A procedure for learning a probabilistic model from mass spectrometry data that accounts for domain specific noise and mitigates the complexity of Bayesian structure learning is presented. We evaluate the algorithm by applying the learned probabilistic model to microorganism detection from mass spectrometry data.
['Anshu Saksena', 'Dennis Lucarelli', 'I.-Jeng Wang']
Bayesian model selection for mining mass spectrometry data