M. Palmblad, A. Tiss, and R. Cramer, Mass spectrometry in clinical proteomics - from the present to the future, PROTEOMICS - CLINICAL APPLICATIONS, vol.4, issue.1
DOI : 10.3748/wjg.v12.i17.2789

N. L. Anderson and N. G. Anderson, The Human Plasma Proteome: History, Character, and Diagnostic Prospects: Fig. 3., Molecular & Cellular Proteomics, vol.2, issue.1, pp.845-867845, 2002.
DOI : 10.1074/mcp.A300001-MCP200

R. Schiess and . Zürich, Proteomic strategy for biomarker discovery, 2008.

R. Aebersold and M. Mann, Mass spectrometry-based proteomics, Nature, vol.1, issue.6928, pp.198-207, 2003.
DOI : 10.1016/S0960-9822(01)00632-7

E. J. Finehout, J. R. Cantor, and K. H. Lee, Kinetic characterization of sequencing grade modified trypsin, PROTEOMICS, vol.68, issue.9, pp.2319-2321, 2005.
DOI : 10.1016/0005-2795(77)90082-4

G. Strubel, Reconstruction de profils moléculaires: Modélisation et inversion d'une chaîne de mesure protéomique, 2008.

W. S. Noble and M. J. Maccoss, Computational and Statistical Analysis of Protein Mass Spectrometry Data, PLoS Computational Biology, vol.83, issue.1, p.1002296, 2012.
DOI : 10.1371/journal.pcbi.1002296.g001

W. E. Haskins, K. Petritis, and J. Zhang, MRCQuant- an accurate LC-MS relative isotopic quantification algorithm on TOF instruments, BMC Bioinformatics, vol.12, issue.1, p.74, 2011.
DOI : 10.1002/rcm.3237

M. Katajamaa, J. Miettinen, and M. Ore?i?, MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data, Bioinformatics, vol.22, issue.5, pp.634-636, 2006.
DOI : 10.1093/bioinformatics/btk039

M. Monroe, N. Toli?, N. Jaitly, J. Shaw, J. Adkins et al., VIPER: an advanced software package to support high-throughput LC-MS peptide identification, Bioinformatics, vol.23, issue.15, pp.2021-2023, 2007.
DOI : 10.1093/bioinformatics/btm281

P. Grangeat, First demonstration on NSE biomarker of a computational environment dedicated to lab-on-chip based cancer diagnosis, Proc. 58th ASMS Int. Conf, 2010.

G. Strubel, J. Giovannelli, C. Paulus, L. Gerfault, and P. Grangeat, Bayesian estimation for molecular profile reconstruction in proteomics based on liquid chromatography and mass spectrometry, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.5979-5982, 2007.
DOI : 10.1109/IEMBS.2007.4353710

T. Schwarz-selinger, R. Preuss, V. Dose, and W. Von-der-linden, Analysis of multicomponent mass spectra applying Bayesian probability theory, Journal of Mass Spectrometry, vol.10, issue.8, pp.866-874, 2001.
DOI : 10.1116/1.577955

M. K. Brusniak, C. S. Chu, U. Kusebauch, M. J. Sartain, J. D. Watts et al., An assessment of current bioinformatic solutions for analyzing LC-MS data acquired by selected reaction monitoring technology, PROTEOMICS, vol.10, issue.Chapter 13, pp.1176-1184, 2012.
DOI : 10.1038/nbt.1546

M. Rauh, LC???MS/MS for protein and peptide quantification in clinical chemistry, Journal of Chromatography B, vol.883, issue.884, pp.883-884, 2012.
DOI : 10.1016/j.jchromb.2011.09.030

V. Lange, P. Picotti, B. Domon, and R. Aebersold, Selected reaction monitoring for quantitative proteomics: a tutorial, Molecular Systems Biology, vol.5, p.222, 2008.
DOI : 10.1038/nbt827

L. Gerfault, P. Szacherski, J. Giovannelli, J. Charrier, P. Mahé et al., A hierarchical SRM acquisition chain model for improved protein quantification in serum samples, Proc. RECOMB-CP, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00676587

J. O. Berger-]-g and . Omenn, Overview of the HUPO plasma proteome project: Results from the pilot phase with 35 collaborating laboratories and multiple analytical groups, generating a core dataset of 3020 proteins and a publicly-available database, Proteomics, vol.5, issue.13, pp.3226-3245, 1985.

G. S. Omenn, R. Aebersold, and Y. Paik, HUPO World Congress of Proteomics: Launching the Second Phase of the HUPO Plasma Proteome Project (PPP-2) 16-20 August 2008, Amsterdam, The Netherlands, PROTEOMICS, vol.35, issue.1, pp.4-6, 2008.
DOI : 10.1093/bioinformatics/btn501

T. Yu and H. Peng, Quantification and deconvolution of asymmetric LC-MS peaks using the bi-Gaussian mixture model and statistical model selection, BMC Bioinformatics, vol.11, issue.1, pp.5591471-2105, 2010.
DOI : 10.1186/1471-2105-11-559

Z. Pápai and T. L. Pap, Analysis of peak asymmetry in chromatography, Journal of Chromatography A, vol.953, issue.1-2, pp.31-38, 2002.
DOI : 10.1016/S0021-9673(02)00121-8

J. Listgarten, R. M. Neal, S. T. Roweis, P. Wong, and A. Emili, Difference detection in LC-MS data for protein biomarker discovery, Bioinformatics, vol.23, issue.2, pp.198-204, 2007.
DOI : 10.1093/bioinformatics/btl326

V. B. , D. Marco, and G. G. Bombi, Mathematical functions for the representation of chromatographic peaks, J. Chromatogr. A, vol.931, pp.1-30, 2001.

V. Brun, C. Masselon, J. Garin, and A. Dupuis, Isotope dilution strategies for absolute quantitative proteomics, Journal of Proteomics, vol.72, issue.5, pp.740-749, 2009.
DOI : 10.1016/j.jprot.2009.03.007

F. Orieux, J. Giovannelli, T. Rodet, A. Abergel, H. Ayasso et al., Super-resolution in map-making based on a physical instrument model and regularized inversion. Application to SPIRE/Herschel, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00674514

J. Idier and E. , Bayesian Approach to Inverse Problems, 2008.
DOI : 10.1002/9780470611197

URL : https://hal.archives-ouvertes.fr/hal-00400668

P. Jallon, A graph based algorithm for postures estimation based on accelerometers data, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, pp.2778-2781, 2010.
DOI : 10.1109/IEMBS.2010.5626363

M. Protter, M. Elad, H. Takeda, and P. Milanfar, Generalizing the Nonlocal-Means to Super-Resolution Reconstruction, IEEE Transactions on Image Processing, vol.18, issue.1, pp.36-51, 2009.
DOI : 10.1109/TIP.2008.2008067

P. Szacherski, J. Giovannelli, and P. Grangeat, Joint Bayesian hierarchical inversion-classification and application in proteomics, 2011 IEEE Statistical Signal Processing Workshop (SSP), pp.121-124, 2011.
DOI : 10.1109/SSP.2011.5967636

URL : https://hal.archives-ouvertes.fr/hal-00585529

R. Pérenon, A. Mohammad-djafari, L. Duraffourg, and P. Grangeat, Quantification moléculaire par spectrométrie de masse à base de NEMS: Modélisation et inversion du problème, Proc. 23rd Colloq. GRETSI, 2011.

A. Barachant, S. Bonnet, M. Congedo, and C. Jutten, Multiclass Brain–Computer Interface Classification by Riemannian Geometry, IEEE Transactions on Biomedical Engineering, vol.59, issue.4, pp.920-928, 2012.
DOI : 10.1109/TBME.2011.2172210

M. Guindani, K. Do, P. Müeller, and J. S. Morris, Bayesian mixture models for gene expression and protein profiles,'' in Bayesian Inference for Gene Expression and Proteomics, pp.238-253, 2006.

I. Kuselman, F. Pennecchi, C. Burns, A. Fajgelj, and P. De-zorzi, Investigating out-of-specification test results of chemical composition based on metrological concepts, Accreditation and Quality Assurance, vol.9, issue.6
DOI : 10.1007/s00769-009-0618-4

N. Parrish, M. R. Gupta, and H. S. Anderson, Robust classification of signal estimates given a channel model, 2011 IEEE Statistical Signal Processing Workshop (SSP), pp.273-276, 2011.
DOI : 10.1109/SSP.2011.5967679

P. P. Wang, D. Ruan, and E. E. Kerre, Fuzzy Logic: A Spectrum of Theoretical & Practical Issues, 2007.
DOI : 10.1007/978-3-540-71258-9

C. P. Robert, The Bayesian Choice: From Decision-Theoretic Foundations to Computational Implementation, 2007.
DOI : 10.1007/978-1-4757-4314-2

C. Robert and G. Casella, Monte Carlo Statistical Methods, 2004.

P. J. Green, Reversible jump Markov chain Monte Carlo computation and Bayesian model determination, Biometrika, vol.82, issue.4, pp.711-732, 1995.
DOI : 10.1093/biomet/82.4.711

T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2009.

P. Szacherski, MRM protein quantification and serum sample classification, Proc. 61st ASMS Conf, pp.1-2, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00909875

F. Adjed, Classification, apprentissage et sélection de modèles pour un mélange de populations appliqués en protéomique, IMS, 2012.

P. Grangeat, Convergence entre l'analyse biostatistique et les méthodes d'inversion hiérarchique bayésienne pour la recherche et la validation de biomarqueurs par spectrométrie de masse, Proc. 24th Colloq. GRETSI, 2013.

L. Gerfault, Statistical analysis of Bayesian hierarchical inversion for MRM protein quantification and QDA serum sample classification, Proc. 62nd ASMS Conf. Mass Spectrometry Allied Topics, 2014.

L. Gerfault, Assessing MRM protein quantification and serum sample classification performances of a Bayesian hierarchical Inversion method on a colorectal cancer cohort, Proc. EuPA Sci. Meeting, 2013.

P. Szacherski, J. Giovannelli, L. Gerfault, and P. Grangeat, Apprentissage supervisé robuste de caractéristiques de classes. Application en protéomique, Proc. 23rd Colloq. GRETSI, 2011.

F. Adjed, J. Giovannelli, A. Giremus, N. Dridi, and P. Szacherski, Variable selection for a mixed population applied in proteomics, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.1153-1157, 2013.
DOI : 10.1109/ICASSP.2013.6637831

URL : https://hal.archives-ouvertes.fr/hal-00879200

P. Szacherski, Reconstruction de profils protéiques pour la recherche de biomarqueurs, Dept. Comput. Sci., Univ. Bordeaux I, 2012.