Spectrum Identification using a Dynamic Bayesian Network Model of Tandem Mass Spectra

Ajit P. Singh, John Halloran, Jeff A. Bilmes, Katrin Kirchoff, William S. Noble
Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence, PMLR R10:774-784, 2012.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR10-singh12a, title = {Spectrum Identification using a Dynamic {B}ayesian Network Model of Tandem Mass Spectra}, author = {Singh, Ajit P. and Halloran, John and Bilmes, Jeff A. and Kirchoff, Katrin and Noble, William S.}, booktitle = {Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence}, pages = {774--784}, year = {2012}, editor = {de Freitas, Nando and Murphy, Kevin}, volume = {R10}, series = {Proceedings of Machine Learning Research}, month = {14--18 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r10/main/assets/singh12a/singh12a.pdf}, url = {https://proceedings.mlr.press/r10/singh12a.html}, 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.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Spectrum Identification using a Dynamic Bayesian Network Model of Tandem Mass Spectra %A Ajit P. Singh %A John Halloran %A Jeff A. Bilmes %A Katrin Kirchoff %A William S. Noble %B Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2012 %E Nando de Freitas %E Kevin Murphy %F pmlr-vR10-singh12a %I PMLR %P 774--784 %U https://proceedings.mlr.press/r10/singh12a.html %V R10 %X 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. %Z Reissued by PMLR on 04 October 2026.
APA
Singh, A.P., Halloran, J., Bilmes, J.A., Kirchoff, K. & Noble, W.S.. (2012). Spectrum Identification using a Dynamic Bayesian Network Model of Tandem Mass Spectra. Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R10:774-784 Available from https://proceedings.mlr.press/r10/singh12a.html. Reissued by PMLR on 04 October 2026.

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