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Understanding the relationships between biomedical terms like viruses, drugs,and symptoms is essential in the fight against diseases. Many attempts havebeen made to introduce the use of machine learning to the scientific process ofhypothesis generation(HG), which refers to the discovery of meaningful implicitconnections between biomedical terms. However, most existing methods fail totruly capture the temporal dynamics of scientific term relations and alsoassume unobserved connections to be irrelevant (i.e., in a positive-negative(PN) learning setting). To break these limits, we formulate this HG problem asfuture connectivity prediction task on a dynamic attributed graph viapositive-unlabeled (PU) learning. Then, the key is to capture the temporalevolution of node pair (term pair) relations from just the positive andunlabeled data. We propose a variational inference model to estimate thepositive prior, and incorporate it in the learning of node pair embeddings,which are then used for link prediction. Experiment results on real-worldbiomedical term relationship datasets and case study analyses on a COVID-19dataset validate the effectiveness of the proposed model.

Temporal Positive-unlabeled Learning for Biomedical Hypothesis Generation via Risk Estimation