Forsuch models, one particularly elegant approach is that ofpassive-aggressive learning[3]. In this framework, a model isonly updated when itfails to classify an example correctly with high confidence.

In this paper, we focus on testing for safety.

Quantifying uncertainty is important for actionable predictions in real-world applications.

Figure S2 shows the inferred bipartite network with the top 200 interactions by BayReL. BayReLinfacthasalsoidentified corresponding drugs that have been proposed to target this gene. Themostsignificantlyenriched GO terms include the MHC class II receptor activity (p-value = 0.00099), chemokine activity (p-value = 0.00175), MHC class II protein complex (p-value = 0.00273), chemokine receptor binding (p-value=0.00404),andregulation Immune escape ofrelapsed amlcells after allogeneic transplantation.