ライフサイエンスコーパス: clustering algorithm

1 ovel graph-based, computationally efficient, clustering algorithm.

2 with an updated version of our purpose-built clustering algorithm.

3 t, intermediate, and long-with a model-based clustering algorithm.

4 uperresolution image reconstruction with the clustering algorithm.

5 imilar accuracy to the standard hierarchical clustering algorithm.

6 dentified with an agglomerative hierarchical clustering algorithm.

7 al and analysis with a specifically designed clustering algorithm.

8 ements a parallelized version of the K-means Clustering algorithm.

9 sing a Dirichlet process mixture model-based clustering algorithm.

10 discovery algorithm with a local permutation-clustering algorithm.

11 by BLAST alignment and through the use of a clustering algorithm.

12 tion data using an unsupervised hierarchical clustering algorithm.

13 ide-chain rotamer, utilizing a density-based clustering algorithm.

14 using topological descriptors and a standard clustering algorithm.

15 expression profiles using a nonhierarchical clustering algorithm.

16 by latent cellular states with a graph-based clustering algorithm.

17 dified dataset is subsequently provided to a clustering algorithm.

18 ure of the stability and predictability of a clustering algorithm.

19 practical and scalable version of the tight clustering algorithm.

20 ing to white and gray matter by using a dual-clustering algorithm.

21 onal studies (ADNI) using a novel multilayer clustering algorithm.

22 somal contacts as features to be used by the clustering algorithm.

23 es were identified using a modified maSigPro clustering algorithm.

24 ned by loop backbone geometry using a new 3D clustering algorithm.

25 sed on likelihood is proposed to improve the clustering algorithms.

26 ts into classes for precision medicine using clustering algorithms.

27 low for easy transitioning between different clustering algorithms.

28 aset constitutes a significant challenge for clustering algorithms.

29 rs across replicate experiments or different clustering algorithms.

30 twork that link unrelated families and stymy clustering algorithms.

31 ation, in comparison with four other popular clustering algorithms.

32 alization and as input to classification and clustering algorithms.

33 st of them belong to the family of heuristic clustering algorithms.

34 incorporates CLICK and several other popular clustering algorithms.

35 ATOR uses a number of rigorous and efficient clustering algorithms.

36 come many of the problems faced by classical clustering algorithms.

37 tiple tissue types, followed by a variety of clustering algorithms.

38 LPWC versus existing time series and general clustering algorithms.

39 imonious results than obtained with standard clustering algorithms.

40 ed with partitional, hierarchical, and fuzzy clustering algorithms.

41 The cells were classified using 10 clustering algorithms.

42 tervals and allows a user to utilize various clustering algorithms.

43 onal data, complementary to the one given by clustering algorithms.

44 onal categories when compared to traditional clustering algorithms.

45 e for assessing the validity of unsupervised clustering algorithms.

46 Use of a model-based clustering algorithm accurately classified more than 400

47 nt several examples that show that our k-ary clustering algorithm achieves results that are superior

48 However, clustering algorithms always detect clusters, even on ra

49 milies identified using an accurate sequence clustering algorithm and containing both TA proteins and

50 ling strategy based on using an unsupervised clustering algorithm and evaluating the performance of C

51 s with performance strongly dependent on the clustering algorithm and number of clusters.

52 ing OTU generation strategy, reference sets, clustering algorithm and specific software implementatio

53 tion of state clusters identified by the box clustering algorithm and the consensus clustering algori

54 c values in 11 categories each using k-means clustering algorithm and then applies a simple combinati

55 that our algorithm outperforms both general clustering algorithms and algorithms designed specifical

56 n are easy to implement within existing PALM clustering algorithms and experimental conditions.

57 magnitude speed-up over respective CPU-based clustering algorithms and is intended as an open-source

58 By using clustering algorithms and Monte Carlo simulations, we qu

59 The method can be implemented using standard clustering algorithms and normalized information distanc

60 e dataset combinations, similarity measures, clustering algorithms and parameters.

61 By application of clustering algorithms and principal component analysis v

62 Data were analyzed using hierarchical clustering algorithms and statistical analyses that iden

63 odes (CAMPAIGN), a central resource for data clustering algorithms and tools that are implemented spe

64 CAMPAIGN is a library of data clustering algorithms and tools, written in 'C for CUDA'

65 P in turn, such as GenCall with the GenTrain clustering algorithm, and require a large reference popu

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

67 yzed via automated event-detection and cycle clustering algorithms, and high quality isochronal activ

68 10 state-of-the-art multi-omics integrative clustering algorithms, and incorporates the most commonl

69 inte grasses using D statistics, model-based clustering algorithms, and multidimensional scaling anal

70 ications for estimating significance levels, clustering algorithms, and process optimization.

71 or performance comparisons between different clustering algorithms, and tailors the cluster analysis

72 ce of the populations identified through the clustering algorithm, antibodies induced against one pre

73 Our approach was a fuzzy C-means clustering algorithm applied to the Kullbach-Leibler dis

74 Here we show that unsupervised clustering algorithms, applied to 96-channel array recor

75 The partitions returned by a clustering algorithm are commonly validated using visual

76 Clustering algorithms are a powerful means to detect coe

77 variety of parametric and graph theoretical clustering algorithms are compared using well-characteri

78 Currently, most sequence clustering algorithms are limited by their speed and sca

79 Clustering algorithms are often used for this purpose.

80 echnical and biological variability-and most clustering algorithms are sensitive to this noise.

81 practice, especially, when a large number of clustering algorithms are to be compared using several m

82 Most clustering algorithms are unable to distinguish between

83 Then, clustering algorithms are used to group together similar

84 We then use this clustering algorithm as a subroutine in a practical algo

85 on of the average linkage-based hierarchical clustering algorithm, as one of the best algorithms avai

86 , we have made the mutation clusters and the clustering algorithm available to the public.

87 that uses affinity propagation, an efficient clustering algorithm based on a message-passing scheme.

88 Then a new weighted co-clustering algorithm based on a semi-nonnegative matrix

89 In this manuscript, we derive clustering algorithms based on appropriate probability m

90 We apply clustering algorithms based on features of RNA secondary

91 Clustering algorithms based on probabilistic and Bayesia

92 e analyzed by using hierarchical and k-means clustering algorithms before and after normalization of

93 Using a novel subspace clustering algorithm, bioassay groups that may inform on

94 ance/similarity measures on three well known clustering algorithms, bottom-up, top-down and k-means w

95 artitions tumors in a manner consistent with clustering algorithms but requires the genetic signature

96 into continental groups using a model-based clustering algorithm, but between closely related popula

97 we demonstrate the power of our fast network clustering algorithm by applying SPICi across hundreds o

98 s increases the quality of output from other clustering algorithms by providing a novel post-processi

99 We propose a spectral co-clustering algorithm called di-sim for asymmetry discove

100 We present a novel clustering algorithm, called CLICK, and its applications

101 biological relevance, unfortunately, a given clustering algorithm can perform poorly under one valida

102 Unsupervised clustering algorithms can be used for stratification pur

103 Classification and clustering algorithms can group proteins together by the

104 These methods, plus a flexible library of clustering algorithms, can be called from a new expandab

105 However, the current speed of the clustering algorithms cannot meet the requirement of lar

106 ose to each other into cities using the City Clustering Algorithm (CCA) defining cities beyond the ad

107 designate metropolitan areas, denoted "City Clustering Algorithm" (CCA).

108 on atoms, we have developed a self-assembled clustering algorithm (CGCYTO).

109 Chromatin-profile Alignment followed by Tree-clustering algorithm (ChAT) employs dynamic programming

110 Hierarchical clustering algorithms clearly differentiated the early a

111 Most existing clustering algorithms cluster genes together when their

112 As a soft clustering algorithm, cluster assignment probabilities f

113 r algorithm with well-known modularity based clustering algorithm CNM.

114 Here, we introduce a scalable subspace clustering algorithm, coherent and shifted bicluster ide

115 chnique we call COmbined Mapping of Multiple clUsteriNg ALgorithms (COMMUNAL).

116 ink prediction, personalized recommendation, clustering algorithms, community detection and so on.

117 its cluster; this is true over the range of clustering algorithms considered.

118 Five unsupervised and three supervised clustering algorithms consistently and accurately groupe

119 The new Consensus Tight-clustering algorithm delivers robust gene clusters and i

120 Most clustering algorithms don't properly account for ambigui

121 We developed a new clustering algorithm, Dynamically Weighted Clustering wi

122 By applying PSI-BLAST and a modified K-means clustering algorithm, eBLOCKs automatically groups prote

123 A newly devised literature-based clustering algorithm enabled the identification of funct

124 es from DNA microarray data based on a novel clustering algorithm EP_GOS_Clust.

125 Whilst many clustering algorithms exist, they are typically unable t

126 atrices calculated, most of the algomerative clustering algorithms fail on large datasets (30,000 + g

127 We have developed a new clustering algorithm for cancer subtype identification,

128 accurate than k-mode, a previously developed clustering algorithm for categorical data.

129 We developed a hierarchical clustering algorithm for classification of the different

130 We propose a novel bi-clustering algorithm for generating non-overlapping clus

131 Here, we describe scHiCluster, a single-cell clustering algorithm for Hi-C contact matrices that is b

132 ed a new methodology called SCAN (Structural Clustering Algorithm for Networks) that can efficiently

133 By using a probabilistic clustering algorithm for replicated measurements, these

134 However, the application of traditional clustering algorithms for extracting these modules has n

135 For this reason, we have created Clustering Algorithms for Massively Parallel Architectur

136 Three clustering algorithms for the multiplex genotyping SNP-I

137 Here, we propose a novel genotype clustering algorithm, GeneScore, based on a bivariate t-

138 ation of a spatial database and two types of clustering algorithms, GeoMEDD uses incoming test data t

139 A clustering algorithm grouped the 150 haplotypes into six

140 A k-means clustering algorithm grouped the differentially expresse

141 Clustering algorithms grouped samples from the same site

142 A wealth of clustering algorithms has been applied to gene co-expres

143 Although a multitude of such network clustering algorithms have been developed over the past

144 While many clustering algorithms have been developed, they all suff

145 Recently unsupervised clustering algorithms have been proposed to identify gen

146 Despite decades of research, existing clustering algorithms have limited effectiveness in high

147 Unfortunately, most clustering algorithms have not been proven sufficiently

148 While classical clustering algorithms have popularly been used to invest

149 MAF] <0.01) is the difficulty that automated clustering algorithms have to accurately detect and assi

150 ing with time-series data, most conventional clustering algorithms, however, either use one-way clust

151 chment analyses and apply a new iterative bi-clustering algorithm (iBBiG) to identify groups of gene

152 A Bayesian clustering algorithm identified five global syndromes of

153 Furthermore, by using a clustering algorithm, identified single-pixel events are

154 showed better performance than the existing clustering algorithm in WGCNA, and identified a module t

155 ossible chemical identity using a multistage clustering algorithm in which metabolic pathway associat

156 eview the principles of cluster analysis and clustering algorithms in a crystallographic context, and

157 where it boosts the performance of existing clustering algorithms in distinguishing between cell typ

158 Many of the methods for biclustering, and clustering algorithms in general, utilize simplified mod

159 rray studies, there are many more choices of clustering algorithms in pattern recognition and statist

160 nd to hinder performance of most traditional clustering algorithms in such a high-dimensional complex

161 Given the availability of numerous clustering algorithms in the machine-learning literature

162 the quality of clusters produced by a given clustering algorithm including their biological relevanc

163 The Monti consensus clustering algorithm is a widely used method which uses

164 The clustering algorithm is based in the geometry and the (P

165 an squared-difference (MSD), Lloyd's K-means Clustering algorithm is more efficient.

166 mbining the layering principle and K-medoids clustering algorithm is proposed to screen representativ

167 An extension to a model-based clustering algorithm is proposed using mixtures of mixed

168 ated segments along the sequence, a boundary-clustering algorithm is used to refine the DCD-linker lo

169 f contradictory clusterings by varying which clustering algorithm is used, which data attributes are

170 The robustness of eleven popular clustering algorithms is evaluated over some two dozen p

171 One solution to the scalability problem of clustering algorithms is to distribute or parallelize th

172 (NR) using even the best currently available clustering algorithms is very time-consuming and only pr

173 Clustering algorithm K-means is adopted to quantify the

174 To address this issue, we developed a new clustering algorithm, k-hulls, that reduces heterogeneit

175 Clustering algorithms like K-Means and standard Gaussian

176 We used the graph-based clustering algorithm MCL to classify all of these specie

177 The hierarchical clustering algorithm method proposed here provides a new

178 ats, distance measures, objective functions, clustering algorithms, methods to choose number of clust

179 r unsupervised clustering that uses multiple clustering algorithms, multiple validity metrics, and pr

180 echanism, adapted to work for any stochastic clustering algorithm, NMF is an efficient method for ide

181 In this paper, we consider six clustering algorithms (of various flavors!) and evaluate

182 We also used our peak-clustering algorithm on experimental data and found that

183 mpute the missing values, and then apply the clustering algorithm on the completed data.

184 e box clustering algorithm and the consensus clustering algorithm on the partial correlation matrices

185 we present an alternative approach by using clustering algorithms on the set of candidate trees.

186 le microarray data using the semi-supervised clustering algorithm Ontology-based Pattern Identificati

187 The use of different clustering algorithms or different parameters often prod

188 technologies through a bipartite-graph-based clustering algorithm, our approach turns a whole genome

189 ervised algorithm (multilevel-weighted graph clustering algorithm) performs very well on the task, ob

190 Clustering algorithms play an important role in the anal

191 orporates a novel stratification-based tight clustering algorithm, principal component analysis and i

192 The robustness of our partial regression clustering algorithm proves the suitability of the combi

193 The tight clustering algorithm provides tight and stable relevant

194 We analyzed how the performance of network clustering algorithms relates to thresholding the networ

195 eveloped a Bayesian model-based multi-tissue clustering algorithm, revamp, which can incorporate prio

196 atterns at a single DNA molecule level using clustering algorithms revealed differential methylation

197 The analysis of data by using different clustering algorithms revealed significantly affected pr

198 Our proposed partial regression clustering algorithm scores top in Gene Ontology driven

199 Both were compared to a previously published clustering algorithm (SHORAH), in order to evaluate thei

200 enes associated with multiple gene-sets, FCM clustering algorithm significantly improves interpretati

201 We present a fast local network clustering algorithm SPICi.

202 es but also outperforms traditional variable clustering algorithms such as hierarchical clustering.

203 have primarily focused on utilizing generic clustering algorithms (such as hierarchical clustering)

204 In population-genetic clustering algorithms, such as BAPS, STRUCTURE and TESS,

205 xpression data, but traditional hierarchical clustering algorithms suffer from several drawbacks (e.g

206 onal families (FunFams) using a hierarchical clustering algorithm supervised by a new classification

207 sed methods of analysis include hierarchical clustering algorithms, t-, F-, and Z-tests, and machine

208 Comparisons between clustering algorithms tend to focus on cluster quality.

209 We present a clustering algorithm that achieves high accuracy across

210 ter large compound sets, we introduce the EI-Clustering algorithm that combines the EI-Search method

211 escribe a generalization of the hierarchical clustering algorithm that efficiently incorporates these

212 We present a new diametrical clustering algorithm that explicitly identifies anti-cor

213 In this article, we present Retro-a novel clustering algorithm that extracts meaningful clusters a

214 called CLUSTERnGO, which uses a model-based clustering algorithm that fulfils this need.

215 specific binomial mixture modeling (BBMM), a clustering algorithm that generates robust genotype like

216 have developed a hierarchical, agglomerative clustering algorithm that groups Saccharomyces cerevisia

217 ass of proteins through the development of a clustering algorithm that groups together extracellular

218 two methodologies with a multiscale mutation clustering algorithm that identifies variable length mut

219 this paper, we introduce a new hierarchical clustering algorithm that overcomes some of these drawba

220 Tracking changes over time requires a clustering algorithm that produces clusters stable under

221 s algorithm is a special case of the maximin clustering algorithm that we introduced previously.

222 of oligonucleotide signatures and subsequent clustering algorithms that automatically estimate the nu

223 Although there are clustering algorithms that can achieve the goal for rela

224 Conventional clustering algorithms that deal with the entire row or c

225 pleiotropic genes are often misclassified by clustering algorithms that impose the constraint that a

226 Using a clustering algorithm, the compounds sort by phenotypic p

227 ornton's method and, together with a residue clustering algorithm, the MODELLER program and the Jmol

228 Along with standard clustering algorithms this application also offers a con

229 Here, we use a clustering algorithm to analyze 76,533 all-trans segment

230 GATE uses a correlation-based clustering algorithm to arrange molecular time series on

231 We develop a clustering algorithm to automatically group pixels with

232 ear-cut guidelines regarding the choice of a clustering algorithm to be used for grouping genes based

233 We used the complete linkage clustering algorithm to build phylogenetic trees to indi

234 points, and developed a novel non-parametric clustering algorithm to cluster all the genotypes.

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

236 We have used the spectral clustering algorithm to cluster the increasingly growing

237 COPD patients and employed a non-supervised clustering algorithm to define and detect changes in NK

238 ion and noncovalent contacts together with a clustering algorithm to define groups of residues with s

239 GPLEXUS integrates Markov Clustering Algorithm to effectively identify functional

240 lar structures, require the application of a clustering algorithm to group localizations that origina

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

242 es using our tool GERMLINE and implemented a clustering algorithm to identify haplotypes shared acros

243 rk analysis, we used a data-driven community-clustering algorithm to identify robust subgroups based

244 ropensity scale for interface residues and a clustering algorithm to identify surface regions with re

245 We used an allele-sharing-based clustering algorithm to infer evidence for four genetica

246 asks), a consensus extension of a multi-task clustering algorithm to infer high-confidence strain-spe

247 interpretable measure of the propensity of a clustering algorithm to maintain output coherence over a

248 rmalized adjacency matrix and use the Markov Clustering Algorithm to partition the graph while mainta

249 Here, by applying an unsupervised clustering algorithm to post-mortem histopathological da

250 ap in conjunction with an in-house-developed clustering algorithm to predict nonspecific ion-binding

251 In addition, we used a clustering algorithm to reveal synchronous transcription

252 ium imaging in combination with a functional clustering algorithm to uncover the functional network s

253 developed systems that explored hierarchical clustering algorithms to automatically identify abstract

254 t of incorporating external information into clustering algorithms to call the genotypes for both dis

255 uction microscopy together with quantitative clustering algorithms to demonstrate a novel approach to

256 analysis and highlight the need for scalable clustering algorithms to extract population information

257 horylation-we demonstrate the sensitivity of clustering algorithms to noise.

258 We develop the Adjacent Site Clustering (A-clustering) algorithm to detect sets of neighboring CpG

259 e successfully combine the ranks of a set of clustering algorithms under consideration via a weighted

260 A clustering algorithm using a threshold of 400 allelic di

261 luate the utility of the kernel hierarchical clustering algorithm using both internal and external va

262 rior to those obtained with the hierarchical clustering algorithm using the Pearson correlation dista

263 us platforms where we rank a total of eleven clustering algorithms using a combined examination of 10

264 s framework, we evaluate six diverse network clustering algorithms using Saccharomyces cerevisiae and

265 Volume segmentation with a k-means clustering algorithm was applied to transverse cine MR i

266 A linear clustering algorithm was developed to identify clusters

267 A density-based clustering algorithm was then used to form spatially con

268 A hierarchical clustering algorithm was used to group study participant

269 incorporates Cepstrum of Bispectrum, ad hoc clustering algorithms, wavelet transforms, least square

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

271 Using a spatial clustering algorithm, we identified 36,780 genomic group

272 Using a clustering algorithm, we identified a new family of 31 s

273 Using two independent graph clustering algorithms, we found that the reconstructed n

274 By employing supervised clustering algorithms, we identified 100 genes whose exp

275 ng unsupervised dimensionality reduction and clustering algorithms, we identified molecularly distinc

276 ical reconstruction microscopy, with protein clustering algorithms, we identify a critical role for C

277 Self-organizing maps and a trajectory clustering algorithm were utilized to identify informati

278 re estimated for each cell, and a variety of clustering algorithms were used to classify the cells.

279 idation strategies that can be used with any clustering algorithm when temporal observations or repli

280 ov state models and present a fast geometric clustering algorithm which combines both the solute-base

281 'parent' sequence and AptaCluster-an aptamer clustering algorithm which is to our best knowledge, the

282 In this paper we propose a new hierarchical clustering algorithm which reduces susceptibility to noi

283 It uses a wrapper around an alignment-clustering algorithm, which allows for indel variation w

284 data or case-parent data via a density-based clustering algorithm, which can be applied to whole-geno

285 ecommend using an agglomerative hierarchical clustering algorithm, which we call the exact linkage al

286 Model-based algorithms are alternative clustering algorithms, which are based on the assumption

287 itates efficient implementations of rigorous clustering algorithms, which otherwise are highly comput

288 ce complexity comparable to greedy heuristic clustering algorithms, while achieving a similar accurac

289 ing values can complicate the application of clustering algorithms, whose goals are to group points b

290 d on directional statistics and an effective clustering algorithm with affinity propagation.

291 It combines any clustering algorithm with silhouette measures to identif

292 We compared our peak-clustering algorithm with SnPM using simulated data.

293 A novel clustering algorithm with stimulating discriminant measu

294 Cluster analysis used the hierarchical clustering algorithm with the Ward minimum variance meth

295 tein datasets with runtime efficient network clustering algorithms without sacrificing the clustering

296 While most available clustering algorithms work well on biological networks o

297 How do clustering algorithms work, which ones should we use and

298 The clustering algorithm works in an origin-shifted four-dim

299 However, not all implementations of clustering algorithms yield the same performance or the

300 probe that are analyzed by a spectral trace clustering algorithm yielding 1D NMR spectra of the indi