Inferring temporal dynamics from cross-sectional data using Langevin dynamics

Pritha Dutta, Rick Quax, Loes Crielaard, Luca Badiali, Peter M. A. Sloot

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)

Abstract

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.
Original languageEnglish
Article number211374
Pages (from-to)211374
Number of pages15
JournalRoyal Society open science
Volume8
Issue number11
DOIs
Publication statusPublished - Nov 2021

Keywords

  • Langevin dynamics
  • cross-sectional data
  • predictive computational models
  • pseudo-longitudinal data

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