0000000000334870
AUTHOR
Elise Mostacci
Méthodes multivariées combinant ondelettes et analyse en composantes principales pour le débruitage de données issues de spectrométrie de masse
International audience; L'identification de nouveaux biomarqueurs diagnostiques ou pronostiques est un des objectifs majeurs en recherche clinique. L'utilisation des technologies à haut débit comme la spectrométrie de masse est prometteuse pour l'identification de tels marqueurs. A partir d'un prélèvement de sang ou de tumeur par exemple, cette technologie permet de traduire sous forme de spectres le profil protéique des individus. Le signal biologique observé dans les spectres est masqué par différentes sources de variabilités techniques, qu'une phase préalable de prétraitement doit permettre de retirer. La méthode classique permettant de retirer le bruit aléatoire de mesure de ce signal c…
Multivariate denoising methods combining wavelets and principal component analysis for mass spectrometry data
The identification of new diagnostic or prognostic biomarkers is one of the main aims of clinical cancer research. In recent years, there has been a growing interest in using mass spectrometry for the detection of such biomarkers. The MS signal resulting from MALDI-TOF measurements is contaminated by different sources of technical variations that can be removed by a prior pre-processing step. In particular, denoising makes it possible to remove the random noise contained in the signal. Wavelet methodology associated with thresholding is usually used for this purpose. In this study, we adapted two multivariate denoising methods that combine wavelets and PCA to MS data. The objective was to o…
Comparison of classification methods that combine clinical data and high-dimensional mass spectrometry data
Background The identification of new diagnostic or prognostic biomarkers is one of the main aims of clinical cancer research. Technologies like mass spectrometry are commonly being used in proteomic research. Mass spectrometry signals show the proteomic profiles of the individuals under study at a given time. These profiles correspond to the recording of a large number of proteins, much larger than the number of individuals. These variables come in addition to or to complete classical clinical variables. The objective of this study is to evaluate and compare the predictive ability of new and existing models combining mass spectrometry data and classical clinical variables. This study was co…