Inferring temporal dynamics from cross-sectional data using Langevin dynamics

Cross-sectional studies are widely prevalent since they are more feasible to conduct compared with longitudinal studies. However, cross-sectional data lack the temporal information required to study the evolution of the underlying dynamics. This temporal information is essential to develop predictiv...

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Autores principales:	Pritha Dutta, Rick Quax, Loes Crielaard, Luca Badiali, Peter M. A. Sloot
Formato:	article
Lenguaje:	EN
Publicado:	The Royal Society 2021
Materias:	cross-sectional data predictive computational models pseudo-longitudinal data Langevin dynamics Science Q
Acceso en línea:	https://doaj.org/article/60c7c0dabd9547d28fbb9d82a7ab112f
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spelling	oai:doaj.org-article:60c7c0dabd9547d28fbb9d82a7ab112f2021-11-10T08:06:33ZInferring temporal dynamics from cross-sectional data using Langevin dynamics10.1098/rsos.2113742054-5703https://doaj.org/article/60c7c0dabd9547d28fbb9d82a7ab112f2021-11-01T00:00:00Zhttps://royalsocietypublishing.org/doi/10.1098/rsos.211374https://doaj.org/toc/2054-5703Cross-sectional studies are widely prevalent since they are more feasible to conduct compared with longitudinal studies. However, cross-sectional data lack the temporal information required to study the evolution of the underlying dynamics. This temporal information is essential to develop predictive computational models, which is the first step towards causal modelling. We propose a method for inferring computational models from cross-sectional data using Langevin dynamics. This method can be applied to any system where the data-points are influenced by equal forces and are in (local) equilibrium. The inferred model will be valid for the time span during which this set of forces remains unchanged. The result is a set of stochastic differential equations that capture the temporal dynamics, by assuming that groups of data-points are subject to the same free energy landscape and amount of noise. This is a ‘baseline’ method that initiates the development of computational models and can be iteratively enhanced through the inclusion of domain expert knowledge as demonstrated in our results. Our method shows significant predictive power when compared against two population-based longitudinal datasets. The proposed method can facilitate the use of cross-sectional datasets to obtain an initial estimate of the underlying dynamics of the respective systems.Pritha DuttaRick QuaxLoes CrielaardLuca BadialiPeter M. A. SlootThe Royal Societyarticlecross-sectional datapredictive computational modelspseudo-longitudinal dataLangevin dynamicsScienceQENRoyal Society Open Science, Vol 8, Iss 11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	cross-sectional data predictive computational models pseudo-longitudinal data Langevin dynamics Science Q
spellingShingle	cross-sectional data predictive computational models pseudo-longitudinal data Langevin dynamics Science Q Pritha Dutta Rick Quax Loes Crielaard Luca Badiali Peter M. A. Sloot Inferring temporal dynamics from cross-sectional data using Langevin dynamics
description	Cross-sectional studies are widely prevalent since they are more feasible to conduct compared with longitudinal studies. However, cross-sectional data lack the temporal information required to study the evolution of the underlying dynamics. This temporal information is essential to develop predictive computational models, which is the first step towards causal modelling. We propose a method for inferring computational models from cross-sectional data using Langevin dynamics. This method can be applied to any system where the data-points are influenced by equal forces and are in (local) equilibrium. The inferred model will be valid for the time span during which this set of forces remains unchanged. The result is a set of stochastic differential equations that capture the temporal dynamics, by assuming that groups of data-points are subject to the same free energy landscape and amount of noise. This is a ‘baseline’ method that initiates the development of computational models and can be iteratively enhanced through the inclusion of domain expert knowledge as demonstrated in our results. Our method shows significant predictive power when compared against two population-based longitudinal datasets. The proposed method can facilitate the use of cross-sectional datasets to obtain an initial estimate of the underlying dynamics of the respective systems.
format	article
author	Pritha Dutta Rick Quax Loes Crielaard Luca Badiali Peter M. A. Sloot
author_facet	Pritha Dutta Rick Quax Loes Crielaard Luca Badiali Peter M. A. Sloot
author_sort	Pritha Dutta
title	Inferring temporal dynamics from cross-sectional data using Langevin dynamics
title_short	Inferring temporal dynamics from cross-sectional data using Langevin dynamics
title_full	Inferring temporal dynamics from cross-sectional data using Langevin dynamics
title_fullStr	Inferring temporal dynamics from cross-sectional data using Langevin dynamics
title_full_unstemmed	Inferring temporal dynamics from cross-sectional data using Langevin dynamics
title_sort	inferring temporal dynamics from cross-sectional data using langevin dynamics
publisher	The Royal Society
publishDate	2021
url	https://doaj.org/article/60c7c0dabd9547d28fbb9d82a7ab112f
work_keys_str_mv	AT prithadutta inferringtemporaldynamicsfromcrosssectionaldatausinglangevindynamics AT rickquax inferringtemporaldynamicsfromcrosssectionaldatausinglangevindynamics AT loescrielaard inferringtemporaldynamicsfromcrosssectionaldatausinglangevindynamics AT lucabadiali inferringtemporaldynamicsfromcrosssectionaldatausinglangevindynamics AT petermasloot inferringtemporaldynamicsfromcrosssectionaldatausinglangevindynamics
_version_	1718440377487196160

Inferring temporal dynamics from cross-sectional data using Langevin dynamics

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