Gradients of Generative Models for Improved Discriminative Analysis of Tandem Mass Spectra

Tandem mass spectrometry (MS/MS) is a high-throughput technology used toidentify the proteins in a complex biological sample, such as a drop of blood. A collection of spectra is generated at the output of the process, each spectrum of which is representative of a peptide (protein subsequence) presen...

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Bibliographic Details
Published inarXiv.org
Main Authors Halloran, John T, Rocke, David M
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 04.09.2019
Subjects
Ascent
Bayesian analysis
Kernels
Mass spectra
Mass spectrometry
Post-processing
Proteins
Search algorithms
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Summary:Tandem mass spectrometry (MS/MS) is a high-throughput technology used toidentify the proteins in a complex biological sample, such as a drop of blood. A collection of spectra is generated at the output of the process, each spectrum of which is representative of a peptide (protein subsequence) present in the original complex sample. In this work, we leverage the log-likelihood gradients of generative models to improve the identification of such spectra. In particular, we show that the gradient of a recently proposed dynamic Bayesian network (DBN) may be naturally employed by a kernel-based discriminative classifier. The resulting Fisher kernel substantially improves upon recent attempts to combine generative and discriminative models for post-processing analysis, outperforming all other methods on the evaluated datasets. We extend the improved accuracy offered by the Fisher kernel framework to other search algorithms by introducing Theseus, a DBN representing a large number of widely used MS/MS scoring functions. Furthermore, with gradient ascent and max-product inference at hand, we use Theseus to learn model parameters without any supervision.
ISSN:2331-8422