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1 al Component Analysis) and HCA (Hierarchical Cluster Analysis).
2          We call this tool CSCA (Chi-Squared Cluster Analysis).
3 ncipal component analysis and heat map-based cluster analysis.
4 , principal component analysis, and stepwise-cluster analysis.
5  Probe Amplification (dcRT-MLPA) followed by cluster analysis.
6 le groups (TPGs) identified via hierarchical cluster analysis.
7  confirmed by calcium signaling and spectral/cluster analysis.
8 hysical activity patterns were identified by cluster analysis.
9 ts with clinical profiles and outcomes using cluster analysis.
10 sis informed the variables used in a k-means cluster analysis.
11 s and multivariate analyses such as nMDS and cluster analysis.
12 eters of presumed PV cells identified by the cluster analysis.
13 pped to these immunological variables in the cluster analysis.
14 analyzed by multivariate factor analyses and cluster analysis.
15 ts with AERD were determined by hierarchical cluster analysis.
16 uning response measures and used them in the cluster analysis.
17 iminant analysis (OPLS-DA), and hierarchical cluster analysis.
18  grouped using multidimensional longitudinal cluster analysis.
19 sing clinical criteria from the multivariate cluster analysis.
20 Reporting System, were used for transmission cluster analysis.
21 groups reached statistical significance in a clustered analysis.
22 lomerulus were identified using unsupervised clustering analysis.
23  the entire pre-propeptide for comprehensive clustering analysis.
24 g the EDSS time series using an unsupervised clustering analysis.
25  patient grouped together by gene expression clustering analysis.
26 trol and GCT, with unsupervised hierarchical clustering analysis.
27 uality were used as input into a model-based clustering analysis.
28 rprints were identified through hierarchical clustering analysis.
29                                         Upon clustering analysis, 12 distinct cell types were identif
30 nt with evolutionary GB structure search and clustering analysis(21,25,26).
31          Often, such SPPs are analyzed using cluster analysis algorithms to quantify molecular cluste
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                                    Combining cluster analysis and HDX kinetics adjudication, we found
35                    Unsupervised hierarchical cluster analysis and minimal spanning tree using intratu
36                                 Hierarchical cluster analysis and PCA principle component analysis te
37  evaluated pCGA/pPLA patterns among sites by cluster analysis and principal component analysis and gr
38                                 Hierarchical cluster analysis and principal component analysis identi
39                    The chemometric analysis (cluster analysis and principal component analysis) of th
40 alytical tool relies on a bioinformatic gene cluster analysis and utilizes a predictive enoylreductas
41 lticolor flow cytometry gating, unsupervised clustering analysis and BAL supernatant cytokine measure
42 the quality of visualization and accuracy of clustering analysis and can discover gene expression pat
43 cally identify ICU patient subgroups through clustering analysis and evaluate whether these groups mi
44                            Based on Bayesian clustering analysis and hybridization simulations, we in
45 ion techniques combined with proximity-based clustering analysis and model simulations to investigate
46                                      Genetic clustering analysis and phylogenetic reconstruction reve
47 xtracted ion chromatograms, and hierarchical cluster analysis), and database searching for metabolite
48 eline clinical phenotypes using hierarchical cluster analysis, and also used Cox regression analysis
49 ly in the polluted water, as classified by a cluster analysis, and at median concentrations of 1.71 x
50 coxon rank-sum tests, Spearman correlations, cluster analysis, and logistic regression.
51 ectrophysiology, chemogenetics, unsupervised cluster analysis, and predictive modeling and found that
52 PT trial (n=4351) using the same data-driven cluster analysis as reported by Ahlqvist and colleagues.
53                                    Following cluster analysis based on amplification fragment length
54                          Here we performed a cluster analysis based on measurements of the responses
55                                              Cluster analysis based on phenotypic data distinguished
56                                              Cluster analysis based on taste values identified 5 tast
57 further compare the landscapes, we develop a cluster analysis based on the structural similarity betw
58                                 Nonetheless, clustering analysis based on the density of the distribu
59                               An independent cluster analysis, based on 10 morphological metrics meas
60                                 Hierarchical cluster analysis, based on the distribution of [(18)F]AV
61 behaviours were identified with hierarchical cluster analysis, based on the phenology and duration of
62                                              Cluster analysis by airway ecology of asthma and COPD in
63                                              Clustering analysis by Principal Component Analysis (PCA
64 n and antioxidant properties of extracts the cluster analysis (CA) was performed to distinguish simil
65 and geographical origin classification while Cluster Analysis (CA) was successful only for botanical
66 DA), principal component analysis (PCA), and cluster analysis (CA).
67                         The multivariate and cluster analysis categorized studied foods into two main
68                                   Cluster Of Clusters Analysis (COCA) is one such approach that has b
69                                              Cluster analysis, combined with visualization of the res
70                                      Dynamic cluster analysis (DCA) is an automated, unbiased techniq
71                                              Cluster analysis defined nonoverlapping groups (termed p
72                                          The cluster analysis demonstrated that the dominant fields o
73                                 Hierarchical clustering analysis demonstrated similar genetic profile
74                                              Clustering analysis demonstrated that these state transi
75                                              Cluster analysis differentiated the collection into four
76                                 Our unbiased clustering analysis enabled us to quantify circuit stabi
77                                              Cluster analysis encompassing data from 7 Czech patients
78 subtypes, pseudo-temporal ordering of cells, clustering analysis, etc.
79              In contrast, full transcriptome clustering analysis failed to uncover this connection.
80          Data were subjected to hierarchical cluster analysis followed by a stepwise discriminant ana
81                              An unsupervised cluster analysis for 3-dimensional data (nonnegative spa
82 s correlation and agglomerative hierarchical cluster analysis for the identification of microplastics
83  of the macroeconomic indicators and perform clustering analysis for positively serially correlated p
84                                              Cluster analysis found an anterior, intermediate, and po
85 lome, three new web tools were developed for cluster analysis, functional annotation and survival ana
86                                              Clustering analysis further identified several functiona
87                                  Likewise, a cluster analysis grouped patients with NTS and malaria t
88                   Research using data-driven cluster analysis has proposed five subgroups of diabetes
89 aches (ANOVA, Principal Components Analysis, Cluster Analysis) have been used for data analysis.
90 h chemometrics analysis such as hierarchical cluster analysis (HCA) (OPUS Version 7.2 software), prin
91 cipal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA) and Linear Discriminate Analysis
92 cipal component analysis (PCA), hierarchical cluster analysis (HCA) and partial least square regressi
93  recognition techniques such as hierarchical cluster analysis (HCA) and principal component analysis
94                                 Hierarchical cluster analysis (HCA) and principal component analysis
95 al component analysis (PCA) and hierarchical cluster analysis (HCA) distinguished SOBs from positive
96 arget Factor Analysis (TFA) and Hierarchical Cluster Analysis (HCA) of chemistry and sensory data was
97 l Components Analysis (PCA) and Hierarchical Cluster Analysis (HCA) of the relative abundance levels
98                               A Hierarchical Cluster Analysis (HCA) revealed that drugs within the sa
99 al component analysis (PCA) and hierarchical cluster analysis (HCA) revealed that lentil and yellow p
100 al component analysis (PCA) and hierarchical cluster analysis (HCA) showed a tendency to form two gro
101                                 Hierarchical cluster analysis (HCA) was performed on the FPA data and
102 e components analysis (PCA) and hierarchical cluster analysis (HCA)).
103 cipal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), and Orthogonal Projection to Lat
104       In addition to performing hierarchical cluster analysis (HCA), multiple chemometric methods of
105 mission spectroscopy (ICP-OES), Hierarchical Cluster Analysis (HCA), One-way ANOVA, and calculation o
106 igh-dimensional data (including hierarchical cluster analysis (HCA), principal component analysis (PC
107 al component analysis (PCA) and hierarchical cluster analysis (HCA).
108 port vector machines (SVMs) and hierarchical cluster analysis (HCA).
109 l components analysis (PCA) and hierarchical cluster analysis (HCA).
110                               A hierarchical clustering analysis (HCA) of the collected time series d
111 al component analysis (PCA) and hierarchical clustering analysis (HCA) were utilized to assess the di
112                                          For cluster analysis, high-dimensional data are associated w
113  Kendrick mass defect plots and hierarchical cluster analysis highlighted compositional differences b
114  experimental results and the Perron-cluster cluster analysis highlighted the importance of a periphe
115                                        Using cluster analysis, ICUs were categorized according to fou
116                                              Cluster analysis identified 3 clusters with mild, modera
117                                              Cluster analysis identified 3 groups of infants defined
118                                 Hierarchical cluster analysis identified 3 inflammatory subendotypes
119                                              Cluster analysis identified 4 serum biomarker-based clus
120                                              Cluster analysis identified three clusters: (i) hypereos
121                                              Cluster analysis identified three groups of high thymol,
122                                 Unsupervised clustering analysis identified 3 molecular types (A, B,
123                                              Clustering analysis identified a CD34(+) subpopulation p
124                      An unbiased, model-free clustering analysis identified distinct groups of OFC ne
125              Remarkably, single-cell RNA-seq clustering analysis identified four cellular/development
126                                 Unsupervised clustering analysis identified four immune signatures, r
127                                              Clustering analysis identified seven groups and defined
128 cross the three joints were analyzed using a cluster analysis, in order to classify the different han
129                                              Cluster analysis, in situ hybridization and RNAi assays
130 3 recently published methods for integrative clustering analysis, including iClusterBayes, Bayesian j
131 ion is absent, and multiresolution consensus cluster analysis indicates a solution with only 3 top-le
132 usting for censoring and grouped patients by cluster analysis into 3 risk groups for resource use.
133         Microorganisms were classified using cluster analysis into four groups named red-orange, oran
134                                              Cluster analysis is a core task in modern data-centric c
135                                              Cluster analysis is also increasingly applied on single-
136                            At the same time, cluster analysis is known to be imperfect and depends on
137                                   While SMLM cluster analysis is now well developed, techniques for a
138                                              Cluster analysis is widely used to identify interesting
139                          A pivotal aspect of clustering analysis is quantitatively comparing clusteri
140 ted that MPS heterogeneity implied by global cluster analysis may be even greater at a single-cell le
141           Here, we present a new open source cluster analysis method for 3D SMLM data, free of user d
142 file different mechanisms of action based on cluster analysis of a set of 12 contractility parameters
143 , the aerosol population was categorised via cluster analysis of aerosol size distributions taken at
144                                 Hierarchical cluster analysis of an ensemble of 21(st)-century simula
145                                              Cluster analysis of behavioral data obtained from a firs
146                                            A cluster analysis of daily wind patterns shows more frequ
147 anagement practices were identified based on cluster analysis of data from 106 interviewee's response
148                                              Cluster analysis of DEGs led to the creation of subgroup
149                                              Cluster analysis of echocardiographic variables identifi
150                                              Cluster analysis of extensively studied neuronal classes
151 entifies gene clusters for each species by a cluster analysis of gene expression data, and subsequent
152                                              Cluster analysis of intact glycoproteomic profiles delin
153 eural network, shows the same results as the cluster analysis of morphological characteristics.
154 rmed detailed functional and mass cytometric cluster analysis of multiple CD8(+) T-cell clones recogn
155                                              Cluster analysis of super-resolution data indicates that
156  t-Stochastic Neighbor Embedding and k-means cluster analysis of surface marker expression, that chro
157                         Unsupervised k-means cluster analysis of the 57 largest principal components
158                             In experiment 3, cluster analysis of the aggression-related measures iden
159 ts analysis followed by unsupervised k-means cluster analysis of the biomarker data was used to ident
160 ional tool for batch-fitting EPR spectra and cluster analysis of the chi(2) landscape in Linux.
161                                            A cluster analysis of the HSPC lineage output highlighted
162 t analysis, followed by unsupervised k-means cluster analysis of the principal components.
163                                              Cluster analysis of these changes confirmed several grou
164                                              Cluster analysis of this large cohort of eyes identified
165                    Unsupervised hierarchical clustering analysis of all the biopsies revealed high si
166                                 Multivariate clustering analysis of behavioral data discriminated 2 g
167                      Finally, a hierarchical clustering analysis of cancer biomarkers and immune medi
168                                              Clustering analysis of gene expression from aortic cells
169                                  Employing a clustering analysis of Gene Ontology terms, we newly ide
170                                              Clustering analysis of in vitro phenotypic traits indica
171                                 Hierarchical clustering analysis of morpho-physiological acclimations
172          We develop NCutYX, an R package for clustering analysis of multilayer omics data.
173                                              Clustering analysis of posttransplant responses revealed
174                                              Clustering analysis of shotgun data revealed composition
175                     Heat map of hierarchical clustering analysis of significantly changed miRNAs and
176                    Importantly, hierarchical clustering analysis of single nucleotide variants (SNVs)
177                                            A clustering analysis of single nucleotide variants reveal
178  tationally feasible to perform hierarchical clustering analysis of tens of millions of sequences.
179                                              Clustering analysis of the 2D RMSD distribution leads to
180                                              Clustering analysis of the copy number profiles revealed
181 al differences were observed in hierarchical clustering analysis of the nontargeted data, with distin
182 a from the Cancer Genome Atlas, we show that cluster analysis on model explanations substantially out
183  compare our ability to recover subtypes via cluster analysis on model explanations to classical clus
184               Patients were classified using cluster analysis on the basis of current and premorbid I
185                     By applying hierarchical cluster analysis on the basis of the EO constituents, tw
186  analysis on model explanations to classical cluster analysis on the original data.
187 ganized as functional networks by applying a clustering analysis on resting-state functional MRI (RSf
188                                              Cluster analysis (on baseline variables) defined 4 patie
189 stinct dyslexic subgroups were identified by cluster analysis - one characterised by significantly lo
190                                       In our cluster analysis, participants' region of residence coul
191 lysis was used to visualize the data set and cluster analysis performed at genus level.
192                           Using hierarchical cluster analysis, performed on summary statistics of eac
193  the widely used antiSMASH biosynthetic gene cluster analysis pipeline and is also available as an op
194 o statistical analysis (Kruskal-Wallis test, cluster analysis, principal component analysis).
195                                              Cluster analysis prioritized clinical criteria of chorio
196 tween homogeneous subgroups within the data, cluster analysis provides an intuitive alternative.
197         Principal component and hierarchical cluster analysis reduced the data to a set of variables
198 nical course of asthma phenotypes defined by clustering analysis remains unknown, although it is a ke
199 using Markov state models and Perron-cluster cluster analysis, respectively.
200                                  The PCA and cluster analysis results distinguished different anatomi
201                                              Cluster analysis revealed 2 distinct molecular subtypes
202                                              Cluster analysis revealed 5 donor groups.
203                                              Cluster analysis revealed 5 ordinal clusters by intensit
204                                              Cluster analysis revealed hundreds of developmentally-dy
205                    A nonbiased, hierarchical cluster analysis revealed multiple clusters of cells res
206                                              Cluster analysis revealed significant class separation o
207               Comparison of the proteomes by cluster analysis revealed that CD62L(dim) neutrophils we
208                                              Cluster analysis revealed that muscarinic and nicotinic
209                                              Cluster analysis revealed that species can be classified
210                                    SSR-based cluster analysis revealed that varieties with interestin
211                                            A clustering analysis revealed that distinct combinations
212                                     Phenetic clustering analysis revealed that the array could distin
213    A subsequent correlation and hierarchical clustering analysis revealed that the default-mode and v
214                                              Clustering analysis revealed that the physicochemical pr
215           Consistent with this, hierarchical clustering analysis revealed that the transcriptional pr
216                                 Phylogenetic clustering analysis revealed that this was caused by exp
217               Further, functional annotation clustering analysis revealed the enrichment of receptor
218 examined by principal component analysis and cluster analysis, revealing a natural separation between
219                                              Cluster analysis separated them into 3 groups according
220 onent analysis and unsupervised hierarchical clustering analysis separated all the lots from five cen
221                                     Overall, cluster analysis showed adalimumab, secukinumab, and ust
222                                         Gene cluster analysis showed differences in temporal expressi
223                                 Hierarchical cluster analysis showed identical NHOC profiles between
224              The results of the hierarchical cluster analysis showed improved clustering reproducibil
225                                              Cluster analysis showed no systematic patterns of sensit
226                             In addition, the cluster analysis showed that 32 morphological characteri
227 assification and protein-protein interaction cluster analysis showed that S-cyanylation is involved i
228                                              Clustering analysis showed smaller changes in protein ab
229                                              Cluster analysis shows the existence of only 10 predicte
230                                              Clustering analysis successfully identified six clinical
231                                              Cluster analysis suggested the following 2 subgroups bas
232                                 Hierarchical clustering analysis suggested the presence of at least t
233                                              Cluster analysis suggests a similarity of the polyphenol
234                                              Cluster analysis suggests initial transmission from HIV-
235                                              Cluster analysis suggests stimulus type as the most, and
236                                     Although cluster analysis techniques have been developed for 2D S
237 ells were considerably more heterogeneous by clustering analysis than the epithelial cells.
238 rons connected to pyramidal neurons and used cluster analysis to classify interneurons according to t
239           We combined this with hierarchical cluster analysis to consider multiple outcomes related t
240 s with bvFTD were employed in a hierarchical cluster analysis to determine the similarity of variance
241                              This study used cluster analysis to find distinct sleep patterns and rel
242                                      We used cluster analysis to group patients by the dose, recency,
243                                 We then used cluster analysis to identify patterns of individual diff
244                     Recent studies have used cluster analysis to identify phenotypic clusters of asth
245 95 to 2015, we combined network modeling and cluster analysis to simultaneously identify the structur
246 , we used variation partitioning and spatial clustering analysis to analyse the results.
247                                      We used clustering analysis to identify putative clusters among
248                         We used hierarchical clustering analysis to prioritize nontarget MS features
249 ted superresolution approaches combined with clustering analysis to study at unprecedented resolution
250                           Using hierarchical clustering analysis, two distinct NEHI endotypes were id
251                                              Cluster analysis unveiled distinct IRF7(hi) versus IRF7(
252                                              Cluster analysis using 50 baseline and 12 longitudinal v
253 ve trajectory subgroups were derived through cluster analysis using estimates of premorbid and curren
254                We performed an imaging-based cluster analysis using quantitative computed tomography-
255                                    Moreover, cluster analysis using SPN discharge properties did not
256                                 We performed clustering analysis using data from patients' hospital s
257                                 Unsupervised clustering analysis using immune cell proportions reveal
258                                    Haplotype clustering analysis using the whole genome resequencing
259                  A multivariate unsupervised cluster analysis, using the resistance data from 32 sens
260                               According to a cluster analysis, varieties Panda, Zaleika, and VB Nojai
261 a simplified isotope pattern and mass defect cluster analysis was developed in R for the screening.
262                                            A cluster analysis was done to identify unique clusters of
263 a set for structure refinement, hierarchical cluster analysis was employed to select the data sets mo
264                   Additionally, hierarchical cluster analysis was performed and significant discrimin
265                                            A cluster analysis was performed on echocardiographic vari
266                    Unsupervised hierarchical cluster analysis was performed on quantitative anthropom
267                                 Hierarchical cluster analysis was performed to assign samples to clus
268 then calculated for each neighborhood, and a cluster analysis was performed to determine aggregation
269                                Pointwise and cluster analysis was performed to determine whether ther
270                               In this study, cluster analysis was used as an objective approach for p
271                                            A cluster analysis was used to classify participants into
272                                 Hierarchical cluster analysis was used to determine subgroups of part
273                                   Supervised cluster analysis was used to generate parametric maps of
274                                              Cluster analysis was used to group individuals based on
275                                 Hierarchical cluster analysis was used to identify and characterize i
276                          Multivariate 2-step cluster analysis was used to identify clusters of eyes i
277                                              Cluster analysis was used to identify distinct donor gro
278 ndencies of these comorbidities, and network-clustering analysis was applied to derive disease subtyp
279 omeric proteins functionally, a hierarchical clustering analysis was conducted on the basis of those
280                             The hierarchical clustering analysis was repeated on a replication sample
281                    Unsupervised hierarchical clustering analysis was used to identify gene expression
282                                By means of a cluster analysis, we assigned groups of similar footprin
283                           Using unsupervised cluster analysis, we find that neurons in the parasubicu
284            Through unsupervised hierarchical cluster analysis, we found electrophysiological diversit
285                                        Using cluster analysis, we here affirmed that BPH evolutionari
286                                        Using cluster analysis, we identified two major electrophysiol
287                           Using hierarchical cluster analysis, we identify 21 epidemic clusters, of w
288           Combining high-content imaging and cluster analysis, we show that in male rats SCI decrease
289                           Using unsupervised clustering analysis, we identified 18 transcriptionally
290 aurosporine), quantitative real-time PCR and clustering analysis, we studied gene-gene interactions i
291       Principal component analysis (PCA) and cluster analysis were performed in order to examine the
292                                      PCA and cluster analysis were performed in order to examine the
293 ipal component and analysis and hierarchical cluster analysis were used for the chromatographic analy
294 In addition, a self-organizing map (SOM) and cluster analysis were used together to reveal whether th
295 roups that were detected in the hierarchical clustering analysis were mapped to the phylogeny.
296 op a supervised machine-learning approach to cluster analysis which is fast and accurate.
297 o standard approaches and if integrated into clustering analysis will enhance the robustness and accu
298                                      We used clustering analysis with 4 factors of surgical intensity
299 scriptomic characterization and hierarchical clustering analysis with adult organ RNA sequencing data
300                                              Cluster analysis yielded 6 groups of preoperative opioid

 
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