ライフサイエンスコーパス: factorization

1 erogeneous data based on non-negative matrix factorization.

2 s vectors recovered with non-negative matrix factorization.

3 -Ontology and orthogonal non-negative matrix factorization.

4 isoform Functions with collaborative matrix factorization.

5 Gene Ontology is integrated into the matrix factorization.

6 l component analysis and non-negative matrix factorization.

7 ssion profile via sparse non-negative matrix factorization.

8 property that is equivalent to their global factorization.

9 iteration, demonstrated by the matrix polar factorization.

10 , a group testing method based on hypergraph factorization.

11 sing network-regularized non-negative matrix factorization.

12 ual clustering technique non-negative matrix factorization.

13 utational signatures via non-negative matrix factorization.

14 y structure to further coordinate the matrix factorization.

15 re by an orthonormal projective non-negative factorization.

16 ional efficiently of the compatibility-based factorizations.

17 al conjectures such as the hardness of prime factorization(1) to provide security against eavesdroppi

18 oaches: (1) quadratically regularized matrix factorization, (2) functional data analysis and (3) cano

19 nalyzed by three-dimensional positive matrix factorization (3D-PMF), showing that PBOA represented th

20 ks were delineated using non-negative matrix factorization, a multivariate analysis technique.

21 With 10-fold cross-validation, tensor factorization achieved AUROC = 0.82 +/- 0.02 and AUPRC =

22 d residual oil estimated via positive matrix factorization) across three U.S. highly urbanized region

23 ing a Markov chain Monte Carlo (MCMC) matrix factorization algorithm (GAPS) with a threshold-independ

24 discovery was based on a non-negative matrix factorization algorithm and significant copy number vari

25 -seq data sets by applying the CoGAPS Matrix Factorization algorithm and the projectR transfer learni

26 Analyses of EMGs using a nonnegative matrix factorization algorithm revealed that in seven of eight

27 the efficiency of the CoGAPS Bayesian matrix factorization algorithm so that it can analyze 1000 time

28 arsity, and a constrained nonnegative matrix factorization algorithm to extract signals from neurons

29 We used a factorization algorithm to identify the muscle synergies

30 vel reformulation of the non-negative matrix factorization algorithm to simultaneously search for syn

31 an unsupervised Bayesian non-negative matrix factorization algorithm using public genome-wide associa

32 ed end-to-end implementation of Shor's prime factorization algorithm, developed as part of a framewor

33 uantum computers; these include Shor's prime factorization algorithm, error correction, Grover's sear

34 ethod using an iterative non-negative matrix factorization algorithm.

35 and immune cells using a non-negative matrix factorization algorithm.

36 rithms, quaternary phase estimation and fast factorization algorithms.

37 (Triple inTegrative fast non-negative matrix factorization), an efficient joint factorization method

38 ped cNMTF (corrected non-negative matrix tri-factorization), an integrative algorithm based on cluste

39 ntronics technology, and demonstrate integer factorization, an illustrative example of the optimizati

40 study, we used iterative non-negative matrix factorization, an unbiased clustering method, on mRNA ex

41 Here, we applied nonnegative matrix factorization, an unsupervised multivariate pattern anal

42 Positive matrix factorization analyses, based on aerosol mass spectromet

43 of OA factors, resolved with Positive Matrix Factorization analysis of AMS data, is directly investig

44 Positive matrix factorization analysis of high-resolution mass spectral

45 Positive matrix factorization analysis of OA mass spectra from an aeroso

46 A factorization analysis on the smoothed discharge rates o

47 Our factorization analysis revealed, in a quantitative way,

48 tworks were derived using nonnegative matrix factorization and analyzed using generalized additive mo

49 was further analyzed by non-negative matrix factorization and demonstrated to be attributable to are

50 tic Regression, LASSO, Random Forest, Tensor Factorization and Gradient Boosting Machine.

51 Leveraging matrix factorization and optimal transport methods, we found th

52 atterns via unsupervised non-negative matrix factorization and quantifies the reproducibility of frag

53 at TCSM outperforms both non-negative matrix factorization and topic modeling-based approaches, parti

54 Non-negative matrix factorization and uncontrolled manifold analysis were pe

55 Within HFpEF, non-negative matrix factorization and weighted gene coexpression analysis id

56 emiparametric model, which combines low-rank factorizations and flexible Gaussian process priors to l

57 ed class discovery using non-negative matrix factorization, and functional annotation using gene-set

58 ipal component analysis, non-negative matrix factorization, and t-distributed stochastic neighbor emb

59 n parcellation technique-non-negative matrix factorization-and applied it to cortical thickness data

60 We use non-negative matrix factorization applied to the imaginary part and the nonr

61 Positive matrix factorization applied to the organic mass spectra sugges

62 ties by using a Bayesian non-negative matrix factorization approach.

63 gion of the phenotype space that has a given factorization as a "type", i.e. as a set of phenotypes t

64 We proposed a non-negative matrix factorization based method to rank, so as to predict, th

65 In particular, considering factorizations based on transcript-fragment compatibilit

66 Non-negative matrix factorization-based clustering of mRNA expression data w

67 his article, we propose a Nonnegative Matrix Factorization-based Immune-TUmor MIcroenvironment Deconv

68 MM patients using unsupervised binary matrix factorization (BMF) clustering and identify six distinct

69 presented a new algorithm for Boolean matrix factorization (BMF) via expectation maximization (BEM).

70 at were based on Bayesian nonnegative matrix factorization (bNMF) clustering.

71 as built based on coupled nonnegative matrix factorization (C-NMF).

72 osaic integration approaches based on matrix factorization cannot efficiently adapt to nonlinear embe

73 methods (HOPACH, sparse non-negative matrix factorization, cluster 'fitness', support vector machine

74 ing characterization and non-negative matrix factorization clustering of signaling profiles.

75 method we call consensus non-negative matrix factorization (cNMF) accurately infers identity and acti

76 h a novel combination of non-negative matrix factorization, compressed sensing and electron tomograph

77 tors were identified through positive matrix factorization coupled to single particle analysis, inclu

78 onality reduction tool, compositional tensor factorization (CTF), that incorporates information from

79 These factorizations decompose a dataset of single-trial popul

80 Deep matrix factorization (DMF) excels at uncovering subtle patterns

81 se a dual-network integrated logistic matrix factorization (DNILMF) algorithm to predict potential dr

82 putation framework, called Extensible Matrix Factorization (EMF).

83 ing subgraph sampling and nonnegative matrix factorization enables the discovery of these latent moti

84 ions were analyzed using non-negative matrix factorization followed by gene ontology filtering and ne

85 Here we present Probabilistic Matrix Factorization for Gene Regulatory Network Inference (PMF

86 del, GENERALIST: GENERAtive nonLInear tenSor-factorizaTion for protein sequences.

87 ngful, technically robust, and generalizable factorization for psychometric tools.

88 computational limitations of Bayesian matrix factorization for single cell data analysis.

89 d a machine learning technique called tensor factorization for the problem of predicting clinical out

90 e incomplete network captured using a matrix factorization formulation to constrain the set of reacti

91 Here, we introduce a matrix factorization framework to integrate physical and functi

92 y analyzed in a multiple non-negative matrix factorization framework, and additional network data are

93 ltisite data with cross-validation yielded a factorization generalizable across populations and medic

94 of graph convolution network (GCN) and graph factorization (GF).

95 Non-Negative Matrix factorization has become an essential tool for feature e

96 e component analysis and non-negative matrix factorization, have the disadvantage that they return di

97 cle, we present a hybrid non-negative matrix factorization (HNMF) method to integrate phenotype and g

98 itions among regions with different regional factorizations, i.e. for the evolution of new types or b

99 Non-negative matrix factorization identified 3 HFpEF transcriptomic subgroup

100 The resulting matrix factorization imputes gene abundance for both zero and n

101 a new software framework for parallel matrix factorization in Version 3 of the CoGAPS R/Bioconductor

102 Positive matrix factorization indicated that although volatilized Aroclo

103 The use of nonnegative matrix factorization indicated that there were five distinct DM

104 cribe online integrative non-negative matrix factorization (iNMF), an algorithm for integrating large

105 orthogonality-regularized nonnegative matrix factorization (iONMF) to integrate multiple data sources

106 In this work, we provide evidence that factorization is a normative principle of biological vis

107 The matrix factorization is an important way to analyze coregulatio

108 rithm based on a semi-nonnegative matrix tri-factorization is applied.

109 Non-negative matrix factorization is distinguished from the other methods by

110 When non-negative matrix factorization is implemented as a neural network, parts-

111 Moreover, because no matrix factorization is involved, SAILER can easily scale to pr

112 Non-negative matrix factorization is one such method and was shown to be adv

113 autoencoders and sparse non-negative matrix factorization is reproducible and can be associated with

114 hine learning technique, non-negative matrix factorization, is applied to analyze the total scatterin

115 =U(i)Sigma(i)V(T), where V, identical in all factorizations, is obtained from the eigensystem SV=VLam

116 alternative method, knowledge-driven matrix factorization (KMF) framework, to reconstruct phenotype-

117 introduce Localized semi-Nonnegative Matrix Factorization (LocaNMF), a method that efficiently decom

118 Among surrogate models, 2(nd)-order factorization machine (FM) models are widely employed as

119 orming other state-of-the-art models such as factorization machine and extreme gradient boosting tree

120 r to Mendel-Impute, our matrix co-clustering factorization (MCCF) model is completely new.

121 ey are each characterized by their choice of factorization method (5 options), choice of probability

122 e tool is built using a probabilistic matrix factorization method and DrugBank v3, and the latent var

123 e AMM to derive an affine nonnegative matrix factorization method for estimating fluorophore endmembe

124 ve matrix factorization), an efficient joint factorization method for single-cell multiomics data.

125 igh REsolution), a segmentation-free spatial factorization method that can handle transcriptome-wide

126 We have developed a factorization method that can overcome this difficulty b

127 ether, and then present a nonnegative matrix factorization method to learn the parameters of the mode

128 Using this observation, the factorization method uses the singular value decompositi

129 We propose a deep neural network tensor factorization method, Avocado, that compresses this epig

130 tagenomic binning based on AFIT and a matrix factorization method.

131 Here, we present a novel matrix factorization methodology for drug-target interaction pr

132 pose a novel su- pervised nonnegative tensor factorization methodology that derives discriminative an

133 However, matrix factorization methods are prone to technical artifacts a

134 However, current matrix factorization methods do not provide clear bicluster str

135 ssification using genomic nonnegative matrix factorization methods identified three distinct genomic

136 We validated various factorization methods on simulated data and on populatio

137 Bayesian factorization methods, including Coordinated Gene Activi

138 Applying matrix factorization methods, we decompose the drug-disease ass

139 s data via TL of clustering, correlation and factorization methods.

140 In this paper, we have utilized the matrix factorization (MF) as a modality for high dimensionality

141 ionary learning (DL), implemented via matrix factorization (MF), is commonly used in computational bi

142 We develop a constrained matrix factorization model, sn-spMF, to learn patterns of tissu

143 lyadic Decomposition and conventional matrix factorization models by evaluation of detecting spatial

144 ometric curves better than shape from motion factorization models using shape or trajectory basis fun

145 propose a multi-similarities bilinear matrix factorization (MSBMF) method to predict promising drug-a

146 intermediate integrative approaches (matrix factorization, multiple kernel methods, ensemble learnin

147 We have developed a nonnegative matrix factorization (NMF) algorithm to detect and separate spe

148 Non-negative Matrix Factorization (NMF) algorithms associate gene expression

149 ervised methods, such as non-negative matrix factorization (NMF) and Convex Analysis of Mixtures (CAM

150 elation analysis (sCCA), non-negative matrix factorization (NMF) and logic data mining MicroArray Log

151 sing iterative consensus non-negative matrix factorization (NMF) based clustering.

152 digital filter based on non-negative matrix factorization (NMF) enables blind deconvolution of tempo

153 In this study, we apply non-negative matrix factorization (NMF) for the unsupervised analysis of ToF

154 the data analysis method non-negative matrix factorization (NMF) has been applied to the analysis of

155 pollutant sources using non-negative matrix factorization (NMF) in a moderately polluted urban area.

156 an be de-convolved using non-negative matrix factorization (NMF) into discrete trinucleotide-based mu

157 Non-negative matrix factorization (NMF) is a technique widely used in variou

158 Non-negative matrix factorization (NMF) is an increasingly used algorithm fo

159 Here, we propose non-negative matrix factorization (NMF) of fragment length distributions as

160 We then use non-negative matrix factorization (NMF) to approximate these protein family

161 analysis methods, we used nonnegative matrix factorization (NMF) to uncover coordinated patterns of c

162 sing N-grams generation, Non-Negative Matrix Factorization (NMF) topics and sentiment characteristics

163 ration (BSS) techniques: non-negative matrix factorization (NMF) using additional sparse conditioning

164 ction principle known as non-negative matrix factorization (NMF) was previously shown to account for

165 calable implementation of nonnegative matrix factorization (NMF) with a new stability-driven model se

166 describe here the use of nonnegative matrix factorization (NMF), an algorithm based on decomposition

167 omponent analysis (PCA), non-negative matrix factorization (NMF), maximum autocorrelation factor (MAF

168 ing PCA, Kernel PCA, and Non-Negative Matrix Factorization (NMF), were compared to nine selection met

169 transformations, such as nonnegative matrix factorization (NMF).

170 motif analysis based on non-negative matrix factorization (NMF).

171 me sequencing data using non-negative matrix factorization (NMF).

172 rties was established by non-negative matrix factorization (NMF).

173 er reference dataset via Non-negative Matrix Factorization (NMF).

174 Sparse non-negative matrix factorizations (NMFs) are useful when the degree of spar

175 ulti-view clustering, nonnegative matrix tri-factorization (NMTF) and nonnegative Tucker decompositio

176 ores are computed by Non-negative Matrix Tri-Factorization (NMTF) method that predicts associations b

177 Here, we present nonnegative spatial factorization (NSF), a spatially-aware probabilistic dim

178 Prime factorization of 51 and 85 can be demonstrated with only

179 es into subgroups by a joint sparse rank-one factorization of all the data matrices.

180 d of an enthalpic and entropic contribution, factorization of both can unravel the complexity of a fl

181 types have and are thus compatible with the factorization of both types.

182 With sparse factorization of data matrices using GPCA, groups of cor

183 Factorization of integers up to 945 is demonstrated with

184 can analyze 1000 times more cells, enabling factorization of large single-cell data sets.

185 At the heart of our framework is a factorization of local neighborhood information in the p

186 f the simplest instance of Shor's algorithm: factorization of N = 15 (whose prime factors are 3 and 5

187 The method, based on the multidimensional QR factorization of numerically encoded multiple sequence a

188 nkey ventral visual hierarchy, we found that factorization of object pose and background information

189 ry method based on joint non-negative matrix factorization of spatial RNA transcripts and histologica

190 Positive matrix factorization of the AMS spectra resolved the organic ae

191 On the basis of a factorization of the likelihood and the constrained sear

192 gorithms they use by adopting an approximate factorization of the likelihood function they optimize.

193 rning to identify a stable and generalizable factorization of the Positive and Negative Syndrome Scal

194 Moreover, an extended model based on a factorization of the sparse-variable GLM allowed us to d

195 ecture lead to the improved performance: (1) factorization of the STRF matrix into a small number of

196 Here we investigate the utility of tensor factorizations of population spike trains along space an

197 However, these approximate factorizations of the likelihood function simplify calcu

198 demonstrate that model simplifications (i.e. factorizations of the likelihood function) adopted by ce

199 bilities, and adopting improved, data-driven factorizations of this likelihood, we demonstrate that s

200 Source apportionment using positive matrix factorization on the hourly data revealed four primary P

201 comparison with classic non-negative matrix factorization on the three well-studied datasets.

202 pal components analysis, non-negative matrix factorization or no DR.

203 terfering subspaces of population activity ('factorization') or encoded in an entangled fashion.

204 Inspired by non-negative matrix factorization, our model fully exploits the unique prope

205 ry-2a algorithm (ART-2a) and positive matrix factorization partition a continuum of particle composit

206 Furthermore, a positive matrix factorization (PMF) analysis demonstrated a predominance

207 HPLC-MS/MS analysis and (2) positive matrix factorization (PMF) analysis of aerosol mass spectromete

208 fraction of BB resolved from positive matrix factorization (PMF) analysis of organic mass spectral da

209 MS and the CO2 analyzer, (2) positive matrix factorization (PMF) analysis to separate the gas- and pa

210 rajectory investigations and Positive Matrix Factorization (PMF) analysis, we deduce that Red Sea Dee

211 Baltic Sea were evaluated by positive matrix factorization (PMF) and principal component analysis (PC

212 Positive matrix factorization (PMF) applied to organic aerosol (OA) data

213 Positive Matrix Factorization (PMF) applied to the data set revealed a f

214 ce apportionment of PM(2.5), positive matrix factorization (PMF) coupled with a bootstrap technique f

215 rganic compound (SVOC) data, positive matrix factorization (PMF) coupled with a bootstrap technique w

216 Positive matrix factorization (PMF) has been applied to single particle

217 mass balance (CMB) model and positive matrix factorization (PMF) in order to quantify PBDE sources an

218 AMS data with a constrained positive matrix factorization (PMF) method using the multilinear engine

219 Five sources based on Positive Matrix Factorization (PMF) modeling include industrial emission

220 factor recently resolved by positive matrix factorization (PMF) of aerosol mass spectrometer data co

221 unique factor resolved from positive matrix factorization (PMF) of AMS organic aerosol spectra colle

222 is source-apportioned using positive matrix factorization (PMF) of data collected from aerosol mass

223 ) were incorporated into the positive matrix factorization (PMF) receptor model to form a receptor-or

224 atistical approach, based on positive matrix factorization (PMF) shows that the COA factor was clearl

225 set has been examined using positive matrix factorization (PMF) to apportion PCB sources in the air,

226 ce apportionment tool called Positive Matrix Factorization (PMF) to identify the sources of PCBs to t

227 Positive matrix factorization (PMF) was applied to identify and apportio

228 Source apportionment by Positive Matrix Factorization (PMF) was carried out to interpret the rea

229 Positive matrix factorization (PMF) was used for apportioning sources of

230 To investigate this issue, Positive Matrix Factorization (PMF) was used to identify the dominant so

231 Positive matrix factorization (PMF) was used to resolve PM0.1 source con

232 ce apportionment study using positive matrix factorization (PMF), performed on long-term PM2.5 chemic

233 by the ACSM were analyzed by positive matrix factorization (PMF), yielding three conventional factors

234 source factors resolved from positive matrix factorization (PMF).

235 ater fish were examined with positive matrix factorization (PMF).

236 (CARP) and were analyzed via Positive Matrix Factorization (PMF).

237 hese data were analyzed with Positive Matrix Factorization (PMF).

238 orks using penalized non-negative matrix tri-factorization (PNMTF).

239 FactVAE integrates the factorization principle into the variational autoencoder

240 teger linear programming solution to the VAF factorization problem in the case of error-free data and

241 e reconstruction of gene network as a matrix factorization problem, we first use the gene expression

242 ations as the variant allele frequency (VAF) factorization problem.

243 it of explicitly enforcing sparseness in the factorization process.

244 Compared to classical non-negative matrix factorization, proposed method: (i) improves color decom

245 we show that our fused regularization matrix factorization provides a novel incorporation of external

246 data preprocessing and normalization, joint factorization, quantile normalization and joint clusteri

247 ticle, a regularized non-negative matrix tri-factorization (R-NMTF) algorithm is introduced to co-clu

248 The problem of finding such a factorization reduces to finding an appropriate represen

249 For instance, Positive Matrix Factorization results are very sensitive to both the fit

250 ion of tissues, and encourages interpretable factorization results.

251 Moreover, C-ZIPTF notably enhances the factorization's consistency.

252 e single-cell Projective Non-negative Matrix Factorization (scPNMF) method to select informative gene

253 We applied non-negative matrix factorization separately to the cortical and muscular da

254 value decomposition and non-negative matrix factorization show that our method provides higher predi

255 We propose a sparse multi-view matrix factorization (sMVMF) algorithm to jointly analyse gene

256 approaches such as sparse nonnegative matrix factorization (sNMF) and EIGENSTRAT have been proposed,

257 cipal component analysis/non-negative matrix factorization step compared with the classifier alone en

258 , termed spike-triggered non-negative matrix factorization (STNMF), can address these issues.

259 residuals using a simple non-negative matrix factorization strategy.

260 notation software package using a novel HMM "factorization" strategy.

261 dent component analysis, non-negative matrix factorization, t-distributed stochastic neighbor embeddi

262 A nonnegative matrix factorization technique separately decomposed clinical (

263 Here, we adopt a joint matrix factorization technique to address this challenge.

264 Although matrix factorization techniques have been widely adopted in lin

265 e volume of data, we propose to apply tensor factorization techniques to reduce the data volumes.

266 Comparisons with existing factorization, techniques, such as singular value decomp

267 h relies on a constrained nonnegative matrix factorization that expresses the spatiotemporal fluoresc

268 nstrate an algorithm for non-negative matrix factorization that is able to learn parts of faces and s

269 have not led to a convincing path to integer factorization that is competitive with the best known cl

270 re a promising, alternative method for prime factorization that uses well-established techniques from

271 ruction with relational inference via tensor factorization to accurately predict disease-gene links.

272 R), that uses integrative nonnegative matrix factorization to address this challenge.

273 prediction task, utilizes collective matrix factorization to compress the data, and chaining to rela

274 o dimensions: it builds on collective matrix factorization to derive different semantics, and it form

275 visualization tool using non-negative matrix factorization to display modality changes.

276 med knots are used in conjunction with prime factorization to encode information.

277 red matrices are constrained by non-negative factorization to ensure that the completed drug-disease

278 nd water were examined using positive matrix factorization to look for evidence that PCBs and PCDD/Fs

279 In parallel, we use nonnegative matrix factorization to predict enhanced gene expression maps o

280 es the multiple alignment, we adapted the QR factorization to produce a minimal basis set of protein

281 , we benchmark and enhance the use of matrix factorization to solve this problem.

282 sults introduce the concept of CX/CUR matrix factorizations to mass spectrometry imaging, describing

283 algorithm, based on the multidimensional QR factorization, to remove redundancy from a multiple stru

284 ionment of urban PM(2.5) via positive matrix factorization uncovers a new source of transported anthr

285 or chemical composition, and Positive Matrix Factorization was used to determine contributions of PM2

286 Using the notion of local factorizations we develop a theory of character identity

287 ptive shrinkage and semi-non-negative matrix factorization, we designed parallelization strategies fa

288 nlike many popular approaches such as matrix factorization, we do not assume that users in each group

289 ction technique known as non-negative matrix factorization, we found that a variety of medial superio

290 Using nonnegative matrix factorization, we measured the contribution of each sign

291 Such integration is possible by matrix factorization, where current approaches have an undesire

292 ditive analysis, e.g. by non-negative matrix factorization, where the mutations within a cancer sampl

293 ancement method based on non-negative matrix factorization which incorporates an iteratively updating

294 novel technique based on non-negative matrix factorization which is able to reconstruct lineage defin

295 tasks, traditional techniques such as matrix factorization (which can be seen as a type of graph embe

296 gy of this network, using data-driven matrix factorization, which allowed for partitioning into a set

297 is based on constrained non-negative matrix factorization with a new biologically motivated regulari

298 n curve fitting known as non-negative matrix factorization with alternating least-squares algorithm (

299 them to subgroups using non-negative matrix factorization with k-means clustering.

300 The classical QR factorization with pivoting, developed as a fast numeric