Sparse regression and optimization in high-dimensional framework for Gene Regulatory Network inference.

Gene regulatory networks (GRNs) are powerful tools to
represent and analyse complex biological systems and enable the
modelling of functional relationships between elements of these
systems. In this talk, I will focus on theoretical analysis and the
use of statistical and optimization methods in the context of GRN
inference. The first part will be dedicated to the study of
statistical learning methods to infer networks from sparse linear
regressions in a high-dimensional setting. Then, I will present an
optimization algorithm to directly estimate relationships in such
networks. I will finally propose an application to cancer data.