ArticleJournal of proteome research2016
Dynamic Bayesian Network for Accurate Detection of Peptides from Tandem Mass Spectra.
Article in Journal of proteome research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Automatic recognition of Pd ions in high-resolution mass spectra of multicomponent samples without visible fine isotope structures.Nature communications · 2026Article
- Challenges and Opportunities for Bayesian Statistics in Proteomics.Journal of proteome research · 2022Review
- Article
- Learning Concave Conditional Likelihood Models for Improved Analysis of Tandem Mass Spectra.Advances in neural information processing systems · 2018Article
- A Matter of Time: Faster Percolator Analysis via Efficient SVM Learning for Large-Scale Proteomics.Journal of proteome research · 2018Article
- Gradients of Generative Models for Improved Discriminative Analysis of Tandem Mass Spectra.Advances in neural information processing systems · 2017Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
A central problem in mass spectrometry analysis involves identifying, for each observed tandem mass spectrum, the corresponding generating peptide. We present a dynamic Bayesian network (DBN) toolkit that addresses this problem by using a machine learning approach. At the heart of this toolkit is a DBN for Rapid Identification (DRIP), which can be trained from collections of high-confidence peptide-spectrum matches (PSMs). DRIP's score function considers fragment ion matches using Gaussians rather than fixed fragment-ion tolerances and also finds the optimal alignment between the theoretical and observed spectrum by considering all possible alignments, up to a threshold that is controlled using a beam-pruning algorithm. This function not only yields state-of-the art database search accuracy but also can be used to generate features that significantly boost the performance of the Percolator postprocessor. The DRIP software is built upon a general purpose DBN toolkit (GMTK), thereby allowing a wide variety of options for user-specific inference tasks as well as facilitating easy modifications to the DRIP model in future work. DRIP is implemented in Python and C++ and is available under Apache license at http://melodi-lab.github.io/dripToolkit .
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Registered trials
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