Uncertainty in Artificial Intelligence
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Spectrum Identification using a Dynamic Bayesian Network Model of Tandem Mass Spectra
Ajit Singh, John Halloran, Jeff Bilmes, Katrin Kirchoff, William Noble
Abstract:
Shotgun proteomics is a high-throughput technology used to identify unknown proteins in a complex mixture. At the heart of this process is a prediction task, the spectrum identification problem, in which each fragmentation spectrum produced by a shotgun proteomics experiment must be mapped to the peptide (protein subsequence) which generated the spectrum. We propose a new algorithm for spectrum identification, based on dynamic Bayesian networks, which significantly outperforms the de-facto standard tools for this task: SEQUEST and Mascot.
Keywords:
Pages: 775-785
PS Link:
PDF Link: /papers/12/p775-singh.pdf
BibTex:
@INPROCEEDINGS{Singh12,
AUTHOR = "Ajit Singh and John Halloran and Jeff Bilmes and Katrin Kirchoff and William Noble",
TITLE = "Spectrum Identification using a Dynamic Bayesian Network Model of Tandem Mass Spectra",
BOOKTITLE = "Proceedings of the Twenty-Eighth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-12)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2012",
PAGES = "775--785"
}


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