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

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.
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IEEE Access, 2014, 2, pp.1248 - 1262. 〈10.1109/ACCESS.2014.2359979〉
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Pierre Grangeat, Pascal Szacherski, Jean-François Giovannelli, Laurent Gerfault, Pierre Mahé, 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〉

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