  Disease-gene prediction (DGP) refers to the computational challenge of
predicting associations between genes and diseases. Effective solutions to the
DGP problem have the potential to accelerate the therapeutic development
pipeline at early stages via efficient prioritization of candidate genes for
various diseases. In this work, we introduce the variational graph auto-encoder
(VGAE) as a promising unsupervised approach for learning powerful latent
embeddings in disease-gene networks that can be used for the DGP problem, the
first approach using a generative model involving graph neural networks (GNNs).
In addition to introducing the VGAE as a promising approach to the DGP problem,
we further propose an extension (constrained-VGAE or C-VGAE) which adapts the
learning algorithm for link prediction between two distinct node types in
heterogeneous graphs. We evaluate and demonstrate the effectiveness of the VGAE
on general link prediction in a disease-gene association network and the C-VGAE
on disease-gene prediction in the same network, using popular random walk
driven methods as baselines. While the methodology presented demonstrates
potential solely based on utilizing the topology of a disease-gene association
network, it can be further enhanced and explored through the integration of
additional biological networks such as gene/protein interaction networks and
additional biological features pertaining to the diseases and genes represented
in the disease-gene association network.
