戻る
「早戻しボタン」を押すと検索画面に戻ります。 [閉じる]

コーパス検索結果 (１語後でソート)

通し番号をクリックするとPubMedの該当ページを表示します
1 for coherence using correlation, factor, and cluster analyses.
2 f pairwise serum-virus vector distances, and cluster analyses.
3 es among these sites based on redundancy and cluster analyses.
4 for improvement), and conducted case-mix and cluster analyses.
5 ents related to outbursts through factor and cluster analyses.
6 e the information among neighboring SNPs for cluster analyses.
7 ion-based subgroups of CRC using model-based cluster analyses.
8 ccus maripaludis using phylogenetic and gene cluster analyses.
9 ints was analyzed by logistic regression and cluster analyses.
10 ial expression and unsupervised hierarchical clustering analyses.
11 types were investigated using odds ratio and clustering analyses.
12 such as principal component and hierarchical clustering analyses.
13 rol subjects for differential expression and clustering analyses.
14 timised using the bidimensional hierarchical clustering analyses.
15  compiled TSG and OCG profiles and performed clustering analyses.
16 Chip hybridization protocol and hierarchical clustering analyses.
17 correlations (CSC) on correlation-based gene-clustering analyses.
18                              IMC revealed by clustering analyses a more prominent, phenotypically shi
19         Principal component and hierarchical cluster analyses also revealed aroma content differences
20                                        Phase clustering analyses also identified reward-related activ
21                  Time-lagged correlation and clustering analyses also revealed different transport pa
22                                        These clustering analyses also show potential subsets of patie
23           Using multivariate statistical and cluster analyses, an ensemble of randomly oriented parti
24                                              Clustering analyses and clinical correlations were used
25     mRNA expression clustering refined prior clustering analyses and identified a poor-survival "neur
26 e used to complete unsupervised hierarchical clustering analyses and identify differentially expresse
27 ll line and compound comparisons, along with clustering analyses and predictions of drug-target inter
28 ned our knowledge of cell type with unbiased clustering analyses and supervised machine learning to d
29                                   Results of cluster analyses are also presented, demonstrating the p
30                       While phylogenetic and cluster analyses are often used to define clonal groups
31                       Gene ranking and graph clustering analyses are integrated into the package.
32         We examine the value of multivariate cluster analyses as a new paradigm that can inform treat
33                                              Clustering analyses at age 23 years (N = 996) identified
34 bour distance, and regularity index, we used cluster analyses based on changes in soma size and round
35 rogeneous condition, as shown by independent cluster analyses based on demographic, clinical, and inf
36 om each geographical locale were analyzed by cluster analyses based on genetic fingerprinting with th
37                            We also conducted cluster analyses based on Local Indicator of Spatial Ass
38                                              Cluster analyses based on nearest-neighbor distance (F,
39                                              Clustering analyses based on these outlier loci indicate
40 tors that undergo some degree of spontaneous clustering, analyses based on multivalence-mediated coop
41  training and technical support to farmers." Cluster analyses, based on participant opinions, grouped
42 CSMSs, a better performance was observed for cluster analyses-based CSMSs.
43                                              Clustering analyses by ADMIXTURE and Principal Component
44                  Results of precomputed gene clustering analyses can be retrieved in tabular or graph
45         In the mixed-membership unsupervised clustering analyses commonly used in population genetics
46 quences of 1,932 SARS-CoV-2 genomes, various clustering analyses consistently identified six types of
47                     Energetic and structural clustering analyses depict a clear trend of differential
48 nd arm movement temporally overlapped, phase clustering analyses enabled us to resolve differences in
49 zed genetic structure across the range using clustering analyses, exact tests for population differen
50                                              Cluster analyses explored whether NCEP identifies a mixt
51      Gene ontology and functional annotation clustering analyses for APY-modified proteins suggested
52    High genetic diversity, nonconcordance of cluster analyses from different genetic loci, and shared
53                                              Cluster analyses grouped risk loci into five major categ
54                                 Hierarchical clustering analyses grouped samples according to their l
55                                              Cluster analyses have enhanced understanding of the hete
56                                     Previous cluster analyses have identified subgroups of asthma.
57             Detailed serum and HIV-1 isolate cluster analyses have shown that in general, the identif
58                                     Further, clustering analyses have identified seven major conforma
59                                              Cluster analyses identified 3 subgroups of GA.
60                           In addition, these cluster analyses identified calpactins I and II as novel
61         Principal component and hierarchical cluster analyses identified gene signatures unique to ea
62 biting different forest blocks, and Bayesian cluster analyses identified hierarchical structure.
63                                        Local cluster analyses identified Iowa and Northeastern Missou
64                 A combination of outlier and cluster analyses identified putative new members of the
65                                 Unsupervised cluster analyses identified six clusters including ventr
66 1) cotton using double feature selection and cluster analyses identified species-specific and stage-s
67                                              Cluster analyses identified three cognitive trajectory s
68                                              Cluster analyses identified three distinct profiles of n
69  forest algorithm) and unsupervised (k-means cluster) analyses identified abdominal and anorectal var
70                                              Clustering analyses identified 4 PASC symptom phenotypes
71                                     Unbiased clustering analyses identified an "intermediate-ILC2" po
72                                     Unbiased clustering analyses identified metaclusters of T and B c
73                                              Clustering analyses identified novel sRNA seed regions a
74                                              Clustering analyses identified other LEC2-induced RNAs n
75                         We conducted spatial cluster analyses, identifying hotspots of PFAS sites at
76 usters were identified using correlation and clustering analyses in the numerical space.
77                                              Cluster analyses independently confirmed these connectio
78 e and population differentiation, as well as clustering analyses, indicate that the genomes of indivi
79        Contamination sufficient to influence clustering analyses is present in public sequence databa
80                            Research based on cluster analyses led to the identification of particular
81  allergic diseases based primarily on recent cluster analyses, molecular phenotyping, biomarkers, and
82 o inflammatory subgroups based on factor and cluster analyses of 13 cytokines (HC Low n = 32, Proband
83                        Based on quantitative cluster analyses of 52 constitutively expressed or behav
84 r membership coefficients from 100 replicate cluster analyses of 600 chickens from 20 different breed
85                Semantic map and hierarchical cluster analyses of baseline marker expression revealed
86                                 Hierarchical cluster analyses of clinical and pathologic features of
87 m cellular inflammation profiles, as well as cluster analyses of clinical variables and molecular and
88                                              Cluster analyses of folding trajectories showed the nati
89 gic significance of results obtained through cluster analyses of gene expression data generated in mi
90                                 In addition, cluster analyses of gene expression profiles among these
91                                              Cluster analyses of large independent cohorts of patient
92 edia of Genes and Genomes pathway enrichment cluster analyses of microarray data using wild-type and
93                                     Separate cluster analyses of multilocus SB and single locus PCR-R
94                                     Unbiased cluster analyses of nephrotic urine proteomes demonstrat
95 ort, two groups were defined by hierarchical cluster analyses of protein data.
96                      Principal component and cluster analyses of ROI volumes were used to identify pa
97 ternative to conventional data reduction and cluster analyses of similarity matrices that is rooted i
98                                              Cluster analyses of six independent samples of 100+ neur
99                                              Cluster analyses of the co-evolution of antibody and T c
100                                              Cluster analyses of the integrated data identified two m
101 sonance imaging, voxel-based morphometry and cluster analyses of the pathological groups here suggest
102                         Pair correlation and cluster analyses of the resulting PALM images identified
103                       As a result, replicate cluster analyses of the same data may produce several di
104                                   Factor and cluster analyses of the sputum cytokine profiles reveale
105                                              Cluster analyses of these spectra showed that UVRR spect
106                        Pathway and annotated cluster analyses of those probe sets predicted that enti
107                                              Cluster analyses of transcriptional profiling data were
108                                      Similar cluster analyses of two published, independent data sets
109 ouped into subpopulations using unsupervised clustering analyses of 38 surface and intracellular mark
110                              Sensitivity and clustering analyses of averaged neuronal response patter
111                                     Unbiased clustering analyses of blastomeres from such embryos rev
112                    Unsupervised hierarchical clustering analyses of CRLF2-rearranged DS-ALL identifie
113                     Notably, statistical and clustering analyses of echinoid temporal gene expression
114 alternative option for biologists to perform clustering analyses of gene expression patterns or trans
115                                              Clustering analyses of gene-specific protein-mRNA ratios
116                                     However, clustering analyses of microsatellite and Chd1 gene sequ
117 terogeneous, multi-modal datasets, including clustering analyses of population subsets.
118 like domains provides a platform for further clustering analyses of sequence similarities among diffe
119                      Principal component and clustering analyses of transcriptomic data revealed rapi
120 ore similar than observations from different clusters, analyses of such data must take intracluster c
121 l subgroups were determined using factor and cluster analyses on 18 sputum cytokines.
122  Using correlation heatmaps and hierarchical cluster analyses, PENK clustered with estimated glomerul
123 s) through cross-sectional, longitudinal and clustering analyses, performed using statistical and mac
124 ts, histograms, expression profile plots and cluster analyses plots.
125                                   Rand index cluster analyses predicted best binning results between
126 ensive fusion gene, expression and molecular cluster analyses provide a molecular portrait of this ra
127                                              Clustering analyses provided further functional clues an
128               High-resolution trajectory and cluster analyses reveal the lineage relationship, spatia
129                                              Clustering analyses reveal five large-scale networks tha
130                                              Cluster analyses revealed 25 putatively preserved patien
131                          The association and cluster analyses revealed excellent coherence between th
132                         Comparative genomics cluster analyses revealed novel gene families (clusters)
133                                              Cluster analyses revealed that for half the sample, incr
134                                              Cluster analyses revealed that most DE genes annotated a
135                                              Cluster analyses revealed that most taxonomic groups cou
136                                              Cluster analyses revealed two main groups of genetically
137                                              Clustering analyses revealed a highly complex spatial st
138                                  A series of clustering analyses revealed a topological migration of
139                                              Clustering analyses revealed behavioral evidence for thr
140                                              Clustering analyses revealed three distinct subgroups id
141                                 Unsupervised clustering analyses revealed variability in the number a
142                                              Clustering analyses revealed variability in TL specifici
143                      Principal Component and Cluster Analyses show that the samples cluster according
144                                              Cluster analyses showed that many of the transcripts wer
145                                      K-means cluster analyses showed that salt concentration was the
146                                     Bayesian-clustering analyses showed two distinct population clust
147 ranulosa group correspond to clades, genetic clustering analyses sometimes grouped distinct taxonomic
148 s errors, the influences of contamination on clustering analyses, such as single-nucleotide polymorph
149                                              Clustering analyses suggest the occurrence of two additi
150                                     However, clustering analyses suggested a more complex heterogeneo
151                                              Cluster analyses tested SPS and its factors independentl
152 69 single nucleotide polymorphisms (SNPs) in clustering analyses, the Suriname sample appears sister
153 trate significant improvements in downstream clustering analyses through the application of our propo
154  phase of TGA together with connectivity and cluster analyses to detect changes in the episodic memor
155                           We used geospatial cluster analyses to identify areas of higher perceived s
156 on, it describes a novel use of hierarchical cluster analyses to identify protein relatedness based o
157 ethodologies such as principal component and cluster analyses to provide a geometric representation o
158 ering implementation, and enables meaningful clustering analyses to be performed using such implement
159                      We then employ spectral clustering analyses to further distill these responses i
160                                      Network cluster analyses using SCAFFoLD, VorteX, and CITRUS reve
161 titions that might be hidden when performing clustering analyses using all available genes.
162                 Tree diagrams constructed by cluster analyses, using eight length factors in a given
163          Also, using principal component and cluster analyses, we determined a strong negative relati
164 eters, and, by using principal component and cluster analyses, we quantitatively categorized neurons
165                                     Aided by clustering analyses, we evaluated how species conformed
166                                        Using clustering analyses, we identified three main molecular
167 Spectral band area analysis and hierarchical cluster analyses were performed to clarify if the 2 peri
168                                              Cluster analyses were performed to identify locations de
169 ng multidimensional scaling and hierarchical cluster analyses were produced.
170                           Both parsimony and cluster analyses were used to divide the genus into two
171                      Principal component and cluster analyses were used to find patterns of cytokine/
172  clinicobiological phenotyping, unsupervised clustering analyses were performed by artificial neural
173 tional enrichment, gene network, and k-means clustering analyses were used to identify molecular path
174 nd multivariate (random forest, hierarchical clustering) analyses were performed to identify clinical
175 rred using principal components and Bayesian cluster analyses, were consistent with three genetic clu
176 es contamination causes errors that confound clustering analyses, while between-species contamination
177                                 Hierarchical clustering analyses with 3288 genes clearly segregated t
178  classes examined, no single copy would bias clustering analyses with regard to other closely related

 
Page Top