ArticleUncertainty in artificial intelligence : proceedings of the ... conference. Conference on Uncertainty in Artificial Intelligence2014
Learning Peptide-Spectrum Alignment Models for Tandem Mass Spectrometry.
Article in Uncertainty in artificial intelligence : proceedings of the ... conference. Conference on Uncertainty in Artificial Intelligence, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Learning Concave Conditional Likelihood Models for Improved Analysis of Tandem Mass Spectra.Advances in neural information processing systems · 2018Article
- Gradients of Generative Models for Improved Discriminative Analysis of Tandem Mass Spectra.Advances in neural information processing systems · 2017Article
- Dynamic Bayesian Network for Accurate Detection of Peptides from Tandem Mass Spectra.Journal of proteome research · 2016Article
- Faster and more accurate graphical model identification of tandem mass spectra using trellises.Bioinformatics (Oxford, England) · 2016Article
- Learning Peptide-Spectrum Alignment Models for Tandem Mass Spectrometry.Uncertainty in artificial intelligence : proceedings of the ... conference. Conference on Uncertainty in Artificial Intelligence · 2014Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
We present a peptide-spectrum alignment strategy that employs a dynamic Bayesian network (DBN) for the identification of spectra produced by tandem 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 spectrum and a peptide-based theoretical spectrum to calculate the best alignment between the two spectra. Unlike all existing state-of-the-art methods 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 outperforms, on a majority of datasets, several widely used, state-of-the-art database search tools for spectrum identification. Furthermore, the proposed approach provides an extensible framework for MS/MS analysis and provides useful information that is not produced by other methods, thanks to its generative structure.
Identifiers
25298752PMC4185971What OpenQuestion holds
Registered trials
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