Robust MS serum sample classification in proteomics by the use of inverse problems

In this communication, we address the problem of robust classification of proteomic serum samples. We propose coupling classification with the inverse problem methodology. The analytical chain comprising a liquid chromatograph and a mass spectrometer in Selected Reaction Monitoring mode is modelled, integrating an implicit hierarchy. We solve the inverse problem by the means of full-Bayesian statistics, resorting to stochastic sampling algorithms for the numerical computations. We compare our joint Inversion-Classification to state-of-the-art methods (Naïve Bayes, logistic regression, fuzzy c-means) using sequential estimations and show very encouraging results on simulated multi-class data.