title: Bayesian model selection for mining mass spectrometry data.
abstract: 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.
authors.affiliation: The Johns Hopkins University, Applied Physics Laboratory, Laurel, MD 20723, USA. mkyan@ee.ryerson.ca
authors.forename: Anshu
authors.initials: A
authors.lastname: Saksena
authors.forename: Dennis
authors.initials: D
authors.lastname: Lucarelli
authors.forename: I-Jeng
authors.initials: IJ
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D016208:Databases, Factual; D013058:Mass Spectrometry / Q000706:statistics & numerical data*; D015233:Models, Statistical; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization