Modeling Relational Drug-Target-Disease Interactions via Tensor Factorization with Multiple Web Sources

Modeling the behaviors of drug-target-disease interactions is crucial in the early stage of drug discovery and holds great promise for precision medicine and personalized treatments. The growing availability of new types of data on the internet brings great opportunity of learning a more comprehensive relationship among drugs, targets, and diseases. However, existing methods often consider drug-target interactions or drug-disease interactions separately, which ignores the dependencies among these three entities. Also, many of them cannot directly incorporate rich heterogeneous information from diverse sources.