Spectrum Identification Using a Dynamic Bayesian Network Model of Tandem Mass Spectra
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 out-performs the de-facto standard tools for this task: SEQUEST and Mascot.
Cite
Text
Singh et al. "Spectrum Identification Using a Dynamic Bayesian Network Model of Tandem Mass Spectra." Conference on Uncertainty in Artificial Intelligence, 2012.Markdown
[Singh et al. "Spectrum Identification Using a Dynamic Bayesian Network Model of Tandem Mass Spectra." Conference on Uncertainty in Artificial Intelligence, 2012.](https://mlanthology.org/uai/2012/singh2012uai-spectrum/)BibTeX
@inproceedings{singh2012uai-spectrum,
title = {{Spectrum Identification Using a Dynamic Bayesian Network Model of Tandem Mass Spectra}},
author = {Singh, Ajit P. and Halloran, John T. and Bilmes, Jeff A. and Kirchhoff, Katrin and Noble, William Stafford},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2012},
pages = {775-785},
url = {https://mlanthology.org/uai/2012/singh2012uai-spectrum/}
}