Local graph-motif features improve gene interaction network prediction
Gene interaction networks specify how genes interact to produce an organisms phenotype. These networks are often incomplete due to absent or unobserved information. Predicting these missing links is critical for many applications, including genome-wide association studies and phenotype prediction. Efforts have previously applied graph neural networks (GNNs) to this missing-link prediction problem, but these techniques too have limitations when the sparsity of the networks is very high. Here, we apply a novel feature engineering technique that uses local graph motif incidence to enhance the feature set for variational graph autoencoders (VGAE). We compare the performance of our technique against state-of-the-art approaches, and then progressively hide more and more of the original graph edges. Our results show that VGAEs with our local-area motif prevalence (LAMP) features outperform state-of-the-art node embeddings for a wide range of missing edges on both a benchmark and a biological dataset. We also observe that this combined VGAE and LAMP technique has the potential to facilitate the search for novel genetic interactions in an experimental adaptive sampling context with far fewer samples. Improvements to gene interaction imputation can lower the barrier to new pharmaceutical and epidemiological discoveries by revealing hidden gene interactions that steer the development of potential drug targets.