1 research outputs found
Modeling Pharmacological Effects with Multi-Relation Unsupervised Graph Embedding
A pharmacological effect of a drug on cells, organs and systems refers to the
specific biochemical interaction produced by a drug substance, which is called
its mechanism of action. Drug repositioning (or drug repurposing) is a
fundamental problem for the identification of new opportunities for the use of
already approved or failed drugs. In this paper, we present a method based on a
multi-relation unsupervised graph embedding model that learns latent
representations for drugs and diseases so that the distance between these
representations reveals repositioning opportunities. Once representations for
drugs and diseases are obtained we learn the likelihood of new links (that is,
new indications) between drugs and diseases. Known drug indications are used
for learning a model that predicts potential indications. Compared with
existing unsupervised graph embedding methods our method shows superior
prediction performance in terms of area under the ROC curve, and we present
examples of repositioning opportunities found on recent biomedical literature
that were also predicted by our method.Comment: Accepted at IJCNN 202