Learning Concave Conditional Likelihood Models for Improved Analysis of Tandem Mass Spectra.

ABSTRACT: The most widely used technology to identify the proteins present in a complex biological sample is tandem mass spectrometry, which quickly produces a large collection of spectra representative of the peptides (i.e., protein subsequences) present in the original sample. In this work, we greatly expand the parameter learning capabilities of a dynamic Bayesian network (DBN) peptide-scoring algorithm, Didea [25], by deriving emission distributions for which its conditional log-likelihood scoring function remains concave. We show that this class of emission distributions, called Convex Virtual Emissions (CVEs), naturally generalizes the log-sum-exp function while rendering both maximum likelihood estimation and conditional maximum likelihood estimation concave for a wide range of

SUBMITTER: Halloran JT 

PROVIDER: S-EPMC6863516 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature