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

Pascal Szacherski; Jean-François Giovannelli; Laurent Gerfault; Pierre Mahé; Jean-Philippe Charrier; Audrey Giremus; Bruno Lacroix; Pierre Grangeat

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, *}

* Corresponding author

1 CEA-LETI - Laboratoire d'Electronique et des Technologies 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

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

Document type :

Journal articles

Domain :

Computer Science [cs] / Signal and Image Processing

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https://hal-cea.archives-ouvertes.fr/cea-01615512
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Submitted on : Thursday, October 12, 2017 - 2:51:11 PM
Last modification on : Monday, February 25, 2019 - 4:34:20 PM

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