Sparse regression and optimization for gene regulatory network inference
Salle de séminaires M.0.1
Labo. MAP5, Université Paris Descartes
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.
Cet exposé rentre dans le cadre des projets RIN Asterics (17B01101GR) et ANR SMILES (ANR-18-CE40-0014).