  Accurate identification of disease genes has consistently been one of the
keys to decoding a disease's molecular mechanism. Most current approaches focus
on constructing biological networks and utilizing machine learning, especially,
deep learning to identify disease genes, but ignore the complex relations
between entities in the biological knowledge graph. In this paper, we construct
a biological knowledge graph centered on diseases and genes, and develop an
end-to-end Knowledge graph completion model for Disease Gene Prediction using
interactional tensor decomposition (called KDGene). KDGene introduces an
interaction module between the embeddings of entities and relations to tensor
decomposition, which can effectively enhance the information interaction in
biological knowledge. Experimental results show that KDGene significantly
outperforms state-of-the-art algorithms. Furthermore, the comprehensive
biological analysis of the case of diabetes mellitus confirms KDGene's ability
for identifying new and accurate candidate genes. This work proposes a scalable
knowledge graph completion framework to identify disease candidate genes, from
which the results are promising to provide valuable references for further wet
experiments.
