Cross-sectional studies are widely prevalent since they are more feasible to conduct compared to longitudinal studies. However, cross-sectional data lack the temporal information required to study the evolution of the underlying processes. Nevertheless, this 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 that can be described as effectively following a free energy landscape, such as protein folding, stem cell differentiation and reprogramming, and social systems involving human interaction and social norms. A crucial assumption in our method is that the data-points are gathered from a system in (local) equilibrium. The result is a set of stochastic differential equations which capture the temporal dynamics, by assuming that groups of data-points are subject to the same free energy landscape and amount of noise. Our method is a 'baseline' method which initiates the development of computational models which can be iteratively enhanced through the inclusion of expert knowledge. We validate the proposed method against two population-based longitudinal datasets and observe significant predictive power in comparison with random choice algorithms. We also show how the predictive power of our 'baseline' model can be enhanced by incorporating domain expert knowledge. Our method addresses an important obstacle for model development in fields dominated by cross-sectional datasets.

翻译：跨部门研究由于比较纵向研究比较比较比较可行,因而广泛广泛存在,因为与纵向研究相比,进行跨部门研究比较比较比较比较比较比较比较比较比较比较比较比较比较比较比较比较比较比较,不过,跨部门数据缺乏研究基础过程演变所需的时间信息,然而,这对于开发预测计算模型至关重要,这是建立因果建模的第一步。我们建议采用一种方法,利用兰埃文动态从跨部门数据中推算计算模型,这种方法可以适用于可以被描述为有效遵循自由能源景观的任何系统,例如蛋白折叠、干细胞差异和重新编程,以及涉及人类互动和社会规范的社会系统。我们方法中的一个关键假设是,数据点是从(本地)均衡的系统收集的。结果就是一套随机差异方程式方程式等方程式,通过假设数据点组群与自由的能源景观和噪音数量相同,可以将计算模型推导出计算模型的发展,而这种计算模型通过专家知识的融合,可以比拟出两种基于人口的长方位的长方位数据选择方法。