0000000000985850

AUTHOR

Catherine Mercier

showing 3 related works from this author

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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Mixed-model of ANOVA for measurement reproducibility in proteomics

2009

This work is a statistical analysis of reproducibility of a MALDI-TOF mass spectrometry experiment. Its aim is to evaluate measurement variability and compare peak intensities from two types of MALDI-TOF platforms. We compared and commented on the abilities of Principal Component Analysis and mixed-model analysis of variance to evaluate the biological variability and the technical variability of peak intensities in different patients. The properties and hypotheses of both methods are summarized and applied to spectra from plasma of patients with Hodgkin lymphoma. Principal Component Analysis checks rapidly the balance between the two variabilities; however, a mixed-model analysis of varianc…

Mixed modelProteomicsQuality Control030213 general clinical medicine[SDV.OT]Life Sciences [q-bio]/Other [q-bio.OT]ProteomeBiophysicsAnalytical chemistryProteomicsBiochemistryMass Spectrometry03 medical and health sciences0302 clinical medicineStatisticsHumans030304 developmental biologyMathematicsMeasurement variabilityMeasurement reproducibility0303 health sciencesReproducibilityAnalysis of VariancePrincipal Component AnalysisComputersReproducibility of ResultsVariance (accounting)Blood ProteinsHodgkin DiseaseSpectrometry Mass Matrix-Assisted Laser Desorption-IonizationPrincipal component analysisAnalysis of varianceAdsorptionPeptidesSoftware
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Variance component analysis to assess protein quantification in biomarker discovery. Application to MALDI-TOF mass spectrometry.

2017

International audience; Controlling the technological variability on an analytical chain is critical for biomarker discovery. The sources of technological variability should be modeled, which calls for specific experimental design, signal processing, and statistical analysis. Furthermore, with unbalanced data, the various components of variability cannot be estimated with the sequential or adjusted sums of squares of usual software programs. We propose a novel approach to variance component analysis with application to the matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) technology and use this approach for protein quantification by a classical signal processing algori…

0301 basic medicineStatistics and ProbabilityMALDI-TOFexperimental designBiometryprotein quantificationQuantitative proteomicsVariance component analysis[ CHIM ] Chemical Sciences01 natural sciencesSignaltechnological variability010104 statistics & probability03 medical and health sciencesstatistical analysis[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[CHIM.ANAL]Chemical Sciences/Analytical chemistryComponent (UML)[SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry Molecular Biology/Genomics [q-bio.GN]biomarker discoverysum of squares type0101 mathematicsBiomarker discoverysignal processingMathematicsSignal processingAnalysis of Variance[ PHYS ] Physics [physics]Noise (signal processing)ProteinsGeneral MedicineVariance (accounting)[SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]030104 developmental biologySpectrometry Mass Matrix-Assisted Laser Desorption-IonizationLinear Modelsvariance components[ CHIM.ANAL ] Chemical Sciences/Analytical chemistryStatistics Probability and UncertaintyBiological systemAlgorithmsBiomarkersBiometrical journal. Biometrische Zeitschrift
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