ライフサイエンスコーパス: dimensionality reduction

1 An important tool in information analysis is dimensionality reduction.

2 ew collective coordinates by using nonlinear dimensionality reduction.

3 ons with low-dimensional functions including dimensionality reduction.

4 nection between RNA velocity and statistical dimensionality reduction.

5 chieved behavior-predictive nonlinear neural dimensionality reduction.

6 n spliced and unspliced mRNA at the level of dimensionality reduction.

7 data-driven approach to fulfill the task of dimensionality reduction.

8 ut irrelevant information, a process akin to dimensionality reduction.

9 broadly applicable data-driven algorithm for dimensionality reduction.

10 ategy to quantify gene relevance and improve dimensionality reduction.

11 sian ideal observer method for task-specific dimensionality reduction.

12 .8 x 10(-5) [FDR </=0.05], P for multifactor dimensionality reduction = 5.9 x 10(-45)).

13 ithms for neural networks implicitly perform dimensionality reduction-a process called feature learni

14 SIPI has higher power than MDR (Multifactor Dimensionality Reduction), AA_Full, Geno_Full (full inte

15 AMBROSIA was compared with multifactor dimensionality reduction across several diverse models a

16 rformed Eigenanatomy, a statistically robust dimensionality reduction algorithm, and used leave-one-o

17 Here we introduce a nonlinear dimensionality reduction algorithm, embodied in the Pyth

18 f any investigation, we recently developed a dimensionality reduction algorithm, sketch-map, that can

19 Finally, using a powerful nonlinear dimensionality reduction algorithm, we show that the act

20 However, success often hinges on dimensionality reduction algorithms for simplifying the

21 ure clustering, selection, normalization and dimensionality reduction algorithms, and five commonly u

22 erages ensemble classification theory within dimensionality reduction, allowing for application to a

23 For comparison, we performed the same dimensionality reduction analyses on the activity of a r

24 rs1143634) was assessed using multifactorial dimensionality reduction analysis.

25 urvival analysis, similar gene detection and dimensionality reduction analysis.

26 propose a neural network-based approach for dimensionality reduction and analysis of biological gene

27 ms existing methods including multifactorial dimensionality reduction and Bayesian epistasis associat

28 orithm that combines kernel-based non-linear dimensionality reduction and binary classification (or r

29 A combination of longitudinal, dimensionality reduction and categorical analysis of the

30 Subsequent multifactor dimensionality reduction and classification and regressi

31 Using unsupervised dimensionality reduction and clustering algorithms, we i

32 Even simple downstream analyses, such as dimensionality reduction and clustering, require days of

33 Dimensionality reduction and expansion ('dimensionality

34 propose an ensemble method for simultaneous dimensionality reduction and feature gene extraction (ED

35 ation and Projection (UMAP) for unsupervised dimensionality reduction and k-means clustering to segme

36 By combining feature selection with dimensionality reduction and machine learning approaches

37 Dimensionality reduction and machine learning techniques

38 th data analysis based on diffusion maps for dimensionality reduction and network synthesis from stat

39 niform Manifold Approximation and Projection dimensionality reduction and PhenoGraph clustering revea

40 efficient population coding scheme based on dimensionality reduction and sparsity constraints.

41 study, we develop a pipeline that integrates dimensionality reduction and statistical modeling to gra

42 o mathematical approaches to simplification, dimensionality reduction and the maximum entropy method,

43 tein conformational search, efficient robust dimensionality reduction and topological analysis via pe

44 Combining dimensionality reduction and transfer learning can reduc

45 Here, we investigate whether applying dimensionality reduction and transfer learning to MIRS d

46 Support Vector Machines (SVM), coupled with dimensionality reduction and variable selection algorith

47 Dimensionality reduction and visual exploration facilita

48 d projection (UMAP) is applied for nonlinear dimensionality reduction and visualization of mass spect

49 ed techniques in unsupervised clustering and dimensionality reduction and, more importantly, support

50 accepted role of PS membranes as providing "dimensionality reduction" and favor a regulatory role fo

51 By using a parameterization, dimensionality reduction, and clustering protocol, a tra

52 workflow: feature calculation and selection, dimensionality reduction, and data processing.

53 estimate the model's parameters, DREISS uses dimensionality reduction, and identifies canonical tempo

54 discordance as the effects of localization, dimensionality reduction, and irreversible enzymatic rea

55 risons with the relevance-chain, multifactor dimensionality reduction, and PDT methods, the results f

56 of neural coding, computational methods for dimensionality reduction, and sensory-perceptual perform

57 risons with the relevance-chain, multifactor dimensionality reduction, and the pedigree disequilibriu

58 plicability of our method in an unsupervised dimensionality reduction application by inferring genes

59 This novel dimensionality reduction approach is broadly applicable

60 ochemical profiles, we apply a bidirectional dimensionality reduction approach taking into account bo

61 ferred using geodesic spectral embeddings, a dimensionality reduction approach that we show to be esp

62 Here, we propose a dimensionality reduction approach to simplify the analys

63 The new methodology is compared to competing dimensionality reduction approaches through a simulation

64 This observation justifies the use of dimensionality reduction approaches to model complex sys

65 By employing dimensionality reduction approaches to simultaneous, lay

66 In this paper, a novel algorithm, Dimensionality Reduction based Optimization for DOT (DRO

67 The dimensionality reduction brings the promise of a decreas

68 he algorithm to perform joint clustering and dimensionality reduction by efficiently optimizing a con

69 ubject groups using k-means clustering after dimensionality reduction by t-neighbor stochastic neighb

70 Proper dimensionality reduction can allow for effective noise r

71 e few relevant coordinates emerging from the dimensionality reduction can correctly identify the tran

72 ons for sequence alignment, quality control, dimensionality reduction, cell clustering, data aggregat

73 Dimensionality reduction clustered ONHs into four distin

74 code covering transformation, normalization, dimensionality reduction, clustering, and pseudotime ana

75 igh-dimensional statistical ideas, including dimensionality reduction, clustering, subsampling, and r

76 eate tree structures, whereas algorithms for dimensionality-reduction create low-dimensional spaces.

77 Customizable settings for dimensionality reduction, data normalization, along with

78 Dimensionality reduction did not improve model generaliz

79 k has many important implications, including dimensionality reduction, differential diagnosis, and es

80 data, computational techniques often rely on dimensionality reduction (DR) as both a pre-processing s

81 Dimensionality reduction (DR) enables the construction o

82 Dimensionality reduction (DR) methods have been develope

83 ssification, as against using the method for dimensionality reduction (DR).

84 .g., inter-landmark distances), unsupervised dimensionality reductions (e.g., principal component ana

85 ndependent marker sets, extended multifactor-dimensionality reduction (EMDR) analysis was employed to

86 ariate analysis that simultaneously provides dimensionality reduction, feature extraction and multi-c

87 ributions, regularly confront the problem of dimensionality reduction: finding meaningful low-dimensi

88 At the individual cell level, dimensionality reduction followed by density based clust

89 lying protein interactions and new tools for dimensionality reduction for flexible protein docking.

90 LCF uses nonlinear dimensionality reduction for pattern recognition.

91 The proposed model uses dimensionality reduction for preprocessing the Likes dat

92 Dimensionality reduction further reveals the similarity

93 lear, however, how the results obtained from dimensionality reduction generalize to recordings with l

94 approach, namely the generalized multifactor dimensionality reduction (GMDR) method, which can entert

95 Dimensionality reduction has been applied in various bra

96 researchers who seek to understand the role dimensionality reduction has had and can have in systems

97 riate data raises the fundamental problem of dimensionality reduction: how to discover compact repres

98 of using multiview methods of clustering and dimensionality reduction; however, none of these methods

99 Here, we test this dimensionality reduction hypothesis by relating a data-d

100 Cell typing, clustering, and dimensionality reduction identified 36 reference cell ty

101 Multifactor dimensionality reduction identified a gene-gene interact

102 Factor analysis is a widely used method for dimensionality reduction in genome biology, with applica

103 on input to motor neurons results in a large dimensionality reduction in motor neuron outputs.

104 tunately, despite the critical importance of dimensionality reduction in scRNA-seq analysis and the v

105 ere assessed through model-based multifactor dimensionality reduction in the PIAMA study, and gene-ge

106 Dimensionality reduction in the training set established

107 Multifactor dimensionality reduction indicated that the best genetic

108 Dimensionality reduction is a critical step in signature

109 Dimensionality reduction is a crucial step in interpreti

110 Parallel multifactor dimensionality reduction is a tool for large-scale analy

111 Dimensionality reduction is an essential first step in d

112 Visualizing data by dimensionality reduction is an important strategy in Bio

113 Dimensionality reduction is an indispensable analytic co

114 Dimensionality reduction is crucial for the visualizatio

115 utational efficiency and easy visualization, dimensionality reduction is necessary to capture gene ex

116 Dimensionality reduction is often used to visualize comp

117 Dimensionality reduction is standard practice for filter

118 The goal of dimensionality reduction is to embed high-dimensional da

119 Dimensionality reduction is widely used in the visualiza

120 To interpret the outputs of dimensionality reduction, it is important to first under

121 Unlike clustering methods for local dimensionality reduction, LLE maps its inputs into a sin

122 s, combined effect analysis, and multifactor dimensionality reduction (MDR) analysis.

123 The computationally efficient multifactor dimensionality reduction (MDR) approach has emerged as a

124 We introduce multifactor-dimensionality reduction (MDR) as a method for reducing

125 Multifactor Dimensionality Reduction (MDR) has been introduced previ

126 Multifactor dimensionality reduction (MDR) is a powerful model-free

127 , we have previously developed a multifactor dimensionality reduction (MDR) method for collapsing hig

128 his problem, we have developed a multifactor dimensionality reduction (MDR) method for collapsing hig

129 natorial approaches, such as the multifactor dimensionality reduction (MDR) method, have emerged as a

130 natorial approaches, such as the multifactor dimensionality reduction (MDR) method, the combinatorial

131 s, a novel approach based on the multifactor dimensionality reduction (MDR) method, to detect genetic

132 We used Multifactor- dimensionality reduction (MDR) program as a non-parametr

133 re investigated by entropy-based multifactor dimensionality reduction (MDR), classification and regre

134 CE algorithm was compared to the multifactor dimensionality reduction (MDR), generalized MDR (GMDR),

135 onlinear stochastic embedding (One-SENSE), a dimensionality reduction method based on the t-distribut

136 We demonstrate that the choice of dimensionality reduction method can significantly influe

137 Spatial PCA (RASP), a novel spatially-aware dimensionality reduction method for ST data.

138 ut, and uses the state-of-the-art non-linear dimensionality reduction method t-Distributed Stochastic

139 the single-cell graph autoencoder (scGAE), a dimensionality reduction method that preserves topologic

140 First, we applied a linear dimensionality reduction method to assess the dimensiona

141 We highlight the importance of selecting a dimensionality reduction method to visualize large multi

142 ining an extended self-organizing maps-based dimensionality reduction method with bootstrap-based non

143 ly developed a higher-order statistics based dimensionality reduction method, called quasi-anharmonic

144 onomic count data, and another commonly used dimensionality reduction method, Principal Component Ana

145 Here, we show that combining a nonlinear dimensionality reduction method, t-statistic Stochastic

146 Here, we develop a dimensionality-reduction method, (Z)ero (I)nflated (F)ac

147 ex biological interactions, yet conventional dimensionality reduction methods (DRMs) often fail to pr

148 Here we apply dimensionality reduction methods (PCA, t-SNE, PCA-t-SNE,

149 However, current dimensionality reduction methods aggregate sparse gene i

150 However, current dimensionality reduction methods are often confounded by

151 We use two dimensionality reduction methods as well, robust Princip

152 We test the performance of these dimensionality reduction methods by applying several goo

153 e the computational scalability of different dimensionality reduction methods by recording their comp

154 We describe the dimensionality reduction methods commonly applied to pop

155 s are inherently complex and existing linear dimensionality reduction methods could be inadequate and

156 However, existing dimensionality reduction methods could not be directly a

157 in scRNA-seq analysis and the vast number of dimensionality reduction methods developed for scRNA-seq

158 We evaluate the performance of different dimensionality reduction methods for neighborhood preser

159 me this limitation, we introduce model-based dimensionality reduction methods for neurons with non-Po

160 we provide important guidelines for choosing dimensionality reduction methods for scRNA-seq data anal

161 ive evaluation of a variety of commonly used dimensionality reduction methods for scRNA-seq studies.

162 d to evaluate the effectiveness of different dimensionality reduction methods in scRNA-seq.

163 Dimensionality reduction methods like principal componen

164 However, existing dimensionality reduction methods often either fail to se

165 Specifically, we compare 18 different dimensionality reduction methods on 30 publicly availabl

166 We also investigated the effect of dimensionality reduction methods on the performance of t

167 omes project data, we examine how non-linear dimensionality reduction methods such as t-distributed s

168 Nonlinear dimensionality reduction methods such as Uniform Manifol

169 Dimensionality reduction methods that rely on correlatio

170 gorithms (e.g. DeepLabCut) with unsupervised dimensionality reduction methods to produce interpretabl

171 Recent studies have applied dimensionality reduction methods to understand how the m

172 We introduce several dimensionality reduction methods to visualize in 2- and

173 Discovery of efficient and accurate dimensionality reduction methods used to display at once

174 , we use normalized edit distance to enhance dimensionality reduction methods, including Isomap and L

175 ing better than approaches based on standard dimensionality reduction methods, such as principal comp

176 Using dimensionality reduction methods, we find that V1-V2 int

177 survival forest and generalized multifactor dimensionality reduction methods.

178 data has motivated continuous development of dimensionality reduction methods.

179 Three multiview dimensionality reduction methods: multiview t-distribute

180 l RNA-seq data are challenging for classical dimensionality-reduction methods because of the prevalen

181 Stimulus dimensionality-reduction methods in neuroscience seek to

182 ilt on a concept intentionally orthogonal to dimensionality-reduction methods.

183 e-scale numerical simulation with nonlinear "dimensionality reduction" methods.

184 Dimensionality reduction, missing data treatment, seed r

185 clinical variables (Model 1), and after PCA dimensionality reduction (Model 2).

186 Dimensionality reduction models following shotgun lipido

187 Dimensionality reduction models, such as Lasso and Elast

188 Principal component analysis (PCA) dimensionality reduction obtained a 2-dimensional repres

189 ing (i) variable importance assessment, (ii) dimensionality reduction of big data and (iii) interpret

190 ework that can help to perform the necessary dimensionality reduction of complex interactions between

191 lysis is a widely used tool for unsupervised dimensionality reduction of high-throughput datasets in

192 of visualization and is well-suited for the dimensionality reduction of large (>100 000 pixels) MSI

193 Dimensionality reduction of Mass Cytometry data further

194 ing techniques that include fast, non-linear dimensionality reduction of oligonucleotide signatures a

195 pathways enable efficient decorrelation and dimensionality reduction of photoreceptor signals while

196 that functional groups of spines defined by dimensionality reduction of receptive field properties e

197 Dimensionality reduction of RNA expression profiles via

198 able Model (GPLVM) is a popular approach for dimensionality reduction of single-cell data and has bee

199 We present a novel framework, ivis, for dimensionality reduction of single-cell expression data.

200 osed to accomplish this, neighbourhood-based dimensionality reduction of spectrograms to produce a la

201 methods suggested for feature extraction and dimensionality reduction of such data.

202 Dimensionality reduction of such high-dimensional data s

203 ecomes constrained, effectively leading to a dimensionality reduction of the learning problem; at the

204 Here, we examine not only the dimensionality reduction of this mapping but also its te

205 Additionally, we perform t-SNE dimensionality reduction on the MALDI-MSI dataset to enh

206 Alternatively, one can perform dimensionality reduction on the output trajectory and ob

207 ly be a byproduct of MSTd neurons performing dimensionality reduction on their inputs.

208 ly be a byproduct of MSTd neurons performing dimensionality reduction on their inputs; and (3) imply

209 method is deterministic and does not rely on dimensionality reduction or optimization methods, it is

210 ontrast to previous algorithms for nonlinear dimensionality reduction, ours efficiently computes a gl

211 indings can help guide the interpretation of dimensionality reduction outputs in regimes of limited n

212 mponent analysis (PCA) is a popular tool for dimensionality reduction, pattern recognition, and visua

213 A single dimensionality reduction plot highlighted all TCR consta

214 el data can be explored through two types of dimensionality reduction plots, t-distributed Stochastic

215 ction, clustering, normalization, selection, dimensionality reduction, predictor construction, best d

216 A sparse decomposition model based on a dimensionality reduction principle known as non-negative

217 been generated during the last two decades, dimensionality reduction problem has been a challenging

218 Here we describe an approach to solving dimensionality reduction problems that uses easily measu

219 al benchmarking on tasks such as single cell dimensionality reduction, protein module discovery and r

220 a network of profiles based on the nonlinear dimensionality reduction results.

221 Multifactor dimensionality reduction revealed that genotyping result

222 Dimensionality reduction reveals fine-scale population s

223 complementary enhanced-sampling techniques, dimensionality reduction schemes, electronic calculation

224 red the scaling trends of two key outputs of dimensionality reduction-shared dimensionality and perce

225 Cluster-wise dimensionality reduction should make it feasible to impr

226 on of metastable states, along with dramatic dimensionality reduction, significantly simplifies the t

227 Here, we demonstrate the application of high-dimensionality reduction/spatial clustering and histopat

228 t solutions to common problems: the need for dimensionality reduction, strategies for topographic or

229 Dimensionality reduction summarizes the complex transcri

230 activity of the sampled neurons via targeted dimensionality reduction (TDR), we found enhanced popula

231 bjective of this work was to develop a novel dimensionality reduction technique as a part of an integ

232 for the RPM algorithm, which is a nonlinear dimensionality reduction technique designed to produce a

233 Using a dimensionality reduction technique known as non-negative

234 Using a statistical nonlinear dimensionality reduction technique on single-trial ensem

235 Feature selection is one such high-dimensionality reduction technique that helps to maximiz

236 oy the diffusion map approach as a nonlinear dimensionality reduction technique to extract a dynamica

237 Here we demonstrate the advantages of a new dimensionality reduction technique, demixed principal co

238 DL-based dimensionality reduction technique, including variationa

239 Mash extends the MinHash dimensionality-reduction technique to include a pairwise

240 classes of procedures, among them classical dimensionality reduction techniques and others incorpora

241 Advanced dimensionality reduction techniques applied to the elect

242 However, conventional dimensionality reduction techniques are typically limite

243 nd on these findings by pursuing more robust dimensionality reduction techniques based on manifold em

244 However, dimensionality reduction techniques can provide approxim

245 However, traditional dimensionality reduction techniques cannot accomplish th

246 nt Analysis (PCA) is one of the most popular dimensionality reduction techniques for the analysis of

247 anatomy using F18-fluorodeoxyglucose PET and dimensionality reduction techniques in two cohorts of pa

248 Clustering and dimensionality reduction techniques often help in discer

249 cartography, particle image velocimetry, and dimensionality reduction techniques reveal that these os

250 Dimensionality reduction techniques revealed robust corr

251 Furthermore, we find that dimensionality reduction techniques such as PCA preferen

252 Dimensionality reduction techniques such as principal co

253 turing local structure compared with popular dimensionality reduction techniques such as t-SNE and UM

254 Here, we explore the application of various dimensionality reduction techniques that have been used

255 equires the use of the latest clustering and dimensionality reduction techniques to automatically seg

256 scale texture analysis as well as non-linear dimensionality reduction techniques to identify salient

257 close the importance of complementing linear dimensionality reduction techniques with nonlinear ones

258 that (i) VAE and MMD-VAE outperform existing dimensionality reduction techniques, (ii) integrated mul

259 After feature selection, seven dimensionality reduction techniques, including principal

260 Different from existing dimensionality reduction techniques, the proposed method

261 g large-scale neural recordings in mice with dimensionality reduction techniques, we observed that su

262 hen focus on one widely used set of methods, dimensionality reduction techniques, which allow users t

263 se of a greater level of information through dimensionality reduction techniques.

264 ce of cellular trajectories rely on unbiased dimensionality reduction techniques.

265 -we demonstrate that simple and well-studied dimensionality-reduction techniques such as principal co

266 Here, by applying dimensionality-reduction techniques to the singing behav

267 eature analysis provides a general two-sided dimensionality reduction that reveals encoding in high d

268 haracterizations adequately reflect the true dimensionality reduction that takes place in the nervous

269 Many recent studies have adopted dimensionality reduction to analyze these populations an

270 ps such as peak alignment, normalization, or dimensionality reduction to be tailored to specific prob

271 using machine learning algorithms to perform dimensionality reduction to capture eigengenome informat

272 d signal processing feature extraction, (ii) dimensionality reduction to differentiate dynamical path

273 classification, unsupervised clustering and dimensionality reduction to learn cancer-relevant data r

274 nformation-theoretic notion of surprisal for dimensionality reduction to promote more meaningful sign

275 re we use a combination of phylogenetics and dimensionality reduction to reevaluate the population st

276 Here, we use dotplots and dimensionality reduction to systematically define LCR ty

277 urrent analyses prioritize noise removal and dimensionality reduction to tackle these challenges and

278 Here, we develop scLENS, a dimensionality reduction tool that circumvents the long-

279 We describe a dimensionality reduction tool, compositional tensor fact

280 Machine learning (ML) provides powerful dimensionality reduction tools.

281 We develop a new approach to dimensionality reduction: tree preserving embedding.

282 ual-algorithm that uses cluster analysis and dimensionality reduction using a cohort of randomly sele

283 , a statistical method for visualization and dimensionality reduction using a tropical polytope with

284 Dimensionality reduction using principal components was

285 use of deep learning on subsampled nonlinear dimensionality reduction using t-SNE and UMAP to extract

286 Dimensionality reduction using the polynomial terms alon

287 g to look at the overall expression dataset, dimensionality reduction using UMAP to find correlations

288 Dimensionality reduction via coarse grain modeling is a

289 images of 1800 microstructures, followed by dimensionality reduction via principal component analysi

290 Using an evolutionary algorithm and dimensionality reduction, we discovered metaparameters,

291 riven modelling approach using probabilistic dimensionality reduction, we investigate covariation acr

292 ximation and Projection (UMAP) for nonlinear dimensionality reduction, we reduce the dataset's dimens

293 Different methods of variable selection and dimensionality reduction were used, and different algori

294 FITF also outperformed multifactor dimensionality reduction when interactions involved addi

295 vercome these difficulties, we propose DR-A (Dimensionality Reduction with Adversarial variational au

296 ribe ACCENSE, a tool that combines nonlinear dimensionality reduction with density-based partitioning

297 ew method (WaveMAP) that combines non-linear dimensionality reduction with graph clustering to identi

298 By performing dimensionality reduction with respect to gene co-express

299 Dimensionality reduction with t-SNE revealed distinct cl

300 We find evidence of goal-directed dimensionality reduction within human ventromedial PFC d