WorldCat Identities

Vignes, Matthieu (1978-....).

Overview
Works: 5 works in 9 publications in 2 languages and 11 library holdings
Roles: Other, Author, Thesis advisor
Publication Timeline
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Most widely held works by Matthieu Vignes
Contribution à la modélisation et l'inférence de réseau de régulation de gènes by Magali Champion( Book )

3 editions published between 2014 and 2015 in English and held by 3 WorldCat member libraries worldwide

This manuscript intends to study a theoretical analysis and the use of statistical and optimization methods in the context of gene networks. Such networks are powerful tools to represent and analyse complex biological systems, and enable the modelling of functional relationships between elements of these systems. The first part is dedicated to the study of statistical learning methods to infer networks, from sparse linear regressions, in a high-dimensional setting, and particularly the L2-Boosting algorithms. From a theoretical point of view, some consistency results and support stability results were obtained, assuming conditions on the dimension of the problem. The second part deals with the use of L2-Boosting algorithms to learn Sobol indices in a sensitive analysis setting. The estimation of these indices is based on the decomposition of the model with functional ANOVA. The elements of this decomposition are estimated using a procedure of Hierarchical Orthogonalisation of Gram-Schmidt, devoted to build an approximation of the analytical basis, and then, a L 2 -Boosting algorithm, in order to obtain a sparse approximation of the signal. We show that the obtained estimator is consistant in a noisy setting on the approximation dictionary. The last part concerns the development of optimization methods to estimate relationships in networks. We show that the minimization of the log-likelihood can be written as an optimization problem with two components, which consists in finding the structure of the complete graph (order of variables of the nodes of the graph), and then, in making the graph sparse. We propose to use a Genetic Algorithm, adapted to the particular structure of our problem, to solve it
Modèles markoviens graphiques pour la fusion de données individuelles et d'intéractions : application à la classification de gènes by Matthieu Vignes( Book )

3 editions published in 2007 in French and held by 3 WorldCat member libraries worldwide

The research work presented in this dissertation is on keeping with the statistical integration of post -genomics data of heterogeneous kinds. Gene clustering aims at gathering the genes of a living organism -modeled as a complex system- in meaningful groups according to experimental data to decipher the roi es of the genes acting within biological mechanisms under study. We based our approach on probabilistic graphical models. More specifically, we used Hidden Markov Random Fields (HMRF) that allow us to simultaneously account for gene-individual features thanks to probability distributions and network data that translate our knowledge on existing interactions between these genes through a non-oriented graph. Once the biological issues tackled are set, we describe the model we used as weil as algorithmic strategies to deal with parameter estimation (namely mean field-like approximations). Then we examine two specificities of the data we were faced to: the missing observation problem and the high dimensionality ofthis data. They lead to refinements ofthe model under consideration. Lastly, we present our experiments both on simulated and real Yeast data to assess the gain in using our method. ln particular, our goal was to stress biologically plausible interpretations of our results
Inferring large graphs using $$\ell _1$$ ℓ 1 -penalized likelihood by Magali Champion( )

1 edition published in 2017 in English and held by 2 WorldCat member libraries worldwide

Correction to: Inferring large graphs using $$\ell _1}$$ ℓ 1 -penalized likelihood by Magali Champion( )

1 edition published in 2017 in English and held by 2 WorldCat member libraries worldwide

Inférence de réseaux causaux à partir de données interventionnelles by Gilles Monneret( )

1 edition published in 2018 in French and held by 1 WorldCat member library worldwide

The purpose of this thesis is the use of current transcriptomic data in order to infer a gene regulatory network. These data are often complex, and in particular intervention data may be present. The use of causality theory makes it possible to use these interventions to obtain acyclic causal networks. I question the notion of acyclicity, then based on this theory, I propose several algorithms and / or improvements to current techniques to use this type of data
 
Audience Level
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Audience Level
1
  General Special  
Audience level: 0.96 (from 0.94 for Inferring ... to 1.00 for Inférence ...)

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