2005 Special Issue: Bayesian model selection for mining mass spectrometry data

Authors:
Anshu Saksena;Dennis Lucarelli;I-Jeng Wang
Affiliations:
The Johns Hopkins University, Applied Physics Laboratory, Laurel, MD 20723, USA;The Johns Hopkins University, Applied Physics Laboratory, Laurel, MD 20723, USA;The Johns Hopkins University, Applied Physics Laboratory, Laurel, MD 20723, USA
Venue:
Neural Networks - 2005 Special issue: IJCNN 2005
Year:
2005

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A tutorial on learning with Bayesian networks

Proceedings of the NATO Advanced Study Institute on Learning in graphical models

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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.