Empowering the discovery of novel target-disease associations via machine learning approaches in the open targets platform.

ABSTRACT:

Background

The Open Targets (OT) Platform integrates a wide range of data sources on target-disease associations to facilitate identification of potential therapeutic drug targets to treat human diseases. However, due to the complexity that targets are usually functionally pleiotropic and efficacious for multiple indications, challenges in identifying novel target to indication associations remain. Specifically, persistent need exists for new methods for integration of novel target-disease association evidence and biological knowledge bases via advanced computational methods. These offer promise for increasing power for identification of the most promising target-disease pairs for therapeutic development. Here we introduce a novel approach by integrating additional target-disease f

SUBMITTER: Han Y 

PROVIDER: S-EPMC9202116 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature