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

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 expan...

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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
Algorithms
Bayesian analysis
Convergence
Emission
Machine learning
Mass spectra
Mass spectrometry
Mathematical models
Maximum likelihood estimation
Message passing
Parameters
Peptides
Proteins
Schedules
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Summary: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, 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 Bayesian networks. Utilizing CVEs in Didea allows efficient learning of a large number of parameters while ensuring global convergence, in stark contrast to Didea's previous parameter learning framework (which could only learn a single parameter using a costly grid search) and other trainable models (which only ensure convergence to local optima). The newly trained scoring function substantially outperforms the state-of-the-art in both scoring function accuracy and downstream Fisher kernel analysis. Furthermore, we significantly improve Didea's runtime performance through successive optimizations to its message passing schedule and derive explicit connections between Didea's new concave score and related MS/MS scoring functions.
ISSN:2331-8422