  The widespread of Coronavirus has led to a worldwide pandemic with a high
mortality rate. Currently, the knowledge accumulated from different studies
about this virus is very limited. Leveraging a wide-range of biological
knowledge, such as gene ontology and protein-protein interaction (PPI) networks
from other closely related species presents a vital approach to infer the
molecular impact of a new species. In this paper, we propose the transferred
multi-relational embedding model Bio-JOIE to capture the knowledge of gene
ontology and PPI networks, which demonstrates superb capability in modeling the
SARS-CoV-2-human protein interactions. Bio-JOIE jointly trains two model
components. The knowledge model encodes the relational facts from the protein
and GO domains into separated embedding spaces, using a hierarchy-aware
encoding technique employed for the GO terms. On top of that, the transfer
model learns a non-linear transformation to transfer the knowledge of PPIs and
gene ontology annotations across their embedding spaces. By leveraging only
structured knowledge, Bio-JOIE significantly outperforms existing
state-of-the-art methods in PPI type prediction on multiple species.
Furthermore, we also demonstrate the potential of leveraging the learned
representations on clustering proteins with enzymatic function into enzyme
commission families. Finally, we show that Bio-JOIE can accurately identify
PPIs between the SARS-CoV-2 proteins and human proteins, providing valuable
insights for advancing research on this new disease.
