TY - JOUR
T1 - Inferring temporal dynamics from cross-sectional data using Langevin dynamics
AU - Dutta, Pritha
AU - Quax, Rick
AU - Crielaard, Loes
AU - Badiali, Luca
AU - Sloot, Peter M. A.
N1 - Funding Information: This work is supported by the NTU Research Scholarship, ZonMw (Netherlands Organization for Health Research and Development, project number: 531003015), Social HealthGames (NWO, the Dutch Science Foundation, project number: 645.003.002), Computational Modelling of Criminal Networks and Value Chains (Nationale Politie, project number: 2454972) and TO_AITION (EU Horizon 2020 programme, call: H2020-SC1-2018-2020, grant number: 848146). Acknowledgements Publisher Copyright: © 2021 The Authors.
PY - 2021/11
Y1 - 2021/11
N2 - 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.
AB - 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.
KW - Langevin dynamics
KW - cross-sectional data
KW - predictive computational models
KW - pseudo-longitudinal data
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85122370962&origin=inward
UR - https://www.ncbi.nlm.nih.gov/pubmed/34804581
UR - https://github.com/Pritha17/langevin-crosssectional
UR - http://www.scopus.com/inward/record.url?scp=85122370962&partnerID=8YFLogxK
U2 - https://doi.org/10.1098/rsos.211374
DO - https://doi.org/10.1098/rsos.211374
M3 - Article
C2 - 34804581
SN - 2054-5703
VL - 8
SP - 211374
JO - Royal Society open science
JF - Royal Society open science
IS - 11
M1 - 211374
ER -