Fig. 1: An overview of the post-hoc interpretation methods applied in this study to detect interactions in visible neural networks.

a Comparing the relative weights of the one-hot encoded input for each SNP reveals the model that the neural network is using for that particular SNP (e.g., linear spaced weights indicate an additive model). PathExplain applies Integrated Gradients on itself to find the Expected Hessians, which can be used to find interaction between inputs. RLIPP (c) is a method to detect if a node has non-linear behavior. The activations towards and from this neuron are regressed to the output with linear regression to provide an estimate of the non-linear gain of that node. d NID uses the assumption that edges with strong weights are more likely to interact with each other than edges with low absolute weights. DFIM (e) compares Deeplift’s attribution scores for all features before and after a feature of interest is perturbed, revealing all features that interact with the feature of interest.