Before revision:  Additionally, for the task of predicting gene expression values using GNNs, with and without additional expression training data, we found that the message passing scheme of GNNs is able to obtain the lowest mean squared error between the tested models both in prediction of unseen test values, and in an auto-encoder scheme for reconstruction of the feature matrix of expression values .
After revision:  Additionally, for the task of predicting gene expression values using GNNs, with and without additional expression training data, we found that the message passing scheme of GNNs is able to obtain the lowest mean squared error between the tested models both in prediction of unseen test values, and in an auto-encoder which is trained on the feature reconstruction task, and showed that it performs better at predicting unobserved node features than auto-encoders that are trained on the graph reconstruction task before learning to predict node features. When applied to the problem of imputing missing data in single-cell RNAseq data, our graph feature auto-encoder outperformed a state-of-the-art imputation method that does not use protein interaction information, showing the benefit of integrating biological networks and omics data using graph representation learning .