NDLI: Variable selection for noisy data applied in proteomics

Content Provider	IEEE Xplore Digital Library
Author	Dridi, N. Giremus, A. Giovannelli, J.-F. Truntzer, C. Roy, P. Gerfaut, L. Charrier, J.-P. Ducoroy, P. Mercier, C. Grangeat, P.
Copyright Year	2014
Description	Author affiliation: Biomerieux, Marcy l'Étoile, France (Charrier, J.-P.) \|\| IMS, Univ. Bordeaux, Talence, France (Dridi, N.; Giremus, A.; Giovannelli, J.-F.) \|\| Plateforme proteomique CLIPP, CHU Dijon, Dijon, France (Truntzer, C.; Ducoroy, P.) \|\| CEA Leti, Grenoble, France (Gerfaut, L.; Grangeat, P.) \|\| Hospices Civils de Lyon, Service de Biostatistique, Univ. Lyon I, Lyon, France (Roy, P.; Mercier, C.)
Abstract	The paper proposes a variable selection method for proteomics. 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: (1) we do not impose ad-hoc relationships between the variables such as a logistic regression model and (2) we account for instrumental variability through measurement noise. We are then dealing with indirect observations of a mixture of distributions and it results in intricate probability distributions. A closed-form expression of the posterior distributions cannot be derived. Thus, we discuss several approximations and study the robustness to the noise level. Finally, the method is evaluated both on simulated and clinical data.
Sponsorship	IEEE Signal Process. Soc.
Starting Page	2833
Ending Page	2837
File Size	125746
Page Count	5
File Format	PDF
ISBN	9781479928934
DOI	10.1109/ICASSP.2014.6854117
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-04
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Proteins Bayes methods Biological system modeling Biomarkers Noise Input variables proteomics Model and variable selection Bayesian approach biological et technological variability Gaussian mixture
Content Type	Text
Resource Type	Article

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