VARIABLE SELECTION FOR NOISY DATA APPLIED IN PROTEOMICS

Abstract : 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: (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. Index Terms— Model and variable selection, Bayesian approach, biological et technological variability, Gaussian mixture, proteomics.
Type de document :
Communication dans un congrès
IEEE International Conference on Acoustics, Speech and Signal Processing, May 2014, Florence, Italy. IEEE International Conference on Acoustics, Speech and Signal Processing
Liste complète des métadonnées

Littérature citée [13 références]  Voir  Masquer  Télécharger

https://hal.archives-ouvertes.fr/hal-01722157
Contributeur : Noura Dridi <>
Soumis le : samedi 3 mars 2018 - 09:00:39
Dernière modification le : vendredi 6 juillet 2018 - 15:06:10
Document(s) archivé(s) le : lundi 4 juin 2018 - 15:30:58

Fichier

article_icassp14_vf.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01722157, version 1

Citation

N. Dridi, A. Giremus, J.-F Giovannelli, C. Truntzer, P. Roy, et al.. VARIABLE SELECTION FOR NOISY DATA APPLIED IN PROTEOMICS. IEEE International Conference on Acoustics, Speech and Signal Processing, May 2014, Florence, Italy. IEEE International Conference on Acoustics, Speech and Signal Processing. 〈hal-01722157〉

Partager

Métriques

Consultations de la notice

281

Téléchargements de fichiers

29