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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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The Royal Society
2021-11-01
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| Series: | Royal Society Open Science |
| Subjects: | |
| Online Access: | https://royalsocietypublishing.org/doi/10.1098/rsos.211374 |
| _version_ | 1818433369321504768 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-14T16:20:00Z |
| format | Article |
| id | doaj.art-60c7c0dabd9547d28fbb9d82a7ab112f |
| institution | Directory Open Access Journal |
| issn | 2054-5703 |
| language | English |
| last_indexed | 2024-12-14T16:20:00Z |
| publishDate | 2021-11-01 |
| publisher | The Royal Society |
| record_format | Article |
| series | Royal Society Open Science |
| spelling | doaj.art-60c7c0dabd9547d28fbb9d82a7ab112f2022-12-21T22:54:49ZengThe Royal SocietyRoyal Society Open Science2054-57032021-11-0181110.1098/rsos.211374Inferring temporal dynamics from cross-sectional data using Langevin dynamicsPritha Dutta0Rick Quax1Loes Crielaard2Luca Badiali3Peter M. A. Sloot4Interdisciplinary Graduate Programme, Nanyang Technological University, SingaporeInstitute for Advanced Study, University of Amsterdam, Amsterdam, The NetherlandsInstitute for Advanced Study, University of Amsterdam, Amsterdam, The NetherlandsComputational Science Lab, University of Amsterdam, Amsterdam, The NetherlandsInstitute for Advanced Study, University of Amsterdam, Amsterdam, The NetherlandsCross-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.https://royalsocietypublishing.org/doi/10.1098/rsos.211374cross-sectional datapredictive computational modelspseudo-longitudinal dataLangevin dynamics |
| spellingShingle | Pritha Dutta Rick Quax Loes Crielaard Luca Badiali Peter M. A. Sloot Inferring temporal dynamics from cross-sectional data using Langevin dynamics Royal Society Open Science cross-sectional data predictive computational models pseudo-longitudinal data 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 | cross-sectional data predictive computational models pseudo-longitudinal data Langevin dynamics |
| url | https://royalsocietypublishing.org/doi/10.1098/rsos.211374 |
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