ライフサイエンスコーパス: eigenvector

1 igenvalues of the transition matrix, not the eigenvectors.

2 by projecting the signal onto characteristic eigenvectors.

3 regression, adjusting for age, sex, and five eigenvectors.

4 framework to predict the shapes of localized eigenvectors.

5 d individuals and the set of eigenvalues and eigenvectors.

6 genes that associate with the most important eigenvectors.

7 The main physical driver of Eigenvector 1 has long been suspected to be the quasar l

8 The dominant trend is known as 'Eigenvector 1', in which many properties correlate with

9 veals that the Eddington ratio indeed drives Eigenvector 1.

10 vestigation into the components of deviating eigenvectors after varimax orthogonal rotation reveals d

11 e, it was shown that the rotation of certain eigenvectors allowed the presence of trace analytes to b

12 An eigenvector analysis indicated that both normal and adap

13 l component analysis, based on an eigenvalue-eigenvector analysis of the scaled sensitivity coefficie

14 he underlying dynamical mechanism through an eigenvector analysis.

15 Eigenvector and degree centrality achieved high accuracy

16 ph-theoretical approach based on centrality (eigenvector and distance-weighted fragmentation) of habi

17 plex environments are linked to the dominant eigenvector and to spectral properties of G0.

18 by the plane spanned by the major and medium eigenvectors and depicted with three-dimensional graphic

19 ids--in persistent homology, and for finding eigenvectors and eigenvalues of the combinatorial Laplac

20 se the mixing matrices into population-level eigenvectors and subject-specific loadings.

21 cation by covariate adjustment using the top eigenvectors and to estimate cluster or group membership

22 imensions of thalamic shape variation (i.e., eigenvectors) and the activation patterns within thalami

23 wo common approaches to determine PCA modes (eigenvectors) and their eigenvalues.

24 Subspaces defined by the top eigenvectors are compared using several statistical metr

25 derived from an elastic network model, whose eigenvectors are reorientated to correct for the distort

26 The dual eigen-analysis identified the HFD eigenvectors as well as the endogenous eigenvectors for

27 ere; these formulas just involve finding the eigenvector associated with the largest eigenvalue of a

28 We used the degree, eigenvector, betweenness and closeness centrality metric

29 efficiently finds the eigenvalue of a given eigenvector but requires fully coherent evolution.

30 In addition, these eigenvectors can be readily identified by commonly used

31 value decomposition (SVD), which produced 20 eigenvectors capturing 97.05% of the total information c

32 complex network of nodes and links, and used eigenvector centrality (EC) to characterize brain networ

33 demonstrate that betweenness centrality and eigenvector centrality are reduced within network hubs,

34 At the cluster level, eigenvector centrality group patterns were similar to DC

35 One caveat is that Eigenvector Centrality is not robust to error unless the

36 Eigenvector centrality predicted both the speed with whi

37 ng the different measures of centrality, the eigenvector centrality reveals to be the best to reduce

38 The dominance of betweenness centrality and eigenvector centrality suggests that cognitive impairmen

39 We present an application of eigenvector centrality to encode the connectivity of pol

40 Reductions in betweenness centrality and eigenvector centrality were particularly evident within

41 g local (degree centrality [DC]) and global (eigenvector centrality) functional relationships across

42 as well as a whole brain centrality measure (eigenvector centrality).

43 rms consistently better than HITS, PageRank, eigenvector centrality, and edge betweenness from the pe

44 etweenness centrality, closeness centrality, eigenvector centrality, and subgraph centrality) are int

45 ion, like reputation, depth algorithms, like Eigenvector Centrality, are required.

46 raph metrics were betweenness centrality and eigenvector centrality, which provide measures of the ex

47 Eigenvector centrality, which quantifies the importance

48 and node degree, betweenness, closeness, and eigenvector centrality.

49 ets and find that the component signs of the eigenvectors contain either geographical information or

50 igen-analysis, in which the sample- and gene-eigenvectors correspond respectively to the macro- and m

51 f 3D diffusion tensors, in which the leading eigenvector corresponded to the principal fiber orientat

52 When this is avoided, the eigenvector corresponding to the smallest eigenvalue of

53 matrices of expression data can be found in eigenvectors corresponding to characteristic expression

54 the negative modes to retain only those with eigenvectors corresponding to double-well potentials doe

55 hput 3C method Hi-C, and we demonstrate that eigenvector decomposition of the obtained maps provides

56 Eigenvector decomposition of this energetic space reveal

57 llow the contact maps to be compared, we use eigenvector decomposition to resolve the principal eigen

58 validate this ICE (iterative correction and eigenvector decomposition) technique on published data o

59 Morlet wavelet transformation of the leading eigenvector-derived, m(1)AChR eigenfunctions locates sev

60 The first two eigenvectors describing the motions of M2 correspond to

61 impact roughly coincides with the endogenous eigenvector driven by aging.

62 classifier whose weights are equal to these eigenvector entries.

63 kably, the same combination of low-frequency eigenvectors exhibit the dominant contribution for both

64 d electromagnon absorption, the polarization eigenvectors exhibited chromatic dispersion, as a result

65 eated by numerically integrating the primary eigenvector field in the heart into coherent streamlines

66 e HFD eigenvectors as well as the endogenous eigenvectors for each tissue.

67 The weighted sum of two eigenvectors formed the peptide design template.

68 n directly relevant to ancestral origin; the eigenvectors generated by Eigenstrat are sample specific

69 The low-frequency subspace of eigenvectors in normal mode analysis was found to be rob

70 with positive degree-degree correlations, an eigenvector localized on the high k-core nodes, or a hig

71 ironmental variables, distance-based Moran's eigenvectors maps (dbMEM) to describe spatial relationsh

72 graph Laplacian and projects the rows of the eigenvector matrix onto the sphere.

73 The eigenvector matrix produced by the method permits the co

74 participation ratio of a network's principle eigenvector - measuring how many nodes effectively contr

75 cause of non-normal dynamics (non-orthogonal eigenvectors near a stable fixed point) inherent in the

76 dominant FC pattern captured by the leading eigenvector of dynamic FC matrices.

77 rete image volumes, and deriving the minimal eigenvector of the center voxel Hessian matrix.

78 The leading eigenvector of the connectivity matrix, which captures n

79 ntribution of this class to the leading left eigenvector of the matrix describing the movements of ge

80 modified algorithm converges to the leading eigenvector of the matrix EC, where C is the input covar

81 network topology and given by the principal eigenvector of the network's adjacency matrix.

82 classifiers can be ranked using the leading eigenvector of this covariance matrix, because its entri

83 We observe that the eigenvectors of a certain sparse matrix exactly match th

84 modularity can be expressed in terms of the eigenvectors of a characteristic matrix for the network,

85 onent analysis of simulation bursts by using eigenvectors of a Markov matrix describing anisotropic d

86 or metapopulation networks, demonstrate that eigenvectors of G0 provide a synthetic and effective too

87 engineering require finding eigenvalues and eigenvectors of large matrices.

88 m(1)AChR amino acid sequence yielded ordered eigenvectors of orthogonal hydrophobic variational patte

89 coupled "eigengenes." The eigengenes are the eigenvectors of the arrays x arrays correlation matrix,

90 ar network, timescales are segregated if the eigenvectors of the connectivity matrix are localized to

91 The finite-basis eigenvalues and eigenvectors of the Hamiltonian were obtained by solving

92 s was represented in the basis formed by the eigenvectors of the Hamiltonian.

93 c potential function along the directions of eigenvectors of the lowest normal modes to determine the

94 From the analysis of the eigenvectors of the transition matrix associated with th

95 Further, we identified nine eigenvectors (or synergies) that are remarkably similar

96 ure, which could be decomposed into discrete eigenvectors, or "ecomodes." The largest ecomode dominat

97 ctroscopic data employing a multidimensional eigenvector phase space approach, an empirical phase dia

98 nding the weight vectors is formulated as an eigenvector problem with a unique solution.

99 esent a way to solve the extremal eigenvalue/eigenvector problem, turning it into a nonlinear classic

100 Improved confidence in eigenvector reproducibility enabled tracking of myolamin

101 The enrichment analysis of the eigenvectors revealed diverse HFD impact on the three ti

102 trol subjects that reveals an agent-specific eigenvector (self eigenmode) associated with imagining o

103 the underlying system and the corresponding eigenvectors simultaneously coalesce.

104 an increase in overlap of the coarse-grained eigenvector space (the motion directions) with that obta

105 Analysis and visualization of the principal eigenvectors support a "plug and clamshell" model of Sec

106 ack box, and FABS-Spectral that utilizes the eigenvector technique (spectral) as black box.

107 The principal axes of inertia are eigenvectors that can be calculated for any rigid body.

108 lytically to produce the eigenvalues and the eigenvectors that specify the NMR parameters.

109 teroviruses, using a five dimensional set of Eigenvectors that summarize over 200 different scalar va

110 ector decomposition to resolve the principal eigenvectors these can then be aligned using standard dy

111 al component, IBI-D, with a nearly identical eigenvector to that previously reported.

112 n model adjusted for age, sex, study and two eigenvectors, two new loci achieved genome-wide signific

113 ality holds if and only if the corresponding eigenvector v(k) is a right basis vector of equal signif

114 this Markov chain enables the calculation of eigenvector values that quantify the importance of each

115 different quantities such as eigenvalues and eigenvectors, we find that the US housing market experie

116 Eigenvectors were extracted from a principal coordinate

117 de analysis that produces much more accurate eigenvectors without the tip effect.