81 Principal component analysis (PCA) revealed clear discrimination between the two produ

82 Principal component analysis (PCA) revealed correlations between the different regimes

83 Principal component analysis (PCA) revealed that some of the CFC intermediates could c

84 Principal component analysis (PCA) separated SD from HP and TD, based on the concentra

85 of the data by Principal Component Analysis (PCA) showed a separation tendency between the two groups

86 statistics and principal component analysis (PCA) showed that hydroxybenzoic and ferulic acids influe

87 esearchers use principal component analysis (PCA) to derive dietary patterns from food data.

88 Application of Principal Component Analysis (PCA) to experimental data showed a good separation of th

89 he device uses principal component analysis (PCA) to reduce spectral dimensionality (1203 features) a

90 Principal component analysis (PCA) was employed as the statistical tool to differentia

91 Principal component analysis (PCA) was employed for image processing of the data.

92 Principal component analysis (PCA) was employed to create a composite measure of four

93 Principal component analysis (PCA) was used as a very helpful tool to discriminate the

94 Principal component analysis (PCA) was used to assess the effect of co-exposure.

95 Principal component analysis (PCA) was used to assess the overall structure, and parti

96 Initially, principal component analysis (PCA) was used to see if clusters of milk from different

97 iques based on Principal Component Analysis (PCA) were applied to each single technique for the ident

98 orrelation and principal component analysis (PCA) were conducted to reveal the relationships among th

99 ysis (HCA) and principal component analysis (PCA) were successfully applied to analyze the output gen

100 ysis (HCA) and principal component analysis (PCA) were successfully performed for the classification

101 Principal component analysis (PCA) with promax rotation was used to examine possible s

102 By combining principal component analysis (PCA) with Raman spectroscopy and circular dichroism (CD)

103 on of tropical principal component analysis (PCA), a statistical method for visualization and dimensi

104 analysis (DA), principal component analysis (PCA), and cluster analysis (CA).

105 iance (ANOVA), principal component analysis (PCA), and partial least squares analysis (PLS) revealed

106 sis, including principal component analysis (PCA), and partial least squares-discriminant analysis (P

107 on algorithms: Principal Component Analysis (PCA), Genetic Algorithm (GA), and the Successive Project

108 Principal component analysis (PCA), hierarchical cluster analysis (HCA) and partial le

109 (MVSA), namely Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), and Orthogona

110 that included principal component analysis (PCA), manifold learning, and unsupervised clustering to

111 biclustering, Principal Component Analysis (PCA), Multidimensional Scaling (MDS), and t-distributed

112 ses, including principal component analysis (PCA), orthogonal partial least squares discriminant anal

113 Furthermore, Principal component analysis (PCA), soft independent modelling of class analogy (SIMCA

114 e a variant of principal component analysis (PCA), sparse contrastive PCA that extracts sparse, stabl

115 ethods such as Principal Component Analysis (PCA), Support Vector Machine (SVM), and Random Forest (R

116 sualized using principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE

117 it to those of principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE

118 Using the principal component analysis (PCA), the riboflavin and NADH spectra allowed clear diff

119 Using principal component analysis (PCA), the treated cells could be clearly distinguished f

120 is (WGCNA) and principal component analysis (PCA), we characterized common and unique transcriptome s

121 maps based on principal component analysis (PCA), which permitted to monitor and visualise the ripen

122 adjust for PS, principal component analysis (PCA)-based ancestry prediction has been widely used.

123 content using principal component analysis (PCA).

124 dentified with principal component analysis (PCA).

125 SD scores and principal component analysis (PCA).

126 as examined by principal component analysis (PCA).

127 analyzed using principal component analysis (PCA).

128 vented, namely Principal Component Analysis (PCA).

129 variables with principal component analysis (PCA).

130 Based on Principal Components Analysis (PCA) and Hierarchical Cluster Analysis (HCA) of the rela

131 thods such as principal components analysis (PCA) and hierarchical cluster analysis (HCA).

132 rimental with principal components analysis (PCA) demonstrating that HpDNA is a promising molecular b

133 Principal Components Analysis (PCA) of flavour volatile data across 37 laboratory roast

134 Principal components analysis (PCA) produced three clear components: an ACC glutathione

135 Principal components analysis (PCA) was used to characterize trait variation, redundanc

136 pplication of principal components analysis (PCA), and we demonstrate that MR image sequences can be

137 s, derived by principal components analysis (PCA), are associated with KOA progression.

138 veloped using principal components analysis (PCA), are mapped to identify populations vulnerable to e

139 onent analysis linear discriminant analysis (PCA-LDA).

140 S, followed by principal component analysis, PCA, and partial least squares discriminant analysis, PL

141 AD model and passive cutaneous anaphylaxis (PCA) model on VAD and vitamin A supplementation (VAS) mo

142 but showed lower amounts of chlorpyrifos and PCA, suggesting local sources of these compounds to othe

143 ug efflux in CRPC cells, but BRB extract and PCA did not.

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

145 plemented chemometric methods (HCA, PAM, and PCA) done on FTIR spectra collected for four high explos

146 accurately depicts BGA through STRUCTURE and PCA.

147 Antibody titers and PCA concentrations at day 30 were significantly higher w

148 re evaluated using ANOVA, post-hoc Tukey and PCA.

149 s associated with decreased skin cis-UCA and PCA content.

150 on methods (PCA, t-SNE, PCA-t-SNE, UMAP, and PCA-UMAP) to biobank-derived genomic data of a Japanese

151 igh fidelity using only the facial video and PCA.

152 RSV-A and RSV-B neutralizing antibodies and PCAs in healthy, nonpregnant women.

153 Finally, BPCAs and PCAs could contribute to the black carbon and nitrogen i

154 This is the first report of BPCAs and PCAs in water-extractable organic carbon from thermally

155 sitive serum gastric parietal cell antibody (PCA) was 61.8%.

156 76-fold; and palivizumab competing antibody (PCA) concentrations 11.69-, 14.38- and 14.24-fold compar

157 ated sensitivity of HVIs created by applying PCA to input variables and whether training input variab

158 ent study, a Pavlovian conditioned approach (PCA) procedure was used to identify rats that are more (

159 AWC-PCA was associated with reduced treatment failure and a

160 eters plus patient-controlled analgesia (AWC-PCA).

161 While the use of AWC-PCA did not translate into a shorter LOS in this study,

162 ents were randomized 1:1 to either EP or AWC-PCA within an enhanced recovery protocol.

163 was 20% in the EP group versus 7% in the AWC-PCA (P = 0.09).

164 oon of day 1 and morning of day 2 in the AWC-PCA group however were regarded as low at all time point

165 ative anesthetic time was shorter in the AWC-PCA group, 49 minutes versus 62 minutes (P = 0.003).

166 rence was noted in LOS (EP 6 d (3-27) vs AWC-PCA 6 d (3-66), P = 0.886).

167 We implemented voxel-based PCA and standard Statistical Parametric Mapping analysis

168 xed models to assess the association between PCA-derived factor scores in quintiles and serum thyroid

169 ovalent pai-pai stacking interaction between PCA and SWNTs.

170 Both PCA and OPLS analyses suggested the same trends.

171 elected compounds which is projected by both PCA and embedded by t-SNE.

172 romatic herbs under analysis was achieved by PCA.

173 oducibility of the analyses was evaluated by PCA of spectral data.

174 ween the control and contaminated samples by PCA and PLS-DA was also achieved by this workflow using

175 c tissue of 4 patients, and prostate cancer (PCA) lesions of 9 patients were analyzed.

176 GT) is a central feature of prostate cancer (PCA) treatment, management, and hereditary cancer assess

177 Prostate cancer (PCA), one of the most common malignant tumors in men, is

178 Compositional PCA identified characterizing taxa and weightings for ca

179 Although compositional PCA methods are not commonly used to describe patterns f

180 The comprehensive PCA analysis allowed discrimination of drying methods an

181 component analysis (PCA), sparse contrastive PCA that extracts sparse, stable, interpretable and rele

182 Background Percutaneous cryoablation (PCA) is an increasingly utilized treatment for stage I r

183 he hydrophilic channels and the well-defined PCA/SWNTs ink deposition on paper in a facile manner req

184 ntioxidant capacity, dietary pattern-derived PCA factors, and seborrheic dermatitis adjusted for conf

185 k-means clustering of the 2-dimensional PCA representation of 12-lead QRS waveforms identified 2

186 ith variables selection resulted in distinct PCA patterns for each spice.

187 This multidisciplinary, consensus-driven PCA genetic implementation framework provides novel guid

188 Three multivariate statistical models, i.e. PCA/LDA, SELECT/LDA and PLS-DA, were built and relevant

189 two powerful chemometrics approaches (i.e., PCA and PLS-DA) were used to classify samples as either

190 the principal components (PCs) obtained from PCA provided classification rates of 100% in training sa

191 Furthermore, PCA of both the peptidomic and proteomic data has allowe

192 res-Discriminant Analysis modelling of fused PCA scores of three independent models was used and comp

193 responses in a low-dimensional space (e.g., PCA, MCA, or t-SNE).

194 eneralized principal component analysis (GLM-PCA) for non-normal distributions, and feature selection

195 cipal component analysis (PCA) and groupwise PCA (GPCA) of the matrix (temporal wise) were used to an

196 e or family history suggestive of hereditary PCA was recommended for GT.

197 However, PCA revealed a cline of genetic diversity reflecting a w

198 ic abrogated p53 function recapitulate human PCA precursor lesions, histopathology, and clinical beha

199 dent and Principal Components Analysis (ICA, PCA) as well as Orthogonal Partial Least Squares Discrim

200 In PCA model, VAD mice showed remarkable more blue dye leak

201 insomnia where the traditional analgesics in PCA were replaced with dexmedetomidine (Dex), an alpha-2

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

203 Although individual PCA enables to detect the influence of different process

204 IV-PCA was noninferior to TEA for the treatment of postoper

205 multimodal, patient-controlled analgesia (IV-PCA) could be noninferior to multimodal thoracic epidura

206 significantly shorter for patients in the IV-PCA group (74 vs 104 h, P < 0.001).

207 r postoperative days (PODs) 0 to 5 in the IV-PCA group was no worse than the mean pain score in the T

208 rst 3 days was significantly lower in the IV-PCA group.

209 red in STs and GTs seven days after the last PCA training session, because oxytocin is known to modul

210 This method leverages PCA and iterative PAM50 calls to derive the gene express

211 atic testing were recommended for metastatic PCA.

212 We used ordination methods (PCA and NMDS) and tests of multivariate dispersion (PERM

213 e we apply dimensionality reduction methods (PCA, t-SNE, PCA-t-SNE, UMAP, and PCA-UMAP) to biobank-de

214 ionic strength buffer or the small molecule PCA.

215 MP-PCA denoising led to a higher median z score of task-bas

216 could be truncated by at least 40% after MP-PCA without degradation of clinically relevant correlati

217 enko-Pastur principal component analysis (MP-PCA) provides a novel strategy to separate functional MR

218 , histograms of z scores for original and MP-PCA denoised data were extracted from relevant regions a

219 y and Accountability Act-compliant study, MP-PCA performance was first evaluated by using simulated f

220 Purpose To determine whether MP-PCA denoising improves activation magnitude for task-bas

221 cid-modified single-walled carbon nanotubes (PCA/SWNTs) were deposited by quantitative inkjet printin

222 ss are critical for metastatic conversion of PCA and may be ideal targets for therapeutic interventio

223 herin, this resulted in an 80% disruption of PCA.

224 bacterial culture systems, but the impact of PCA upon Fe and Mn cycling in the rhizosphere is unknown

225 determine the 10-year oncologic outcomes of PCA for stage I RCC in a prospective manner.

226 Based on the principles of PCA, we developed Patient-Controlled Sleep (PCSL) for ch

227 t associated with receiving a nerve block or PCA.

228 described an RFV code consistent with a PAMA PCA, up to 22.9% (4681 of 20 506; 95% confidence interva

229 IC flux was dominated by pentachloroanisole (PCA) at Lomonosovfonna (23.5 pg cm(-2) yr(-1)) and Kongs

230 s and of these 20/22 patients had a positive PCA (P = 0.007).

231 zospheres inoculated with the PCA-producing (PCA(+)) strain Pseudomonas synxantha 2-79 than in rhizos

232 a metal center, a 2-pyridinecarbothioamide (PCA), and a hydroxamic acid, which is found in the antic

233 tification scheme combining QRS area and QRS PCA group identified patients with LBBB with similar out

234 mes but had significant differences from QRS PCA groups.

235 aveforms identified 2 patient subgroups (QRS PCA groups).

236 Compared with QRS PCA Group 2 (n=425), Group 1 (n=521) had lower risk for

237 Results: PCA revealed an ADCRP that involved regions with relativ

238 computation cost because it requires running PCA separately for each study sample on the augmented da

239 tative elemental fingerprint of the samples, PCA and LDA were applied.

240 QTL and QTL for principal component scores (PCA QTL), may have been critical for evolutionary diverg

241 P = 0.024) occurred more frequently in serum PCA positive patients.

242 showed higher prevalences of positive serum PCA (P = 0.057), hypergastrinemia (P = 0.062) and female

243 imensionality reduction methods (PCA, t-SNE, PCA-t-SNE, UMAP, and PCA-UMAP) to biobank-derived genomi

244 SSM/PCA pattern expression strengths were tested for their a

245 Conclusion: SSM/PCA-derived binding patterns of (18)F-flortaucipir diffe

246 tern expression strengths in 2 different SSM/PCA components yielded a prediction accuracy of 98%.

247 upported by AMOVA (F(GT) = 0.26), STRUCTURE, PCA, and F(ST) analyses.

248 In this study PCA (first three components explained 81.5% of the datas

249 covariance matrices as a framework to study PCA.

250 thylating bacteria: Geobacter sulfurreducens PCA and Desulfovibrio desulfuricans ND132.

251 Supervised PCA explained 62% of variance in the input variables and

252 y occurring on extreme heat days; supervised PCA could not be applied to block group data.

253 The supervised PCA identified a subset of analytes whose abundance stro

254 eat-related health outcomes; b) "supervised"-PCA applied only to variables significantly correlated w

255 ge III/IV OSCC undergoing resective surgery, PCA was significantly reduced in the postoperative sampl

256 We report here that PCA models harboring conditional LSL/Kras(G12D) or BRAF(

257 Results indicate that PCA improved the performance of MDS compared to traditio

258 id not differ significantly, indicating that PCA-mediated redox transformations of Fe and Mn were tra

259 The PCA allowed separating the samples into three classes: c

260 The PCA analysis confirmed that 'Nada' and 'Crvena Ranka' de

261 The PCA analysis yielded a 4 symptom components solution for

262 The PCA and cluster analysis results distinguished different

263 The PCA biplot showed the highest correlation between antich

264 The PCA cohort outperformed both RN- and PN-matched subgroup

265 The PCA content in the stratum corneum of AD(-)PA(+) subject

266 The PCA framework generated by the analysis of the two black

267 The PCA identified 4 components for each ligand, reflecting

268 The PCA identified vegetable, Western, fat-rich and fruit di

269 The PCA patterns for both CE and biosensor data provided goo

270 The PCA performed discriminated the majority of the GlcN-ami

271 The PCA plots were able to classify up to 84.75% of rice wit

272 The PCA revealed that CEP profiles are major determinants of

273 The PCA/SWNTs ink concentration can reach ~4 mg mL(-1) and w

274 x (BMI), specific gravity (SG), and, for the PCA, other factor scores.

275 Nevertheless, the PCA and neural data suggested that writing abilities wer

276 The first 5 principal components (PC) of the PCA explained 65% of variability.

277 c antibodies were then functionalized on the PCA/SWNTs.

278 e, by identifying key bands that lead to the PCA separation we could correlate spectral peaks to spec

279 d irrigated rhizospheres inoculated with the PCA-producing (PCA(+)) strain Pseudomonas synxantha 2-79

280 We demonstrate that even though PCA ignores the information regarding the class labels o

281 e, 10-second PIRL-MS samplings and real-time PCA-LDA analysis using the above model.

282 According to PCA, acacia honey samples can be clearly isolated and fo

283 ral analgesia was observed to be superior to PCA for pain control in patients undergoing open hepatic

284 We focus here on tropical PCA for dimension reduction and visualization over the s

285 fied had the highest load over the first two PCA components (explaining 67.2% variability) and can be

286 cluster, while, regarding other honey types, PCA can offer the possibility to distinguish sunflower a

287 Unsupervised PCA analysis showed distinct grouping of samples from he

288 A to construct HVIs using: a) "unsupervised"-PCA applied to variables selected a priori as risk facto

289 We used PCA to construct HVIs using: a) "unsupervised"-PCA appli

290 aoperative nerve blocks and 106 (85.5%) used PCAs.

291 Using PCA, Western and prudent dietary patterns were derived.

292 HVIs calculated using PCA are sensitive to input data and scale.

293 We observed two factors using PCA, one representing the di(2-ethylhexyl) (DEHP) and an

294 can infer relative chromosome ordering using PCA on a thresholded inter-chromosomal contact matrix.

295 able biophysical variables compared to using PCA-derived inputs.

296 models for the meat discrimination, whereas PCA was used for exploratory studies.

297 of different origins were not observed with PCA, hence, feed-forward multi-layer perceptron artifici

298 plied to detect S. oryzae infested rice with PCA and MLR techniques.

299 Distinct symptom phenotypes are seen with PCA analysis.

300 age 40 years or 10 years before the youngest PCA diagnosis in a family was recommended for BRCA2 carr