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Ising models are a simple generative approach to describing interactingbinary variables. They have proven useful in a number of biological settingsbecause they enable one to represent observed many-body correlations as theseparable consequence of many direct, pairwise statistical interactions. Theinference of Ising models from data can be computationally very challenging andoften one must be satisfied with numerical approximations or limited precision.In this paper we present a novel method for the determination of Isingparameters from data, called GNisi, which uses a Graph Neural network trainedon known Ising models in order to construct the parameters for unseen data. Weshow 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.

GNisi: A graph network for reconstructing Ising models from multivariate binarized data