Sparse regression and optimization in high-dimensional framework: application to Gene Regulatory Networks.

In this presentation, we focus on a theoretical analysis and the use of statistical and optimization methods in the context of sparse linear regressions in a high-dimensional setting. The first part of this work is dedicated to the study of statistical learning methods, more precisely penalized methods and greedy algorithms. The second part concerns the application of these methods for gene regulatory networks inference. Gene regulatory networks are powerful tools to represent and analyse complex biological systems, and enable the modelling of functional relationships between elements of these systems. We thus propose to develop optimization methods to estimate relationships in such networks.