GRL-PUL: Predicting microbe-drug association based on graph representation learning and positive unlabeled learning

In this work, a computational model was designed for predicting microbe–drug associations, introducing a novel negative sampling strategy that leverages topological features to extract reliable negative samples from a large pool of unlabeled data.