Ensembles of knowledge graph embedding models improve predictions for drug discovery.

ABSTRACT: Recent advances in Knowledge Graphs (KGs) and Knowledge Graph Embedding Models (KGEMs) have led to their adoption in a broad range of fields and applications. The current publishing system in machine learning requires newly introduced KGEMs to achieve state-of-the-art performance, surpassing at least one benchmark in order to be published. Despite this, dozens of novel architectures are published every year, making it challenging for users, even within the field, to deduce the most suitable configuration for a given application. A typical biomedical application of KGEMs is drug-disease prediction in the context of drug discovery, in which a KGEM is trained to predict triples linking drugs and diseases. These predictions can be later tested in clinical trials following extensive experimenta

SUBMITTER: Rivas-Barragan D 

PROVIDER: S-EPMC9677479 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature