  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.
