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1 y using the characteristic modes obtained by singular value decomposition.
2 ributions due to absorbing side chains using singular value decomposition.
3 e information were used in analyses based on singular value decomposition.
4 raining randomness and the non-uniqueness of singular value decomposition.
5 stprocessing method was implemented by using singular value decomposition.
6  of the lag-distribution of the angles using singular value decomposition.
7 oncentration is too small for application of singular value decomposition.
8 y subjecting the set of collected spectra to singular value decomposition.
9 dard IRLS algorithms since it avoids forming singular value decompositions.
10 OADP uses a computationally efficient online singular value decomposition algorithm, which can greatl
11                                              Singular value decomposition analyses of the CD spectra
12      We have developed two novel methods for Singular Value Decomposition analysis (SVD) of microarra
13  of multiprotein complexes are determined by singular value decomposition analysis and clustering.
14 orption data in combination with generalized singular value decomposition analysis and multiexponenti
15 pectral analyses, which is demonstrated by a singular value decomposition analysis for Raman spectra
16                                              Singular value decomposition analysis of the circular di
17                                              Singular value decomposition analysis of the time-resolv
18                                     A sparse singular value decomposition analysis of variability in
19                                              Singular value decomposition analysis provides predictiv
20 nd perform clustering (here using randomized singular value decomposition and BH-tSNE).
21 oach outperforms standard approaches such as singular value decomposition and Fourier analysis.
22       Multivariate curve resolution based on singular value decomposition and global analysis is appl
23                              Here we combine singular value decomposition and global analysis of NMR
24                 The data were analyzed using singular value decomposition and global exponential fitt
25                 The data were analyzed using singular value decomposition and global exponential fitt
26 00 ns to 1 s time interval, were analyzed by singular value decomposition and global exponential fitt
27                                              Singular value decomposition and global exponential fitt
28 -angle X-ray scattering and then analyzed by singular value decomposition and global fitting.
29                             Here, we applied singular value decomposition and gradient analyses to ch
30                    Global analysis, based on singular value decomposition and matrix least-squares al
31 times from 50 ns to 50 ms and analyzed using singular value decomposition and multiexponential fittin
32 es that popular embedding techniques such as singular value decomposition and node2vec fail to captur
33  existing factorization, techniques, such as singular value decomposition and non-negative matrix fac
34 alues, a technology is proposed based on the singular value decomposition and on the separation of di
35                Spectral clustering using the singular value decomposition and other bioinformatic tec
36 s of O2(*-) adduct formation and decay using singular value decomposition and pseudoinverse deconvolu
37 ion, Bayesian principal components analysis, singular value decomposition and random forest), on the
38                                              Singular value decomposition and reference to an indepen
39                Here, using the properties of singular value decomposition and subsampling algorithm,
40 , coupled with principal component analysis, singular-value decomposition and model reduction.
41 orded in the absence of dye were analyzed by singular-value decomposition and multiexponential fittin
42 present a data-analytical approach, based on singular-value decomposition and nonlinear Laplacian spe
43 components analysis and, more generally, the Singular Value Decomposition are fundamental data analys
44 S-SVM classifiers and generalized eigenvalue/singular value decompositions are successfully used in m
45                         An approach based on singular value decomposition as opposed to simulated ann
46            Tissue clutter was rejected using singular value decomposition based spatiotemporal clutte
47 re facilitates aberration correction using a singular value decomposition-based beamformer.
48 nt in computational efficiency over previous Singular Value Decomposition-based implementations.
49              We describe a generalization of singular value decomposition-based reconstruction for wh
50  of genetic associations, we apply truncated singular value decomposition (DeGAs) to matrices of summ
51 e in processing time compared with classical singular value decomposition denoising.
52 rix algebra can be used to perform truncated singular-value decomposition despite the nonlinear geome
53                                              Singular-value-decomposition fits of the chemical shift
54 only such framework to date, the generalized singular value decomposition (GSVD), is limited to two m
55 ts of DNA microarray gene expression data by singular value decomposition has uncovered underlying pa
56 atical framework of Higher-Order Generalized Singular Value Decomposition (HO-GSVD).
57        We describe the use of a higher-order singular value decomposition (HOSVD) in transforming a d
58  assignment based on peak-shape analysis via singular value decomposition in combination with detaile
59 etween genes, among comparison groups, using singular value decomposition in combination with inner p
60  of the labeled segments were obtained using singular value decomposition in combination with target
61                       We describe the use of singular value decomposition in transforming genome-wide
62 an be extracted using a technique called Lag singular value decomposition (LagSVD), which considers t
63 d the principal component analysis using the singular value decomposition method for detecting the gl
64 t efficient denoising algorithms require the singular value decomposition of a matrix with a size tha
65                            In this work, the singular value decomposition of a sparse tetrapeptide fr
66                                          The singular value decomposition of recent metagenomic data
67                                      Through singular value decomposition of SSH, we are able to dete
68                                              Singular value decomposition of the data yields a set of
69 tion, bimodularity can be optimized with the singular value decomposition of the directed modularity
70 e flux solution space is illustrated through singular value decomposition of the randomly sampled poi
71                                              Singular value decomposition of the scattering curves sh
72                                              Singular value decomposition of UV spectra obtained as a
73  to predict the need for >=3 shocks based on singular value decompositions of ECG wavelet transforms.
74 cation (inter alia) to the evaluation of the singular value decompositions of numerically low-rank ma
75                                              Singular value decompositions of one-dimensional and two
76          The first three vectors obtained by singular-value decomposition of each set of unfolding sp
77                                 LaSSI uses a singular value decomposition on chemical descriptors to
78                           The method employs singular value decomposition on the square root of the C
79 in the time-resolved scattering patterns and singular value decomposition revealed that the expansion
80         Factor analysis of the CD spectra by singular value decomposition revealed that the experimen
81 he time-dependent difference Fourier maps by singular value decomposition reveals that only one signi
82 es and denoises the dataset using randomized Singular Value Decomposition (rSVD), followed by the imp
83 d with the analysis of roll-call votes using singular value decomposition, successfully uncovers poli
84      The reverse engineering analysis uses a Singular Value Decomposition (SVD) algorithm to solve th
85                                              Singular value decomposition (SVD) analysis indicated th
86                                              Singular value decomposition (SVD) analysis was applied
87                                            A singular value decomposition (SVD) analysis was made of
88                 The data were analyzed using singular value decomposition (SVD) and global exponentia
89 m infrared (FTIR) spectroscopy combined with singular value decomposition (SVD) and global fitting we
90 ivariate dimension reduction techniques, the Singular Value Decomposition (SVD) and Independent Compo
91                              On the basis of singular value decomposition (SVD) and multiexponential
92                                  We employed singular value decomposition (SVD) and subsequent recons
93 culation of both a similarity matrix and its singular value decomposition (SVD) are computationally i
94 e implemented and evaluated three methods: a Singular Value Decomposition (SVD) based method (SVDimpu
95 y-line high-frequency ultrasound imagers and singular value decomposition (SVD) clutter filtering for
96               Single exponential fits to the singular value decomposition (SVD) components of the SAX
97    An orthogonal basis was constructed using singular value decomposition (SVD) for each GC/MS two-wa
98 mprovement of the 3DCC method by introducing singular value decomposition (SVD) for processing of the
99 re varied in the refolding kinetics, and the singular value decomposition (SVD) method was employed t
100            We developed a novel method using singular value decomposition (SVD) normalization to disc
101      This new method, called LaSSI, uses the singular value decomposition (SVD) of a chemical descrip
102                    The treatment is based on singular value decomposition (SVD) of a matrix of comput
103                     One such approach is the singular value decomposition (SVD) of extreme pathway ma
104                                              Singular value decomposition (SVD) of matrices of extrem
105 cted transcription factor binding sites with singular value decomposition (SVD) of the inferred motif
106 component GRSs' weights are derived from the singular value decomposition (SVD) of the matrix of appr
107                              We describe the singular value decomposition (SVD) of yeast genome-scale
108 h 4DSF (clinical standard) and s4DSF and (b) singular value decomposition (SVD) on original (clinical
109 hen combining adaptive demodulation (AD) and singular value decomposition (SVD) techniques.
110 s temperature data matrices were analyzed by singular value decomposition (SVD) to ascertain the mini
111 anges in base pair stacking were analyzed by singular value decomposition (SVD) to determine the 10 n
112          In this equilibrium study, we apply singular value decomposition (SVD) to elucidate both the
113 oyed principal components analysis (PCA) and singular value decomposition (SVD) to interpret HSV-2 ge
114                                              Singular value decomposition (SVD) was applied to the 22
115                                 In addition, singular value decomposition (SVD), a mathematical metho
116                                     Finally, singular value decomposition (SVD), a mathematical metho
117 linear algebra approaches, in particular the singular value decomposition (SVD), coupled with closely
118 s: K-nearest neighbors (KNN), Mean, MinProb, Singular Value Decomposition (SVD), Multivariate Imputat
119 n and feature extraction has been the matrix singular value decomposition (SVD), which presupposes th
120                 Image data were reduced with singular value decomposition (SVD), which produced 20 ei
121 a global exponential fitting procedure after singular value decomposition (SVD).
122 obal exponential fitting procedure following singular value decomposition (SVD).
123 t read depth that is based on local adaptive singular value decomposition (SVD).
124  based on linear least-squares fitting using singular value decomposition (SVD).
125 ucted metabolic networks were analysed using singular value decomposition (SVD).
126 sing a Fourier trig transform, followed by a Singular Values Decomposition (SVD).
127 zed the unstructured state of IA(3) by using singular-value decomposition (SVD) to analyze the CD dat
128 gulatory programs and propose a thresholding singular value decomposition (T-SVD) regression method f
129 servation, the factorization method uses the singular value decomposition technique to factor the mea
130                          In addition, use of singular value decomposition techniques and finite impul
131  all reactive species are deconvoluted using singular-value decomposition techniques that yield spect
132 rocedure by the higher dimensional analog of singular value decomposition, tensor decomposition.
133 f a decomposition technique (space-frequency singular value decomposition) that is shown to be a usef
134                                         From singular value decomposition, the major CD spectral comp
135 copy, differential scanning calorimetry, and singular-value decomposition, the number of species pres
136 rthermore, based on this new concept, we use Singular Value Decomposition to analyze real protein dat
137 pose an algorithm, called EigenMS, that uses singular value decomposition to capture and remove biase
138                                      It uses singular value decomposition to construct a family of ca
139 ed average thermodynamic data were fit using singular value decomposition to determine the eight non-
140 eated, which was subsequently factorized via singular value decomposition to extract pair-wise cosine
141                                       We use singular value decomposition to identify drug-selective
142 is differentiated from the noise by applying singular value decomposition to sets of target sequences
143  made possible in part by the application of singular value decomposition to the MISC data using a pr
144               When the data were analyzed by singular value decomposition, two dominant characteristi
145                                              Singular value decomposition was applied to separate the
146 he proposed EMP-SVD (Ensemble Meta Paths and Singular Value Decomposition), we introduce five meta pa
147 g the Poisson factor model, entitled Poisson Singular Value Decomposition with Offset (PSVDOS).
148                                     Whitened singular value decomposition (WSVD) with internal refere
149 arisons indicate that k-nearest neighbor and singular value decomposition yielded a greater number of

 
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