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

Abstract : 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.
Type de document :
Communication dans un congrès
2012 IEEE International Workshop on Genomic Signal Processing and Statistics, Dec 2012, Washington, DC, United States. 2012
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https://hal.archives-ouvertes.fr/hal-00744709
Contributeur : Pascal Szacherski <>
Soumis le : mardi 23 octobre 2012 - 16:34:44
Dernière modification le : lundi 25 février 2019 - 16:34:18

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  • HAL Id : hal-00744709, version 1

Citation

Pascal Szacherski, Jean-François Giovannelli, Laurent Gerfault, Audrey Giremus, Pierre Grangeat. Robust MS serum sample classification in proteomics by the use of inverse problems. 2012 IEEE International Workshop on Genomic Signal Processing and Statistics, Dec 2012, Washington, DC, United States. 2012. 〈hal-00744709〉

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