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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8580443/ https://www.ncbi.nlm.nih.gov/pubmed/34804581 http://dx.doi.org/10.1098/rsos.211374 |
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author | Dutta, Pritha Quax, Rick Crielaard, Loes Badiali, Luca Sloot, Peter M. A. |
author_facet | Dutta, Pritha Quax, Rick Crielaard, Loes Badiali, Luca Sloot, Peter M. A. |
author_sort | Dutta, Pritha |
collection | PubMed |
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 | Online Article Text |
id | pubmed-8580443 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | The Royal Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-85804432021-11-19 Inferring temporal dynamics from cross-sectional data using Langevin dynamics Dutta, Pritha Quax, Rick Crielaard, Loes Badiali, Luca Sloot, Peter M. A. R Soc Open Sci Computer Science and Artificial Intelligence 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. The Royal Society 2021-11-10 /pmc/articles/PMC8580443/ /pubmed/34804581 http://dx.doi.org/10.1098/rsos.211374 Text en © 2021 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited. |
spellingShingle | Computer Science and Artificial Intelligence Dutta, Pritha Quax, Rick Crielaard, Loes Badiali, Luca Sloot, Peter M. A. Inferring temporal dynamics from cross-sectional data using Langevin dynamics |
title | 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_short | Inferring temporal dynamics from cross-sectional data using Langevin dynamics |
title_sort | inferring temporal dynamics from cross-sectional data using langevin dynamics |
topic | Computer Science and Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8580443/ https://www.ncbi.nlm.nih.gov/pubmed/34804581 http://dx.doi.org/10.1098/rsos.211374 |
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