  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.
