Markov Random Fields and Mass Spectra Discrimination

TL;DR

This paper introduces a new interpretable method using Markov Random Fields to identify small biomarker groups for cancer discrimination based on mass spectra, improving biological interpretability over traditional black-box classifiers.

Contribution

The paper develops efficient signature discovery algorithms with rigorous theoretical validation, enabling interpretable biomarker signatures for cancer discrimination from mass spectra.

Findings

Successfully identified biomarker signatures for colorectal and ovarian cancer datasets.

Validated the approach with theoretical results and benchmark tests.

Produced interpretable signatures that outperform traditional methods in biological relevance.

Abstract

For mass spectra acquired from cancer patients by MALDI or SELDI techniques, automated discrimination between cancer types or stages has often been implemented by machine learnings. These techniques typically generate "black-box" classifiers, which are difficult to interpret biologically. We develop new and efficient signature discovery algorithms leading to interpretable signatures combining the discriminating power of explicitly selected small groups of biomarkers, identified by their m/z ratios. Our approach is based on rigorous stochastic modeling of "homogeneous" datasets of mass spectra by a versatile class of parameterized Markov Random Fields. We present detailed algorithms validated by precise theoretical results. We also outline the successful tests of our approach to generate efficient explicit signatures for six benchmark discrimination tasks, based on mass spectra acquired from colorectal cancer patients, as well as from ovarian cancer patients.