Ising models are a simple generative approach to describing interacting binary variables. They have proven useful in a number of biological settings because they enable one to represent observed many-body correlations as the separable consequence of many direct, pairwise statistical interactions. The inference of Ising models from data can be computationally very challenging and often one must be satisfied with numerical approximations or limited precision. In this paper we present a novel method for the determination of Ising parameters from data, called GNisi, which uses a Graph Neural network trained on known Ising models in order to construct the parameters for unseen data. We show that GNisi is more accurate than the existing state of the art software, and we illustrate our method by applying GNisi to gene expression data.

翻译：在描述相互作用的二进制变量时,Ising模型是一种简单的基因化方法,在多个生物环境中被证明是有用的,因为它们使得人们能够将观察到的多身体关系作为许多直接的、双向的统计互动的分离结果。Ising模型从数据中得出的推论在计算上具有极大的挑战性,而且往往必须满足于数字近似或有限的精确度。在本文中,我们提出了一个从数据中确定Ising参数的新方法,称为Ganisi,它使用一个以已知的Ising模型为培训的图形神经网络来建立已知的不可见数据的参数。我们表明Ganisi比艺术软件的现有状态更准确,我们通过将Ganisi应用于基因表达数据来说明我们的方法。