Active learning for Bayesian network models of biological networks using structure priors

Abstract

Active learning methods aim at identifying measurements that should be done in order to benefit a learning problem maximally. We use Bayesian networks as models of biological systems and show how active learning can be used to select new measurements to be incorporated via structure priors. Improved performance of the methods is demonstrated with both simulated and real datasets.