Uncertainty in Artificial Intelligence
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Learning Peptide-Spectrum Alignment Models for Tandem Mass Spectrometry
John Halloran, Jeff Bilmes, William Noble
We present a peptide-spectrum alignment strategy that employs a dynamic Bayesian network (DBN) for the identification of spectra produced by tan- dem mass spectrometry (MS/MS). Our method is fundamentally generative in that it models peptide fragmentation in MS/MS as a physical process. The model traverses an observed MS/MS spec- trum and a peptide-based theoretical spectrum to calculate the best alignment between the two spectra. Unlike all existing state-of-the-art meth- ods for spectrum identification that we are aware of, our method can learn alignment probabilities given a dataset of high-quality peptide-spectrum pairs. The method, moreover, accounts for noise peaks and absent theoretical peaks in the observed spectrum. We demonstrate that our method out- performs, on a majority of datasets, several widely used, state-of-the-art database search tools for spectrum identification. Furthermore, the pro- posed approach provides an extensible framework for MS/MS analysis and provides useful informa- tion that is not produced by other methods, thanks to its generative structure.
Pages: 320-329
PS Link:
PDF Link: /papers/14/p320-halloran.pdf
AUTHOR = "John Halloran and Jeff Bilmes and William Noble",
TITLE = "Learning Peptide-Spectrum Alignment Models for Tandem Mass Spectrometry",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "320--329"

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