title: Bayesian estimation for molecular profile reconstruction in proteomics based on liquid chromatography and mass spectrometry.
abstract: There is a need to precisely measure concentration of proteins in biological substance for early diagnosis of disease or knowledge of fundamental biological processes. Many apparatus have been proposed, and now data processing methods have to be investigated. This paper focuses on data processing of proteomic experiments combining nano liquid chromatography and mass spectrometry. Experimental fluctuations of this process raise an interest for robust methods. Consequently, we propose a model of this acquisition system and a probabilistic Bayesian method to estimate the proteins' concentrations and system parameters.
authors.affiliation: CEA-Leti Minatec, 17 rue des Martyrs, 38054 Grenoble cedex 9, France. gregory.strubel@cea.fr
authors.forename: G
authors.initials: G
authors.lastname: Strubel
authors.forename: J F
authors.initials: JF
authors.lastname: Giovannelli
authors.forename: C
authors.initials: C
authors.lastname: Paulus
authors.forename: L
authors.initials: L
authors.lastname: Gerfault
authors.forename: P
authors.initials: P
authors.lastname: Grangeat
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D002853:Chromatography, Liquid / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D013058:Mass Spectrometry / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D010449:Peptide Mapping / Q000379:methods*; D020543:Proteome / Q000032:analysis* / Q000737:chemistry*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D020543:Proteome