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

ABSTRACT: Tandem mass spectrometry (MS/MS) is a high-throughput technology used to identify 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) [7] 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 a

SUBMITTER: Halloran JT 

PROVIDER: S-EPMC6863505 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature