  Identification of disease genes, which are a set of genes associated with a
disease, plays an important role in understanding and curing diseases. In this
paper, we present a biomedical knowledge graph designed specifically for this
problem, propose a novel machine learning method that identifies disease genes
on such graphs by leveraging recent advances in network biology and graph
representation learning, study the effects of various relation types on
prediction performance, and empirically demonstrate that our algorithms
outperform its closest state-of-the-art competitor in disease gene
identification by 24.1%. We also show that we achieve higher precision than
Open Targets, the leading initiative for target identification, with respect to
predicting drug targets in clinical trials for Parkinson's disease.
