ライフサイエンスコーパス: factorization

1 Gene Ontology is integrated into the matrix factorization.

2 l component analysis and non-negative matrix factorization.

3 ssion profile via sparse non-negative matrix factorization.

4 property that is equivalent to their global factorization.

5 erogeneous data based on non-negative matrix factorization.

6 s vectors recovered with non-negative matrix factorization.

7 -Ontology and orthogonal non-negative matrix factorization.

8 ional efficiently of the compatibility-based factorizations.

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

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

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

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

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

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

15 We used a factorization algorithm to identify the muscle synergies

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

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

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

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

20 Positive matrix factorization analyses, based on aerosol mass spectromet

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

22 Positive matrix factorization analysis of high-resolution mass spectral

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

24 Our factorization analysis revealed, in a quantitative way,

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

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

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

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

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

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

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

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

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

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

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

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

37 These factorizations decompose a dataset of single-trial popul

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

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

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

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

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

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

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

45 Positive matrix factorization indicated that although volatilized Aroclo

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

127 it of explicitly enforcing sparseness in the factorization process.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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