0000000000454794

AUTHOR

Jean-philippe Charrier

showing 2 related works from this author

Strategies for improving production and purification of a recombinant protein: rP30 of Toxoplasma gondii expressed in the yeast Schizosaccharomyces p…

2007

Abstract Many problems concerned with the production and the purification of recombinant proteins must be addressed prior to launching an industrial production process. Among these problems, attention is focused on low-level expression that complicates the purification step and can jeopardise the process. The expression of a membrane protein, rP30, of Toxoplasma gondii in the yeast Schizosaccharomyces pombe led to a secretion of only 0.5 μg ml−1. In order to obtain a sufficient quantity for biochemical characterization and evaluation in vitro diagnostic test development, strategies for both production and purification had to be optimized. First, the influence of four nitrogen sources (three…

Clinical BiochemistryIon chromatographyProtozoan ProteinsAntigens ProtozoanRaw materialBiochemistryChromatography AffinityAnalytical Chemistrylaw.inventionAffinity chromatographylawSchizosaccharomycesYeast extractAnimalsBiomassChromatographybiologyChemistryCell BiologyGeneral Medicinebiology.organism_classificationYeastRecombinant ProteinsBiochemistrySchizosaccharomyces pombeFermentationRecombinant DNAFermentationToxoplasmaJournal of chromatography. B, Analytical technologies in the biomedical and life sciences
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VARIABLE SELECTION FOR NOISY DATA APPLIED IN PROTEOMICS

2014

International audience; The paper proposes a variable selection method for pro-teomics. It aims at selecting, among a set of proteins, those (named biomarkers) which enable to discriminate between two groups of individuals (healthy and pathological). To this end, data is available for a cohort of individuals: the biological state and a measurement of concentrations for a list of proteins. The proposed approach is based on a Bayesian hierarchical model for the dependencies between biological and instrumental variables. The optimal selection function minimizes the Bayesian risk, that is to say the selected set of variables maximizes the posterior probability. The two main contributions are: (…

0209 industrial biotechnologybusiness.industryComputer scienceInstrumental variablePosterior probabilityBayesian probabilityPattern recognitionFeature selection02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingLogistic regression01 natural sciences010104 statistics & probability020901 industrial engineering & automationCohortProbability distributionBayesian hierarchical modelingArtificial intelligence0101 mathematicsbusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingSelection (genetic algorithm)[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
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