ML Anthology

A Hidden Markov Model for De Novo Peptide Sequencing

Bernd Fischer, Volker Roth, Jonas Grossmann, Sacha Baginsky, Wilhelm Gruissem, Franz Roos, Peter Widmayer, Joachim M. Buhmann
NeurIPS 2004 pp. 457-464
/neurips/2004/fischer2004neurips-hidden/

Abstract

De novo Sequencing of peptides is a challenging task in proteome re- search. While there exist reliable DNA-sequencing methods, the high- throughput de novo sequencing of proteins by mass spectrometry is still an open problem. Current approaches suffer from a lack in precision to detect mass peaks in the spectrograms. In this paper we present a novel method for de novo peptide sequencing based on a hidden Markov model. Experiments effectively demonstrate that this new method signif- icantly outperforms standard approaches in matching quality.

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Cite

Text

Fischer et al. "A Hidden Markov Model for De Novo Peptide Sequencing." Neural Information Processing Systems, 2004.

Markdown

[Fischer et al. "A Hidden Markov Model for De Novo Peptide Sequencing." Neural Information Processing Systems, 2004.](https://mlanthology.org/neurips/2004/fischer2004neurips-hidden/)

BibTeX

@inproceedings{fischer2004neurips-hidden,
  title     = {{A Hidden Markov Model for De Novo Peptide Sequencing}},
  author    = {Fischer, Bernd and Roth, Volker and Grossmann, Jonas and Baginsky, Sacha and Gruissem, Wilhelm and Roos, Franz and Widmayer, Peter and Buhmann, Joachim M.},
  booktitle = {Neural Information Processing Systems},
  year      = {2004},
  pages     = {457-464},
  url       = {https://mlanthology.org/neurips/2004/fischer2004neurips-hidden/}
}