Classification of Proteomic MS Data as Bayesian Solution of an Inverse Problem

Pascal Szacherski (1) , Jean-François Giovannelli (2) , Laurent Gerfault (1) , Pierre Mahé (3) , Jean-Philippe Charrier (4) , Audrey Giremus (5) , Bruno Lacroix (6) , Pierre Grangeat (1)

1 CEA-LETI - Commissariat à l'énergie atomique et aux énergies alternatives - Laboratoire d'Electronique et de Technologie de l'Information
2 IMS - Laboratoire de l'intégration, du matériau au système
3 CBIO - Centre de Bioinformatique
4 Recherche Technologique, bioMerieux SA
5 UB - Université de Bordeaux
6 Biomérieux Lyon

The cells in an organism emit different amounts of proteins according to their clinical state (healthy/pathological, for instance). The resulting proteomic profile can be used for early detection, diagnosis, and therapy planning. In this paper, we study the classification of a proteomic sample from the point of view of an inverse problem with a joint Bayesian solution, called inversion-classification. We propose a hierarchical physical forward model and present encouraging results from both simulation and clinical data.

Keywords: INDEX TERMS Statistical signal processing; inverse problems; mathematical modelling; classification; algorithms; probability; proteins; proteomics; selective reaction monitoring; mass spectrometry; liquid; chromatography

Domains: Computer Science [cs]; Signal and Image Processing

Pascal Szacherski, Jean-François Giovannelli, Laurent Gerfault, Pierre Mahé, Jean-Philippe Charrier, et al.. Classification of Proteomic MS Data as Bayesian Solution of an Inverse Problem. IEEE Access, 2014, 2, pp.1248 - 1262. ⟨10.1109/ACCESS.2014.2359979⟩. ⟨cea-01615512⟩