It is useful to analyze correlation graphs

Gorban, Alexander N.; Tyukina, Tatiana A.; Покидышева, Л. И.; Smirnova, Elena V.

doi:10.1016/j.plrev.2021.10.002

Cited by 4 publications

(2 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The application of statistical-mechanics-inspired tools in public health is still in its infancy [18,19] and/or confined to very specific issues [20,21]. In this work, relying upon a very general consideration by Alexander Gorban and colleagues, 'It is useful to analyse correlation graphs' [22], we demonstrate how a raw (albeit very reliable) indicator, allcause mortality, is amenable to a statistical mechanics approach opening new avenues to epidemiological and environmental research.…”

Section: Discussionmentioning

confidence: 88%

Reconstruction of the Temporal Correlation Network of All-Cause Mortality Fluctuation across Italian Regions: The Importance of Temperature and Among-Nodes Flux

Gigante

Giuliani

2022

Entropy

View full text Add to dashboard Cite

All-cause mortality is a very coarse grain, albeit very reliable, index to check the health implications of lifestyle determinants, systemic threats and socio-demographic factors. In this work, we adopt a statistical-mechanics approach to the analysis of temporal fluctuations of all-cause mortality, focusing on the correlation structure of this index across different regions of Italy. The correlation network among the 20 Italian regions was reconstructed using temperature oscillations and traveller flux (as a function of distance and region’s attractiveness, based on GDP), allowing for a separation between infective and non-infective death causes. The proposed approach allows monitoring of emerging systemic threats in terms of anomalies of correlation network structure.

show abstract

Section: Discussionmentioning

confidence: 88%

Reconstruction of the Temporal Correlation Network of All-Cause Mortality Fluctuation across Italian Regions: The Importance of Temperature and Among-Nodes Flux

Gigante

Giuliani

2022

Entropy

View full text Add to dashboard Cite

show abstract

“…As aptly envisaged by Gorban et al [ 32 ], the character of complex systems resides in the emergence of peculiar correlation structures among different features of the system at hand. In dynamics terms, the eigenvectors of the correlation matrix of the different features (corresponding to principal components) characterize a system trajectory in its phase space, generating an unbiased picture of the attractor states of the dynamics by the action of Takens’s theorem [ 33 ] This implies that any choice of n relevant features (with n > p being p the actual attractor dimension) computed along the trajectory can faithfully reproduce the attractor dynamics of the system at hand.…”

Section: Methodsmentioning

confidence: 99%

A Statistical Journey through the Topological Determinants of the β2 Adrenergic Receptor Dynamics

Paola

Poudel

Parise

et al. 2022

Entropy

View full text Add to dashboard Cite

Activation of G-protein-coupled receptors (GPCRs) is mediated by molecular switches throughout the transmembrane region of the receptor. In this work, we continued along the path of a previous computational study wherein energy transport in the β2 Adrenergic Receptor (β2-AR) was examined and allosteric switches were identified in the molecular structure through the reorganization of energy transport networks during activation. In this work, we further investigated the allosteric properties of β2-AR, using Protein Contact Networks (PCNs). In this paper, we report an extensive statistical analysis of the topological and structural properties of β2-AR along its molecular dynamics trajectory to identify the activation pattern of this molecular system. The results show a distinct character to the activation that both helps to understand the allosteric switching previously identified and confirms the relevance of the network formalism to uncover relevant functional features of protein molecules.

show abstract

Gradistics: An underappreciated dimension in evolutionary space

Vinogradov

Anatskaya

2023

Biosystems

View full text Add to dashboard Cite

It is useful to analyze correlation graphs

Cited by 4 publications

References 41 publications

Reconstruction of the Temporal Correlation Network of All-Cause Mortality Fluctuation across Italian Regions: The Importance of Temperature and Among-Nodes Flux

Reconstruction of the Temporal Correlation Network of All-Cause Mortality Fluctuation across Italian Regions: The Importance of Temperature and Among-Nodes Flux

A Statistical Journey through the Topological Determinants of the β2 Adrenergic Receptor Dynamics

Gradistics: An underappreciated dimension in evolutionary space

Contact Info

Product

Resources

About