ライフサイエンスコーパス: eigenvector

1 parison to the JSE correction of the leading eigenvector.

2 JSE is a consistent estimator of the leading eigenvector.

3 d individuals and the set of eigenvalues and eigenvectors.

4 genes that associate with the most important eigenvectors.

5 igenvalues of the transition matrix, not the eigenvectors.

6 by projecting the signal onto characteristic eigenvectors.

7 lationships between a system's most dominant eigenvectors.

8 stem of linear equations, or find the matrix eigenvectors.

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

10 framework to predict the shapes of localized eigenvectors.

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

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

13 veals that the Eddington ratio indeed drives Eigenvector 1.

14 Analysis of Hi-C contact maps and eigenvectors across the three carnivore families reveale

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

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

17 An eigenvector analysis indicated that both normal and adap

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

19 he underlying dynamical mechanism through an eigenvector analysis.

20 Eigenvector and degree centrality achieved high accuracy

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

22 ories through the effective rank of both the eigenvector and the dynamics matrices.

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

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

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

26 oordinate formed by three orthogonal Hessian eigenvectors and further adapting the gray level occurre

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

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

29 D algorithm (TCEVD) to compute and store the eigenvectors and values of the dFC matrices, and then al

30 The inclusion of spatial eigenvectors and vulnerability indicators such as health

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

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

33 al changes in helix angle (HA) and secondary eigenvector angle (E2A), which reflects orientations of

34 packing/interstitial expansion), and second eigenvector angle (measuring sheetlet orientation).

35 to those with G+LVH+ but had elevated second eigenvector angle (P<0.001 after adjustment for left ven

36 igenvector angle was elevated, and secondary eigenvector angle mobility was reduced compared with con

37 Reductions in secondary eigenvector angle suggest that the myocardial sheetlets

38 Diastolic secondary eigenvector angle was correlated with amyloid burden mea

39 In CA, diastolic secondary eigenvector angle was elevated, and secondary eigenvecto

40 sotropy, and sheetlet orientation (secondary eigenvector angle).

41 , higher mean diffusivity, and higher second eigenvector angle; all P<0.001) and MVD (lower stress my

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

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

44 ings of both homological and non-homological eigenvectors are studied.

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

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

47 The eigenvector associated with the largest eigenvalue of th

48 cts symmetric delay embeddings, and extracts eigenvectors associated with the eigenvalue of minimal m

49 The ability to visualize phonon eigenvectors at specific crystallographic sites with unp

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

51 sition and entire network structure (degree, eigenvector, betweenness, clustering coefficient).

52 We show that eigenvector bias can have a substantial impact on varian

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

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

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

56 Hypergraph network analysis with eigenvector centralities was applied to identify systemi

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

58 vity for temporal networks, named multilayer eigenvector centrality (mlEVC), we applied a consensus h

59 rtionately higher degree, were more central (eigenvector centrality and coreness), and were more like

60 reased association strength, social time and eigenvector centrality and significantly decreased close

61 etwork statistics, but synchronizability and eigenvector centrality are consistently less reliable th

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

63 loid-beta42 and phosphorylated tau levels on eigenvector centrality during hippocampal CA1 downregula

64 in regional functional network dynamics (ie, eigenvector centrality dynamics) between cognitively imp

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

66 id-beta42 (<1098) correlated negatively with eigenvector centrality in the anterior cingulate cortex

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

68 functional connectivity was quantified using eigenvector centrality mapping.

69 of abnormal biomarkers, age correlated with eigenvector centrality negatively in the insula and midc

70 ncy, and altered the location of the highest eigenvector centrality nodes from cortex to the hypothal

71 rates that caregivers have the largest modal eigenvector centrality of the four groups.

72 During resting state functional MRI, similar eigenvector centrality patterns in the cingulate had pre

73 horylated tau levels (>19.2) correlated with eigenvector centrality positively in the ventral striatu

74 Eigenvector centrality predicted both the speed with whi

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

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

77 r structure by investigating the tendency of eigenvector centrality to concentrate on a small fractio

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

79 such as degree, closeness, betweenness, and eigenvector centrality to prioritise biologically releva

80 However, eigenvector centrality was lower in calves with lung con

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

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

83 eing better socially integrated (i.e. higher Eigenvector centrality) were more likely to retrieve foo

84 contact networks (lower degree, strength and eigenvector centrality), and initiated contact (higher o

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

86 e identified the effect of healthy ageing on eigenvector centrality, a measure of each voxel's overal

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

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

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

90 Nodes are targeted based on eigenvector centrality, betweenness centrality, or at ra

91 f the unobserved network statistics, such as eigenvector centrality, or response functions by or of t

92 When benchmarking cycles with eigenvector centrality, we find that cycle participation

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

94 Eigenvector centrality, which quantifies the importance

95 ast on hub node, and they are related to the eigenvector centrality.

96 sing measures of betweenness, closeness, and eigenvector centrality.

97 er simulated conflicts, measured as grooming eigenvector centrality.

98 igher association strength, social time, and eigenvector centrality.

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

100 This occurs as system eigenvectors coalesce near the exceptional point and a b

101 parameter space whereby some eigenvalues and eigenvectors coalesce simultaneously.

102 es where eigenvalues and their corresponding eigenvectors coalesce(1-4).

103 nal points (EPs), where both eigenvalues and eigenvectors coalesce.

104 ouvillian superoperator and their associated eigenvectors coalesce.

105 on-Hermitian operators where eigenvalues and eigenvectors coalesce.

106 or more eigenvalues, and their corresponding eigenvectors, coalesce and become degenerate.

107 he variates quickly converge to the ratio of eigenvector components of the positive growth mode, whic

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

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

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

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

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

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

114 atedness matrix (GRM) as well as its leading eigenvectors, corresponding to the principal components

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

116 Eigenvector decomposition of this energetic space reveal

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

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

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

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

121 the optimal number of clusters (K) involving eigenvector distribution analysis.

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

123 We used Leading Eigenvector Dynamics Analysis to examine whole-brain dyn

124 ., emotion dynamics) and fMRI (i.e., leading eigenvector dynamics analysis; LEiDA) data analysis, we

125 classifier whose weights are equal to these eigenvector entries.

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

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

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

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

130 framework that uses gaps between consecutive eigenvectors for TAD boundary identification.

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

132 we introduce an algorithm that uses the gene eigenvectors from a reference dataset to establish a glo

133 s within families were strongly supported by eigenvectors from the Hi-C contact maps.

134 state spin-spin coupled manifold wherein 16 eigenvectors generate the 3D quantum spin-space describe

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

136 ent with changes in the spatial frequency of eigenvector grid patterns, distance estimates between re

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

138 tor in the HL regime called "James-Stein for eigenvectors" (JSE) and its close relationship with the

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

140 Population history was modeled using spatial eigenvector mapping based on geographic distances reflec

141 ormation of Montana (USA) using Phylogenetic Eigenvector Maps applied to the following osteohistologi

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

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

144 The eigenvector matrix produced by the method permits the co

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

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

147 The eigenvector of 1 module was correlated with the expressi

148 as excess dispersion, in the leading sample eigenvector of a factor-based covariance matrix estimate

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

150 g suggests that the ions (LD1: most dominant eigenvector of LDA) and electrons (LD2: second most domi

151 DA) and electrons (LD2: second most dominant eigenvector of LDA) represent the predators and preys, r

152 The eigenvector of one MA-associated module (turquoise) was

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

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

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

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

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

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

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

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

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

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

163 engineering require finding eigenvalues and eigenvectors of large matrices.

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

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

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

167 ptional point (EP), at which eigenvalues and eigenvectors of the corresponding non-Hermitian Hamilton

168 s a fungal phylogeny and show that including eigenvectors of the covariance matrix as covariates decr

169 ng the principal components of it, i.e., the eigenvectors of the density matrix with the largest eige

170 dicted with the only help of eigenvalues and eigenvectors of the graph Laplacian matrix.

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

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

173 The eigenvectors of the human connectome graph Laplacian, du

174 across scales, we leverage the longest-lived eigenvectors of the inferred Markov chain to perform a t

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

176 as been found that the alignment between the eigenvectors of the SGS stresses and the resolved strain

177 n rodents and a grid-cell model based on the eigenvectors of the successor representation, human posi

178 node2vec and the spectral embedding via the eigenvectors of the symmetric normalized Laplacian matri

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

180 simple edits, a closed-form formula for the eigenvectors of the transition probability matrix and a

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

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

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

184 molecular electronic Hamiltonian eigenvalue-eigenvector problem on a D-Wave 2000Q quantum annealer.

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

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

187 -driven approaches (expected sensitivity and eigenvector rankings) based on the time-varying Jacobian

188 mbiguously relating them to the ground state eigenvectors reference frame.

189 effects on plant biomass, using phylogenetic eigenvector regression and variance decomposition.

190 Improved confidence in eigenvector reproducibility enabled tracking of myolamin

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

192 k was also correlated with the same module's eigenvector (rho = -0.25; P = 3.3 x 10(-5)).

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

194 We describe a data-driven eigenvector shrinkage estimator in the HL regime called

195 the underlying system and the corresponding eigenvectors simultaneously coalesce.

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

197 Developing the framework in the eigenvector space is orders of magnitude faster and more

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

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

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

201 Obtaining sparse estimates of the eigenvectors that constitute the basis matrix that is us

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

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

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

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

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

207 e comparisons performed on the most relevant eigenvectors using several statistical metrics quantify

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

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

210 The MA-associated turquoise module eigenvector was additionally correlated with clinical fe

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

212 Eigenvectors were extracted from a principal coordinate

213 each other overall, their respective leading eigenvectors were poorly aligned.

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