  Disease gene prioritization assigns scores to genes or proteins according to
their likely relevance for a given disease based on a provided set of seed
genes. Here, we describe a new algorithm for disease gene prioritization based
on continuous-time quantum walks using the adjacency matrix of a
protein-protein interaction (PPI) network. Our algorithm can be seen as a
quantum version of a previous method known as the diffusion kernel, but,
importantly, has higher performance in predicting disease genes, and also
permits the encoding of seed node self-loops into the underlying Hamiltonian,
which offers yet another boost in performance. We demonstrate the success of
our proposed method by comparing it to several well-known gene prioritization
methods on three disease sets, across seven different PPI networks. In order to
compare these methods, we use cross-validation and examine the mean reciprocal
ranks and recall values. We further validate our method by performing an
enrichment analysis of the predicted genes for coronary artery disease. We also
investigate the impact of adding self-loops to the seeds, and argue that they
allow the quantum walker to remain more local to low-degree seed nodes.
