Classification based on extensions of LS-PLS using logistic regression: application toclinical and multiple genomic data

International audiencePrediction from high-dimensional genomic data is an active field in today's medical research. Most of the proposed prediction methods make use of genomic data alone without considering established clinical data that often are available and known to have predictive value. Recent studies suggest that combining clinical and genomic information may improve predictions. We consider in this paper methods for classification purposes that simultaneously use both types of variables, but applying dimension reduction only to the high-dimensional genomic ones. A usual way to deal with that is the use of a two-step approach. In step one, dimensionality reduction technique is just performed on the genomic dataset. In step two, the selected genomic variables are merged with the clinical variables to build a classification model on the combined dataset. Nevertheless, the reduction dimension is built without taking into account the link between the response variable and the clinical data. To address this issue, using Partial Least Squares (PLS) as reduction technique, we propose here a one step approach based on three extensions of LS-PLS (LS for Least Squares) method for logistic regression context. We perform a simulation study to evaluate these approaches compared to methods using only the clinical data or only genetic data. Then, we illustrate their performances to classify two real data sets containing both clinical information and gene expression

La pharmacométrie

International audienceCette revue scientifiques et techniques en français aborde le thème de la pharmacométrie. La pharmacométrie a été récemment définie comme la science de la pharmacologie clinique quantitative. La pharmacologie étudie l'interaction entre notre organisme et le médicament. Cette interaction recouvre la pharmacocinétique (PK) (ce que notre organisme fait subir au médicament) et la pharmacodynamie (PD) (ce que le médicament fait subir à notre organisme). Cette revue présente tout d'abord le type de données rencontrées en pharmacologie clinique et les modèles dynamiques et statistiques qui sous-tendent l'approche de population. Dans un contexte de modélisation des données, les méthodes d'estimation des paramètres des modèles ainsi que les étapes de construction et de validation de modèles son présentés. Pour finir, les méthodes d'évaluation et d'optimisation des protocoles qui s'appuient sur ces modèles seront présentées avec l'ensemble de ces outils Tout au long de cet article, nous illustrons nos propos par une application à la PK et la PD de la warfarine

La pharmacométrie

International audienceCette revue scientifiques et techniques en français aborde le thème de la pharmacométrie. La pharmacométrie a été récemment définie comme la science de la pharmacologie clinique quantitative. La pharmacologie étudie l'interaction entre notre organisme et le médicament. Cette interaction recouvre la pharmacocinétique (PK) (ce que notre organisme fait subir au médicament) et la pharmacodynamie (PD) (ce que le médicament fait subir à notre organisme). Cette revue présente tout d'abord le type de données rencontrées en pharmacologie clinique et les modèles dynamiques et statistiques qui sous-tendent l'approche de population. Dans un contexte de modélisation des données, les méthodes d'estimation des paramètres des modèles ainsi que les étapes de construction et de validation de modèles son présentés. Pour finir, les méthodes d'évaluation et d'optimisation des protocoles qui s'appuient sur ces modèles seront présentées avec l'ensemble de ces outils Tout au long de cet article, nous illustrons nos propos par une application à la PK et la PD de la warfarine

Extension of the EM-algorithm using PLS to fit linear mixed effects models for high dimensional repeated data

International audienceIn studies where individuals contribute more than one observations, such as longitudinal or repeated measures studies, the linear mixed model provides a framework to take correlation between these observations into account. By introducing random effects, mixed models allow to take into account the variability of the response among the different individuals and the possible within-individual correlation. In addition, recent studies have collected high-dimensional data, which involve new statistical issue as the sample size is relatively small compared to the number of covariates. To deal with high dimensional data, reduction dimension method can be used which aims at summarizing the numerous predictors in form of a small number of new components (often linear combinations of the original predictors). The traditional approach is the Principal Component Regression which is an application of Principal Component Analysis (PCA) to regression model. PCA is applied without considering of the link between the outcome and the independent variables. An alternative method is the Partial Least Square (PLS) that takes this link into account. To solve the high-dimensional issue in the repeated/longitudinal data context, we propose an approach adapted from the Expectation-Maximization (EM) algorithm for linear mixed models by incorporating a PLS step to reduce the high-dimensional data to low-dimensional features. Under this algorithm framework, we use simulation studies to investigate the performance and computational properties of this extension of EM-algorithm using PLS (EM-PLS) and compare it with other reduction dimension approaches. To illustrate the practical usefulness of the approach, we apply the EM-PLS algorithm developed in this work to fit real data sets including for instance cell-cycle gene expression data observed over several time points or brain images collected during repeated sessions

Machine learning at TIMC: Stock of the situation

International audienc

Evaluation et optimisation de protocoles dans les modèles non linéaires à effets mixtes (application à la modélisation de la pharmacologie des antiretroviraux)

PARIS7-Bibliothèque centrale (751132105) / SudocSudocFranceF

A Comparison of Methods for Analysing Logistic Regression Models with both Clinical and Genomic Variables

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Design evaluation and optimisation in multiple response nonlinear mixed effect models: PFIM 3.0

International audienceNonlinear mixed effect models (NLMEM) with multiple responses are increasingly used in pharmacometrics, one of the main examples being the joint analysis of the pharmacokinetics (PK) and pharmacodynamics (PD) of a drug. Efficient tools for design evaluation and optimisation in NLMEM are necessary. The R functions PFIM 1.2 and PFIMOPT 1.0 were proposed for these purposes, but accommodate only single response models. The methodology used is based on the Fisher information matrix, developed using a linearisation of the model. In this paper, we present an extended version, PFIM 3.0, dedicated to both design evaluation and optimisation for multiple response models, using a similar method as for single response models. In addition to handling multiple response models, several features have been integrated into PFIM 3.0 for model specification and optimisation. The extension includes a library of classical analytical pharmacokinetics models and allows the user to describe more complex models using differential equations. Regarding the optimisation algorithm, an alternative to the Simplex algorithm has been implemented, the Fedorov-Wynn algorithm to optimise more practical D-optimal design. Indeed, this algorithm optimises design among a set of sampling times specified by the user. This R function is freely available at http://www.pfim.biostat.fr. The efficiency of this approach and the simplicity of use of PFIM 3.0 are illustrated with a real example of the joint PKPD analysis of warfarin, an oral anticoagulant, with a model defined by ordinary differential equations