ライフサイエンスコーパス: clustering algorithm

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

2 uperresolution image reconstruction with the clustering algorithm.

3 imilar accuracy to the standard hierarchical clustering algorithm.

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

5 dentified with an agglomerative hierarchical clustering algorithm.

6 al and analysis with a specifically designed clustering algorithm.

7 somal contacts as features to be used by the 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 using topological descriptors and a standard clustering algorithm.

14 es were identified using a modified maSigPro clustering algorithm.

15 expression profiles using a nonhierarchical clustering algorithm.

16 were analyzed with the use of a hierarchical clustering algorithm.

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

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

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

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

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

22 rs across replicate experiments or different clustering algorithms.

23 twork that link unrelated families and stymy clustering algorithms.

24 The cells were classified using 10 clustering algorithms.

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

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

27 alization and as input to classification and clustering algorithms.

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

29 incorporates CLICK and several other popular clustering algorithms.

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

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

32 matic framework for assessing the results of clustering algorithms.

33 onal categories when compared to traditional clustering algorithms.

34 e for assessing the validity of unsupervised clustering algorithms.

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

36 low for easy transitioning between different clustering algorithms.

37 aset constitutes a significant challenge for clustering algorithms.

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

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

40 of type I and type II errors) of a sequence clustering algorithm, although the existence of highly i

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

42 Furthermore, clustering algorithm analysis showed distinctive gene ex

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

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

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

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

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

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

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

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

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

52 lustering, ranging from distance measures to clustering algorithms and multiple-cluster memberships.

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

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

55 By application of clustering algorithms and principal component analysis v

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

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

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

59 lt parser, several multiple alignment tools, clustering algorithms and various filters for the elimin

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

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

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

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

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

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

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

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

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

69 Clustering algorithms are a powerful means to detect coe

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

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

72 Clustering algorithms are often used for this purpose.

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

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

75 Most clustering algorithms are unable to distinguish between

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

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

78 Clustering algorithms attempt to partition the genes int

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

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

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

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

83 We apply clustering algorithms based on features of RNA secondary

84 Clustering algorithms based on probabilistic and Bayesia

85 Clustering algorithms based on probability models offer

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

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

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

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

90 of quality comparable to a leading heuristic clustering algorithm, but with the key advantage of sugg

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

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

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

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

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

96 Unsupervised clustering algorithms can be used for stratification pur

97 Classification and clustering algorithms can group proteins together by the

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

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

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

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

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

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

104 Hierarchical clustering algorithms clearly differentiated the early a

105 Most existing clustering algorithms cluster genes together when their

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

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

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

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

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

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

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

113 For this purpose we implemented two clustering algorithms derived from k-means and Partition

114 ces the number of false positives, (2) a new clustering algorithm designed specifically for grouping

115 Most clustering algorithms don't properly account for ambigui

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

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

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

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

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

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

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

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

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

125 Audic and Claverie have proposed a clustering algorithm for protein-coding regions in micro

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

127 ierarchy of models generated using a k-means clustering algorithm for the potential ligand.

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

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

130 Three clustering algorithms for the multiplex genotyping SNP-I

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

132 A clustering algorithm grouped the 150 haplotypes into six

133 A k-means clustering algorithm grouped the differentially expresse

134 Clustering algorithms grouped samples from the same site

135 The clustering algorithm has potential applications in defin

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

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

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

139 Many clustering algorithms have been proposed for the analysi

140 Many different heuristic clustering algorithms have been proposed in this context

141 Recently unsupervised clustering algorithms have been proposed to identify gen

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

143 Unfortunately, most clustering algorithms have not been proven sufficiently

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

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

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

147 A Bayesian clustering algorithm identified five global syndromes of

148 Clustering algorithms identified 224 genes with expressi

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

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

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

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

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

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

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

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

157 developed a number of rigorous and efficient clustering algorithms, including two with guaranteed glo

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

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

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

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

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

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

164 Clustering algorithm K-means is adopted to quantify the

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

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

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

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

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

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

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

172 fully applied our methodology to compare six clustering algorithms on four gene expression data sets.

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

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

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

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

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

178 Clustering algorithms play an important role in the anal

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

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

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

182 Sequence homologies, various ORF-clustering algorithms, relative gene positions on the ch

183 Clustering algorithms reveal that lymphoblastic leukemia

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

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

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

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

188 We present a fast local network clustering algorithm SPICi.

189 A two-way hierarchical clustering algorithm successfully distinguished adenoma

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

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

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

193 that normally hinder other protein sequence clustering algorithms, such as the presence of multi-dom

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

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

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

197 We present a clustering algorithm that achieves high accuracy across

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

213 focus on several important issues related to clustering algorithms that have not yet been fully studi

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

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

216 les were compared among tumors using various clustering algorithms, thereby separating the tumors int

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

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

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

220 We develop a clustering algorithm to automatically group pixels with

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

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

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

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

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

226 GPLEXUS integrates Markov Clustering Algorithm to effectively identify functional

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

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

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

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

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

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

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

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

235 Our methodology is to apply a clustering algorithm to the data from all but one experi

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

237 erent data analysis techniques and different clustering algorithms to analyze the same data set can l

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

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

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

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

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

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

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

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

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

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

248 several commonly used expression-based gene clustering algorithms using a figure of merit based on t

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

250 An efficient two-way clustering algorithm was applied to both the genes and t

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

252 A linear clustering algorithm was developed to identify clusters

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

254 A hierarchical clustering algorithm was used to group study participant

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

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

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

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

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

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

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

262 cDNA microarrays and a clustering algorithm were used to identify patterns of g

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

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

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

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

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

268 TFCC is a semi-supervised clustering algorithm which relies on the assumption that

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

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

271 Our clustering algorithm, which is based on the concept of a

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

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

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

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

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

277 It combines any clustering algorithm with silhouette measures to identif

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

279 A novel clustering algorithm with stimulating discriminant measu

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

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

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

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

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

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

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