ライフサイエンスコーパス: singular value decomposition

1 ributions due to absorbing side chains using singular value decomposition.

2 e information were used in analyses based on singular value decomposition.

3 of the lag-distribution of the angles using singular value decomposition.

4 oncentration is too small for application of singular value decomposition.

5 y subjecting the set of collected spectra to singular value decomposition.

6 y using the characteristic modes obtained by singular value decomposition.

7 Singular value decomposition analyses of the CD spectra

8 We have developed two novel methods for Singular Value Decomposition analysis (SVD) of microarra

9 of multiprotein complexes are determined by singular value decomposition analysis and clustering.

10 orption data in combination with generalized singular value decomposition analysis and multiexponenti

11 pectral analyses, which is demonstrated by a singular value decomposition analysis for Raman spectra

12 Singular value decomposition analysis of the circular di

13 Singular value decomposition analysis of the time-resolv

14 A sparse singular value decomposition analysis of variability in

15 Singular value decomposition analysis provides predictiv

16 oach outperforms standard approaches such as singular value decomposition and Fourier analysis.

17 Multivariate curve resolution based on singular value decomposition and global analysis is appl

18 Here we combine singular value decomposition and global analysis of NMR

19 The data were analyzed using singular value decomposition and global exponential fitt

20 00 ns to 1 s time interval, were analyzed by singular value decomposition and global exponential fitt

21 Singular value decomposition and global exponential fitt

22 The data were analyzed using singular value decomposition and global exponential fitt

23 -angle X-ray scattering and then analyzed by singular value decomposition and global fitting.

24 Global analysis, based on singular value decomposition and matrix least-squares al

25 times from 50 ns to 50 ms and analyzed using singular value decomposition and multiexponential fittin

26 existing factorization, techniques, such as singular value decomposition and non-negative matrix fac

27 Spectral clustering using the singular value decomposition and other bioinformatic tec

28 s of O2(*-) adduct formation and decay using singular value decomposition and pseudoinverse deconvolu

29 ion, Bayesian principal components analysis, singular value decomposition and random forest), on the

30 Singular value decomposition and reference to an indepen

31 , coupled with principal component analysis, singular-value decomposition and model reduction.

32 orded in the absence of dye were analyzed by singular-value decomposition and multiexponential fittin

33 present a data-analytical approach, based on singular-value decomposition and nonlinear Laplacian spe

34 components analysis and, more generally, the Singular Value Decomposition are fundamental data analys

35 S-SVM classifiers and generalized eigenvalue/singular value decompositions are successfully used in m

36 An approach based on singular value decomposition as opposed to simulated ann

37 e in processing time compared with classical singular value decomposition denoising.

38 rix algebra can be used to perform truncated singular-value decomposition despite the nonlinear geome

39 Singular-value-decomposition fits of the chemical shift

40 only such framework to date, the generalized singular value decomposition (GSVD), is limited to two m

41 ts of DNA microarray gene expression data by singular value decomposition has uncovered underlying pa

42 atical framework of Higher-Order Generalized Singular Value Decomposition (HO-GSVD).

43 We describe the use of a higher-order singular value decomposition (HOSVD) in transforming a d

44 assignment based on peak-shape analysis via singular value decomposition in combination with detaile

45 etween genes, among comparison groups, using singular value decomposition in combination with inner p

46 of the labeled segments were obtained using singular value decomposition in combination with target

47 We describe the use of singular value decomposition in transforming genome-wide

48 an be extracted using a technique called Lag singular value decomposition (LagSVD), which considers t

49 d the principal component analysis using the singular value decomposition method for detecting the gl

50 t efficient denoising algorithms require the singular value decomposition of a matrix with a size tha

51 In this work, the singular value decomposition of a sparse tetrapeptide fr

52 The singular value decomposition of recent metagenomic data

53 Through singular value decomposition of SSH, we are able to dete

54 Singular value decomposition of the data yields a set of

55 e flux solution space is illustrated through singular value decomposition of the randomly sampled poi

56 Singular value decomposition of the scattering curves sh

57 Singular value decomposition of UV spectra obtained as a

58 cation (inter alia) to the evaluation of the singular value decompositions of numerically low-rank ma

59 The first three vectors obtained by singular-value decomposition of each set of unfolding sp

60 LaSSI uses a singular value decomposition on chemical descriptors to

61 The method employs singular value decomposition on the square root of the C

62 in the time-resolved scattering patterns and singular value decomposition revealed that the expansion

63 Factor analysis of the CD spectra by singular value decomposition revealed that the experimen

64 he time-dependent difference Fourier maps by singular value decomposition reveals that only one signi

65 d with the analysis of roll-call votes using singular value decomposition, successfully uncovers poli

66 Singular value decomposition (SVD) analysis indicated th

67 Singular value decomposition (SVD) analysis was applied

68 A singular value decomposition (SVD) analysis was made of

69 The data were analyzed using singular value decomposition (SVD) and global exponentia

70 m infrared (FTIR) spectroscopy combined with singular value decomposition (SVD) and global fitting we

71 ivariate dimension reduction techniques, the Singular Value Decomposition (SVD) and Independent Compo

72 On the basis of singular value decomposition (SVD) and multiexponential

73 e implemented and evaluated three methods: a Singular Value Decomposition (SVD) based method (SVDimpu

74 Single exponential fits to the singular value decomposition (SVD) components of the SAX

75 An orthogonal basis was constructed using singular value decomposition (SVD) for each GC/MS two-wa

76 mprovement of the 3DCC method by introducing singular value decomposition (SVD) for processing of the

77 re varied in the refolding kinetics, and the singular value decomposition (SVD) method was employed t

78 We developed a novel method using singular value decomposition (SVD) normalization to disc

79 This new method, called LaSSI, uses the singular value decomposition (SVD) of a chemical descrip

80 One such approach is the singular value decomposition (SVD) of extreme pathway ma

81 Singular value decomposition (SVD) of matrices of extrem

82 cted transcription factor binding sites with singular value decomposition (SVD) of the inferred motif

83 We describe the singular value decomposition (SVD) of yeast genome-scale

84 s temperature data matrices were analyzed by singular value decomposition (SVD) to ascertain the mini

85 anges in base pair stacking were analyzed by singular value decomposition (SVD) to determine the 10 n

86 In this equilibrium study, we apply singular value decomposition (SVD) to elucidate both the

87 oyed principal components analysis (PCA) and singular value decomposition (SVD) to interpret HSV-2 ge

88 Singular value decomposition (SVD) was applied to the 22

89 In addition, singular value decomposition (SVD), a mathematical metho

90 Finally, singular value decomposition (SVD), a mathematical metho

91 linear algebra approaches, in particular the singular value decomposition (SVD), coupled with closely

92 Image data were reduced with singular value decomposition (SVD), which produced 20 ei

93 a global exponential fitting procedure after singular value decomposition (SVD).

94 obal exponential fitting procedure following singular value decomposition (SVD).

95 t read depth that is based on local adaptive singular value decomposition (SVD).

96 based on linear least-squares fitting using singular value decomposition (SVD).

97 ucted metabolic networks were analysed using singular value decomposition (SVD).

98 zed the unstructured state of IA(3) by using singular-value decomposition (SVD) to analyze the CD dat

99 gulatory programs and propose a thresholding singular value decomposition (T-SVD) regression method f

100 servation, the factorization method uses the singular value decomposition technique to factor the mea

101 In addition, use of singular value decomposition techniques and finite impul

102 f a decomposition technique (space-frequency singular value decomposition) that is shown to be a usef

103 From singular value decomposition, the major CD spectral comp

104 copy, differential scanning calorimetry, and singular-value decomposition, the number of species pres

105 rthermore, based on this new concept, we use Singular Value Decomposition to analyze real protein dat

106 pose an algorithm, called EigenMS, that uses singular value decomposition to capture and remove biase

107 It uses singular value decomposition to construct a family of ca

108 ed average thermodynamic data were fit using singular value decomposition to determine the eight non-

109 eated, which was subsequently factorized via singular value decomposition to extract pair-wise cosine

110 is differentiated from the noise by applying singular value decomposition to sets of target sequences

111 When the data were analyzed by singular value decomposition, two dominant characteristi

112 Singular value decomposition was applied to separate the

113 g the Poisson factor model, entitled Poisson Singular Value Decomposition with Offset (PSVDOS).

114 arisons indicate that k-nearest neighbor and singular value decomposition yielded a greater number of