ライフサイエンスコーパス: cluster analysis

1 information on particle types (from K-means cluster analysis).

2 al Component Analysis) and HCA (Hierarchical Cluster Analysis).

3 MMP-8 response patterns were explored by the cluster analysis.

4 rize phenotypes of near fatal asthma using a cluster analysis.

5 atic discriminant analysis (QDA) and K-means cluster analysis.

6 th similar behavior by applying hierarchical cluster analysis.

7 nical heterogeneity identifiable by means of cluster analysis.

8 ts ability to detect the cancer subtype in a cluster analysis.

9 essfully differentiated from the controls by cluster analysis.

10 n of Staphylococcus epidermidis by hierarchy cluster analysis.

11 TENOR) study were evaluated in this post hoc cluster analysis.

12 ated groups of proteins are revealed through cluster analysis.

13 MMP-8 response patterns were explored by cluster analysis.

14 racterizations well agree with the data from cluster analysis.

15 nical characteristics by using a statistical cluster analysis.

16 hysical activity patterns were identified by cluster analysis.

17 ected using factor analysis for unsupervised cluster analysis.

18 ts with clinical profiles and outcomes using cluster analysis.

19 sis informed the variables used in a k-means cluster analysis.

20 s and multivariate analyses such as nMDS and cluster analysis.

21 eters of presumed PV cells identified by the cluster analysis.

22 pped to these immunological variables in the cluster analysis.

23 groups reached statistical significance in a clustered analysis.

24 patient grouped together by gene expression clustering analysis.

25 the entire pre-propeptide for comprehensive clustering analysis.

26 g the EDSS time series using an unsupervised clustering analysis.

27 d, children were grouped, using partitioning cluster analysis, according to the distribution of 23 va

28 tute-space functional data were subjected to cluster analysis algorithms (K-means, partition around m

29 Often, such SPPs are analyzed using cluster analysis algorithms to quantify molecular cluste

30 Employing SVM cluster analysis allowed for the classification 251 prot

31 The use of structure-based clustering analysis allowed us to identify molecular fea

32 The cluster analysis also identifies 20 novel tetraloop fold

33 9 and 2013 were merged and then latent class cluster analysis and generalized linear Poisson model we

34 l gene expression, unsupervised hierarchical cluster analysis and integrated pathway analysis reveale

35 The cluster analysis and linear discriminant analysis showed

36 Hierarchical cluster analysis and PCA principle component analysis te

37 The chemometric analysis (cluster analysis and principal component analysis) of th

38 orrespondence between our earlier phenotypic cluster analysis and subsequent follow-up clinical and m

39 alytical tool relies on a bioinformatic gene cluster analysis and utilizes a predictive enoylreductas

40 Further, clustering analysis and 3D structured illumination micro

41 Using this clustering analysis and envelope sequence data, we ident

42 cally identify ICU patient subgroups through clustering analysis and evaluate whether these groups mi

43 Based on Bayesian clustering analysis and hybridization simulations, we in

44 ion techniques combined with proximity-based clustering analysis and model simulations to investigate

45 xtracted ion chromatograms, and hierarchical cluster analysis), and database searching for metabolite

46 ly in the polluted water, as classified by a cluster analysis, and at median concentrations of 1.71 x

47 combination of principal component analysis, cluster analysis, and discriminant analysis.

48 es, principal component analysis, hierarchic cluster analysis, and predictive techniques, such as ran

49 All of these genes were included in a clustering analysis, and enrichment of biological functi

50 ional connectivity structure as indicated by clustering analysis, and was found even in participants

51 Cluster analysis applied to data from nine countries (n

52 We formulated a clustering analysis approach with the estimation of cert

53 ree features of such data can cause standard cluster analysis approaches to be ineffective: (i) the d

54 hods, principal component analysis (PCA) and cluster analysis, are employed to characterize and class

55 Following cluster analysis based on amplification fragment length

56 further compare the landscapes, we develop a cluster analysis based on the structural similarity betw

57 Unsupervised clustering analysis based on pathways from the Kyoto Enc

58 An independent cluster analysis, based on 10 morphological metrics meas

59 Hierarchical cluster analysis, based on the distribution of [(18)F]AV

60 behaviours were identified with hierarchical cluster analysis, based on the phenology and duration of

61 QDA and cluster analysis both successfully identified 93.78% of

62 Clustering analysis by Principal Component Analysis (PCA

63 The multivariate and cluster analysis categorized studied foods into two main

64 steps: spike pattern feature-extraction, pre-clustering analysis, clustering classification, and unbi

65 Cluster analysis, combined with visualization of the res

66 erences and similarities, while hierarchical cluster analysis correctly grouped the fruits according

67 Dynamic cluster analysis (DCA) is an automated, unbiased techniq

68 iased way--using statistical methods such as clusters analysis--different phenotypes of NFA.

69 Hierarchical clustering analysis displayed two broad microbial signat

70 In cluster analysis, disregarding clinical diagnosis, the o

71 properties and GABA expression, unsupervised cluster analysis divided MePV neurons into three types o

72 Statistical analysis (PCA and cluster analysis) divided the samples in four groups on

73 Cluster analysis emphasised discriminated attributes bet

74 demonstrate the utility of mPAM for accurate clustering analysis, especially with higher-dimensional

75 subtypes, pseudo-temporal ordering of cells, clustering analysis, etc.

76 Of importance, the cluster analysis extends to all profiled proteins and th

77 In contrast, full transcriptome clustering analysis failed to uncover this connection.

78 Data were subjected to hierarchical cluster analysis followed by a stepwise discriminant ana

79 An unsupervised cluster analysis for 3-dimensional data (nonnegative spa

80 Clinical cluster analysis from the Severe Asthma Research Program

81 lome, three new web tools were developed for cluster analysis, functional annotation and survival ana

82 aches (ANOVA, Principal Components Analysis, Cluster Analysis) have been used for data analysis.

83 cognition techniques, including hierarchical cluster analysis (HCA) and linear discriminant analysis

84 cipal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA) and Linear Discriminate Analysis

85 ps with multivariate methods of hierarchical cluster analysis (HCA) and principal component analysis

86 Both hierarchical cluster analysis (HCA) and principal component analysis

87 al component analysis (PCA) and hierarchical cluster analysis (HCA) distinguished SOBs from positive

88 Hierarchical Cluster Analysis (HCA) revealed an expected segregation

89 al component analysis (PCA) and hierarchical cluster analysis (HCA) were applied to evaluate the obta

90 e components analysis (PCA) and hierarchical cluster analysis (HCA)).

91 tical data were evaluated using hierarchical cluster analysis (HCA), Fisher-ratio (F-ratio), interval

92 In addition to performing hierarchical cluster analysis (HCA), multiple chemometric methods of

93 mission spectroscopy (ICP-OES), Hierarchical Cluster Analysis (HCA), One-way ANOVA, and calculation o

94 employing similarity analysis, hierarchical cluster analysis (HCA), principal component analysis (PC

95 port vector machines (SVMs) and hierarchical cluster analysis (HCA).

96 A hierarchical clustering analysis (HCA) of the collected time series d

97 f (13)C signals across spectra, hierarchical clustering analysis (HCA) was performed for pattern reco

98 al component analysis (PCA) and hierarchical clustering analysis (HCA) were utilized to assess the di

99 For cluster analysis, high-dimensional data are associated w

100 experimental results and the Perron-cluster cluster analysis highlighted the importance of a periphe

101 Cluster analysis identified 2 dietary patterns for the n

102 Cluster analysis identified 3 clusters with mild, modera

103 Cluster analysis identified 3 groups of infants defined

104 The cluster analysis identified 49 cold spot neighborhoods,

105 Cluster analysis identified 5 phenotypes: moderate-to-se

106 Cluster analysis identified a subset of early mRNAs that

107 d vascular pathology with atypical language, cluster analysis identified an association of handedness

108 Cross-sectional severe asthma cluster analysis identified different phenotypes.

109 Cluster analysis identified five PFS endotypes linked to

110 The cluster analysis identified four distinct clinical pheno

111 Cluster analysis identified several distinct temporal pa

112 Cluster analysis identified three clusters: (i) hypereos

113 Cluster analysis identified three groups of high thymol,

114 A hierarchical cluster analysis identified winter foraging strategies b

115 Remarkably, single-cell RNA-seq clustering analysis identified four cellular/development

116 Advanced clustering analysis identified functional subsets includ

117 Clustering analysis identified TH2-high and TH2-low subj

118 emonstrated further diversity, with unbiased clustering analysis identifying six distinct subgroups.

119 Cluster analysis, in conjunction with similarity profile

120 cross the three joints were analyzed using a cluster analysis, in order to classify the different han

121 Cluster analysis, in situ hybridization and RNAi assays

122 rophysiology with morphology reconstruction, cluster analysis, in vivo retrograde labeling, and immun

123 Cluster analysis indicates distinct profiles for each of

124 Microorganisms were classified using cluster analysis into four groups named red-orange, oran

125 dable GBS methods coupled with complementary cluster analysis is a powerful tool for fine-scale popul

126 The field of cluster analysis is crowded with diverse methods that ma

127 Since cluster analysis is for discovery, we would suggest tryi

128 At the same time, cluster analysis is known to be imperfect and depends on

129 While SMLM cluster analysis is now well developed, techniques for a

130 The primary goal in cluster analysis is to discover natural groupings of obj

131 g principal component analysis, hierarchical cluster analysis, k-means clustering, and vertex compone

132 incipal component analysis (PCA) and k-means cluster analysis (KCA), respectively.

133 Here, we present a new open source cluster analysis method for 3D SMLM data, free of user d

134 Cluster analysis of 2498 low-energy poses resulting from

135 Cluster analysis of adults with symptomatic airflow obst

136 , the aerosol population was categorised via cluster analysis of aerosol size distributions taken at

137 Unsupervised cluster analysis of all and as few as two assays demonst

138 Cluster analysis of clinical variables identified 4 dist

139 (3+) macrocycles which, through hierarchical cluster analysis of cytotoxicity data for the lead compo

140 anagement practices were identified based on cluster analysis of data from 106 interviewee's response

141 ion map of the NADPH-d system among species, cluster analysis of data from the whole brain and hindbr

142 Cluster analysis of DEGs led to the creation of subgroup

143 Cluster analysis of echocardiographic variables identifi

144 Cluster analysis of extensively studied neuronal classes

145 entifies gene clusters for each species by a cluster analysis of gene expression data, and subsequent

146 Further, cluster analysis of gene expression from treated DU145 a

147 We performed an unsupervised cluster analysis of gene expression profiles in 150 psor

148 nsion reduction procedure performed prior to cluster analysis of high dimensional molecular data.

149 ristics of asthma phenotypes determined by a cluster analysis of IgE responsiveness and the relations

150 Further, cluster analysis of in vivo gene expression and in vitro

151 Cluster analysis of microarray gene expression demonstra

152 eural network, shows the same results as the cluster analysis of morphological characteristics.

153 rmed detailed functional and mass cytometric cluster analysis of multiple CD8(+) T-cell clones recogn

154 algorithms, including unbiased hierarchical cluster analysis of phenotypic data (67 continuous varia

155 Cluster analysis of super-resolution data indicates that

156 Unsupervised k-means cluster analysis of the 57 largest principal components

157 In experiment 3, cluster analysis of the aggression-related measures iden

158 t analysis, followed by unsupervised k-means cluster analysis of the principal components.

159 Cluster analysis of the relationships between individual

160 Single nucleotide polymorphism (SNP)-based cluster analysis of the S. Enteritidis genomes revealed

161 We performed a 2-step cluster analysis of the subgroup of subjects with OA.

162 cs approach and Support Vector Machine (SVM) cluster analysis of three conditioned media derived frac

163 An unsupervised clustering analysis of a test series of 98 samples ident

164 Unsupervised hierarchical clustering analysis of all the biopsies revealed high si

165 Multivariate clustering analysis of behavioral data discriminated 2 g

166 fore and after vaccination, using a two-step clustering analysis of CyTOF data, which is suitable for

167 Unsupervised clustering analysis of demographic and Paneth cell data

168 Clustering analysis of in vitro phenotypic traits indica

169 lts indicate our approach was applicable for clustering analysis of influenza viral sequences.

170 Hierarchical clustering analysis of morpho-physiological acclimations

171 Additionally, hierarchical clustering analysis of neutralization resistance pattern

172 Clustering analysis of significant genes and transcripti

173 Heat map of hierarchical clustering analysis of significantly changed miRNAs and

174 tationally feasible to perform hierarchical clustering analysis of tens of millions of sequences.

175 Clustering analysis of the 2D RMSD distribution leads to

176 A hierarchical clustering analysis of the calculated fluxes and enzyme

177 Patients were classified using cluster analysis on the basis of current and premorbid I

178 By applying hierarchical cluster analysis on the basis of the EO constituents, tw

179 ganized as functional networks by applying a clustering analysis on resting-state functional MRI (RSf

180 es identified by using a previously reported cluster analysis or newly homemade clusters do not behav

181 dy titers were grouped into 4 categories via cluster analysis-orange-red, yellow-orange, orange-blue,

182 ment, differential gene expression analysis, clustering analysis, pathway visualization, gene-set enr

183 The cluster analysis performed in the OA subgroup identified

184 Using hierarchical cluster analysis, performed on summary statistics of eac

185 Use of cluster analysis permitted the three kinds of honey to b

186 the widely used antiSMASH biosynthetic gene cluster analysis pipeline and is also available as an op

187 o statistical analysis (Kruskal-Wallis test, cluster analysis, principal component analysis).

188 R spectra, with various techniques including cluster analysis, principal component analysis, and disc

189 Unsupervised cluster analysis produces groups that significantly corr

190 milar expression profiles across treatments, cluster analysis provides insight into gene functions an

191 However, cluster analysis provides no support for a signature tha

192 using Markov state models and Perron-cluster cluster analysis, respectively.

193 Cluster analysis revealed 2 distinct molecular subtypes

194 Cluster analysis revealed 5 donor groups.

195 Functional annotation cluster analysis revealed broad ontologies enriched in t

196 Cluster analysis revealed hundreds of developmentally-dy

197 A nonbiased, hierarchical cluster analysis revealed multiple clusters of cells res

198 ment between classification methods, yet the cluster analysis revealed novel correlations with clinic

199 Cluster analysis revealed significant class separation o

200 A hierarchical cluster analysis revealed that 56 of the 57 families of

201 Comparison of the proteomes by cluster analysis revealed that CD62L(dim) neutrophils we

202 Cluster analysis revealed that species can be classified

203 SSR-based cluster analysis revealed that varieties with interestin

204 Unsupervised hierarchical clustering analysis revealed a disease-specific pattern

205 Hierarchical clustering analysis revealed an increased prevalence of

206 A model-based clustering analysis revealed that five genetic groups ex

207 The clustering analysis revealed that maternal and paternal

208 Phenetic clustering analysis revealed that the array could distin

209 Bayesian-based clustering analysis revealed the existence of three gene

210 examined by principal component analysis and cluster analysis, revealing a natural separation between

211 We applied unsupervised clustering analysis, revealing 22 distinct subpopulation

212 dependent on processing conditions and, in a cluster analysis, samples which were presumably subjecte

213 rincipal component analysis and hierarchical cluster analysis segregated Raman signatures of whole mi

214 Cluster analysis separated them into 3 groups according

215 onent analysis and unsupervised hierarchical clustering analysis separated all the lots from five cen

216 Overall, cluster analysis showed adalimumab, secukinumab, and ust

217 Cluster analysis showed correlation between spring pH an

218 Gene cluster analysis showed differences in temporal expressi

219 In addition, the cluster analysis showed that 32 morphological characteri

220 Multidimensional cluster analysis showed two equal-sized patient agglomer

221 We report Single Molecule Cluster Analysis (SiMCAn), which utilizes hierarchical c

222 Using an unsupervised hierarchal cluster analysis, subjects with similar histories tended

223 Clustering analysis successfully identified six clinical

224 Cluster analysis suggested the following 2 subgroups bas

225 Hierarchical clustering analysis suggested the presence of at least t

226 The cluster analysis suggests that the intermediate state ma

227 Although cluster analysis techniques have been developed for 2D S

228 Factor and cluster analysis techniques were used to determine 3 nov

229 These data can be subjected to a cluster analysis that makes it possible to objectively c

230 Spatiotemporal clustering analysis that leverages on the blinking photo

231 pal component analysis (PCA) was employed in cluster analysis to capture data patterns and to highlig

232 evacuation travel times to safety, and (iii) cluster analysis to classify communities with similar vu

233 rons connected to pyramidal neurons and used cluster analysis to classify interneurons according to t

234 We combined this with hierarchical cluster analysis to consider multiple outcomes related t

235 s with bvFTD were employed in a hierarchical cluster analysis to determine the similarity of variance

236 This study used cluster analysis to explore clinical phenotypes in chron

237 This study used cluster analysis to find distinct sleep patterns and rel

238 We used 2-step cluster analysis to identify objective call types and di

239 We then used cluster analysis to identify patterns of individual diff

240 Recent studies have used cluster analysis to identify phenotypic clusters of asth

241 roscopy in a combination with a hierarchical cluster analysis to mitigate the effect of scattering an

242 , we used variation partitioning and spatial clustering analysis to analyse the results.

243 We used clustering analysis to identify putative clusters among

244 factors for clinical severity and conducted clustering analysis to identify viral clusters in childr

245 ted superresolution approaches combined with clustering analysis to study at unprecedented resolution

246 Cluster analysis used the hierarchical clustering algori

247 Cluster analysis using 50 baseline and 12 longitudinal v

248 Meta-analysis and cluster analysis using metafor, meta and Cluster in R.

249 icated methods for disease subtyping perform cluster analysis using patients' clinical features.

250 We performed an imaging-based cluster analysis using quantitative computed tomography-

251 A cluster analysis using two-steps algorithm was performed

252 These measurements were used to sort RGCs by cluster analysis using Ward's Method.

253 We performed clustering analysis using data from patients' hospital s

254 We sought to evaluate whether unbiased clustering analysis using dense phenotypic data (phenoma

255 Unsupervised clustering analysis using immune cell proportions reveal

256 According to a cluster analysis, varieties Panda, Zaleika, and VB Nojai

257 With the use of these measures, cluster analysis was applied to classify the phenotypes

258 Cluster analysis was applied to compare the liposoluble

259 a simplified isotope pattern and mass defect cluster analysis was developed in R for the screening.

260 comparative approach built upon hierarchical cluster analysis was developed to gain further insight i

261 Additionally, hierarchical cluster analysis was performed and significant discrimin

262 Cluster analysis was performed in 389 participants who c

263 A cluster analysis was performed on 45 baseline clinical v

264 A cluster analysis was performed on echocardiographic vari

265 Cluster analysis was performed on those with rhinitis at

266 then calculated for each neighborhood, and a cluster analysis was performed to determine aggregation

267 m eosinophil percentages over time, a 2-step cluster analysis was performed to identify patient clust

268 K-means cluster analysis was undertaken among 309 currently whee

269 Multivariate analysis, based on cluster analysis was used as a first approach to disting

270 A cluster analysis was used to classify participants into

271 Supervised cluster analysis was used to generate parametric maps of

272 Cluster analysis was used to group individuals based on

273 Cluster analysis was used to identify distinct donor gro

274 ndencies of these comorbidities, and network-clustering analysis was applied to derive disease subtyp

275 omeric proteins functionally, a hierarchical clustering analysis was conducted on the basis of those

276 Clustering analysis was performed using point process me

277 Unsupervised hierarchical clustering analysis was used to identify gene expression

278 Hierarchical clustering analysis was used to permit visualization of

279 s PCA (Principal Component Analysis) and CA (Cluster Analysis), was successful for infusions made fro

280 timization, virtual screening, and structure clustering analysis, was developed and used to identify

281 Based on the clustering analysis we grouped all identified RNHL domai

282 By means of a cluster analysis, we assigned groups of similar footprin

283 Using self-organizing map cluster analysis, we detect robust circulation pattern t

284 Using cluster analysis, we found a single dominant configurati

285 Through unsupervised hierarchical cluster analysis, we found electrophysiological diversit

286 Using cluster analysis, we here affirmed that BPH evolutionari

287 Using cluster analysis, we identified two major electrophysiol

288 Using hierarchical cluster analysis, we identify 21 epidemic clusters, of w

289 By clustering analysis, we found connections between nutrie

290 nd gene expression and functional enrichment clustering analysis, we identified Irp2 as a regulator o

291 aurosporine), quantitative real-time PCR and clustering analysis, we studied gene-gene interactions i

292 rocyanidins, antioxidant capacity assays and cluster analysis were measured.

293 Principal component analysis (PCA) and cluster analysis were performed in order to examine the

294 PCA and cluster analysis were performed in order to examine the

295 In addition, a self-organizing map (SOM) and cluster analysis were used together to reveal whether th

296 roups that were detected in the hierarchical clustering analysis were mapped to the phylogeny.

297 sparkling wines samples through hierarchical cluster analysis, which seemed to have an organised dist

298 o standard approaches and if integrated into clustering analysis will enhance the robustness and accu

299 The use of cluster analysis with SIMPROF provided a robust statisti

300 To identify symptom clusters we used cluster analysis with the hierarchical cluster agglomera