ライフサイエンスコーパス: clustering

1 mulated data were analyzed with hierarchical clustering.

2 omponent analysis, k-means, and hierarchical clustering.

3 hylogenetic analysis was performed to detect clustering.

4 genetic analysis was used to examine genetic clustering.

5 n goats, showing a high degree of farm-level clustering.

6 ing equations to account for household-level clustering.

7 -Vangl2 PCP axis to control mesenchymal cell clustering.

8 ing sites, suggesting underlying cis-element clustering.

9 s, where it also improves resolution of cell clustering.

10 ignificantly impairs integrin activation and clustering.

11 plotype network revealed a strong geographic clustering.

12 on-negative matrix factorization (NMF) based clustering.

13 integrates feature selection with iterative clustering.

14 ushes collapse and Ki-67 promotes chromosome clustering.

15 ts (BSP), multivariate analyses and Bayesian clustering.

16 eled fluorescent PIP2 in liposomes, implying clustering.

17 ojection-based algorithm that is scalable to clustering 10 million cells.

18 Functional clustering (20% of the traits) seemed to be associated w

19 We present SAME-clustering, a mixture model-based approach that takes cl

20 han other competitors while maintaining high clustering accuracy and robustness.

21 ns show that DESC offers a proper balance of clustering accuracy and stability, has a small footprint

22 tatistical approaches including unsupervised clustering, agglomerative hierarchical clustering and co

23 -seq count data, and present a co-occurrence clustering algorithm to cluster cells based on the dropo

24 patterns, we introduce a weighted univariate clustering algorithm to guarantee linear runtime, optima

25 A clustering algorithm using a threshold of 400 allelic di

26 Using a graph-based clustering algorithm, we found that certain tissue-speci

27 While classical clustering algorithms have popularly been used to invest

28 on structure metrics, multivariate analyses, clustering algorithms, and Bayesian methods, we found ev

29 LPWC versus existing time series and general clustering algorithms.

30 Mixed TL/LS clustering also was found.

31 Unsupervised clustering analyses revealed variability in the number a

32 ial expression and unsupervised hierarchical clustering analyses.

33 Clustering analysis demonstrated that these state transi

34 Our unbiased clustering analysis enabled us to quantify circuit stabi

35 of the macroeconomic indicators and perform clustering analysis for positively serially correlated p

36 Clustering analysis identified a CD34(+) subpopulation p

37 Unsupervised clustering analysis identified four immune signatures, r

38 Clustering analysis of posttransplant responses revealed

39 Clustering analysis of the copy number profiles revealed

40 Phylogenetic clustering analysis revealed that this was caused by exp

41 ells were considerably more heterogeneous by clustering analysis than the epithelial cells.

42 nt with evolutionary GB structure search and clustering analysis(21,25,26).

43 lomerulus were identified using unsupervised clustering analysis.

44 unwanted sources of variation that influence clustering and a lack of canonical markers for certain c

45 set and testing a real one, two hierarchical clustering and a principal component analysis, were perf

46 Once adjusting for clustering and age, the difference in decline between th

47 Multiple clustering and classification methods were compared for

48 In contrast, categorical approaches, such as clustering and community detection, aim to identify subt

49 vised clustering, agglomerative hierarchical clustering and correlation coefficients to provide insig

50 ab5 exchange factor Rabex-5 impairs both BAX clustering and cytochrome c release.

51 temporally coincides with mitochondrial BAX clustering and cytochrome c release.

52 EpiGo-KRAB is sufficient to induce genomic clustering and de novo heterochromatin-like domain forma

53 t transformation, a data-driven hierarchical clustering and dimension reduction technique.

54 oint supervised classification, unsupervised clustering and dimensionality reduction to learn cancer-

55 The proposed method outperforms both clustering and energy ranking-based methods, all the whi

56 RNA-seq data with emphasis on improving both clustering and gene differential expression analysis.

57 Clustering and gene ontology (GO) enrichment analyses of

58 described a multi-step algorithm, Iterative Clustering and Guide-gene Selection (ICGS), which applie

59 human therapeutics that benefit from target clustering and higher-order antigen-binding valency.

60 the mechanism of soluble antigen-induced BCR clustering and internalization in a cultured human B cel

61 -induced myosin accumulation, which leads to clustering and locally enhanced flow.

62 Clustering and microcolony assembly are dependent on exo

63 rized using measures of network segregation (clustering and modularity), network integration (global

64 determine appropriate adjustment factors for clustering and multimorbidity.

65 Hospital-level clustering and Nationwide Readmissions Database sampling

66 vanced sequel of a web server for filtering, clustering and networking of chemical compound libraries

67 information, statistical analysis, structure clustering and other principles to estimate the relative

68 d multipolarity by improving both centrosome clustering and pole coalescence.

69 eters recapitulated growth-dependent SBF/MBF clustering and predicted TF dynamics that were confirmed

70 ffects models accounting for clinical centre clustering and repeated measures by individual.

71 ntified two stromal features, namely stromal clustering and stromal barrier, which represented the me

72 d glaucoma dashboard." We used density-based clustering and the VF decomposition method called "arche

73 However, widely used clustering and visualization algorithms produce a fixed

74 common populations that outperforms popular clustering and visualization algorithms, as demonstrated

75 fect models, Moran's I statistic for spatial clustering, and Empirical Bayesian Kriging.

76 effects of cohesin and transcription on CTCF clustering, and highlights the power of quantitative sup

77 stream differential expression, unsupervised clustering, and pseudotemporal trajectory analyses, as w

78 on, normalization, dimensionality reduction, clustering, and pseudotime analysis that can serve as a

79 torization, quantile normalization and joint clustering, and visualization.

80 ese challenges, we developed an unsupervised clustering approach for discovering differential pathway

81 nalysis of BAL constituents with an unbiased clustering approach revealed distinct cytokine/chemokine

82 present SVXplorer, which uses a graph-based clustering approach streamlined by the integration of no

83 X-ray fluorescence data in conjunction with clustering approaches can be used to effectively and non

84 We then applied network and clustering approaches to identify bronchiolitis endotype

85 s and more biologically plausible than other clustering approaches.

86 Mechanisms of this clustering are debated.

87 the mechanisms of AIS Na(+) and K(+) channel clustering are understood, the molecular mechanisms that

88 Microglial clustering around plaques was impaired, plaques were mor

89 We present the Single-Cell Clustering Assessment Framework, a method for the automa

90 of mitochondria within the neuron allows for clustering at regions of high-energy demand.

91 al stages, revealing a strong preference for clustering at the polar domains.

92 h active EoE were identified by unsupervised clustering based on expression of IL4, IL5, IL13, C-C mo

93 Clustering based on plasma protein profiles delineated a

94 Clustering-based methods need a certain number of models

95 However, only a subset of colonizers display clustering behavior and growth following a power law.

96 ll be widely applicable to investigations of clustering behaviors in other signaling proteins.

97 ns where we detect the strongest evidence of clustering belong to just two functional groups: Compone

98 Patients without DMR had higher degrees of clustering between tumor cells and CTLs, and between tum

99 t the beta3-subunit is not required for this clustering but beta3 does significantly change the distr

100 e cell types and states through unsupervised clustering, but the ever increasing number of cells and

101 ing a random-effect to account for potential clustering by center RESULTS:: A total of 1163 women con

102 ates; PFGE patterns did not reliably predict clustering by cgMLST analysis.

103 ld (pulse oximetry) and clinical guidelines, clustering by child, and CHW or HC catchment area.

104 ional hazards regression models adjusted for clustering by facility and a priori baseline covariates

105 nd presentation features, and accounting for clustering by hospital.

106 l while accounting for repeated measures and clustering by household.

107 of the gut microbial community demonstrated clustering by physical activity (p = 0.001) but not by h

108 nsultations, medications, calendar year, and clustering by practice.

109 eralized mixed-effects models to account for clustering by subject.

110 In addition, analysis of beta1 integrin clustering by super-resolution imaging demonstrates that

111 The similarity or distance measure used for clustering can generate intuitive and interpretable clus

112 filtering, normalization, batch correction, clustering, cell type annotation and differential gene e

113 y improves several analysis tasks, including clustering, cell type identification, and integration wi

114 hey are able to assemble bipolar spindles by clustering centrosomes into two spindle poles.

115 GC-MS data showed a clustering closely matching the one found by sensory ana

116 Closed structures (clustering coefficient), in which collaborators also col

117 structure (degree, eigenvector, betweenness, clustering coefficient).

118 n gray matter volumes, degree, strength, and clustering coefficient.

119 riety of nonequilibrium phenomena, including clustering, collective motion, and spatio-temporal chaos

120 Clustering commonly constitutes a central component in a

121 Clustering concordance analyses demonstrated some differ

122 Hierarchical clustering confirmed group separation.

123 DSG3-CAART IFN-gamma secretion and homotypic clustering, consistent with an activated phenotype.

124 iance scales with body size due to landscape clustering, consumers that forage for clustered foods ar

125 By clustering copy number calls, we reconstructed histories

126 tion Index quartile, adjusted for geographic clustering, demographic, clinical, and hospital characte

127 t matrix-free divisive hierarchical spectral clustering different from prevalent single-resolution cl

128 w that it achieves high accuracy on genotype clustering, doublet detection and ambient RNA estimation

129 clusters exist in solution or are formed by clustering during droplet evaporation has been debated.

130 stic regression was performed to account for clustering effect within hospitals and adjusting for pat

131 Results show that our SAME-clustering ensemble method yields enhanced clustering, i

132 eMake pipeline for flexible and parallelized clustering evaluation and selection.

133 ques confirmed and extended the hierarchical clustering findings.

134 classification with automated un-supervised clustering for generating training data provides an effe

135 mitochondria and functionally uncouples BAX clustering from cytochrome c release, while knockdown of

136 cs tasks such as gene prioritization, sample clustering, gene set analysis, and expression signature

137 based on plant productivity and phenology by clustering global 0.083 degree resolution normalized dif

138 e decrease of entropy due to the residential clustering has a parallel and independent effect increas

139 tailed and quantitative understanding of cis-clustering has been hindered by a lack of experimental a

140 However, detailed mechanisms of channel clustering have only recently been identified: they incl

141 previous work and shows that four VZVsncRNA clustering in and near ORF61 and antisense to the latenc

142 a complex landscape with protective epitopes clustering in at least 6-7 antigenic sites.

143 on rapidly and demonstrate the importance of clustering in exploring pathogenic disease mechanisms of

144 dom effect regression analyses adjusting for clustering in health centres.

145 the discovery of HIMs based on network motif clustering in heterogeneous interactomes.

146 alcohol), and TB, taking into account their clustering in individuals.

147 operated Ca(2+) entry (SOCE) and ORAI1-STIM1 clustering in Jurkat T cells.

148 rface mutation led to a loss of induced PIP2 clustering in MACA, indicating the importance of protein

149 emonstrate this by applying them to receptor clustering in platelets, nuclear pore components, endocy

150 We observed clustering in specific units and on specific shifts, wit

151 es distinct clinical and molecular disorders clustering in the GEFD1 and seventh spectrin repeat doma

152 ween the role of protein curvature and lipid clustering in the relaxation of large membrane deformati

153 ation solutions with those from unsupervised clustering in which no labels are assigned to the data.

154 E-clustering ensemble method yields enhanced clustering, in terms of both cluster assignments and num

155 ernative condensation chemistry also induces clustering independent of sumoylation.

156 Mutual clustering information exhibits none of the undesirable

157 ble low and high estimates for the degree of clustering, informed by multimorbidity studies.

158 that exist in solution and species formed by clustering inside droplets as solvent evaporation occurs

159 ectra under previous conditions is formed by clustering inside the electrospray droplet, but <=5.6% a

160 cant age-dependent concentration differences clustering into four temporal stages, and resulting in a

161 diel expression pattern, with 61% of contigs clustering into modules with statistically significant d

162 Clustering is an essential step in the analysis of singl

163 he third, most robust, model poses that this clustering is due to a short-ranged internuclear attract

164 Two suggest that the initial clustering is due to nuclear repulsion from the cell pol

165 Surprisingly, Ndc80 complex clustering is independent of the organization and number

166 Moreover, this clustering is similar in yeast and human kinetochores de

167 ductions to Guangdong, although phylogenetic clustering is uncertain because of low virus genetic var

168 through the application of persistence-based clustering, is capable of probing densely packed structu

169 of individual Nav1.5 alpha-subunits, but the clustering itself depends on other factors.

170 estigated whether a temporal-intensity voxel clustering (kinetic spatial filtering, or KSF) improved

171 OCA, and present Kernel Learning Integrative Clustering (KLIC) as an alternative strategy.

172 ion, which can introduce bias into the final clustering labels.

173 election despite some issues associated with clustering large decoy sets and decoy sets that do not s

174 nt with ZDHHC14's importance for Kv1 channel clustering, loss of ZDHHC14 decreases outward currents a

175 Understanding this clustering may allow identification and targeting of poc

176 Clustering may thus have been underestimated.

177 s within a list of GO terms using the Markov Clustering (MCL) algorithm, based on the overlap of gene

178 The AJCC staging system can be thought as a clustering mechanism that groups patients based on their

179 We observed cell reorientation, clustering, membrane damage, growth inhibition, and in t

180 We present Epiclomal, a probabilistic clustering method arising from a hierarchical mixture mo

181 an artificial-cell-type aware surface marker clustering method for CITE-seq.

182 We present Spectrum, a new spectral clustering method for complex omic data.

183 ance matrix based on SNP data with the UPGMA clustering method found the best fit to dissect the gene

184 s-validation, one for testing) using a novel clustering method that ensures there are no homologous p

185 Further, we present a post-processing clustering method that improves the average relative F1

186 We developed a gradient-based unsupervised clustering method to extract the patterns learned by the

187 We present the chromosome clustering method, establish its optimality and runtime

188 existence of genetic subtypes of DLBCL using clustering methodologies.

189 ground truth compounds and compared to other clustering methods (DBSCAN and OPTICS) that were used in

190 When traditional clustering methods are applied to this type of data, imp

191 Automated algorithmic clustering methods that are able to cluster features in

192 Hierarchical clustering methods were applied to identify immune cell

193 Various diversity matrices and clustering methods were evaluated for a comprehensive ch

194 g different from prevalent single-resolution clustering methods.

195 a random effect to adjust for within-school clustering, minimisation variables, baseline cluster-lev

196 e correlation among treatment groups and the clustering/multiplicity of measurements within an indivi

197 Instead, it stimulates lysosome clustering near the nucleus as seen in TMEM106B-deficien

198 This clustering occurs only after microtubule attachment, and

199 te basement membrane analog, also causes the clustering of AQP4 and beta-DG.

200 Moreover, spatial clustering of co-active inputs appears to be reserved fo

201 These data suggest that clustering of endocrine cells during islet morphogenesis

202 of two extreme responders shows differential clustering of exhausted CD8 + T-cells with PD-L1 + macro

203 analysis using graph diffusion and multitask clustering of FMR1 CLIP-seq and transcriptional targets

204 function independently and helps explain the clustering of FSGS-associated mutations.

205 ll clusters are identified based on unbiased clustering of gene expression profiles and canonical mar

206 tion network analysis (WGCNA) and Multiscale Clustering of Geometric Network (MEGENA), were applied o

207 detection according to grade group (i.e., a clustering of Gleason grades).

208 A robust, unsupervised bootstrap clustering of immune cell subsets coupled with random fo

209 Furthermore, clustering of microglia revealed that IDOL-ASO treatment

210 pervised algorithms were applied for natural clustering of MIR and TD-NMR data in two groups.

211 bserved: (i) mRNA expression showed distinct clustering of MSF, (ii) six of seven cases with recurren

212 on, to describe the molecular identities and clustering of mWake(+) cells, we provide detailed analys

213 We identified clustering of non-coding SVs around neuroactive ligand-r

214 Hierarchical clustering of OMP occurrence data revealed the relevance

215 ponses are nucleated via the AKAP5-dependent clustering of P2Y(11)/ P2Y(11)-like receptors, AC5, PKA

216 ute few records), we do not find evidence of clustering of participants; instead, participants fall a

217 ry metabolic enzymes and examples of genomic clustering of pathway genes.

218 Clustering of patients based on plasma TNF and KYN/TRP y

219 the charged ends of an I-BAR domain, we find clustering of phosphatidylinositol 4,5-bisphosphate-like

220 A tendency towards clustering of positive IOD events is evident in our reco

221 Clustering of reflectance spectra allowed materials at i

222 sory factors on the endosome membrane drives clustering of retromer-bound integral membrane cargo pri

223 eneous subpopulations are then determined by clustering of scRNA-seq data.

224 We illustrate this by utilizing DR-A for clustering of scRNA-seq data.

225 40 bp) and CapA (~1,500 bp), from short-read clustering of sequencing datasets from S. boliviensis.

226 mical dealloying process that results in the clustering of solute atoms.

227 Hierarchical clustering of sTREM2 and cytokine concentrations also di

228 A-first) or GAD (GADA-first) by unsupervised clustering of temporal lipidome, identifying a subgroup

229 domic dataset, identified lipids driving the clustering of the groups.

230 Clustering of the resulting phenotypic fingerprints reve

231 Significant spatial clustering of the selected child health outcomes was obs

232 infected with HIV (n = 23) were analyzed for clustering of their viral sequences (genetic distance, <

233 c AARS distribution was observed featuring a clustering of tRNA anti-codon binding domains on one MSC

234 Clustering of tumors according to genome graph-derived f

235 Our simulations demonstrate dynamic clustering of twitcher-type bacteria with polydomains of

236 idation machinery, wherein receptor-mediated clustering of upstream autophagy factors drives continue

237 and their cellular homologs reveals distinct clustering of viral sequences into divergent clades, ind

238 tection of rare transcriptomic profiles, and clustering on large-scale scRNA-seq datasets.

239 cific cis-interactions contribute to lateral clustering on lipid bilayers.

240 50 hidden units, we found that hierarchical clustering on the low-dimensional embedding corresponds

241 mice, are endowed with four genes (Aox1-4), clustering on the same chromosome, each encoding a funct

242 The effect of Tir clustering on the viability of EPEC-infected intestinal

243 iquitous in bioinformatics, but using single clustering or classification methods to process scRNA-se

244 Clustering patients according to their PK parameter valu

245 Geographic clustering patterns highlighted wide transmission and co

246 e complementary benchmark datasets: mutation clustering patterns in the protein 3D structures, litera

247 We show that CITE-sort produces the best clustering performance across the board.

248 that the network topological centrality and clustering performance of SCI sub-compartment prediction

249 also noted that cells exhibited a pronounced clustering phenotype when exposed to near-inhibitory amo

250 Whether this clustering plays a critical role in membrane fusion is p

251 ing profound disruption of AIS Na(+) channel clustering, progressive loss of nodal Na(+) channels, an

252 ated by nucleocytoskeletal connections, Par3 clustering proximal to nuclear lamina folds, and retrogr

253 f sulfur cathodes in terms of liquid-species clustering, reaction kinetics, and solid deposition.

254 Indeed, telomere clustering relies only on liquid properties of the conde

255 alyses, such as dimensionality reduction and clustering, require days of runtime and hundreds of giga

256 However, clustering requires upfront algorithm and hyperparameter

257 A fixed clustering 'resolution' hampers our ability to identify

258 is therefore advisable to obtain a range of clustering results from multiple models and hyperparamet

259 This clustering results in the formation of gel-like or even

260 Spectrum gives improved runtimes and better clustering results relative to other methods.

261 The clustering revealed 2 distinct PCOS subtypes: a "reprodu

262 Hierarchical clustering revealed a group of samples that was suggesti

263 Partition-against-medians clustering revealed several clusters of unique root phen

264 hemical space, being used for visualization, clustering, scaffold-diversity analysis and active-serie

265 single-cell regulatory network inference and clustering (SCENIC) algorithm, we were able to establish

266 rently, the Gini coefficient and the maximum clustering set-proportion statistic (MCS-P) are used to

267 hospital characteristics and within-hospital clustering, showed that target: stroke was associated wi

268 We propose a clustering similarity measure called Lag Penalized Weigh

269 g, a mixture model-based approach that takes clustering solutions from multiple methods and selects a

270 ples were studied to evaluate within-patient clustering stability in patients with severe asthma.

271 ple kernel learning (MTMKL) method with a co-clustering step based on a cutting-plane algorithm to id

272 Unbiased hierarchical clustering suggested that samples from specific centers

273 Next, hierarchical clustering technique was applied to detect gene modules.

274 Using clustering techniques applied to targeted sequencing dat

275 e demonstrated the importance of integrative clustering techniques for combining information from mul

276 rincipal component analysis and hierarchical clustering tested for transcriptomic distinctiveness bet

277 Clustering the optical flow into regions that move in si

278 Clustering the surfaces into 28 archetypical cell shapes

279 example, in single cell RNA-seq analysis for clustering the transcriptomes of individual cells.

280 We applied longitudinal k-means clustering to derive exacerbation trajectories among 887

281 sentations of patient complaints followed by clustering to group HF patients by similarity of complai

282 s (PCA), manifold learning, and unsupervised clustering to identify eyes with similar global, hemifie

283 First, we applied hierarchical clustering to intrinsically define the functional bounda

284 We used polarity-insensitive k-means clustering to segment resting-state high-density (128-ch

285 ach of the symptoms by applying unsupervised clustering to the feature weights in the models.

286 ch applies intra-gene correlation and hybrid clustering to uniquely resolve novel transcriptionally c

287 d systemic blood pressure, and within-person clustering, to provide absolute differences in width and

288 ster identification is important for guiding clustering-triggered interventions to disrupt new transm

289 Mechanistically, Tir clustering triggers rapid Ca2+ influx, which induces lip

290 We performed nonsupervised clustering using latent class analysis to identify subgr

291 involves identifying typhoon track vectors, clustering vectors using a self-organizing map, extracti

292 Extensive clustering was observed-overall, there was a 20.40 great

293 To evaluate whether centrosome clustering was occurring, we next analysed the number of

294 Gibbs clustering was performed to identify motifs of binding p

295 ago-adjusted for age, country, and household clustering-was 0.13 (95% CI: 0.08, 0.20), P < 0.001 for

296 Using Ripley's K to quantify clustering, we found that FLAPS configurations were subs

297 sis root transcriptome data and coexpression clustering, we identified over 100 putative CREs (pCREs)

298 Supervised clustering with markers of boundary cells and segment ce

299 e HAPLEXR algorithm which combines haplotype clusterings with allelic dosages.

300 enes were associated with CM, but due to the clustering within CC of carriage of these genes, it was